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Background: During online search queries, Google uses machine learning algorithms to provide frequently associated (“People Also Ask” [PAA]) questions with corresponding websites answering the ques...

Personality not only plays essential roles in people's real lives, but also becomes an important factor for various online services. Traditional approaches to personality assessment usually ask users to answer a long list of questions and are thus not practical in many online systems. It is more desirable to use some universally observable data in the system to perform personality assessment. This paper reports a controlled study to investigate common mouse operations as a potential new type of data source for online personality assessment. We establish an elaborate personality-mouse behavior dataset from 146 subjects and propose kinematic and adjustment features to characterize mouse motion patterns. Statistical approaches and machine learning algorithms are employed to examine the connections between personality preferences and mouse motion features via correlation analysis, discernibility analysis, and personality recognition experiments. The results reveal some interesting expressions of cognitive personality perspectives reflected in mouse motion patterns, such as fast starting acceleration and better controller. Performance evaluation shows it is possible to recognize different personality preferences using mouse motion features with accuracies ranging from 60.6% to 78.3%. Our findings suggest a potential to use mouse operational behaviors as a new data source for personality assessment in various information systems.

A long-standing vision of indoor localization is to eliminate infrastructure and deployment costs. Recent innovations make it possible to enable device-to-device (D2D) localization while requiring multiple antennas for the systems. We ask the following question: can we localize the more generally-used single-antenna devices (e.g., IoT) using another single-antenna device (e.g., smartphone or smartwatch) in a smart environment where low-cost backscatter tags are widely deployed on walls or smart objects. In this paper, we present TagLoc, a light-weight system that enables device-to-device localization without relying on large antenna arrays. Our observation is that the reflected signals from the ambient smart environment can be exploited to eliminate the requirement of bulky antenna arrays that are unachievable for the simple-designed IoT devices. Specifically, TagLoc creates multiple direction signatures using backscatter arrays in smart environments. Then, the receiver can accurately estimate the direction signatures from the transmitter to the arrays and then localize the target by cooperating all tag arrays. We prototype TagLoc using two single-antenna Intel NUCs with off-the-shelf Intel 5300 WiFi cards and customized backscatter tags. The results show TagLoc can achieve robust performance in a real indoor environment with a median localization error of 0.82m.

Given a set of agents qualifying or disqualifying each other, group identification is the task of identifying a socially qualified subgroup of agents. Social qualification depends on the specific rule used to aggregate individual qualifications . The classical bribery problem in this context asks how many agents need to change their qualifications in order to change the outcome in a certain way. Complementing previous results showing polynomial-time solvability or NP-hardness of bribery for various social rules in the constructive (aiming at making specific agents socially qualified) or destructive (aiming at making specific agents socially disqualified) setting, we provide a comprehensive picture of the parameterized computational complexity landscape. Conceptually, we also consider a more fine-grained concept of bribery cost, where we ask how many single qualifications need to be changed, nonunit prices for different bribery actions, and a more general bribery goal that combines the constructive and destructive setting.

Reachability queries ask whether a vertex can reach another vertex on large directed graphs. It is one of the most fundamental graph operators and has attracted researchers in both academics and industry to study it. The main technical challenge is to support fast reachability queries by efficient managing the three main costs: the index construction time, the index size and the query processing time on large/small and sparse/dense graphs. As real world graphs grow bigger in size, these problems remain open challenges that demand high performance solutions. In this paper, we propose a Multi-Dimensional Graph Labeling approach (called MGTag) to supporting fast reachability queries. MGTag is novel in three aspects. First, it recursively partitions a graph into multiple subgraphs with disjoint vertex sets, called non-shared graphs, and several inter-partition edges, called cross-edges. Second, we build a four-dimensional label - one dimension of layer, one dimension of non-shared graph and two dimensions of interval for each vertex in non-shared graphs. Finally, with the four-dimensional labeling scheme, we design algorithms to answer reachability queries efficiently. The extensive experiments on 28 large/small and dense/sparse graphs show that building the high dimensional index is quickly and the index size is also competitive compared with most of the state-of-the-art approaches. The results also show that our approach is more scalable and efficient than the state-of-the-art approaches in answering reachability queries on large/small and sparse/dense graphs.

Our ability to briefly retain information is often limited. Proactive Interference (PI) might contribute to these limitations (e.g., when items in recognition tests are difficult to reject after having appeared recently). In visual Working Memory (WM), spatial information might protect WM against PI, especially if encoding items together with their spatial locations makes item-location combinations less confusable than simple items without a spatial component. Here, I ask (1) if PI is observed for spatially distributed items, (2) if it arises among simple items or among item-location combinations, and (3) if spatial information affects PI at all. I show that, contrary to views that spatial information protects against PI, PI is reliably observed for spatially distributed items except when it is weak. PI mostly reflects items that appear recently or frequently as memory items, while occurrences as test items play a smaller role, presumably because their temporal context is easier to encode. Through mathematical modeling, I then show that interference occurs among simple items rather than item-location combinations. Finally, to understand the effects of spatial information, I separate the effects of (a) the presence and (b) the predictiveness of spatial information on memory and its susceptibility to PI. Memory is impaired when items are spatially distributed, but, depending on the analysis, unaffected by the predictiveness of spatial information. In contrast, the susceptibility to PI is unaffected by either manipulation. Visual memory is thus impaired by PI for spatially distributed items due to interference from recent memory items (rather than test items or item-location combinations).

In this paper, we provide a new Markovian-type model for stock price trend analysis at the transaction level, and illustrate its use for trading in conjunction with a controller which makes buy and sell decisions. Central to our formulation is a sequence of i.i.d. random variables T_k which corresponds to the number of transactions between reversals in price direction. For a trader, this is an important indicator of the duration of a trend. For processes with large T_k, there is an incentive to try and capitalize by buying stock when a temporary trend is up and selling when it is down. The extent to which this is possible is determined by a model parameter p_e, called the probability of efficiency, which indicates the likelihood that the bid, ask and current price are such that one can seamlessly enter or exit the market without slippage. The degree to which a trader can exploit trending behavior is quantified in our main result which provides the expected value of the trading gain resulting from a strategically constructed feedforward switching controller. The paper also includes an example illustrating application of the theory using historical data.

Many service providers explicitly ask customers for a tip. This may create social pressure, thus resulting in lower tips. Building on the theory of psychological reactance, we propose that an explicit request to tip has a detrimental impact on tip size. Across two studies, a field experiment and an online experiment, we test this effect and examine how the physical presence of the server moderates this relationship. We find that an explicit request to tip negatively affects tip size, while server’s physical presence alleviates this effect. The findings also show that social pressure hampers perceived control, which in turn has a detrimental effect on tip size. In light of these findings, service providers might want to revisit their strategies to enhance tipping.

The access to increasing volumes of scientific and clinical data, particularly with the implementation of electronic health records, has reignited an enthusiasm for artificial intelligence and its application to the health sciences. This interest has reached a crescendo in the past few years with the development of several machine learning and deep learning based medical technologies. The impact to research and clinical practice within gastroenterology and hepatology has already been significant, but the near future promises only further integration of artificial intelligence and machine learning into this field. The concepts underlying artificial intelligence and machine learning initially seem intimidating, but with increasing familiarity they will become essential skills in every clinician's toolkit. In this Review, we provide a guide to the fundamentals of machine learning, a concentrated area of study within artificial intelligence that has been built upon a foundation of classical statistics. The most common machine learning methodologies, including those involving deep learning, are also described.

In-band full-duplex (IBFD) technology is promised to deliver us from the scarcity of spectral resources as well as create novel multifunction wireless systems [1] . This concept was initially investigated in the 1960s for continuous-wave radar applications [2] , and, in the past decade, it has undergone a shift in research toward communications [3] – [7] and converged RF systems [8] , [9] . Since 2010, more than 600 IEEE publications alone have focused on at least one aspect of the technology, including more than 400 conference papers, 200 journal articles, and 20 magazine features [10] . After digesting these numbers, it is fair to ask: Why has IBFD functionality not been incorporated into a wireless standard yet?

In sample surveys, when we need information regarding rare sensitive issues which people often do not prefer to share with others. In such situations this is also awkward for interviewers to ask the direct questions related to confidential and private matters of interviewees. An approach towards the open queries about sensitive issues generally results in the high non-response rates or misleading answers. The aim of this paper is to develop an effective randomized response model to overcome with these types of challenges arising due to sensitive nature of characteristic under study. In this paper we have proposed three-stage randomized response model for estimating mean number of individuals who possessed rare sensitive attribute which makes use of Poisson distribution. The properties of the proposed estimation procedures have been deeply examined when the parameter of a rare unrelated attribute is known as well as unknown. Privacy protection of respondents is also an equally important matter of concern. So measure of privacy protection for the proposed randomized response model has also been examined. Empirical studies are performed to support the theoretical results, which show the dominance of the proposed estimators over well-known contemporary estimators. From the findings of this study we may conclude that proposed randomized response model is rewarding in terms of percent relative efficiencies and privacy protection and may be recommended to survey practitioners for real life applications.

Voice assistants offer users access to an increasing variety of personalized functionalities. Researchers and engineers who build these experiences rely on various signals from users to create the machine learning models powering them. One type of signal is explicit feedback. While collecting explicit user feedback in situ via voice assistants would help improve and inspect the underlying models, from a user perspective it can be disruptive to the overall experience, and the user might not feel compelled to respond. However, careful design can help alleviate the friction in the experience. In this paper, we explore the opportunities and the design space for voice assistant explicit feedback elicitation. First, we present four usage categories of explicit feedback in situ for model evaluation and improvement, derived from interviews with machine learning practitioners. Then, using realistic scenarios generated for each category, we conducted an online study to evaluate multiple voice assistant designs. Our results reveal that when the voice assistant is introduced as a learner or a collaborator, users were more willing to respond to its request for feedback and felt less disruptive. In addition, giving users instructions on how to initiate feedback themselves can reduce the perceived disruptiveness compared to asking users for feedback directly. Based on our findings, we discuss the implications and potential future directions for designing voice assistants to elicit user feedback for personalized voice experiences.

Digitization has given as a goliath whole of data that joins fragile information. Endeavours are attempting so hard to secure the data as far as mystery, insightfulness, and realness. One medium used by various associations for securing unapproved get to is through the intrusion detection system. This zone remains dynamic in researching as the peculiarity regarding intruders is growing exponentially on a regular reason. These solicitations successful figuring’s and systems that can recognize and take way better decisions practically increasingly current ambushes. A couple of AI based methodologies are existing recorded as a hard copy which can be upgraded for reduced wrong cautions. We have done a wide ask about experimentation on the AWID dataset for way better comes to fruition on DoS ambushes. We have used an embedded ridge-based decrease approach and ensemble classifier that gave us 99.94% exactness.

Spatial audio is an important component of realistic simulations in virtual environments. However, there have been shortcomings in existing spatial audio rendering systems, due to the computational cost of rendering realistic sound propagation. In this article, we present a novel spatial audio rendering method using 3-D graphs to simulate sound propagation in real time. We then ask if such a distinction in accuracy makes a difference to task performance, and test this proposition in a 3-D game. We conclude that improvements in spatial audio rendering do increase task performance.

Ask a developer, “What are the fundamental characteristics of object-oriented programming (OOP)?” and you will get an immediate reply saying, “Classes (and objects), inheritance, abstraction, encapsulation, and polymorphism are the most important characteristics in OOP. In addition, when you analyze enterprise code that is based on OOP, you’ll find different forms of polymorphism.” But the truth is, a novice programmer rarely uses the power of polymorphism. This chapter focuses on this topic. It shows you some simple but powerful code examples using this principle.

In human–robot cooperation, it is effective for a robot to ask a human partner for help to reduce the time required for a task. However, the person might not be able to help the robot if she or he is engaged in something else. In this study, we focused on the person’s subjective estimation of the time for a task and investigated its effect on the likelihood of agreeing to help the robot. For this purpose, we developed a Help-Estimator system that decides whether the robot should request help by considering the required time for both the person and the robot to finish their tasks. We conducted an experiment to evaluate such help requests that incorporate the human’s subjective time estimation for a task. We found that appropriate timing for requesting help, as estimated from the person’s viewpoint, increases the likelihood of the person helping the robot and improves the person’s impression of the robot as a partner.

Data-driven decision making for sustainable development requires domain expertise to ask the right questions; high-quality, relevant data; appropriate, nuanced statistical analyses; and the power t...

In combinatorial auctions for the procurement of transportation services, each carrier has to determine the set of profitable contracts to bid on and the associated ask prices. This is known as the Bid Construction Problem (BCP). Our paper addresses a BCP with stochastic clearing prices taking into account uncertainty on other competing carriers’ offers. Contracts’ selection and pricing decisions are integrated to generate multiple combinatorial bids. Our paper is the first to propose an exact non-enumerative method to solve this NP-Hard problem. Our computational results demonstrate that optimal solutions can be obtained on instances with up to 50 contracts. We also propose a hybrid heuristic that yields most of the these optimal solutions in a fraction of the run time, and provides competitive solutions for the remaining instances. Finally, our results show the good performance of our exact method for a set of auction contexts we simulate.

This editorial provides context for a series of published case reports in ACI Open by summarizing activities and outputs of joint electronic health record integration and pharmacogenomics workgroups in the NIH-funded electronic Medical Records and Genomics (eMERGE) Network. A case report is a useful tool to describe the range of capabilities that an IT infrastructure or a particular technology must support. The activities we describe have informed infrastructure requirements used during eMERGE phase III, provided a venue to share experiences and ask questions among other eMERGE sites, summarized potential hazards that might be encountered for specific clinical decision support (CDS) implementation scenarios, and provided a simple framework that captured progress toward implementing CDS at eMERGE sites in a consistent format.

Suppliers of tourist services continuously generate big data on ask prices. We suggest using this information, in the form of a price index, to forecast the occupation rates for virtually any time-space frame, provided that there are a sufficient number of decision makers “sharing” their pricing strategies on the web. Our approach guarantees great transparency and replicability, as big data from OTAs do not depend on search interfaces and can facilitate intelligent interactions between the territory and its inhabitants, thus providing a starting point for a smart decision-making process. We show that it is possible to obtain a noticeable increase in the forecasting performance by including the proposed leading indicator (price index) into the set of explanatory variables, even with very simple model specifications. Our findings offer a new research direction in the field of tourism demand forecasting leveraging on big data from the supply side.

The Future Conversations workshop at CHIIR'21 looked to the future of search, recommendation, and information interaction to ask: where are the opportunities for conversational interactions? What do we need to do to get there? Furthermore, who stands to benefit? The workshop was hands-on and interactive. Rather than a series of technical talks, we solicited position statements on opportunities, problems, and solutions in conversational search in all modalities (written, spoken, or multimodal). This paper -co-authored by the organisers and participants of the workshop- summarises the submitted statements and the discussions we had during the two sessions of the workshop. Statements discussed during the workshop are available at https://bit.ly/FutureConversations2021Statements.

The question of what types of units and domains are needed in order to capture phonological change is a reasonable one to ask. To answer this question, however, we first need to properly define how we understand phonological change, and the definition that we adopt for that clearly depends on the phonological framework that is assumed. I consider several influential frameworks here and then come to the conclusion that the same condition holds for all of them: change can only be described in terms of the same units (and domains) as are used for synchronic description. This leads to the following conclusion: the set of units for phonological change is a subset of the set of units that are needed for synchronic phonological description. However, it is also unlikely that some units needed for synchronic description can be fully ignored for all descriptions of changes, which leads us to the conclusion that the set of units that are needed for phonological change is also a superset of that set. The sets are thus equal: the phonological units needed for synchronic description are the units needed to account for phonological change, and the question above is meaningless.

In today’s highly connected cyber-physical environments, users are becoming more and more concerned about their privacy and ask for more involvement in the control of their data. However, achieving effective involvement of users requires improving their privacy decision-making. This can be achieved by: (i) raising their awareness regarding the direct and indirect privacy risks they accept to take when sharing data with consumers; (ii) helping them in optimizing their privacy protection decisions to meet their privacy requirements while maximizing data utility. In this article, we address the second goal by proposing a user-centric multi-objective approach for context-aware privacy management in connected environments, denoted δ- Risk . Our approach features a new privacy risk quantification model to dynamically calculate and select the best protection strategies for the user based on her preferences and contexts. Computed strategies are optimal in that they seek to closely satisfy user requirements and preferences while maximizing data utility and minimizing the cost of protection. We implemented our proposed approach and evaluated its performance and effectiveness in various scenarios. The results show that δ- Risk delivers scalability and low-complexity in time and space. Besides, it handles privacy reasoning in real-time, making it able to support the user in various contexts, including ephemeral ones. It also provides the user with at least one best strategy per context.

Each generation of cellular communication systems is marked by a defining disruptive technology of its time, such as orthogonal frequency division multiplexing (OFDM) for 4G or Massive multiple-input multiple-output (MIMO) for 5G. Since artificial intelligence (AI) is the defining technology of our time, it is natural to ask what role it could play for 6G. While it is clear that 6G must cater to the needs of large distributed learning systems, it is less certain if AI will play a defining role in the design of 6G itself. The goal of this article is to paint a vision of a new air interface which is partially designed by AI to enable optimized communication schemes for any hardware, radio environment, and application., Comment: 7 pages, 6 figures, accepted for publication in the IEEE Communications Magazine

The number of users who have the appropriate knowledge to answer asked questions in community question answering is lower than those who ask questions. Therefore, finding expert users who can answer the questions is very crucial and useful. In this article, we propose a framework to find experts for given questions and assign them the related questions. The proposed model benefits from users’ relations in a community along with the lexical and semantic similarities between new question and existing answers. Node embedding is applied to the community graph to find similar users. Our experiments on four different Stack Exchange datasets show that adding community relations improves the performance of expert finding models.

The things in the Internet of Things are becoming more and more socially aware. What social means for these things (more often termed as “social objects”) is predominately determined by how and when objects interact with each other. In this paper, an agent-based model for Social Internet of Things is proposed, which features the realization of various interaction modalities, along with possible network structures and mobility modes, thus providing a novel model to ask interesting “what-if” questions. The scenario used, which is the acquisition of shared resources in a common spatial and temporal world, demands agents to have ad-hoc communication and a willingness to cooperate with others. The model was simulated for all possible combinations of input parameters to study the implications of competitive vs. cooperative social behavior while agents try to acquire shared resources/services in a peer-to-peer fashion. However, the main focus of the paper was to analyze the impact of profile-based mobility, which has an underpinning on parameters of extent and scale of a mobility profile. The simulation results, in addition to others, reveal that there are substantial and systematic differences among different combinations of values for extent and scale.

The article describes the methodology to solve tasks of Olympiads in informatics using recursive numbers generation. The study is based on the sequential solution of increasingly complex tasks. For each task, the following materials are given: the condition of the task, the idea of the solution with a proposal to come up with an independent implementation, the solution in the Pascal programming language. Distance learning system DL.GSU.BY is the effective technical base for teaching. It allows offer the student a condition of the task; send the solution for review; get a verdict from the system — a correct or incorrect solution; for incorrect solutions, the number of the test on which the solution did not pass is indicated. A student can take a test (input and output data), on which his solution did not pass, figure out what the error is in his program, correct and send the solution again. In addition, for each task there is a link on it to the topic in the forum, where you can ask a question on solving this problem and / or read the answer if the questions have already been asked before.

The auditory system is capable of producing a wide range of information through the acquisition and perception of the vibrations present in the environment, even when the receptor is not directly facing the stimulus’s source. Said information can be crucial for survival and useful for a variety of systems like the visual system and the motor system. Despite that, the quantity of studies involving the auditory system or its interactions with other systems is limited, even though anatomical evidence recognizes this relationships’ existence. In this work, we study its interaction with the motor system. A bio-inspired model that explores the relationship between the auditory and motor systems, grounded on neuroscientific research, is presented to address this proposal. To validate our proposal, a case study in which we endow a virtual entity with our proposed model. Then, we ask both a group of persons and the virtual creature to compute and face towards the direction were the sound was originated.

The aims of this paper are (i) to provide a detailed taxonomy of noncanonical uses of interrogative sentences, i.e. when they are used not to ask a question but to convey some information, or to ask a question albeit not that expressed by the interrogative sentence exploited in the act, (ii) to identify properties of circumstances where an interrogative sentence is being used in this way, and (iii) to propose some maxims that govern the rational use of questions. Four main categories of such cases are presented, and a few further subclasses are differentiated. I show how these types are interrelated, and what logical features differentiate them. I also propose a hypothesis for when an interrogative sentence is not being used in its primary mode. Studies on circumstances in which questions are used in other ways can shed light on maxims that govern asking and questioning in a rational conversation; therefore, some possible maxims of this kind are proposed.

This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. MPNet uses neural networks to learn general near-optimal heuristics for path planning in seen and unseen environments. It takes environment information such as raw point-cloud from depth sensors, as well as a robot's initial and desired goal configurations and recursively calls itself to bidirectionally generate connectable paths. In addition to finding directly connectable and near-optimal paths in a single pass, we show that worst-case theoretical guarantees can be proven if we merge this neural network strategy with classical sample-based planners in a hybrid approach while still retaining significant computational and optimality improvements. To train the MPNet models, we present an active continual learning approach that enables MPNet to learn from streaming data and actively ask for expert demonstrations when needed, drastically reducing data for training. We validate MPNet against gold-standard and state-of-the-art planning methods in a variety of problems from 2D to 7D robot configuration spaces in challenging and cluttered environments, with results showing significant and consistently stronger performance metrics, and motivating neural planning in general as a modern strategy for solving motion planning problems efficiently., Supplementary material including implementation parameters and project videos are available at https://sites.google.com/view/mpnet/home. This work has been accepted for publication at IEEE Transactions on Robotics

In this essay I view writing as a communicative practice, and ask how and in what ways teachers and students define the role of writing in relation to digital video composition. While there is much...

When we ask what sex is, we ask what it means to be human. It is a simple yet powerfully disruptive and generative question with far-reaching implications. Fortifying perennial dualisms, the study ...

The article examines the advantages of practical training through independent learning over traditional lessons and the problems encountered in independent learning, and offers suggestions on how to prevent these problems and use them in the educational process. They are limited to specific information, instructions, tasks and assignments given by the teacher. Such students don't even ask too many questions. They observe, listen to, and are content with the behavior of their teachers.

Online learning platforms, such as Coursera, Edx, Udemy, etc., offer thousands of courses with different content. These courses are often of discrete content. It leads the learner not to find a learning path in a vast volume of courses and contents, especially when they have no experience in advance. Streamlining the order of courses to create a well-defined learning path can help e-learners achieve their learning goals effectively and systematically. The learners usually ask the necessary skills that they expect to earn (query). The need is to develop a recommender system that can search for suitable learning paths. This study proposes a multi-objective optimization model as a knowledge-based recommender. Our model can generate an appropriate learning path for learners based on their background and job goals. The recommended studying path satisfies several learner criteria, such as the critical learning path, number of enrollments, learning duration, popularity, rating of previous learners, and cost. We have developed Metaheuristic algorithms includes the Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), to solve the proposed model. Finally, we tested proposed methods with a dataset consisting of Coursera’s courses and Vietnam work’s jobs. The test results show the effectiveness of the proposed method.

User reviews are a new source of information in the hospitality and tourism sector. Usually, these reviews contain comments of users and assessments expressed through ordered qualitative scales. The website Booking.com uses a smiley face scale to ask users the degree of satisfaction regarding several aspects of accommodations. The scoring system of the website assigns numerical values to each item of the smiley face scale. However, when users perceive different proximities between pairs of items of an ordered qualitative scale, these numerical codifications are because they may misrepresent the original ordinal information. In this paper, we analyze the drawbacks and limitations of the scoring system of Booking.com and we manage its smiley face scale through a purely ordinal procedure. This procedure avoids assigning numerical codifications to items of scales and it takes into account how users perceive the proximities between pairs of items. The findings show the importance of considering how users understand qualitative scales when they face subjective assessments through ordered qualitative scales.

The trend towards individual products and decreasing lot sizes demands more flexible production. One approach is skill-based engineering in which product properties are mapped to machine capabilities. The machine skills require uniform semantics, unique designations, and widespread communication standards. The integration of machines and their capabilities via OPC UA into the Cyber-Physical Production System is an already implemented approach. Until now, skills have been used for simple actions that do not require time consuming calculations or simulations in advance. The paper shows existing OPC UA skill models and extends them by a feasibility and context check. In the future these models will allow to ask a Cyber-Physical Production Module in advance if a skill can be executed with a defined parameter set. This results in an interface to the digital twin of the Cyber-Physical Production Module.

Due to the increasing individualization of products, modern production facilities ask for highly flexible logistic systems like arm-type-robots on mobile platforms. To ensure good dynamics, minimal use of space and good reachability of connected machines, an ideal geometry of those systems would tend to be unstable due to the high center of mass, little weight and small ground base. The current paper describes briefly the development, modelling, simulation and validation of different types of stabilization strategies of robots on mobile platforms. It assesses the developed active stabilization devices and integrated control algorithms concerning their effectiveness and applicability within a production environment.

Software developers have heavily used online question-and-answer platforms to seek help to solve their technical problems. However, a major problem with these technical Q8A sites is “answer hungriness,” i.e., a large number of questions remain unanswered or unresolved, and users have to wait for a long time or painstakingly go through the provided answers with various levels of quality. To alleviate this time-consuming problem, we propose a novel D EEP A NS neural network–based approach to identify the most relevant answer among a set of answer candidates. Our approach follows a three-stage process: question boosting, label establishment, and answer recommendation. Given a post, we first generate a clarifying question as a way of question boosting. We automatically establish the positive , neutral + , neutral - , and negative training samples via label establishment. When it comes to answer recommendation, we sort answer candidates by the matching scores calculated by our neural network–based model. To evaluate the performance of our proposed model, we conducted a large-scale evaluation on four datasets, collected from the real-world technical Q8A sites (i.e., Ask Ubuntu, Super User, Stack Overflow Python, and Stack Overflow Java). Our experimental results show that our approach significantly outperforms several state-of-the-art baselines in automatic evaluation. We also conducted a user study with 50 solved/unanswered/unresolved questions. The user-study results demonstrate that our approach is effective in solving the answer-hungry problem by recommending the most relevant answers from historical archives.

In today’s digital world people are keen on finding the knowledge they need by surfing the internet to find the answers to their questions. To this aim, many Community Question Answering (CQA) systems are established, in which people can ask their question and receive the required information. The gathered data in such systems is a rich repository for people to search through the available questions that have been answered before. CQA users, however, are not always successful in finding their answers in their native CQA systems. One solution to enrich the searching process is translating input questions and searching them in other CQA systems. This solution is useless as the process of translating each question is time-consuming. To make the non-English CQA systems richer in finding the available answers, the systems can develop a model to find similar English questions. To help Persian CQA systems in providing the answers to the questions, we propose a cross-lingual question retrieval model to retrieve relevant English questions to any input Persian question. In the proposed model, we benefit from a translation model-based retrieval using neural cross-lingual word embedding. Our experiment shows that the proposed model achieves 71.4% MRR and 83.5% success@5 using supervised cross-lingual word embedding.

An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. Herein, we work with a mathematical model of murine cancer immunotherapy that has been previously-validated against the average of an experimental dataset. We ask the question: what happens if we try to use this same model to perform personalized fits, and therefore make individualized treatment recommendations? Typically, this would be done by choosing a single fitting methodology, and a single cost function, identifying the individualized best-fit parameters, and extrapolating from there to make personalized treatment recommendations. Our analyses show the potentially problematic nature of this approach, as predicted personalized treatment response proved to be sensitive to the fitting methodology utilized. We also demonstrate how a small amount of the right additional experimental measurements could go a long way to improve consistency in personalized fits. Finally, we show how quantifying the robustness of the average response could also help improve confidence in personalized treatment recommendations.Author summaryAs we enter the era of healthcare where personalized medicine becomes a more common approach to treating cancer patients, harnessing the power of mathematical models will only become more essential. Using a preclinical dataset on cancer immunotherapy, we explore the challenges and limitations that arise when trying to move from fitting and making predictions for the population-level average, to fitting and making predictions for an individual. We find that the standard of approach of picking a single fitting methodology and a single cost function may end up having limited predictive value when applied to individual data. We also show how having a small amount of the right additional experimental data, and establishing the robustness of average treatment response, can help improve confidence in personalized model predictions.

Research on limit order book markets has been rapidly growing and nowadays high-frequency full order book data is widely available for researchers and practitioners. However, it is common that research papers use the best level data only, which motivates us to ask whether the exclusion of the quotes deeper in the book over multiple price levels causes performance degradation. In this paper, we address this question by using modern Machine Learning (ML) techniques to predict mid-price movements without assuming that limit order book markets represent a linear system. We provide a number of results that are robust across ML prediction models, feature selection algorithms, data sets, and prediction horizons. We find that the best bid and ask levels are systematically identified not only as the most informative levels in the order books, but also to carry most of the information needed for good prediction performance. On the other hand, even if the top-of-the-book levels contain most of the relevant information, to maximize models' performance one should use all data across all the levels. Additionally, the informativeness of the order book levels clearly decreases from the first to the fourth level while the rest of the levels are approximately equally important., NeurIPS 2021 Workshop on Machine Learning meets Econometrics (MLECON2021)

Exploring the production of knowledge with quantitative methods is the foundation of scientometrics. In an application of machine learning to scientometrics, we here consider the classification problem of the mapping of academic publications to the subcategories of a multidisciplinary journal—and hence to scientific disciplines—based on the information contained in the abstract. In contrast to standard classification tasks, we are not interested in maximizing the accuracy, but rather we ask, whether the failures of an automatic classification are systematic and contain information about the system under investigation. These failures can be represented as a ’misclassification network’ inter-relating scientific disciplines. Here we show that this misclassification network (1) gives a markedly different pattern of interdependencies among scientific disciplines than common ’maps of science’, (2) reveals a statistical association between misclassification and citation frequencies, and (3) allows disciplines to be classified as ’method lenders’ and ’content explorers’, based on their in-degree out-degree asymmetry. On a more general level, in a wide range of machine learning applications misclassification networks have the potential of extracting systemic information from the failed classifications, thus allowing to visualize and quantitatively assess those aspects of a complex system, which are not machine learnable.

En el mundo tecnológico actual, donde las redes están creciendo rápidamente, se exige que los protocolos de red sean robustos, confiables y versátiles. Es por ello que antes de ser propuestos para su uso, deben ser estudiados a fondo, para lo que se requiere utilizar herramientas informáticas que permitan examinar su comportamiento en diferentes entornos. Es así que, con este fin, se utilizan simuladores de redes. Uno de estos simuladores es el ns-2, que ha sido utilizado de manera exhaustiva en ámbitos académicos y científicos por casi dos décadas. Sin embargo, con el lanzamiento de un nuevo simulador, el ns-3, que difiere en muchos aspectos de su antecesor y requiere la reformulación de los programas escritos para la versión anterior, surge el interrogante si la actualización a las nuevas versiones es realmente necesaria. Es así que, nos preguntamos si el simulador de redes que utilizamos es suficiente para nuestro trabajo o si la nueva versión aportará considerables beneficios en las investigaciones actuales y futuras. Determinar si la inversión de tiempo en el aprendizaje de una nueva herramienta, producirá suficientes mejorías en las tareas que requiere la investigación de redes, es una decisión importante. Este informe presenta de manera comparativa las dos versiones del simulador. Se describen las ventajas y desventajas, alcances, limitaciones y requisitos para la utilización de cada una de las versiones.

It is increasingly evident that if researchers and policymakers want to meaningfully develop an understanding of responsible innovation, we must first ask whether some sociotechnical systems should...

Since the emergence of so-called fake news on the internet and in social media, platforms such as Facebook have started to take countermeasures, and researchers have begun looking into this phenomenon from a variety of perspectives. A large number of scientific work has investigated ways to detect fake news automatically. Less attention has been paid to the subsequent step, i.e., what to do when you are aware of the inaccuracy of claims in social media. This work takes a user-centered approach on means to counter identified mis- and disinformation in social media. We conduct a three-step study design on how approaches in social media should be presented to respect the users' needs and experiences and how effective they are. As our first step, in an online survey representative for some factors to the German adult population, we enquire regarding their strategies on handling information in social media, and their opinion regarding possible solutions --- focusing on the approach of displaying a warning on inaccurate posts. In a second step, we present five potential approaches for countermeasures identified in related work to interviewees for qualitative input. We discuss (1) warning, (2) related articles, (3) reducing the size, (4) covering, and (5) requiring confirmation. Based on the interview feedback, as the third step of this study, we select, improve, and examine four promising approaches on how to counter misinformation. We conduct an online experiment to test their effectiveness on the perceived accuracy of false headlines and also ask for the users' preferences. In this study, we find that users welcome warning-based approaches to counter fake news and are somewhat critical with less transparent methods. Moreover, users want social media platforms to explain why a post was marked as disputed. The results regarding effectiveness are similar: Warning-based approaches are shown to be effective in reducing the perceived accuracy of false headlines. Moreover, adding an explanation to the warning leads to the most significant results. In contrast, we could not find a significant effect on one of Facebook's current approaches (reduced post size and fact-checks in related articles).

The personal satisfaction with the formal attributes of buildings has an underlying essence and needs some exploratory attempts to secure a reliable set of individual attribute satisfactions. This paper aims to discover reliable methods for extracting the personal preferences for the formal attributes by examining the accuracy of several questioning methods. Focusing on building facades, the attributes are defined at first to cover a wide range of architectural forms. The study then introduces eight methods of extracting personal preferences: four attribute-based methods directly ask participants for their attribute preferences, and four building-based methods extract each attribute satisfaction from the analysis of appreciation of architectural forms. A survey then extracts individuals’ satisfaction with the attributes via each method; the outcomes of each questioning method are examined by applying them into preference prediction of another set of building images integrated into the survey. The analysis shows that the most accurate results are achieved when participants directly express their opinions about the attributes illustrated in a building’s image. Among the building-based methods, considering all the visible attributes in the analysis of the building preferences can reveal the second-most accurate data. Finally, although the combination of both methods enhanced the result’s accuracy, the former method is more efficacious while a lower number of attributes are considered and knowledgeable people are addressed; otherwise, the latter method is practically more valid for laypeople and scalable to a large number of people. Article received: May 28, 2020; Article accepted: July 11, 2020; Published online: September 15, 2020; Original scholarly paper How to cite this article: Tayyebi, Seyed Farhad and Yuksel Demir. "Extracting Personal Preferences for Architectural Attributes: Examining the Reliability of Several Direct and Indirect Questioning Methods." AM Journal of Art and Media Studies 22 (2020): 111-134. doi: 10.25038/am.v0i22.381

Over the course of this study, we analyze the problem-solving schemes proposed for two sequences of estimation problems. In the first sequence, the problems ask for estimates directly, whereas in the second, each estimate is asked for based on a contextualized situation. The first objective is to identify, statistically, if there is a link between the problem-solving strategy and the characteristics of the context within which the estimation task occurs. The second objective is to analyze the impact that the structure of the word problem has on students’ success. To these ends, we analyzed the written answers of N=224 and N=87 university students who are in teacher training. The results show that there is a significant relationship (independent of the sequence) between the variables of the problems based on context and the proposed problem-solving strategy. Moreover, we observe that modifying the structure of the word problem makes the development of a problem-solving scheme more difficult only when the required estimate is not asked for directly. The results allow us to make conclusions about the potential use of these sequences to promote flexibility in approaches to problem solving.

Online questionnaires and surveys are one of the most efficient methods that are used by people all around the world for statistics, to ask questions and to evaluate something; however, this is because of its benefits, like reducing time, getting real answers apart from manipulation or fake results, and it is easy for everyone to use. There are a lot of online survey forms found on Google and using these e-forms are easy; all that one has to do is open the site, register through email and then choose the type of survey one wants to fill in. However, despite the plurality of e-forms, it is essential for companies to have special forms that are relevant to office work and the nature of work conducted by the companies; the forms should also be specific to be used by the companies’ managers and employees. The aim of this study is to create an online questionnaires and surveys website that is specifically to be used by companies. This website makes the work of the companies more efficient and faster; so if they want to evaluate the performance of anything or ask the employees about something or to vote, they can easily use our new website. Keywords: Online application, questionnaires and surveys, NetBeans.

In this work we ask whether and to what extent applying a predictor of a publication’s impact that is better than early citations has an effect on the assessment of the research performance of individual scientists. Specifically, we measure the total impact of Italian professors in the sciences and economics over time, valuing their publications first by early citations and then by a weighted combination of early citations and the impact factor of the hosting journal. As expected, the scores and ranks of the two indicators show a very strong correlation, but significant shifts occur in many fields, mainly in economics and statistics, and mathematics and computer science. The higher the share of uncited professors in a field and the shorter the citation time window, the more recommendable is recourse to the above combination.

The EQ-5D-5L is a commonly used instrument for assessing the utility of different health states. Health state utility values are a key component of health technology evaluations. Such evaluations are used to support evidence-based decisions surrounding health resource allocations and therefore rely on the accuracy of the valuation set used. This paper takes an alternative approach to developing an EQ-5D-5L value set for Canada. The aim is to introduce a robust method that is likely to generate a value set with improved accuracy and that can be used to generate value sets for other populations without the need for modification. The common approach to developing a valuation set for preference-based instruments is to ask a population sample to value a subset of the health states using an established preference elicitation technique. The relationship between the elicited health states and the preferences is used to inform a model to predict the utility values for the unsampled health states described by the instrument. The true relationship is unknown and the functional forms chosen in the modelling process vary across valuation studies. We use nonparametric local constant regression to estimate an EQ-5D-5L value set for Canada and propose this method as an alternative for value set development because it does not require the specification of a functional form at the outset. Compared to the existing valuation model for Canada, the nonparametric method improves in-sample fit, reducing the average squared prediction error by 94.46% and the mean absolute error by 79.37%. In four of five sets of out-of-sample studies, this new approach performs significantly better than 9 comparison models. Despite lacking any restriction on the functional form of the resulting valuations, the valuation set generated by this new approach is logically consistent. 100% of the pairs of health states in which one state is dominant have health state values which respect this ordering. The value set also appears to differ substantially from the comparators. Overall, the results suggest that nonparametric regression is a promising tool for the estimation of EQ-5D-5L valuation sets and may be a good option in a standardised methodology for value set development.