Reviewerly/exHarmony huggingface dataset:
Large-Scale Evolutionary Optimization via Multi-Task Random Grouping

Evolutionary Algorithms (EA) are known to suffer from the curse of dimensionality resulting in poor performances when handling large-scale problems. Cooperative coevolution aims to overcome these issues in a divide and conquer approach by decomposing the original problem into several lower-dimensional sub-problems. For each sub-problem a chosen EA is applied for a defined number of function evaluations. This is repeated in a round robin like fashion until a terminating condition is met. A recently proposed area in the evolutionary computation field is the evolutionary multitask optimization (EMTO) framework. By jointly optimising several tasks, EMTO aims to exploit beneficial information across multiple tasks to improve the performance compared to optimising each task in isolation. In this paper, we consider a large-scale problem as a multi-task optimization problem by considering each sub-problem as an independent task. Applying an EMTO algorithm, knowledge transfer across sub-problems is carried out explicitly to improve the optimization of each sub-problem. We evaluate the effectiveness of our proposed algorithms empirically on a suite of separable and non-separable benchmark problems of varying dimensions.
Grounding Cryptocurrencies

Abstract Chapter 6 documents a more specific and exploratory follow the thing research technique to uncover the digital-material architecture of Bitcoin. Treating the Bitcoin code as both a text and material, a single bitcoin is followed through the decentralised protocol “from” Australia “to” the United States. By tracing the spatial relationships between miscellaneous paraphernalia that facilitate the transaction, from proprietary software to Bitcoin mining rigs, the chapter navigates the material culture of the Bitcoin blockchain. This involves opening up source code for inspection to uncover the functional performativity of the network. The spatial lens used reveals several material infrastructures such as undersea cables, data centres, pools of Bitcoin mines, active nodes, and third-party wallet software, that assemble to form operational modes of centralisation.
Overcoming AI applications challenges in health: Decision system DINAR2

Expectations from a doctor’s interactions with artificial intelligence (AI) are often not fulfilled in real life. A medical case may be outside the domain of the AI system. A physician’s incorrect description of a medical case leads to incorrect AI decisions, only worsening the quality of the final decision. The use of AI systems may not fit into the day-to-day work of a doctor. This chapter discusses the sources and possible solutions to these problems. We use the decision support system DINAR2 to attempt to solve these problems in practice. The chapter analyzes the reasons for the efficiency and long-term demand for DINAR2.
Dimensionality reduction

The analysis of biological signals requires very effective predictive models that take as input pre-processed signals and return as output a prediction associated with the task at hand. Whereas the predictive model is critical for the success of the model, an almost similarly important task has to do with processing inputs to reduce their initial dimensionality and identify those variables that are more relevant for building a predictive model. This is precisely the topic covered in this chapter that deals with the dimensionality reduction problem; we formulate the task in the context of supervised learning and review the two main approaches for solving it: feature selection and feature transformation. These interrelated tasks aim at processing the input signals to extract meaningful information while maintaining it at a manageable dimension. Both approaches are described, including their associated challenges and representative methodologies. The goal of this chapter is to serve as an introductory reference for non-experts in machine learning.
AI Powered Language Teaching Bot Using Dialogflow

Oftentimes, we as humans want an instant answer and reply to the many questions in our heads, and it is not always possible to get answers for our queries at one go. However, with the advancement in the field of artificial intelligence, we can overcome these challenges with the help of a chatbot. This chatbot is a software influenced by an AI that gives an automatic response to the user. In this paper, the authors have utilized a tool called Dialogflow for implementation of a chatbot that can simulate a language translator for Paite. Dialogflow has a pre-trained machine learning-based model that allows us to feed in our data for automatic response. The proposed application is to give translation in Paite for the input words and sentences. The Chatbot serves as a Paite language teaching bot in this application. This application is beneficial for both the end user and the developer as the user gets his/her desired response, and the developer saves his/her time giving response to the many queries received daily. Furthermore, we will discuss in detail about the workflow and the methodology behind implementation of this work.
Visible-Infrared Cross-Modal Person Re-identification based on Positive Feedback

Visible-infrared person re-identification (VI-ReID) is undoubtedly a challenging cross-modality person retrieval task with increasing appreciation. Compared to traditional person ReID that focuses on person images in a single RGB mode, VI-ReID suffers from additional cross-modality discrepancy due to the different imaging processes of spectrum cameras. Several effective attempts have been made in recent years to narrow cross-modality gap aiming to improve the re-identification performance, but rarely study the key problem of optimizing the search results combined with relevant feedback. In this paper, we present the idea of cross-modality visible-infrared person re-identification combined with human positive feedback. This method allows the user to quickly optimize the search performance by selecting strong positive samples during the re-identification process. We have validated the effectiveness of our method on a public dataset, SYSU-MM01, and results confirmed that the proposed method achieved superior performance compared to the current state-of-the-art methods.
Surprisingness - A Novel Objective Interestingness Measure in Hypergraph Pattern Mining from Knowledge Graphs for Common Sense Learning

Pattern mining usually results in huge amounts of patterns, among which only small percentages are interesting. In this paper, Surprisingness (including Surpringness_I and Surpringness_II) is proposed as an innovative objective multivariate interestingness measure for automatically identifying interesting patterns from a large quantity of patterns. Surprisingness is applicable in unstructured or semi-structured, multi-domain or mixed-domain data compared to existing measures. An experiment has been conducted enabling unsupervised learning of common sense, interesting patterns and exceptions from a knowledge graph database built from Wikipedia <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> extracted data (represented as directed labeled hypergraphs), using Surpringness.
Scope and Challenges in Conversational AI using Transformer Models

Conversational AI is an interesting problem in the field of Natural Language Processing and combines natural language processing with machine learning. There has been quite a lot of advancements in this field with each new model architecture capable of processing more data, better optimisation and execution, handling more parameters and having higher accuracy and efficiency. This paper discusses various trends and advancements in the field of natural language processing and conversational AI like RNNs and RNN based architectures such as LSTMs, Sequence to Sequence models, and finally, the Transformer networks, the latest in NLP and conversational AI. The authors have given a comparison between the various models discussed in terms of efficiency/accuracy and also discussed the scope and challenges in Transformer models.
Pedagogical Characteristics of Learning Support

The study of learning support is carried out in the context of inclusive education and with a view to its purpose to respond to the diversity of student needs. As an act of pedagogical interaction, support has a key role in the formation of the complex autonomy of children and students. Defining pedagogical characteristics of learning support allows us to discover its meaning and power of action in pedagogical practice at school level, the learning space and the systematization of the responsibilities of those involved.
Index Optimization Using Wavelet Tree and Compression

Retrieving of relevant information from a large corpus is a challenging task nowadays. Wavelet tree is an accomplished data structure to store and retrieve text, image, audio, and video data in efficient space and time. It has turn to be a leading tool in modernized full-text indexing or in its proficiency in compression. This study presents the contribution of wavelet trees to design indexing to retrieve information in the field of web, healthcare, agriculture, bioinformatics, and earthquake detection. In this paper, we proposed a technique of LZW compression on wavelet tree. It performs compression on the wavelet tree. This paper consists of several concepts of wavelets which show about the indexing procedure, empirical measures, wavelet packets, wavelet entropy, wavelet matrix, and complexity of wavelets. Further, the literature discusses the open issues where wavelet trees can be used to design indexing of other databases.
Semantic Segmentation of 3D Facial Models Using 2D Annotations

Semantic information retrieved from the human face can improve human-machine interaction, add new level of information compression and expand the multi-modality in data analysis. Extracting such information is done using semantic segmentation of images of the human face. It consists of automatically identifying the areas of human facial image, defining the different face parts, that meaningful information for humans. These areas include nose, eyes, forehead, ears, etc. In this work we propose a new algorithm based on auto-encoder architecture for semantic segmentation of 3D models of the human face. These models are represented as mesh objects which further motivates us to use graph-convolutional neural networks for the implementation of the auto-encoder. Since no data of 3D face models with annotated facial parts is available, we approach the problem using publicly available 2D annotated data and analysis-by-synthesis approach. Experimental results validate our approach for 3D face semantic segmentation.
Digital Democracy: A Secure Platform for Voting

The voting system is the way to elect country representatives. It is a set of rules that determine how the elections and referendums are conducted and how their results are determined. Voting is commonly related to politics and is finished with exploitation and a manual approach. The voters stand to vote for his or her decision. There are many ways to conduct voting, and for a developing country like Sri Lanka, the polling power to have an excellent representative to develop the country into a better and prosperous one. Usually, the existing voting system is operated manually and sometimes may lead to malpractices. Many have suggested diverse ways to overcome the problems, and sometimes those recommendations have failed. The most reliable way to accomplish this is to expand the technology from manual voting systems to digital voting systems. There are several significant issues with introducing such a digital voting system. A significant problem here is the low level of technical knowledge among the people of Sri Lanka. Such a system would be a problem for some at once. This research paper aims to put forward a study on why and how it is necessary to shift from a standard voting system to a digital platform and how it saves time and energy.
Inferring Human Activity Using Wearable Sensors

This paper presents methods that use data from wearable sensors, such as those found in low-cost commodity hardware, to infer the human activity (such as reading or walking) corresponding to the sensor readings. A related task is the identification of individuals based on the same data. The classification accuracy of the methods used in this work is higher than earlier work using the same dataset. Further, a significant reduction in the number of sensor data streams produces only a very small impact on this accuracy, which is a feature of practical significance due to implications for network bandwidth and energy budgets in such systems.
Development of an Underwater Robot Kit for Marine Education about Handling Underwater Equipment and Educational Effects Seen from Practical Examples

In recent years, underwater research and marine development have been accelerated in worldwide, and human resource development has become an issue in the industrial world. Japanese high schools, universities, etc. do not have any institutions that provide basic education on practical underwater equipment, and there are few textbooks. In addition, in biological and natural science faculties and departments, the content of education is limited to how to use underwater equipment, and there are few opportunities to learn development and maintenance. However, many of these graduates who have jobs in the marine industry are faced with on-site equipment maintenance and repair. It takes time and money to develop human resources to learn how to handle underwater equipment from zero base, which may cause a delay in the competition in the world. Therefore, we have developed a learning kit that allows you to learn from the basics how to handle underwater equipment.
Identification of Important Utterances in Narrative Speech Using Attentive Listening Responses

Since narratives contain a variety of knowledge, it is useful to record their contents. It is difficult to read the transcription even if the narrative speech is transcribed directly because the transcription contains redundant contents. Thus, it is effective to summarize the narrative transcriptions. This paper proposes a method for identifying important utterances in narratives with the aim of summarizing them. The method uses not only the narrative by the speaker but also the attentive listening responses by the listeners for identifying the important utterances. Attentive listening responses are conversational responses to positively show that listeners are attentively listening to the narratives, e.g., back-channel feedbacks. The more important an utterance in a narrative is, the more likely the listeners seem to react to it. In this study, we focus on attentive listening responses as the listener’s reactions. We experimentally evaluated the effectiveness of using attentive listening responses in identifying important utterances in narratives.
Long-term deep object tracking

Visual object tracking is one of the oldest tasks in computer vision. Interestingly, when it comes to longer sequences object tracking must address significant challenges that relate to model decay, that is the worsening of the model due to added bias, and target disappearance and reappearance. While model decay is also present in short-term tracking, in longer sequences the gradually added bias becomes so much more significant, that the tracker fails completely. The success of deep learning has influenced visual object tracking, especially in the context of long-term sequences. The reason is that with the commonly-used deep Siamese tracker design, one can relay all appearance comparisons to an offline learning of a similarity function. The offline learning of the Siamese trackers, in turn, eliminates model decay. However, eliminating model decay comes at the cost of possibly missing the target object in cases where the appearance of the target changes significantly compared to the first frame. For this reason, Siamese tracker variants with built-in invariances and equivariances are also proposed, allowing for adapting to appearance variations without exacerbating model decay. All considering, Siamese trackers perform well and are recommended in long-term visual object tracking.
Coping with Inconsistencies in Legal Reasoning

Law cases are triggered by conflicting opinions on juridical matter. Legal discourse reflects these contradictions. One may “index them away” in logical formalization. That, however, usually leads to unduly abstract—or, under-complex—formal models which thus become non-descriptive, i.e. naive and boring. Wouldn’t it be better to accept the real differences in a specific legal dispute and to adequately reflect them in the logical description?We demonstrate how D2, a system designed by the Polish logician Stanisław Jaśkowski, and alternative systems provide a promising methodological basis for legal reasoning.
An Early Warning System for Evaluating Effects of Medical Treatment using Machine Learning

The development of AI-based medical change tracking and impact analysis tools can have a beneficial effect on a patient's recovery in real-time. The study presents a system for patient medical change tracking and impact analysis using machine learning, particularly, principal component analysis and Bayesian structural networks. We found that the proposed system achieved an acceptable statistical significance level for all the patient data tested. Moreover, in cases where there are spurious changes due to extra missing values and/or newly administered medical tests causing the change, the causal impact analysis was able to capture them as bogus. Consequently, we can say that the proposed system can potentially offer real-time monitoring and tracking of patients for the clinicians. In addition, we believe that the approach provides a promising future in interpreting large quantities of patient data for establishing cause-effect relationships for critically ill patients.
Device Light Fingerprints Identification Using MCU-Based Deep Learning Approach

We introduce device identification using the light fingerprint by a MCU-based deep learning approach. At first, we observe that minor differences exist for individual components of lighting equipment. The corresponding difference produces a unique phenomenon in the frequency spectrum. Therefore, we adopt deep learning approaches for developing a mobile phone light fingerprint identification system and implementing it on a low-cost microcontroller platform. The screen light of the mobile phone is analyzed to obtain the features of unique light fingerprints. We utilize the convolutional neural network, the improved multi-class greedy autoencoder and variational autoencoder with domain adaptation techniques to develop the identification algorithm. Finally, the Bayesian optimization technique is used to optimize the hyper-parameters of models for implementing in the microprocessor. The corresponding comparisons are introduced to demonstrate the performance. The multi-class greedy autoencoder algorithm produces results with an overall accuracy rate and abnormal sample detection rate of 99.67% and 99.85%, respectively. Only a single model needs to be added or deleted for updating new authentication data and this does not affect the identification ability of all models. This results in greater flexibility in real-life applications and potential for expansion to other fields, such as smart buildings and automated robots.
Outlook: Forward Thinking for 2022

Each January issue of Computer is devoted to invited articles offering forward-looking ideas. This 2022 issue discusses artificial intelligence hardware concerns, the future of software, the current state of open banking, future obstacles in deploying autonomous machines, ethical dimensions of social media, and a 200-year look back at the invention and contribution made by Charles Babbage.
AI-based Holl-filling with Temporal Consistency

Depth image-based rendering (DIBR) has been recently considered as a significant technology for generating 3D virtual views. However, the hole-filling method is still the main challenge in the DIBR engine because of the occlusions of foreground objects. While looking into the video sequences, the occluded parts in a frame may be revealed in the past or future frames. The temporal information could be the useful cues to recover the occlusions of the current frame. In this paper, we design an encoder-decoder neural model with a bounded region attention module to effectively fill the holes. This attention module is aim to extract the useful hints from neighbor frames.
Facial Expressions Recognition by Structuring the Embeddings Space

Lately, Facial Expression Recognition (FER) gained in popularity in the medical field due to their importance in understanding the human psychology or detecting various pathologies. In this paper, we propose a more efficient solution for recognizing facial expressions using a more convenient structuring of the data. To accomplish this aspect, a clustering loss entitled Large Margin was used in addition to the classic Cross Entropy, in order to improve discrimination in the embeddings space. The main study consisted of trying different distance metrics (L1, L2, cosine similarity) between embeddings, to examine which of them offers a better structuring of the data. The solution is tested on two benchmarks with very good results.
Forensic Investigation for Cyber Space: Importance of Cyber Forensics in Business and Global Economy

Cyber forensics is a technique of electronic finding used to identify and disclose technological criminal evidence. The current study was an attempt to add to the emerging body of knowledge about the inherent problems of cyber forensics. This paper provides an overview of cyber forensics, including recent and future advances in the area. The specific subjects covered included the importance of computer forensics and how new technology enables the development of computer forensics in a business and global economic context.
Peculiarities of teaching technical disciplines at the department of information science and special equipment in the online format

The research subject is the peculiarities of teaching technical disciplines at the department of information science and special equipment of Barnaul Juridical Institute of the Ministry of Internal Affairs of Russia. The research object is the educational process in a distance format. The authors consider such aspects of the topic as the peculiarities of online lectures and practical lessons. Special attention is given to the ways of solving the problem of the absence of special equipment at students&amp;rsquo; places. The article contains examples of educational materials for the discipline &amp;ldquo;Special equipment of internal affairs bodies&amp;rdquo; and &amp;ldquo;Special Equipment&amp;rdquo; used at the department. The authors formulate the structures and peculiarities of lectures and practical lessons in online format. The authors&amp;rsquo; special contribution is practical recommendations about the organization of various lessons in online format. The scientific novelty of the research consists in the development of the author&amp;rsquo;s technique of formation of practical skills of students during the lessons in technical disciplines in online format. The technique contains the concrete action plan for a teacher, the samples of guidelines materials, and assessment criteria, and helps effectively form competences in the field of special equipment for visual surveillance and documenting, during a two-hour practical lesson. Approaches used in this research can be also used for other educational specializations.