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This paper presents a new approach for the detection of copy-move image forgery, a commonly employed technique for image manipulation. The proposed method combines a modified version of the Gabor Filter and Centre Symmetric Local Binary Pattern (CS-LBP) for feature extraction, aiming to meet the growing demand for accurate forgery detection. The process involves pre-processing the image and extracting features using the Gabor filter and CS-LBP at varying scales and orientations. Key points are matched using the Manhattan distance to identify forged regions. Classification of the forged images is achieved using Hybrid Neural Networks with Decision Tree (HNN-DT). In order to assess the performance of the presented method, diverse image datasets are used and compared to existing feature extraction techniques. The results demonstrate the effectiveness of the modified Gabor filter with CS-LBP in accurately classifying forged images. Specifically, the HNN-DT method with Gabor filter CS-LBP feature extraction surpasses HNN-DT with SURF and PCA feature extraction in terms of classification accuracy and overall performance. Evaluation on the CoMoFoD database confirms superior results compared to existing techniques, establishing the proposed method as a reliable approach for distinguishing between authentic and forged images. Consequently, it serves as a robust solution for image classification and forgery detection. The presented work focuses on detecting mainly three types of image forgery, namely retouching, splicing, and cloning. It is applicable to various image formats, including JPEG and BMP, and is designed specifically for forensic applications in image forgery detection. [ABSTRACT FROM AUTHOR]

In this paper, the problem of improving reliability and reducing routing overhead in dynamic scalable Wireless Ad hoc NETworks (WANETs) is considered. Having in mind that the number of mobile network users is constantly growing, dynamic network topology and scalability need to be taken into account when designing or choosing an appropriate routing protocol. In this paper, we have proposed an improvement of Ad hoc On Demand Distance Vector (AODV) protocol, called Neighborhood-Density AODV (ND-AODV) to reduce routing overhead in large scale dynamic WANETs. Instead of hop-count metric, our protocol uses Expected Transmission Count (ETX) based metric, called Power Light Reverse ETX (PLRE), which significantly improves the reliability of AODV protocol. Proposed protocol with metric, ND-AODV-PLRE, is compared to AODV and Probabilistic AODV (PAODV) protocols with PLRE metric in the terms of basic key performance indicators such as throughput, packet loss ratio, overhead, average, initial, and jitter of end to end delay. In order to show that it is suitable for various types of large scale dynamic WANETs, the ND-AODV-PLRE protocol is tested in two scenarios, one using the Manhattan Grid, and the other Random Waypoint mobility model. The results obtained in Network Simulator 3 (NS-3) have shown significant improvements of the proposed protocol in both scenarios. NS-3 implementation code is made publicly available. [ABSTRACT FROM AUTHOR]

Background: People with mental illnesses are disproportionately entangled in the criminal legal system. Historically, this involvement has resulted from minor offending, often accompanied by misdemeanor charges. In recent years, policymakers have worked to reduce the footprint of the criminal legal system. This paper seeks to better understand how misdemeanor systems intervene in the lives of people with mental illnesses. Methods: System mapping exercises were conducted with misdemeanor system stakeholders from the jurisdictions of Atlanta, Chicago, Manhattan, and Philadelphia. Narrative detail on decision-making and case processing, both generally and in relation to specific types of behavior, including trespassing, retail theft/shoplifting, and simple assault, were coded and analyzed for thematic patterns. Based on the qualitative analysis, this paper offers a conceptual diagram of contexts shaping misdemeanor system interventions among people with mental illnesses. Results: All four sites have been engaged in efforts to reduce the use of misdemeanor charges both generally and in relation to people with mental illnesses. Decision-makers across all sites experience contexts that shape how, when, and where they intervene, which are: (1) law and policy environments; (2) location of the behavior; (3) expectations of stakeholders; (4) knowledge of mental illnesses; and (5) access to community resources. Law and policy environments expand or constrain opportunities for diversion. The location of offending is relevant to who has a stake in the behavior, and what demands they have. Clinical, experiential, and system-level knowledge of mental illnesses inform a chain of decisions about what to do. The capacity to address mental health needs is contingent on access to social services, including housing. Conclusion: People making decisions along the criminal legal continuum are critical to illuminating the dynamic, inter-related contexts that facilitate and frustrate attempts to address defendants' mental health needs while balancing considerations of public safety. Multi-sector, scenario-based or case study exercises could help identify concrete ways of improving each of the contexts that surround whole-of-system decisions. [ABSTRACT FROM AUTHOR]

Semantic relatedness is one of the most significant aspects of natural language processing. It has been identified as a critical technology for developing intelligent systems like Siri, Microsoft Ice, Cortana, and Xiaoai. In 2014, SemEval ranked SR as the top task. While many existing studies have focused on analyzing the entailment of single phrases, advancements in deep learning have made it possible to analyze complete sentences or texts. While the natural parallelism of membrane computing has shown promise for data processing, harnessing this potential to advance semantic relatedness remains an open problem yet to be tackled. This paper proposes a novel Siamese Manhattan LSTM-SNP approach (SiMaLSTM-SNP) for the SR problem. The approach uses a collaborative Word2vec and 10-Layer Attention strategy to represent and extract sentence pairs and a Siamese LSTM-SNP structure to calculate the hidden states of sentences. The multi-head self-attention layer identifies text associations and redistributes hidden state weights. The last hidden state is extracted, and the relatedness score is calculated using the Manhattan distance. The experiments demonstrate that SiMaLSTM-SNP outperforms 17 classical SR baselines and 7 novel approaches on the standard datasets SICK and STS in terms of mean square error performance. This indicates that SiMaLSTM-SNP can accurately capture the semantic distinction between two sentences and effectively preserve their semantic information. [ABSTRACT FROM AUTHOR]

A well-known theorem in plane geometry states that any set of n non-collinear points in the plane determines at least n lines. Chen and Chvátal asked whether an analogous statement holds within the framework of finite metric spaces, with lines defined using the notion of betweenness. In this paper, we prove that in the plane with the L 1 (also called Manhattan) metric, a non-collinear set of n points induces at least ⌈ n / 2 ⌉ lines. This is an improvement of the previous lower bound of n/37, with substantially different proof. As a consequence, we also get the same lower bound for non-collinear point sets in the plane with the L ∞ metric. [ABSTRACT FROM AUTHOR]

The so-called Hamming distance measures the difference between two binary strings A and B. In simplified form, it measures the number of changes in A to get B. This type of distance is very useful in classical computing in applications such as error correction. It is also advantageous in quantum computing, being for example widely used in quantum machine learning. Since current quantum computers have limited resources, this type of distance is particularly attractive because it can be computed using fewer qubits and operations than other distances such as Euclidean or Manhattan distances. In this paper, two circuits for calculating Hamming distances using exclusively Clifford+T gates are presented. The aim of both circuits is to reduce the quantum cost and number of T gates needed to compute the Hamming distance. The T gate is more expensive than the other gates, so this reduction will have a significant impact on the total cost of the circuits. Furthermore, the proposed circuits are implemented using only Clifford+T gates. The circuits implemented exclusively with this group of gates are compatible with proven error detection and correction codes. [ABSTRACT FROM AUTHOR]

Conformal Cyclic Cosmology (CCC) is a cyclic model of the universe put forward by Sir Roger Penrose (Cycles of Time: An Extraordinary New View of the Universe, Random House, Manhattan, 2010). A conformal invariance assumption in the neighbourhood of the crossover region between cycles (which Penrose calls aeons) allows successive space-times to be related by a conformal rescaling. A major open problem is how to choose the conformal factor in a unique way, and is a fundamental hurdle to further study. Proposals have been put forward by Newman (Gen Relativ Gravit 46(5):1–13, 2014), Tod (Gen Relativ Gravit 47(3):1–13, 2015) and Nurowski (Class Quantum Gravity 38(14):145004, 2021), but they disagree in one way or another with Penrose's original assumptions as well as each other. In this paper we compare these different models in detail and rule out certain choices for the conformal factor that have been put forward by Penrose. We extend the results of Newman and fix inconsistencies that arose in his calculations. A new class of solutions are put forward which agree with Penrose's assumptions exactly so long as a certain additional relation is satisfied. [ABSTRACT FROM AUTHOR]

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) are among the most employed approaches for aggregating performances in Multi-Criteria Decision-Making (MCDM). TOPSIS and SAW are two MCDM methods based on the value function approach and are often used in combination with other MCDM methods in order to produce rankings of alternatives. In this paper, first, we analyse some common features of these two MCDM methods with a specific reference to the additive properties of the value function and to the sensitivity of the value function to trade-off weights. Based on such methodological insights, an experimental comparison of the results provided by these two aggregation methods across a computational test is performed. Specifically, similarities in rankings of alternatives produced by TOPSIS and SAW are evaluated under three different Minkowski distances (namely, the Euclidean, Manhattan and Tchebichev ones). Similarities are measured trough a set of statistical indices. Results show that TOPSIS, when used in combination with a Manhattan distance, produces rankings which are extremely similar to the ones resulting from SAW. Similarities are also Experimental results confirm that rankings produced by TOPSIS methods are closer to SAW ones when similar formal properties are satisfied. [ABSTRACT FROM AUTHOR]

One of the main steps in the data lifecycle is to publish it for data analysts to discover hidden patterns. But, data publishing may lead to unwanted disclosure of personal information and cause privacy problems. Data anonymization techniques preserve privacy models to prevent the disclosure of individuals' private information in published data. In this paper, a distributed in-memory method is proposed on the Apache Spark framework to preserve the ℓ-diversity privacy model. This method anonymizes large-scale data in a three-phase process, which includes, seed selection, data clustering for ℓ -diversity, and finalizing phase. In this method, a hierarchical kmeans-based data clustering algorithm has been designed for data anonymization. One of the major challenges of anonymization methods is to establish a better trade-off between data utility and privacy. Therefore, for calculating the distance between records and forming more cohesive ℓdiverse-clusters, the authors have designed two Manhattan-based and Euclidean-based distance functions to satisfy the requirements of the ℓ-diversity model. Given the 100-fold speed of the Spark compared to MapReduce, the proposed method is presented using in-memory RDD programming in Apache Spark, to address the runtime, scalability, and performance in large-scale data anonymization as it exists in the previous MapReduce-based algorithms. Our method provides general knowledge to use parallel in-memory computation of Spark in big data anonymization. In experiments, this method has obtained lower information loss and loses about 1% to 2% accuracy and FMeasure criteria; therefore, it establishes a better trade-off than the state-of-the-art MapReduce-based Mondrian methods [ABSTRACT FROM AUTHOR]

Superpixel segmentation is a popular image preprocessing technology in image processing. Among the various methods used to calculate uniform superpixel, the performance of linear spectral clustering (LSC) is better than the state-of-the-art superpixel segmentation algorithms. However, this method is slow on images and has low accuracy on non-convex images. In order to improve this problem, we propose a subsampled clustering method that can accelerate LSC. Meanwhile, this paper presents an improved distance measurement method based on non-convex image features and Manhattan distance, which can achieve high accuracy on non-convex images. The proposed method is evaluated on the BSDS500 dataset. The experimental results confirmed that this method runs faster than LSC, and at the same time produces almost the same superpixel segmentation accuracy on the images. In addition, the proposed method improves the accuracy of superpixel segmentation on non-convex images. [ABSTRACT FROM AUTHOR]

Precise wind speed prediction is crucial for the management of the wind power generation systems. However, the stochastic nature of the wind speed makes optimal interval prediction very complicated. In this paper, a hybrid approach consisting of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), temporal convolutional network with attention mechanism (ATCN), and bidirectional long short-term memory network (Bi-LSTM) is proposed for wind speed interval prediction (WSIP). First, ICEEMDAN is used to pre-process the raw data by decomposing the wind signal to several intrinsic mode functions. ATCN is used to reduce the uncertainty from the denoised data and extract the important temporal and spatial characteristics. Then, Bi-LSTM is used to forecast the high-quality intervals for the wind speed. Existing approaches observe a decline in the forecasting performance when the time ahead increases. As a result, the hybrid approach is evaluated using 5-min, 10-min, and 30-min ahead WSIP. To evaluate the novelty of the proposed approach, an experiment is conducted utilising wind speed data from the Garden City, Manhattan wind farm. The experimental results demonstrate that the proposed framework outperformed the comparison models with percentage improvements of 36%, 47%, and 17% for 5-min, 10-min, and 30-min ahead WSIP. [ABSTRACT FROM AUTHOR]

The paper presents a study of equilibrium in a two-sided market for network platforms with cross externalities between buyers and sellers. The proposed model is a generalization of Armstrong's (2006) monopoly model for the case of a duopoly in a two-sided market for network platforms located on a plane. The paper solves the problem of optimal pricing and investigates the question of the optimal location of platforms in the market, provided that the heterogeneous utility of agents of both groups (buyers and sellers) is formed taking into account the Hotelling specification with the Manhattan metric. [ABSTRACT FROM AUTHOR]

The indoor scenes usually exhibit strong structural regularity; on the other hand, the local geometric features are scarce and difficult to be distinguished. The properties make the solutions of the existing global registration algorithms ambiguous. In addition, finding global optimal solution is usually computationally expensive. These problems make the global registration of indoor point clouds a challenging task. Based on the Manhattan-world assumption and color information, we propose a fast and effective algorithm for the global registration of indoor colored point clouds in this paper. We introduce the Manhattan-world assumption into the global registration of indoor point cloud to limit the number of all possible rotation solutions. As a result, our algorithm solves the problem very fast. Furthermore, we utilize the color information hierarchically to eliminate the ambiguity induced by the structural models. The limited rotation set is filtered by the global color histograms, and the final solution is evaluated by the local features. The experiments demonstrate that our algorithm can complete the global registration for the point clouds with hundreds of thousands of points in 0.1 second with comparable accuracy with others. [ABSTRACT FROM AUTHOR]

On-demand mobility systems in which passengers use the same vehicle simultaneously are a promising transport mode, yet difficult to control. One of the most relevant challenges relates to the spatial imbalances of the demand, which induce a mismatch between the position of the vehicles and the origins of the emerging requests. Most ridepooling models face this problem through rebalancing methods only, i.e., moving idle vehicles towards areas with high rejections rate, which is done independently from routing and vehicle-to-orders assignments, so that vehicles serving passengers (a large portion of the total fleet) remain unaffected. This paper introduces two types of techniques for anticipatory routing that affect how vehicles are assigned to users and how to route vehicles to serve such users, so that the whole operation of the system is modified to reach more efficient states for future requests. Both techniques do not require any assumption or exogenous knowledge about the future demand, as they depend only on current and recent requests. Firstly, we introduce rewards that reduce the cost of an assignment between a vehicle and a group of passengers if the vehicle gets routed towards a high-demand zone. Secondly, we include a small set of artificial requests, whose request times are in the near future and whose origins are sampled from a probability distribution that mimics observed generation rates. These artificial requests are to be assigned together with the real requests. We propose, formally discuss and experimentally evaluate several formulations for both approaches. We test these techniques in combination with a state-of-the-art trip-vehicle assignment method, using a set of real rides from Manhattan. Introducing rewards can diminish the rejection rate to about nine-tenths of its original value. On the other hand, including future requests can reduce users' traveling times by about one-fifth, but increasing rejections. Both methods increase the vehicles-hour-traveled by about 10%. Spatial analysis reveals that vehicles are indeed moved towards the most demanded areas, such that the reduction in rejections rate is achieved mostly there. [ABSTRACT FROM AUTHOR]

The paper presents a new mixed-integer programming formulation for the maximally diverse grouping problem (MDGP) with attribute values. The MDGP is the problem of assigning items to groups such that all groups are as heterogeneous as possible. In the version with attribute values, the heterogeneity of groups is measured by the sum of pairwise absolute differences of the attribute values of the assigned items, i.e. by the Manhattan metric. The advantage of the version with attribute values is that the objective function can be reformulated such that it is linear instead of quadratic like in the standard MDGP formulation. We evaluate the new model formulation for the MDGP with attribute values in comparison with two different MDGP formulations from the literature. Our model formulation leads to substantially improved computation times and solves instances of realistic sizes (for example the assignment of students to seminars) with up to 70 items and three attributes, 50 items and five attributes, and 30 items and ten attributes to (near) optimality within half an hour. [ABSTRACT FROM AUTHOR]

Recently, Internet of Things (IoT) and multimedia are gaining popularity because of their usages in various applications. Numerous sensors and automated devices are generating huge volumes of data. Therefore, it is required to efficiently and effectively analyze this voluminous data. It can be achieved by using appropriate machine leaning techniques such as clustering. Among the clustering techniques, the k-means method/algorithm is one of the simplest, effective and commonly used methods. For making the cluster, it uses a measure of similarity/distance among the data observations. Nearby/similar data observations are placed within the same cluster whereas distant/dis-similar data observations are placed in other clusters. Hence, the similarity/distance metric plays a major role on the performance and accuracy of the k-means. Therefore, using an appropriate similarity/distance metric, the performance and accuracy of the k-means can be improved. K-means algorithm is majorly implemented using Euclidean distance metric. With the objective to explore the better and/or alternate similarity/distance metric(s) for k-means, a case study, based on empirical evaluation, of thirteen different similarity/distance metrics on six well-known datasets is performed and presented in this paper. By using the efficient and effective similarity / distance metrics, the performance and accuracy of the k-means algorithm can be improved which leads to formation of good clusters of various data observations or things or images etc. The results of the empirical study are analyzed and compared on the basis of widely used statistical clustering evaluation/validation measures. Based on the comparative results, these metrics are assigned with the ranks. Overall, the results demonstrate that Manhattan and Minkowski distance metrics gives better results for k-means algorithm. [ABSTRACT FROM AUTHOR]

For the HSI classification, the recently introduced nearest regularized subspace (NRS) classifier outperform the sparse representation-based classification (SRC) and collaborative representation-based classification (CRC). The NRS method has two major distance measurements parts to classify approximation of the testing simple properly. One is the residual between the approximation and the corresponding pixel, and the other is the elements in the Tikhonov matrix. The main contribution of this paper is to find the optimum distance measures for NRS to increase the performance results and minimize the running time compared to their previous versions and other existing methods. The experiments were conducted with four distance measures such as Manhattan distance (MD), Euclidean distance(ED), Chi-square (X2) and Cosine distance (CS). The different distance measures are implemented in residual and Tikhonov Matrix calculations. To sum up, sixteen (16) distance measurement combinations are tested on Four Datasets, such as Indian Pines, Pavia University, KSC and Center of Pavia. The experiments show higher accuracy and reduced time as compared to other methods, with NRS_X2-MD and NRS_MD-MD as the top two combinations. [ABSTRACT FROM AUTHOR]

Recent years witness the unprecedented proliferation of human-related trajectory data, such as GPS data and Local-Based Social Networks data. Each kind of trajectory data has its pros and cons in instrumenting and charactering human mobility and activities, which provides a valuable opportunity to understand the in-depth intelligence of human travel behaviors from the perspective of data fusion. In this paper, we focus on the motivations of passengers' travel behaviors, i.e., trip purposes. Rather than deploying sensors to collect data regarding passengers behaviours directly, we propose a device-free sensing way which leverages the taxi trajectory data left when passengers taking taxis. In another word, the data is collected by GPS devices installed in taxis. Specifically, we establish a visual analytics system called VizTripPurpose to understand taxi trip purposes by fusing the taxi trajectory data and human check-in data. Specifically, our system allows for not only obtaining taxi trip purpose on both collective and individual scales, but also visually enable analysts, such as urban planners, to (1) intuitively understand the time-evolving taxi trip purpose patterns by profiling them at different time periods in the multi-scale spatial domain; (2) timely infer the trip purpose for a given taxi trip containing origin, destination, and time information. To illustrate the effectiveness of our system, we provide case studies on real taxi trip data and Foursquare check-in data generated in the region of Manhattan, New York City (NYC), US. [ABSTRACT FROM AUTHOR]

Accurate and automated identification of the deceased victims with dental radiographs plays a significant role in forensic dentistry. The image processing techniques such as segmentation and feature extraction play a crucial role in image retrieval in accordance with the matching image. The raw image undergoes segmentation, feature extraction and distance-based image retrieval. The ultimate goal of the proposed work is the automated quality enhancement of the image by providing advanced enhancement techniques, segmentation techniques, feature extraction, and matching techniques. In this paper, multi-orientation local ternary pattern-based feature extraction is proposed for feature extraction. The grey level difference method (GLDM) is adopted to extract the texture and shape features that are considered for better results. The image retrieval is done by the computation of similarity score using distances such as Manhattan, Euclidean, vector cosine angle, and histogram intersection distance to obtain the optimal match from the database. The manually picked dataset of 200 images is considered for performance analysis. By extracting both the shape features and texture features, the proposed approach achieved maximum accuracy, precision, recall, F-measure, sensitivity, and specificity and lower false-positive and negative values. [ABSTRACT FROM AUTHOR]

Facial identification for surgical and non-surgical datasets is getting popular. The reason behind this popularity is the growing need of a robust facial recognition system which is consistent to occlusion, spoofing attacks and last but most important plastic surgery effects. Plastic therapies are undertaken by individuals to beautify their external appearance but it is also undertaken by impostors to commit crimes and falsify their true identities. This research work aims at developing a facial recognition system which can identify genuine and impostor pairs. The projected methodology optimizes face detection via Back-Propagation Neural Network (BPNN) and dimensionality reduction by means of Speeded Up Robust Features followed by Multi-K-Nearest-Neighbor technique. The novelty is the production of an innovative-fangled T-Database which trains the BPNN. Thus, BPNN converges faster and achieves higher recognition. The proposed scheme is not applied till date on a medically altered dataset. We have applied five distance metrics and integrated them to acquire T-Dataset, which is fed to the BPNN. This scheme is tested on surgical and non-surgical datasets and it is deduced that higher recognition is achieved with non-surgical databases as compared to surgical ones. For both surgical and non-surgical datasets, the computational cost attained is the modest. [ABSTRACT FROM AUTHOR]

Scene understanding from the single image of an indoor scene is identified as a challenging task. This involves interpreting the assessment of multiple scene components, such as to identify the spatial layout from room, detect the objects in 3D space and classify the scene, to understand the nature of an indoor scene. Assessing the spatial structure of indoor scenes offers important geometric details and limits for various activities, such as indoor 3D-reconstruction, navigation, scene awareness and virtual reality. Most of the layout estimation function lacks the clutter and decorations present in the background, instead concentrating mainly on the horizontal wall contours. Room layout states the orientations, heights, and positions of walls with respective to its camera center. Then, a set of estimated boundaries or corner positions, or as a 3D mesh is characterized from layout. Nevertheless, identifying the 3D model from a single 2D picture is an ill-posed and challenging problem. Large number of techniques applies "Manhattan assumption" for layout estimation. The layout estimation approach is combined with few computer vision based techniques like semantic segmentation, object orientation estimation, scene classification, and 3D object detection to achieve indoor scene understanding. Additionally, the object detection and semantic segmentation is also reviewed along with this layout estimation approach. This survey provides valuable information to all researchers and those who are looking for better techniques in layout estimation. [ABSTRACT FROM AUTHOR]

We study the generalized minimum Manhattan network (GMMN) problem: given a set P of pairs of points in the Euclidean plane R 2 , we are required to find a minimum-length geometric network which consists of axis-aligned segments and contains a shortest path in the L 1 metric (a so-called Manhattan path) for each pair in P . This problem commonly generalizes several NP-hard network design problems that admit constant-factor approximation algorithms, such as the rectilinear Steiner arborescence (RSA) problem, and it is open whether so does the GMMN problem. As a bottom-up exploration, Schnizler (2015) focused on the intersection graphs of the rectangles defined by the pairs in P , and gave a polynomial-time dynamic programming algorithm for the GMMN problem whose input is restricted so that both the treewidth and the maximum degree of its intersection graph are bounded by constants. In this paper, as the first attempt to remove the degree bound, we provide a polynomial-time algorithm for the star case, and extend it to the general tree case based on an improved dynamic programming approach. [ABSTRACT FROM AUTHOR]

Microarray data have become an integral part of the clinical and drug discovery process. Due to its voluminous and heterogeneous nature, the question arises of the interpretability and stability of the traditional gene selection method. To enhance the stability of the gene selection method, so that the results are better explicable, an ameliorated Extended ReliefF gene selection algorithm is proposed. It encodes gene affinity information using a new mathematical formula based on Bayes' theorem and Manhattan distance for calculating the nearest neighbor in a pooled sample. It works in four aspects: initializing sample gene weight, improving gene weight, maximizing sample gene weight and finally adopting mutation operation. The proposed method selects the most informative genes which are highly perceptive to the prognosis of the disease. Further, to accomplish the accuracy and stability of the algorithm, soft classification is performed on Relieved_F, STIR, VLS-RelifF, I-RelieF, conventional ReliefF and proposed extended ReliefF algorithms using three classifiers namely Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forest (RF) on ten microarray datasets. According to the findings, MLP training times are much longer than those of RF and SVM. From a network perspective, SVM is much faster at training, whereas MLP excels in terms of accuracy. With a rise in gene similarity among the genes selected from the multiple training sets, the approach becomes more stable. As a result, it can be seen that the recommended gene selection algorithm greatly outperforms the other feature selection methods in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Structural geometry constraints, such as perpendicularity, parallelism and coplanarity, are widely existing in man-made scene, especially in Manhattan scene. By fully exploiting these structural properties, we propose a monocular visual-inertial odometry (VIO) using point and line features with structural constraints. First, a coarse-to-fine vanishing points estimation method with line segment consistency verification is presented to classify lines into structural and non-structural lines accurately with less computation cost. Then, to get precise estimation of camera pose and the position of 3D landmarks, a cost function which combines structural line constraints with feature reprojection residual and inertial measurement unit residual is minimized under a sliding window framework. For geometric representation of lines, Plücker coordinates and orthonormal representation are utilized for 3D line transformation and non-linear optimization respectively. Sufficient evaluations are conducted using two public datasets to verify that the proposed system can effectively enhance the localization accuracy and robustness than other existing state-of-the-art VIO systems with acceptable time consumption. [ABSTRACT FROM AUTHOR]

Ocular diseases are a prevalent disease among the aging population across the world. The retinal damage and vision loss can be substantially decreased through early-stage diagnosis with computer-aided ocular disease diagnosis. With the use of color fundus photography for obtaining digital retinal fundus images, there is a growth in the online accessibility of digital fundus images. For diagnosing ocular diseases, an attempt was made to model graphs from images for feature learning. Three feature detection algorithms, namely scale-invariant feature transform, binary robust invariant scalable keypoints and oriented fast and rotated BRIEF (ORB) techniques are computed individually. As graphs are represented in the non-Euclidean domain, the graph neural network is used to learn the node embedding to model the network for ocular disease diagnosis. Three distance measures: the Euclidean, Manhattan and Chebyshev distances, are computed for analyzing the discriminative power of the model. The proposed RDD-Net model is trained and evaluated on the ODIR-2019 dataset with eleven different performance indicators. The results show that mapping images to non-Euclidean geometric space have obtained a successful diagnosis of ocular diseases from digital fundus images. The ORB descriptor outperforms the other two feature descriptors as well as the existing algorithms for ocular disease diagnosis. Results of the Chebyshev distance measure show superior performance when compared to the other two distance measures based on computation time and performance evaluation metrics. The proposed RDD-Net achieves an F1-score of 0.9970 and a sensitivity of 0.9969 with the ORB descriptor and shows state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Player selection is an important aspect of team-based sports such as cricket. Various situations, like players getting injured, rested, or falling under disciplinary action, etc., are common in cricket, and in those circumstances, the proper substitution of players is very important. We present an innovative knowledge-based intelligent framework for substitute suggestions by employing various clustering techniques like DBSCAN, Spectral clustering. We compared it with the substitution made in the real-time team selection process and obtained a large similarity between that and the recommendations generated using Spectral clustering. We have also compared our proposed results with existing state-of-the-art works in our experimental setup, where they have used the K-means clustering technique. The results highlighted that Spectral clustering is the best choice for substitute recommendations among the mentioned clustering techniques. We also present an intelligent framework for team selection and apply various similarity measures like Euclidean distance, Cosine Similarity, Manhattan distance, and Pearson Correlation Coefficient to find the most accurate combination of players. The recommendations obtained from the Pearson Correlation Coefficient have a maximum accuracy of 77.50% with a high F-measure (0.77) and present different directions for the team line-up formation. [ABSTRACT FROM AUTHOR]

Route schema is difficult to plan for tourists, because they demand to pick points of interest (POI) in unknown areas that align with their preferences and limitations. This research proposes a novel personalized method for POI route recommendation that employs contextual data. The proposed approach enhances the existing methods by considering user preferences and multifaceted tourism contexts. Due to the sparsity of the data, the proposed method employs two-level clustering (DBSCAN based on the Manhattan distance) that reduces the time to discover POI. In specific, this approach utilizes the following: first, a topic pattern model is employed to discover the users' attraction diffusion while improving the user–user similarity model using a novel asymmetric schema. Second, it has used explicit demographic information to alleviate the cold start issue, and third, it proposes a new strategy for assessing user preferences and also combined the context parameters in the form of a vector model with the Term Frequency Inverse Document Frequency technique to find contexts' similarity. Furthermore, our framework discovers a list of optimal candidate trips by involving personalized POIs in sequential patterns' mining (SPM); also, it used an adjusted forgotten function to involve the date context of each trip. Based on two datasets (Flickr and Gowalla), our methodology beats other prior approaches in F-score, RMSE, MAP, and NDCG factors in the experimental evaluation. [ABSTRACT FROM AUTHOR]

From n-size samples of k-variate points, we construct n × n distance-matrices based on the widely used Euclidean, Manhattan and Hausdorff coefficients and study (individually and in pairs) their properties P, R and ρ using theoretical analysis and both computer-generated and empirical data. The concordance PEM is shown by analysis of uniformly-distributed data to decrease asymptotically as k → ∞ to exp [‒ exp [‒ γ]] ≅ 0.5704, and PEH and PMH to decrease to zero, as also in generated N(0,1) and empirical data. In geological data, PEM is higher than predicted for 10 < k < 50. The robustness R of single matrices increases asymptotically as k → ∞ to 1.0, but the benefit to phenetic analysis is offset by the opposite response in P. For paired matrices, the distance-correlation coefficient ρE2M is > 0.9 and independent of k for both U(0,1) and N(0,1); ρEH and ρEM decline to zero as k → ∞. [ABSTRACT FROM AUTHOR]

Agility gives the ability to be highly responsive to changes and make improvements in the way the agile project is documented. The just-in-time and just barely good enough documentation may miss out on important executable specifications. Interestingly, the frequently asked 'how-to-do' questions on the popular question answering (Q&A) websites like stack overflow are strong indicators of gaps in documentation and respondent answers can complement conventional software documentation practices. Social interaction within these QA websites generates partially structured content commonly referred to as crowd knowledge that can offer a peer-reviewed re-documentation by integrating the answers of these 'how-to-do' concerns. But finding the best, value-added answer to the question which can contribute toward an enriched and curated documentation is computationally difficult. Moreover, query duplicates can cause seekers to spend more time finding these best answers. As a solution, the research proffers a novel question-answering crowd documentation model (QACDoc) which is based on a socially mediated documentation mechanics involving the dynamics of community-based web. Firstly, duplicate questions are detected using Siamese neural architecture where two identical hierarchical attention networks are used to generate vectors for similarity matching. Semantic matching is done using Manhattan distance function, and a multi-layer perceptron is trained to output the predictions. Next, all respondents of semantically matched questions are grouped to form an intent-based crowd and lastly, top k-experts are identified using representative social presence features for expert ranking. The crowd documentation is then filtered to only include answers of these identified experts. [ABSTRACT FROM AUTHOR]

We study competitive location problems in a continuous setting, in which facilities have to be placed in a rectangular domain R of normalized dimensions of 1 and ρ ≥ 1 , and distances are measured according to the Manhattan metric. We show that the family of balanced facility configurations (in which the Voronoi cells of individual facilities are equalized with respect to a number of geometric properties) is considerably richer in this metric than for Euclidean distances. Our main result considers the One-Round Voronoi Game with Manhattan distances, in which first player White and then player Black each place n points in R; each player scores the area for which one of its facilities is closer than the facilities of the opponent. We give a tight characterization: White has a winning strategy if and only if ρ ≥ n ; for all other cases, we present a winning strategy for Black. [ABSTRACT FROM AUTHOR]

Allele-specific expression (ASE) analysis detects the relative abundance of alleles at heterozygous loci as a proxy for cis-regulatory variation, which affects the personal transcriptome and proteome. This study describes the development and application of an ASE analysis pipeline on a unique cohort of 87 well phenotyped and RNA sequenced patients from the Maastricht Cardiomyopathy Registry with dilated cardiomyopathy (DCM), a complex genetic disorder with a remaining gap in explained heritability. Regulatory processes for which ASE is a proxy might explain this gap. We found an overrepresentation of known DCM-associated genes among the significant results across the cohort. In addition, we were able to find genes of interest that have not been associated with DCM through conventional methods such as genome-wide association or differential gene expression studies. The pipeline offers RNA sequencing data processing, individual and population level ASE analyses as well as group comparisons and several intuitive visualizations such as Manhattan plots and protein–protein interaction networks. With this pipeline, we found evidence supporting the case that cis-regulatory variation contributes to the phenotypic heterogeneity of DCM. Additionally, our results highlight that ASE analysis offers an additional layer to conventional genomic and transcriptomic analyses for candidate gene identification and biological insight. [ABSTRACT FROM AUTHOR]

In this work, the time series of growth rates regarding confirmed cases and deaths of COVID-19 for several sampled countries are investigated via an introduction of an orthonormal basis. This basis, which is served as the feature benchmark, reveals the hidden features of COVID-19 via the magnitude of Fourier coefficients. These coefficients are ranked in the form of ranking vectors for all the sampled countries. Based on these and Manhattan metric, we then perform spectral clustering to categorise the countries. Unlike the classical cosine similarity analysis which, relatively speaking, is a composite index and hard to identify the features of the categorised countries, spectral analysis delves into the internal structures or dynamical trend of the time series. This research shows there is no single feature that dominates the trend of the growth rates. It also reveals that results from the spectral analysis are different from the ones of cosine similarity. In the end, some approximated values of the confirmed cases and deaths are also calculated by the spectral analysis. [ABSTRACT FROM AUTHOR]

Due to the probabilistic completeness and asymptotic optimality, the RRT* algorithm can find sub-optimal solutions and solve path planning problems effectively compared with other strategies. The B-RRT* algorithm introduces the bidirectional search to obtain faster convergence and shorter paths. To solve the problems of path planning in complex environments with concavities, convexities, narrow channels, mazes and multiple obstacles, an adaptive extension bidirectional RRT* (AEB-RRT*) algorithm is proposed. The AEB-RRT* algorithm simultaneously extends from both the starting point and the ending point, and adaptively adjusts the sampling probability and different expansion methods according to the number of collision detection failures. After acquiring a feasible path, it is then post-processed by interpolation and the principle of triangular inequality to obtain a shorter collision-free path. And the Manhattan distance replaces the Euclidean distance to calculate the distances between nodes in order to achieve higher computational efficiency. The experimental results show that the AEB-RRT* algorithm outperforms other path planning algorithms in stronger search ability, obtaining near-optimal or best paths with high efficiency and better robustness. Furthermore, it is successfully applied to solve the 6-DOF arc welding robot path planning problem. [ABSTRACT FROM AUTHOR]

Buildings contribute about 40% of greenhouse gas emissions in the USA. The estimation of urban building fossil fuel CO2 emissions with the fine spatial and temporal resolution is critical for climate change studies and developing optimal plans for reaching emission reduction goals. This study proposed a new method to map fossil fuel CO2 emissions at the building level and hourly scale. Specifically, a building energy use model was developed and used for modeling building energy consumption at the hourly scale through integrating building prototypes from the accessor's data, the number of floors from remote sensing data, floor area from footprint data, and energy use intensity from the Energy Plus model. Furthermore, the greenhouse gas equivalencies calculator was applied to convert calculated building energy consumption to greenhouse gas emissions at the building level and hourly scale. Research results indicate that the spatial distribution pattern of fossil fuel CO2 emissions is largely decided by building type, building height, and building density. In particular, the largest fossil fuel CO2 emissions are located in the center of Manhattan which is composed of large offices and hotels. In terms of temporal pattern, stable and similar monthly greenhouse gas emissions could be detected throughout four seasons, and two small emission peaks could be found in around July and December which are caused by high cooling electricity use and high gas heating consumption. The hourly emissions patterns also indicate the active relationship between fossil fuel CO2 emissions and daily human activities. [ABSTRACT FROM AUTHOR]

Taxi demand forecasting is crucial to building an efficient transportation system in a smart city. Accurate taxi demand forecasting could help the taxi management platform to allocate taxi resources in advance, alleviate traffic congestion, and reduce passenger waiting time. Thus, more efforts in industrial and academic circles have been directed towards the cities' taxi service demand prediction (CTSDP). However, the complex nonlinear spatio-temporal relationship in demand data makes it challenging to construct an accurate forecasting model. There remain challenges in perceiving the micro spatial characteristics and the macro periodicity characteristics from cities' taxi service demand data. What's more, the existing methods are significantly insufficient for exploring the potential multi-time patterns from these demand data. To meet the above challenges, and also stimulated by the human perception mechanism, we propose a Multi-Sensory Stimulus Attention (MSSA) model for CTSDP. Specifically, the MSSA model integrates a detail perception attention and a stimulus variety attention for capturing the micro and macro characteristics from massive historical demand data, respectively. The multiple time resolution modules are employed to capture multiple potential spatio-temporal periodic features from massive historical demand data. Extensive experiments on the yellow taxi trip records data in Manhattan show that the MSSA model outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

To tackle the problem that traditional particle-filter- or correlation-filter-based trackers are prone to low tracking accuracy and poor robustness when the target faces challenges such as occlusion, rotation and scale variation in the case of complex scenes, an accurate reliable-patch-based tracker is proposed through exploiting and complementing the advantages of particle filter and correlation filter. Specifically, to cope with the challenge of continuous full occlusion, the target is divided into numerous patches by combining random with hand-crafted partition methods, and then, an effective target position estimation strategy is presented. Subsequently, according to the motion law between the patch and global target in the particle filter framework, two effective resampling rules are designed to remove unreliable particles to avoid tracking drift, and then, the target position can be estimated by the most reliable patches identified. Finally, an effective scale estimation approach is presented, in which the Manhattan distance between the reliable patches is utilized to estimate the target scale, including the target width and height, respectively. Experimental results illustrate that our tracker can not only be robust against the challenges of occlusion, rotation and scale variation, but also outperform state-of-the-art trackers for comparison in overall performance. [ABSTRACT FROM AUTHOR]

Serum protein levels are related to physiological and pathological status of animals and could be affected by both genetic and environmental factors. This study aimed to evaluate genetic variation of serum protein profile in sheep. Blood samples were randomly collected from 96 Lori-Bakhtiari ewes, a heavy meat-type breed. Total protein, albumin, globulin, α1, α2, β and γ globulins and IgG levels were measured in blood serum. The samples were genotyped using the Illumina OvineSNP50 BeadChip. The studied traits adjusted for age, birth type, birth season and estimate of breeding value for body weight were considered as pseudo-phenotypes in genome-wide association analysis. In the GWAS model, the first five principal components were fitted as covariates to correct the biases due to possible population stratification. The Plink, R and GCTA software were used for genome-wide association analysis, construction of Q-Q and Manhattan plots and estimation of genetic variances, respectively. Noticeable genomic heritabilities ± SE were estimated for total and γ globulins (0.868 ± 0.262 and 0.831 ± 0.364, respectively), but other protein fractions had zero or close to zero estimates. Based on the Bonferroni adjusted p values, four QTLs located on 181.7 Mbp of OAR3, 107.7 Mbp of OAR4, 86.3 Mbp of OAR7 and 83.0 Mbp of OAR8 were significantly associated with α1, β, β and γ globulins, respectively. The results showed that the PKP2, IGF2R, SLC22A1 and SLC22A2 genes could be considered as candidate genes for blood serum proteins. The present study showed significant genetic variations of some blood protein fractions. [ABSTRACT FROM AUTHOR]

We solve two related extremal-geometric questions in the n-dimensional space equipped with the maximum metric. First, we prove that the maximum size of a right-equidistant sequence of points in equals . A sequence is right-equidistant if each of the points is at the same distance from all the succeeding points. Second, we prove that the maximum number of points in with pairwise odd distances equals 2n. We also obtain partial results for both questions in the n-dimensional space with the Manhattan distance. [ABSTRACT FROM AUTHOR]

We study the Manhattan and Lorentz mirror models on an infinite cylinder of finite even width n, with the mirror probability p satisfying p < C n - 1 , C a constant. We show that the maximum height along the cylinder reached by a walker is order p - 2 . We observe an algebraic structure, which helps organise our argument. The models on the cylinder can be thought of as Markov chains on the Brauer (in the Mirror case) or Walled Brauer (in the Manhattan case) algebra, with the transfer matrix given by multiplication by an element of the algebra. [ABSTRACT FROM AUTHOR]