Reviewerly/exHarmony huggingface dataset:
E-governance Experimental Framework Using a Mediated Artificial Superintelligence (mASI) System Research Study

This paper outlines the experimental framework for an e-governance study by the AGI Laboratory. The goal of this research study is to identify indications of the relative performance of e-governance methodologies and how those methods might be improved by applying advanced agent and collective based AI software. The agent in this study will be based on the Independent Core Observer Model Cognitive Architecture modified with mASI (mediated Artificial Superintelligence) collective system architecture. The study will apply different groups and methods to a static set of questions analyzing the quality of those results. We hope to identify the best application model for e-governance using this kind of technology and help us identify additional paths for research with the mASI research program and systems as applied to e-governance.
Data-driven Branch-and-bound Algorithms for Constrained Simulation-based Optimization

The wide use of detailed simulations for complex systems has led to a growing interest for methods that can optimize simulation-dependent problems using data, without explicit equations or derivatives. Due to the lack of derivatives and the dependence on sampling, simulation-based optimization algorithms lack convergence guarantees and often require a significant number of samples to identify an optimal solution with consistency. Moreover, the presence of black-box constraints is an open challenge because it further complicates sampling and identification of unknown feasible spaces. Previously, we have introduced the Data-Driven Spatial Branch-and-Bound algorithm for box constrained problems, which employs data-driven convex under estimators, finds upper and lower bounds on the optimal objective value and progressively prunes suboptimal subspaces until convergence. In this work, we present recent advances of this framework for handling simulation-based and equation-based constraints. We demonstrate the performance of these features with respect to convergence and sampling requirements through benchmark constrained optimization problems.
Blockchain-Based Load Carrier Management in the Physical Internet

Although recent studies have shown that the concepts of the Physical Internet (PI) can sustainably improve logistics processes, this new paradigm has not yet become established in practice. Companies fear losing control over their business and resist disclosing internal data. Even if some see a long-term improvement in the PI, current practical tests fail because the potential benefits can only be leveraged with a sufficiently large number of participants. We are therefore convinced that the introduction of the PI can only take place in small steps, in which companies retain control over their business. The technical platform required to operate the PI must be set up in a kind of parallel operation and disseminated via incentives without interfering with critical process flows. Such an incentive could be provided, e.g., by a shareable accounting platform that documents the exchange of pallets, boxes, etc. and keeps balance lists in sync with any trading partner. Companies continue to control their processes themselves, but at the same time they enable the construction of a backbone for distributed data exchange with arbitrary partners – a first step towards the establishment of a PI platform. In this paper we show how such a load carrier management platform can look like, how manipulation can be prevented with the use of Blockchain and how human handling can be improved.
Embedded and Parallel Implementation of the Stereo-Vision System for the Autonomous Vehicle

Stereo-vision is the most widely used technique in the development of environmental perception systems for intelligent transportation. The main requirement for the application of stereo vision on a vehicle is the processing time which must be very fast for autonomous driving in real time, whereas the computation of the correspondence of the images in the algorithm of stereo-vision requires a more computing power. This article presents an implementation of the stereo vision system to generate a scene disparity map using the sum of absolute differences (SAD), and the triangulation method for calculating the distance between the obstacle and the stereoscopic system. A parallel treatment is used to speed data processing and this algorithm is implemented in embedded platform. Several experiments are performed from a profiling analysis to have a statistical analysis for optimization.
Gaussian-Chaos-Enhanced Sub-dimensional Evolutionary Particle Swarm Optimization Algorithms for Global Optimization

In this paper, a sub-dimensional evolutionary of the particle swarm optimization algorithm based on Gaussian-chaos enhancement strategies (GC-SDPSO) is proposed. The advantage of the sub-dimensional evolutionary particle swarm algorithm is that each dimension of the particle is iterated in turn, rather than a whole. In order to overcome the shortcomings of sub-dimensional evolution algorithm, we introduce the complementary strategy of Gaussian and chaos in the iterative process. Gaussian mutation and chaos perturbation is performed with high aggregation degree to improve the global convergence and to ensure particle diversity. To evaluate the efficiency of the GC-SDPSO algorithm, we also choose nine different types of benchmark functions to test. Compared with SDPSO, GSDPSO, experimental results showed that GC-SDPSO is effective; convergence speed and the quality of the solution are improved.
Representation of graphs by means of generalized metrics

The paper deals with spaces with metrics of nonnegative integer values, called here discrete gem spaces. We recall the concept of proper continuity for such spaces. Undirected connected graphs can be seen as such spaces and we show that they form the class of spaces satisfying the property corresponding to convexity in Euclidean spaces. Moreover, the whole category of discrete gem spaces and properly continuous maps is shown to be equivalent to a subcategory of the category of connected looped digraphs and their homomorphisms.
Wireless channel prediction using ensemble of Extreme Learning Machines

In this article, we examine the possibilities and provide justification for extreme learning machines (ELMs) ensemble application in prediction of wireless channel condition. Single-input single-output (SISO) system in environments classified as microcellular and picocellular is used for analysis of the prediction model. Effectiveness and accuracy of ensemble based ELM algorithm to predict signal-to-noise ratio (SNR) in the channel is confirmed using, as performance indicators, the normalized mean squared error (NMSE) and time consumption. Moreover, the ensemble can effectively improve the generalization of the model compared to the single ELM. The results also show that ELM scan generates diverse prediction results, even when using the same training set.
F2DeepRS: A Deep Recommendation Framework Applied to ICRC Platforms

The applications of recommendation system (RS) are ubiquitous in our daily lives. Many RSs have exhibited excellent performances, especially those based on neural networks in recent years. However, many existing methods gained superiority in performance at the expense of hardware or computational costs, which are not applicable to applications that run on information and computation resource constrained (ICRC) platforms. The ICRC applications, such as in-vehicle infotainment system where there is no powerful computer resources, pose new challenges for developing a RS, including uncertainty in the availability of user profile or item content, less powerful computation resources but huge size of user and item pools, etc. With a focus on applications in such context, we developed a Fast and Flexible Deep Recommendation System (F2DeepRS) framework. In this framework, existing user/item knowledge learned by other even non-neural models can be leveraged to make the learning process computationally efficient. And the framework can be flexibly configured to incorporate user/item content information depending on availability. Experiments were conducted using R2-Yahoo! Music dataset, which is, to our best knowledge, the largest public dataset publicly accessible to our knowledge. The performance is compared with both traditional and state-of-the-art RS. The results have indicated that the proposed F2DeepRS framework is competitive in preference prediction tasks, and particularly, is very computationally efficient.
Attendance System Using Face Recognition Library

Among several methods used for monitoring the attendance of students, facial recognition is not mostly acclaimed. The emerging image processing technology is not a prevailing part of regular attendance monitoring systems regardless of the numerous benefits. To eliminate data handling processes, it is required to design an intelligent system that detects a student’s face and verifies it from the database. This paper proposes a system that uses TensorFlow for face identification and verification and displays students’ attendance on a web-based/local GUI. This system is capable of generating real-time output based on video feed obtained from the classroom. The outcome is labeled with the name of the student as entered in the database. This system functions on the Google Colab platform on Graphics Processing Units (GPUs). In its preliminary stage, a local dataset of a student under diverse light conditions has been experimented upon to study the behavior of the Face Recognition algorithm in illumination. The results suggest that the algorithm is effective under low light conditions as well. This paper primarily engenders significant advances in image processing through facial recognition library highlighting Machine Learning applications in everyday circumstances.
TruLook: A Framework for Configurable GPU Approximation

In this paper, we propose TruLook, a framework that employs approximate computing techniques for GPU acceleration through computation reuse as well as approximate arithmetic operations to eliminate redundant and unnecessary exact computations. To enable computational reuse, GPU is enhanced with small lookup tables that are placed close to the stream cores that return already computed values for exact and potential inexact matches. Inexact matching is subject to a threshold controlled by the number of mantissa bits involved in the search. Approximate arithmetic is provided by a configurable approximate multiplier that dynamically detects and approximates operations which are not significantly affected by approximation. TruLook guarantees the accuracy bound required for an application by configuring the hardware at runtime. We have evaluated TruLook efficiency on a wide range of multimedia and deep learning applications. Our evaluation shows that with 0% and less than 1% quality loss budget, TruLook yields on average 2.1× and 5.6× energy-delay product improvement over four popular networks on the ImageNet dataset.
Collaborative Multilabel Classification

In multilabel classification, strong label dependence is present for exploiting, particularly for word-to-word dependence defined by semantic labels. In such a situation, we develop a collaborative-learning framework to predict class labels based on label-predictor pairs and label-only data. For example, in image categorization and recognition, language expressions describe the content of an image together with a large number of words and phrases without associated images. This article proposes a new loss quantifying partial correctness for false positive and negative misclassifications due to label similarities. Given this loss, we develop the Bayes rule to capture label dependence by nonlinear classification. On this ground, we introduce a weighted random forest classifier for complete data and a stacking scheme for leveraging additional labels to enhance the performance of supervised learning based on label-predictor pairs. Importantly, we decompose multilabel classification into a sequence of independent learning tasks, based on which the computational complexity of our classifier becomes linear in the size of labels. Compared to existing classifiers without label-only data, the proposed classifier enjoys the computational benefit while enabling the detection of novel labels absent from training by exploring label dependence and leveraging label-only data for higher accuracy. Theoretically, we show that the proposed method reconstructs the Bayes performance consistently, achieving the desired learning accuracy. Numerically, we demonstrate that the proposed method compares favorably in terms of the proposed and Hamming losses against binary relevance and a regularized Ising classifier modeling conditional label dependence. Indeed, leveraging additional labels tends to improve the supervised performance, especially when the training sample is not very large, as in semisupervised learning. Finally, we demonstrate the utility of the proposed approach on the Microsoft COCO object detection challenge, PASCAL visual object classes challenge 2007, and Mediamill benchmark.
Semi‐supervised Robust Feature Selection with ℓq‐Norm Graph for Multiclass Classification

Flexible manifold embedding (FME) is a semi-supervised dimension reduction framework. It has been extended into feature selection by using different loss functions and sparse regularization methods. However, these kind of methods used the quadratic form of graph embedding, thus the results are sensitive to noise and outliers. In this paper, we propose a general semi-supervised feature selection model that optimizes an ℓ q -norm of FME to decrease the noise sensitivity. Compare to the fixed parameter model, the ℓ q -norm graph brings flexibility to balance the manifold smoothness and the sensitivity to noise by tuning its parameter. We present an efficient iterative algorithm to solve the proposed ℓ q -norm graph embedding based semi-supervised feature selection problem, and offer a rigorous convergence analysis. Experiments performed on typical image and speech emotion datasets demonstrate that our method is effective for the multiclass classification task, and outperforms the related state-of-the-art methods.
Fostering Empathy and Privacy: The Effect of Using Expressive Avatars for Remote Communication

The importance of effective remote communication is currently being emphasized as education moves from offline to online due to acceleration of the Untact Era (a contact-free society). In this study, we analyze the hindrances to effective distance learning, observe the effect on users of communicating through Expressive Avatar (EA), and suggest effective ways to use EA. First, through a questionnaire, 84% of the respondents quantitatively demonstrated experiencing a psychological burden due to exposure of their faces, in addition to low concentration during remote classes. Second, through in-depth interviews, they were found to prefer to replace their images with two- and/or three-dimensional avatars and believed that the avatars’ unique characteristics were important. Content using EA is expected to contribute to improved quality in distance learning by reducing psychological burden and promoting transmission of emotions.
Features of Group Work Organization in the Digital Space by the Example of Discipline «Digital Mathematics»

The possibilities of organizing group work using digital resources are discussed in the article. Currently, there is a clear need on the part of the state and employers for specialists who can work in a team and implement ambitious projects. At the same time, according to the authors, since the 90s, there has been an asymmetry in pedagogy towards individual forms of education and the individualization of the educational process in general. As a result, a significant proportion of graduates are not ready to work in the team. It is possible to overcome this contradiction by using a variety of forms of group work, starting from the first year. The authors present their experience in organizing such activities for first-year undergraduate economics students. A statistical analysis is presented that confirms the positive impact of group work on the academic success of students. A survey conducted among students also confirms their interest in developing teamwork skills.
Programming Languages: A Usage-based Statistical Analysis and Visualization

Understanding the popularity, and its trend, of programming languages can be important to anticipate which languages are best studied for future use, which are widely supported for use in enterprise projects, and investigate which skills are easiest to recruit for. In addition, popularity of languages used together can indicate tight communities of languages. This project aims to discover the current state of language popularity and use historical data to understand their respective trends. Data was sourced and processed from GitHub project language data and StackOverflow question tags be-tween 2008 and 2020. This data is then collated into visualizations with the aim of understanding popularity of languages in isolation, and in tandem. Several different visualizations were utilized, each giving different perspectives on the data. The findings were analyzed and discussed with a view to adequately identify the current trends in programming languages. From the results, it is understood that JavaScript is leading programming language in popularity in isolation. HTML and CSS are languages are commonly used in tandem with JavaScript.
The Necessity and Influencing Factors of Constructing a Diversified Cooperative Educational Mechanism for E-Commerce Major

Multi-subject collaborative education is bound to become the only way for the development of applied undergraduate colleges. This article will analyze the necessity of constructing a multi-subject collaborative education mechanism from multiple perspectives and analyze the multi-subject collaborative education of applied undergraduate colleges. Summarizing the main forms and cooperation modes of people, analyzing the influencing factors of the multi-subject collaborative education of applied undergraduate colleges and universities, can provide a new path and new thinking for e-commerce professional construction and talent training under the background of new economy and new liberal arts construction. By inspiring and guiding college students’ needs for professional and practical training, this research pays more attention to cultivating students’ learning autonomy, independence and creativity, and at the same time further enhances and broadens students’ social practice, strives to accelerate the reform of teaching practice, and enhance students. The effect of professional learning has strong practical significance and social value for promoting the deep integration of the diversified collaborative education of talent training forms and university teaching.
An Analysis of the Inferential Meaning of Chinese Even-Construction for Semantic Parsing

The even-construction in Chinese, whose form is lian + X + dou + Y, has been thoroughly investigated in Chinese linguistics. However, in natural language processing, the representation and acquisition of the full meaning of the construction remains to be a tough question. From the perspective of construction grammar, we compare the semantic functions of the even-construction and the corresponding subject-predicate structure. The meanings of the even-construction include a basic proposition and an inferential meaning. The basic proposition of the construction is the occurrence of an unlikely event. There are three kinds of interrelated but distinguishable inferential meanings: (1) the occurrence of an unlikely event means some related and more likely events will happen or have happened; (2) the occurrence of an unlikely event indicates an entity involved in the event has some special attributes; (3) the speaker expresses some subjective attitudes towards the occurrence of an unlikely event. Based on the analysis of the meaning of the even-construction, the ways to compute the meaning are discussed in this paper. It is also pointed out that the semantic computation of the construction depends on much linguistic knowledge, world knowledge and common sense, and temporary contextual information in communication.
Approaching deterministic and probabilistic truth: a unified account

Abstract The basic problem of a theory of truth approximation is defining when a theory is “close to the truth” about some relevant domain. Existing accounts of truthlikeness or verisimilitude address this problem, but are usually limited to the problem of approaching a “deterministic” truth by means of deterministic theories. A general theory of truth approximation, however, should arguably cover also cases where either the relevant theories, or “the truth”, or both, are “probabilistic” in nature. As a step forward in this direction, we first present a general characterization of both deterministic and probabilistic truth approximation; then, we introduce a new account of verisimilitude which provides a simple formal framework to deal with such issue in a unified way. The connections of our account with some other proposals in the literature are also briefly discussed.
Piano performance support system with a head-mounted display

In this paper, we propose a method to analyze and support the performance status from the images of hands and keys obtained by installing a web camera on a head-mounted display (HMD).
Self-Calibration Spherical Video Stabilization Based on Gyroscope

With the development of handheld video capturing devices, video stabilization becomes increasingly important. The gyroscope-based video stabilization methods perform promising ability, since they can return more reliable three-dimensional (3D) camera rotation estimation, especially when there are many moving objects in scenes or there are serious motion blur or illumination changes. However, the gyroscope-based methods depend on the camera intrinsic parameters to execute video stabilization. Therefore, a self-calibrated spherical video stabilization method was proposed. It builds a virtual sphere, of which the spherical radius is calibrated automatically, and then projects each frame of the video to the sphere. Through the inverse rotation of the spherical image according to the rotation jitter component, the dependence on the camera intrinsic parameters is relaxed. The experimental results showed that the proposed method does not need to calibrate the camera and it can suppress the camera jitter by binding the gyroscope on the camera. Moreover, compared with other state-of-the-art methods, the proposed method can improve the peak signal-to-noise ratio, the structural similarity metric, the cropping ratio, the distortion score, and the stability score.
Vision vibration measurement based on coded illumination in a single frame

A vision-based mechanical vibration measurement method is presented and verified by experiments in this paper. The coded illumination is projected on the objects by a digital light processing projector with a digital micromirror device in it. The projection patterns are designed to be concentric. In one integration time of the camera, the projector is exposed several times, which embeds temporal information in the images. A single frame can be divided into subframes by separating the coded concentric patterns. The centroids of the coded concentric patterns are fitted, and the centroids are treated as virtual feature points with vibration information. The acquisition devices are common low-speed cameras, and they record the vibration whose frequency exceeds the camera frame rate. The temporal resolution is increased by 10 times, corresponding to the 400 Hz sampling frequency in the experiment. We can measure the vibration of multiple points with different sampling frequencies. The frequency measurement accuracy is in the subhertz level in low-frequency measurement, relative error is always slightly greater than 0.01 in high-frequency measurement, and the amplitude resolution is 130 µm.