Reviewerly/exHarmony huggingface dataset:
Chatbots to Support Children in Coping with Online Threats: Socio-technical Requirements

Online threats to children, in the form of cyberbullying, grooming, and sexting, have reached unprecedented and alarming levels. Support is still lacking despite endless efforts by child safety organisations and online safety educational programmes. This is mainly due to children feeling apprehensive in such situations, ashamed of revealing their distressing encounter to an adult or even for not having anyone to approach with their concerns. This paper investigates how children envision the potential support of a chatbot in such contexts. We captured design requirements for such a chatbot through a participatory design approach involving 110 schoolchildren in the UK. Using LEGO figures, they elaborated and performed stories featuring the interaction of a child under threat with a chatbot. The analysis of the dialogues in their performances and their reflections resulted in a set of expected tasks for the chatbot, a conversation flow, and novel socio-technical requirements addressing potential users' main concerns and expectations.
Multi-Programming-Language Commits in OSS: An Empirical Study on Apache Projects

Modern software systems, such as Spark, are usually written in multiple programming languages (PLs). Besides benefiting from code reuse, such systems can also take advantages of specific PLs to implement certain features, to meet various quality needs, and to improve development efficiency. In this context, a change to such systems may need to modify source files written in different PLs. We define a multi-programming-language commit (MPLC) in a version control system (e.g., Git) as a commit that involves modified source files written in two or more PLs. To our knowledge, the phenomenon of MPLCs in software development has not been explored yet. In light of the potential impact of MPLCs on development difficulty and software quality, we performed an empirical study to understand the state of MPLCs, their change complexity, as well as their impact on open time of issues and bug proneness of source files in real-life software projects. By exploring the MPLCs in 20 non-trivial Apache projects with 205,994 commits, we obtained the following findings: (1) 9% of the commits from all the projects are MPLCs, and the proportion of MPLCs in 80% of the projects goes to a relatively stable level; (2) more than 90% of the MPLCs from all the projects involve source files written in two PLs; (3) the change complexity of MPLCs is significantly higher than that of non-MPLCs in all projects; (4) issues fixed in MPLCs take significantly longer to be resolved than issues fixed in non-MPLCs in 80% of the projects; and (5) source files that have been modified in MPLCs tend to be more bug-prone than source files that have never been modified in MPLCs. These findings provide practitioners with useful insights on the architecture design and quality management of software systems written in multiple PLs.
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models

Training models on low-resource named entity recognition tasks has been shown to be a challenge [1], especially in industrial applications where deploying updated models is a continuous effort and crucial for business operations. In such cases there is often an abundance of unlabeled data, while labeled data is scarce or unavailable. Pre-trained language models trained to extract contextual features from text were shown to improve many natural language processing (NLP) tasks, including scarcely labeled tasks, by leveraging transfer learning. However, such models impose a heavy memory and computational burden, making it a challenge to train and deploy such models for inference use. In this work-in-progress we combined the effectiveness of transfer learning provided by pre-trained masked language models with a semi-supervised approach to train a fast and compact model using labeled and unlabeled examples. Preliminary evaluations show that the compact models can achieve competitive accuracy with × 36 compression rate when compared with a state-of-the-art pre-trained language model, and run significantly faster in inference, allowing deployment of such models in production environments or on edge devices.
Human Movement Direction Prediction using Virtual Reality and Eye Tracking

One way of potentially improving the use of robots in a collaborative environment is through prediction of human intention that would give the robots insight into how the operators are about to behave. An important part of human behaviour is arm movement and this paper presents a method to predict arm movement based on the operator's eye gaze. A test scenario has been designed in order to gather coordinate based hand movement data in a virtual reality environment. The results shows that the eye gaze data can successfully be used to train an artificial neural network that is able to predict the direction of movement ~500ms ahead of time.
Consensus‐based nonlinear smoother for distributed continuous‐discrete systems

Abstract In order to estimate the state of general distributed continuous‐discrete nonlinear system, a new consensus on the measurement and information cubature smoothing method is proposed in this article. The distributed continuous‐discrete nonlinear dynamic system refers to a system whose process is described as a stochastic differential equation (SDE) and whose measurements are provided by a wireless sensor network (WSN) at discrete times. First, the cubature rule is embedded into the consensus on measurements and information extended Kalman filter (CMIEKF) architecture by using the statistical linear regression (SLR) method, and then the presented consensus‐based method is obtained according to the Rauch–Tung–Striebel (RTS) smoothing methodology. In a numerical simulation, the performances of the presented smoothing method and the conventional nonlinear estimating methods are compared and analyzed. The numerical results show that, compared with other distributed estimation methods, the newly proposed smoothing algorithm enjoys not only higher accuracy but also stronger robustness.
End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping

As communication systems are foreseen to enable new services such as joint communication and sensing and utilize parts of the sub-THz spectrum, the design of novel waveforms that can support these emerging applications becomes increasingly challenging. We present in this work an end-to-end learning approach to design waveforms through joint learning of pulse shaping and constellation geometry, together with a neural network (NN)-based receiver. Optimization is performed to maximize an achievable information rate, while satisfying constraints on out-of-band emission and power envelope. Our results show that the proposed approach enables up to orders of magnitude smaller adjacent channel leakage ratios (ACLRs) with peak-to-average power ratios (PAPRs) competitive with traditional filters, without significant loss of information rate on an additive white Gaussian noise (AWGN) channel, and no additional complexity at the transmitter.
Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm

This paper explores a unified approach to the improvement of clusters and the detection of anomalies in a dataset. It presents a novel approach for the formation of tighter and better clusters than traditional methods such as the K-means algorithm. It is evaluated against intrinsic measures for unsupervised learning such as the silhouette coefficient, Calinski Harabasz index, and the Davies Bouldin index. The proposed method decreases the intracluster variance of N clusters, until the variance approaches a global minimum. The method also extends K-means as a novel method for the detection of anomalies in the dataset. The proposed algorithm's performance is evaluated by extrinsic measures such as the Jaccard similarity score, the V-measure cluster, and the F1 Score. The algorithm is tested upon synthetic and real datasets, UCI Breast Cancer and UCI Wine Quality, to showcase its effectiveness in both cases. The proposed algorithm reduced the variance of the synthetic and real dataset, Wine Quality, by 18.7% and 88.1% respectively. It was also effective in increasing the accuracy and F1 score by 22.5% and 20.8% in the case of the Wine Quality dataset.
A Deeper Look into DeepCap

Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality. Many previous performance capture approaches either required expensive multi-view setups or did not recover dense space-time coherent geometry with frame-to-frame correspondences. We propose a novel deep learning approach for monocular dense human performance capture. Our method is trained in a weakly supervised manner based on multi-view supervision completely removing the need for training data with 3D ground truth annotations. The network architecture is based on two separate networks that disentangle the task into a pose estimation and a non-rigid surface deformation step. Extensive qualitative and quantitative evaluations show that our approach outperforms the state of the art in terms of quality and robustness. This work is an extended version of [1] where we provide more detailed explanations, comparisons and results as well as applications.
Research on Cultivating Senior Technical Talents Based on the Internet of Things Platform

Based on the discussion of the perception layer, network layer and application layer of the Internet of Things (IOT), this paper constructs a platform for cultivating senior technical talents of the Internet of things, which takes the application layer as the core, radiates the perception layer and network layer, provides the comprehensive and high-quality characteristics of senior technical talents, and discusses the senior technical talents with advanced engineering technology background, and create senior technical personnel with high starting point, strong application ability and strong engineering adaptability.
COVID19-Routes: A Safe Pedestrian Navigation Service

COVID-19 has become a global pandemic during 2020 due to its high contagiousness and the high mobility of the world's population today.In just one year, this virus has caused millions of infections and deaths worldwide.These numbers will continue to grow until the population becomes immune to the virus thanks to an effective vaccine.Until this is possible, the only viable strategy is to try to stop its expansion through preventive measures such as limiting mobility, the use of masks, etc.In order to support these measures, this article presents a service to provide safe navigation solutions to reduce the likelihood of infection by avoiding potential conflict areas in the city.To identify these hotspots, a strategy that combines a rule-based system and a common-sense knowledge base is proposed.Through this strategy, an occupation model and a danger model are inferred.This requires the prior capture of knowledge about the general functioning of the city, its inhabitants and the virus.The proposed service makes decisions from these two models.Finally, a validation process has been carried out through surveys to evaluate the proposed solution.Obtained results demonstrate the potential of the proposed solution as a tool to identify safe routes that allow citizens to move around the city with low exposure to COVID-19.
High Performance Advanced Encryption Standard system using Secure Double Rate Registers

Power Analysis Attack (PAA) are a class of Side Channel Attacks (SCA) which is based on power consumption measurements whose major concern is the protection of secret data stored in cryptographic devices. This paper reports the advantages of Secure Double Rate Registers (SDRR) as a Register Transfer Level (RTL) countermeasure in order to increase the security of cryptographic devices against PAA. SDRR is exploited in AES 128 bit architecture, the random data in the entire clock cycle is evaluated by the combinational path. One of the main advantage is that our technique does not require duplication of combinational path to process the random data thereby limiting area overhead unlike previous RTL countermeasures. This work reported in this paper gives the comparative study between conventional advanced encryption standard (AES) algorithm with normal registers and AES algorithm with secure double rate registers (SDRR). This proposed system is implemented, simulated using Verilog HDL and synthesized by Xilinx tool.
Enhancing Collaborative Filtering Recommendation by User Interest Probability

Traditional collaborative filtering recommendation is one of the most commonly used algorithms in current recommendation systems, and it is also the mainstream algorithm used in the e-commerce industry. The basic principle of collaborative filtering is that users with similar interests will have similar interest bias in the future. However, the traditional collaborative filtering algorithm faces some of the following problems in the e-commerce industry: (1) lacking of confidence based on user interest bias similarity; (2) without consideration of the time factor; (3) no correlation between behaviors. Therefore, the collaborative filtering model based on user interest probability (PUCF) proposed in this paper to solves the above problems. Firstly, Wilson confidence interval is used to solve the problem of confidence, the behavioral time decay factor is obtained through the normalized time. And then considering the problem of conversion between behaviors. Besides the algorithm assigns different calculation weights to the existence of progressive behavior relationships, and to a certain extent considers the influence of the internal connections between behaviors on user interest. Through looking for the optimal parameters and making comparative experiments, it shows that the effect of collaborative filtering model based on user interest probability (PUCF) proposed in this paper is better than other collaborative filtering methods.
Focus Guided Light Field Saliency Estimation

Light field imaging enables us to capture all light rays in a visual scene. As light fields are four-dimensional, their captures come with an increased amount of information to take advantage of. This has stimulated ongoing light field specific research into virtual viewpoints and shallow depth of field rendering, commonly called refocusing. However, the computation time and memory required to perform these operations can make tasks such as real-time rendering impractical. One solution is to exploit the salient information of light fields to focus resources on regions that attract visual attention when using these algorithms. Although saliency estimation methods for light fields have been previously explored, these focus mainly on salient object segmentation with the goal of generating one saliency map per light field.Aiming to create a basis for a 4D saliency prediction model analogous to light fields, this paper proposes a saliency estimation method specific to light fields that considers the refocusing operation. The proposed method modifies an existing view rendering algorithm with focus guidance, obtained from the light field disparity. This facilitates the construction of saliency maps without the need to render the corresponding view itself, which will help to speed up processing operations that are compatible. The results show that the proposed saliency estimation approach yields very good predictions of visual attention across multiple planes of the light field. We anticipate that this approach can be extended for a range of rendering applications.
Domain Isolation in FPGA-Accelerated Cloud and Data Center Applications

Cloud and data center applications increasingly leverage FPGAs because of their performance/watt benefits and flexibility advantages over traditional processing cores such as CPUs and GPUs. As the rising demand for hardware acceleration gradually leads to FPGA multi-tenancy in the cloud, there are rising concerns about the security challenges posed by FPGA virtualization. Exposing space-shared FPGAs to multiple cloud tenants may compromise the confidentiality, integrity, and availability of FPGA-accelerated applications. In this work, we present a hardware/software architecture for domain isolation in FPGA-accelerated clouds and data centers with a focus on software-based attacks aiming at unauthorized access and information leakage. Our proposed architecture implements Mandatory Access Control security policies from software down to the hardware accelerators on FPGA. Our experiments demonstrate that the proposed architecture protects against such attacks with minimal area and communication overhead.
Multi-Agent Reinforcement Learning Approaches to RF Fingerprint Enhancement

Deep learning based RF Fingerprinting has shown great promise for IoT device security. This work explores various multi-agent reinforcement learning approaches to enable RF Fingerprint enhancement for an ensemble of transmitters. A RiftNetTM Reconstruction Model (RRM) is used to learn a latent Wi-Fi signal representation and how to reconstruct from that latent representation at the transmitter such that the reconstruction uniquely excites parts of the front-end to enhance the fingerprint. Deep reinforcement learning is then employed to learn the RRM control policy. Details on the design of the control interface, state representation, and rewards structure are presented for four different policy approaches. The resulting computational and security characteristics are discussed.
Priority-enabled Load Balancing for Dispersed Computing

Opportunistic managed access to local in-network compute resources can improve the performance of distributed applications and reduce the dependence on shared network resources. Instead of backhauling application data to a centralized cloud data center for processing, networked services may be adaptively and continuously dispersed into shared compute resources that are closer to the source of need. While this approach has several benefits, support for mission-aware access to computation is often an afterthought, and is implemented as a brittle extension over traditional load-balancer solutions.In this work, we investigate the design of two priority-aware resource allocation strategies and two load-balancing dispatching strategies as first class citizens in an open-source dispersed computing middleware. We present a control theoretic analysis of these load-balancing primitives to identify weaknesses and strengths in our design, and recommend future directions. In parallel, we prototype two priority-aware allocation algorithms to validate our priority predictions. In initial experiments our prototype shows substantial gains in processing prioritized load. Finally, we make our source-code and experimental configurations open source.
A Two Phase Smart Code Editor

Intelligent code completion has become an area of interest to accelerate software development. In this study, novel methods for code completion are proposed and the performance of these methods on code completion is demonstrated. Deep Learning models are applied over large datasets to achieve code completion in dynamically written programming languages. In this study, an LSTM model that performs next word prediction is trained on the JavaScript programming language. The code completion system consists of two phases. In the first phase, multiple completion suggestions are generated from the LSTM model. In the second phase, these suggestions are checked by the JavaScript Parser and the best results are achieved. This system is an alternative solution where the model can be trained without preprocessing the training set with a Parser. The proposed method has been tested on the test dataset and the success of the code completion is shown in a quantitative way.
Digitalization of education: trends in teacher training

Taking into account interstate and national requirements, an analysis of the significance was carried out and trends in the preparation of teachers for professional activity in the context of the digitalization of education were identified. It is noted that the ongoing scientific research affects various areas of training future teachers, however, they mainly consider certain aspects of professional training of teachers. Among the promising directions for the development of the process of training teachers are identified: the development of methodological concepts, strategic national projects and programs for the training of teachers, taking into account the prospects for economic development, digitalization of society and the education system; the transition to understanding the digitalization of professional pedagogical education as a process that includes the formation of future teachers’ personal readiness for professional activity in the context of the digitalization of society and education; provision of the process of training pedagogical personnel with hardware, network and software resources adequate to the needs; reducing the level of academic load on teachers who train teachers; improving the quality of professional pedagogical education based on personalization and targeting; ensuring the parity of interests of commercial organizations, state and public institutions, personal interests of future teachers; purposeful formation of future teachers’ competence in the design and modernization of educational space based on IR technologies; formation of professional competence among future teachers in the field of ensuring the protection of children and youth in the information space.
A Weakly Supervised Gas-Path Anomaly Detection Method for Civil Aero-Engines Based on Mapping Relationship Mining of Gas-Path Parameters and Improved Density Peak Clustering

Gas-path anomalies account for more than 90% of all civil aero-engine anomalies. It is essential to develop accurate gas-path anomaly detection methods. Therefore, a weakly supervised gas-path anomaly detection method for civil aero-engines based on mapping relationship mining of gas-path parameters and improved density peak clustering is proposed. First, the encoder-decoder, composed of an attention mechanism and a long short-term memory neural network, is used to construct the mapping relationship mining model among gas-path parameters. The predicted values of gas-path parameters under the restriction of mapping relationships are obtained. The deviation degree from the original values to the predicted values is regarded as the feature. To force the extracted features to better reflect the anomalies and make full use of weakly supervised labels, a weakly supervised cross-entropy loss function under extreme class imbalance is deployed. This loss function can be combined with a simple classifier to significantly improve the feature extraction results, in which anomaly samples are more different from normal samples and do not reduce the mining precision. Finally, an anomaly detection method is deployed based on improved density peak clustering and a weakly supervised clustering parameter adjustment strategy. In the improved density peak clustering method, the local density is enhanced by K-nearest neighbors, and the clustering effect is improved by a new outlier threshold determination method and a new outlier treatment method. Through these settings, the accuracy of dividing outliers and clustering can be improved, and the influence of outliers on the clustering process reduced. By introducing weakly supervised label information and automatically iterating according to clustering and anomaly detection results to update the hyperparameter settings, a weakly supervised anomaly detection method without complex parameter adjustment processes can be implemented. The experimental results demonstrate the superiority of the proposed method.