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Abstract: Current electronic voting systems are not sufficient to satisfy trustworthy elections as they do not provide any proof or confirming evidence of their honesty. This lack of trustworthiness is the main reason why e-voting is not widespread even though e-voting is expected to be more efficient than the current plain paper voting. Many experts believe that the only way to assure voters that their intended votes are casted is to use paper receipts. In this paper, we propose an efficient scheme for issuing receipts to voters in an e-voting environment using the well-known divide-and-choose method. Our scheme requires neither special printers or scanners, nor frequent observations of voting machines. In addition, our scheme is more secure than the previous schemes. [Copyright &y& Elsevier]

Organic molecules having extended π-conjugated moieties is useful for creating ‘dynamic’ functional materials by modulating the photophysical properties and molecular packing through non-covalent interactions. Herein, we report the photoluminescence properties of a luminogen, NBA, exhibiting aggregation-induced emission (AIE) characteristics, synthesized by Knoevenagel condensation reaction between 2-Hydroxy naphthaldehyde and malononitrile. NBA emits strongly upon aggregation and in solid state with large Stokes shift whereas it is non emissive in pure solvents. The aggregation induced emission behavior of the compound was carried out in DMSO (good solvent)-water mixture (poor solvent) with water fraction ( f w) ranging from 0% to 98%. The AIE property of the luminogen were further exploited for fabricating rewritable fluorescent paper substrates that found applications in security printing and data storage where the written images or letters stored on the filter paper are invisible under normal light. [ABSTRACT FROM AUTHOR]

Abstract: Information seeking is completely ignored in research on aviation psychology and more generally in issues of air transport safety. But information seeking occupies a central place in the pilots’ work. And this activity is undergoing an important development: the transition from paper to electronic documents. A contribution to the assessment of this transition is presented here. The assessment is focused on deterioration of information access, understanding and exploitation, and their possible negative consequences in terms of reliability. The exploitation of the documentation by pilots, and particularly the information seeking activity, are described and linked to their contexts (i.e. the main tasks of flying that require information seeking). Then an users test is presented where some information-seeking tasks are evaluated in terms of cost and errors, by comparing electronic and paper documents. The results show that electronic documents provide new functions that seem useful but also that too many new functions generate difficulties. Then it seems that pilots need some time to learn how to use these new documents. Reducing attention needed to perform a task such as calculation is risky. Some aspects of information seeking by the pilots should be better analysed and evaluated. [Copyright &y& Elsevier]

Current reliable strategies for information security are all chosen using incomplete information. With standards, problems resulting from incomplete information can be reduced, since with standards, we can decrease the choices and simplify the process for reliable supply and demand decision making. This paper is to study the certification of information security management systems based on specifications promulgated by the Bureau of Standards, Metrology and Inspection (BSMI), Ministry of Economic Affairs in accordance with international standards and their related organizations. And we suggest a certification requirement concept for five different levels of “Information and Communication Security Protection System” in our country, the Republic of China, Taiwan. [Copyright &y& Elsevier]

Common personalization approaches involve re-ranking search results. In such way, documents likely to be preferred by the user are presented higher. In this paper, we focus on research-paper retrieval. We propose a scientometric re-ranking approach based on the scientometric preferences of a particular researcher. The researcher creates its own definition of document quality by the mean of scientometric indicators. These indicators are the base of the scientometric score calculation, which serves to results re-ranking. The originality of our approach was the incorporation of different scientometric indicators into researcher’s preferences which have significantly improved ranking performance. [ABSTRACT FROM AUTHOR]

Spatial similarity provides an important basis for geographic information processing and is widely applied in multi-source data fusion and update, data retrieval and query, and cartographic generalization. To address the shape description and similarity measurement of polygon entities, this study presents a new hierarchical shape description approach and examines its application in similarity measurement of polygon entities. Using the rotation and segmentation methods, we first constructed a hierarchical shape description model for target polygon entities, followed by measurement of global and hierarchical shape description of polygon entities, respectively, using the farthest-point-distance and geometric feature description methods. Finally, we constructed a comprehensive similarity measurement model through a weighted integration of position, size, direction, and shape. The hierarchical shape description approach proposed in this paper can be applied to the shape similarity measurement of polygon elements, similarity measurement after spatial object simplification, and multi-scale polygon entity matching. The experimental results showed that the hierarchical shape description approach and similarity measurement model are able to effectively measure spatial similarity between different polygon entities, and have obtained good application results. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet leads to the rapid growth of network information, we call it information explosion. The Internet is full of information, and it is difficult for users to find this information and useful knowledge of the ocean. The Web has become the world's largest information repository, and there is an urgent need for efficient access to the valuable knowledge of vast amounts of web information. The purpose of this paper is to study the process and algorithm analysis of text mining system based on artificial intelligence. This paper presents an algorithm of document feature acquisition based on genetic algorithm. Selecting suitable features is an important task in specific text classification and information retrieval. Finding appropriate feature vectors to represent the text will undoubtedly help with subsequent sorting and grouping. Based on the genetic algorithm of variable length chromosome, this paper improves the crossover, mutation and selection operations, and proposes an algorithm to obtain text feature vectors. This method has a wide range of applications and good results. [ABSTRACT FROM AUTHOR]

The exponential growth of textual data poses a monumental challenge for extracting meaningful knowledge. Manually identifying descriptive keywords or keyphrases for each document is infeasible given the massive daily generated text. Automatic keyphrase extraction is, therefore, essential. However, current techniques struggle with learning the most salient semantic features from lengthy documents. This hybrid keyphrase extraction framework uniquely combines the complementary strengths of graph-based and textual feature methods. Our approach demonstrates improved performance over relying solely on statistical or graphical. Graph-based systems leverage word co- occurrence networks to score importance. Textual methods extract keyphrases using linguistic properties. Together, these complementary techniques overcome the limitations of relying on any strategy. The hybrid approach is evaluated on standard SemEval 2017 Task 10 and SemEval 2010 Task 5 benchmark datasets for scientific paper keyphrase extraction. Performance is quantified using the F1 score relative to human-annotated ground truth keyphrase. Results will quantify effectiveness on long documents with thousands of terms where only a few keywords represent salient concepts. Results show our technique effectively identifies the most salient semantic keywords, overcoming limitations of current techniques that struggle to mix features of graphical or statistical methods. Our experiments demonstrate that the proposed hybrid approach achieves superior F1 scores compared to current state-of-the-art methods on benchmark datasets. These results validate that synergistically combining graph and textual features enables more accurate keyphrase extraction, especially for long documents laden with extraneous terms. [ABSTRACT FROM AUTHOR]

Some previous empirical studies have shown that the performances of the listwise learning to rank approaches are in general better than the pointwise or pairwise learning to rank techniques. The listwise learning to rank methods which directly optimize information retrieval indicators are a type of essential and popular method of learning to rank. However, the existing learning to rank approaches based on Generative Adversarial Networks (GAN) do not utilize a loss function based on information retrieval indicators to optimize the generator and/or discriminator. Thus, an approach of learning to rank that combines approximate Normalized Discounted Cumulative Gain (NDCG) ranking indicators with Conditional Generative Adversarial Networks (CGAN) is proposed in this paper, named NCGAN-LTR. The NCGAN-LTR approach constructs loss functions of the generator and discriminator based on the Plackett-Luce model and an approximate version of the NDCG ranking indicator, which is utilized to train the network parameters of CGAN. The experimental results on four benchmark datasets of learning to rank, i.e., TREC TD2004, OHSUMED, MQ2008, and MSLR-WEB10K demonstrate that our proposed NCGAN-LTR approach has superior performance across almost various ranking indicators of information retrieval compared with the IRGAN-List approach. [ABSTRACT FROM AUTHOR]

We introduce the combination of two techniques: Sparse Retrieval and Dense Retrieval, while experimenting with different training approaches to find the optimal method for the Vietnamese Legal Text Retrieval task. Moreover, the Question Answering task was only built on the open domain of UIT-ViQuAD but shown promising results on the in-domain legal dataset. Finally, we also mentioned the data augmentation of legal documents up to 3GB to train the Phobert language model, improve this backbone with Condenser, Cocondenser in this paper. Furthermore, these techniques can be utilized for other information retrieval assignments in languages with limited resources. [ABSTRACT FROM AUTHOR]

Abstract Participating in research and teaching activities of universities, and then providing valuable information and policy suggestions are an important task of academic librarians. This paper demonstrates systematically in practice that how academic librarians can involve and contribute in the research activities of universities. Our demonstration is launched through comparative studies of research productivities and research impacts among universities. These universities are divided into two counterparts. One is the Chinese universities which aim to construct the world-class mathematics discipline, and the other is the university which already owns the world-class mathematics discipline. During these comparative studies, we demonstrate in practice that how to seek the research question, how to collect and process the data, how to design the research methods, how to analyze the data, and how to propose policy suggestions. Contributions of this paper are twofold. The first aspect is a systematic demonstration in practice. This demonstration shows to academic librarians that how to involve and contribute in the research activities of universities. The academic librarianship thus can be strengthened, as it provides a way for us academic librarians to promote our roles in teaching and research activities of universities. The second aspect is to gain insights in the research productivities and research impacts of Chinese universities which aim to construct the world-class mathematics discipline. During this process, we design a bibliometric indicator for evaluating the research impacts of universities based on academic papers, launch comparative studies with the university that already owns the world-class mathematics discipline, and propose policy suggestions for China to scientifically achieve its goal. This also provides references for other countries to launch similar studies. [ABSTRACT FROM AUTHOR]

Outsourcing data to the cloud offers various advantages, such as improved reliability, enhanced flexibility, accelerated deployment, and so on. However, data security concerns arise due to potential threats such as malicious attacks and internal misuse of privileges, resulting in data leakage. Data encryption is a recognized solution to address these issues and ensure data confidentiality even in the event of a breach. However, encrypted data presents challenges for common operations like access control and range queries. To address these challenges, this paper proposes Secure Multi-dimensional Data Retrieval with Access Control and Range Search in the Cloud (SMDR). In this paper, we propose SMDR policy, which supports both access control and range queries. The design of the SMDR policy cleverly utilizes the minimum and maximum points of buckets, enabling the SMDR policy is highly appropriate for supporting range queries on multi-dimensional data. Additionally, we have made modifications to Ciphertext Policy-Attribute Based Encryption (CP-ABE) to enable effective integration with the SMDR policy, and then constructed a secure index using the SMDR policy and CP-ABE. By utilizing the secure index, access control and range queries can be effectively supported over the encrypted multi-dimensional data. To evaluate the efficiency of SMDR, extensive experiments have been conducted. The experimental results demonstrate the effectiveness and suitability of SMDR in handling encrypted multi-dimensional data. Additionally, we provide a detailed security analysis of SMDR. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of maximizing the effectiveness of the ranking produced by information retrieval or recommender systems and at the same time maximizing two fairnesses, that of the group and that of the individual. The context of this paper is therefore that of access to information carried out by users, who aim to satisfy their own information needs, to documents produced by authors and curators who aim to be exposed in a fair manner, i.e. without discriminating between groups nor individuals. The paper describes a general method based on the spectral decomposition of mixtures of symmetric matrices, each of which represents a variable to be maximized, and experiments conducted with the use of a test collection. The method described in this paper has explained if and how the trade-offs between effectiveness, group fairness and individual fairness manifest themselves. The experimental results show that maintaining an acceptable level of effectiveness and fairness at the same time is feasible and (b) the trade-offs exist but the order of magnitude of the variations depends on the measure of effectiveness used and therefore by what the user's model of access to information is as well as on the fairness measures and therefore on how authors or editors should be exposed. • Modern information access systems should balance fairness and effectiveness. • A single eigensystem achieves simultaneous maximization. • Fairness, effectiveness, and access measures are crucial in trade-offs. [ABSTRACT FROM AUTHOR]

In this paper, we present a survey of modeling and simulation approaches to describe information retrieval basics. We investigate its methods, its challenges, its models, its components and its applications. Our contribution is twofold: on the one hand, reviewing the literature on discovery some search techniques that help to get pertinent results and reach an effective search, and on the other hand, discussing the different research perspectives for study and compare more techniques used in information retrieval. This paper will also shedding the light on some of the famous AI applications in the legal field. [ABSTRACT FROM AUTHOR]

There has been an increased interest in the creation of AI social agents who possess complex behaviors that allow them to perform social interactions. Behavior trees provide a plan model execution that has been widely used to build complex behaviors for AI social agents. Behavior trees can be represented in the form of a memory structure known as cognitive scripts, which would allow them to evolve through further development over multiple exposure to repeated enactment of a particular behavior or similar ones. Behavior trees that share the same context will then be able to learn from each other resulting in new behavior trees with richer experience. The main challenge appears in the expensive cost of retrieving contextually similar behavior trees (scripts) from a repertoire of scripts to allow for that learning process to occur. This paper introduces a novel application of map-and-reduce technique to retrieve cognitive with low computational time and memory allocation. The paper focuses on the design of a corpus of cognitive scripts, as a knowledge engineering key challenge, and the application of map-and-reduce with semantic information to retrieve contextually similar cognitive scripts. The results are compared to other techniques used to retrieve cognitive scripts in the literature, such as Pharaoh which uses the least common parent (LCP) technique in its core. The results show that the map-and-reduce technique can be successfully used to retrieve cognitive scripts with high retrieval accuracy of 92.6%, in addition to being cost effective. [ABSTRACT FROM AUTHOR]

In the era of rapid paper publications in various venues, automatic citation recommendations would be highly useful to researchers when they write papers. Local citation recommendation aims to recommend possible papers to cite given local citation contexts. Previous work mainly computes the similarity score between citation contexts and cited papers on a one-to-one basis, which is quite time-consuming. We train a pair of neural network encoders that map citation contexts and all possible cited papers to the same vector space, respectively. After that, we index the positions of all cited papers in the vector space. This makes our process for searching recommended papers considerably faster. On the other hand, existing methods tend to recommend papers that are highly similar to each other, which makes recommendations lack diversity. Therefore, we extend our algorithm to perform multi-topic recommendations. We generate multi-topic training examples based on the index we mentioned earlier. Furthermore, we specially design a multi-group contrastive learning method to train our model so that it can distinguish different topics. Empirical experiments show that our model outperforms previous methods by a wide margin. Our model is also light weighted and has been deployed online so that researchers can use it to obtain recommended citations for their own paper in real-time. • Proposed FLCR algorithm with sentence-transformer & k-d tree. • Introduced multi-topic citation recommendation for diverse contexts. • Developed large-scale dataset with 1.7 million citation contexts for evaluation. • Demonstrated significant performance improvement over previous methods. • Deployed demo for real-time citation recommendations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multi-granularity information association approaches are used to uncover multi-level geological knowledge and support accurate retrieval of mineral resource geological reports. • Extracting geological multiple feature information (topic, time, space, figure, and table) based on the multi-granularity of geological reports. • Mining potential multiple feature information associations by an improved apriori algorithm. Massive geologic report contains all kinds of multimodal geologic data information (geologic text, geologic maps, geologic tables, etc.), which contain a lot of rich geologic basic knowledge and expert experience knowledge about rocks and minerals, stratigraphic structure, geologic age, geographic location, and so on. Accurate retrieval of specific information from massive geologic data has become an important need for geologic information retrieval. However, the majority of existing research primarily revolves around extracting and associating information at a single granularity to facilitate geological semantic retrieval, which ignores many potential semantic associations, leading to ambiguity and fuzziness in semantic retrieval. To solve these problems, this paper proposes a multi-granularity (document-chapter-paragraph) geological information retrieval framework for accurate semantic retrieval. The framework firstly extracts topic feature information, spatiotemporal feature information, figure and table feature information based on the multi-granularity of geological reports. Then, an improved apriori algorithm is used to mine and visualize the associations among the feature information to discover the semantic associations of the geological reports at multiple levels of granularity. Finally, experiments are designed to validate the application of the proposed multi-granularity information retrieval framework on the accurate retrieval of geological reports. The experimental results show that the proposed multi-granularity information retrieval framework in this paper can dig deeper into underlying geo-semantic information and realize accurate retrieval. [ABSTRACT FROM AUTHOR]

Scientific literature retrieval provides convenience for researchers to find scientific literature related to the query. It is an important part of scientific research to search related papers given a paper title as query. However, for scientific literature retrieval tasks, most of the existing retrieval methods do not consider sentence-level semantic matching so that the retrieval performance is limited. With the success of neural networks, neural information retrieval methods have been widely studied and achieved good retrieval results. In this paper, we propose a semi-supervised semantic-enhanced scientific literature retrieval framework. The framework is composed of two networks: a self-attention convolutional encoder-decoder network and a sentence-level attention scientific literature retrieval network. By joint training of the two networks, the proposed semi-supervised semantic-enhanced scientific literature retrieval model can fully capture the rich semantic information of scientific text data and leverages human labeled scientific text data to improve the discriminativeness of the learned semantic representation. The retrieval results on two scientific literature datasets demonstrate that the proposed method significantly and consistently outperforms the other baseline methods. [ABSTRACT FROM AUTHOR]

Tagging can be regarded as the action of connecting relevant user-defined keywords to an item, indirectly improving the quality of the information retrieval services that rely on tags as data sources. Tag recommendation dramatically enhances the quality of tags by assisting users in tagging. Although there exist many studies on tag recommendation for textual content, few of them consider two characteristics in real applications, i.e., the long-tail distribution of tags and the topic-tag correlation. In this paper, we propose a Topic-Guided Tag Recommendation (TGTR) model to recommend tags by jointly incorporating dynamic neural topic. Specifically, TGTR first generates dynamic neural topic that would indicate the tags by a neural topic generator. Then, a sequence encoder is used to distill indicative features from the post. To effectively leverage the topic and alleviate the data imbalance, we design a multi-tag topical attention mechanism to get a tag-specific post representation for each tag with the help of dynamic neural topic. These three modules are seamlessly joined together via an end-to-end multi-task learning model, which is helpful for the three parts to enhance each other and balance the effects of topics and tags. Extensive experiments have been conducted on four real-world datasets and demonstrate that our model outperforms the state-of-the-art approaches by a large margin, especially on tail-tags. The code, data and hyper-parameter settings are publicly released for reproducibility. [ABSTRACT FROM AUTHOR]

Dust detection using remotely sensed measurements has been one of the challenging problems encountered by atmospheric scientists. MODerate Resolution Imaging Spectroradiometer (MODIS) on the Terra (T) and Aqua (A) platforms have been a versatile sensor for well over 21 and 18 years respectively, and have been extremely useful in the retrieval of aerosol information over the entire globe. The MODIS radiances from the Level 1B in general are expected to be within 5% accuracy in the reflective wavelengths and within 1% in the thermal emissive wavelengths. In this paper, we evaluate the sensitivity of previously developed dust detection technique based on thermal emissive wavelengths, which correspond to MODIS bands 20, 29, 31, and 32 respectively. The Thermal Emissive Dust Index (TEDI) performed very comparably to the traditional Aerosol Optical Thickness (AOT) retrievals by MODIS reflective channels. Since the MODIS Thermal Emissive Bands (TEB) are well calibrated on-orbit using a BlackBody (BB) source, the calibration of these long wave infrared bands is quite robust. As A-MODIS continues to perform well beyond its designed lifetime of 6 years, the instrument has undergone various levels of degradation during its mission time. As a consequence, it is imperative to check the impacts of calibration on the higher-level retrievals. In this paper, we rigorously analyze the sensitivity of TEDI due to the impact of calibration by the afore-mentioned TEB. The perturbation of the dominant (linear) calibration term demonstrated the following: first, there was a correlation in the sensitivity of the TEDI due to the uncertainty in the linear calibration term. Based on a perturbation in the linear calibration term for all aforementioned bands over a range of ±5% yielded the TEDI sensitivity to vary from approximately −3.2% to about −3.6%. When considering the uncertainty in each individual band significant changes were observed. The least change was observed for the perturbation in the calibration of band 20 with the TEDI sensitivity and the largest sensitivity in TEDI was observed in the perturbation of band 31 calibration. Thus, in the case of TEDI, noticeable sensitivity due to calibration uncertainty was observed in bands 29, 31, and 32, reiterating the importance of the TEB calibration in these bands. Also, the dust detection scheme based on A-MODIS was successfully transferred to the follow-on sensors such as Suomi (SNPP) and NOAA 20 (N20) VIIRS. The results presented in this paper would be extremely helpful in understanding impacts of calibration on the higher-level products for both current and future missions based on the MODIS heritage. Finally, the work also identifies the importance of radiometric fidelity in maintaining the accuracy of the dust detection. Results presented will show drastic improvement of the Saharan dust detection after the reduction of the electronic crosstalk in the 8.5 µm channel of T-MODIS. [ABSTRACT FROM AUTHOR]

Several methods of preference modeling, ranking, voting and multi-criteria decision-making include pairwise comparisons. It is usually simpler to compare two objects at a time, furthermore, some relations (e.g., the outcome of sports matches) are naturally known for pairs. This paper investigates and compares pairwise comparison models and the stochastic Bradley–Terry model. It is proved that they provide the same priority vectors for consistent (complete or incomplete) comparisons. For incomplete comparisons, all filling in levels are considered. Recent results identified the optimal subsets and sequences of multiplicative/additive/reciprocal pairwise comparisons for small sizes of items (up to n = 6). Simulations of this paper show that the same subsets and sequences are optimal in the case of the Bradley–Terry and the Thurstone models as well. This somehow surprising coincidence suggests the existence of a more general result. Further models of information and preference theory are subject to future investigation to identify optimal subsets of input data. • Multiplicative/additive/reciprocal pairwise comparisons vs. Bradley–Terry model. • Their equivalence is shown in case of consistency. • All filling in levels are investigated. • The same subsets of comparisons are shown to be optimal even in inconsistent cases. • Results raise new research direction: Are such coincidences valid in further models? [ABSTRACT FROM AUTHOR]

Efficient information search from building information models (BIMs) requires deep BIM knowledge or extensive engineering efforts for building natural language (NL)-based interfaces. To address this challenge, this paper introduces a dynamic prompt-based virtual assistant framework dubbed "BIMS-GPT" that integrates generative pre-trained transformer (GPT) technologies, supporting NL-based BIM search. To understand users' NL queries, extract relevant information from BIM databases, and deliver NL responses along with 3D visualizations, a dynamic prompt-based process was developed. In a case study, BIMS-GPT's functionality is demonstrated through a virtual assistant prototype for a hospital building. When evaluated with a BIM query dataset, the approach achieves accuracy rates of 99.5% for classifying NL queries with incorporating 2% of the data in prompts. This paper contributes to the advancement of effective and versatile virtual assistants for BIMs in the construction industry as it significantly enhances BIM accessibility while reducing the engineering and training data prerequisites for processing NL queries. • Introduced a dynamic prompt-based virtual assistant for BIM information search • Integrated BIM and GPT technologies for developing natural language interfaces • Explored prompt engineering for GPT to interpret NL queries and summarize NL answers • Improved BIM accessibility by enabling NL-based interactions with 3D visualizations • Reduced engineering and training data prerequisites for processing BIM NL queries [ABSTRACT FROM AUTHOR]

In recent times, prediction error expansion (PEE) based reversible data hiding (RDH) schemes have gained significant traction due to their performance in terms of embedding capacity and image quality. However, the major part of their performance is dependent on how good the prediction has been. For a good prediction, various predictors such as median edge detection (MED), rhombus mean, least square, convolution neural network based predictor (CNNP) have been introduced. In this paper, a review of the working predictors being used in PEE-RDH is presented and discussed. In addition, a new predictor using extreme gradient boosting (XGBoost) is introduced in reversible data hiding. The XGBoost predictor makes use of a machine learning algorithm, where several optimization techniques are combined to get accurate results. To evaluate the performance comprehensively, experimental results considering different test images have been used and analyzed. From the analysis, it has been found that the XGBoost provides better prediction accuracy than some of the existing predictors. However, its performance is not up to the level of some other popular predictors such as least square, CNNP. • The paper first presents a detailed a review of various predictors being used in RDH. • The rhombus mean predictor is found to be the simplest with prediction accuracy. • A new XGBoost predictor has also been introduced for RDH. • The performance analysis and comparisons are also presented. • Further, future research directions have been recommended. [ABSTRACT FROM AUTHOR]

Singing melody extraction from polyphonic musical audio is one of the most challenging tasks in music information retrieval (MIR). Recently, data-driven methods based on convolutional neural networks (CNNs) have achieved great success for this task. In the literature, harmonic relationship has been proven crucial for this task. However, few existing CNN-based singing melody extraction methods consider the harmonic relationship in the training stage. The state-of-the-art CNN based methods are not capable of capturing such long-dependency harmonic relationship due to limited receptive field and unacceptable computation cost. In this paper, we introduce a neural harmonic-aware network with gated attentive fusion (NHAN-GAF) for singing melody extraction. Specifically, in the 2-D spectrograms modeling branch, we propose to employ multiple parallel 1-D CNN kernels to capture the harmonic relations between 1–2 octaves along the frequency axis in the spectrogram. Considering the advantage of jointly using Time–Frequency (T-F) domain and time domain information, we use two-branch neural nets to learn discriminative representation for this task. A novel gated attentive fusion (GAF) network is suggested to encode potential correlations between the two branches and fuse the descriptors learned from raw waveform and T-F spectrograms. Moreover, the idea of GAF can be exploited to the multimedia applications with multimodal analysis. With the two proposed components, our proposed model is capable of learning the harmonic relationship in the spectrogram and better capturing the contextual but discriminative features for singing melody extraction. We use part of the vocal tracks of the RWC dataset and MIR-1 K dataset to train the model and evaluate the performance of the proposed model on the ADC 2004, MIREX 05 and MedleyDB datasets. The experimental results show that the proposed method outperforms the state-of-the-art ones. [ABSTRACT FROM AUTHOR]

• Data Integration of two or more heterogeneous databases. • Syntactic and semantic analysis of textual data. • Semi-automatic process. [Display omitted] One of the most difficult issues today, is the integration of data from various sources. Thus, it arises the need of automatic Data Integration (DI) methods. However, in the literature there are fully automatic or semi-automatic DI techniques, but they require the involvement of IT-experts with specific domain skills. In this paper we present a novel DI methodology for which it is not required the involvement of IT-experts; in this methodology syntactically/semantically similar entities present in the sources are merged, by exploiting an information retrieval technique, a clustering method and a trained neural network. Although the suggested process is completely automated, we planned some interactions with the Company Manager, a figure who is not required to have IT-skills, but whose only contribution will be to define limits and tolerance thresholds during the DI process, based on the interests of the company. The validity of the proposed approach showed an integration accuracy between 99 % − 100 %. [ABSTRACT FROM AUTHOR]

In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Google, Amazon, etc. Cloud system presents several benefits to users in terms of easy operations, low implementation, and maintenance expenses. However, significant risks are encountered in the data security procedures of cloud systems. Although the area is frequently being analyzed and reformed, the concern of cloud data protection and user reliability remains under uncertainty due to growing cyber-attack schemes as well as cloud storage system errors. To deal with this risk and contribute to the endeavor of providing optimal data security solutions in cloud data storage and retrieval system, this paper proposes a Symmetric Searchable Encryption influenced Machine Learning based cloud data encryption and retrieval model. The proposed model enhances data security and employs an effective keyword ranking approach by using an Artificial Neural Network. The comparative assessment of the proposed model against multiclass SVM and Naïve Bayes has established the better operational potentiality of the model. The effectiveness of the proposed work is justified by the association between high TPR and low FPR. Further, a low CCR of 0.6973 adds up to the success of the proposed work. [ABSTRACT FROM AUTHOR]

Vehicle re-identification (re-ID) has numerous applications in real life, such as video surveillance, information retrieval, and public security. However, it suffers from the misalignment of vehicles, which is a critical problem caused by inaccurate detection and different views. This paper proposes a novel network named Part Alignment Network (PAN) which can properly handle the misalignment of vehicles. It is therefore adapted to the vehicle re-ID task, avoiding the use of any additional pre-processing steps such as the annotation of vehicle key points and part segmentation by hand. In PAN, cross-correlation is adopted to the alignment of vehicle parts. Then, an effective network architecture is designed to extract the discriminative aligned features. By combining complementary aligned features and original features, more robust feature representations are learned. To show the effectiveness of PAN, this paper conducts experiments on three vehicle re-ID databases (VD1, VD2, and VehicleID), on which it improves the current state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In our knowledge data about supplementations of recreational athletes are very limited in France. The health of these athletes is mostly under the supervision of their general practitioner. If it was shown that these athletes use substances, their general practitioners will need to find effective ways to protect their health. The main objective of this study is to estimate the prevalence of use of performance enhancing substances among athletes consulting in primary care center. Others goals are to collect information about motivations of user's, person advising consumer's and places to purchase substances. All major athletes consulting in general medical practice between the 24th of August 2020 and the 06th of November 2020 were invited to answer an anonymous questionnaire. A total of 40 randomized physicians of Herault participate in the study. We installed posters and flyers in waiting room to provide information and allow the athlete to submit to online version or paper version of questionnaire. A total of 281 athletes submitted to the questionnaire with 54.5% of male (n = 153) and 45.5% of female (n = 128) and an average age of 39.7 years (± 14.9). Over than 96% was recreational athletes (n = 272) and 59.9% of them report using a least one substance. About 66.9% of consumers report using dietary supplement, 67.5% medications and 13.6% illicit drugs or anabolic agents. Motivations and places to purchase depend on substances. Near of 60% of athletes consulting in primary care center report use performances enhancing substances. It seems important that physicians are aware of this in order to help the athlete to protect his health. [ABSTRACT FROM AUTHOR]

Bug triaging is a critical and time-consuming activity of software maintenance. This paper aims to present an automated heuristic approach combined with fuzzy multi-criteria decision-making for bug triaging. To date, studies lack consideration of multi-criteria inputs to gather decisive and explicit knowledge of bug reports. The proposed approach builds a bug priority queue using the multi-criteria fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and combines it with Bacterial Foraging Optimization Algorithm (BFOA) and Bar Systems (BAR) optimization to select developers. A relative threshold value is computed and categorization of developers is performed using hybrid optimization techniques to make a distinction between active, inactive, or new developers for bug allocation. The harmonic mean of precision, recall, f-measure, and accuracy obtained is 92.05%, 89.21%, 85.09%, and 93.11% respectively. This indicates increased overall accuracy of 90%±2% when compared with existing approaches. Overall, it is a novel solution to improve the bug assignment process which utilizes intuitive judgment of triagers using fuzzy multi-criteria decision making and is capable of making a distinction between active, inactive, and new developers based on their relative workload categorization. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the problem of expertise matching with various constraints. Expertise matching, which aims to find the alignment between experts and queries, is a common problem in many applications such as conference paper–reviewer assignment, product–reviewer alignment, and product-endorser matching. Most existing methods formalize this problem as an information-retrieval problem and focus on finding a set of experts for each query independently. However, in real-world systems, various constraints are often needed to be considered. For example, in order to review a paper, it is desirable that there is at least one senior reviewer to guide the reviewing process. An important question is: “Can we design a framework to efficiently find the optimal solution for expertise matching under various constraints?” This paper explores such an approach by formulating the expertise matching problem in a constraint-based optimization framework. In the proposed framework, the problem of expertise matching is linked to a convex cost flow problem, which guarantees an optimal solution under various constraints. We also present an online matching algorithm to support incorporating user feedbacks in real time. The proposed approach has been evaluated on two different genres of expertise matching problems, namely conference paper–reviewer assignment and teacher–course assignment. Experimental results validate the effectiveness of the proposed approach. Based on the proposed method, we have also developed an online system for paper–reviewer suggestions, which has been used for paper–reviewer assignment in a top conference and feedbacks from the conference organizers are very positive. [Copyright &y& Elsevier]

Context encompasses the classification of a certain environment by its key attributes that take the role of semantic markers. It is an abstract representation of a certain data environment. In texts, the context classifies and represents a piece of text in a generalized form. Context can be a recursive construct when summarizing information on a more coarse-grained level. This paper presents identification and standardization of context on different levels of granularity that finally supports faster and more precise information retrieval. The prototypical system presented here applies supervised learning for a semi-automatic approach to extract, distil, and standardize data from text. The approach is based on named-entity recognition and simple ontologies for identification and disambiguation of context. Even though the prototype shown here still represents work in progress, it already demonstrates its potential for mining texts on different levels of context granularity. The paper presents the design of the Contexter system that supports identification and classification of misinformation and fake news around the topic Covid-19. [ABSTRACT FROM AUTHOR]

In natural language, the same meaning can be expressed by different texts. The process of determining the inference relationship occurring between a text T and a hypothesis H is called Natural Language Inference (NLI). The NLI task aims to provide a generic framework that captures, in a unifying manner, the inference across Natural Language Processing applications such as question answering, summarization, information retrieval, and machine translation. Many tasks and datasets have been created to support the development and evaluation of the ability of the NLI task in different languages. For the Arabic language, interest in this field is gradually increasing. This paper aims to provide an overview of state-of-the-art NLI approaches for Arabic, the relevant knowledge resources and the used tools in order to support a better understanding of this growing field. Moreover, this paper points to classify the proposed approaches for Arabic NLI and compare existing NLI systems. [ABSTRACT FROM AUTHOR]

Information retrieval is a scorching today when the flare-up of data on the Internet increasingly day by day. Due to the infinite growth and availability of the Internet; numbers of educational sites are amplified. Huge Expansion in knowledge puts a special effect on the education system. This helps in upgrading higher education. Interest in Web usage mining has grown swiftly in its short history, both in exploring interest and research. Web usage mining is a type of data mining that extracts the server log information after users browse web pages. For extracting and retrieval information machine learning approaches are designed to recognize useful information from text documents automatically. The main aim of this paper is to cram weblogs using the clustering technique with WEBASTRO tool for predicting next user movement and user behaviour analysis. This paper presents an ideal approach for web mining and predicting their behaviour for the next visit and automatic web site modification. [ABSTRACT FROM AUTHOR]

• This paper focuses on content-based information retrieval with similarity fusion. • Contextual information of fused similarity graph is used to enhance performance. • Sparsity of fused similarity matrix is utilized to improve retrieval efficiency. • The effectiveness of the proposed model is verified in 4 different retrieval tasks. • The proposed model can be modified and applied in other important retrieval tasks. Similarity networks contain important topological features and patterns critical to understanding interactions among samples in a large dataset. To create a comprehensive view of the interactions within a dataset, the Similarity Network Fusion (SNF) technique has been proposed to fuse the similarity networks based on different data types into one similarity network that represents the full spectrum of underlying data. In this paper, a modified version of SNF, which is named as Contextual Information based SNF (CI-SNF), is proposed. In CI-SNF, first, modified Jaccard distance is performed on the SNF fused similarity to utilize the contextual information contained in the fused similarity network. Second, the local consistency of samples from the same category is enhanced by speculating that the samples which are located high in the Jaccard distance based ranking list of a specific query are from the same category as the query. Third, the inverted index technique is introduced to utilize the sparsity property of the locally consistent similarity network to enhance the computational efficiency. To verify the effectiveness and efficiency of CI-SNF model, it is applied in four different tasks, Cover Song Identification (CSI), image classification, cancer subtype identification, and drug taxonomy, respectively. Extensive experiments on thirteen challenging datasets demonstrate that CI-SNF scheme outperforms state-of-the-art similarity fusion algorithms including SNF in all four tasks. It is also verified that utilizing the contextual information contained in the SNF-based similarity network helps to enhance the performance of the SNF-based scheme, further. [ABSTRACT FROM AUTHOR]

This paper presents findings from the analysis of data from 177 individuals who solved 401 separate design problems. The findings from this body of research show that individuals with more expertise have processes that are more complex, consider a broader set of information, spend more time problem solving and are more likely to demonstrate a cascade pattern in their design activities. This paper also demonstrates the utility of a commitment to a single research methodology, creating a design timeline representation that is both concrete (visible) and sticky (memorable), and using that representation to tell a consistent story about design processes. Examples of using the timelines to teach about design processes demonstrate timelines are indeed sticky for students of design. • Individuals with more design expertise demonstrate more complexity in their design processes. • Design timelines convey the complexity of design processes and can be used to compare processes. • Design timelines can convey a unique design signature for each instance of a design process. • Design timelines provide useful contrasts to traditional node-and-arc design models. • Design timelines are effective tools for teaching design supporting design awareness. [ABSTRACT FROM AUTHOR]

Clustering is a traditional data mining problem that has attracted researchers from different disciplines because its solution can be applied to many useful problems in our daily life. Since the era of big data is coming, how to "reduce the computing time" of an "effective clustering algorithm" has been a promising research issue in recent years. Thus, this paper presents an effective clustering algorithm, by using the so-called searched information to determine later search directions, and then has it implemented on Spark to accelerate its response time for analyzing large-scale datasets. Simulation results show that the proposed algorithm provides a better result than the other clustering algorithms compared in this paper because it is less sensitive to the initial solutions. The simulation results further show that cloud computing platform is capable of enhancing the performance of the proposed algorithm. • This study presents a high-performance clustering algorithm based on searched experiences. • The results show that it is able to find a better result than traditional clustering algorithms. • We apply it to Spark to show its possibility to accelerate the response time on a cloud system. [ABSTRACT FROM AUTHOR]

Nowadays, the internet plays a major role in online information retrieval. As, we are well aware the web provides information related all the fields like national, lifestyle, movies, shopping, spirituality, sports, entertainment and much more. One cannot assume that the web retrieved information is believable or trustworthy due to multiple answers to the same query. The paper aims to study many research articles which are related to truth information discovery. However, the paper found that there is no proper research to find trustworthiness of news content which is extracted from multiple information sources with minimum misclassification error and retrieval time. To alleviate these issues, the truth content discovery algorithm is proposed to produce trustworthy information with minimal time along with multiple domain news information. The system provides reliable information from a various source of news provider with minimum classification error. The proposed method ranked the news content index based on query matches from extracted information. It minimizes the query retrieval time and classification error. Based on the experimental evaluation, proposed TCD + J48 algorithm produced the best result compared to existing approaches. It minimizes reduces 7.54 ms the query retrieval time (QRT), 5% Error rate (ER) and 0.98 and Mean Normalized Absolute Distance (MNAD). [ABSTRACT FROM AUTHOR]

Assessment literacy has been recognized as important for teachers because it helps them use information about student learning to teach more effectively by responding to students' learning needs. The aim of this paper is to present an updated, expanded model of assessment literacy that represents three key aspects of assessment literacy in context. The paper reports the results of a Delphi inquiry with international experts on educational assessment and teacher education. Conclusions include implications for research on educational assessment and teacher professional development. • Critical literature review on teacher assessment literacy was performed. • A new model was proposed to a selected panel of experts. • Experts provided feedback on this new model. • Experts' feedback made explicit the cultural and contextual nature of assessment literacy. • Paths for future in-depth studies are presented. [ABSTRACT FROM AUTHOR]

The electronic environment has significantly shifted library capabilities and user expectations for the delivery of government information and services. At the same time, many laws of the federal government have pushed for the creation and distribution of government information through electronic channels. However, the Federal Depository Library Program (FDLP) continues as though these changes will not have a large impact on the program. It is time for a meaningful reevaluation of the FDLP program and approaches to government information by academic libraries generally. Drawing upon a range of library research and policy analysis, this paper argues for changes in both concept and practice in the provision of government information by FDLP and other academic libraries and in the educational preparation of future academic librarians. Further, this paper asserts that such a discussion needs to occur beyond considerations of the current economic environment, which some libraries may view as a reason to restructure the FDLP to achieve cost reductions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Deep Reinforcement learning based approach for Learning to rank task. Reinforcement Learning has been applied in the ranking task with good success, but the existing Policy Gradient based approaches suffer from noisy gradients and high variance, resulting in unstable learning. The natural policy gradient methods like REINFORCE perform Monte Carlo sampling, thus taking samples randomly, which leads to high variance. As the action space becomes large, i.e., with a very large number of documents, traditional RL techniques lack the complex model required in the scenario to deal with a large number of items. We propose a Deep Reinforcement learning based approach for learning to rank task in this paper to address these issues. By combining Deep learning with the Reinforcement Learning framework, our approach can learn a complex function as deep neural networks can provide significant function approximation. We used Actor-Critic framework where the critic network can reduce variance by utilizing techniques such as clipped delayed policy updates, clipped double q learning, etc. Also, due to the enormous space of the web, the most relevant results are needed to be returned for the corresponding query from within a large action space. Policy gradient algorithms have been effectively applied to problems in large action spaces(items) with deep neural networks as they don't rely on finding value for each action(item) as in value-based methods. Further, we use an actor-network with a CNN layer in the ranking process to capture the sequential patterns among the documents. We utilize the TD3 method to train our Reinforcement Learning agent with a listwise loss function, which performs delayed policy updates resulting in value estimates with lower variance. To the best of our knowledge, this is the first Deep reinforcement learning method applied in Learning to Rank for document retrieval. We performed experiments on the various Letor datasets and showed that our method outperforms various state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

The purpose of this practical case study is to examine the development of Dataverse, a global research data management consortium. This paper is the second in a project focusing on data discoverability and current metadata implementation on the Dataverse portals established by 27 university libraries worldwide. Five research questions were proposed to identify the most popular metadata standards and elements, search interface options, and result display formats by those portals. The data were collected from 27 university libraries worldwide between December 1, 2020 and January 31, 2021. According to the results of the descriptive analyses, the most popular metadata elements for the dataset overview were Subject and Description , while Dataset persistent ID , Publication Date , Title, Author , Contact , Deposit Date , Depositor , Description , and Subject were the most popular elements for the metadata record of each dataset. Publication Year , Author Names , and Subject were found to be the most common search facets used by the portals. English was the most common language used for the search interfaces and metadata descriptions. Based on their findings from this evidence-based study, the authors recommend future research on the development of institutional data portal infrastructure, on stakeholder outreach and training, and on user studies on dataset retrieval. [ABSTRACT FROM AUTHOR]

Product quantization (PQ) is a fundamental technique for approximate nearest neighbor (ANN) search in many applications such as information retrieval, computer vision and pattern recognition. In the existing PQ-based methods for approximate nearest neighbor search, the reachable best search accuracy is achieved by using fixed codebooks. The search performance is limited by the quality of the hard codebooks. Unlike the existing methods, in this paper, we present a novel codebook-softened product quantization (CSPQ) method to achieve more quantization levels by softening codebooks. We firstly analyze how well the database vectors match the trained codebooks by examining quantization error for each database vector, and select the bad-matching database vectors. Then, we give the trained codebooks b -bit freedom to soften codebooks. Finally, by minimizing quantization errors, the bad-matching vectors are encoded by softened codebooks and the labels of best-matching codebooks are recorded. Experimental results on SIFT1M, GIST1M and SIFT10M show that, despite its simplicity, our proposed method achieves higher accuracy compared with PQ and it can be combined with other non-exhaustive frameworks to achieve fast search. [ABSTRACT FROM AUTHOR]

With the rapid growth in the number of tweets published daily on Twitter, automated classification of tweets becomes necessary for broad diverse applications (e.g., information retrieval, topic labeling, sentiment analysis, rumors detection) to better understand what these tweets are, and what the users are expressing in this social platform. Text classification is the process of assigning one or more pre-defined categories to text according to its content. Tweets are short, and the short text does not have enough contextual information, which is part of the challenge in their classification. Adding to the challenge is the increase in ambiguity since the diacritical marking is not explicitly specified in most Modern Standard Arabic (MSA) texts. Not to mention the Arabic tweets are known to contain fused text of MSA and dialectal Arabic. In this paper, we propose a scheme to classify the textual tweets in the Arabic language based on its linguistic characteristics and content into five different categories. We explore two different textual representations: word embedding using Word2vec and stemmed text with term frequency-inverse document frequency (tf-idf). We tested three different classifiers: Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Random Forest (RF). All the classifiers had their hyperparameters tuned. We collected and manually annotated a dataset of approximately 35,600 Arabic tweets for the experiments. Statistically, the RF and the SVM with radial basis function (RBF) kernel performed equally well when used with stemming and tf-idf , achieving macro- F 1 scores ranging between 98.09% and 98.14%. The GNB with word embedding was disappointingly low performer. Our result tops the current state-of-the-art score of 92.95% using a deep learning approach, RNN-GRU (recurrent neural network-gated recurrent unit). [ABSTRACT FROM AUTHOR]

Retrieval of mathematical information from scientific documents is one of the crucial tasks. Numerous Mathematical Information Retrieval (MIR) systems have been developed, which mainly focus on the improvement over the indexing and the searching mechanism, the poor results obtained for evaluation measures depict major limitations of such systems. These enhance the scope of improvement and new innovations through the inclusion of functionalities, which can resolve the challenges of MIR system. Further, to improve the performance of the MIR systems, this paper proposed a formula embedding and generalization approach with the context, in addition to innovative relevance measurement technique. In this approach, documents are preprocessed by the document preprocessor module and extracted the formulas in Presentation MathML format with their context. The formula embedding and generalization modules of the proposed approach formed the binary vectors where '1' represents the presence, and '0' represents the absence of a particular entity in a formula, and subsequently, the vectors of formulas with context are indexed by the indexer. The innovative relevance measurement technique of the proposed approach ranked those documents first, which are retrieved by both formula embedding and generalization modules as compared to the individual one. The proposed approach has been tested on the MathTagArticles of Wikipedia of NTCIR-12, and the obtained results verify the significance of the context of the formula and the dissimilarity factor in the retrieval of mathematical information. [ABSTRACT FROM AUTHOR]

Search systems have played an essential role in improving user experience and information accessibility on the web, allowing users to express their information needs (provided as search queries) and serving users with the results that satisfy those needs. However, a user's search task can be complex and may not be expressed using a single search query, requiring the user to write several queries to fulfill all the aspects of his or her needs. In such scenarios, an intelligent search system would be beneficial to identify and understand the original search task issued by a user and then suggest several search tasks (in a form of key-phrases or short topics) related to the original search task. Aiming to tackle this limitation, this paper proposes a framework for applying several unsupervised learning approaches, including topic modeling and log mining. The results of applying these approaches to large user session data show that, indeed, these approaches would be applicable in search suggestion and task recommendation, reaching a significant improvement over a strong baseline. [ABSTRACT FROM AUTHOR]

Consolidation of the research information improves the quality of data integration, reducing duplicates between systems and enabling the required flexibility and scalability when processing various data sources. We assume that the combination of a data lake as a data repository and a data wrangling process should allow low-quality or "bad" data to be identified and eliminated, leaving only high-quality data, referred to as "research information" in the Research Information System (RIS) domain, allowing for the most accurate insights gained on their basis. This, however, would lead to increased value of both the data themselves and data-driven actions contributing to more accurate and aware decision-making. This cleansed research information is then entered into the appropriate target Current Research Information System (CRIS) so that it can be used for further data processing steps. In order to minimize the effort for the analysis, the proliferation and enrichment of large amounts of data and metadata, as well as to achieve far-reaching added value in information retrieval for CRIS employees, developers and end users, this paper outlines the concept of a curated data lake with the data wrangling process, showing how it can be used in CRIS to clean up data from heterogeneous data sources during their collection and integration. [ABSTRACT FROM AUTHOR]