Reviewerly/exHarmony huggingface dataset:
A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality

Although deep learning has recently increased in popularity, it suffers from various problems including high computational complexity, energy greedy computation, and lack of scalability, to mention a few. In this paper, we investigate an alternative brain-inspired method for data analysis that circumvents the deep learning drawbacks by taking the actual dynamical behavior of biological neural networks into account. For this purpose, we develop a general framework for dynamical systems that can evolve and model a variety of substrates that possess computational capacity. Therefore, dynamical systems can be exploited in the reservoir computing paradigm, i.e., an untrained recurrent nonlinear network with a trained linear readout layer. Moreover, our general framework, called EvoDynamic, is based on an optimized deep neural network library. Hence, generalization and performance can be balanced. The EvoDynamic framework contains three kinds of dynamical systems already implemented, namely cellular automata, random Boolean networks, and echo state networks. The evolution of such systems towards a dynamical behavior, called criticality, is investigated because systems with such behavior may be better suited to do useful computation. The implemented dynamical systems are stochastic and their evolution with genetic algorithm mutates their update rules or network initialization. The obtained results are promising and demonstrate that criticality is achieved. In addition to the presented results, our framework can also be utilized to evolve the dynamical systems connectivity, update and learning rules to improve the quality of the reservoir used for solving computational tasks and physical substrate modeling.
On a Novel Representation of Multiple Textual Documents in a Single Graph

This paper introduces a novel approach to represent multiple documents as a single graph, namely, the graph-of-docs model, together with an associated novel algorithm for text categorization. The proposed approach enables the investigation of the importance of a term into a whole corpus of documents and supports the inclusion of relationship edges between documents, thus enabling the calculation of important metrics as far as documents are concerned. Compared to well-tried existing solutions, our initial experimentations demonstrate a significant improvement of the accuracy of the text categorization process. For the experimentations reported in this paper, we used a well-known dataset containing about 19,000 documents organized in various subjects.
Learning Robot Skills with Temporal Variational Inference

In this paper, we address the discovery of robotic options from demonstrations in an unsupervised manner. Specifically, we present a framework to jointly learn low-level control policies and higher-level policies of how to use them from demonstrations of a robot performing various tasks. By representing options as continuous latent variables, we frame the problem of learning these options as latent variable inference. We then present a temporal formulation of variational inference based on a temporal factorization of trajectory likelihoods,that allows us to infer options in an unsupervised manner. We demonstrate the ability of our framework to learn such options across three robotic demonstration datasets.
A New Approach for Modeling Correlated Gaussian Errors using Frequency Domain Overbounding

This paper presents a new method to overbound Kalman filter (KF) based estimate error distributions in the presence of uncertain, time-correlated noise. Each noise component is a zero-mean Gaussian random process whose autocorrelation sequence (ACS) is stationary over the filtering duration. We show that the KF covariance matrix overbounds the estimate error distribution when the noise models overbound the Fourier transform of a windowed version of the ACS. The method is evaluated using covariance analysis for an example application in GPS-based relative position estimation.
Human Activity and Gesture Recognition: A Review

Human activity recognition is the method of extracting and predicting the movements of the human body often indoors by using any hardware device such as a camera or sensor-based device. At an earlier stage collecting data from sensors is quite expensive but recently we have smartphones and other personal devices which have accelerometer-based sensors that track our activities. HAR is a classification method in which people have a great interest because we can find different actions of the human body like sitting, walking, running, jumping, jogging etc. by using body-worn sensors like accelerometer, gyroscope and applying methods like convolution neural network and other deep learning methods. The main objective of this review is to study different human activities, compounds and methods that are used to recognize actions and position of the body.
Learning Lexical Subspaces in a Distributional Vector Space

In this paper, we propose LexSub, a novel approach towards unifying lexical and distributional semantics. We inject knowledge about lexical-semantic relations into distributional word embeddings by defining subspaces of the distributional vector space in which a lexical relation should hold. Our framework can handle symmetric attract and repel relations (e.g., synonymy and antonymy, respectively), as well as asymmetric relations (e.g., hypernymy and meronomy). In a suite of intrinsic benchmarks, we show that our model outperforms previous approaches on relatedness tasks and on hypernymy classification and detection, while being competitive on word similarity tasks. It also outperforms previous systems on extrinsic classification tasks that benefit from exploiting lexical relational cues. We perform a series of analyses to understand the behaviors of our model. 1 Code available at https://github.com/aishikchakraborty/LexSub .
Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations

It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regarding these dependencies can greatly benefit optimization. We specifically consider the setting where partial evaluations are possible, meaning that the partial modification of a solution can be efficiently evaluated. Such problems are potentially very difficult, for example, non-separable, multimodal, and multiobjective. The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) can effectively exploit partial evaluations, leading to a substantial improvement in performance and scalability. GOMEA was recently shown to be extendable to real-valued optimization through a combination with the real-valued estimation of distribution algorithm AMaLGaM. In this article, we definitively introduce the Real-Valued GOMEA (RV-GOMEA), and introduce a new variant, constructed by combining GOMEA with what is arguably the best-known real-valued EA, the Covariance Matrix Adaptation Evolution Strategies (CMA-ES). Both variants of GOMEA are compared to L-BFGS and the Limited Memory CMA-ES (LM-CMA-ES). We show that both variants of RV-GOMEA achieve excellent performance and scalability in a GBO setting, which can be orders of magnitude better than that of EAs unable to efficiently exploit the GBO setting.
Design and implementation of power and area optimized AES architecture on FPGA for IoT application

Purpose The growing trends in the usage of hand held devices necessitate the need to design them with low power consumption and less area design. Besides, information security is gaining enormous importance in information transmission and data storage technology. In addition, today’s technology world is connected, communicated and controlled via the Internet of Things (IoT). In many applications, the most standard and widely used cryptography algorithm for providing security is Advanced Encryption Standard (AES). This paper aims to design an efficient model of AES cryptography for low power and less area. Design/methodology/approach First, the main issues related to less area and low power consumption in the AES encryption core are addressed. To implement optimized AES core, the authors proposed optimized multiplicative inverse, affine transforms and Xtime multipliers functions, which are the core function of AES’s core. In addition, to achieve the high throughput, it uses the multistage pipeline and resource reuse architectures for SBox and Mixcolumn of AES. Findings The results of optimized AES architecture have revealed that the multistage pipe line and resource sharing are optimal design model in Field Programmable Gate Array (FPGA) implementation. It could provide high security with low power and area for IoT and wireless sensors networks. Originality/value This proposed optimized modified architecture has been implemented in FPGA to calculate the power, area and delay parameters. This multistage pipeline and resource sharing have promised to minimize the area and power.
Opacity Measures of Fuzzy Discrete Event Systems

Opacity, as an important security property, has been well investigated in crisp discrete event systems. However, in some practical systems, the general behaviors, secret and nonsecret behaviors obtained by the intruder need to be expressed as fuzzy sets rather than crisp sets. Under this situation, the intruder makes inferences by fuzzy logic rather than classic logic, thus the notions of the fuzzy opacity measures are desired to be established. First, by means of the basic laws of fuzzy logic, a general fuzzy opacity measure, as a template, is presented, in this article, for the given generalized characterizations of secret and nonsecret behaviors. Moreover, a specific measure, called as fuzzy initial-final opacity (FIFO) measure, is investigated under the assumption that the secrets and nonsecrets can be coded into the initial-final state-pairs. In addition, two variants of FIFO measure are also proposed under the assumptions that the secrets and nonsecrets are only related to the initial-states or final-states. With these fuzzy opacity measures, the system designers can evaluate the opacity degree of the systems to be designed. To show the applicability of the fuzzy opacity measures to practical systems, we finally present an illustrative example of evaluating the location privacy protection systems in location-based services by means of the fuzzy opacity measures.
Professional Self-Identification of Future Educators as a Form of Personal Growth

The article covers the issue of professional self-identification of future specialists.The meaning of the concept of "self-identification" has been revealed, the circle of related concepts highlighted, and the process of self-identification in the context of personal self-determination analyzed.The purpose and objectives of the study have led to the need for a comprehensive use of the basic general scientific methods which are interchangeable at different stages of study.Among them, we should point out theoretical methods (scientific analysis, comparison, systematization, classification, integration of information obtained from psychological, pedagogical literature to design the scholarly apparatus of the study) and empirical research methods (diagnostic techniques: observation, surveying, testing).Professional selfidentification is a very important component in the path of professional development of a would-be specialist, as a future teacher must not only have theoretical knowledge and empirical skills, he must also clearly understand the purpose, objects, means, possible difficulties, prospects of his activity, etc.Based on the psychological and pedagogical analysis of scholarly ideas, the meaning and content of definitions, concepts, and conceptual provisions revealing the specific features of the future educators' professional self-identification have been established.It has been proved that a teacher is one of the most responsible professions; helping people, he must be able to maintain aloofness as well as provide professional assistance, and for this, he requires a high level of professional training.It has been determined that the concept of "professional self-identification" correlates with such currently popular notions as selfdetermination, self-actualization, self-fulfillment, and self-awareness.The study materials may be applied when preparing the lectures on pedagogical subjects such as Pedagogy, History of Pedagogy, Educational and Developmental Psychology, Methodology of Pedagogical Work.
Ivy

This paper describes Ivy, an office chair that represents sitting time of an office worker through growing ivy strands. The longer one sits, the more strands will grow onto the chair. By means of a qualitative interface called Ivy, we illustrate a design approach that is currently underrepresented in sedentary behavior interventions. With this approach, we counter the current trend of digitalization and quantification of health interventions. Instead of graphs and numbers, Ivy uses data physicalization as a qualitative interface that represents sitting. We describe the design, the process, and future research steps of Ivy as a critical perspective on sedentary behavior interventions. We aim to spark discussion amongst designers and researchers in the field of Human-Computer Interaction to use qualitative interfaces as a promising approach to deepen the user's relationship with the targeted behavior and enrich the ability to construct meaning from the feedback.
Power Inequities: Observations on the Development of Information and Communication Technologies, from an African Place

Using power to achieve political goals is not a new strategy. This paper unpacks observations about the use of power in the development of information and communication technologies (ICTs) in Africa. This analysis shows the use of unequal powers by non-African academics, development actors and technical experts (including the power to set the agenda, fund and build), embedded deeply in the current structures of ICT development for Africa. It also looks at how benefits accrue to non-local development actors and outlines some of the unmitigated risks for Africa.
Human-Drone Interaction: Using Pointing Gesture to Define a Target Object

This paper focuses on exploring the optimal gesture interface for Human-Drone Interaction in a firefighting scenario. For this purpose, we conducted a Preliminary Interview and User Study with seven subjects from the Kobe Firefighting Brigade, Japan. As a drone’s flight and locomotion properties significantly affect the user’s mental and physical expectations, differently compared to other grounded robots, a careful investigation of user-defined design preferences and interactions is required. This work proposes an examination and discussion, with experienced firefighters, about Human-Drone Interactions when relying solely on the drone’s monocular camera, without relying on other devices such as GPS or external cameras. The User Study had three main elements: A drone, a building, and the volunteering firefighters. During the Study, each firefighter should specify a window to the drone. The drone would theoretically use that window to enter the building and perform some designed tasks, like information gathering, thus saving the firefighter time and effort. Results show that the subjects always chose Pointing Gestures and voice commands as a means to communicate to the drone a target window. We built A prototype application with the resulting gesture from this investigation. In the prototype, a drone can understand to which object the user is pointing.
Identifying VoIP traffic in VPN tunnel via Flow Spatio-Temporal Features

The persistent emergence of new network applications, along with encrypted network communication, has make traffic analysis become a challenging issue in network management and cyberspace security. Currently, virtual private network (VPNs) has become one of the most popular encrypted communication services for bypassing censorship and guarantee remote access to geographically locked services. In this paper, a novel identification scheme of VoIP traffic tunneled through VPN is proposed. We employed a set of Flow Spatio-Temporal Features (FSTF) to six well-known classifiers, including decision trees, K-Nearest Neighbor (KNN), Bagging and Boosting via C4.5, and Multi-Layer perceptron (MLP). The overall accuracy, precision, sensitivity, and F-measure verify that the proposed scheme can effectively distinguish between the VoIP flows and Non-VoIP ones in VPN traffic.
Hubungan Efikasi Diri dan Motivasi Belajar Terhadap Hasil Belajar Berbasis E-Learning pada Mahasiswa Program Studi Pendidikan Matematika Universitas Flores

The objective of this study is to explore the correlation between self-efficacy and learning motivation towards the learning outcomes based on e-learning among the 4th semester students of mathematics education program, Universitas Flores. The research method used in this study was quantitative research with Linear Regression Analysis use of SPPS involving 40 students of the 4th semester randomly chosen. Data were collected through online questionnaire with Google Form. The Kolmogorov-Smirnov analysis and hypothesis testing with Bivariate Pearson Correlation through SPSS were used. The findings showed that self-efficacy (X1) and learning motivation (X2) had a positive impact towards learning outcomes based on e-learning (Y), as a result from the correlation analysis from R for X1 against Y= 0.895 and X2 against Y=0.923, and significant value between X1 and X2 against Y resulted with (**). Therefore, there was a positive influence with 1% significant. In the other words, the more increase or the better the self-efficacy and learning motivation are, the more increase the learning outcomes from the students.
A Literature Review of AR-Based Remote Guidance Tasks with User Studies

The future of work is increasingly mobile and distributed across space and time. Institutions and individuals are phasing out desktops in favor of laptops, tablets and/or smart phones as much work (assessment, technical support, etc.) is done in the field and not at a desk. There will be a need for systems that support remote collaborations such as remote guidance. Augmented reality (AR) is praised for its ability to show the task at hand within an immersive environment, allowing for spatial clarity and greater efficiency, thereby showing great promise for collaborative and remote guidance tasks; however, there are no systematic reviews of AR based remote guidance systems. This paper reviews the literature describing AR-based remote guidance tasks and discusses the task settings, technical requirements and user groups within the literature, followed by a discussion of further areas of interest for the application of this technology combined with artificial intelligence (AI) algorithms to increase the efficiency of applied tasks.
FetchSGD: Communication-Efficient Federated Learning with Sketching

Existing approaches to federated learning suffer from a communication bottleneck as well as convergence issues due to sparse client participation. In this paper we introduce a novel algorithm, called FetchSGD, to overcome these challenges. FetchSGD compresses model updates using a Count Sketch, and then takes advantage of the mergeability of sketches to combine model updates from many workers. A key insight in the design of FetchSGD is that, because the Count Sketch is linear, momentum and error accumulation can both be carried out within the sketch. This allows the algorithm to move momentum and error accumulation from clients to the central aggregator, overcoming the challenges of sparse client participation while still achieving high compression rates and good convergence. We prove that FetchSGD has favorable convergence guarantees, and we demonstrate its empirical effectiveness by training two residual networks and a transformer model.
Probabilistic Graph Convolutional Network via Topology-Constrained Latent Space Model

Although many graph convolutional neural networks (GCNNs) have achieved superior performances in semisupervised node classification, they are designed from either the spatial or spectral perspective, yet without a general theoretical basis. Besides, most of the existing GCNNs methods tend to ignore the ubiquitous noises in the network topology and node content and are thus unable to model these uncertainties. These drawbacks certainly reduce their effectiveness in integrating network topology and node content. To provide a probabilistic perspective to the GCNNs, we model the semisupervised node classification problem as a topology-constrained probabilistic latent space model, probabilistic graph convolutional network (PGCN). By representing the nodes in a more efficient distribution form, the proposed framework can seamlessly integrate the node content and network topology. When specifying the distribution in PGCN to be a Gaussian distribution, the transductive node classification problems can be solved by the general framework and a specific method, called PGCN with the Gaussian distribution representation (PGCN-G), is proposed. To overcome the overfitting problem in covariance estimation and reduce the computational complexity, PGCN-G is further improved to PGCN-G+ by imposing the covariance matrices of all vertices to possess the identical singular vectors. The optimization algorithm based on expectation-maximization indicates that the proposed method can iteratively denoise the network topology and node content with respect to each other. Besides the effectiveness of this top-down framework demonstrated via extensive experiments, it can also be deduced to cover the existing methods, graph convolutional network, graph attention network, and Gaussian mixture model and elaborate their characteristics and relationships by specific derivations.
Local-Source Enhanced Residual Network for Steganalysis of Digital Images

Steganalysis refers to the study of identifying hidden messages in images inserted by steganography. Although detection performance is greatly improved when adopting convolutional neural networks (CNNs), they require sophisticated tricks, such as preprocessing for suppression of image content, using absolute and truncated activation functions, and utilizing domain knowledge. These tricks help networks train stably and mitigate the convergence problem of early stages in training, but they also restrict the flexibility of CNNs, which limits their performance. In this paper, we propose a local-source enhanced residual network (LSER) with end-to-end learning. The LSER is simple in its architecture but has two distinct characteristics from previous methods. First, the LSER uses residual blocks without any normalization. We find batch normalization is an unnecessary module in our framework. Second, a local-source skip connection is added to bypass features of different levels, which allows more abundant feature representation. Moreover, the LSER exhibits state-of-the-art results compared with the existing work in both spatial and JPEG domain steganalysis. Furthermore, a simple self-ensemble method further improves its performance without any side information.