Reviewerly/exHarmony huggingface dataset:
Identifying Proficient Cybercriminals Through Text and Network Analysis

A few highly skilled cybercriminals run the Crime as a Service business model. These expert hackers provide entry-level criminals with tools that allow them to enhance their cybercrime operations significantly. Thus, effectively and efficiently disrupting highly proficient cybercriminals is of a high priority to law enforcement. Such individuals can be found in vast underground forums, though it is particularly challenging to identify and profile individual users. We tackle this problem by combining two analysis methods: text analysis with Latent Dirichlet Allocation (LDA) and Social Network Analysis with centrality measures. In this paper, we use LDA to eliminate around 79% of hacker forum users with very low to no technical skills, while also inferring the forum roles held by the remaining users. Furthermore, we use centrality measures to identify users with hugely popular public posts, including users with very few public posts who receive much attention from their peers. We study various preprocessing methods, wherein we achieve our results by following a series of rigorous preprocessing steps. Our proposed method works towards overcoming current challenges in identifying and interrupting highly proficient cybercriminals.
Digital Full-Face Mask Display with Expression Recognition using Embedded Photo Reflective Sensor Arrays

This paper presents a thin digital full-face mask display that can reflect an entire facial expression of a user onto an avatar to support augmented face-to-face communication in real environments. Although camera-based facial expression recognition technology has enabled people to augment their faces with avatars, application was limited to face-to-face communication in virtual environments. To enable digital facial augmentation with an avatar in a real space, we propose a digital face mask display system that integrates a lightweight flexible display with a thin facial expression recognition system. The thin wearable facial expression recognition system was implemented with photo reflective sensor arrays which can measure facial expressions at 40 feature points distributed across an entire face. We investigated a ten-class facial expression identification model based on an SVM training algorithm. The trained model achieved an average accuracy of 79% when identifying the facial expressions of multiple users. User experiments indicated that the proposed thin digital full-face mask display allows the wearer to control the facial expression of the avatar with a fast response rate and create a positive sense of self-agency and self-ownership toward the augmented avatar face.
Blockchain-based machine-to-machine communication in the industry 4.0 applied at the industrial mining environment

The Industrial Internet of Things is expected to attract significant investments for Industry 4.0. In this new environment, blockchain have immediate potential in industrial applications, providing immutable, traceable and auditable communication. However, works on the blockchain literature targets environments that do not meet the reality and requirements of a factory, in addition to needing cloud infrastructure in which communication delay increases. In this context, this article presents a blockchain-based machine-to-machine communication architecture, which it is deployed directly to industrial devices. Evaluation results reveal the need for the evolution of the current stage of blockchain technologies to meet the strict requirements time demanded by machine-to-machine communication in the industrial mining environment.
Synchronous and Asynchronous Learning Methods under the light of General Data Protection Regulation

The continuous evolution of technology affects various areas of people's daily lives. One of them is the field of education, where the use of technological means allows alternative ways of teaching. The spread of the SARS-CoV-2 coronavirus, which has led to the closure of educational institutions in many countries, has increased the significance of educational platforms. Some platforms allow synchronous communication between the tutor and the student, i.e., the tutoring process takes place at a predetermined time, simulating conventional training. Asynchronous educational platforms enable the student to study and solve exercises in the time and at the pace of their own choice. However, there are questions about users' data, especially considering the General Data Protection Regulation <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> (GDPR) in force since 25 May 2018. In this article, we describe the features that distinguish synchronous from asynchronous learning systems and identify their points of impact with specific GDPR elements, respectively. Particularly for the asynchronous method, we focus on platforms that process user data and appropriately adapt the educational material. Having identified the impact points, we address the issue by providing guidelines for similar system designers. We also compare the two methods in terms of their benefits, taking into account the design needed for GDPR compliance.
DISTANCE EDUCATION: UNIVERSITY INSTRUCTOR AND STUDENT PERSPECTIVE

Рассмотрен вопрос организации и реализации дистанционного обучения в высших учебных заведениях в период самоизоляции и карантина, установленных во многих странах в связи с распространением новой короновирусной инфекции. Приведены примеры основных проблем и тенденций, выявленных социологическими опросами преподавателей и студентов вузов, проведенными в России и за рубежом. Описаны результаты онлайн-опроса преподавателей и студентов Томского государственного педагогического университета, проведенного в весеннем семестре 2020 г. Определены возможные направления вуза по работе с цифровыми технологиями в образовании и внедрении успешных кейсов и практик. The paper considers the issue of organization and implementation of distance education in higher education institutions during the period of self-isolation and quarantine, established in many countries in connection with the spread of a new coronoviral infection. Examples of the main problems and trends revealed by sociological surveys of university instructors and students conducted in Russia and abroad have been described. Thus, the number of educational institutions had to introduce three main types of teaching and learning including asynchronous with students studying independently, synchronous based on web-conferencing services and blended covering both the types; the most popular trends in dealing with the implementation of digital teaching and learning indicated student volunteering activities in providing technological and psychological support to teachers and school students. The results of the online survey of university instructors and students in Tomsk State Pedagogical University conducted at the end of the spring semester 2020 have been exposed. The author identifies some possible directions of the university on work with digital technologies in education and implementation of successful cases and practices: an introductory stage of training university students and instructors how to use digital tools in the teaching and learning process, updating university digital platforms and tools, providing on-going technological and methodological support to university instructors, arranging seminars on best teaching and learning practices.
LSBCTR: A Learning Style-Based Recommendation Algorithm

This Research Full Paper presents a hybrid algorithm for the recommendation of Learning Objects (LO) aimed at students' learning profiles. In this sense, the Learning Styles-based Collaborative Topic Recommender (LSBCTR) algorithm was developed based on the Collaborative Topic Regression (CTR) model, a hybrid recommendation algorithm that combines a method of Collaborative Filtering (CF) and probabilistic topic modeling. The Learning Style is incorporated into the CTR to predict LO classification. The proposed model controls which classifications are more effective in the students' learning process and which LO recommendations fit better to the student's learning profile. Experiments were carried out in a real-world dataset collected from a Virtual Learning Environment (VLE) that was based on the inventory proposed by Felder and Soloman.
Semantic Segmentation of Brain MRI Based on U-net Network and Edge Loss

Brain MRI analysis is of great significance for extracting clinical information from patients and providing diagnostic recommendations for doctors. However, brain MRI is difficult to detect and segment because of its complex structure and great difference. To deal with these problems, in the field of medical image semantic segmentation, the model based on U-net structure in the depth neural network model has excellent performance. In this work, in order to increased the influence of image edge pixels on segmentation results and improved the accuracy of medical image segmentation, we provided a more optimized U-net model that can be applied to medical image semantic segmentation. The model integrated the convolution block attention module and after the feature extraction, combined with the edge detection network, the segmentation effect of edge pixels was enhanced. At the same time, by improving the residual convolution block, the amount of parameters was greatly reduced. On the BraTS-2017 data set, we had good experiment results.
Mental health diagnosis of college students based on facial recognition and neural network

In recent years, college campus incidents caused by mental health problems have been increasing year by year, and college students’ mental health problems have become the focus of attention of schools, society and parents. Based on this, this paper proposes a facial emotion recognition method for college students. By using moving target detection, target classification, target tracking, and a series of image preprocessing techniques, this method achieves intelligent monitoring of the area where college students are located and can automatically alert when a potentially dangerous target is found. Moreover, this method uses a combination of shape features and motion features to select and extract feature quantities. In addition, the method calculates the similarity between the target and candidate target corresponding sub-models, and according to the ability of each feature to distinguish between the target and the background, monitors the student’s mental health in real time and prevents various problems from occurring. Through experimental research, we can see that the model constructed in this paper has good performance.
Enhanced grey wolf optimization algorithm for group decision making in unmanned clusters

Unmanned platform cluster collaborative work will be a common mode to complete related tasks. The uncertainty and diversity of environment and task types make group decision-making a key technology to support unmanned cluster collaborative work. Gray wolf group has strong cognitive and cooperative ability, and can quickly track and surround prey in complex environment. The intelligent behavior characteristics such as information cognition and division of labor and cooperation are consistent with the decision-making needs of unmanned group. As a new swarm intelligence optimization algorithm, grey wolf optimization (GWO) algorithm is very suitable for solving such complex decision optimization problems. Aiming at the shortcomings of traditional GWO algorithm, such as slow convergence speed and easy to fall into local optimum, an enhanced grey wolf optimization (EGWO) algorithm is proposed to decompose the cooperative hunting movement of wolves in stages. The mathematical model mapped from search and track, pursuit, encirclement and attack prey is updated by three mathematical methods: nonlinear time-varying weighted weight differential mutation disturbance factor Archimedes spiral. The task stage switching model is constructed to deal with the state mutation in real time to enhance the correct decision-making and rapid response of the cluster. The experimental results show that the egwo algorithm can effectively improve the optimization accuracy and convergence speed of group decision-making.
Adaptive Quantum Process Tomography via Linear Regression Estimation

This paper proposes a recursively adaptive tomography protocol to improve the precision of quantum process estimation for finite dimensional systems. The problem of quantum process tomography is firstly formulated as a parameter estimation problem which can then be solved by the linear regression estimation method. An adaptive algorithm is proposed for the selection of subsequent input states given the previous estimation results. Numerical results show that the proposed adaptive process tomography protocol can achieve an improved level of estimation performance.
Towards an Extended POMDP Planning Approach with Adjoint Action Model for Robotic Task

In real-world environments, robotic task planning is expected to handle both partial observability and unexpected dynamics of the environment. A robust plan for the task requires the robot's observation actions to concurrently run with the task actions, to observe and adapt to environmental changes. The Partially Observable Markov Decision Process (POMDP) has been widely applied for planning under partially observable domains. For realistic robotic tasks, however, the POMDP model and planning algorithm are quite restrictive and unrealistic. One limitation is that task actions are modelled as atomic entities that only have endpoint effects, with no conditions specified at arbitrary points during task action execution. Also, the observation is obtained only after each task action execution, with no intermediate observations and decision-making during task action execution. To mitigate the limitations of POMDP planning, this paper first proposes an Adjoint Action Model (AAM) that explicitly defines the continuous interaction between robot's observation and task actions. Then we extend the POMDP task action model with intermediate invariant conditions which specifies the runtime properties of action execution. Finally, we propose the AAM-extended POMDP planning approach which handles observation action planning and task replanning for task action execution. We experimentally demonstrate that the plan from our proposed approach is more effective and robust to cope with the environment dynamics, comparing with the standard POMDP planning approach.
A Multi-Label Method of State Partition and Fault Diagnosis Based on Binary Relevance Algorithm

Prognostics and health management (PHM) is an important topic of rolling bearing, so partition of healthy stage and different degree of fault states are equally important as fault types. This study uses a multi-label learning method, Binary Relevance (BR) algorithm, for fault diagnosis of bearing data based on both of states partition and fault types judgment. The Binary Relevance algorithm simplifies the classification process by transforming the multi-classification problem into multiple binary classification problems. Using extreme learning machine ensures the classification speed and effect. By comparative experiments on XJTU-SY bearing datasets, the effectiveness of our method is proved to be superior.
Do Programmers Prefer Predictable Expressions in Code?

Abstract Source code is a form of human communication, albeit one where the information shared between the programmers reading and writing the code is constrained by the requirement that the code executes correctly. Programming languages are more syntactically constrained than natural languages, but they are also very expressive, allowing a great many different ways to express even very simple computations. Still, code written by developers is highly predictable, and many programming tools have taken advantage of this phenomenon, relying on language model surprisal as a guiding mechanism. While surprisal has been validated as a measure of cognitive load in natural language, its relation to human cognitive processes in code is still poorly understood. In this paper, we explore the relationship between surprisal and programmer preference at a small granularity—do programmers prefer more predictable expressions in code? Using meaning‐preserving transformations , we produce equivalent alternatives to developer‐written code expressions and run a corpus study on Java and Python projects. In general, language models rate the code expressions developers choose to write as more predictable than these transformed alternatives. Then, we perform two human subject studies asking participants to choose between two equivalent snippets of Java code with different surprisal scores (one original and transformed). We find that programmers do prefer more predictable variants, and that stronger language models like the transformer align more often and more consistently with these preferences.
Forward Stepwise Deep Autoencoder-Based Monotone Nonlinear Dimensionality Reduction Methods

Dimensionality reduction is an unsupervised learning task aimed at creating a low-dimensional summary and/or extracting the most salient features of a dataset. Principal components analysis (PCA) is a linear dimensionality reduction method in the sense that each principal component is a linear combination of the input variables. To allow features that are nonlinear functions of the input variables, many nonlinear dimensionality reduction methods have been proposed. In this paper we propose novel nonlinear dimensionality reduction methods based on bottleneck deep autoencoders (Kramer, 1991). Our contributions are two-fold: (1) We introduce a monotonicity constraint into bottleneck deep autoencoders for estimating a single nonlinear component and propose two methods for fitting the model. (2) We propose a new, forward stepwise (FS) deep learning architecture for estimating multiple nonlinear components. The former helps extract interpretable, monotone components when the assumption of monotonicity holds, and the latter helps evaluate reconstruction errors in the original data space for a range of components. We conduct numerical studies to compare different model fitting methods and use two real data examples from the studies of human immune responses to HIV to illustrate the proposed methods.
Time-Optimal and Collision-Free Path Planning for Dual-Manipulator 3D Printer

To meet the demand for high additive manufacturing efficiency, we are working on an FDM (Fused Deposition Modeling) printer called DEXTER which has dual Selective Compliance Articulated Robot Arms (SCARA). We develop a path planner to produce time-optimal motion paths for each of DEXTER's arms while guaranteeing that the arms do not collide with one another. We present a collision-free path planner for DEXTER's arms using an improved Sampling-Based Model Predictive Optimization (SBMPO) based on A* type optimization by adding efficient collision determination and a new type of cost function. The simulation results show that the improved SBMPO can be used to efficiently generate smooth collision-free paths and trajectories with bounded velocity and acceleration.
Path planning for autonomous mobile robot using transfer learning-based Q-learning

Transfer learning is the process of reusing the experience of agents in source tasks to improve the performance in new target tasks. In recent years, transfer learning has received more and more attention over the reinforcement learning settings. However, when applied to reinforcement learning, many problems will be exposed, such as how is the target task different from the source task, if the mappings between the tasks are required and what knowledge is transferred. Transfer learning algorithms have mainly been applied in discrete gridworld tasks. We first introduce the traditional Q-learning to the transfer algorithm based on the measurement of distance between two MDPs. Further, inspired by Q(lambad) algorithm, this paper investigates the improved Q-learning transfer algorithm to improve the learning efficiency. Finally, the simulation is shown to verify the effectiveness of the proposed algorithms.
A Bootstrapped Model to Detect Abuse and Intent in White Supremacist Corpora

Intelligence analysts face a difficult problem: distinguishing extremist rhetoric from potential extremist violence. Many are content to express abuse against some target group, but only a few indicate a willingness to engage in violence. We address this problem by building a predictive model for intent, bootstrapping from a seed set of intent words, and language templates expressing intent. We design both an n-gram and attention-based deep learner for intent and use them as colearners to improve both the basis for prediction and the predictions themselves. They converge to stable predictions in a few rounds. We merge predictions of intent with predictions of abusive language to detect posts that indicate a desire for violent action. We validate the predictions by comparing them to crowd-sourced labelling. The methodology can be applied to other linguistic properties for which a plausible starting point can be defined.
Sequential ensemble transform for Bayesian inverse problems

We present the Sequential Ensemble Transform (SET) method, an approach for generating approximate samples from a Bayesian posterior distribution. The method explores the posterior distribution by solving a sequence of discrete optimal transport problems to produce a series of transport plans which map prior samples to posterior samples. We prove that the sequence of Dirac mixture distributions produced by the SET method converges weakly to the true posterior as the sample size approaches infinity. Furthermore, our numerical results indicate that, when compared to standard Sequential Monte Carlo (SMC) methods, the SET approach is more robust to the choice of Markov mutation kernels and requires less computational efforts to reach a similar accuracy when used to explore complex posterior distributions. Finally, we describe adaptive schemes that allow to completely automate the use of the SET method.
Synthesizing Missing Data using 3D Reversible GAN for Alzheimer's Disease

Multi-modal brain data has been extensively used for improving the accuracy of disease diagnosis by providing complementary information. A problem using multi-modality data is that the data is commonly incomplete for many subjects in the ADNI dataset. A straightforward strategy to tackle this challenge is to simply discard subjects with missing data, but this will greatly reduce the number of training subjects for learning reliable diagnostic models. In this work, we first adopted the RevGAN model to complete missing data. After that, a 3D convolutional neural network was designed to perform AD diagnosis by all subjects (with both real images and synthetic PET images). We tested our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results have demonstrated that our synthesized PET images with 3D-RevGAN are reasonable, and our method is successful in Alzheimer's diagnosis.
Scale-free features of temporal localization of deformation in late stages of creep failure

The last stage of material failure often shows a regime with power-law acceleration as the material lifetime is approached. We study this experimentally in tensile creep with paper samples using digital image correlation. The last, tertiary creep stage exhibits scale-free features in the sample response (strain rate) and its fluctuations. It is accompanied by an increasing localization of strain at the location of final failure. The main features are reproduced by a material model built on a viscoelastic fiber bundle model.