Reviewerly/exHarmony huggingface dataset:
A Bibliographical Study of Software Product Management Research

Software Product Management (SPM) is a relatively young research area which aims to understand how to productise a software product or a service as well as how to align it with the organisation's strategy. While the research of an academic discipline of SPM started to emerge as yearly as 1990s, the most impactful works have been published during 2006–2007. To understand how this young field has emerged and developed, this paper presents a bibliometric study on SPM publications found from Scopus ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{n}=142$</tex> ). The identified studies have been produced by a small set of authors and institutions, which are mainly located in Europe. By using Bibliographic Coupling and Co-Citation metrics, the study shows that Software Product Management literature is drawing from several different related fields. Furthermore, the studied literature is tightly interconnected. The study also shows that the SPM field might be lacking a coherent intellectual background and new openings due to scattered research foci. To prevent this development, this work calls for a formation of a shared research agenda for the Software Product Management field.
A retrospective on the intellectual adventures of think tanks in biosecurity before and after the COVID-19 pandemic outbreak

Think tanks play a fundamental role in shaping policy agendas in Western countries, especially in the US. As international biosecurity is turning from a moderate to a serious concern, the convergence of biosecurity subjects and think tanks is evidently increasing. Examining the involvement and implication of think tanks in biosecurity policy formulation domestically and internationally is, therefore, of great value. This article takes a brief look at the intellectual output of over 30 think tanks during the last five years, before and after the outbreak of COVID-19, and tries to build an understanding of the extent to which these think tanks informed strategic, operational, and tactical decisions, with the aim of providing a better basis for dealing with sophisticated biological threats.
A Medical University School of Pedagogical Excellence as the Environment for Creativity

The article deals with the integration of formal and informal education of university instructors within the framework of the School of Pedagogical Excellence (SPE) as an environment for pedagogical creativity. The authors analyze the general cultural competencies in the educational standards of higher education, reflecting the requirements for the creative training of university graduates. A brief overview of researches on educational technologies, creativity and professional development of university instructors, including medical ones, is given. The article presents the approaches to solving the problems of continuous pedagogical training of medical university instructors in terms of the SPE potential. The structure and activities of the SPE are demonstrated. The University instructors are trained in 11 additional professional programs of advanced pedagogical education and retraining. This diversity gives a possibility to satisfy various educational needs of staff members. The Center for Pedagogical Innovations of the University performs the function of psychological and andragogical support for teaching doctors in creativity and distribution of innovative experience. It also accumulates the experience and options for non-formal education. The results of the study can be used to create a system for continuous pedagogical training of university instructors of medical and other professional areas.
Limited Sampling Reference Frame for MaskTrack R-CNN

With the great achievement for the computer vision tasks, e.g., image classification, object detection and segmentation, people are diving into more complex vision tasks. Video instance segmentation is a new task which includes detection, segmentation and tracking of instances simultaneously in a video. Occluded Video Instance Segmentation (OVIS) is used for this task, and it includes many heavily occluded scenes. Besides, there is a long range for the length of videos in this dataset. In order to track instances in videos with different lengths, we make some improvements based on MaskTrack R-CNN. Based on these optimizations, a refinement model can be well used to detect and segment instances, which acquires a better track accuracy in long videos. Furthermore, we apply Stochastic Weights Aver-aging training strategy to get a better result. Finally, The proposed method can achieve the mAP score of 28.9 for the validation set and 32.2 for the test set on the OVIS dataset.
Towards Reliable AI for Source Code Understanding

Cloud maturity and popularity have resulted in Open source software (OSS) proliferation. And, in turn, managing OSS code quality has become critical in ensuring sustainable Cloud growth. On this front, AI modeling has gained popularity in source code understanding tasks, promoted by the ready availability of large open codebases. However, we have been observing certain peculiarities with these black-boxes, motivating a call for their reliability to be verified before offsetting traditional code analysis. In this work, we highlight and organize different reliability issues affecting AI-for-code into three stages of an AI pipeline- data collection, model training, and prediction analysis. We highlight the need for concerted efforts from the research community to ensure credibility, accountability, and traceability for AI-for-code. For each stage, we discuss unique opportunities afforded by the source code and software engineering setting to improve AI reliability.
Broad coverage emotion annotation

Abstract In this paper we present the emotion annotation of 1.5 billion words Portuguese corpora, publicly available. We motivate the annotation process and detail the decisions made. The resource is evaluated, being applied to different areas: to study Lusophone literature, to obtain paraphrases, and to do genre comparison.
Efficient Pairwise Neuroimage Analysis Using the Soft Jaccard Index and 3D Keypoint Sets

We propose a novel pairwise distance measure between image keypoint sets, for the purpose of large-scale medical image indexing. Our measure generalizes the Jaccard index to account for soft set equivalence (SSE) between keypoint elements, via an adaptive kernel framework modeling uncertainty in keypoint appearance and geometry. A new kernel is proposed to quantify the variability of keypoint geometry in location and scale. Our distance measure may be estimated between O (N 2) image pairs in [Formula: see text] operations via keypoint indexing. Experiments report the first results for the task of predicting family relationships from medical images, using 1010 T1-weighted MRI brain volumes of 434 families including monozygotic and dizygotic twins, siblings and half-siblings sharing 100%-25% of their polymorphic genes. Soft set equivalence and the keypoint geometry kernel improve upon standard hard set equivalence (HSE) and appearance kernels alone in predicting family relationships. Monozygotic twin identification is near 100%, and three subjects with uncertain genotyping are automatically paired with their self-reported families, the first reported practical application of image-based family identification. Our distance measure can also be used to predict group categories, sex is predicted with an AUC = 0.97. Software is provided for efficient fine-grained curation of large, generic image datasets.
Corpus Linguistic Technology as a Tool to Improve Creative Thinking in the Interpretation of English Language Idioms

The article validates the possibility and efficiency of improving creative thinking ability while teaching the interpretation of idioms based on corpus linguistic technology to EFL (English as a Foreign Language) students. Creative thinking ability is defined as the ability to generate, develop, and test hypotheses in the process of the interpretation of idioms based on corpus linguistic technology. The goal of the current research is to identify specific features of the students’ interpretation activity while working with corpus linguistic technology and, on this basis, substantiate the possibilities of developing creative thinking ability by means of corpus linguistic technology while teaching the interpretation of idioms. The article provides examples of corpus-based exercises aimed at developing students’ creative thinking ability while teaching the interpretation of idioms. The methodology has been tested on the EFL students of Bachelor study program in Public Relations implemented by Peter the Great St. Petersburg Polytechnic University in the years 2020–2021. The authors describe a case study to reveal how corpus linguistic technology can be used in foreign language teaching and detail the procedure of the learners’ work with the basic corpus tools while interpreting idioms. The result of the experimental training indicates that the average level of creative thinking ability increased, confirming the hypothesis of the research on the effectiveness of the corpus linguistic technology as a tool to improve EFL students’ creative thinking ability in the process of teaching the interpretation of idioms.
Neural architecture tuning with policy adaptation

Neural architecture search (NAS) is to automatically design task-specific neural architectures, whose performance has already surpassed those of many manually designed neural networks. Existing NAS techniques focus on searching for the neural architecture and training the optimal network weights from the scratch. Nevertheless, it could be essential to study how to tune a given neural architecture instead of producing a completely new neural architecture in some scenarios, which may lead to a more optimal solution by combining human experience and the advantages of the machine's automatic searching. This paper proposes to learn to tune the architectures at hand to achieve better performance. The proposed Neural Architecture Tuning (NAT) algorithm trains a deep Q-network to tune neural architectures given a random architecture so that we can achieve better performance on a reduced space. We then apply adversarial autoencoder to make the learned policy be generalized to a different searching space in real-world applications. The proposed algorithm is evaluated on the NAS-Bench-101 dataset. The results indicate that our NAT framework can achieve state-of-the-art performance on the NAS-Bench-101 benchmark, and the learned policy can be adapted to a different search space while maintaining the performance.
Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable selection

Abstract We introduce a framework for efficient Markov chain Monte Carlo algorithms targeting discrete-valued high-dimensional distributions, such as posterior distributions in Bayesian variable selection problems. We show that many recently introduced algorithms, such as the locally informed sampler of Zanella (J Am Stat Assoc 115(530):852–865, 2020), the locally informed with thresholded proposal of Zhou et al. (Dimension-free mixing for high-dimensional Bayesian variable selection, 2021) and the adaptively scaled individual adaptation sampler of Griffin et al. (Biometrika 108(1):53–69, 2021), can be viewed as particular cases within the framework. We then describe a novel algorithm, the adaptive random neighbourhood informed sampler, which combines ideas from these existing approaches. We show using several examples of both real and simulated data-sets that a computationally efficient point-wise implementation (PARNI) provides more reliable inferences on a range of variable selection problems, particularly in the very large p setting.
Face hallucination based on degradation analysis for robust manifold

Recently, face hallucination, also termed face super-resolution (SR), has been widely studied and achieved significant progress. The algorithm based on manifold learning is one of the primary methods for SR. However, when recovering the high-resolution (HR) counterparts from extremely low-resolution (LR) images, the basic assumption of manifold consistence between LR/HR spaces is doubtful and vulnerable. To address this issue, some algorithms usually employ the cascaded models in line with the inherent magnification factors, such as 2, 4 and 8, to maintain the manifold consistence. As a simple cascade mechanism, the inherent factors cannot ensure the optimal performance without the relationship between the manifold relevance and the down-sampling scale. In this paper, we explore the relevance with the groups of gradually down-sampled training sets and divide the scales into different classes for robust manifold consistence. And then, to map the optimal coefficients from LR spaces to HR target ones better, we introduce the weight-mapping neighbour embedding model. Qualitative and quantitative evaluations demonstrate that the enhancing of the manifold relevance can promote effective face hallucination. Based on the weight-mapping and scale clustering, our algorithm achieves better results compared with the state-of-the-art methods.
Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment

Accurately grading open-ended assignmentsin large or massive open online courses is nontrivial. Peer review is a promising solution but can be unreliable due to few reviewers and an unevaluated review form. To date, no work has leveraged sentiment analysis in the peer-review process to inform or validate grades or utilized aspect extraction to craft a review form from what students actually communicated. This article utilizes, rather than discards, student data from review form comments to deliver better <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information</i> to the instructor. In this article, we detail the process by which we create our domain-dependent lexicon and aspect-informed review form as well as our entire sentiment analysis algorithm, which provides a fine-grained sentiment score from text alone. We end by analyzing validity and discussing conclusions from our corpus of over 6800 peer reviews from nine courses to understand the viability of sentiment in the classroom for increasing the information from and reliability of grading open-ended assignments in large courses.
SCOPA

The recent advent of cross-lingual embeddings, such as multilingual BERT (mBERT), provides a strong baseline for zero-shot cross-lingual transfer. There also exists increasing research attention to reduce the alignment discrepancy of cross-lingual embeddings between source and target languages, via generating code-switched sentences by substituting randomly selected words in the source languages with their counterparts of the target languages. Although these approaches improve the performance, naively code-switched sentences can have inherent limitations. In this paper, we propose SCOPA, a novel technique to improve the performance of zero-shot cross-lingual transfer. Instead of using the embeddings of code-switched sentences directly, SCOPA mixes them softly with the embeddings of original sentences. In addition, SCOPA utilizes an additional pairwise alignment objective, which aligns the vector differences of word pairs instead of word-level embeddings, in order to transfer contextualized information between different languages while preserving language-specific information. Experiments on the PAWS-X and MLDoc dataset show the effectiveness of SCOPA.
Transform Without Encode is not Sufficient for SIFA and FTA Security: A Case Study

Statistical Ineffective Fault Analysis (SIFA) and Fault Template Attack (FTA) are two recently proposed classes of Fault Attacks (FA), which evade almost all existing FA countermeasures, even while they are combined with Side-Channel Analysis (SCA) countermeasures such as masking. Protecting against these attacks requires an entirely new class of mechanisms, and only a handful of suggestions have been made in the context of SIFA so far. Recently, a countermeasure targeting both of these attack classes has been proposed in DATE 2021 [], claiming security for single-bit faults. In this paper, we present successful SIFA and FTA attacks against this countermeasure using single-bit faults only. Considering the fact that the target countermeasure is a partial instantiation of one of the earliest SIFA countermeasures proposed in [] (which, on the contrary, is secure against SIFA), this attack establishes that any unproven modification to a countermeasure can be fatal. The proposed attacks were validated in simulation considering state-of-the-art fault models.
Face Detection and Face Re-identification System Using Deep Learning and OpenVINO

Face detection and face re-identification are two important processes commonly used in the surveillance system especially in Intelligent Surveillance System to identify and track people from the video footages collected from a network surveillance system that are stored inside a centralized server. This system will be helpful in crime detection, criminal tracking, and factory visitor tracking if it can be done in real time and has high accuracy. It will open the potential to revolutionize the industry and the way businesses work today. In this paper, an embedded system equipped with fast and accurate face reidentification using a modified single shot detector (SSD), a landmark regressor and a MobileNet V2 model is proposed. From the result obtained, the proposed system is tested on multiple faces and is able to achieve 99.01% re-identification accuracy. Furthermore, the proposed system also achieves an average processing time of 60ms using the low-cost Intel UP board with Intel Atom CPU and OpenVINO (a neural network acceleration platform).
Producing a Dissimilar Joint of Copper to Austenitic Stainless Steel by Ultrasonic Welding

Abstract There are several possibilities for establishing a cohesion joint between dissimilar metals. In the case of thin sheets, the ultrasonic welding process is suitable. This process can establish a cohesion joint rapidly, with a low heat input between the thin sheets. The authors have tried to determine the optimal ultrasonic welding parameters for copper and austenite stainless steel joining by using an experimental method of joining. Suitable results were obtained by welding tests due dissimilarities in the chemical, physical and mechanical properties of the copper and stainless steel. A standard size sheet thickness and test sample was used for the welding by different parameters. The parameters were refined based on the theoretical and practical knowledge during the experiments. The experimental welding was made by a Branson L20 type welder machine. The joint made by the different parameters was inspected by shearing-tensile tests (maximal force level).
A Signal Detection Scheme Based on Deep Learning in OFDM Systems

Channel estimation and signal detection are essential steps to ensure the quality of end-to-end communication in orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we develop a DDLSD approach, i.e., Data-driven Deep Learning for Signal Detection in OFDM systems. First, the OFDM system model is established. Then, the long short-term memory (LSTM) is introduced into the OFDM system model. Wireless channel data is generated through simulation, the preprocessed time series feature information is input into the LSTM to complete the offline training. Finally, the trained model is used for online recovery of transmitted signal. The difference between this scheme and existing OFDM receiver is that explicit estimated channel state information (CSI) is transformed into invisible estimated CSI, and the transmit symbol is directly restored. Simulation results show that the DDLSD scheme outperforms the existing traditional methods in terms of improving channel estimation and signal detection performance.
Fuzzy-Based Smart Path Guidance System for Visually Impaired and Deaf Person

In this globe, millions of people are deaf or visually impaired. It is exceptionally rare case someone to be born with this condition, but the possibility of developing deafness or blindness later in life is very probable. The conception of the navigational assistance system has been a remarkable techno-logical advancement. With the developments of wireless communications, the deaf and visually impaired people's assistive device demand also growing for safe and reliable movement. The aim of the research proposed a fuzzy-based simulation model to assist deaf and visually impaired people for safe mobility. For accurate simulation data analysis based on maximum possibility used fuzzy logic interface tools with multiple inputs and output systems. The fuzzy logic interface rules have been prepared based on deaf or visually impaired people's requirements and specifications. The developed model helps the user to avoidance objects and safe navigation.
System for Analyzing Crime News by Mining Live Data Streams with Preserving Data Privacy

Data stream mining is an emerging field of data science. It is a process of extraction of knowledge from streaming data using incremental algorithms. Mining streaming data comes with different challenges [3] like concept drift, handling incomplete and delayed information, skewness of data, and privacy preservation. Privacy of streaming data should be maintained during the process of its mining and processing to protect sensitive information from attackers and also to preserve user-sensitive personal data that is vulnerable to malicious purposes. In this paper, we proposed a system for mining crime news data streams along with privacy preservation of sensitive data using K-anonymization and Apache Spark. The knowledge gained through the process of mining streaming data is visualized in the form of real-time updating charts which provides the end-user with useful insights about current crime rates and statistics in the popular cities of India.
Optimizing the Performance of KNN Classifier for Human Activity Recognition

Recently, computer and smartphone devices have been increasingly employed in various life aspects. Human Activity Recognition (HAR) is one of the most emerging fields that can be clearly noticed in terms of computer related researches. This study proposes a classification model to recognize human six basic activities based on smartphone embedded sensors. For feature selection, we utilized three optimization search algorithms which are: Particle Swarm Optimization (PSO), Greedy, and Genetic algorithm. K-nearest neighbors (KNN) classifier, with Manhattan distance function, is adopted to measure the models performance on two publicly available well-known datasets (i.e., WISDM and UCI-HAR). All experiments are achieved using 10-folds cross validation technique, and the results are evaluated and compared using different metrics, for instance, accuracy, root mean square error, f-measure, and number of features. The experimental results proved that PSO based KNN classifier presents the best performance compared to the other two algorithms, in addition to the baseline KNN classifier. Moreover, the proposed model outperforms those state-of-the-art models presented in related literature regarding the UCI-HAR dataset with performance rate of 98.89%, and close to the best performance achieved on WISDM dataset with performance rate of 91.07%.