Reviewerly/exHarmony huggingface dataset:
Voice-Based Interaction for an Aging Population: A Systematic Review

In the past twenty years, voice-based systems have emerged as a technology with high usability and accessibility. Research in the field of Human-Computer Interaction (HCI) has investigated this trend and how these systems can bridge the gap in technology use across all generations, specifically for older adults. This study aims to conduct a systematic literature review of research on voice-based systems for older adults published in the past twenty years. These articles were obtained from reputed databases, filtered out based on specific eligibility criteria, and perused by the authors of this study. This study answers questions concerning the research methodologies often employed for this particular topic and the trends in existing voice-based systems studied in research experiments. It discusses the benefits of voice technologies for the elderly, their challenges, and how to address them.
Execution Time Analysis of Multithreading and Multiprocessing on Seam Carving Algorithm

The objective of this research is to demonstrate the performance difference exhibited by the seam carving algorithm when executed sequentially on a conventional CPU as opposed to when it is run parallelly. Multithreading and multiprocessing were two of the parallelization methods used in this project. These execution times were recorded, and an execution time analysis was performed. In the end, the researchers compared the two to determine which parallelization technique performed better. The researchers believe that the applications of this algorithm when parallelized can be significantly expanded. This expansion could potentially allow it to be used in applications such as thumbnail generation, responsive web development, and a complete contentaware alternative to image resizing (skewing and stretching).
Detecting Hate Speech in Hindi in Online Social Media

Because of the rise in online hatred, the research communities of artificial intelligence, particularly natural language processing, have been developing models for identifying online hatred. Recently, code-mixing, or the usage of multiple languages in social media conversations, has made multilingual hatred a significant difficulty for automated detection. The crucial task involved in NLP is identifying inciting hatred in writings on social networking sites. This work has several relevant applications, including analysis of sentiments, cyberbullying in online world, and societal & political conflict studies. Using tweets that have been put online on Twitter, we analyze the issue of hatred detection in multilingual functionality in this paper. The tweets have the text annotations and the speech category (Normal speech or Hate speech) to which these belong. We, therefore, recommend a monitored method for detecting hatred. Additionally, the classification approach is provided, which uses certain characters level, words level, and lexicons-based features for identifying hate speech in the corpus. We obtain results of 96% accuracy in identifying posts across four classifiers. Index Terms—Hate speech, Multilingual, Code-mixing, NLP
A Novel Technique to Detect URL Phishing based on Feature Count

The advent of internet access to people across the globe, increasing levels of connectivity, remote employment, de-pendence on technology, and automation have presented a rapid rise in cybercrime. There are numerous methods of cybercrime that are frequently used, with phishing being the most significant. Various algorithms have been proposed to classify malicious and legitimate URLs, blacklisting and whitelisting algorithms being the oldest, used for computer security. But these algorithms are time-consuming and involve human labor. Hence, machine learning algorithms were incorporated to improve the effectiveness of classifying URLs. Recent research includes detecting phishing URLs using machine learning classifiers but the accurate classification is a long-term goal. This paper focuses on evaluating models for classifying URLs using different feature counts and deriving a specific model to provide good enough classification with less time complexity.
Analysing Shifts in Perceptions of Indians during COVID-19 Pandemic by Mining Tweets

Current pandemic situation has a significant impact affecting human life not only socially and economically, but emotionally and psychologically as well. This impact can be easily observed on social media platforms. Along with the knowledge exchange related to Covid-19 pandemic on social media, there is an emotional trauma wave that can be felt by carefully analyzing the activities of this social media. Keeping this view in thought, we analyze around 12000 tweets of Indian people to find out whether there is a trend shift of thinking pattern and mindset of Indian people as the pandemic progresses. The study is bifurcated into stages to clearly see the paradigm shift. We use tweets since twitter is a rich medium that can be leveraged to its optimum to have a good amount of understanding of the sentiments of the people. Analyzing the twitter dataset, we derive results and find out whether the amount of negative tweets v/s positive (or motivational) tweets have increased or not as the pandemic progresses. The study is supported by graphical visualizations of the polarity of the tweets month wise. Further, Wordmap approach is used to perform qualitative mining analysis in addition to the sentiment score based calculation. This work helps us to understand how the public opinions are changing with the changes in the spread dynamics of the virus. This kind of mood mining helps in identifying the Covid-19 situation from the psychological perspective that whether there is a sense of fear among people or they are quite optimistic of the situation. It can help in a great extend to the strategic and decision making bodies to plan out for future decisions. Further, such kind of studies can be used as reference to provide insights about mental health of people for any future incident or event of such nature.
Feature Selection using Enhanced Nature Optimization Technique

An essential study issue now is the preference of highly discriminative traits from a huge feature collection. By eliminating a significant number of noisy, redundant features, this has the potential to enhance classification performance while lowering the cost of system diagnostics. A feature selection process has been implemented using nature-inspired algorithms. Each of these algorithms needs its starting population to be initialized, and how well that initialization is done has a big impact on the outcome. This paper presents a newly hybrid nature-inspired Algorithm which is comprised by Harris-hawk Algorithm with Visual Geometry Group for selection of traits on High-Dimensional-datasets. Our main idea is to overcome the overfitting issue of feature selection and also overcome convergence problem arise in nature inspired algorithm by introducing visual geometry group Convolution neural network based deep neural network. Then, we compared our upgraded approach to the most significant nature-inspired optimization technique to show that our technique is more accurate and categorized using the Acute lymphoblastic leukemia & Breast cancer High Dimensional datasets.
Counterfeit Drug Prevention in Pharma Supply Chain using Blockchain Technology

Fake drugs are a growing serious issue related to the healthcare industry which causes extraordinary dangers to the society and the environment. Tracing the drugs at every step of the pharma supply chain is a difficult task. The goal of the research is to create a blockchain-based model that can forestall drug forging in drug store networks. The scope of the research lies where irregularities in the pharmaceutical supply chain affect public health and economy. The advanced elements of Blockchain makes it fit for complete recognizability of medications from producer to end patients, and the capacity to prevent fake prescriptions or medications. Drugs change proprietorship from producers to distributors and afterward retailers before it arrives at the client. Through the proposed model, the producer would have the option to transfer the subtleties relating to a medication, after which it will be sent for endorsement to the Government. From there on, distributors and retailers, in view of their prerequisites, can demand the endorsed drugs. Later on, in the event that a patient needs some drug, the person should demand it on the blockchain network. The solicitation will be shipped off the closest retailer/drug store and from that point, the patient can gather their medicine. Hyperledger fabric is utilized because they are permissioned and have an open source blockchain framework, meaning all the organization and pears are well known and confirmed. Our execution of the proposed blockchain based model features that the model can effectively forestall any medication being fake. This will be advantageous for the clients getting impacted with fake medications.
Trends in Research Topic Representation

In this paper, insights and data analyses are presented for tracking the use of phrases in the representation of key domain areas in scientific publications, over time. A domain refers to a particular branch of scientific knowledge and hence largely defines the main topic or theme of any scientific research paper. These domains can be extracted from scientific publications over a fixed time period. Varied phrases can then be analyzed whether they are representative of the same domain. The representative phrases are then analyzed as to whether they are trending in usage over time or are being phased out. Thus, insights are proposed for a domain to be qualified by its most trending representative phrase. These insights are supported by rigorous data analyses.
Simplified Micropayment Mechanism to Eliminate the Risk of Double Payment in E-Commerce

In recent years, the emergence of the Internet and E-commerce has steered significant growth in digital transactions. Businesses today need mobile wallets, credit and debit cards, and e-cash to digitize payments. Digital payment systems are in transition and promise amazing advancements, but they also pose many risks and as the number of online transactions is increasing tremendously, we need a security system that follows all security norms. In this paper, we study digital transaction systems and evaluate various components of E-commerce plat-forms to address the security of these services. We evaluate the attributes that affect the security of digital payment processes and identify several barriers that hinder their performance to propose a simplified payment mechanism for micro-payments that eliminates the double payment problem.
A Case Study to Analyze the Impact of Social Media on Video Game Sales

Social media data is becoming more and more valuable in analyzing patterns in human behavior as a result of the ongoing expansion of online engagement. Twitter is a great place to find current user-generated data. Anyone can utilize this platform to contribute their own ideas, opinions, or feedback on a variety of issues through Tweets. These topics might come from a variety of industries, including business, politics, and entertainment. On average, 6,000 tweets are uploaded on Twitter every second, for a daily total of over 500 million messages. Understanding the causes and impacts of social, environmental, or financial events can be made much easier by the amount of publicly available information. The data is incredibly beneficial for retailers for market research and sales forecasting when paired with cutting-edge data analysis and Artificial Intelligence (AI) solutions. The proposed study examine the sentiments and statistics that have been submitted by users on the Twitter platform in order to determine whether “Social Media Influencing” has any effect on the video game business.
A Novel Approach to Object Detection: Object Search

Most object detection algorithms attempt to detect all objects present in an image and accordingly classify them. While that approach is useful for various domains and applications, there are also many cases where we would only want to search for a particular object in a given image. For such cases, there is potential to optimize the search by focusing on the object we are looking for and ignoring the rest of the information in the image to the maximum possible extent, thereby greatly improving the computation speed. In this light, I have developed a model which can search for an object given in an image (the object image) in another image where the object mayor may not be present (the target image). The design takes inspiration from Siamese Neural Networks and techniques applied in other object detection algorithms and combines them with a novel technique and loss. I have trained and tested the model using images from the COCO dataset. It has shown improvement in computation speed compared to other state-of-the-art models for the desired task, along with appreciable accuracy.
High Efficient Quality of Video Compression using Variational Autoencoders in Deep Learning

The past few years researchers are rising dominance of deep learning and artificial intelligence in huge range of various applications like image and video compression. The paper provides a deep concept over for highly loss video compression model. In the existing neural networks of video encoding presence of quantity and level of redundancy provides unique problems. The ELBO utilized in variational autoencoders is strongly related to the rate distortion loss, which the autoencoder and prior are jointly trained to reduce. Although proposed method is straightforward, in this paper discover that it beats cutting-edge learning video compression networks interpolation. The work where systematically assess key design decisions, including the type of autoregressive prior and the usage of frame-based or spatio-temporal autoencoders. And also provide three modifications to the fundamental concept that highlight its advantages over more traditional compression techniques. In this paper proposed semantic compression, which trains the model to devote more bits to important objects. This work investigates the proposed multimodal compression, where it will presents how well our approach works when it comes to combining different modalities that have been collected by unconventional imaging sensors, such quad cameras. And finalize that approach makes unique video compression applications possible that were not possible with conventional codecs. Finally, at the end of the work in our multimodal compression studies significantly improves compression performance with the PSNR of 48. For future scope of work, a simple decoder can recreate videos with sufficient quality without compromising classification accuracy.
Blockchain Data Transaction with Leakage Tracing Based on Digital Fingerprint

Data transaction promotes data circulation and mitigates the issues of Data Island. However, unlike traditional physical goods, data can be easily copied. This feature always induces leakage after selling the right of use in the transaction, resulting in the loss of users’ interest. In fact, it is difficult to restrain when buyers make backups and leak after purchasing the complete data. Therefore, it’s necessary to design corresponding schemes to ensure the data security in the transaction process, find the data after the buyer leaked and identify the malicious leaker. In this paper, we propose a data transaction architecture with leakage tracing by blockchain and digital fingerprint. In our architecture, buyers interact with sellers who pass the screening through the smart contracts. The digital fingerprint is utilized to avoid the malicious behavior of sellers and track leaked data by buyers. Then we provide an improvement plan to optimize the scheme of leakage tracing in data transactions by combining reporting and deposit mechanisms. In addition, we develop a Decentralized Application (DApp) to build the model for data transaction and leakage tracing. Experiments on the DApp demonstrate the effectiveness and feasibility of our proposed architecture.
Accelerating Federated Learning with Two-phase Gradient Adjustment

With the advent of the Internet of Things (IoT) era and 5G, ubiquitous sensing devices (e.g., smartphones, surveillance sites, and security cameras) have been widely used in various fields, resulting in the generation of a huge amount of monitoring data. The rise of federated learning makes it possible to leverage monitoring data to train deep neural networks through cloud-edge collaboration without compromising privacy. However, the non identically and independently distributed (called Non-IID) data collected by IoT devieces creates a client drift phenomenon, resulting in a slow convergence of the global model. To this end, we propose a new Federated learning framework based on Gradient Variance Reduction with a correction weight control mechanism and Global gradient descent with Momentum, named FedGVRGM to conduct gradient correction and reduce the negative impacts of prediction parameters. Specifically, in the local training phase, FedGVRGM combines gradient variance reduction with a correction weight control mechanism to further correct the local model parameters, thus reducing the dispersion of model parameters among clients. In the global aggregation phase, FedGVRGM integrates the historical change states of the global model through the gradient descent with momentum to reduce the oscillations and improve the convergence speed of the global model. We refer to the above methods of gradient adjustment in the local and global training phases as FedGVR and FedGM, respectively. Numerous evaluations are conducted on CIFAR-100, CIFAR-10, and MNIST datasets to prove that FedGVRGM has a faster convergence rate than other stateof-the-art approaches such as Federated Averaging (FedAvg), FedProx, FedReg, FedGVR, and FedGM.
A Nginx-based Dynamic Feedback Load Balancing Algorithm With Adaptive Heartbeat Detecting

Load balance technology of web server clusters which assigns customer requests to suitable servers, is a major tool for quality-of-service guarantees. Nginx, a popular frontend web server, is lightweight and requires low-complexity load balancing algorithms. This study proposed a Nginx-based dynamic feedback load balancing algorithm that delivers customer requests to server nodes in accordance with the nodes’ status. Specifically, an adaptive heartbeat detection mechanism that adjusts heartbeat packet transmission in line with the server node status is designed to collect real-time node status information. The simulation study indicates that our algorithm can obtain better load balancing with a considerable reduction in heartbeat packet transmissions compared with baseline algorithms.
GraphGRU: A Graph Neural Network Model for Resource Prediction in Microservice Cluster

Predicting resource usage of workloads in large scale production clusters is very important to understand the characteristics of applications. It is also very important for cluster operators to manage cluster resources more efficiently. Traditional statistical-based prediction methods face challenges in predicting resource usage in large scale dynamic and complex clusters. The current commonly used deep learning methods such as Recurrent Neural Networks (RNN) usually use the historical data in single node to predict the future resource usage. While most of the modern applications (e.g., microservices) are distributively deployed to the cluster, the traditional single node resource prediction methods cannot predict the resource usages well. To solve this problem, in this paper we propose a new deep learning model called GraphGRU which is based on graph attention networks (GAT) to predict the resource usages from the cluster perspective. We use the Dynamic Time Warping (DTW) algorithm to construct a graph structure for multiple physical nodes in the cluster and also use a method similar to data compensation to co-train the model with both horizontal and vertical data. We validate our model on the Alibaba microservices dataset which is captured from a large scale production cluster. Compared to the traditional deep learning methods, our model improves the prediction accuracy by up to 48.27%.
UltraBD: Backdoor Attack against Automatic Speaker Verification Systems via Adversarial Ultrasound

Automatic speaker verification (ASV) systems have been widely applied in voice user interfaces to conduct person identification and access control via voiceprints. A typical ASV system consists of three stages, i.e., training, enrollment, and verification. Previous work has revealed that the ASV system can be bypassed at the training stage by backdoor attacks and at the verification stage by adversarial example attacks. In this paper, we propose a new type of backdoor attack aimed at the enrollment stage via adversarial ultrasound, named UltraBD, which is highly imperceptible, synchronization-free, and content-independent. By simultaneously injecting the ultrasound backdoor examples when the legitimate user initiates the enrollment, the polluted voiceprints stored in the ASV systems grant access to both the legitimate user and the adversary with relatively high confidence. Despite the challenges, i.e., when, what, and how the legitimate user articulates at the enrollment stage can be remarkably unpredictable and various, we managed to launch UltraBD by augmenting the generation and optimization process of the ultrasound backdoor examples with the randomness of synchronous time and relative amplitude ratio. Furthermore, we optimize the modulation mechanism of adversarial ultrasound by tuning the baseband signal on limited signal frequency points to improve its robustness in the physical world setting. We validate UltraBD on two common datasets together with two open-source ASV models. Results show that UltraBD can be robust to various configurations, e.g., different speakers and utterance content. In sum, our attack calls attention to a new attack surface of ASV systems and sheds light on its fundamental mechanisms.
Key Cryptographic Methods in the Cloud: A Comparative Study

With cloud computing becoming an inseparable part of human daily life and making its way in every field of technology during the last decade with smarter human generation and adoption of new data policies, data security has become a significant pillar of trust. The standard of data security ensures the quality of privacy while availing various services over the internet. To achieve a step forward against always developing exploiters various algorithms have been developed which come under the wing of cryptography. Cryptography is the branch that ensures confidentiality, integrity, authentication, privacy, and security of the data of a consumer. This paper has dis-cussed and analyzed DES, triple DES, Blowfish, AES, IDEA, RC4, RSA, and ECC cryptography algorithms based on their ability of key size, block size, encryption time, decryption time, and total time in the Node JavaScript environment. Since, JavaScript powers 80% of the internet, this paper provides a production-level real-world analysis of various algorithms at present time.