Reviewerly/exHarmony huggingface dataset:
Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning

To be able to train a model in a way where access to datasets remains private, Federated Learning (FL) was proposed as a machine learning technique to address the privacy challenges by enabling the model to be trained across nodes that hold their datasets locally. Moreover, blockchain-based FL was recently proposed as a distributed FL architecture to achieve decentralized FL. In this setting, the chief that was responsible of the aggregation in FL is eliminated from the learning process. Instead, the workers collaborate amongst themselves to train the global model. This decentralization incurs additional delays and FL-based applications relying on it need to account for it in their deployments. In this work, we study the end-to-end learning completion latency particularly when this system is under attack. Blockchain-based FL systems need to detect targeted/untargeted poisoning attacks, thus, we investigate a realistic decentralized FL process protected against them. The technique that we propose consists in decoupling the mechanisms used in the defenses proposed against these attacks. The operations of the monitoring and detection phases to assess the behavior of the workers are conducted in parallel for an improved decentralized FL deployment. We demonstrate that our technique improved time efficiency, robustness and network scalability. Parallelizing the operations resulted in a decrease in latency over the communication network and a decrease in computation, and consensus delays during the blockchain and FL process.
A Study of Risk Management Frameworks and Security Testing For Secure Software Systems

The information system is used for data processing to accomplish organizations' missions and business functions. However, there have been significant issues with system security in recent years due to various vulnerabilities and malicious code. Various security measures have been availed to detect the vulnerabilities and risks of the software systems to ensure their security. Since most defects of the software systems occur during the development stage, measures to address the issue should support the detection of risks and security threats. As a result, this study investigates risk management frameworks and security testing tools to ensure software systems' security. This study shows that risk management frameworks, such as mobile security and security testing, effectively detect potential risks and defects to secure software systems. The control over the activities involved in the development of software systems helps to ensure secure software. Recommendations suggest taking into account the behaviors of programmers to enhance the security of tools used.
Image Translation of Bangla and English Sign Language to Written Language using Convolutional Neural Network

One particular thing that differentiates humans from other species is their abilities to interact. to communicate with others, humans invented languages as units. There are 6500 languages in this world and English has been established as a global language. However, there are a ton of physically disabled human beings who are deprived of expressing their emotions through verbal language. Therefore, sign language has been discovered by expressing feelings with the help of signs which is mainly done by moving body parts: hands in particular. Although, it is so rare to find a research where both Bangladeshi Sign Language (BdSL) and American Sign Language (ASL) is translated, previously some of the prominent researchers worked on primary or secondary ASL and BdSL datasets separately and obtained high accuracy (> 97%) based on their algorithmic approaches. The executed system focuses on implementing both of the aforementioned sign languages combinedly as well as working on both the primary and secondary datasets using single algorithmic approach in order to resolve the two way communication and a better understanding of communicating through sign language. In this thesis, the advantages of real world pictures of Bangladeshi Sign Languages will be used to run an algorithm which will convert sign language to written language using Sequential Convolutional Neural Network method. The system will be able to detect both BdSL and ASL regarding any background with the accuracy of 95.23% and 98.45% respectively.
Modeling of Photovoltaic-Wind Hybrid Systems

This article proposes a model for photovoltaic-wind hybrid systems. The system under investigation comprises five PV modules attached to the blades of a vertical-axis wind turbine. The solar irradiation readings at each PV module are monitored at three different tilt angles: 90°(default), 50° <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">,</sup> and 10°. These measurements are taken on many selected days at random times around noon. This paper calculates the solar irradiation received at each PV module and estimates the optimum tilt angle of the system. A simple clear sky model is used for the calculation, the input data of the model is the solar geometry, including location latitude, solar azimuth angle and solar elevation angle, no aerosol or atmospheric data is necessary. A comparison between measurement and calculated data is conducted, and the result shows that the optimum tilt angle is 50°. The mean absolute percentage error is computed between the measured and calculated values and shows that it ranges between 11% and 17% at a 90° tilt angle, and between 5% and 17.4% at a 50° tilt angle.
PV/IoT based Smart Traffic Management System

Slower speeds, longer travel times, and increased vehicle queueing are all signs of traffic congestion on roads. A tie-up occurs when the number of vehicles on the road exceeds the capacity of the road. Within the urban cities of Egypt, the holdup is also a serious problem, which is caused when demand exceeds the available road capacity. Building highways and other physical infrastructure isn't the only way to control traffic flow. It's more of a combination of such appropriate infrastructure and the extraction of new ways of thinking and the application of new technologies. As known, the problems of traffic congestion in Egypt lead to dangerous accidents, slower traffic, high fuel consumption while idling and waiting, and without giving enough space for the passage of emergency vehicles such as ambulances and firefighters. The paper aims to minimize traffic congestion; in addition, it properly calculates fines using an image processing system, and it discovers a new way to connect drivers and the traffic system using IoT technology.
The use of computer vision and data mining in obtaining subconscious user experience

In general, whenever a computer processes visual input, such for example a photo, video, or live stream from camera on a smartphone, it uses computer vision in doing so. Simply said, computer vision is used precisely to recognize and process the inputs. This system allows the scanner of a barcode to “read” a cluster of lines and convert it into a sequence of numbers. Likewise, when the smartphone can detect if it is the phone user's face through a facial scan. Similar applications can also be found in industry, in autonomous vehicles, where it is used to monitor driving lanes or passing vehicles, or in obtaining implicit feedback in marketing research when evaluating user experience in digital environments, for example. This is the area that User Experience (UX) and User Interface (UI) designers are dealing with. Their task is to design a sufficiently intuitive and interesting solution that will be available no matter what device is used (computer/smartphone) and at the same time the customers / visitors can easily reach their goal or get the information they need in just a couple of clicks. The foundations for UX design changes can now be gathered not only through online analytical tools, but especially by using neuromarketing tools that use advanced computer vision and data mining systems. Our paper highlights new possibilities for testing the design of UX digital interfaces using a stationary eye camera (Eye Tracking) and face reading (Facial coding).
Multi-layer segmentation solution to filter the noise of optical flow vectors to assist robots in object recognition inside buildings

In this paper, a qualitative optical flow processing solution is proposed to filter out the noisy vectors. The qualitative processing solution is performed based on the results of the multi-layered segmentations of the optical flow vectors before calculating a filter threshold related to the mean/average of the vectors in the segmented optical flow image area (called quarter). The advantage of the proposed solution is that the filter threshold is calculated using simple arithmetic operations and its value varies according to different noise quarters. Experimental results in an indoor environment will demonstrate that the ability to recognize objects based on computer vision after filtering optical flow noise helps the robot move safely to a desired location.
Temporal Attention for Monocular Depth Estimation in Aerial Scenarios

Monocular depth estimation (MDE) is a key task for a large set of computer vision applications, convolutional neural networks (CNNs) being nowadays employed for this task. The objective of measuring the world from a single image is cumbersome, especially in case of highly complex scenarios where there is a lack in scene structure. State of the art deep learning-based methods cope with this problem by employing very powerful feature extractors, mixed with additional scene priors such as geometrical or semantic information. The usage of such approaches generally leads to high amounts of resources, computations which make the system incapable for real-time processing. In this work we propose a novel method that tries to account for the time constraints while providing accurate depth maps from a monocular system. Thus, instead of providing geometric or semantic priors which need complex additional processing (generally an additional CNN), we aid the depth estimation process with features extracted and preserved from previous frames. To this end, we propose a novel temporal attention sub-network, that properly extracts the aforementioned features and it combines them with the last available depth map. This sub-network is then inserted into a novel CNN architecture, that proves to generate better depth maps. We test the efficiency of our method on aerial images and obtain an improved accuracy while keeping the amount of resources as low as possible.
Comparison of Transformer Models for Information Extraction from Court Room Records in Pakistan

The legal domain has many opportunities when it comes to improvement and innovation through computational advancements. In Pakistan, as the number of reported judgments continues to grow at a rapid rate, it has become essential to process this massive chunk of data to better meet the requirements of the respective stakeholders. However, extracting the required information from this unstructured legal text is challenging. In this paper, we have compared different variations of BERT to see which would be more suited for a machine learning system that can automatically extract information from these publicly available judgments of the Supreme Court of Pakistan. A labelled dataset comprising of thirteen entities has been created using the publicly available legal judgments from the Supreme Court. Different pre-trained BERT models, namely BERT <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BASE</inf> -uncased, BERT <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BASE</inf> -cased and LegalBERT, are then further trained and fine-tuned on the created dataset for Named Entity with F1 scores of 92.47%, 94.72% and 92.5 % respectively. The BERT models have been found to improve the F1 scores of previous studies on a dataset available from Lahore High Court, having smaller number of labels, with the F1 scores of 82.3%, 93.21% and 85.06%, respectively.
Hireblock: Hyperledger-based Human Resource Recruitment System

Human Resource (HR) plays a vital role in the development of the organization. Existing solutions such as LinkedIn, Indeed, Moster J ob.com, Internshala, Angel List provides access to great list of candidates. The existing employment-oriented online solutions are following content based filtering method by allowing user to find the right person for the right job and vice-versa. These solutions though automate a large base of talent acquisition process, they do not guarantee candidate and document validation. Lacking of document verification feature leads to many forgery cases. Online identity theft is a huge problem for all online businesses. Document verification is an integral tool in the verification of a customer's identity. Further, the existing career and recruitment services are suffering from data clogging issue. Data clogging force the organization to get rid of unused set of data thereby introducing the problem of data breaches, compromise of privacy. All the above-mentioned problems leads to a never ending tedious cycle of relying on third party vendors/software to resolve various problems. To address aforementioned issues this paper propose a novel approach of creating a Hyperledger-based Human Resource Recruitment Pool - Hireblock. The proposed recruitment model provides document verification, reliability, decentralization, security, and scalability. We have implemented proposed Hyperledger based Hireblock model.
Light-Weight Face Mask Detector

People’s lives have been severely disrupted recently due to the COVID-19 outbreak’s fast worldwide proliferation and transmission. An option for controlling the epidemic is to make individuals wear face masks in public. For such regulation, automatic and effective face detection systems are required. A facial mask recognition model for real-time video-recorded streaming is provided in this research, which categorizes the pictures as (with mask) or (without mask). A dataset from Kaggle was used to develop and assess the model. The suggested system is computationally more precise, efficient and lightweight when compared to other systems like VGG-16, DenseNet-121, and Inception-V3 which helped the developed model meet low end PC system requirements. The collected data set contains exactly 12,000 images and has a 98.1% performance training accuracy and a validation accuracy of 98.2%, which is achieved by using MobileNetV2.
Credit-card Fraud Detection System using Big Data Analytics

Due to the rapid emergence and evolution of online transactions, credit cards have become the most popular payment method. Credit card fraud involves using fake credit cards to purchase goods without paying. On the other hand, researchers have proposed a wide range of anti-fraud systems with quick research and development around information technology and data mining including neural networks and decision trees, to advanced machine learning and deep learning methods. This paper presents a framework that combines the capabilities of Apache Spark and machine learning to analyze and monitor a large amount of data. It allows users to perform credit-card fraud detection and improve the classification performance on a real credit-card dataset. The obtained results showed an enhancement in the classification accuracy over the existing results.
Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations

Explainable Artificial Intelligence (XAI) has recently gained a swell of interest, as many Artificial Intelligence (AI) practitioners and developers are compelled to rationalize how such AI-based systems work.Decades back, most XAI systems were developed as knowledge-based or expert systems.These systems assumed reasoning for the technical description of an explanation, with little regard for the user's cognitive capabilities.The emphasis of XAI research appears to have turned to a more pragmatic explanation approach for better understanding.An extensive area where cognitive science research may substantially influence XAI advancements is evaluating user knowledge and feedback, which are essential for XAI system evaluation.To this end, we propose a framework to experiment with generating and evaluating the explanations on the grounds of different cognitive levels of understanding.In this regard, we adopt Bloom's taxonomy, a widely accepted model for assessing the users' cognitive capability.We utilize the counterfactual explanations as an explanation-providing medium encompassed with user feedback to validate the levels of understanding about the explanation at each cognitive level and improvise the explanation generation methods accordingly.
Urdu News Classification: An Empirical Study Using Machine Learning Techniques

Text is a rich source of information and there is unlimited text on the internet. Automatic text classification is a technique to label those text documents with predefined categories. This has various applications including sentiment analysis, spam detection, NLP etc. There is much work done on english text classification but there is a huge gap with Urdu. There isn't any standard algorithm known that outperforms all others. Also it is observed that classifiers usually perform better when the text is preprocessed, but there aren't any standard stemmer, stop word list, tokenizer etc. available for urdu text. Urdu is rich morphologically and it's a challenge to design preprocessing tools for urdu. This research tends to reduce the gap by testing different classification algorithms using different dimensionality reduction combinations on urdu news data set to know which performs better. It also includes designing a stemmer, tokenizer and preparing a stop word list. In this research it was concluded that SVM performed better with the combination of both preprocessing techniques. Fasttext library was also tested for urdu text classification which achieved 95%accuracy and f-score 1 %less than SVM. Another approach used is that topic modeling has been performed using LDA and documents have been weighed as topics. Classification using documents as topics didn't perform well but Random Forest performed better than Naive Bayes and SVM. It's in future work to design a POS tagger that may improve performance of stemmer and to test deep learning methods for urdu text classification.
Mask R-CNN Based Real Time near Drowning Person Detection System in Swimming Pools

To find the drowning person in time in swimming pool to reduce the drowning person mortality rate. We used the Mask R-CNN algorithm, and optimizing the convolution backbone of the traditional Mask R-CNN algorithm by adding features of cascaded with pyramid model to design a swimmer drowning detection system. Through real-time recognition of the posture of swimmers in the swimming pool, it can determine the drowning person and alert in time. The system's proposed algorithm has been put to the test on multiple real-world video sequences taken in swimming pools, and the findings show that it is very accurate and capable of monitoring people in real time. The experimental results show that the detection speed of the system is 6 FPS, while the detection rate is 94.1 %, while the false detection rate is 5.9%. The effect is good, which satisfying the anticipated requirements.
Multi-Network Blood Cell Classification System Based on Decision Fusion

The classification and identification of blood cells from microscopic images is very important for the detection of serious diseases. To increase the efficiency of the identification and counting of cells from 8 classes such as Basophils, Eosinophils, Erythroblasts, Immature Granulocytes, Lymphocytes, Monocytes, Neutrophils, and Thrombocytes, a special advance was taken using artificial intelligence for image analysis. This article proposes a collective intelligence decision system based on the fusion of individual decisions of four efficient convolutional neural networks, VGG 16, Xception, ResNet-50, and NasNetLarge. The fusion is based on the efficiency of each network's performance in recognizing (correctly classifying) the membership class of the analyzed cell. In this sense, a weight is established for each network associated with a certain class. The global system has better performances than each individual network and is better or comparable to those reported in recent works.
Abnormal Wedge Bond Detection Using Convolutional Autoencoders in Industrial Vision Systems

Anomaly detection using machine vision images is of utmost importance for mission-critical tasks in industrial applications. Convolutional autoencoders have been widely adopted for unsupervised defect identification on vision data. This type of deep learning vision model is able to identify the anomaly pattern of target objects/areas without incurring the heavy workload on annotation/labelling. This paper investigates the challenging problem of anomaly detection in the quality control of wedge bond production. We report the progress of using SSIM (Structural Similarity)-based autoencoder in the abnormal wedge bond detection. The main idea is to leverage the advantages of both SSIM and autoencoder on identifying the abnormal features of wedge bond. SSIM analyzes the image difference in terms of luminance, contrast and structure. An autoencoder is trained to identify such visual differences in the prediction images. We conduct extensive experiments using images captured from real wedge production line for the anomaly detection. The evaluation results demonstrate the effectiveness and accuracy of the SSIM-based autoencoder.
Ensemble Learning for Sentiment Analysis of Translation-Based Textual Data

Ensemble learning is a technique that combines several learners to generate a model characterized by more generalized predictions than the constituent learners. Despite the number of conducted studies about ensemble learning of sentiment analysis and the ones that studied the impact of translation on sentiment analysis, the studies that consider ensemble learning on translated text are limited. Here different techniques of ensemble learning such as bagging, boosting, and stacking were applied to classify an English dataset that was translated to modern standard Arabic, which in turn translated manually to Bahraini dialects. Interestingly, this study revealed the outperformance of stacking ensemble based on LSTM as base-learners and decision tree as meta-learner over the other ensemble techniques by achieving 99.52%, 99.25%, and 98.52% of mean accuracy in English, modern standard Arabic, and Bahraini dialects, respectively.