Reviewerly/exHarmony huggingface dataset:
Simplified abstract syntax tree based semantic features learning for software change prediction

Abstract Software change prediction aims to identify the change‐prone parts of source code, which can help software practitioners allocate resources more efficiently, increase the quality of software products, and reduce maintenance costs. In recent years, researchers have built many change prediction models based on product and process metrics using traditional classification algorithms. However, source code contains rich semantic structural information, which traditional features cannot usually capture. Therefore, extracting the semantic features of code can help improve the performance of existing models. To bridge the gap between semantic features and change prediction, we introduce a novel change prediction approach based on a simplified abstract syntax tree (AST). Specifically, we first extract semantic features from partial AST nodes that pay attention to the syntax and semantic of code instead of all AST nodes. Then, a bidirectional recurrent neural network is utilized to model the deep semantic information of the code for change prediction. We also propose a new dataset that to some extent alleviates the data‐imbalance problem, which has become an active research topic. We conducted extensive experiments on the proposed dataset. The results show the effectiveness of semantic features for change prediction. Further, our model outperformed a state‐of‐the‐art code representation method.
A short note on polynomials f(X) = X + AX1+q2(q−1)/4 + BX1+3q2(q−1)/4 ∈ 𝔽q2[X], q even

An alternative proof of the necessary conditions on [Formula: see text] for [Formula: see text] to be a permutation polynomial in [Formula: see text], [Formula: see text] even, is given. This proof involves standard arguments from algebraic geometry over finite fields and fast symbolic computations.
T-Rank: Graph Data Analytics for Urban Traffic Modeling

Evaluating specific points in urban passages and identifying the factors causing traffic jams are of particular importance. In this paper, we seek for a mathematical model of urban traffic that identifies the factors affecting traffic and evaluating their impact. Such models are capable of predicting the impact of the latent factors on the urban traffic. Our model uses graph evaluation algorithms, similar to ones used in web-page ranking. The model validation is done based on the data gathered by ANPR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> cameras that detect the plate numbers of the vehicles in the traffic zones. The data analytics in Apache Spark environment shows that our model archives an accuracy of more than 80% in daily traffic density prediction. The model also predicts the ratio of the vehicles that park in the monitored zone with respect to the ones that pass through the zone accurately.
Reference Model for Agile Development of Machine Learning-based Service Systems

In this study, as a reusable knowledge for the effective system development we built a reference agile development model for machine learning(ML)-based systems. For this purpose, we collected project data on ML-based service system development projects (ML projects). From the collected practice data, we identified the common activities in these projects to extend the existing development models such as a generic agile development model and the workflow model for ML projects. Through the reference model represented by ArchiMate that is an enterprise modeling language, it was confirmed that the project participants from the business division can understand the activities that they should be involved in.
CULTURAL PROBES METHOD IN DESIGN RESEARCH: CREATIVITY IN SKETCHES

Despite the abundance of methods and models, the design research is still often entangled in concepts irrelevant to design practice. This paper rethinks the discourse of design research and the evolving role of a designer. The most important attempts to build a design as an academic discipline are mentioned, along with research communities that are operating in the field of design. Art-and-design-based approach in design research in Donald Schön’s works on architecture and design practice, rises the importance of sketches in the creative idea generation. Implementing sketching techniques into the cultural probes, as one of an art-and-design-based research method, opens up ways to innovative design solutions. This paper discusses the relationship between the method and sketching in the context of the case study “When Nobody Sees”. Sketching is discussed as an art-based tool for problem-setting.
Efficient Object Detection using Deep Reinforcement Learning and Capsule Networks

Recently, with the advent of deep learning, significant advances in computer vision have been made. One of the traditional and important tasks in computer vision is object detection. Object detection methods based on pre-defined anchors or region proposal suffer from high computational complexity. We propose a new method based on deep reinforcement learning and capsule networks for object detection in images. Capsules are a group of neurons that communicate with each other in the form of routing. The main idea is to use capsule networks as the heart of deep reinforcement learning and train an intelligent agent for a more accurate search of objects and localize them in the image. We also defined a new function called CEU and used it as part of the movement reward function. We evaluated our method on two well-known object detection benchmark datasets called PASCAL Visual Object Classes (VOC) 2007, and 2012. Experiments illustrate that the proposed method achieved higher precision than similar methods in terms of not being a region proposal.
The Worksheet School: COVID-19 Lockdown and Online Schooling in Public Schools of Delhi

The COVID-19 pandemic led to the closure of educational institutions across India, which meant that all schooling activities shifted overnight to online platforms. This posed a problem for students from low-income communities who may not have the necessary infrastructure to participate fully in online school. To mitigate this, the Government of Delhi introduced a worksheet based education system for all public schools in the national capital to ensure no student was denied education. This paper discusses a qualitative study conducted between July to December 2020 with grade 10 students from Delhi public schools, their teachers and parents to understand: (1) the effect of a new shared home-school context and (2) the emergence of a 'Worksheet School' infrastructure created to facilitate learning and teaching in low-resourced schools. We reveal how the home as a new site for learning affected students' participation in online school, how the worksheets were re-purposed to support schooling activities and how WhatsApp was used for delivering educational instructions and the fostering of community learning. We discuss implications for equitable access to online learning materials and future work that aims to further study online learning environments in low-resource communities.
Long and short-term neural network news recommendation model based on self-attention mechanism

Information overload has become a huge barrier for people to effectively obtain information while enjoying the convenience of the big data era. Personalized news recommendation system can effectively help news platforms find articles that best suit user’s preferences from a large amount of news and enhance user experience. Existing news recommendation systems usually process user's preferences in a unified manner, ignoring the difference between long-term preferences and short-term preferences. In response to the problem above, this paper studies the long and short-term memory model based on GRU (Gated Recurrent Unit). This paper uses the LFM(Latent Factor Model) to extract user's long-term preferences and uses the GRU to obtain user’s short-term preferences from browsing history. Aiming at the problem of user interest transfer in the short term, this paper uses a self-attention mechanism based on time interval to characterize the degree of it. Experiments on two real-world datasets show our approach can effectively improve the performance of news recommendation.
Anonymity, fidelity to law, and digital Civil disobedience

Making use of the liberal concept of civil disobedience, this paper assesses, under which circumstances instances of illegal digital protest—called “hacktivism”—can be justified vis-à-vis the pro tanto political obligation to obey the law. For this, the paper draws on the three main criteria for liberal civil disobedience—publicity, nonviolence, and fidelity to law—and examines how these can be transferred to the realm of the digital. One of the main disanalogies between street and cyberspace protests is the tendency of hacktivists to remain anonymous, which in turn calls into question their fidelity to law (the third criterion). The paper argues that there are functionally equivalent alternatives to what can be called the “acceptance-of-legal-consequences-condition” (ALCC) associated with the fidelity to law. As a result, the ALCC is not a necessary condition for hacktivists to showcase their fidelity to law, thereby resolving the apparent disanalogy.
Timing System of Track and Field Competition Based on Data Analysis Algorithm

Today, in the context of the big data era, increasingly advanced data processing technologies have been widely used in various fields for data analysis. At the same time, people are paying more and more attention to the standardization of competitions and the fairness of results in sports events. Data analysis is reflected in sports competitions. For example, coaches analyze training or competition data to help athletes improve their competitive level, formulate competition strategies, and optimize the configuration of team athletes. Through the analysis, exploration, display and prediction of sports data, it provides value to data users. At the same time, the track and field timing system automatically records and automatically classifies, archives and displays the performance of each athlete, making the timing method more efficient and convenient, and also plays an important role in making the game more accurate and fair and reducing labor costs. The automatic timing system for track and field competitions monitors the athlete's time spent in the competition and the distance from the finish line in real time through wireless communication between the base station at the finish line and the intelligent terminal. At the same time, combined with data analysis algorithms, a more convenient and safer and more reliable automatic timing method is used to determine the conditions for athletes to reach the end point, so as to achieve the goals of accurate ranging, cost reduction, and error reduction. This article adopts experimental analysis and data analysis methods to explore the timing system that is more suitable for track and field competitions by understanding the algorithms related to data analysis, so as to achieve accurate ranging, reduce costs, and reduce errors. According to the research results, the response time of the system is kept within 5 s. The performance of the system tested this time is still very good, and it has certain application value.
Collectively playable wearable music: Practice-situated approaches to participatory relational inquiry

Abstract We present two practice-situated participatory investigations using networked wearable sensors to develop movement-responsive collectively playable musical instruments: a series of four collocated workshops for expert dancers and a distance learning course in which students use wearable technology to enhance embodied learning and feelings of connectedness telematically. We reflect on our exploration of techniques for structuring ensemble improvisations augmented with bespoke digital musical instruments using aggregate statistical measures, such as variance of participants’ physical orientation as an index of group intention. Participatory design exchanges top-down design methodologies with bottom-up approaches consulting actors’ interests. We follow this approach by evolving our instruments through abductive experiments and trial-and-error tinkering, without strong theories, methods, or models, using elementary signal processing techniques that are meaningfully understood and modified by participants. Our experiences suggest useful scaffolding techniques for educational transdisciplinary research-creation communities seeking to explore relational ensemble dynamics in telematic and/or physically collocated settings using accessible wearable technologies. Through creative inquiry and participation, technical objects can become bearers of sense and meaning rather than instating mystifying or alienating relations for the participants.
Observations on the Theory of Digital Signatures and Cryptographic Hash Functions

As the demand for effective information protection grows, security has become the primary concern in protecting such data from attackers. Cryptography is one of the methods for safeguarding such information. It is a method of storing and distributing data in a specific format that can only be read and processed by the intended recipient. It offers a variety of security services like integrity, authentication, confidentiality and non-repudiation, Malicious. Confidentiality service is required for preventing disclosure of information to unauthorized parties. In this paper, there are no ideal hash functions that dwell in digital signature concepts is proved.
Meaning and reference from a probabilistic point of view

The rise of Bayesian models of cognition requires that traditional questions in epistemology and metaphysics, such as how models relate to reality and how one observer's models relate to another's, be reframed in probabilistic terms. In this paper we take up these questions beginning from a subjective (Bayesian) conception of probability, in which distinct observers hold potentially different probabilistic models of the world, with no one observer necessarily possessing the "true" one. The key question is what terms in a probabilistic theory mean-that is, what they refer to and what their truth conditions are. We address this question with tools from information theory. We introduce the translation uncertainty, a generalization of the Kullback-Leibler divergence that expresses the discrepancy between two observers' probabilistic models of a common environment. We derive a number of basic information-theoretic relationships among observers, showing for example that the probability that two Bayesian observers will classify the world similarly (called the concordance) depends on the translation uncertainty between their respective models of the world. Our framework suggests a pathway to a semantics for a "probabilistic language of thought."
Competencies of a Bibliographer: Continuity of Development and Break Points

The competence of librarians-bibliographers is a topical subject of discussion in the professional community, as well as the framework of the activities of international and national library associations. The dynamically changing external environment is influencing the changing role and requirements of librarians-bibliographers: from traditional custodians of knowledge to new ones – collaborators, research partners, promoters and data stewards. Within the framework of the III International Bibliographic Congress in 2021, a foresight session “Bibliographer’s Competences: Continuity of Development and Break Points” was held, the purpose of which was to build a model of the competencies of a modern bibliographer. The foresight session was based on the system of methods - expert assessment, moderation and facilitation of discussions, brainstorming, as well as a survey of specialists on the strategically important competencies of a modern bibliographer. The assessment of the results of the survey was formed on the basis of the skills constructor. It was built as a result of the analysis of educational standards, curricula, literature on the modern development of bibliographic activity and the competence of librarians. The assessment of the results was carried out according to the standard Likert scale of importance. The degree of competencies importance was considered in 4 blocks: 1) general professional and professional competences; 2) skills in analytical and synthetic information processing, 3) other “hard” skills of a bibliographer; 4) “soft” skills. Each block of the survey was completed with open questions, allowing respondents the indication of any competencies they think should be included. It was determined that the importance of the competencies of a bibliographer for libraries of different types is different, in this regard, the article presents the summary rating of the competencies of a bibliographer, as well as a comparative analysis of the importance of the competencies of a bibliographer working in libraries of different types (university / scientific and public).
An improved network-based recommendation model via inhibiting algorithm bias

As an effective tool of information filtering, the network-based recommendation algorithms encounter the challenging problem of recommendation bias induced by the object heterogeneity. Previous solutions usually make the improvement based on some specific algorithm, however, are difficult to generalize to different algorithms. In this article, we propose an improved model with a general formula, by inhibiting recommendation bias described by the eigenvalue and eigenvectors of the algorithm similarity matrix, and applied the model into ten different algorithms. Based on four real recommender systems, the experimental results show that nearly all the algorithms are improved in three aspects of recommendation accuracy, diversity and novelty, for all the four datasets. The recommendation accuracy of cold objects is also elevated. Especially, two excellent algorithms are further improved without introducing any other parameter. Our work may shed a new light on developing general recommendation algorithms from the perspective of revealing intrinsic feature in recommender systems.
Forensic analysis of note and journal applications

As the rate of smartphones into the general population has increased, apps that provide a wide range of functions have been developed. The usage records can remain within the app, and are a significant target for analysis from a digital forensic perspective. However, some apps protect app data by providing security features. From the perspective of digital forensic investigation, there is a need for research into apps that provide security features, so that investigators can efficiently collect forensically useful data. In this study, we analyzed the secret values used for locking, and the methods for storing user-created content of 56 note and journal apps that provide security features within the app. We found that 95% of apps that provide security features store users' data insecurely. We propose methods of acquiring a password from another app, using the user's secret value that can be obtained within the app. • Identifing the security settings provided by each app. • Identifing the user secret value used for security settings, and the storage location and storage type of the content. • Analyzing vulnerabilities of note and journal apps. • Providing decryption methods for encrypted app data.
Fast reconstruction technology of a laser beam spatial transmission characteristic curve

The traditional multiposition method of the M2 factor measurement system is a good method, but it is relatively time consuming, so it cannot meet the requirements of the transient test of a Gaussian beam. To solve this problem, a quadriwave lateral shearing interferometer and a wavefront construction method based on a difference Zernike polynomial are analyzed. This interferometer uses a special grating to select four replicas of the wavefront, and the interferogram generated by four replicas includes difference wavefront information. Then the difference Zernike polynomial method is used to analyze quadriwave lateral shearing interferograms. The characteristic parameters are obtained after finding the optimal terms of the Zernike polynomials. As a result, the errors of F-number, beam radius, and radius of curvature are 3.7%, 3.8%, and 0.6%, respectively, which verifies this method to calculate parameters of Gaussian beam. In addition, we also find that the shear amount has influence on the reconstructed wavefront. It is showed that as the amount of shear increases from 20 pixels to 90 pixels, the peak-to-valley (P-V) values and RMS values both gradually decrease with a nonlinear relationship, which could be used to decrease the error of wavefront reconstruction further.
Learning to rank from relevance judgments distributions

Abstract LEarning TO Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document‐topic pair. Within the Cranfield framework, relevance labels result from merging either multiple expertly curated or crowdsourced human assessments. In this paper, we explore how to train LETOR models with relevance judgments distributions (either real or synthetically generated) assigned to document‐topic pairs instead of single‐valued relevance labels. We propose five new probabilistic loss functions to deal with the higher expressive power provided by relevance judgments distributions and show how they can be applied both to neural and gradient boosting machine (GBM) architectures. Moreover, we show how training a LETOR model on a sampled version of the relevance judgments from certain probability distributions can improve its performance when relying either on traditional or probabilistic loss functions. Finally, we validate our hypothesis on real‐world crowdsourced relevance judgments distributions. Overall, we observe that relying on relevance judgments distributions to train different LETOR models can boost their performance and even outperform strong baselines such as LambdaMART on several test collections.
Research on Pedestrian Tracking Algorithm Based on Deep Learning

Pedestrian tracking is an important task in the field of computer vision and is the basis for other advanced vision tasks such as human pose estimation, motion recognition and behavioural analysis, and is widely used in emerging areas such as autonomous driving, intelligent security and service robotics. The detection-based tracking framework in this paper relies heavily on pedestrian detection, and excellent detection algorithms can significantly improve tracking performance. To address the problem of poor pedestrian detection accuracy in the presence of small targets, occlusion and congestion, this paper designs a joint attention module, combines this module with the YOLO-v4 target detection model to design a joint attention-based pedestrian detection algorithm. It conducts experiments on the OTB100- UP&T pedestrian detection dataset. The experimental results demonstrate the effectiveness of the algorithm.