Reviewerly/exHarmony huggingface dataset:
ARB-BERT: An Automatic Aging-Related Bug Report Classification Method based on BERT

Software aging is a common phenomenon in most software systems. It refers to the increase of failure rates or the decline of performance in a long-running software system, mainly caused by Aging-Related Bugs (ARBs). Failure incurred by software aging may cause economic loss and may lead to casualties in security-critical systems. Automatic classification of ARB reports is an effective method to ensure the software system's quality by helping us detect and fix the bugs in software systems. There are two challenges in the automatic classification of ARB reports at present. Firstly, it is difficult to distinguish the ARB reports since the semantics of the text is ambiguous and hard to be recognized; secondly, the number of ARB reports is much smaller than other types of bugs, which may lead to class imbalance. Therefore, An ARB Report Automatic Classification Method based on BERT(ARB-BERT) is proposed in this paper to alleviate these problems. We combined back-translation, random under-sampling and random over-sampling to reduce the class imbalance problem in classification. By considering the ARB reports' characteristics, we utilize BERT as the semantic model, extracting feature vectors containing more accurate and sufficient information. The experimental results show that our method can improve the accuracy, precision, F-measure, and recall value compared with previous methods.
Video Instance Segmentation with Recurrent Graph Neural Networks

Video instance segmentation is one of the core problems in computer vision. Formulating a purely learning-based method, which models the generic track management required to solve the video instance segmentation task, is a highly challenging problem. In this work, we propose a novel learning framework where the entire video instance segmentation problem is modeled jointly. To this end, we design a graph neural network that in each frame jointly processes all detections and a memory of previously seen tracks. Past information is considered and processed via a recurrent connection. We demonstrate the effectiveness of the proposed approach in comprehensive experiments. Our approach, operating at over 25 FPS, outperforms previous video real-time methods. We further conduct detailed ablative experiments that validate the different aspects of our approach.
Development of usable applications featuring QR codes for enhancing interaction and acceptance: a case study

QR codes provide potential opportunities to improve interaction in different areas of application.Even so, this technology has been utilized in punctual and specific standalone applications, being barely applied to broader contexts, such as the educational one, where different scenarios can be considered to take advantage of the same QR-code approach.In this paper, key issues for the development of applications that utilize QR codes to improve sharing and availability of resources are presented, specifically focusing on improving interaction and acceptance.In this way, a holistic approach has been developed where the system is aimed at improving the interaction between educators and students.On the one hand, educators use a web interface to manage educational resources and announcements and automatically generate QR codes.On the other hand, students scan those QR codes using a mobile device, obtaining updated information related to their lectures.This scenario has been evaluated by educators and students, obtaining good results in terms of acceptance, cognitive burden, and overall usability.The approach provides development clues and the evaluation method that can be applied to different contexts, thus representing a generalizable solution.
PPOAccel: A High-Throughput Acceleration Framework for Proximal Policy Optimization

Reinforcement Learning (RL) is a major branch of AI that enables agents to learn optimal decision making via interaction with the environment. Proximal Policy Optimization (PPO) is the state-of-the-art policy optimization based RL algorithm which achieves superior overall performance on various benchmarks. A PPO agent iteratively optimizes its policy - a function which chooses optimal actions approximated by a DNN, with each iteration consisting of two computationally intensive phases: Sample Generation - where agents inference on its policy and interact with the environment to collect data, and Model Update - where the policy is trained using the collected data. In this paper, we develop the first high-throughput PPO accelerator on CPU-FPGA heterogeneous platform. Our unified systolic-array based design accelerates both the inference and the training of the deep neural network used in a RL algorithm, and is generalizable to various MLP and CNN models across a wide range of RL applications. We develop novel optimizations to simultaneously reduce data access and computation latencies, specifically: (a) optimal data flow mapping to systolic array, (b) novel memory-blocked data layout to enable streaming stall-free data access in both forward and backward propagations, and, (c) a systolic array compute sharing technique to mitigate load imbalance in the training of two networks. We evaluate our design on widely used robotics and gaming benchmarks, achieving 1.4×–26× and 1.3×–2.7× improvements in throughput, respectively, when compared with state-of-the-art CPU/CPU-GPU implementations.
Intelligent Radio Resource Allocation for Human-Robot Collaboration

The recent surge in human-controlled robotics and haptic devices research is expediting a paradigm shift in today’s communication networks towards human-to-robot (H2R) centric technologies that support Industrial Internet of Things (IIoT) and Industry 5.0. In both IIoT and Industry 5.0, human skills are extended through collaboration with robots that are geographically separated from the human. Depending on the dynamicity of the actual use case, human-to-robot communications necessitate low-latency networking. While Long Term Evolution (LTE) cellular technology has been successful in fulfilling the bandwidth demands of massively-connected sensors and devices of Industry 4.0, it is insufficient to meet the low latency demands of the future Industry 5.0 where dynamic interactions between humans and robots are paramount. In reducing the latency caused by radio resource contention in wireless H2R communications, in this work, we propose a novel approach that exploits an Attention-based Recurrent Neural Network (Att-RNN) to improve the Semi-Persistent Scheduling (SPS) resource allocation scheme adopted by LTE and new radio (NR) standards developed for the fifth generation (5G) mobile networks. We conduct a series of real haptic experiments to collect H2R traffic traces to train, test and evaluate the accuracy of Att-RNN in predicting H2R traffic. Then, with extensive simulations based on the empirical H2R traffic traces, we show that our proposed Att-RNN SPS scheme outperforms classic SPS and other existing resource allocation schemes in terms of reduced latency and improved resource allocation efficiency, thus making Att-RNN SPS a suitable candidate in future Industry 5.0 deployments.
Adaptively Customizing Activation Functions for Various Layers

To enhance the nonlinearity of neural networks and increase their mapping abilities between the inputs and response variables, activation functions play a crucial role to model more complex relationships and patterns in the data. In this work, a novel methodology is proposed to adaptively customize activation functions only by adding very few parameters to the traditional activation functions such as Sigmoid, Tanh, and rectified linear unit (ReLU). To verify the effectiveness of the proposed methodology, some theoretical and experimental analysis on accelerating the convergence and improving the performance is presented, and a series of experiments are conducted based on various network models (such as AlexNet, VggNet, GoogLeNet, ResNet and DenseNet), and various datasets (such as CIFAR10, CIFAR100, miniImageNet, PASCAL VOC, and COCO). To further verify the validity and suitability in various optimization strategies and usage scenarios, some comparison experiments are also implemented among different optimization strategies (such as SGD, Momentum, AdaGrad, AdaDelta, and ADAM) and different recognition tasks such as classification and detection. The results show that the proposed methodology is very simple but with significant performance in convergence speed, precision, and generalization, and it can surpass other popular methods such as ReLU and adaptive functions such as Swish in almost all experiments in terms of overall performance.
Automatic Generation of Intonation Marks and Prosodic Segmentation for Belarusian NooJ Module

The article depicts the problem of automated text segmentation into syntagmas at the punctuational and the semantic levels of Belarusian. It is aimed at generating prosodic transcription and delimiting long sentences. Its implementation is essential for improving the synthetic speech generated by Belarusian text-to-speech systems using prepared syntactic grammars in NooJ.
Dynamic Sequential Recommendation: Decoupling User Intent from Temporal Context

Nowadays sequential recommender systems have been equipped with various deep learning techniques such as recurrent neural networks and self-attention mechanisms. Despite recent advances, the most striking difference between the field of recommendation and other fields of study—the dynamic property—has often been ignored when modeling user behavior. In contrast to the case of language models where word frequency is relatively stable, the item popularity distribution changes periodically or irregularly. To address this time-related problem, we propose a novel time-aware framework for dynamic sequential recommendation. More precisely, based on factorizing the point-wise mutual information between the item and the context, the user intrinsic interest is decoupled from the temporal context. The sequential model consists mainly of two parts: i) a time-invariant main network, intricately structured with most of the model parameters and stable to temporal dynamics; ii) a time-sensitive bias network, frequently updated to represent the evolution of item popularity. The proposed strategy can be overlaid on the state-of-the-art sequential models to simultaneously capture the sequential and temporal patterns. Furthermore, the connection with the matrix factorization is illustrated to intuitively demonstrate the decoupling of user intent. Finally, the effectiveness of the proposed strategy on retrieval and ranking metrics is demonstrated through extensive experiments from several real-world scenarios, including MovieLens, Amazon and Alibaba datasets.
Early Alert of Elderly Cognitive Impairment using Temporal Streaming Clustering

more than 44 million people have been diagnosed with dementia worldwide, and this number is estimated to triple by next three decades. Given this increasing trend of older adults with cognitive impairment (CI; dementia and mild cognitive impairment) and its significant underdiagnosis, early identification of CI and understanding its progression is a critical step towards a better quality of life for the aging population. Early alert of individual health changes could facilitate better ways for clinicians to diagnose CI in its early stages and come up with more effective treatment plans. However, there is a lack of approaches to characterize patient health conditions accounting for temporal information in an unsupervised manner. Limited CI cases and its costly ascertainment in clinical settings also make unsupervised learning more promising in CI research. In this paper, a streaming clustering model was used to determine distinct patterns of older adults' health changes from their clinical visits in Mayo Clinic Study of Aging. The streaming clustering was also examined to study its ability to generate early alerts for potential incidents of CI. Our analysis demonstrated that temporal characteristics incorporated in a streaming clustering model has a promising potential to increase power in predicting CI.
A 3D Point-of-Intention Estimation Method Using Multimodal Fusion of Hand Pointing, Eye Gaze and Depth Sensing for Collaborative Robots

Hand pointing psychologically expressing intention has been fused with eye gaze to assist in detecting the point of intention (POI). Ideally, a POI detection approach using a pair of hand-pointing and eye-gaze rays may achieve precise POI positioning in 3-dimensional (3D) space; however, an object sometimes occludes the detection process in real applications. This paper proposes a method of 3D POI estimation using multimodal fusion of hand pointing, eye gaze and depth sensing for collaborative robots. In this method, depth sensors are used to assist in the fusion of the original hand-pointing and eye-gaze rays to determine the POI based on a volume of interest (VOI) to find 3D POI. At real production sites where humans work with collaborative robots, workpieces may sometimes occlude the depth sensors, resulting in blind views. This problem is solved by increasing the number of depth sensors and arranging them with overlapping view ranges to cover otherwise blind views due to occlusion. The number of depth sensors is determined based on the largest possible obstacle size determined by users in advance. Experiments performed by 10 participants confirm the effectiveness of POI estimation in the presence of obstacles; specifically, with our proposed method, we measured 3D POIs at 50 cm, 70 cm, 75 cm, 80 cm, 85 cm, 90 cm, and 95 cm with average distance errors of 0.82%, 0.68%, 0.68%, 0.72%, 0.76%, 0.87%, and 0.81%, respectively, compared with the conventional POI estimation method based on the fusion of hand pointing and eye gaze.
Graduating from ‘new-school’ – Germany’s procedural approach to regulating online discourse

For some time now, media regulators in Germany and beyond have been trying to cope with the rise of digital intermediaries like social networks and search engines. In this context, the German Network Enforcement Act (‘NetzDG’) has become the epitome of the concept of new-school speech regulation: the increasing co-optation of intermediaries for governmental content moderation – with all its potential harms to free expression and democratic discourse. Contrary to this trend, however, recent legislative measures in Germany, including the Interstate Media Treaty (IMT), suggest that the country is exploring new avenues in digital intermediary regulation. Not only does the new German legislation focus on procedural instead of substantive aspects of content moderation, offering a case study for concepts promoted by recent literature on platform regulation. By introducing a positive bias for certain ‘content of value,’ the country also includes the role of beneficial content in its regulation. This paper analyzes the new German countermovement against new-school regulation and presents its potential impact for future regulatory concepts.
Semantic-enabled architecture for auditable privacy-preserving data analysis

Small and medium-sized organisations face challenges in acquiring, storing and analysing personal data, particularly sensitive data (e.g., data of medical nature), due to data protection regulations, such as the GDPR in the EU, which stipulates high standards in data protection. Consequently, these organisations often refrain from collecting data centrally, which means losing the potential of data analytics and learning from aggregated user data. To enable organisations to leverage the full-potential of the collected personal data, two main technical challenges need to be addressed: (i) organisations must preserve the privacy of individual users and honour their consent, while (ii) being able to provide data and algorithmic governance, e.g., in the form of audit trails, to increase trust in the result and support reproducibility of the data analysis tasks performed on the collected data. Such an auditable, privacy-preserving data analysis is currently challenging to achieve, as existing methods and tools only offer partial solutions to this problem, e.g., data representation of audit trails and user consent, automatic checking of usage policies or data anonymisation. To the best of our knowledge, there exists no approach providing an integrated architecture for auditable, privacy-preserving data analysis. To address these gaps, as the main contribution of this paper, we propose the WellFort approach, a semantic-enabled architecture for auditable, privacy-preserving data analysis which provides secure storage for users’ sensitive data with explicit consent, and delivers a trusted, auditable analysis environment for executing data analytic processes in a privacy-preserving manner. Additional contributions include the adaptation of Semantic Web technologies as an integral part of the WellFort architecture, and the demonstration of the approach through a feasibility study with a prototype supporting use cases from the medical domain. Our evaluation shows that WellFort enables privacy preserving analysis of data, and collects sufficient information in an automated way to support its auditability at the same time.
Gen<sup>2</sup>Out: Detecting and Ranking Generalized Anomalies

In a cloud of m-dimensional data points, how would we spot, as well as rank, both single-point- as well as group-anomalies? We are the first to generalize anomaly detection in two dimensions: The first dimension is that we handle both point-anomalies, as well as group-anomalies, under a unified view - we shall refer to them as generalized anomalies. The second dimension is that Gen<sup>2</sup>Out not only detects, but also ranks, anomalies in suspiciousness order. Detection, and ranking, of anomalies has numerous applications: For example, in EEG recordings of an epileptic patient, an anomaly may indicate a seizure; in computer network traffic data, it may signify a power failure, or a DoS/DDoS attack.We start by setting some reasonable axioms; surprisingly, none of the earlier methods pass all the axioms. Our main contribution is the Gen<sup>2</sup>Out algorithm, that has the following desirable properties: (a) Principled and Sound anomaly scoring that obeys the axioms for detectors, (b) Doubly-general in that it detects, as well as ranks generalized anomaly&#x2013; both point- and group-anomalies, (c) Scalable, it is fast and scalable, linear on input size. (d) Effective, experiments on real-world epileptic recordings (200GB) demonstrate effectiveness of Gen<sup>2</sup>Out as confirmed by clinicians. Experiments on 27 real-world benchmark datasets show that Gen<sup>2</sup>Out detects ground truth groups, matches or outperforms point-anomaly baseline algorithms on accuracy, with no competition for group-anomalies and requires about 2 minutes for 1 million data points on a stock machine.
Deep Learning for Radar Signal Detection in the 3.5 GHz CBRS Band

This paper presents a comprehensive framework for generating radio frequency (RF) datasets, designing deep learning (DL) detectors, and evaluating their detection performance using both simulated and experimental test data. The proposed tools and techniques are developed in the context of dynamic spectrum use for the 3.5 GHz Citizens Broadband Radio Service (CBRS), but they can be utilized and expanded for standardization of machine learned spectrum awareness technologies and methods. In the CBRS band, environmental sensing capability (ESC) sensors are required to detect the presence of federal incumbent signals and trigger protection mechanisms when necessary. To support the development and evaluation of detection techniques for ESC sensors, we provide software tools for generation and augmentation of simulated radar datasets as well as baseline DL detectors that can be replicated, evaluated, and tested in a simulated or an experimental environment. We find that all the proposed detectors exceed ESC requirements for incumbent detection. The software tools, the pre-trained DL models and their configurations, and the experimental setup are made available in the public domain.
GPS Spoofing Detection by Neural Network Machine Learning

GPS today is ubiquitous. It provides real-time positioning, navigation, and timing (PNT) data for countless military and civilian users worldwide. Yet, a proliferation of GPS degrading and denying devices threatens GPS PNT capabilities. The biggest threat is spoofing, which deceives a GPS receiver into accepting false signals as genuine. Current antispoofing techniques are highly dependent on specific spoofing scenarios, choice of mathematical models, values of thresholds, and often require significant hardware and/or software modifications to existing GPS receivers. Efforts are also underway to replace GPS with alternate sources. However, no such sensor can match the availability, accuracy, and global utility of GPS. This article examines an alternate antispoofing method using neural networks. The supervised machine learning technique uses well-understood GPS observables such as pseudorange, carrier phase, Doppler shift, and carrier-to-noise density ratio to distinguish between authentic and spoofed signals. Network architecture, training sample size, number of hidden layers, distribution of neurons between hidden layers, and number of hidden neurons are examined. Results show that the proposed method is capable of detecting spoofing signals with a high probability of correct classification and a low probability of misclassification.
Vision based Human Activity Recognition using Deep Transfer Learning and Support Vector Machine

In this computer era, the interaction between human and machine is an extremely interesting area due to the advent of deep transfer learning and the availability of vision based human activity recognition datasets. Transfer learning is one of the decisive innovations in the field of deep learning due to its promising results. Therefore, this paper proposes the deep transfer learning method to extract the features in conjunction with a multi-class support vector machine for the classification of human activities using RGB videos. A fine-tuned pre-trained convolutional neural network based on Visual Geometry Group network 19 (VGGNet-19) has been applied to extract the visual features from the RGB videos. Next, a multi-class support vector machine is used to classify the extracted features into the different categories of human activities. The proposed approach is evaluated on a popular sports activity dataset such as: UCF Sports Action dataset. During experimentation the proposed approach achieves 97.13% recognition accuracy on this dataset. The experimental results also show the effectiveness of the proposed approach over handcrafted feature engineering methods such as histogram of oriented gradient and optical flow.
Software Architecture Design of Animation Studies Platform Using Structure-Behavior Coalescence Method

Animation education in the new media era is moving toward the goal of cultivating high-end talents. The development of an architecture-oriented animation studies platform provides guarantee for the training of talents in terms of teaching quality. This research uses the Internet as the medium and mobile phones and computer clients as the main technology platforms, starting from the software architecture and constructing the system model of the animation studies platform according to the Structure-Behavior Coalescence (SBC) method. The core theme of Model-Based Systems Engineering (MBSE) is a modeling language with model consistency of systems structure and systems behavior. This paper developed Structure-Behavior Coalescence State Machine (SBC-SM) as the formal language for the MBSE animation studies platform design model singularity. The model consistency will be fully guaranteed in the MBSE animation studies platform design when the SBC state machine approach is adopted. It not only improves the efficiency of platform development but also reduces the difficulty and risk of platform development.