Reviewerly/exHarmony huggingface dataset:
Novel Metrics for Social Collaboration Processes

At present, the development and maintenance of many software projects are carried out by means of social collaboration. So social collaboration managers need appropriate service support to help them complete staffing, time planning and so on. Measuring the properties of the collaboration and applying them to recommendations can improve recommendation results. This paper proposes a framework for measuring the properties of collaboration process. This framework measures the collaboration process in terms of difficulty and smoothness. Then an empirical analysis is carried out based on the data of bugzilla platform's bug fix collaboration process. The value of the collaboration process property measurement were confirmed by recommendation accuracy. The empirical research results show that analyzing the properties of the collaboration process and establishing a measurement framework and applying it to the service recommendation can effectively improve the service effect.
Intelligent and Adaptive Web Page Recommender System

In this manuscript, an intelligent and adaptive web page recommender system is proposed that provides personalized, global, and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: uniformity and recommendation strength. The system continuously tracks the user's responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset, which is a significant improvement over the 70% F1 measure reported by automatic clustering-based genetic algorithm, the prior web recommender system.
A solution of the hypersingular integro-diﬀerential equation with determinants of the Vronsky type

In this paper, we consider a new hypersingular integro-diﬀerential equation of arbitrary order on a closed curve located in the complex plane. The integrals in the equation are understood in the sense of the ﬁnite Hadamard part. The equation refers to linear integro-diﬀerential equations with variable coeﬃcients of a particular form. A characteristic feature of the equation is its representation with the help of determinants close to the Vronsky ones. The method of analytical continuation, properties of determinants, and generalized Sokhotsky formulas are used for the study. The equation reduces to the Riemann boundary value problem of a jump in a certain class of functions. If the Riemann boundary problem turns out to be solvable, then one should solve linear inhomogeneous diﬀerential equations in the class of analytic functions in the domains of the complex plane. The analysis of the obtained solutions in an inﬁnitely distant point is not evident. The study has a complete look. The conditions for the solvability of the original equation are explicitly written out. When they are fulﬁlled, the solution is explicitly written, to which an example is given.
A Framework for Partitioning Support Vector Machine Models on Edge Architectures

Current IoT applications generate huge volumes of complex data that requires agile analysis in order to obtain deep insights, often by applying Machine Learning (ML) techniques. Support vector machine (SVM) is one such ML technique that has been used in object detection, image classification, text categorization and Pattern Recognition. However, training even a simple SVM model on big data takes a significant amount of computational time. Due to this, the model is unable to react and adapt in real-time. There is an urgent need to speedup the training process. Since organizations typically use the cloud for this data processing, accelerating the training process has the advantage of bringing down costs. In this paper, we propose a model partitioning approach that partitions the tasks of Stochastic Gradient Descent based Support Vector Machines (SGD-SVM) on various edge devices for concurrent computation, thus reducing the training time significantly. The proposed partitioning mechanism not only brings down the training time but also maintains the approximate accuracy over the centralized cloud approach. With a goal of developing a smart objection detection system, we conduct experiments to evaluate the performance of the proposed method using SGD-SVM on an edge based architecture. The results illustrate that the proposed approach significantly reduces the training time by 47%, while decreasing the accuracy by 2%, and offering an optimal number of partitions.
Personalized Multi-modal Video Retrieval on Mobile Devices

Current video retrieval systems on mobile devices cannot process complex natural language queries, especially if they contain personalized concepts, such as proper names. To address these shortcomings, we propose an efficient and privacy-preserving video retrieval system that works well with personalized queries containing proper names, without re-training using personalized labelled data from users. Our system first computes an initial ranking of a video collection by using a generic attention-based video-text matching model (i.e., a model designed for non-personalized queries), and then uses a face detector to conduct personalized adjustments to these initial rankings. These adjustments are done by reasoning over the face information from the detector and the attention information provided by the generic model. We show that our system significantly outperforms existing keyword-based retrieval systems, and achieves comparable performance to the generic matching model fine-tuned on plenty of labelled data. Our results suggest that the proposed system can effectively capture both semantic context and personalized information in queries.
Don’t Judge Me by My Face: An Indirect Adversarial Approach to Remove Sensitive Information From Multimodal Neural Representation in Asynchronous Job Video Interviews

Use of machine learning for automatic analysis of job interview videos has recently seen increased interest. Despite claims of fair output regarding sensitive information such as gender or ethnicity of the candidates, the current approaches rarely provide proof of unbiased decision-making, or that sensitive information is not used. Recently, adversarial methods have been proved to effectively remove sensitive information from the latent representation of neural networks. However, these methods rely on the use of explicitly labeled protected variables (e.g. gender), which cannot be collected in the context of recruiting in some countries (e.g. France). In this article, we propose a new adversarial approach to remove sensitive information from the latent representation of neural networks without the need to collect any sensitive variable. Using only a few frames of the interview, we train our model to not be able to find the face of the candidate related to the job interview in the inner layers of the model. This, in turn, allows us to remove relevant private information from these layers. Comparing our approach to a standard baseline on a public dataset with gender and ethnicity annotations, we show that it effectively removes sensitive information from the main network. Moreover, to the best of our knowledge, this is the first application of adversarial techniques for obtaining a multimodal fair representation in the context of video job interviews. In summary, our contributions aim at improving fairness of the upcoming automatic systems processing videos of job interviews for equality in job selection.
Quality Control Systems in UK : An On-site Assessments of Testing and Inspection Institutions

With reference to the quality standard, ISO/IEC 17025, we conducted on-site assessments of and comparisons between test and inspection institutions in Korea and the UK. The diversity of evaluation methods used ‘in the field’ within the UK is greater than the range in Korea. Nevertheless, when assessing on-site processes in both countries, it is clear that many aspects of the quality assessment, including the roles of the evaluation committee members, the preparation of documents for evaluation, the opening meeting, the evaluation process in the field, and the closing meeting, occur in a similar order in the UK as in Korea. However, one important difference between the on-site evaluations in Korea and the UK was noted; particularly, in the UK, the level of excellence achieved by the evaluated institution is highlighted during the closing meeting.
Momentum-driven adaptive synchronization model for distributed DNN training on HPC clusters

• Big Data application for Deep Learning. • DNN training on High-Performance Computing Platform. • DNN training slows down due to the stale gradients. • Stochastic Error Constraint for DNN training. • Adaptive Learning Rate for DNN training. Building a distributed deep learning (DDL) system on HPC clusters that guarantees convergence speed and scalability for the training of DNNs is challenging. The HPC cluster, which consists of multiple high-density multi-GPU servers connected by the Infiniband network (HDGib), compresses the computing and communication time for distributed DNNs' training but brings new challenges. The convergence time is far from linear scalability (with respect to the number of workers) for parallel DNNs training. We thus analyze the optimization process and identify three key issues that cause scalability degradation. First, the high-frequency update for parameters due to the compression of the computing and communication times exacerbates the stale gradient problem, which slows down the convergence. Second, the previous methods used to constrain the gradient noise (stochastic error) of the SGD are outdated, as HDGib clusters can support more strict constraints due to the Infiniband network connections, which can further constrain the stochastic error. Third, the same learning rate for all workers is inefficient due to the different training stages of each worker. We thus propose a momentum-driven adaptive synchronization model that focuses on solving the above issues and accelerating the training procedure on HDGib clusters. Our adaptive k -synchronization algorithm uses the momentum term to absorb the stale gradients and adaptively bind the stochastic error to provide an approximate optimal descent direction for the distributed SGD. Our model also includes an individual dynamic learning rate search method for each worker to further improve training performance. Compared with previous linear and exponent decay methods, it can provide a more precise descent distance for distributed SGD based on different training stages. Extensive experimental results indicate that the proposed model effectively improves the training performance of CNNs, which retains high accuracy with a speed-up of up to 57.76% and 125.3% on the CPU-based and GPU-based clusters, respectively.
The research on the layout mode of the main network and the secondary network of the diamond distribution network

Aiming at the problem of the lack of the research on the coordinated layout mode of the switch station and the ring station in the development of the diamond distribution network proposed in the Shanghai area, it constructed the Shanghai diamond distribution network collaborative layout mode of the main and the secondary network. Firstly, it introduced the wiring mode of the diamond distribution network, and equivalented the power supply range of the substation into a regular hexagon, and established the diamond-shaped power supply model of the diamond distribution network, and analyzed the factors in the layout mode of the main and the secondary network in the diamond distribution network; Secondly, it constructed the layout model of the main network in the diamond distribution network considering the combination of reliability and economy;Finally, within the power supply range of the switch station,it constructed the layout model of the secondary network in the diamond distribution network considering the combination of reliability and economy. In the end, the best layout model of the main and secondary main network of the diamond distribution network was obtained, which provided the quantitative support for the planning of the diamond distribution network in Shanghai, and gived the guiding significance.
Applications of Unconstrained Optimization

Optimization problems occur in many disciplines, for example, in engineering, physical sciences, social sciences, and commerce. In this chapter, we demonstrate the usefulness of the unconstrained optimization algorithms studied in this book by applying them to a number of problems in engineering. Applications of various constrained optimization algorithms will be presented in Chap. 16.
A Generative Model-Based Predictive Display for Robotic Teleoperation

We propose a new generative model-based predictive display for robotic teleoperation over high-latency communication links. Our method is capable of rendering photo-realistic images of the scene to the human operator in real time from RGB-D images acquired by the remote robot. A preliminary exploration stage is used to build a coarse 3D map of the remote environment and to train a generative model, both of which are then used to generate photo-realistic images for the human operator based on the commanded pose of the robot. Data captured by the remote robot is used to dynamically update the 3D map, enabling teleoperation in the presence of new and relocated objects. Various experiments validate our proposed method’s performance and benefits over alternative methods.
<scp>XR</scp> Accessibility Initiatives in Academic Libraries

Abstract Virtual reality and other extended reality (XR) technologies are being integrated into academic libraries to support research and teaching in innovative ways; however, new technologies are often adopted without considering disabled users. XR accessibility depends on establishing appropriate policies and best practices. This paper examines the current state of XR accessibility initiatives in academic libraries by reporting findings from a survey sent to the academic library community from February to May 2021.
Continuing Along the Cow Path

Recent calls to incorporate cultural approaches to technical communication research offers new avenues for imagining where technical communication and user experience emerge. Taking up these calls, this case study explores how elements of documentation design in a Jewish cemetery function as cultural interfaces and why socially informed design benefits users by highlighting technical documentation's role in fostering community.
GeoCQA: A Large-Scale Geography-Domain Chinese Question Answering Dataset from Examination

We present GeoCQA, the largest multiple-choice Chinese Question answering dataset in the geographic domain, evaluating the high-level reading ability of logic reasoning and prior geographic domain knowledge integration of a question answering (QA) model. GeoCQA contains 58,940 questions from real-world scenarios and has been collected from the high school geography examination which aims to evaluate students' mastery of the geographic concept and their ability to use geographic knowledge to solve problems. To investigate the challenges of GeoCQA to existing methods, we implement both rule-based and best neural methods and find that the current best method can achieve 71.90% of test accuracy, while unskilled humans and skilled humans can reach 80% and 96% accuracy respectively, which shows that GeoCQA is challenging to the current methods and the performance still has space to improve. We will release GeoCQA and our baselines to bring more data sources to the community and hope that it can help to promote much stronger Chinese QA models in the future (https://github.com/db12138/GeoCQA).
The RF national policy for senior citizens and its implementation in St. Petersburg libraries

Abstract : The author analyzes the state policy of the Russian Federation oriented toward using and developing the potential of senior citizens, in particular its administration in library and information services. She reviews the documents on active ageing and senior citizen participation in economic and socio cultural development, i. e. federal laws, decrees, presidential messages, strategies, concepts, national programs, etc. Special attention is given to the related government programs and legal acts adopted in St. Petersburg. The priorities of the national and regional policy in the area are analyzed. The accomplished projects of Russian and, in particular, St. Petersburg public libraries are characterized. Personality orientation, humanism and social viability make the principles of library senior user services. The key vectors and formats of services are reviewed.
Reproducibility Companion Paper

In this companion paper, we provide details of the artifacts to support the replication of "Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework", which was presented at MM'20. The Inter-intra Contrastive (IIC) framework aims to extract more discriminative temporal information by extending intra-negative samples in contrastive self-supervised learning. In this paper, we first summarize our contribution. Then we explain the file structure of the source code and detailed settings. Since our proposal is a framework which contain a lot of different settings, we provide some custom settings to help other researchers to use our methods easily. The source code is available at https://github.com/BestJuly/IIC.
OpenNFCSense: Open-Source Library for NFCSense

OpenNFCSense is an open-source library for NFCSense, a system for detecting the movements of near-field communication (NFC) tags using a commodity, low-cost RC522 NFC reader. With a user-defined tag profile, the users can use its application programming interface (API) to obtain the NFC tagged objects’ motion speed, motion frequency, and motion type while recognizing these tagged objects. Since NFC tags support straightforward augmentation to existed physical objects, such as modular building blocks and mechanical toys, OpenNFCSense offers an easy and safe method for rapid prototyping tangible user interfaces for designers.
Transfer Clustering Using a Multiple Kernel Metric Learned Under Multi-Instance Weak Supervision

Multiple kernel clustering methods have been quite successful recently especially concerning the multi-view clustering of complex datasets. These methods simultaneously learn a multiple kernel metric while clustering in an unsupervised setting. With the motivation that some minimal supervision can potentially increase their effectiveness, we propose a Multiple Kernel Transfer Clustering (MKTC) method that can be described in terms of two tasks: a source task, where the multiple kernel metric is learned, and a target task where the multiple kernel metric is transferred to partition a dataset. In the source task, we create a weakly supervised multi-instance subset of the dataset, where a set of data instances are together provided some labels. We put forth a Multiple Kernel Multi-Instance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -Means (MKMIKM) method to simultaneously cluster the multi-instance subset while also learning a multiple kernel metric under weak supervision. In the target task, MKTC transfers the multiple kernel metric learned by MKMIKM to perform unsupervised single-instance clustering of the entire dataset in a single step. The advantage of using a multi-instance setup for the source task is that it requires reduced labeling effort to guide the learning of the multiple kernel metric. Our formulations lead to a significantly lower computational cost in comparison to the state-of-the-art multiple kernel clustering algorithms, making them more applicable to larger datasets. Experiments over benchmark computer vision datasets suggest that MKTC can achieve significant improvements in clustering performance in comparison to the state-of-the-art unsupervised multiple-kernel clustering methods and other transfer clustering methods.
Semantic Interoperability of CV Builder: Problem and Practical Solutions

The article presents the results and practical solutions of the Operational Programme for the European Union Funds’ Investments in 2014–2020 project “Automated CV Builder Based on Artificial Intelligence Module”. The article discusses the problem of structuring curriculum vitae (CV) data, specifics of evaluation process, evaluation of candidate’s technical and personal skills, data validity, and data presentation problems. The results of the carried out empirical research and the systematic review of the literature are described, and the new CV builder information model is presented.
Designing Sensory and Social Tools for Neurodivergent Individuals in Social Media Environments

Sensory guides and social narratives are learning tools that provide sensory and social support to neurodivergent individuals. These tools—and their design guidelines—have historically been developed for physical environments, such as museums and classrooms. They lack support for social media environments, where sensory stimuli and social contexts can be complex and uncertain. We address these challenges by designing a novel social media sensory guide and social narrative, specifically adapted for social media interaction. We leverage our use case, Twitter Spaces—an audio-only conversation feature in beta. The goal of this pilot study is to determine whether neurodivergent users want sensory guides and social narratives adapted for social media, and if users find them helpful in setting expectations for social media interaction. We evaluate these tools with eight neurodivergent Twitter users, using tasks and thinking aloud. Results indicate a strong potential for adoption of both tools among neurodivergent individuals to reduce overstimulation in social media environments.
Method of Automatically Detecting the Abnormal First Arrivals Using Delay Time (December 2020)

Abnormal first-arrival times in the automatic first-arrival picking significantly affect the structural inversion and static correction. After implementing an automatic picking, the manual intervention is still required to correct or eliminate false first-arrival times, which seriously affects the efficiency of the first-arrival picking. Therefore, it is necessary to develop automatic removal methods for abnormal first-arrival times. Here, we propose a distance-based outlier detection algorithm to automatically and effectively eliminate the abnormal first-arrival times. In order to identify the abnormal first-arrival times, each first-arrival time is decomposed into a receiver delay time and a shot delay time. Then, according to offset and azimuth information, we eliminate the influences of offset and shot delay times on the first-arrival times in a single shot to get the receiver delay times. We further rearrange the delay times in polar coordinates. Finally, we apply a distance-based outlier detection algorithm to the delay times on different azimuths and delete the first-arrival times that correspond to the detected outliers. Our study indicates that the distance-based outlier detection algorithm can effectively eliminate the abnormal first-arrival times. The results from real data processing demonstrate that the proposed automatic removal approach of the abnormal first-arrival times can effectively improve the quality of the first-arrival times. In addition, we find that our proposed algorithm has a better performance than a least-squares regression method for low signal-to-noise ratio (SNR) seismic data.