Reviewerly/exHarmony huggingface dataset:
Protecting Business from Data Breach

Computer security measures are applied to protect company data from unauthorized access. The aspects of such computer security, need to be known to the business professionals and owners. This paper proposes a new view of computer security in the business world by informing business professionals and owners about data breaches. The suggested methods are not very complex as they are meant to be used by companies who do not yet have a computer security team, though we highly recommend investing company money into professional computer security roles. This research is done while considering the rapid growth of both the tech industry and the everyday vulnerabilities that come along with any evolving sector of business. The results show that computer usage has only increased in the past 10 years. With this increase, data breaches have also increased. In the business world, research shows this could be very detrimental for most businesses. Investing in Computer Security and protecting your company from a data breach is ideal in the capitalist, technological we live in today.
An Abstraction Layer Exploiting Voice Assistant Technologies for Effective Human—Robot Interaction

A lot of people have neuromuscular problems that affect their lives leading them to lose an important degree of autonomy in their daily activities. When their disabilities do not involve speech disorders, robotic wheelchairs with voice assistant technologies may provide appropriate human–robot interaction for them. Given the wide improvement and diffusion of Google Assistant, Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa, etc., such voice assistant technologies can be fully integrated and exploited in robotic wheelchairs to improve the quality of life of affected people. As such, in this paper, we propose an abstraction layer capable of providing appropriate human–robot interaction. It allows use of voice assistant tools that may trigger different kinds of applications for the interaction between the robot and the user. Furthermore, we propose a use case as a possible instance of the considered abstraction layer. Within the use case, we chose existing tools for each component of the proposed abstraction layer. For example, Google Assistant was employed as a voice assistant tool; its functions and APIs were leveraged for some of the applications we deployed. On top of the use case thus defined, we created several applications that we detail and discuss. The benefit of the resulting Human–Computer Interaction is therefore two-fold: on the one hand, the user may interact with any of the developed applications; on the other hand, the user can also rely on voice assistant tools to receive answers in the open domain when the statement of the user does not enable any of the applications of the robot. An evaluation of the presented instance was carried out using the Software Architecture Analysis Method, whereas the user experience was evaluated through ad-hoc questionnaires. Our proposed abstraction layer is general and can be instantiated on any robotic platform including robotic wheelchairs.
Adaptive Compression for Online Computer Vision: An Edge Reinforcement Learning Approach

With the growth of computer vision-based applications, an explosive amount of images have been uploaded to cloud servers that host such online computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the de facto compression and encapsulation method for images. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model—for example, the standard quality level of JPEG leads to 50% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models (e.g., InceptionNet and ResNet). Knowing this, designing a better JPEG configuration for online computer vision-based services is still extremely challenging. First, cloud-based computer vision models are usually a black box to end-users; thus, it is challenging to design JPEG configuration without knowing their model structures. Second, the “optimal” JPEG configuration is not fixed; instead, it is determined by confounding factors, including the characteristics of the input images and the model, the expected accuracy and image size, and so forth. In this article, we propose a reinforcement learning (RL)-based adaptive JPEG configuration framework, AdaCompress. In particular, we design an edge (i.e., user-side) RL agent that learns the optimal compression quality level to achieve an expected inference accuracy and upload image size, only from the online inference results, without knowing details of the model structures. Furthermore, we design an explore-exploit mechanism to let the framework fast switch an agent when it detects a performance degradation, mainly due to the input change (e.g., images captured across daytime and night). Our evaluation experiments using real-world online computer vision-based APIs from Amazon Rekognition, Face++, and Baidu Vision show that our approach outperforms existing baselines by reducing the size of images by one-half to one-third while the overall classification accuracy only decreases slightly. Meanwhile, AdaCompress adaptively re-trains or re-loads the RL agent promptly to maintain the performance.
Deepfakes Detection Methods: A Literature Survey

Recently, a great amount of concern has been attracted to the phenomena of DeepFake, which has been created for capturing and reenacting faces in a video and swap a face with someone else’s face using neural networks. In Deepfake technology, a computer-generated fake video shows fictional contents as real things. Many unbelievable applications in this technology are starting to be explored. Currently, malicious usages of fake videos are gaining in the computerized world like fake news, celebrity pornographic videos, revenge porn, and financial frauds. So, celebrities, politicians and famous people are facing mostly to Deepfake detection problem. Human’s naked eyes are weak to directly identify the difference between real videos and Deepfake videos because they are quite realistic. So, bothered people require an automated computerized Deepfake detection tool. This paper reviews existing Deepfake detection methods using traditional methods and deep learning technologies. Further this paper discussing the limitations of current methods and availability of datasets in the society. According to the literature, there is not a highly accurate and automated detection method to identify Deepfakes. The unavailability of efficient Deepfake detection method is a big challenge to the world due to the ease of generating Deepfake videos and their rapid spread. However, there are many efforts to solve this phenomenon and deep learning related methods show remarkable performance than other methods.
How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?

Federated learning (FL) has been considered as a promising learning framework for future machine learning systems due to its privacy preservation and communication efficiency. In beyond-5G/6G systems, it is likely to have multiple FL groups with different learning purposes. This scenario leads to a question: How does a wireless network support multiple FL groups? As an answer, we first propose to use a cell-free massive multiple-input multiple-output (MIMO) network to guarantee the stable operation of multiple FL processes by letting the iterations of these FL processes be executed together within a large-scale coherence time. We then develop a novel scheme that asynchronously executes the iterations of FL processes under multicasting downlink and conventional uplink transmission protocols. Finally, we propose a simple/low-complexity resource allocation algorithm which optimally chooses the power and computation resources to minimize the execution time of each iteration of each FL process.
Invited: Accelerating Fully Homomorphic Encryption with Processing in Memory

Fully homomorphic encryption (FHE) provides a promising solution for future computing needs by allowing privacy-preserving computation. However, its practical use has been limited by the huge latency overhead it incurs while computing. This is primarily due to the huge size of encrypted data and intermediate processing required to compute on it. In this paper, we present insights into the benefits of accelerating FHE with processing in-memory (PIM). PIM is an excellent match for the FHE since it provides extensive parallelism, in-situ operations, and bit-level granularity. We present FHE-PIM, which implements basic polynomial primitives with PIM and uses them to accelerate key FHE operations in memory. This can significantly make the time-consuming procedure of FHE bootstrapping faster in memory. We compare the speedup of FHE-PIM for various FHE operations with their CPU implementations. FHE-PIM can achieve an estimated average throughput improvement of 88$,397 \times$ as compared to CPU for FHE arithmetic operations.
Service robotics development trends in the Russian Federation

The paper is devoted to the analysis of service robotics development trends in the Russian Federation. In recent years, a positive direction has been the assistance of the state in disseminating information and other types of support to national robotics manufacturers. The leading domestic manufacturers of service robots are Alfa Robotics, Promobot, R. Bot, Way-Bot, Android Technology, and Neurobotics. They have developed a whole line of robots that can be used in hotels, airports, shopping centers, banks as a consultant and assistant. The robot communicates in 10 languages, helps to navigate in the room, identifies the person and conducts electronic registration.
Concept Drift-Based Runtime Reliability Anomaly Detection for Edge Services Adaptation

To meet the rapidly increasing need of computation-intensive and latency-sensitive applications, mobile edge computing (MEC has attracted tremendous attention from both academia and industry. However, the runtime reliability of edge services fluctuates over time due to the dynamics in their internal states and the external environment. This causes the distribution of edge services' reliability data streams to vary in the form of concept drift. Severe negative reliability drifts indicate that an edge service may be suffering from a performance anomaly or a runtime failure. To ensure the stable operation of edge services, we propose A-Detection, a concept drift-based runtime reliability anomaly detection approach for edge services adaptation. We integrate reservoir sampling and singular value decomposition (SVD for large-scale streaming data sampling and feature extraction. Jensen Shannon (JS divergence is utilized to develop a dissimilarity metric of data stream distribution, called FDC, for runtime edge service reliability anomaly detection. When an anomaly is detected in a running edge service, checkpoint-retry is combined with computation offloading to implement runtime reliability adaptation. Extensive experimental results verify and demonstrate the effectiveness and efficiency of A-Detection.
Co-evolutionary hybrid intelligence

Artificial intelligence is one of the drivers of modern technological development. The current approach to the development of intelligent systems is data centric. It has several limitations: it is fundamentally impossible to collect data for modeling complex objects and processes; training neural networks requires huge computational and energy resources; solutions are not explainable. The article discusses an alternative approach to the development of artificial intelligence systems based on the human-machine hybridization and their co-evolution.
Sizing Methodology of Floating Photovoltaic Plants in Dams of Semi-Arid Areas

Abstract Floating photovoltaic (FPV) plants in reservoirs can contribute to reduce water evaporation, increase power generation efficiency, due to the cooling process, and reduce competitiveness in land use. Based on this motivation, we propose a new methodology for sizing FPV plants in dams of semi-arid regions using the flood duration curve. The methodology innovations are no use of commercial software, the possibility of choosing the reliability level, the application in reservoirs of semi-arid areas of the world, and the use of a graphic analysis of the reservoir hydrological behavior. The case studies in the Brazilian and Australian semi-arid consider two scenarios: high reliability level (90%, scenario 1) and low reliability level (70%, scenario 2). The reliability level is linked to the electricity production; the evaporation reduction is proportional to the FPV plant area.
Feature Matching-based Approaches to Improve the Robustness of Android Visual GUI Testing

In automated Visual GUI Testing (VGT) for Android devices, the available tools often suffer from low robustness to mobile fragmentation, leading to incorrect results when running the same tests on different devices. To soften these issues, we evaluate two feature matching-based approaches for widget detection in VGT scripts, which use, respectively, the complete full-screen snapshot of the application ( Fullscreen ) and the cropped images of its widgets ( Cropped ) as visual locators to match on emulated devices. Our analysis includes validating the portability of different feature-based visual locators over various apps and devices and evaluating their robustness in terms of cross-device portability and correctly executed interactions. We assessed our results through a comparison with two state-of-the-art tools, EyeAutomate and Sikuli. Despite a limited increase in the computational burden, our Fullscreen approach outperformed state-of-the-art tools in terms of correctly identified locators across a wide range of devices and led to a 30% increase in passing tests. Our work shows that VGT tools’ dependability can be improved by bridging the testing and computer vision communities. This connection enables the design of algorithms targeted to domain-specific needs and thus inherently more usable and robust.
Towards establishing a research lineage via identification of significant citations

Abstract Finding the lineage of a research topic is crucial for understanding the prior state of the art and advancing scientific displacement. The deluge of scholarly articles makes it difficult to locate the most relevant previous work. It causes researchers to spend a considerable amount of time building up their literature list. Citations play a crucial role in discovering relevant literature. However, not all citations are created equal. The majority of the citations that a paper receives provide contextual and background information to the citing papers. In those cases, the cited paper is not central to the theme of citing papers. However, some papers build upon a given paper and further the research frontier. In those cases, the concerned cited paper plays a pivotal role in the citing paper. Hence, the nature of the citation that the former receives from the latter is significant. In this work, we discuss our investigations towards discovering significant citations of a given paper. We further show how we can leverage significant citations to build a research lineage via a significant citation graph. We demonstrate the efficacy of our idea with two real-life case studies. Our experiments yield promising results with respect to the current state of the art in classifying significant citations, outperforming the earlier ones by a relative margin of 20 points in terms of precision. We hypothesize that such an automated system can facilitate relevant literature discovery and help identify knowledge flow for a particular category of papers.
Construction of New Intelligent Teaching Scene Based on Multimodal Wireless Sensing Technology

With the continuous development of computer technology and the gradual popularization of information technology application, the construction of intelligent teaching scene based on wireless sensing technology plays a more and more important role in modern information education. Taking a primary school as an example, this paper introduces multimodal wireless sensing technology into the construction of intelligent teaching system. The purpose of this paper is to explore the construction of a new teaching scene. Firstly, this paper deeply analyzes the sensing mechanism of wireless signal and optimizes the sensing mode, deployment structure, and signal processing in practical application, so that the system can run more effectively in the actual environment. Then, based on multimodal wireless sensing technology, this paper designs and optimizes the basic architecture and functions of intelligent teaching scene. The results show that combining the characteristic information of each mode to get the information conducive to identity confirmation, which can get better recognition performance and improve the accuracy. Combining the information of multiple modes can greatly improve the recognition performance. The user interest model combined with dynamic and static is used to optimize the system recommended resources, so that students can obtain high-quality and highly matched learning resources more quickly and accurately, so as to improve students’ learning efficiency in resource acquisition.
Evaluating Performance, Power and Energy of Deep Neural Networks on CPUs and GPUs

Deep learning has achieved accuracy and fast training speed and has been successfully applied to many fields, including speech recognition, text processing, image processing and video processing. However, the cost of high power and energy comes together with the high accuracy and training speed of Deep Neural Network (DNN). This inspires researchers to perform characterization in terms of performance, power and energy for guiding the architecture design of DNN models. There are three critical issues to solve for designing a both accurate and energy-efficient DNN model: i) how the software parameters affect the DNN models; ii) how the hardware parameters affect the DNN models; and iii) how to choose the best energy-efficient DNN model. To answer the three issues above, we capture and analyze the performance, power and energy behaviors for multiple experiment settings. We evaluate four DNN models (i.e., LeNet, GoogLeNet, AlexNet, and CaffeNet) with various parameter settings (both hardware and software) on both CPU and GPU platforms. Evaluation results provide detailed DNN characterization and some key insights to facilitate the design of energy-efficient deep learning solutions.
Two-dimensional Bhattacharyya bound linear discriminant analysis with its applications

The recently proposed L2-norm linear discriminant analysis criterion based on Bhattacharyya error bound estimation (L2BLDA) was an effective improvement over linear discriminant analysis (LDA) and was used to handle vector input samples. When faced with two-dimensional (2D) inputs, such as images, converting two-dimensional data to vectors, regardless of the inherent structure of the image, may result in some loss of useful information. In this paper, we propose a novel two-dimensional Bhattacharyya bound linear discriminant analysis (2DBLDA). 2DBLDA maximizes the matrix-based between-class distance, which is measured by the weighted pairwise distances of class means and minimizes the matrix-based within-class distance. The criterion of 2DBLDA is equivalent to optimizing the upper bound of the Bhattacharyya error. The weighting constant between the between-class and within-class terms is determined by the involved data that make the proposed 2DBLDA adaptive. The construction of 2DBLDA avoids the small sample size (SSS) problem, is robust, and can be solved through a simple standard eigenvalue decomposition problem. The experimental results on image recognition and face image reconstruction demonstrate the effectiveness of 2DBLDA.
Negative emotional dynamics shape cognition and performance with MetaTutor: Toward building affect-aware systems

Significant efforts are currently being made to design affect-aware systems to classify, monitor, and scaffold emotional experiences across a range of settings. However, most investigations are limited in their view of emotions due to less sophisticated methodologies and analytical techniques. To address these issues, we captured 174 undergraduates’ emotions over time and defined them by multiple dimensions: (1) temporality, (2) valence, and (3) activation during learning with an intelligent tutoring system called MetaTutor. Latent growth models revealed the stability of negative activating emotions over time was related to performance, and changes in negative deactivating emotions were related to time engaging in cognitive strategies. Finally, a random forest classifier revealed high accuracy in predicting high (top 30%) and low performance groups (bottom 30%) using pre-test scores, changes in negative deactivating emotions, and time engaging in cognitive strategies. These findings have important implications for designing affect-aware systems that can potentially leverage emotion interventions based on if, when, and how an emotion changed (or remained stable) to optimize cognition and performance with emerging technologies.
SRaSLR: A Novel Social Relation Aware Service Label Recommendation Model

With the rapid development of new technologies such as cloud, edge and mobile computing, the number and diversity of available services are dramatically exploding and services have become increasingly important to people's daily work and life. As a consequence, using service label recommendation techniques to automatically categorize services plays a crucial role in many service computing tasks, such as service discovery, service composition, and service organization. There have been many service label recommendation studies that have achieved remarkable performance. However, these studies mainly focus on using the text information in service profiles to recommend labels for services while overlooking those social relations that widely exist among services. We argue that such social relations can help to obtain more precise recommendation results. In this paper, we propose a novel Social Relation aware Service Label Recommendation model called SRaSLR, which combines text information in service profiles and social network relations among services. A deep learning based model is constructed based on feature fusion of the two perspectives. We conduct extensive experiments on the real-world Programmable Web dataset, and the experiment results show that SRaSLR yields better performance than existing methods. Additionally, we discuss how service social network affects service label recommendation performance based on the experiment results.
Rewrite to Reinforce: Rewriting the Binary to Apply Countermeasures against Fault Injection

Fault injection attacks can cause errors in software for malicious purposes. Oftentimes, vulnerable points of a program are detected after its development. It is therefore critical for the user of the program to be able to apply last-minute security assurance to the executable file without having access to the source code. In this work, we explore two methodologies based on binary rewriting that aid in injecting countermeasures in the binary file. The first approach injects countermeasures by reassembling the disassembly whereas the second approach leverages a full translation to a high-level IR and lowering that back to the target architecture.