Reviewerly/exHarmony huggingface dataset:
A Fine-Grained Analysis on the Inconsistent Changes in Code Clones

Existing studies report that inconsistent changes in code clones can introduce bugs or inconsistencies in a software system's code-base. However, inconsistent changes can often be intentional and these might not lead to bugs. Thus, it would be beneficial if we could have an automatic mechanism for proactively determining which inconsistent changes are likely to introduce bugs in a code-base. With this focus, in our research we investigate the underlying factors affecting the possibility that an inconsistent change made to code clones will lead to bugs. We extract the evolutionary history of the clone fragments in open-source software systems and analyze whether clone-types, former clone evolutionary patterns, and clone proximity have impacts on the bug-proneness of the inconsistent changes to clones. We perform our investigation on six subject systems written in three different programming languages (Java, C, and C#) and find that inconsistent changes in Type 3 clones have the highest possibility of introducing bugs among all three clone-types (Type 1, 2, and 3). Moreover, similarity preserving inconsistent changes are significantly more likely to introduce bugs compared to diverging inconsistent changes. Proximity as well as granularity of code clones have significant impacts on their possibilities of experiencing bug-fixes after having inconsistent changes. Findings from our research can be important for minimizing bugs and inconsistencies in software systems during their evolution.
Research on Shaking Table Test of Earthquake Simulation Based on Hybrid Integration Algorithm

Aiming at the problem of poor accuracy of acceleration waveform reproduction during the shaking table test of earthquake simulation, a hybrid integration algorithm based on seismic acceleration signal is proposed to control the trend term error.The low-frequency attenuation integral algorithm in the frequency domain is used to integrate the original seismic acceleration signal once to obtain the vibration velocity signal, then, the polynomial fitting integration algorithm is introduced to integrate of the vibration velocity signal once in the time domain to obtain the vibration displacement signal.Through these two integrals, the sensitivity of low frequency band during frequency domain integration and the accumulation of small errors in time domain are reduced.Finally, the hybrid integration algorithm is used to perform seismic wave reproduction experiments on the seismic simulation shaker and good results are obtained, which proves that the hybrid integration calculation has high practical value and practical significance in improving the accuracy of the vibration table waveform reproduction.
Blockchain in Charity: Platform for Tracking Donations

The paper explores the possibilities of using blockchain technology in charity. Problems in this area require implementation of new storage tools and the transfer of information between donors, foundations, donation recipients and other charitable actors to ensure data security, the integrity of funds, and the control of donations. Using the blockchain will allow for an increase in the confidence of potential donors in charitable organizations through guaranteed data security, the ability to track the movement of funds and transactions. In this article, the authors analyze needs, review existing charity platforms on the basis of blockchain in Russia and in the world. They offer an example of the implementation of a platform for placing and tracking donations of funds for charitable purposes using distributed registry technologies. While researching, the authors cooperated with local Funds and NPOs to validate the solution, get a better understanding of the ecosystem needs and share this experience in the paper.
Minimal Complexity Support Vector Machines for Pattern Classification

Minimal complexity machines (MCMs) minimize the VC (Vapnik-Chervonenkis) dimension to obtain high generalization abilities. However, because the regularization term is not included in the objective function, the solution is not unique. In this paper, to solve this problem, we discuss fusing the MCM and the standard support vector machine (L1 SVM). This is realized by minimizing the maximum margin in the L1 SVM. We call the machine Minimum complexity L1 SVM (ML1 SVM). The associated dual problem has twice the number of dual variables and the ML1 SVM is trained by alternatingly optimizing the dual variables associated with the regularization term and with the VC dimension. We compare the ML1 SVM with other types of SVMs including the L1 SVM using several benchmark datasets and show that the ML1 SVM performs better than or comparable to the L1 SVM.
Spatio-Temporal Representation for Skeleton-based Human Action Recognition

In recent years, human action recognition (HAR) has found its ways in a diverse range of applications such as video surveillance, gaming and robotics. Input data for HAR come from different sources such as RGB, depth or skeletal data depending on types of cameras in use. Skeletal data are of great interest recently due to computation and storage efficiency, which attracts a lot of research in computer vision on skeletal-based HAR. In this paper, we propose a new action recognition scheme using Relative Joint Positions with temporal derivatives of joint positions including joint velocity and joint acceleration. Using handcraft feature extraction, our proposed scheme benefits from representation in both spatial domain and temporal domain. Our proposed method outperforms state-of-the-art methods on five benchmark datasets MSR-Action3D, UTKinect-Action, Florence3D-Action, MSRAction Pairs and G3D-Gaming. For MSR-Action3D, our proposed method achieves accuracy of up to 96.88%, averaged over all three action subsets. Largest accuracy improvement is observed on Action Set 1 of MSR-Action3D with 2.98% better than existing methods.
Generative models in pathology: synthesis of diagnostic quality pathology images †

Abstract Within artificial intelligence and machine learning, a generative model is a powerful tool for learning any kind of data distribution. With the advent of deep learning and its success in image recognition, the field of deep generative models has clearly emerged as one of the promising fields for medical imaging. In a recent issue of The Journal of Pathology , Levine, Peng et al demonstrate the ability of generative models to synthesize high‐quality pathology images. They suggested that generative models can serve as an unlimited source of images either for educating freshman pathologists or training machine learning models for diverse image analysis tasks, especially in scarce cases, while resolving patients’ privacy and confidentiality concerns. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley &amp; Sons, Ltd.
Advanced deep learning methods for biomedical information analysis: An editorial

Vital signs reflect patients' current health status. Different patients are suitable for distinct references of vital signs due to the disease type and individual physique. Personalized Vital Signs Control (PVSC) helps clinicians with the recommendation of the optimal references for real-time treatment. However, a data-driven approach needs to overcome the unclear ground truth problem, requires complex feature integration and continuous value space, and is expected to ensure the safety of fragile patients. But none of the existing approaches can overcome all of these challenges simultaneously. This work emphasizes PVSC as a sequence decision-making problem and applies multiple reinforcement learning methods to it. We propose a novel adaptive medical control model. The model combines the deep deterministic policy gradient reinforcement learning algorithm, supervised experience knowledge, and recurrent neural network module, named PVSC-RL. We test the model on a real medical database, MIMIC-III, with 15,232 sepsis and 13,608 heart failure records. Experimental results show that using the policy of PVSC-RL, the survival rate of sepsis and heart failure patients by blood glucose control and blood pressure control can be increased by 3.45%, 15.21%, 4.19%, 12.13%. Meanwhile, the safety rate is also be increased by 3.09%, 1.02%, 3.54%, 4.04%.
Real-time Upper Body Reconstruction and Streaming for Mixed Reality Applications

In view of the challenges of real-time 3D reconstruction and transmission, the research on tele-immersion systems has been quite intense. We present an end-to-end, realtime 3D reconstruction system of the human body's upper part in mixed reality applications, implemented with the use of a single depth camera on the capture side, whereas no special setup is required. Our system captures the scene, extracts the user's point cloud and by quantizing it, achieves real-time mesh generation and streaming. This system, together with the appropriate virtual (VR) or augmented (AR) reality equipment, creates a sense of a more direct, face-to-face communication, as if both users were in the same environment.
Designing for Triggering Self-Investigations and Reflections on Factors Related to Sleep Health

While sleep health is increasingly becoming important to many people, it is challenging to maintain good sleep quality. Because sleep can be affected by many internal and external factors, which may differ among individuals, it is therefore important to support people to explore and reflect on what factors truly affect their sleep as most existing sleep self-tracking tools mainly focus on showing how individuals slept. In this study, we developed two design probes to support users to investigate personal sleep factors through self-experimenting and reflection. The tangible probe enables individuals to track three personal sleep factors flexibly, while the digital visualization one provides weekly reports of their sleep together with the three tracked factors. We conducted a preliminary field study with five participants over four weeks. The results show that our designs could support users to investigate their personal concerned sleep factors in terms of the causal relationship with the subjective sleep quality.
Demystifying the MLPerf Training Benchmark Suite

MLPerf, an emerging machine learning benchmark suite, strives to cover a broad range of machine learning applications. We present a study on the characteristics of MLPerf benchmarks and how they differ from previous deep learning benchmarks such as DAWNBench and DeepBench. MLPerf benchmarks are seen to exhibit moderately high memory transactions per second and moderately high compute rates, while DAWNBench creates a high-compute benchmark with low memory transaction rate, and DeepBench provides low compute rate benchmarks. We also observe that the various MLPerf benchmarks possess unique features that allow unveiling various bottlenecks in systems. We also observe variation in scaling efficiency across the MLPerf models. The variation exhibited by the different models highlight the importance of smart scheduling strategies for multi-GPU training. Another observation is that dedicated low latency interconnect between GPUs in multi-GPU systems is crucial for optimal distributed deep learning training. Furthermore, host CPU utilization increases with an increase in the number of GPUs used for training. Corroborating prior work, we also observe and quantify improvements possible by mixed-precision training using Tensor Cores.
Designing Moodle features as e-learning for learning mathematics in COVID-19 pandemic

Technology plays a significant role in the process of teaching and learning mathematics in the COVID-19 pandemic. Moodle is software with an open-source system e-learning platform designed to help educators to create quality for resources and communication tools online courses in learning mathematics. The objects of the research are to know the teachers' need analysis about e-learning like Moodle, the framework features of the Moodle as e-learning learning for learning mathematics in the COVID-19 pandemic and get the best features of the Moodle. The research methodology used a qualitative descriptive approach. The collecting data used questionnaires for the mathematics teachers in one of the schools. The research subjects were fourth-grade teachers consist of eight teachers as mathematics teachers from three private schools in Indonesia. The result of this research was the teachers need e-learning like Moodle in teaching and learning mathematics, the features of Moodle for learning mathematics was different with another Moodle, and the teachers believed the Moodle could facilitate them in teaching and learning mathematics in COVID-19 pandemic.
Short search space and synthesized-reference re-ranking for face image retrieval

Face image retrieval underpins numerous applications in many computer vision domains, however facial appearance variations including age, gender and race make this task challenging. Prior art methods rely on geometric properties and relationship between local features. However, their performance is still short of what is needed, mainly because (1) they ignore the demographic information, and (2) lack age-invariant re-ranking while retrieving face images. In this paper, we aim to build a two-stage face retrieval approach. First, we search for candidate face images using demographic-assisted clustering resulting in a short search space. Second, we develop a generative model to compensate aging variations between query and candidate face images resulting into an independent aging synthesized face images reference set. We then use this reference set to re-rank candidate face images resulting into the final retrieval of face images. We show that the proposed face retrieval approach outperforms the state-of-the-art methods in terms of both the precision and scalability on publicly available longitudinal datasets including CACD and MORPH II.
Inequality-Type Zhang Equivalency Originating from Neural Dynamics for Time-Varying Problems Solving

In this paper, a concept of Zhang equivalency (ZE) is presented, and its type of inequality to inequality (simply termed, inequality type) is further discussed. By applying Zhang neural dynamics method, we thus propose the order-1, order2, and order-3 ZE general formulas of such an inequality type, respectively. Then, the inequalities in four different situations are investigated, i.e., the robot situation, matrix situation (including matrix-vector situation), state-control situation, and state situation. We propose the corresponding order-1, order-2, and order-3 ZE formulas to prepare for solving corresponding time-varying inequality-related problems.
Deep Learning Performance In Sentiment Analysis

A problem of sentiment analysis is the text representation, that encodes text into a continuous vector by arranging projections from semantics to the points in high dimensional space. Deep learning methods have been widely used to solve various sentiment analysis problems. To improve the performance of deep learning in sentiment analysis requires a good method of text representation to be used as an embedding layer. In this study, we analyzed deep learning with the Recurrent Neural Network (RNN) method with Long Short-Term Memory (LSTM) variants in sentiment classification. We compare the performance of the LSTM network with Word2Vec to the network without Word2Vec as word embedding. Sentiment data used is derived from user-provided reviews of the applications in Google Play. The training process involves the Dropout layer and Early Stopping points to prevent overfitting. The results showed that the LSTM network using a word embedding Word2Vec is better than without Word2Vec. LSTM with Word2Vec 300 words dimension got a low error value of 0.3287 with an accuracy of 86.76%. While the LSTM testing results without Word2Vec get the lowest error of 0.3751 with an accuracy of 84.14%.
The Interactive Show

Advances in the fields of natural language processing, machine learning and speech recognition have led to the increasing adoption of conversational agents in a variety of domains. However, the use of conversational agents as a childcare assistant for parents of young children has not been realized. We propose the use of conversational agents into children's programs to produce an interactive show, which can be employed as conversational companion for children, and a childcare assistant for parents of young children who have limited access to childcare options. Key features of the interactive show include understanding, responsiveness and maintaining an uninterrupted conversation flow. We argue that having a truly interactive show can engage children for a longer period of time without being distracted, which can give parents some time to attend to their needs in the absence of help.
The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets

Imbalanced class distribution in the medical dataset is a challenging task that hinders classifying disease correctly. It emerges when the number of healthy class instances being much larger than the disease class instances. To solve this problem, we proposed undersampling the healthy class instances to improve disease class classification. This model is named Hellinger Distance Undersampling (HDUS). It employs the Hellinger Distance to measure the resemblance between majority class instance and its neighbouring minority class instances to separate classes effectively and boost the discrimination power for each class. An extensive experiment has been conducted on four imbalanced medical datasets using three classifiers to compare HDUS with a baseline model and three state-of-the-art undersampling models. The outcomes display that HDUS can perform better than other models in terms of sensitivity, F1 measure, and balanced accuracy.
Manipulation of Articulated Objects Using Dual-arm Robots via Answer Set Programming

Abstract The manipulation of articulated objects is of primary importance in Robotics and can be considered as one of the most complex manipulation tasks. Traditionally, this problem has been tackled by developing ad hoc approaches, which lack flexibility and portability. In this paper, we present a framework based on answer set programming (ASP) for the automated manipulation of articulated objects in a robot control architecture. In particular, ASP is employed for representing the configuration of the articulated object for checking the consistency of such representation in the knowledge base and for generating the sequence of manipulation actions. The framework is exemplified and validated on the Baxter dual-arm manipulator in the first, simple scenario. Then, we extend such scenario to improve the overall setup accuracy and to introduce a few constraints in robot actions execution to enforce their feasibility. The extended scenario entails a high number of possible actions that can be fruitfully combined together. Therefore, we exploit macro actions from automated planning in order to provide more effective plans. We validate the overall framework in the extended scenario, thereby confirming the applicability of ASP also in more realistic Robotics settings and showing the usefulness of macro actions for the robot-based manipulation of articulated objects.
<i>VibeRing</i>

Some Internet of Things (IoT) devices - a.k.a. "smart Things" - collect meaningful information when they are in use and in physical contact with their user (e.g., a blood-pressure monitor). A Thing's wireless connectivity allows it to transfer that data to its user's trusted device, such as a smartphone. However, an adversary could also establish a communication channel with the Thing, impersonate the user and obtain access to the user's information collected by that Thing. Thus, it is essential that the Thing connects to the correct user's device. Bootstrapping such communication channels usually require an out-of-band channel to share a secret, e.g., by asking the user to input a PIN. However, manual PIN entry is cumbersome, especially when a Thing is used transiently. In this paper, we investigate the use of vibration, generated by a custom Ring, as an out-of-band communication channel to unobtrusively share a secret with a Thing. This exchanged secret can be used to bootstrap a secure wireless channel over which the Ring (or another trusted device) and the Thing can communicate. We present the design, implementation, and evaluation of this system, which we call VibeRing. Through a user study we demonstrate that it is possible to share a secret with various objects accurately and securely.
Recognizing 3D Trajectories as 2D Multi-stroke Gestures

While end users can acquire full 3D gestures with many input devices, they often capture only 3D trajectories, which are 3D uni-path, uni-stroke single-point gestures performed in thin air. Such trajectories with their $(x,y,z)$ coordinates could be interpreted as three 2D stroke gestures projected on three planes,\ie, $XY$, $YZ$, and $ZX$, thus making them admissible for established 2D stroke gesture recognizers. To investigate whether 3D trajectories could be effectively and efficiently recognized, four 2D stroke gesture recognizers, \ie, \$P, \$P+, \$Q, and Rubine, are extended to the third dimension: $\$P^3$, $\$P+^3$, $\$Q^3$, and Rubine-Sheng, an extension of Rubine for 3D with more features. Two new variations are also introduced: $\F for flexible cloud matching and FreeHandUni for uni-path recognition. Rubine3D, another extension of Rubine for 3D which projects the 3D gesture on three orthogonal planes, is also included. These seven recognizers are compared against three challenging datasets containing 3D trajectories, \ie, SHREC2019 and 3DTCGS, in a user-independent scenario, and 3DMadLabSD with its four domains, in both user-dependent and user-independent scenarios, with varying number of templates and sampling. Individual recognition rates and execution times per dataset and aggregated ones on all datasets show a highly significant difference of $\$P+^3$ over its competitors. The potential effects of the dataset, the number of templates, and the sampling are also studied.
Controlling the Deep Learning-Based Side-Channel Analysis: A Way to Leverage from Heuristics

Deep neural networks have become the state-of-the-art method when a profiled side-channel analysis is performed. Their popularity is mostly due to neural nets overcoming some of the drawbacks of “classical” sidßhe need for feature selection or waveform synchronization, in addition to their capability to bypass certain countermeasures like random delays. To design and tune a neural network for side-channel analysis systematically is a complicated task. There exist hyperparameter tuning techniques which can be used in the side-channel analysis context, like Grid Search, but they are not optimal since they usually rely on specific machine learning metrics that cannot be directly linked to e.g. the success of the attack.We propose a customized version of an existing statistical methodology called Six Sigma for optimizing the deep learning-based side-channel analysis process. We demonstrate the proposed methodology by successfully attacking a masked software implementation of AES.