Reviewerly/exHarmony huggingface dataset:
Channel estimation algorithm based on attention mechanism

Abstract As the key to wireless communication, channel estimation has become a hot research topic in recent years. In this paper, we propose a deep learning method based on the channel estimation of inverse convolutional network and expanded convolutional network to address the problems that the performance of traditional channel estimation algorithms in orthogonal frequency division multiplexing (OFDM) systems can hardly meet the communication requirements of complex scenarios and are greatly affected by noise. The method constructs a lightweight deconvolutional network using the correlation of channels, and achieves channel interpolation and estimation step by step with a few layers of deconvolutional operations, which achieves channel estimation with low complexity. To improve the estimation performance, an expanded convolutional network is further constructed to suppress the channel noise and improve the accuracy of channel estimation. The simulation results show that the channel estimation can be performed at different signal levels. The simulation results show that the proposed deep learning method based on deconvolution and dilation convolution has lower estimation error and lower complexity than the traditional methods under different signal-to-noise ratios (SNR).
EBBE-Text: Explaining Neural Networks by Exploring Text Classification Decision Boundaries

While neural networks (NN) have been successfully applied to many NLP tasks, the way they function is often difficult to interpret. In this article, we focus on binary text classification via NNs and propose a new tool, which includes a visualization of the decision boundary and the distances of data elements to this boundary. This tool increases the interpretability of NN. Our approach uses two innovative views: (1) an overview of the text representation space and (2) a local view allowing data exploration around the decision boundary for various localities of this representation space. These views are integrated into a visual platform, EBBE-Text, which also contains state-of-the-art visualizations of NN representation spaces and several kinds of information obtained from the classification process. The various views are linked through numerous interactive functionalities that enable easy exploration of texts and classification results via the various complementary views. A user study shows the effectiveness of the visual encoding and a case study illustrates the benefits of using our tool for the analysis of the classifications obtained with several recent NNs and two datasets.
EntailSum: An Entailment-Based Approach to Aspect-Based Text Summarization with Automated Aspect Adaptation

Aspect-based summarization differs from generic text-summarization in which the generated summary must be conditioned on a given topic. A fundamental challenge to the aspect-based summarization approach is the lack of labeled data for training models, which limits the usage of supervised methods. One approach to address this issue is to introduce human intervention to generate unique datasets per aspect. However, there is a large number of possible aspects to summarize which makes this option impossible to scale. This limits the use of typical modeling techniques, and requires methods which excel in few-shot, or ideally zero-shot regimes. Hence, in this research, we propose a modular, two-step approach that does not need any aspect-based supervision. This research combines recent advances in zero-shot text classification and generic summarization in a novel way. The backbone of the proposed approach is a transformer network trained for the task of textual entailment, which is used to reduce a document to the set of on topic sentences. In the experiments, our model achieves a new state of the art compared to other unsupervised models on the MA-News dataset (ROUGE-1 35.70 and ROUGE-2 15.52), and even outperforms fine-tuned models without any supervision of its own.
A New Black Widow Algorithm for Discontinuous Optimization in Cloud Task Environment

Abstract Task scheduling in cloud environment has received extensive attention due to its complex characteristics, and most of the previous methods ignore the discontinuity problem of workflow scheduling. Therefore, a new black widow optimization algorithm (NBWO) is proposed. NBWO proposed a new reproductive strategy to select excellent male individuals and female individuals to obtain better offspring. In NBWO, the number of female individuals is fixed, and males obtain mating rights through competition; NBWO improves the mutation strategy, which makes the exploration ability and development ability of the algorithm more balanced. Applying NBWO to cloud computing task scheduling, the experimental results show that NBWO has excellent performance in dealing with discontinuous optimization problems.
Image recoloring for Red-Green dichromats with compensation range-based naturalness preservation and refined dichromacy gamut

People with color vision deficiency (CVD) have difficulty discriminating colors, which can cause loss of chromatic contrast in the perception of affected individuals. To compensate for contrast loss, image recoloring approaches have been proposed in the existing studies. In state-of-the-art studies, recoloring models were built based on an approximated gamut of CVD in the CIE L*a*b* (Lab) color space, which significantly deviates from the original gamut. In addition, luminance was not considered during recoloring. Moreover, existing methods also present problems, such as high computational costs and insufficient naturalness preservation . In this paper, we propose a novel recoloring method to compensate for CVD that enhances contrast through adopting a luminance channel-considered optimization model while preserving naturalness by imposing hard constraints on the amount of changes to the original colors. To obtain a better compensation effect, we fit a new curved surface for representing the gamut of dichromacy in the Lab color space more accurately. Furthermore, a discrete solver is implemented to solve the optimization problem efficiently. For effective assessment, qualitative, quantitative, and subjective experiments were conducted, and a new metric, called preference, is proposed to evaluate the contrast enhancement and naturalness preservation comprehensively.
On the advantages of P2P ML on mobile devices

Many fields make use nowadays of machine learning (ML) enhanced applications for cost optimization, scheduling or forecasting, including the energy sector. However, these very ML algorithms consume a significant amount of energy, sometimes going against the whole purpose of their employment. To this day, solutions for an energy-efficient execution of these algorithms have not been addressed adequately. In this paper, we demonstrate the advantage of executing ML algorithms on mobile devices (ARM) over a standard server machine (RISC), from the perspective of energy. To do so, we first propose a novel methodology to quantify the amount of energy consumed by an ML algorithm. Then, we compare the energy consumption of existing algorithms running on mobile devices and server machines. To motivate running ML algorithms on mobile devices, we also propose a new peer-to-peer personalized ML algorithm (P3) that shows better convergence properties than related works, and provably converging to a ball centered at a critical point of a non-convex cost function, under mild assumptions. Most importantly, we show that running the P3 algorithm on mobile devices is extremely energy-efficient, consuming 2700x, 200x and 20x less energy than centralized learning algorithms for 10, 100, and 300 peers respectively. Finally, unlike centralized learning algorithms, the proposed P2P algorithm can generate personalized models, and does not have issues of single-point-of-failure nor data privacy. Thus, we give evidence on the supremacy of our proposed P3 algorithm over the other state-of-the-art centralized ML ones.
Combined Use of a Bilateral and Median Filter to Suppress Gaussian Noise in Images

The paper is devoted to the problem of processing images with Gaussian noise. The method proposes a new way of denoising. This algorithm can effectively clean the image from noise and keep the edges of objects precise. The research was conducted in MATLAB R2019b, the quality of the proposed filter was tested using a peak signal-to-noise ratio and a structural similarity index. This method can de-noise images more efficiently in comparison with most existing methods. The proposed method de-noise better by 1–6 dB than comparable methods in the work. It can be used in applications, image processing, or as a precleaning program for an image recognition neural network.
Unsupervised Anomaly Detection by Autoencoder with Feature Decomposition

In unsupervised anomaly detection tasks, a crucial challenge is modeling the underlying structure of normal data without knowing the definition or ratio of anomalies. The introduction of robustness against anomalous data in autoencoder architecture is a significant research focus in order to address this challenge. In this paper, we propose a model implemented by an autoencoder with two decoders, called Feature Decomposition AutoEncoder (FDAE). It maps all data into a high-dimensional latent feature space. Many studies have proved RSR technology and RPCA technology to improve the performance of anomaly detection models. FDAE employs RSR and RPCA techniques in the latent space to decompose latent features into normal features and abnormal features, then decodes them separately using two decoders. Furthermore, we design an optimization strategy to enable FDAE to prioritize modeling the underlying structure of normal data from unlabeled data to reduce the interference caused by unknown anomalous data. We demonstrate the high performance of FDAE in unsupervised anomaly detection tasks through experiments on five public datasets. In addition, we study the variation of FDAE’s anomaly detection capability under different noise scenarios on the MNIST dataset.
The Impact of Forecasting Horizon and Resolution on PV Power Prediction using Artificial Neural Networks

Solar energy forecasting becomes a necessity in order to balance the supply and demand of this energy, therefore promote its planning, transmission and distribution. This work clarifies the impact of forecast horizon and resolution on model accuracy by exploiting the power of the Long Short-Term Memory (LSTM) model, which manages the shape of multidimensional data to embed small resolution in the input data. Four forecasting horizons were assessed (1 day, 1 hour, 15 minutes and 5 minutes) using Multilayer Perceptron (MLP) model and three forecasting horizons (1 day, 1 hour and 15 minutes) were used within LSTM model to predict Photovoltaic (PV) power generated from 11 PV panels located in Eugene university, Oregon. The findings of this study indicate that in short term forecasting horizon, generally, the more the horizon decreases the more accurate the model becomes, as well as small data resolution is recommended.
Wearable Smart Phone Sensor Fall Detection System

One of the most important measures that developed countries need along with economic success is the provision of contemporary health services for the elderly. Inadequate care for the elderly can put them in danger of falls with serious injury or even death. When an older person falls due to illness, immediate lack of care may lead to death. In this paper, a wearable smart phone sensor fall event detection system is introduced. The proposed system utilizes real-time raw sensory accelerometer, gyroscope and gravity data collected from the AndroSensor App and is then processed and applied to a machine learning classifier. The smart phone is positioned at the waistline to yield the most accurate fall detection results. The system is trained and can detect common activities, like sleeping, walking, sitting, jogging, and events, such as falling in any direction. An full accuracy is almost accomplished using Support Vector Machine classifier in comparison with other classifiers as addressed by other previous research works. Nevertheless, the system requires more validation with elderly people under the supervision of caregivers in a controlled environment.
AIFIS: Artificial Intelligence (AI)-Based Forensic Investigative System

The scope of forensic investigations has recently expanded. Since most Internet of Things (IoT) devices are plug and play and do not have much memory or storage to pre-process data, it is a challenge for forensic investigators to identify and obtain relevant evidence to reconstruct attacks. As a solution, we propose using artificial intelligence (AI)-inspired techniques to automate the forensic analysis process by emulating attacks in the process of identifying and collecting forensic evidence. We used a differentiable inductive logic programming (∂ILP) system to obtain attack emulation information from different sources, such as device- and subsystem-level vulnerabilities gathered by assessing device components in an enterprise network, and to predict potential attacks from previous attacks on similar configurations. Our experimental results showed that the proposed methodology could successfully generate rules that can assist forensic examiners in identifying evidence to emulate attacks without execution.
Move With the Theremin: Body Posture and Gesture Recognition Using the Theremin in Loose-Garment With Embedded Textile Cables as Antennas

We present a novel intelligent garment design approach for body posture/gesture detection in the form of a loose-fitting blazer prototype, “the MoCaBlazer.” The design is realized by leveraging conductive textile antennas with the capacitive sensing modality, supported by an open-source electronic theremin system (OpenTheremin). The use of soft textile antennas as the sensing element allows flexible garment design and seamless tech-garment integration for the specific structure of different clothes. Our novel approach is evaluated through two experiments involving defined movements (20 arm/torso gestures and eight dance movements). In cross-validation, the classification model yields up to 97.18% average accuracy and 92% f1-score, respectively. We have also explored real-time inference enabled by a radio frequency identification (RFID) synchronization method, yielding an f1-score of 82%. Our approach opens a new paradigm for designing motion-aware smart garments with soft conductive textiles beyond traditional approaches that rely on tight-fitting flexible sensors or rigid motion sensor accessories.
On the Application of Partial Domain Adaptation for PBSHM

To address limitations presented by the unavailability of labelled data in structural health monitoring, transfer learning – in the form of domain adaptation – can facilitate leveraging information from a population of physical or numerical structures, by inferring a mapping that aligns the feature spaces. Thus, data from a similar source structure can be used to train a classifier that generalises to a target structure. Conventional unsupervised domain adaptation methods would assume that the data available from both structures belongs to the same number of damage-states or disparate environmental conditions; however, in practical scenarios, the data in the target structure may pertain to a subset of the available classes in the source structure; this is a partial domain adaptation problem. Conventional domain adaptation methods are prone to performance degradation in this scenario. To address this issue, this paper proposes a novel statistic alignment method and instance-weighting strategy. A numerical population of structures demonstrates that these methods facilitate transfer where a number of state-of-the-art domain adaptation algorithms cannot.
Potential of embedded vision platforms in development of spatial AI enabled CPS

Motivated by the recent trends in the field of em-bedded vision platforms, we discuss potential of such solutions in providing foundations for the next generation of Cyber-Physical Systems (CPS). Improved capabilities and reduced price of these platforms will have profound effect on their everyday usage and applications. In comparison to speech and natural language processing, which have established speech recognition and machine translation applications as indispensable in many contemporary CPSs, the vision community is still searching for an application that would be so necessary and desirable to make most of the consumers buy specific vision hardware just to run it. That would be the ultimate proof of the core value of the technology in the market. Thus, also vision problems come with a longstanding tradition and history of numerous solutions, it is still hard to point out a single application that would incorporate many specific vision tasks into one device, and which would be ubiquitously useful and affordable to all (e.g. like smartphone has done in the fields of communication and personal computing). However, with development of new miniaturization technologies and spatial AI it is reasonable to expect that there will be more possibilities for designing CPS with capabilities of visual understanding of outdoor, dynamic and uncontrolled environments. One step in such direction are embedded vision platforms that besides powerful computing capabilities also provide multimodal perception, and thus improve the algorithm performance. As an example, we will discuss stereo depth perception in the context of new spatial AI platforms like OAK-D lite, and point out some possibilities for its improvement and integration into future CPS.
Legal aspects, public interest, and legitimate interest in processing personal data to operate autonomous buses in the regular transportation system

Abstract Autonomous or robot vehicles are manufactured with such advanced technologies that they are technically able to operate without human intervention. The number of sensors deployed in automated vehicles is increasing, which runs a high risk of the excessive collection and processing of personal data. Considering European Union and Finnish perspectives on robot buses as an instance of advancements of autonomous vehicles, this article briefly outlines the general legal issues related to developing robot buses, and focuses on public interest and legitimate interest as lawful bases for processing personal data in running such vehicles in the mass transportation system.
Information Technologies for Popularization Increase of Commercial Internet Resources

The study showed that at the present stage of development of information technology, portable devices account for more than half of all Internet traffic, and its consideration is mandatory in the process of promoting commercial Internet resources. Given that the authors have analyzed the main approaches to promotion in the article and showed that an important aspect of promotion is to support Mobile First technology. During the study, the mechanisms of operation of well-known search engines were analyzed and the emphasis was placed on the operation of the Google system, which is the most common in Ukraine. The analysis of the commercial segment showed that the need to create a mobile version is determined by the following three factors: search delivery, loyalty and conversion. Further work was aimed at identifying and researching the parameters that most influence the process of ranking the mobile version of the resource. The speed of the Internet resource; size and design of pages; quantity and quality of backlinks; dynamic display of content is defined and mathematically described among them, in accordance with Mobile First. Two strategies for the promotion of commercial Internet resources using Mobile First technology have been developed. The initial strategy includes a list of recommendations that should be applied in the process of creating a new Internet resource, adaptive – aimed at customers who already have a working resource. The peculiarities of the decision-making method - the decision tree used in the designed system in the process of forming recommendations are described. The apparatus of algorithms algebra for synthesis of the models demonstrating the basic stages of popularization of commercial Internet resources according to Mobile Friendly technology is applied. The development of a software product that works in demo mode, which implements these promotion strategies in accordance with Mobile First has become the result of the study.
Organization of Joint Work on IT Projects in the Context of Distance Learning

Project activity for IT specialists in the field of academic formation reflects modern requirements of both professional and universal competencies. The transition to a distance learning format set forth the need to change the methodology for organizing work on IT projects and the use of digital technology tools. This paper presents the intricacies of joint work on IT projects in the conditions of team distributed development, as well as online services for the implementation of all development life cycle stages.
Best Practices in Teaching Foreign Students by Means of Creating Digital Environment

The article deals with the processes of teaching foreign students in the digital environment. Special attention is given to the experience of Lipetsk State Technical University in developing educational programs for international students using digital technologies.
Human Action Recognition Using Squeezed Convolutional Neural Network

In recent years, Convolutional Neural Network (CNN) gained great success in many applications, especially in computer vision. In this paper we propose a novel deep learning approach for Human Action Recognition inspired by one of the most popular architectures being SqueezeNet with an incredibly small model size. The results show that the proposed model performs well on a KTH dataset as well as UCF-Sport dataset
A Deep Learning Based Fast Face Detection and Recognition Algorithm for Forensic Analysis

Face detection and recognition applications have become an important research issue with the advent of artificial intelligence and deep learning studies, which have drawn much attention to researchers in recent years. The knowledge of retrieving contents from image, video, and audio files is seen as one of the vital influences in the field of digital forensics. In this study, we proposed a software that is supported with deep learning model which can analyze video and image files extracted from forensic evidence for either face detection or object recognition in a folder given as input and file a report of the images and videos that have been realized via this proposed application. The proposed deep learning model trained with YOLOv5 object detection algorithms can easily detect faces that are completely or partially visible under different light levels or in the image. This study shows that deep learning supported solutions can be easily preferred for real-time applications and time-consuming implementations to reduce fatigue and error-prone incurred during forensic investigations.