Reviewerly/exHarmony huggingface dataset:
A Framework for Multi-Task Learning in Dynamic Adaptive Streaming Over HTTP

This paper presents a framework with a taxonomy for multi-task learning in the context of Dynamic Adaptive Streaming over HTTP (DASH). DASH is a widely used technology for video streaming, and multi-task learning has emerged as a promising approach to enhance the performance and user experience of DASH systems by jointly optimizing multiple related tasks. The framework provides a structured approach to design, train, and evaluate multi-task learning models in DASH, while the taxonomy categorizes the key components and approaches within the framework. The taxonomy includes task types, multitask learning approaches, input features, and training strategies. Task types encompass video quality adaptation, buffer management, bandwidth estimation, content pre-fetching, and resource allocation, representing the specific tasks involved in DASH. Multi-task learning approaches encompass methodologies such as shared representation learning, task-specific layers, multi-head architectures, knowledge distillation, and reinforcement learning, offering flexibility in model design and optimization. Input features cover video characteristics, network conditions, device capabilities, and user preferences, providing the necessary information for informed decision-making across tasks. Training strategies include joint training, alternate training, hierarchical training, task weighting, and task balancing, determining how the multi-task learning model is trained and optimized. By following the presented framework and taxonomy, researchers and practitioners can systematically approach the design, training, and evaluation of multi-task learning models in DASH. The framework enables the development of efficient and adaptive video streaming systems by leveraging the interdependencies among tasks. The taxonomy helps organize the components and approaches within the framework, aiding in a better understanding of the various aspects of multi-task learning in the DASH context. Overall, this framework and taxonomy provide a valuable resource for advancing the field of multitask learning in the dynamic and complex domain of video streaming over HTTP.
Untangle: Aiding Global Function Pointer Hijacking for Post-CET Binary Exploitation

In this paper, we combine static code analysis and symbolic execution to bypass Intel’s Control-Flow Enforcement Technology (CET) by exploiting function pointer hijacking. We present Untangle, an open-source tool that implements and automates the discovery of global function pointers in exported library functions and their call sites. Then, it determines the constraints that need to be satisfied to reach those pointers. Our approach manages naive built-in types and complex parameters like structure pointers. We demonstrate the effectiveness of Untangle on 8 of the most used open source C libraries, identifying 57 unique global function pointers, reachable through 1488 different exported functions. Untangle can find and verify the correctness of the constraints for 484 global function pointer calls, which can be used as attack vectors for control-flow hijacking. Finally, we discuss current and future defense mechanisms against control-flow hijacking using global function pointers.
The Constraints Encountered during Interpretation of Idioms in Witimbule Programme, Radio Mambo Broadcast in Western Kenya

The use of idioms in conversation among the Luhya speaking communities, especially during Witimbule Programme that is aired in Radio Mambo broadcast in Western Kenya, is common. Hardly does a social ceremony end without application of idioms among the Luhya people. The fact that they are interesting to use during the speeches, there are challenges that arise and if not resolved, they can cause misinterpretation, hence miscommunication. This study employs relevance theory by Wilson and Sperber (1995 /2002) which highlights that when the information is complex, it stands the risk of being misinterpreted or rejected by the mind. The study found that the hindrances to interpretation of idioms include deficiency in cognitive environment knowledge, dialectical constraints due to lexical mismatch among the Luhya speaking dialects ,lack of context ,ambiguity of idioms, lack of subject part of the idiom and lack of native speakers intuition .The results reveal that deficiency in cognitive environment knowledge was the main cause of difficulties encountered during interpretation of idioms in conversation among the Luhya speaking dialects. Dialectical variation contributed insignificantly to misinterpretation of idioms. From this assumption, the best strategy of interpreting idioms is the knowledge of cognitive environment approach besides others.
Bidirectional Long-Short Term Memory with Byte Pair Encoding and Back Translation for Bangla-English Machine Translation

Machine Translation (MT) has emerged as an important research area in the computational intelligence domain to translate huge online resources cost-effectively. Among different MT approaches, Neural based Machine Translation (NMT) is in widespread use nowadays. In NMT, Bidirectional Long-Short Term Memory (BiLSTM) is popular due to its higher accuracy and improved performance. This study investigates different performance improvements with standard BiLSTM for Bangla-English MT (BEMT). Attention (ATT) mechanism, Byte Pair Encoding (BPE) and Back Translation (BT) are integrated independently and combinedly through a systematic approach. Four different models such as BiLSTM+ATT, BiLSTM+ATT+BPE, BiLSTM+ATT+BT and BiLSTM+ATT+BPE+BT have been tested rigorously on a benchmark corpus. The effectiveness of ATT, BPE and BT on a BiLSTM model are identified by comparing the performance of the individual models. Finally, the integrated model of BPE and BT with BiLSTM showed the best generalization ability and outperformed other models.
Classification of Tumor Cell Using a Naive Convolutional Neural Network Model

Early detection of tumor tissue leading to cancer is the most burning health issue of the present world due to an increase of radiation, ultraviolet light, radon gas, infectious agents etc. To diagnose the tumor cell promptly nowadays computer-aided detection (CAD) systems using a convolutional neural network (CNN) draws a significant role in the health sector. Many complicated CNN model has been introduced to effectively classify tumor cell but in this study, we proposed a relatively less complex deep learning approach that is as effective and reliable as a renowned pre-trained models such as VGG19, Inception-v3, Resnet-50 and DenseNet-201. Our proposed architecture can perfectly classify the tumor cell based on the PatchCamelyon (PCam) dataset with appeasement validation accuracy 94.70% using less computational parameters, comparatively mentioned pre-trained model.
Trimming the Fat: An Insightful Exploration of Feature Selection and Dimensionality Reduction

Feature selection and dimensionality reduction are crucial techniques in the field of data analysis and machine learning. They aim to identify and retain the most informative and relevant features while discarding redundant or noisy ones. This short review delves into the concepts, methods, and benefits of feature selection and dimensionality reduction. It explores various approaches, such as filter, wrapper, and embedded methods, as well as popular dimensionality reduction techniques like Principal Component Analysis (PCA) and t-SNE. The review highlights the importance of these techniques in enhancing model performance, reducing computational complexity, and improving interpretability. By summarizing the key insights and challenges associated with feature selection and dimensionality reduction, this review aims to provide a comprehensive overview and serve as a foundation for further exploration in this field.
Adversary decision-making using Markov models

This study conducts three experiments on adversary decision-making modeled as a graph. Each experiment has the overall goal to understand how to exploit an adversary’s decision-making in order to obtain desired outcomes, as well as specific goals unique to each experiment. The first experiment models adversary decision-making using an Absorbing Markov chain (AMC). A sensitivity analysis of states (nodes in the graph) and actions (edges in the graph) is conducted which informs how downstream adversary decisions could be manipulated. The next experiment uses a Markov decision process (MDP). Assuming the adversary is initially blind to the rewards they will receive when they take an action, a Q´learning algorithm is used to determine the sequence of actions that maximizes the adversary rewards (called an optimum policy). This experiment gives insight in the possible decision-making of an adversary. Lastly, in the third experiment a two-player Markov game is developed, played by an agent (friend) and the adversary (foe). The agents goal is to decrease the overall rewards the adversary receives when it follows optimum policy. All experiments are demonstrated using specific examples.
Expert System for Diagnosis Coronavirus Disease

The latest issue of the disease called COVID-19 has become famous all over the world. Hence, through this problem, it found that this disease have the same symptom with other diseases such as Influenza and also normal flu. Detecting diseases at early stage can enable to overcome and treat them appropriately. This is because many of peoples does not know and does not aware of the symptom of this various diseases. In an effort to address those problems, an Expert System for Corona Earlier Detection has been proposed to help the doctors to detect those various diseases in human body. Through this research, the researcher will held an interview with the doctors to collect data and information about those diseases’ symptoms and also search for the related articles to make sure this research going successfully. The method that will be used in this research is Certainty Factor. To conclude, this system will be useful to the healthcare department as it will give earlier detection when the patient are positively exposed to the disease that is known by Corona.
The Utilization of Information System for Crime Rate Modelling in Surabaya Using K-means

This study aims to model the crime rate in the city of Surabaya using the k-means clustering method. The data used is crime data that occurred in Surabaya in previous years, which includes the type of crime, location of crime, and crime rate. The k-means clustering method is used to classify crime data in the Surabaya area for 2020-2022 consisting of cluster 3, namely areas with moderate crime rates covering 6 sub-districts (1,260 cases), cluster 1 with areas with high crime rates, namely 12 sub-districts with 2,363 cases, and cluster 2 areas with low crime rates consisting of 13 districts with 2,178 cases based on data on the number of crimes. The geospatial visualization system is used to visually display modeling results, making it easier for interested parties to identify the location of a crime. The results of this study are expected to provide useful information for interested parties, such as the police and the community, in taking preventive action regarding crime rates in Surabaya.
Diagnosing AI Explanation Methods with Folk Concepts of Behavior

We investigate a formalism for the conditions of a successful explanation of AI. We consider “success” to depend not only on what information the explanation contains, but also on what information the human explainee understands from it. Theory of mind literature discusses the folk concepts that humans use to understand and generalize behavior. We posit that folk concepts of behavior provide us with a “language” that humans understand behavior with. We use these folk concepts as a framework of social attribution by the human explainee—the information constructs that humans are likely to comprehend from explanations—by introducing a blueprint for an explanatory narrative that explains AI behavior with these constructs. We then demonstrate that many XAI methods today can be mapped to folk concepts of behavior in a qualitative evaluation. This allows us to uncover their failure modes that prevent current methods from explaining successfully—i.e., the information constructs that are missing for any given XAI method, and whose inclusion can decrease the likelihood of misunderstanding AI behavior.
A Comparative Study on Deep Learning and Machine Learning Models for Human Action Recognition in Aerial Videos

Unmanned Aerial Vehicle )UAV( finds its significant application in video surveillance due to its low cost, high portability and fast-mobility. In this paper, the proposed approach focuses on recognizing the human activity in aerial video sequences through various keypoints detected on the human body via OpenPose. The detected keypoints are passed onto machine learning and deep learning classifiers for classifying the human actions. Experimental results demonstrate that multilayer perceptron and SVM outperformed all the other classifiers by reporting an accuracy of 87.80% and 87.77% respectively whereas LSTM did not produce very good results as compared to other classifiers. Stacked Long Short-Term Memory networks (LSTM( produced an accuracy of 71.30% and Bidirectional LSTM yielded an accuracy of 76.04%. The results also indicate that machine learning models performed better than deep learning models. The major reason for this finding is the lesser availability of data and the deep learning models being data hungry models require a large amount of data to work upon. The paper also analyses the failure cases of OpenPose by testing the system on aerial videos captured by a drone flying at a higher altitude. This work provides a baseline for validating machine learning classifiers and deep learning classifiers against recognition of human action from aerial videos.
Evaluating the efficacy of different adversarial training strategies

Adversarial training (AT) is considered the most effective strategy to defend a machine learning model against adversarial attacks. There are many different methods to perform AT, but the underlying principle is the same, namely, augment the training data with adversarial examples. In this work, we investigate the efficacy of four different adversarial example generation strategies on AT of a given classification model. The four methods represent different categories of attack and data. Specifically, two of the adversarial generation algorithms perform attacks in the pixel domain, while others operate in the latent space of the data. On the other hand, two of the methods generate adversarial data samples designed to be near the model decision boundaries, while the other two generate generic adversarial examples (not necessarily at the boundary). The adversarial examples from these methods are used to adversarially train models on MNIST and CIFAR10. In the absence of a good metric to measure robustness of a model, capturing the effect of AT using a single metric can be a challenge. Hence, we opt to evaluate the robustness improvements resulting of the adversarially trained model using a variety of empirical metrics introduced in the literature that measure local Lipshitz value of a network (CLEVER), smoothness of decision boundaries, robustness to adversarial perturbations and defense transferability
Questions Boards Should Ask

Boards may ask a set of non-technical yet probing questions to ascertain the maturity level of the way an organization understands and manages cybersecurity risk.
Conclusion

Most organizations rely on imperfect information technology (IT) to enable business and operational functions. These imperfections expose attractive perforations for some unauthorized users. Understanding what these users are after and what is critical to the business is vital for getting a handle on cybersecurity risk management. With that handle, a management approach may be put into place and supported by proper measures that quantify uncertainty as uncertainty changes. These basic foundational components help focus attention and resources for organizational management: the ability to understand, manage, and properly measure cybersecurity risk.
Perlocutionary Acts in the movie "Dead Poets Society"

This study aims to analyze the perlocutionary acts used by the main character of Dead Poet Society, Mr. Keating, towards his students. The study employed the qualitative approach, examining the data from Mr. Keating’s words and actions. The study then analyzed the data by classifying the speech acts, on the aspect of perlocutionary acts, and also comparing the speech acts to the actions and the values in the movie Dead Poets Society. The findings of the study revealed that Mr. Keating frequently used the perlocutionary acts to inspire and motivate the students. Therefore, he used a variety of teaching methods in his class in order to continuously encourage and motivate his students to work toward their aspirations.
An Exact Quantum Annealing-Driven Branch and Bound Algorithm for Maximizing the Total Weighted Number of on-Time Jobs on a Single Machine

This paper considers the problem of maximizing the total weighted number of on-time jobs on a single machine. Using the problem as a case study, we present a new approach to solving NP-hard discrete optimization problems using D-Wave’s QPU quantum processor architecture implementing quantum annealing. Although optimization on a quantum machine does not guarantee optimality, the hybrid method of construction of the partitioning and constraint algorithm proposed in this paper, using together CPU and QPU as well as Lagrange relaxation for upper bounds determination, makes it possible to determine the exact, optimal solution.
Machine Learning Approach for Detection of Phishing Website

Abstract: Phishing attacks are the easiest way to get sensitive information from innocent users. The purpose of phishing is to obtain critical information like the private information of users. Phishing sites pretend to be relevant sites and it is more difficult to distinguish these sites. It is one of the greatest threats that every individual and management has ever faced. URLs are referred to as web pages through which users locate pieces of information on the Internet. This paper focuses on phishing website detection which is important for the prevention of our sensitive information. There are different types of websites with different features. Therefore, we need to use a specific set of features on websites to protect against phishing. A machine learning model has been proposed to detect phishing websites. To detect phishing sites, we proposed the machine learning model. The essential objective of this paper is to classify the various algorithm of machine learning by extracting and analyzing various features of legitimate and phishing URLs.
Few-Shot Multi-Agent Perception with Ranking-Based Feature Learning

In this article, we focus on performing few-shot learning (FSL) under multi-agent scenarios in which participating agents only have scarce labeled data and need to collaborate to predict labels of query observations. We aim at designing a coordination and learning framework in which multiple agents, such as drones and robots, can collectively perceive the environment accurately and efficiently under limited communication and computation conditions. We propose a metric-based multi-agent FSL framework which has three main components: an efficient communication mechanism that propagates compact and fine-grained query feature maps from query agents to support agents; an asymmetric attention mechanism that computes region-level attention weights between query and support feature maps; and a metric-learning module which calculates the image-level relevance between query and support data fast and accurately. Furthermore, we propose a specially designed ranking-based feature learning module, which can fully utilize the order information of training data by maximizing the inter-class distance, while minimizing the intra-class distance explicitly. We perform extensive numerical studies and demonstrate that our approach can achieve significantly improved accuracy in visual and acoustic perception tasks such as face identification, semantic segmentation, and sound genre recognition, consistently outperforming the state-of-the-art baselines by 5%-20%.
A Society that Counts to Two Again

The place of research object will be taken by the digital society and man belonging to this era. Between the researcher and the object under study (digital society, modern man) there will be a prism through which it will be possible to consider the problems of interest to us and we will be able to discern the prism of research better. This position of the observer, as well as the means and methods of observation, will help us look differently at the object of our interest: changes in a society driven by digitalization, the role of numbers in modern society, and the updated properties of a digit.
A Healthcare System for Cardiac Disease Diagnosis Using Distributed Computing

The sector of health care makes extensive use of cloud computing and wireless devices that are based on the internet of things. These kinds of wireless and wearable sensors are being used to monitor patients who are at risk for developing chronic diseases. This not only shields patients from attacks that come out of the blue but also makes it easier for them to monitor their treatment regimen. However, the quantity of data acquired from numerous sensors is substantial, and if the data is kept in a centralised location, there is a possibility that the power supply will fail. As a result, in this paper, we suggest the creation of a technology called Smart Fog, which is capable of edge-fog computing and would be able to detect and diagnose heart patients at an early stage. It is anticipated that the overall efficacy of the system would improve as a result of the implementation of the suggested model, which is founded on the concepts of distributed computing and deep learning. With the application of the distributed computing approach, the risk associated with a single point of failure would be eliminated, and in addition, a less amount of data would be required to transfer the information between the cloud and the sensors.
Investigation of Cryptography for Secure Communication and Data Privacy Applications

In many applications, secure communication and data privacy are crucially supported by cryptography. The study of cryptography is now essential for creating strong and dependable security systems due to the growing risks to sensitive information in the digital era. The fundamentals of cryptography, its guiding principles, and its useful applications in securing communication channels and preserving data privacy are explored in this research article.Beginning with symmetric and asymmetric encryption techniques, the inquiry first looks at the fundamental ideas of encryption and decryption. It explores the mathematical underpinnings of cryptography, including discrete logarithms, prime numbers, and modular arithmetic, which serve as the foundation for many cryptographic systems.The paper also examines the various cryptographic protocols and algorithms that are frequently used in secure communication systems. It examines well-known encryption algorithms like Elliptic Curve Cryptography (ECC), Rivest-Shamir-Adleman (RSA), and Advanced Encryption Standard (AES). To determine whether a given algorithm is appropriate for a given use case, its advantages, disadvantages, and distinguishing characteristics are examined.The inquiry also looks at other cryptographic methods including digital signatures, hashing, and key management in addition to encryption. In secure communication systems, these methods are essential for guaranteeing data integrity, authentication, and non-repudiation.