Reviewerly/exHarmony huggingface dataset:
Self-supervised Learning for Predicting Invisible Enemy Information in StarCraft II

In real-time strategy games such as StarCraft II, players gather resources, make buildings, produce various units, and create strategies to win the game. Especially, accurately predicting enemy information is essential to victory in StarCraft II because the enemy situation is obscured by the fog of war. However, it is challenging to predict the enemy information because the situation changes over time, and various strategies are used. Also, previous studies for predicting invisible enemy information in StarCraft do not use self-supervised learning, which is extracting effective feature spaces. In this study, we propose a deep learning model combined with a contrastive self-supervised learning to predict invisible enemy information to improve the model performance. The effectiveness of the proposed method is demonstrated by quantitatively and qualitatively.
DL 101: Basic introduction to deep learning with its application in biomedical related fields

Deep learning is a subfield of machine learning used to learn representations of data by successive layers. Remarkable achievements and breakthroughs have been made in image classification, speech recognition, et cetera, but the full capability of deep learning is still under exploration. As statistical researchers and practitioners, we are especially interested in leveraging and advancing deep learning techniques to address important and impactive problems in biomedical and other related fields. In this article, we provide a basic introduction to Feedforward Neural Networks (FNN) along with some intuitive explanations behind its strong functional representation. Guidance is provided on how to choose quite a few hyperparameters in neural networks for a specific problem. We further discuss several more advanced frameworks in deep learning. Some successful applications of deep learning in biomedical fields are also demonstrated. With this beginner's guide, we hope that interested readers can include deep learning in their toolbox to tackle future real‐world questions and challenges.
Real-World Computer Vision for Real-World Applications: Challenges and Directions

Recent advancements in machine learning, particularly deep learning, have driven the development of next-generation of smart and autonomous applications, especially those that need an in-depth understanding of humans’ behaviors and interactions with the physical world. However, there is a gap between current research in computer vision and the inherent real-world limitations of applications. This paper offers holistic solutions embracing real-world computer vision challenges and bringing computer vision to a broad range of applications. The core significance of this paper is creating a holistic privacy-aware ensemble of novel computer vision algorithms and training principles to understand humans’ behaviors and interactions with the physical world. To this context, this paper presents multiple fundamental contributions intersecting classical computer vision, deep learning research, and information theory principles. The key contributions include novel privacy-aware identity neural person re-identification, domain-invariant training to bridge the gap between the training data-set and real-world data limitation, enhancing visual resiliency, and knowledge amalgamation across multiple concurrent vision tasks to create full situational awareness.
The Prediction Model of Dance Talent Training in Fuzzy Neural Network Algorithm

In view of the existing problems in the training of dance talents, particularly in China, the focal and key goal of this investigation is to examine and train a predictive model using the fuzzy neural network algorithm. In order to enhance the training, improve the quality of dance talents, and further promote the development of dance professional education, this paper implements the fuzzy neural network procedure to train prediction models for dance talents. Moreover, we carry out research and, then, establish a prediction model, through the fuzzy neural network algorithm, to predict the quality and effect of dance talent training. The model can be, then, used to deliver a fundamental and a key reference for the training of dance talents in the social sectors across the world. The experimental setup and obtained outcomes show that the suggested algorithm has good usability for the training and prediction of dance talents in terms of accuracy. We observed that the fuzzy-based technique is approximately 17.6% more precise than the classical scheme. Moreover, the prediction correctness was observed more than 98.5%.
Deep Learning Applied to Automatic Modulation Classification at 28 GHz

Automatic Modulation Classification (AMC) is a fast-expanding technology, which is used in software defined radio platforms, particularly relevant to fifth generation and sixth generation technology. Modulation classification as a specific topic in AMC applies Deep Learning (DL) in this work, which contributes a creative way to analyse the signal transmitted in low Signal to Noise Ratio (SNR). We describe a dynamic system for the Millimeter wave (mmW) bands in our work. The signals collected from the receiving system is without phase lock or frequency lock. DL is applied to our system to classify the modulation types within a wide range of SNR. In this system, we provided a method named Graphic Representation of Features (GRF) in order to present the statistical features in a spider graph for DL. The RF modulation is generated by a lab signal generator, sent through antennas and then captured by an RF signal analyser. In the results from the system with the GRF techniques we find an overall classification accuracy of 56% for 0 dB SNR and 67% at 10 dB SNR. Meanwhile the accuracy of a random guess with no classifiers applied is only 25%. The results of the system at 28 GHz are also compared to our previous work at 2 GHz.
Combining Rule-Based System and Machine Learning to Classify Semi-natural Language Data

Command-line commands form a special kind of semi-natural language. Analyzing their structure and classifying them is a useful approach in the field of cyber security to detect anomalous commands used by malicious actors. Without any contextual knowledge, commands’ analysis is a difficult task as similar-looking commands might be performing different tasks, and commands with different aliases might be performing the same tasks. To understand command-line commands’ structure and their syntactic and semantic meanings, we created a rule-based system based on expert opinions. Using this system, we classified command-line commands into similar and not-similar classes. This rule-based system transformed command-line commands’ data into a binary classified form. We trained three machine learning models (a logistic regression document classifier, a deep learning document classifier, and a deep learning sentence-pair classifier) to learn the set of rules created in the rule-based system. We used Mathews Correlation Coefficient (MCC) score for the models’ performance comparison. The logistic regression model shows an MCC score of 0.85, whereas both the Deep Learning (DL) models scored above 0.98. DL document classifier and DL sentence-pair classifier achieved an accuracy of 0.943 and 0.983 respectively on unseen data. Our proposed hybrid approach solves the complex problem of classifying semi-natural language data. This approach can be used to create a domain-specific set of rules, and classify any semi-natural language data into multi-classes.
LUSTRE: An Online Data Management and Student Project Resource

We advocate for greater emphasis in training students about data management, within the context of supporting experience in reproducible workflows. We introduce the Lancaster University STatistics REsources (LUSTRE) package, used to manage student research project data in psychology and build capacity with respect to data acumen. LUSTRE provides a safe space to engage students with open research practices—by making tangible different phases of the reproducible research pipeline, while emphasizing its value as a transferable skill. It is an open-source online data catalogue that captures key data management information about a student research project of potential relevance to data scientists. Embedded within a taught programme, it also highlights concepts and examples of data management processes. We document a portfolio of open teaching resources for LUSTRE, and consider how others can implement or adapt them to facilitate data management and open research. We discuss the role of LUSTRE as a; (a) resource and set of activities for promoting good data management practices; (b) framework to enable the delivery of key concepts in open research; (c) an online system to organize and showcase project work.
An Exploration of Students and Lecturers’ Insights of Online University Learning Implemented During the COVID‐19 Contagion

The consequences of the coronavirus pandemic present serious threats to the normal functioning of the global economy, societies worldwide and specific institutions, including educational services. Confronting challenges arising from the fact that the changes needed to be enacted quickly, educational providers have resorted to online learning solutions via various platforms to limit the ongoing spread of COVID-19. To deepen knowledge and understanding of the critical benefits and difficulties that online learning has created during the pandemic, the present study investigates student and lecturer perceptions regarding the use and execution of online learning in higher education institutions. The sample is comprised of students and lecturers based in Al-Baha University, Saudi Arabia. The study unearths the major difficulties encountered in using online learning methods and identifies the key factors which have enabled more effective use of these methods. The study will use interviews to collect qualitative data on Al-Baha University students and lecturers’ insights and use thematic analysis to analyse and interpret the ensuing data. The resultant data and analyses will be used to formulate practitioner recommendations as to how online learning systems can be more effectively deployed throughout the pandemic and in future scenarios in which online learning becomes essential to education.
Trustworthy Artificial Intelligence for Cyber Threat Analysis

Artificial Intelligence brings innovations into the society. However, bias and unethical exist in many algorithms that make the applications less trustworthy. Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are getting more accurate for identifying not only signature-based but also behavior-based threats. Quantum mechanics brings a new dimension in improving classification speed with exponential advantage. In this research, we developed a machine learning-based cyber threat detection and assessment tool. It uses two-stage (unsupervised and supervised learning) analyzing method on 822,226 log data recorded from a web server on AWS cloud. The results show the algorithm has the ability to identify the threats with high confidence.
Arcface Based Open Set Recognition for Industrial Fault

In industry, fault classification is important to avoid economic losses. Fault type classification is an important because fault detection and classification before equipment shutdown allows accurate maintenance. However, it is difficult to define all fault types in advance. It is impossible to know everything in advance what kind of fault will occur. Therefore, we propose an Arcface-based open set recognition method. We propose an algorithm that can classify a known fault type or an unknown fault type by fusion of a deep learning-based classification model and a distribution model that can estimate whether it is a known type. We apply the proposed method to the AHU dataset. The proposed model shows better performance compared to the existing methods.
Deep Decoupling Classification and Regression for Visual Tracking

Classification and regression are two tasks that most Siamese-based trackers need to handle. However, most of the existing trackers only learn one feature embedding to handle these two types of task, making it difficult to optimize both simultaneously. To solve this problem, this article tries to deeply decouple classification and regression in the model structure. Specifically, two feature extraction backbone networks are used to divide the model into two branches to extract the heterogeneous features suitable for the two tasks, respectively. Inspired by the core idea of transformer, information interaction and fusion between multiple branches are achieved by the cross-attention mechanism, which can fully exploit the deep information dependence between multiple branches. In addition, the concept of channel-level information interaction is proposed by innovatively changing the generation mode of vector groups in the attention module. The experiments show that double Siamese tracker (DST) designed in this article greatly improves the accuracy of classification and regression. DST runs at 60 frames per second (FPS) on GPU, far above the real-time requirement.
Investigation of the turning speed for immersive environments using freehand‐based technique

Abstract Designing a natural, usable, and efficient gesture‐based virtual locomotion interface in immersive environments has been challenging. Freehand‐based virtual locomotion technique (VLT) has the advantage of eliminating the need to see the handhold devices and has been implemented in many applications. Translation control and direction control are two essential components of freehand‐based VLT; however, the transfer function of turning speed in direction control has not been well explored. In this article, we systematically investigate the effects of turning speed on freehand‐based virtual locomotion for the first time. A transfer function that maps the position of the hand to the turning speed is defined, and three parameters related to the turning speed are investigated. In the experiment, participants are asked to travel in the virtual environment from a first‐person perspective with different parameters. Statistical analysis of the experimental results reveals the relationship between the turning speed and the traveling performance as well as the traveling experience.
Modern and Contemporary Literature Courses in Colleges and Universities Using the Teaching Mode of Deep Learning

With the continuous promotion and development of Chinese around the world, increasingly foreign students come to China to learn Chinese. What they have learned is not only language skills but also the culture behind the language, because language and culture are integrated and inseparable. The purpose of this paper is to explore the teaching research of the teaching mode of using deep learning in contemporary literature courses in colleges and universities. This paper firstly introduces the distance and open education, which refers to a new type of education that uses modern teaching information technology to organize classroom teaching and deliver teaching content under the guidance of the concept of openness. The biggest difference between distance and open education and traditional education lies in the location and method of teaching. Then, the teaching of deep learning theory is explained, and the optimization algorithm of teaching and learning and the problem of education optimization are expounded. The algorithm successfully simulates the process of teachers passing professional knowledge to students and students’ self-learning professional knowledge. Finally, the teaching of modern and contemporary literature courses is investigated and analyzed. The results of this experimental survey show that more than 40% of the learners believe that modern and contemporary culture courses help to develop their Chinese communication skills. At the same time, this course will help foreign students to better master Chinese and understand China, which is beneficial and harmless. It can also change the world’s stereotype of China and shape a new image of China.
Establishing an International Engagement Model of Digital Identity Based on Blockchain

Blockchain technology has been changing the trust system through machine endorsement and mathematical algorithms, laying a technical foundation for network identity from centralized to decentralized management. The decentralized identity with core features, such as “no need for a management center and self-managed identities” have become a key direction for the evolution of a new generation of digital identity based on blockchain. Internationally, the United States, the EU, and other countries have promoted the technological exploration and application innovation of the decentralized identity, aiming to seize the international discourse power in the digital space. This paper establishes an evaluation model of international engagement in the decentralized identity field and takes the United States as a case to analyze the current development status and international engagement of the decentralized identity from multiple dimensions. Furthermore, it proposes some suggestions for other countries to improve the international engagement in the decentralized identity field.
A New Collaborative Filtering Algorithm Integrating Time and Multisimilarity

Aiming at the problem of low recommendation accuracy of existing recommendation algorithms, an algorithm integrating time factors and multisimilarity is proposed to improve the impact of long-term data, user attention, and project popularity on the recommendation algorithm and the similarity of user attributes is introduced to improve the problem of cold start to a certain extent. Considering that the longer the time, the less likely it is to be selected again, time is introduced into the algorithm as a weight factor. When the behavior occurs, i.e., interest in the project, so as to judge the similarity between users, not just the score value, we normalize the popularity to avoid misjudgment of high scoring and popular items. Because new users do not have past score records, the problem of cold start can be solved by calculating the similarity of user attributes. Through the comparative experiment on Movielens100K dataset and Epinions dataset, the results show that the algorithm can improve the accuracy of recommendation and give users a better recommendation effect.
Methodological proposal for constructing a classifier algorithm in clinical diagnostics of diseases using Bayesian methods

Abstract Objectives To develop a methodological proposal to build clinical classifiers using information about signs and symptoms reported by the patient in initial the consultation and laboratory test results. Methods The proposed methodology considers procedures typical of the Bayesian paradigm of statistics as predictive probabilities and the sequential use of the Bayes formula. Additionally, some procedures belonging to classical statistics, such as Youden’s index and ROC curves, are applied. The method assumes two possible scenarios; when the patient only reports the signs and symptoms and the physician does not have access to information from laboratory tests. The other one is when the physician, besides the patient’s information, knows the blood test results. The method is illustrated using data from patients diagnosed with dengue. Results The performance of the proposed method depends of the set of signs and symptoms and the laboratory tests considered by the doctor as good indicators of presence of the sick in the individual. Conclusions The classifier can be used as a screening tool in scenarios where there is no extensive experience treating sick individuals, or economic and social conditions do not allow laboratory methods or gold standard procedures to complete the diagnosis.
Review of "Mathematical Muffin Morsels - Nobody wants a small piece by William Gasarch, Erik Metz, Jacob Prinz, and Daniel Smolyak World Scientific, 2021 210 pages, Softcover, $59.99."

You have 5 muffins and 3 (as usual) hungry students. They ask that you divide the muffins equitably between them. Do you cut every muffin into 3 parts, and give 5 separate pieces of size 1/3 to each student? Well no, because that's too easy, and more importantly no student wants any piece that's less than a third of a muffin. Can this be done? Indeed it can! It turns out you can guarantee that everyone gets a piece of size at least 5/12. Can everyone be guaranteed a bigger piece than that? Alas, the answer is no. That's the bad news. The good news is that one could write a book about it, and these guys wrote a great one.
More than buttons on controllers

People can understand how human interaction unfolds and can pinpoint social attitudes such as showing interest or social engagement with a conversational partner. However, summarising this with a set of rules is difficult, as our judgement is sometimes subtle and subconscious. Hence, it is challenging to program agents or non-player characters (NPCs) to react towards social signals appropriately, which is important for immersive narrative games in Virtual Reality (VR). We present a collaborative work between two game studios (Maze Theory and Dream Reality Interactive) and academia to develop an immersive machine learning (ML) pipeline for detecting social engagement. Here we introduce the motivation and the methodology of the immersive ML pipeline, then we cover the motivation for the industry-academia collaboration, how it progressed, the implications of joined work on the industry and reflective insights on the collaboration. Overall, we highlight the industry-academia collaborative work on an immersive ML pipeline for detecting social engagement. We demonstrate how creatives could use ML and VR to expand their ability to design more engaging commercial games.
Multimodal adaptive empathic agent architecture

Empathic behavior between humans often has a positive effect, particularly in healthcare, since it facilitates relationship, improves engagement, and reduces stress and anxiety. Despite this importance of empathic communication and social relationship in healthcare, the effects of empathic behavior of embodied virtual agents that interact with patients in a multimodal and adaptive way have not been widely explored. The proposed multimodal adaptive empathic agent architecture (MAEAA) endows a therapeutic embodied virtual agent (EVA) with adaptive empathic behavior during interaction with a user. This architecture relies on user-agent interaction relationship and focuses on (1) the interpretation of user's behavior using multimodal input, and (2) the generation of multimodal empathic behavior during interaction. The solution have been used to develop a user study for the empathic interaction with students in the healthcare context [3].
Towards isolated execution at the machine level

Isolated execution with CPU-level protection, such as process sandboxes, virtual machines, and trusted execution environments, has long been studied to mitigate software vulnerabilities. However, the complexity of system software inevitably leads to vulnerabilities in isolated execution environments themselves, and the increase in hardware complexity makes it even more challenging to avoid hardware vulnerabilities. In this paper, we explore the possibility of isolated execution at the machine level using physically separated machines as an extreme case of isolation. We take advantage of recent hardware technologies to enable relatively low-latency communication between physical machines while dramatically reducing the attack surface and trusted computing base size compared to sharing computing resources on a single machine. As the first step in this direction, we discuss the security and performance of isolating processes to another machine with remote system calls and show its feasibility with preliminary experiments.