Reviewerly/exHarmony huggingface dataset:
Toward Machine Learning Based Binary Sentiment Classification of Movie Reviews for Resource Restraint Language (RRL)—Hindi

Sentiment analysis has significantly progressed in English, whereas Hindi research is still nascent. Despite being the third most spoken language worldwide, Hindi remains an RRL. Movie reviews are a treasure trove of opinionated content fueled by people’s passionate engagement with film industry. The proliferation of great use of Hindi in writing reviews has catalyzed our endeavor to devise an approach for bipolar sentiment classification of movie reviews. We compiled and manually annotated a Hindi Language Movie Review (HLMR) dataset comprising 10K reviews for experiments, and challenges associated with Hindi have also been identified. In addition to HLMR, two publicly available IIT-P movie and product review datasets are used. Following dataset preprocessing, we explored TF-ISF with word-level N-gram features for text representation. Studies suggest that performance of machine learning approaches can be enhanced by hyperparameter tuning and ensemble learning. Several baseline classifiers were initially applied, and their parameters were hyper-tuned using Grid search. Subsequently, ensemble-based classifiers were applied individually. Lastly, we propose a simplistic yet powerful stacked ensemble-based architecture (SEBA), which effectively classifies Hindi reviews by leveraging the strengths of both approaches. Comprehensive experiments were conducted on all deployed datasets. Empirical results demonstrate that SEBA outperformed individual baselines and exhibited superior performance with unigrams and TF-ISF as features across deployed datasets. SEBA achieved an accuracy, precision, and recall of 0.808% and an F1-score of 0.807% on the HLMR dataset. These findings strongly advocate for the effectiveness of proposed solution and indicate its suitability for online deployment in binary review classification tasks.
Free Willy: Prune System Calls to Enhance Software Security

Many privilege escalation exploits on Linux abuse vulnerable system calls to threaten the system's security. Therefore, various static and dynamic analysis based seccomp policy generation frameworks emerged. Yet, they either focus on a subset of the available binaries or are constrained by the inherent properties of dynamic, testing-based analysis, which are prone to false negatives. In this paper, we present Jesse, a static-analysis-based framework for generating seccomp policies for ELF binaries. We design and implement an abstract-interpretation-based constant propagation that helps the analyst identify vital system calls for arbitrary, non-obfuscated binaries. Using the extracted results, Jesse allows producing effective seccomp policies, reducing the system's attack vector. To assess Jesse's effectiveness and accuracy, we have applied our system to over 1,000 ELF binaries for Debian 10, and show that---contrary to existing solutions---Jesse produces accurate and safely approximated results, without relying on any properties of the target binaries. In addition, we conduct a case study in which we combine Jesse's constant propagation strategy with container debloating techniques to produce seccomp policies that restrict up to five times more system calls than the Docker's default seccomp policy on average.
Resource-Aware Scene Text Recognition Using Learned Features, Quantization, and Contour-Based Character Extraction

Scene texts serve as valuable information for humans and autonomous systems to make informed decisions. Processing scene texts poses significant difficulties for computer systems due to several factors, primarily due to variations in image characteristics. These factors make it very challenging for computer systems to accurately detect and interpret scene texts, despite being easily understandable to humans. To address this problem, scene text detection and recognition methods leverage computer vision and/or deep learning methods. Deep learning methods require substantial resources, including computing power, memory, and energy. As such, their use in real-time embedded applications, particularly those that run on integer-only hardware, is very challenging due to the resource-intensive nature of these methods. In this paper, we developed an approach to address this challenge and to showcase its effectiveness, we trained end-to-end models for shipping container number detection and recognition. By doing so, we were able to demonstrate the accuracy and reliability of our proposed method for processing scene texts on integer-only hardware. Our efforts to optimize the models yielded impressive results. We reduced the model size by a factor of 3.8x without significantly affecting the models’ performance. Moreover, the optimized models were 1.6x faster, and the maximum RAM usage was 6.6x lower than the base models. These results demonstrate the efficiency and practicality of our approach for scene text processing on integer-only embedded hardware.
Deep Learning in Medical Imaging

Medical image processing tools play an important role in clinical routine in helping doctors to establish whether a patient has or does not have a certain disease. To validate the diagnosis results, various clinical parameters must be defined. In this context, several algorithms and mathematical tools have been developed in the last two decades to extract accurate information from medical images or signals. Traditionally, the extraction of features using image processing from medical data are time-consuming which requires human interaction and expert validation. The segmentation of medical images, the classification of medical images, and the significance of deep learning-based algorithms in disease detection are all topics covered in this chapter.
Applying Gamification to Prioritize Requirements in Agile Projects

Context: In agile development methodologies, requirements prioritization is a complex collaborative, continuous, and cyclical process. However, it is considered a tedious task. The motivation for stakeholders' engagement, for participating and contributing to the prioritization process, is quite critical. Gamification appears as a possible way to fill this gap, as it is an attempt to enhance activities by promoting similar experiences to those of playing games. Objective: This paper proposes an approach for the use of gamification within the requirements prioritization task, in projects that apply agile methodologies. The proposal aims at promoting stakeholders' engagement and participation in the activity. Method: The PRIUS Gamified proposal was developed, and the PRIUS system was implemented; both integrate gamification elements for prioritizing agile artifacts asynchronously. To evaluate the proposal, a controlled experiment was conducted, using the PRIUS system, in an academic environment. Conclusions: The results suggest that the proposed approach has positive impacts on some aspects of engagement of users in requirements prioritization using Agile software development.
Location-Invariant Radio Frequency Fingerprint for Base Station Recognition

Pseudo base station (BS) is an illegal radio device that exploits the security vulnerabilities of fifth generation (5G) communications and then implements corresponding network attacks, such as spoofing attack. These malicious actions cause security and privacy threats and hinder the wide deployment of wireless access networks. To address these problems, efficient BS identification mechanisms are necessary. Radio frequency fingerprinting (RFF) and channel fingerprinting are potential solutions. Unfortunately, existing solutions are limited to be applicable to scenarios where the locations of pseudo BSs are fixed, while the emerging challenges brought by movable pseudo BSs can hardly be addressed by mature wireless security mechanisms. In this letter, we propose a signal echoing protocol (SEP) to reduce the influence on wireless channels and construct the location-invariant RFF. When BS and user equipment (UE) communicate using SEP, UE can accurately estimate the channel to recognize different BSs. Numerical results demonstrate that the proposed scheme can reach a high classification accuracy for ten BSs, by 95.2% at the signal-noise ratio (SNR) of 25 dB.
Prediction of common labels for universal domain adaptation

Universal domain adaptation (UniDA) is an unsupervised domain adaptation that selectively transfers the knowledge between different domains containing different label sets. However, the existing methods do not predict the common labels of different domains and manually set a threshold to discriminate private samples, so they rely on the target domain to finely select the threshold and ignore the problem of negative transfer. In this paper, to address the above problems, we propose a novel classification model named Prediction of Common Labels (PCL) for UniDA, in which the common labels are predicted by Category Separation via Clustering (CSC). It is noted that we devise a new evaluation metric called category separation accuracy to measure the performance of category separation. To weaken negative transfer, we select source samples by the predicted common labels to fine-tune model for better domain alignment. In the test process, the target samples are discriminated by the predicted common labels and the results of clustering. Experimental results on three widely used benchmark datasets indicate the effectiveness of the proposed method.
On Open Gateway from GSMA – Is It a Revolutionary or Too Little and Too Late Deal?

In this article, we review a new GSMA initiative - Open Gateway. This is a set of programming interfaces for developers and service providers to access the infrastructure of a telecommunications operator. In its initial launch, this initiative includes 8 programming interfaces. They mainly focus on security. There are no software interfaces associated with voice calls at all, for example. The GSMA notes that this is only the beginning, and other APIs may follow. But in the first release, well-defined interfaces were chosen. How to evaluate this implementation? Will there be users at the new gateway? For what types of services (services) can this offer be suitable? Open Gateway did not appear out of nowhere. Telecommunications applications have been successfully developed for a long time without the participation of telecommunications operators. Will the new solution meet the needs of developers, and what may appear in subsequent implementations?
Platform Attestation in Consumer Devices

Platform attestation allows consumer devices to report their security state to relying parties such as cloud services and network gateways. In contrast to more restricted forms of remote attestation, such as key attestation, platform attestation provides more information to the verifier, but is complex to deploy, which has hindered its adoption in the industry. Recently, new approaches such as device health attestation (DHA) have been introduced that simplify the remote attestation process especially from the relying party's perspective. A common denominator in these developments is the use of an external, usually cloud-based verification service that is physically separate from the relying party. The service transforms attestation evidence into a health report - a standard and simplified format that is easier for relying parties to process. In this paper, we survey the state of art in platform attestation in the industry, focusing on Windows DHA, Samsung Knox DHA, Android Play Integrity, Huawei SysIntegrity, and Apple's App integrity and Device Check.
Research and Application of the Adaptive Model of the Human Visual System for Improving the Effectiveness of Objective Video Quality Metrics

Video traffic from content delivery networks occu-pied 82% of all consumed bandwidth in 2022. Nevertheless, the available bandwidth is sometimes volatile and limited. Adaptive video streaming or, in other words, prediction of quality is the key to increasing throughput and reducing storage. Unfortunately, while developing video quality metrics, a problem exists in the algorithmic representation of the human visual system, such as the cognitive component, namely the delay of human reaction to artifacts, which is not represented in the current works. The presented new methodology of data collection of the delay of the human visual system response to video artifacts in modern terms of providing information in natural conditions is presented. New knowledge of the human visual system adaptation or other words time of reaction of perception of artefacts, including the response to motion perceptions necessary for correct work of video quality assessments, is presented and tested. The proposed work introduced that the use of new data on the human visual system adaptation gives an improvement in the performance of video quality assessment metrics.
Comparison of Unigram, HMM, CRF and Brill's Part-of-Speech Taggers Available in NLTK Library

Part-of-speech tagging is for many NLP researchers the first task they encounter in the field of natural language processing. This task is undoubtedly related to part-of-speech taggers. We focus on a detailed description of the functioning of the unigram, hidden Markov model, conditional random fields and Brill taggers, followed by a comparison of these models. We use implementations available in the natural language toolkit library, without addressing the selection of the best parameters. We focus on finding out which tagger produces the best results using default settings or in other words, which one works best in “take it as it is” mode. To determine this, we make an experiment in which we track various metrics such as prediction time, accuracy on unknown words, number of correctly labeled sentences and others. From the results of the experiment, we find out that the CRF tagger achieves the highest accuracy among all participants in the experiment. It is also able to tag previously unseen words with the highest accuracy among all taggers compared.
DeFi Gaming Platform Using the Layer 2 Benefits

Decentralized finance (DeFi) and blockchain-based gaming platforms are rapidly gaining popularity in the digital world. However, the high gas fees associated with on-chain transactions make it difficult to use these platforms for mass adoption. In this paper, we propose a DeFi gaming platform that leverages the benefits of layer 2 solutions to reduce gas fees and improve user experience. Our platform utilizes state channels, where players can interact with each other without the need for on-chain transactions. Our platform's architecture and smart contracts are designed to ensure security, transparency, and fairness. We demonstrate the feasibility and effectiveness of our platform through several test scenarios. In addition, we implement two approaches to validate the game results for different games. Our results show that using a layer 2 solution significantly reduces gas fees, making DeFi gaming accessible to a wider audience. Our proposed platform can potentially revolutionize the gaming industry by providing a seamless, decentralized, and rewarding gaming experience to users.
AMPEL: An Approach for Machine-learning Based Prediction and Evaluation of the Learned Success of Social Media Posts

In this paper, we present AMPEL, a system that assists social media managers in creating successful posts for company pages on social media platforms. The AMPEL workflow classifies posts as either successful or unsuccessful, using Facebook as an example. The system makes a prediction of success for a new post during its creation, prior to its publication, which is a major advantage in comparison to existing systems. Posts that are classified as unsuccessful can be revised by the author until the prediction is successful. The system also evaluates previously published posts for success, allowing for comparison between the predicted success and actual success achieved. The two classification models are built using Random Forest, XGBoost, and neural network-based classification algorithms. The system is evaluated using two separate corpora of posts from different industries. We also demonstrate a prototype of AMPEL and show that it achieves good results with very reliable predictions of success. Our evaluation shows that AMPEL can replace manual review by a human social media manager in many applications.
Deep Interference Recognition for Spread Spectrum Communications using Time-Frequency Transformer

Spread spectrum techniques have been widely adopted in both military and commercial communications because of their anti-interference and anti-interception properties. How-ever, when the interference power is high and the spread spectrum gain is not sufficient to achieve satisfactory system performance, additional anti-interference techniques need to be employed, and the design of the interference sensing and recognition method is a basic prerequisite of effective interference suppression. In this work, considering the superiority of transformer networks in the field of deep learning, we propose a novel interference recognition method with short time Fourier transform (STFT) analysis and transformer. For making full use of the hidden information in both time and frequency domain, the proposed method intro-duces the STFT analysis to extract the high-dimensional feature. Furthermore, the idea of transformer is adopted in our method to introduce the attention mechanism focusing on the time-frequency correlation of the received signals and improve the recognition accuracy. Simulation results demonstrate the superiority of the proposed method versus other baseline competitors.
Automatic modulation recognition of communication signal based on wavelet transform combined with singular value and NCA-CNN

In communication signal recognition, there are problems such as a tedious feature extraction process and low applicability of extracted features. This paper simulates wireless communication channels and suggests an algorithm that uses nearest neighbor component analysis (NCA) along with convolutional neural networks (CNN) for classification. The algorithm chooses wavelet entropy (WE), wavelet approximate energy ratio (WAER), and the first 2–4 singular values as the core features. Eight different forms of modulations, including GFSK, CPFSK, B-FM, DSB-AM, SSB-AM, BPSK, QPSK and PAM4 would be automatically classified using the technique. According to the experiment results, the average recognition accuracy for the eight signals is 93.6% when the signal-to-noise ratio is 30dB. In addition, this paper also discusses the results and accuracy of the model to identify 6 and 10 types of signal modulation and studies the accuracy of the recognition under different signal-to-noise ratios, verifying the robustness of the model.
Survey on Different Techniques of Medical Image Watermarking to Ascertain Authenticity and Ownership

The aim of medical image watermarking is to preserve the integrity of a medical image after embedding the required data. With the evolution of the internet in almost every domain of life, telemedicine is a word that has gained popularity in the last four to five years. This article provides an overview of the research that are carried out on medical image watermarking techniques in the last several years. To carry out this survey, research papers on different techniques for medical image watermarking have been studied from Google Scholar, ACM, IEEE, etc. 89% of the data is considered from reputed journals, and the rest of the papers are from different conferences, and book chapters. This article describes the comparison between various techniques used for medical image watermarking. It helps out in identifying the gaps between the different techniques so that a naive approach can be designed to get better results.
Health Management System Based on Blockchain Technology

Medical institution and other surgical facilities are typically the only cognition that proctor patients' vital signs, which makes the activity time-consuming and overpriced. Rapid improvement in information and communication technology existing many chance for the initiation of remote monitoring systems that decrease costs and travel time while boosting patient emotional state and medical effective. This artifact goal is to declare a system for closely watching a patient's essential signs so that doctors can do so wherever they are. Integration of technology enhances patient data access and enhances patient-doctor communication.
Image Generation Based on Text Using BERT And GAN Model

One of the most challenging and important problems in deep learning is creating visuals using a text description. The sub-domain of text-to-image generation is text- to-face image generation. The end objective is to deliver the image utilizing the client-determined face portrayal. Our proposed paradigm includes both images and text. There are two phases to the proposed work. The conversion of the text into semantic features is demonstrated in the first phase. These semantic features have been used in the second phase to train the image decoder to produce accurate natural images. Creating an image based on a written description is more applicable to public safety responsibilities. The fully trained GAN that has been proposed outperformed by producing high-quality images from the input phrase.
Devnagari Character Recognition using Optical Character Recognition (OCR)

In the age of digital applications, optical character recognition (OCR) is a useful tool. There are many different languages and scripts used around the world, with more than 300 million individuals in India speak Hindi as their first language. OCR previously created for the Devnagari script has a very low recognition rate. In this paper, we propose an OCR system based on a Convolutional Neural Network (CNN) model. It increases the handwritten data's efficiency or accuracy by 98.32%. We do several steps for getting this efficiency like preprocessing of data, grayscale images, binarization, reshaping, normalization, modelling, and Evaluation of the data with the help of plotting.
Cost Identification for Deploying website on AWS

In this present competitive business environment, most of the organisations and developers are moving their web applications towards cloud- based operating models to achieve maximum cost optimization. There are many commercial cloud providers available in the present times. But the main limitation which hinders the users is that they find it very confusing and difficult to choose the correct set of resources and services according to their business requirements to achieve maximum cost efficiency. To solve this problem, we propose a cost predictive model that’ll predict the cost of Hosting a website based on the selected resources and services. It will also help the user to choose the appropriate resources and services according to their needs that should be considered to achieve cost efficiency.
Intelligent Face Emotion Detection Application for Blind People

Facial expressions are recognized by the various quality of the human face such as postures, emotions, and orientation of the face. As many people around us are visually impaired and suffer problems in detecting emotions of the human facial expressions. In this paper, we are implementing an application on the identification of facial expressions for blind people which helps visually impaired people to recognize facial expressions with a different sound on different expressions such as happy, anger, sad, disgust, fear, neutral, surprise, etc. Technologies such as image processing and computer vision are used in this application. The application of identification of facial expressions uses the camera to capture the expressions of the human face. It also detects the movements of eyes, nose, lips, etc., and analyzes by comparing with the trained data using classifiers to recognize the facial expressions of the human face and by using algorithms of machine learning, it converts expressions into sound based on different expressions so that blind people can recognize expressions of the human face.