Reviewerly/exHarmony huggingface dataset:
Energy Optimization Analysis on Internet of Things

Energy enhancement and renewable energy integration are crucial facilitators of renewable energy conversion and climate shift alleviations. The technological advancements supported by the current fifth generation (5G), like Internet of Things (IoT), Internet of Flying Things, and Internet of Drones have shown different merits within the energy market, including energy generation, storage, and delivery, as well as increased demand. It is predicted that the IoTs are to be utilized to enhance energy usage, increase sustainable energy use, and reduce the environmental effects of energy application. This study examines the latest studies on the IoT application in smart grids and overall energy networks. The study further details IoT's supporting technologies, like cloud storage and multiple data analysis tools. Moreover, a detailed identified problems for IoT deployment in the energy sector, like security and privacy and solutions, for instance, blockchain. The study offers energy regulators, managers, and analysts on the position of IoT in energy system optimization.
Methods for Learning to Quantify

Abstract This chapter is possibly the central chapter of the book, and looks at the various supervised learning methods for learning to quantify that have been proposed over the years. These methods belong to two main categories, depending on whether they have an aggregative nature (i.e., they require the classification of all individual unlabelled items as an intermediate step) or a non-aggregative nature (i.e., they perform no classification of individual items). In turn, the aggregative methods may be seen as belonging to two main sub-categories, depending on whether the classification of individual unlabelled items is performed by classifiers trained via general-purpose learners or via special-purpose, quantification-oriented learners.
An Optimized Low-Cost 4 × 4 Multiplier Circuit Design Using Reversible Logic Gates

Reversible logic gates, which are proven to provide zero power dissipation under ideal conditions, are in high demand for future computing technologies. Using reversible logic gates, this study provides an improved design for a low-cost 4 × 4 multiplier circuit module. The module can implement 8 × 8, 16 × 16 and up higher order multiplier circuits. In terms of gate counts, garbage outputs, constant inputs, and quantum cost, the suggested multiplier circuit using minimum number of half adders and full adders excels existing designs. Moreover, this concept can be used to implement different complex systems in the field of nanotechnology. The RTL design of the suggested multiplier circuit is then shown, followed by a simulation waveform that illustrates how the design works. The Xilinx ISE 14.7 software tool is also used to evaluate experimental results based on the on-chip power, delay, and other relevant parameters.
Depression Detection using AI, ML and NLP

Depression is a serious mental illness that can be debilitating. It can interfere with daily activities and cause feelings of sadness, hopelessness, and despair. It is a mental health condition that can range in severity from mild to severe. The severity of depression can be assessed based on the number, intensity, and duration of symptoms, as well as their impact on an individual's daily functioning. Mild depression typically involves symptoms such as feeling sad, low energy, and a lack of motivation. These symptoms may not significantly interfere with an individual's ability to function in their daily life, and they may still be able to maintain their social and occupational obligations. Moderate depression involves more severe and persistent symptoms, such as a sense of hopelessness, persistent feelings of sadness, and difficulty concentrating or making decisions. These symptoms may make it challenging to fulfill daily responsibilities, and may result in social isolation or problems at work or school. Severe depression involves symptoms that significantly impair an individual's ability to function in their daily life, such as suicidal thoughts, complete loss of interest in activities, and difficulty with basic tasks such as personal hygiene or eating. Severe depression is a medical emergency and requires immediate professional intervention to prevent harm to oneself or others
Email Spam Detection Using Multi-head CNN-BiGRU Network

Spam emails refer to unsolicited email messages, usually sent in bulk to a large list of recipients with the purpose of marketing, or luring individuals to download malware or click on phishing links. Although much research has been done on this topic, our study reveals that less attention is given to combatting the tactics used by spammers, like purposely forging spellings and the usage of smart sentence structuring. This work attempts to fill this void by using sub-wording and context-based methods. The sub-wording method is used to combat the usage of forged spellings while the context-based method is used to counter smart sentence structuring tactics used by spammers. Also, most of the prevalent methods have been evaluated only for one class weight. As the models’ performance can vary over class weights, we have used two class weights i.e., balanced and unbalanced for evaluation. The proposed model is designed by using a multi-head approach integrated with the Convolutional Neural Network and Bidirectional Gated Recurrent Unit network. The proposed model has been evaluated on the Lingspam and Spamtext datasets. Another issue neglected in prevalent methods is catastrophic forgetting, to consider it we have used a combination of the two datasets. The evaluation is done in terms of f1_score, accuracy, and receiver operator characteristic curve. The proposed model performs well, giving 99.75% accuracy for the Spamtext dataset and 99.79% accuracy for the Lingspam dataset. Obtained results are compared with state of art models available in the previous literature works.
A study on automatic flake surface segmentation of stone tools by calculating shape features

Stone tools are valuable archaeological findings dating to prehistoric age. The ancient people hit a rock with a stone hammer to make stone tools, it also produces flakes. To analyze human activities, reassembling stone tools is effective. When these stone tools are assembled using a computer, the flake surfaces of each stone must be extracted. The segmentation process is necessary for reassembling. This study aims for the segmentation in order to extract flake surfaces, and it requires identifying the orientation of the point normal vector. To do this, the surface of a flake must be considered. If the external space of the object can be correctly defined, directing normal vector is sufficient. While the surface is defined by adopting the voxelization technique, a surface is determined by a voxel that contains points. In addition, external and internal voxels are identified using the 3D flood filling algorithm. We applied normal vector estimation to region-growing segmentation and confirmed its effectiveness experimentally.
Liver tumor detection and classification from abdominal ultrasound images with CenterNet using contrastive learning

Abdominal ultrasound examination is considered to be highly challenging because of its need to diagnose from moving images taken while handling devices. Previous method consisted of a two-stage inference step where tumors in the input ultrasound image was detected and then the cropped area was classified. However, this previous method may be inaccurate because the tumour detection model is not suitable due to the inability to use global features for classification against the cropped diagnostic image. Therefore, we propose a method that uses SimSiam to pretrain CenterNet and infer using only a single model. The proposed method improves classification accuracy by 3%, and improves memory usage and inference speed by 50% and 33% respectively.
Multi-Criteria Decision Making and Recommender Systems

Multi-criteria decision making (MCDM) is a popular branch of decision making, where the decision makers need to make a choice based on a number of decision criteria. This process is applicable in various domains of our daily life. For example, a person who is booking a hotel may need to take into account several factors such as location, safety, budget, and room cleanliness. Similarly, a consumer who is placing an order online may consider factors such as budget, quality, functionality, and delivery time. Recommender systems have become a popular solution to assist users in decision making, and these systems can also be developed with multiple criteria and user preferences in mind. This tutorial provides a comprehensive review of MCDM schemes and the development of multi-criteria recommender systems (MCRS). It explores various MCDM solutions and discusses how the current development of MCRS can be linked to these MCDM theories and solutions. The tutorial also highlights the gap between MCDM and the practice of MCRS, which could inspire future development of MCRS.
Reproducibility as a Stepping Stone to Intelligent Assistants for Data Analysis: Recreating a Study of Physical Activity, Sleep, and Work Shift in Nurses

This paper presents our work in reproducing a published study with a framework and implemented system that are intended to support automation of future analyses. This automation will allow us to develop intelligent systems to assist non-experts in getting useful information out of their own data. Our framework takes high-level questions, automatically elaborates them, and run the necessary analyses on the data. Our TGN10+ system implements that framework and was used to reproduce an analysis of behavioral, psychological, and physiological data of healthcare workers to uncover differences in subjects that can help improve their work conditions. TGN10+ can also be used to update the results when new data becomes available, and could be used to analyze new data using the same method. Our ultimate goal is to develop intelligent systems that will empower healthcare workers to analyze their own data, and ultimately anyone who wants to understand their own health and wellbeing.
A Vietnamese Spelling Correction System

This paper presents a new Vietnamese spelling correction system that allows users to correct spelling errors in their text. Our system is an interactive writing assistant that integrates advanced technologies in natural language processing to (i) identify spelling errors and (ii) replace those errors with their corrected version. To the best of our knowledge, our system is the first Vietnamese spelling correction tool that interacts with the users via Web Interface, Microsoft Word and Chrome extensions to provide the best user experience. We also perform automatic and human evaluations to demonstrate the effectiveness of our system. Our system is publicly available at https://grammar.vinai.io/.
SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE) 2023

This is the third edition of the SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE) workshop. As in the two previous editions, also this year, the main objective is to create an opportunity for all those interested in exploring and implementing new technologies to promote the inclusion of people by breaking down possible cultural, social, and language obstacles. Particular attention is paid to those who may have more difficulty than others in establishing interpersonal connections. To achieve this ambitious goal, social robots can also play a key role. This year, the invited talk will focus on models and methods to make human-robot interaction more and more effective. The final goal is to provide the robot with the perspective capabilities typical of human beings and the ability to adapt and learn. Only in this way, the robot will be able to establish an effective connection with any human being, overcoming the difficulties arising from the differences between the traits that characterize one individual compared to another.
Zero knowledge proof and ZK-SNARK for private blockchains

Comprehensive adoption of distributed ledger technology and blockchain in enterprises might disrupt financial and other sectors. At the same time, there are some barriers to rolling out the technology in practice. One of the significant issues concerns information security and privacy in the blockchain. Some methods for private transactions such as mixed networks, ring signatures, and off-chain protocols address certain data privacy issues, but do not provide the blockchain characteristics such as decentralized storing systems and immutability verification of private data. This article examines zero-knowledge proof (ZKP) methods for corporate blockchain networks. The article reviews existing methods for private transactions, observes the implementation of ZKP methods, and discusses performance and scalability issues.
Secure Multi-party Computation in Federated Learning

In the previous chapters, we mainly explored security and privacy in federated learning from the viewpoint of artificial intelligence, which are relatively new and usually more idea based. In general, these methods are somewhat ad hoc and lack theoretical support.
Poisoning Attacks and Counterattacks in Federated Learning

In this chapter, we will discuss a popular and major attack format, poisoning attack, in federated learning environment due to the open nature and large scale of federated learning. Poisoning attacks happen at the phase of model training, while inference attacks we discussed in Chap. 2 occur at the testing or prediction stage of artificial intelligence applications.
Decision-level fusion detection method of visible and infrared images under low light conditions

Abstract Aiming at the problem of poor effect of object detection with visible images under low light conditions, the decision-level fusion detection method of visible and infrared images is studied. Taking YOLOX as the object detection network based on deep learning, a decision-level fusion detection algorithm of visible and infrared images based on light sensing is proposed. Experiments are carried out on LLVIP dataset, which is a visible-infrared paired dataset for low light vision. Through comparative analysis, it is found that the decision-level fusion algorithm based on Soft-NMS and light sensing obtained the optimal AP value of 69.0%, which is 11.4% higher than the object detection with visible images and 1.1% higher than the object detection with infrared images. The experimental results show that the decision-level fusion algorithm based on Soft-NMS and light sensing can effectively fuse the complementary information of visible and infrared images, and improve the object detection effect under low light conditions.
Cognitive Modeling for Personalized, Adaptive Signaling for Cyber Deception

This chapter presents an overview of the use of cognitive models for personalized adaptive signaling for cyber deception. It provides a general introduction to cognitive modeling techniques and concepts, including general goals, capabilities and limitations, cognitive architectures, and instance-based learning. It establishes that cognitive models’ reliance on generative mechanisms has predictive capabilities beyond those of purely data-driven techniques such as machine learning that can be used to evaluate the effectiveness of cyber defense techniques without requiring full implementation and test. Cognitive models can account for the entire range of human performance, including levels of expertise and individual differences. Techniques such as knowledge-tracing and model-tracing can align a specific cognitive model against an individual behavior trace, enabling personalized interventions. Because cognitive models are analytically tractable, they can guide, inform, and optimize the design of cyber deception techniques. We illustrate these concepts using an insider attacking game meant to abstract the dynamics and decision-making characteristics of real-world cyber defense. Finally, we conclude by discussing future research directions in the development and application of cognitive models to cyber deception for defense.
Using Redirection to Create a Swimming Experience in VR for the Sitting Position

As a primary locomotion method for some VR applications (e.g. diving, physical exercise, etc.), swimming creates an immersive and entertaining experience. Existing VR swimming methods often use complex and expensive setups to fix users in the mid-air to restore prone positions. While these methods provide realistic experiences, the hanging postures place an additional burden on the users and make them difficult to freely switch between the two swimming positions (prone and supine). Using redirection technology, we propose a new, easy, and effective method to simulate swimming in a comfortable sitting position. For visual simulation, this method redirects the -30° to 30° torso tilt range of the pitch axis in a sitting position to -90° to 90° through a rotation gain to the user’s view. For vestibular and proprioceptive simulation, we use the swimming action, the torso forward/backward leaning for swimming postures’ (backstroke and breaststroke) switching, the pillow and seat back for simulating the buoyancy of water. Preliminary experimental results show that our swimming method can create a good swimming experience for seated users.
Visualizing Decisions and Analytics of Artificial Intelligence based Cancer Diagnosis and Grading of Specimen Digitized Biopsy: Case Study for Prostate Cancer 

The rise in Artificial Intelligence (AI) and deep learning research has shown great promise in diagnosing prostate cancer from whole slide image biopsies. Intelligent application interface for diagnosis is a progressive way to communicate AI results in the medical domain for practical use. This paper aims to suggest a way to integrate state-of-the-art deep learning algorithms into a web application for visualizations of decisions and analytics of an AI based algorithms applied on cancer digitized specimen biopsies. Performed primarily by visualizing evidence and explanation of the decision using image from the biopsy. By creating smart visualizations of tissue biopsy images, from magnified regions to augmented sharper images along with image masks that highlight cancerous regions of tissue in addition to intelligent analytics and distribution charts related to cancer prediction, we aim to communicate these easily interpretable results to assist pathologists in their workflow for grading prostate cancer biopsies and concerned medical team to make better decisions for prostate cancer diagnosis as case study
iSee: Intelligent Sharing of Explanation Experience by Users for Users

The right to obtain an explanation of the decision reached by an Artificial Intelligence (AI) model is now an EU regulation. Different stakeholders of an AI system (e.g. managers, developers, auditors, etc.) may have different background knowledge, competencies and goals, thus requiring different kinds of interpretations and explanations. Fortunately, there is a growing armoury of tools to interpret ML models and explain their predictions, recommendations and diagnoses which we will refer to collectively as explanation strategies. As these explanation strategies mature, practitioners will gain experience that helps them know which strategies to deploy in different circumstances. What is lacking, and is addressed by iSee, is capturing, sharing and re-using explanation strategies based on past positive experiences. The goal of the iSee platform is to improve every user’s experience of AI, by harnessing experiences and best practices in Explainable AI.
Chicken swarm optimization with an enhanced exploration–exploitation tradeoff and its application

The chicken swarm optimization (CSO) is a novel swarm intelligence algorithm, which mimics the hierarchal order and foraging behavior in the chicken swarm. However, like other population-based algorithms, CSO also suffers from slow convergence and easily falls into local optima, which partly results from the unbalance between exploration and exploitation. To tackle this problem, this paper proposes a chicken swarm optimization with an enhanced exploration–exploitation tradeoff (CSO-EET). To be specific, the search process in CSO-EET is divided into two stages (i.e., exploration and exploitation) according to the swarm diversity. In the exploratory search process, a random solution is employed to find promising solutions. In the exploitative search process, the best solution is used to accelerate convergence. Guided by the swarm diversity, CSO-EET alternates between exploration and exploitation. To evaluate the optimization performance of CSO-EET in both theoretical and practical problems, it is compared with other improved CSO variants and several state-of-the-art algorithms on two groups of widely used benchmark functions (including 102 test functions) and two real-world problems (i.e., circle packing problem and survival risk prediction of esophageal cancer). The experimental results show that CSO-EET is better than or at least comparable to all competitors in most cases.
A User Interface for Explaining Machine Learning Model Explanations

Explainable Artificial Intelligence (XAI) is an emerging subdiscipline of Machine Learning (ML) and human-computer interaction. Discriminative models need to be understood. An explanation of such ML models is vital when an AI system makes decisions that have significant consequences, such as in healthcare or finance. By providing an input-specific explanation, users can gain confidence in an AI system's decisions and be more willing to trust and rely on it. One problem is that interpreting example-based explanations for discriminative models, such as saliency maps, can be difficult because it is not always clear how the highlighted features contribute to the model's overall prediction or decisions. Moreover, saliency maps, which are state-of-the-art visual explanation methods, do not provide concrete information on the influence of particular features. We propose an interactive visualisation tool called EMILE-UI that allows users to evaluate the provided explanations of an image-based classification task, specifically those provided by saliency maps. This tool allows users to evaluate the accuracy of a saliency map by reflecting the true attention or focus of the corresponding model. It visualises the relationship between the ML model and its explanation of input images, making it easier to interpret saliency maps and understand how the ML model actually predicts. Our tool supports a wide range of deep learning image classification models and image data as inputs.