Reviewerly/exHarmony huggingface dataset:
An MFCC-based Secure Framework for Voice Assistant Systems

Voice assistant systems accept voice commands to perform various routine tasks such as navigation, playing music, temperature control, and so on. Voice assistants are commonly employed in home automation to serve the elderly and disabled people. Despite of their popularity in our daily lives, voice assistants are still far from perfect as these systems suffer from lack of security and accessibility issues. Available voice assistants respond to voice commands from any individual without proper user authentication. Hence, these systems are vulnerable to various security attacks where any adversary can control the voice assistant and perform any desired action. Moreover, popular voice assistants are also platform-dependent and only function on specific kind of devices. In the light of these limitations, we propose a Mel-frequency cepstral coefficients MFCC-based framework to secure the voice assistants via user authentication. MFCC efficiently recognizes the voice features which are then employed to distinguish between authorized and unauthorized users. Moreover, the voice assistant for the proposed framework is also implemented via cross-platform technology to guarantee increased accessibility. The proposed framework is tested on authorized and unauthorized user voices under different conditions with positive outcomes. The positive results prove the feasibility of our proposed framework for securing the voice assistant systems.
TCS Security Analysis in Intel SGX Enclave MultiThreading

With the rapid development of Internet Technology in recent years, the demand for security support for complex applications is becoming stronger and stronger. Intel Software Guard Extensions (Intel SGX) is created as an extension of Intel Systems to enhance software security. Intel SGX allows application developers to create so-called enclave. Sensitive application code and data are encapsulated in Trusted Execution Environment (TEE) by enclave. TEE is completely isolated from other applications, operating systems, and administrative programs. Enclave is the core structure of Intel SGX Technology. Enclave supports multi-threading. Thread Control Structure (TCS) stores special information for restoring enclave threads when entering or exiting enclave. Each execution thread in enclave is associated with a TCS. This paper analyzes and verifies the possible security risks of enclave under concurrent conditions. It is found that in the case of multithread concurrency, a single enclave cannot resist flooding attacks, and related threads also throw TCS exception codes.
Software Tool for Translation of Natural Language Text to SQL query

The paper presents a software tool that, through natural language analysis and processing, handles the task of translating natural language text into a relational database query. Retrieving information about objects in the database is implemented in Python. The software tool is a kind of compiler from natural language to standard SQL i.e. when it finds an input term or phrase, it formalizes it and replaces it with an appropriate SQL statement. The software tool combines activities of analysis, processing, and monitoring of relevant data, processes, and results. If the question can be translated into a valid query, the process continues with a search for an appropriate question model, and applicability, correctness, and applicability checks.
The Automation of Quality Control for Large Irradiance Datasets

The automation of quality control for large irradi-ance datasets is presented in this article. Data-driven techniques in photovoltaic systems have increased significantly and the need for sufficient and time-efficient quality control procedures has increased. The methodology usually includes a process in which the user has to manually review the erroneous data that has been flagged by the quality control procedure. As datasets increase, the time to review these flags increases substantially. A three-step quality control procedure is presented that includes an automatic elimination process of erroneous irradiance measurements and a novel empirical formulation for automatically identifying mea-surement tracking errors. The tracking error was introduced as other formulations were not adequate in removing these data points automatically. The first step is a time-series visualisation, the second step is the automatic removal of missing and du-plicate values, and the third step is an automatic flagging and elimination step to identify erroneous data. Two South African case studies were used to illustrate the practical application of the automatic quality control procedure. The automatic quality control procedure successfully removes the night-time data points as well as the tracking errors. An initial flagging procedure for reviewing data was also included as part of the quality control procedure; however, these data points were found to consist of closure test flags. Upon further investigation, the flagged data did not indicate that the measurements were faulty, and therefore the automated elimination process shows adequate performance in removing data without this manual review process. The automatic process proves to be a time-efficient method to remove erroneous data and is therefore recommended as a minimum quality control procedure for large irradiance datasets.
Development and deployment of a Hybrid Controller for a Dual-Axis Solar Tracker System

This paper presents the development and control of a photovoltaic Dual-Axis Solar Tracker system. The system constructed in this study caters to the application of renewable energy harvesting techniques to a mobile street vending cart solution. The strategy of control deployed is that of a Hybrid Controller, which is a novel combination of Astronomical equations based on open-loop control and Differential Flatness based control over the tracking movement of the photovoltaic panel used. The development takes into account the with mechanical and embedded designs with focus on manufacturing aspects. Finalised prototype was tested in overcast and sunny conditions to evaluate performance against a static solar energy photovoltaic harvesting system. The developed tracker photovoltaic increased the energy acquisition by more than 25 %.
Metainformation Extraction from Encrypted Streaming Video Packet Traces

In this study, we present a metainformation extraction pipeline and a Proof of Concept (PoC) implementation of a recognizer capable of classifying video titles, series titles as well as video genres from encrypted video streams. We show in a promising evaluation, using the Netflix and SVT Play catalogues as examples, that our PoC is capable of learning abstract data from the packet bursts visible in DASH encrypted streams (such as if a video fingerprint is rather a Drama or a Romance movie). This is, to the best of our knowledge, the first demonstration of successful extraction of video metainformation from coarse-grain encrypted network packet traces. While advocating updates in the DASH protocol in order to preserve viewers' privacy, our results also pave the way to future computer forensics systems capable of successful content classification over encrypted channels.
Big Data for Real-Time Processing on Streaming Data: State-of-the-art and Future Challenges

As many commercial businesses aspire for a competitive edge, real-time analysis on streaming data employing a big data methodology has lately become widespread. The ability to efficiently arrange massive amounts of data to make a business decision empowers data warehousing. Dealing with this sort of data poses considerable obstacles; as a result, several ways of assessing data in the form of streams have been created. Many solutions for dealing with enormous volumes of data and making decisions based on off-line batch processing have been investigated. In this paper, we explore the most recent developments in big data approaches for real-time analysis on streaming data to answer four research questions related to streaming data source, stream pre-processing, types of streaming data processing, used machine learning model, in addition to the validation and evaluation criteria. The system architecture associated with the used big data tools, the architecture hardware specs as well as other current platforms appropriate for large data streaming analytics are also considered. Furthermore, we outline numerous difficulties encountered in big data stream processing. The purpose of this review is to fill a gap in the surveyed area by offering thorough evaluation of various big data frameworks, architectures, and categories for cutting-edge approaches, as well as critical analyses of their performance and discussions of their applications, trends, and future directions to serve as guides for readers in this burgeoning study area.
Blockchain-based Pharmaceutical Drug Supply Chain Management System

Blockchain technology was recently invented in financial technology to provide an irreversible, consensus-based, and secure environment. There are areas where trust and transparency are crucial, and that is when blockchain comes to the rescue. It is essential to transmit pharmaceutical data to several parties reliably. Similar to prescription drugs, blockchain technology enables traceability and visibility to supply chains, offering complete information from producers to buyers. Large manufacturers now gather and manage data, and drugstore stores abuse their centralized systems. Many existing approaches and methods enable pharmaceutical data to be kept and centralized between the tending supplier and other stakeholders. This article introduces blockchain technology, reviews and analyses the most recent and significant research using blockchain technology in pharmaceutical supply chains, identifies the challenges connected with blockchain technology, and suggests some future research. Here, we propose an enhanced supply chain management system where the medicine data is available on blockchain to verify all stakeholders and consumers.
Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detection

Adversarial patch-based attacks aim to fool a neural network with an intentionally generated noise, which is concen-trated in a particular region of an input image. In this work, we perform an in-depth analysis of different patch generation parameters, including initialization, patch size, and especially positioning a patch in an image during training. We focus on the object vanishing attack and run experiments with YOLOv3 as a model under attack in a white-box setting and use images from the COCO dataset. Our experiments have shown, that inserting a patch inside a window of increasing size during training leads to a significant increase in attack strength compared to a fixed position. The best results were obtained when a patch was positioned randomly during training, while patch position additionally varied within a batch.
Integrating Blockchain with IoT for Mitigating Cyber Threat In Corporate Environment

The rising number of smart devices in the corporate environment poses a security threat that requires attention; organizations need to address them prior as these devices can work as a gateway for a major breach in their environment. This paper focuses on IoT-based solution towards the bring-your-own-device (BYOD) model in a corporate environment where a user brings their own devices and connects to a private network that brings enormous threats to it. A relatively efficient and security-enhanced model is proposed that addresses the resource constraint while also contributing in the direction of security achievement. It is intended to provide ease to users while not compromising on service delay or introducing resource challenges. Moreover, the comparative result concludes that the proposed model works well in our private testing environment, whereas, no additional resource utilization is involved to achieve maximized security in blockchain-IoT.
Channel boosting based detection and segmentation for cancer analysis in histopathological images

Human immune system plays a vital role in cancer prevention. Tumor Infiltrating Lymphocytes (TILs) are specialized immune cells that infiltrate tumor region. Studies have shown that the presence of TILs reflect towards cancer prognosis. The amount of TILs in tumor stroma can be counted manually under a microscope by an expert pathologist. This manual task is very laborious and time-consuming, and may face human subjectivity. Therefore, an automated system is required to effectively detect lymphocytes and to overcome the burden on pathologist. Automation of lymphocyte detection is a challenging task, due to its morphological variations, clustered appearance and presence of artifacts. To address these challenges, we have designed a computer aided diagnostic system called Pyramid Vision Transformer Channel Boosted Lymphocyte Detection (PVTCB-Lymph-Det) by exploiting deep CNN architectures based on channel boosting. In this work, we have incorporated the idea of channel boosting to enhance the learning capacity of the proposed model. The proposed network is based on a transformer and Convolutional Block Attention Mechanism (CBAM) with ResNet-50 to perform feature extraction. The proposed technique with Channel Boosting has performed well for lymphocyte detection and has an F-score of 88.92%. Moreover, PVTCB-Lymph-Det can be adapted to detect lymphocytes and may aid pathologists in solving medical diagnostic problems.
Human Body 3D Reconstruction and Gait Analysis via Features Mining Framework

Technology increasingly governs the globe, and machines are becoming increasingly sophisticated. To make living better, scientists are aiming to offer machines having the ability to understand the environment and smart skills development tools and techniques. Multiple techniques have been utilized to investigate event classification in videos and sequential images such as shape, height, or orientation of body segments along with their contextual influences. In this research article, a robust approach has been proposed for the human body 3D reconstruction and gait analysis. Initially, the crowd-based datasets are considered as the input, pre-processing steps are performed to reduce noise and computational costs. Then, human detection from given frame data, human 2D model, and human 3D reconstructions are implemented followed by features extraction using degree of freedom, local and global coordinate system, and motion direction flow (MDF) features. Finally, the classification stage with a data mining approach and classification through a genetic algorithm has been proposed. Two publicly available datasets are chosen for this research, namely, the mpii-video pose and pose track dataset. We attain the landmark recognition accuracy for the mpii-video pose dataset of 80.38% and gait analysis of 83.68%. For the pose track dataset, we attained a landmark recognition mean accuracy of 80.07% and a gait analysis mean accuracy of 81.81%. The proposed method represents a substantial enhancement associated with current state-of-the-art systems.
Simulation and Development of an Autonomous Drone for Delivery of Medicines during COVID-19

The main goal of this research paper is to develop an autonomous medicine delivery quadcopter and validate a simulator model for it. It is intended to use this drone in crisis of COVID-19 due to restriction of social distancing and unavailability of regular hospital facilities. A simulator is then modified and used as a pre-mission tool to predict mission outcome and after validation, it will be used to predict complex missions without actually risking the expensive drone. An efficient payload system is designed and constructed for the quadcopter to fulfill its delivery purpose. Once the drone is assembled along with the payload mechanism, its physical parameters are calculated using SolidWorks. The same parameters such as performance coefficients and moments of inertia are then updated in the simulator’s quadcopter properties. The equations of motions model used is improved by including physical theoretical effects. In the end, same autonomous delivery mission tests have been done for the real quadcopter and the simulator in order to compare the results and show the effect of improved equations of motion and physical parameters.
Multi-Feature Behavior Relationship for Multi-Behavior Recommendation

Multi-behavior recommendation aims to model the interaction information of multiple behaviors to enhance the target behavior’s recommendation performance. Despite progress in recent research, it is challenging to represent users’ preferences using the multi-feature behavior information of user interactions. In this paper, we propose a Multi-Feature Behavior Relationship for Multi-Behavior Recommendation (MFBR) framework, which models the multi-behavior recommendation problem from both sequence structure and graph structure perspectives for user preference prediction of target behaviors. Specifically, the MFBR model is designed with a sequence encoder and a graph encoder to construct behavioral representations of different aspects of the user; the correlations between behaviors are modeled by a behavioral relationship encoding layer, and the importance of different behaviors is finally learned in order to construct the final representation of user preferences. Experimental validation conducted on two real-world recommendation datasets shows that our MFBR consistently outperforms state-of-the-art methods.
Improved scheme and evaluation method for progressive visual cryptography

Abstract Visual cryptography (VC) is a powerful technique with high security and requires no PC or device to reconstruct the secret information. Progressive visual cryptography (PVC) is a variation of the VC scheme in which the quality of the reconstructed image is improved by increasing the number of shared images. The previous study focused directly on maximizing the value of the quality in the completely reconstructed image; thus, there is a difference in the quality of the shared images. In this paper, we focus on the aforementioned issue and propose a new approach based on inductive reasoning. Our basic idea is to maximize the quality of the reconstructed images each time the number of shared images increases. We call this method the bottom-up approach . Moreover, hitherto, PVC has been evaluated based on the value of relative difference or by sight. Such evaluation methods are only subjective or difficult to execute without the knowledge of basis matrices. In addition, PVC users cannot easily confirm the effectiveness of their shared images. In this paper, we propose a new information-theoretic evaluation method for PVC, which only uses shared images, to solve the aforementioned problems. Our proposed method can objectively and quantitatively evaluate PVC based on the numerical value, and PVC users can easily confirm the effectiveness of their shared images.
Analysis of heating of printed circuit board conductors on a metal base for spacecraft devices at pulsed current

The article presents the results of the analysis of heating of printed circuit conductors of printed circuit boards mounted on a metal base, on-board device of spacecraft when pulsed current flows through them. Thermal transient and impulse characteristics of a system consisting of a printed conductor and a printed circuit board are considered. The analysis of the dependence of the temperature rise rate of printed conductors and the duration of transient processes on the linear dimensions of the system elements is given. It is noted in the article that the time of transient processes most strongly depends on the thickness of the layer of insulating materials. All the results were obtained in the process of modeling transients in the ANSYS 2019 R1 Transient Thermal module. Practical recommendations on the consideration of thermal transients in printed conductors in the design of printed circuit boards are given.
Cross‐domain sentiment classification using decoding‐enhanced bidirectional encoder representations from transformers with disentangled attention

Summary Cross‐domain sentiment classification is a significant task of sentiment analysis that objectives to predict the opinion orientation of text documents in the target domain by using the source domain's learned classifier. Most of the existing approaches of domain‐adaptation in sentiment classification focus on sharing low‐dimensional features across the domain using domain independent and specific features to mitigate the gap between domains. Earlier cross‐domain sentiment classification approaches mainly focused on document level and sentence level, they cannot consider the full impact of aspect words, position of the words, and long‐term dependencies. To address this concern, we propose a model for cross‐domain sentiment classification, which is based on decoding‐enhanced BERT with disentangled attention (DeBERTa). DeBERTa is a pretrained language model based on transformer architecture. In this article, we perform sentence and aspect embedding to mine wordpiece information from text document. DeBERTa language‐model utilize disentangled attention mechanism and an enhanced mask decoder to understand the expression features. Disentangled attention mechanism is used to encode each word into two vectors (i.e., content and position vector). In order to predict the masked tokens during model pretraining, an enhanced mask decoder is employed, which incorporates absolute positions in the decoding layer. Finally, experiments are conducted on the benchmark dataset that demonstrates the superiority of fine‐tuned DeBERTa model for cross‐domain sentiment classification tasks.
Analysis of Research Trends in Skin Care using Keyword Network Analysis

This study tried to examine the research trends related to skin care by using keyword network analysis on data extracted from the KCI journal D/B provided by the National Research Foundation of Korea. Text refinement was performed by extracting key words from a total of 504 papers published from 2011 to August 2022. The tools used for analysis are Textom and UCINET6. As a result, first, the keyword frequency was ‘cosmetics’ the most, followed by‘appearance management behavior’, ‘skin care shop’, ‘satisfaction’, ‘female’, ‘medical’, ‘service’, and ‘acne’. Second, as a result of examining the frequency of skin care related keywords according to the study period, the 1st period (2011-2016) and the 2nd period (2017-2022) both included ‘cosmetics’ as the keyword the most. In the second period, keywords such as ‘skin care shop’, ‘medical’, ‘aging’, and ‘acne’ appeared in high rankings. Third, keywords such as ‘cosmetics’, ‘satisfaction’, ‘skin care room’, ‘appearance management behavior’ and ‘service’ are keywords with high connection centrality, proximity centrality, and dominance centrality. It was confirmed that it was actively progressing.