Reviewerly/exHarmony huggingface dataset:
Autonomous Detection and Assessment with Moving Sensors

Current approaches to physical security suffer from high false alarm rates and frequent human operator involvement, despite the relative rarity of real-world threats. We present a novel architecture for autonomous adaptive physical security called autonomous detection and assessment with moving sensors (ADAMS). ADAMS is a framework for reducing nuisance and false alarms by placing mobile robotic platforms equipped with sensors outside the normal asset perimeter. These robotic agents integrate sensor data from multiple perspectives over time, and autonomously move to obtain the best new data to reduce uncertainty in the threat scene. Inferences drawn from data fused over time provide ultimate decisions regarding whether to alert human operators. This paper describes the framework and algorithms used in a prototype ADAMS implementation. We describe the results of simulations comparing this framework to alternate paradigms. These simulations show ADAMS has a 4x increase in the range at which threats are identified versus traditional static sensors, and a 5x reduction in false alarms triggered versus frameworks where all sensor detections become alarms, leading to reduced operator load. Further, these simulations show this framework for reacting to new potential threats significantly outperforms methods which merely patrol the site. We also present the results of preliminary hardware trials of an exemplar prototype system, providing limited validation of the simulations in a real-time physical demonstration.
Research and Implementation of a Humanoid Robot Control Method Based on Action Mapping

Abstract In order to reduce programming difficulty and let humanoid robot act more humanlike, we designed a robotics control method for humanoid robots. Firstly, based on human body movements collected by Kinect camera, we calculated 3D data of joint point with pinhole model, and established space model of robot and skeleton model of human. Then, we transformed 3D coordinates of human joints into joint values by space vector method, mapping to the joints of the robot and guides the robot to move. Finally, with semi-autonomous control experiment, it shows that humanoid robot action mapping strategy is feasible and effective. Compared with the existing similar research, this paper implements the action guidance control of humanoid robot, enhances the intelligent degree of robot and expands the application scene and scope of humanoid robot. Depending on the human-like appearance, humanoid robot with action mapping could provide a much more intelligent interaction method.
Performance Prediction Method of Bridge Asphalt Pavement Material Based on Grey Theory

Under the combined action of natural environment and traffic load, the performance of asphalt pavement materials for asphalt concrete bridges is declining, which directly affects the speed of vehicles and the safety, economy and comfort of transportation. Therefore, according to the series grey prediction theory of grey system theory, the index prediction model is established for the basic indexes (PCI, SSI, RQI, SFC) in the service performance of bridge asphalt pavement, and the accuracy of the model is verified by residual error and correlation degree. Through the case analysis, it is concluded that the prediction results of the prediction model are similar to the actual situation, and the corresponding maintenance measures are put forward according to the prediction results. The research conclusions can provide a strong reference for the operation management and maintenance of highway bridges, and have certain guiding significance for improving the durability of bridges.
Africa: Entrepreneurs and Entrepreneurship in the Continent

The continent of Africa is fast evolving economically, socially, technologically, and globally. As the continent moves toward an entrepreneurial-based economy, earlier perspectives and opinions are slowly but surely changing.
A Monocular Motion Estimation Algorithm Based on Region Separation

The offset of feature points is one of the main reasons for the inaccuracy of the monocular Simultaneous Localization and Mapping Eight-Point Algorithm. In order to overcome the problems of high iteration times and insufficient score in global sampling estimation of fundamental matrix, this paper proposes an improved algorithm for monocular motion estimation. On the basis of ORB-SLAM, a method of clustering and segmenting images is introduced into the Frontend. Firstly, after eliminating mismatches, the Density-Based Spatial Clustering of Applications with Noise algorithm is used to separate the feature points of two images into a number of small regions. Then, according to the size of intra cluster divergence, the small regions are sorted and relabeled. Finally, with our multi regional extraction strategy, the feature points in different regions are selected for Eight-Point Algorithm and the matching degree of the estimated results to all the feature points is detected, with the highest score to be best estimation result. By comparing with the RANSAC method of ORB-SLAM, it is concluded that our proposed algorithm improves the accuracy of motion estimation and reduces the average estimation time of fundamental matrix based on TUM dataset.
Technology-based education

Abstract This study aims to determine the benefits of technology and the opportunity to do business in the world of education. To analyze the data, this research used descriptive and quantitative methods, namely descripting the data in a subjective and objective form of words and images. The results of the study showed that technology plays a very important role in education because it is very helpful in improving knowledge when students do not have or cannot contact their supervisor/teacher. This study concludes that in the world of education, it cannot be denied that efficient and effective learning process information technology is needed at this time. Therefore, the opportunity to start an Education Technology business is very promising because it is considered efficient and effective.
Hybrid Learning with Teacher-student Knowledge Distillation for Recommenders

Latent variable models have been widely adopted by recommender systems due to the advancements of their learning scalability and performance. Recent research has focused on hybrid models. However, due to the sparsity of user and/or item data, most of these proposals have convoluted model architectures and objective functions. In particular, the latter are mostly tailored for sparse data from either user or item spaces. Although it is possible to derive an analogous model for both spaces, this makes a system overly complicated. To address this problem, we propose a deep learning based latent model called Distilled Hybrid Network (DHN) with a teacher-student learning architecture. Unlike other related work that tried to better incorporate content components to improve accuracy, we instead focus on model learning optimization. To the best of our knowledge, we are the first to employ teacher-student learning architecture for recommender systems. Experiment results show that our proposed model notably outperforms state-of-the-art approaches. We also show that our proposed architecture can be applied to existing recommender models to improve their accuracies.
Improving Candidate Generation for Low-resource Cross-lingual Entity Linking

Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches based on resources from Wikipedia have proven successful in the realm of relatively high-resource languages (HRL), but these do not extend well to low-resource languages (LRL) with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for resources in the LRL by utilizing resources in closely-related languages, but the performance still lags far behind their high-resource counterparts. In this paper, we first assess the problems faced by current entity candidate generation methods for low-resource XEL, then propose three improvements that (1) reduce the disconnect between entity mentions and KB entries, and (2) improve the robustness of the model to low-resource scenarios. The methods are simple, but effective: we experiment with our approach on seven XEL datasets and find that they yield an average gain of 16.9% in Top-30 gold candidate recall, compared to state-of-the-art baselines. Our improved model also yields an average gain of 7.9% in in-KB accuracy of end-to-end XEL.
Adapting a method for tracking the movement of the camera in the visualization of augmented reality

Abstract This paper presents an adapted method for tracking camera motion in the visualization of augmented reality. A tracking algorithm based on significant points and descriptors without the use of optical flow is described. The tracking module is written in C++. Experiments have been carried out of the precision of the system using the camera motion-tracking module created.
GIST: distributed training for large-scale graph convolutional networks

Abstract The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the number of model parameters. Although some work has explored training on large-scale graphs, we pioneer efficient training of large-scale GCN models with the proposal of a novel, distributed training framework, called . disjointly partitions the parameters of a GCN model into several, smaller sub-GCNs that are trained independently and in parallel. Compatible with all GCN architectures and existing sampling techniques, (i) improves model performance, (ii) scales to training on arbitrarily large graphs, (iii) decreases wall-clock training time, and (iv) enables the training of markedly overparameterized GCN models. Remarkably, with , we train an astonishgly-wide 32–768-dimensional GraphSAGE model, which exceeds the capacity of a single GPU by a factor of $$8\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>8</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> , to SOTA performance on the Amazon2M dataset.
In-Depth Insights into Swarm Intelligence Algorithms Performance

Solving hard optimization problems is one of the most important research topics due to the countless applications in different areas. Since solving such problems is of great importance, numerous metaheuristics were developed, many of which belong to the group of swarm intelligence optimization algorithms. In recent decades, there has been an explosion in the number of the proposed swarm intelligence algorithms most commonly compared to other metaheuristics using one statistic such as average or median which can lead to putting algorithms in different rankings even though there are only small differences between them. In order to provide more insights into swarm intelligence algorithms’ performance, a deep statistical comparison is used. Five representative swarm intelligence optimization algorithms are ranked based on the obtained solutions values and their distribution in the search space while solving the CEC2013 benchmark functions. The used analysis differentiates algorithms that have statistically significant performance and measure the qualities of the exploration and the exploitation abilities of the tested algorithms.
Hyperbolic Attributed Network Embedding with self-adaptive Random Walks

Network embedding aims to learn low-dimensional vectors for vertices in complex networks. In real-world systems, nodes in networks are commonly associated with diverse attributes. However, classic approaches generally ignored the implicit relations and hierarchical information introduced by attributes. Motivated by this, we propose a new method named AHANE, short for Adaptive Hyperbolic Attributed Network Embedding, to learn the vertex representations of attributed networks. We perform a biased self-adaptive random walk, generating vertices sequences that can well retain the distribution of vertices in attributed networks. And then propose a novel framework to optimize both the explicit relations (i.e., observed directly connected links between nodes) and implicit relations (i.e., unobserved but transitive links through attributes) by using hyperbolic skip-gram model. We conducted extensive experiments on real datasets related to vertex classification, link prediction and nearest nodes searching. Experimental results on real-world datasets demonstrate the efficiency and effectiveness of AHANE.
Extreme Subject Targeting for Deep Learning Based Action Recognition

Human action recognition occupies an important place in the computer vision community. This is particularly true as deep learning-based approaches have emerged. Recent attempts to solve this problem suggested developing attention driven architectures that look for the most discriminant features of the RGB frames to model and recognize an action. In this paper, we push this concept to its extreme by intentionally providing the model with the only information it needs. In particular, we propose to investigate the contribution of focusing the learning of actions onto the targeted subjects in each frame of an RGB video instead of considering the whole frame. The proposed learning procedure has been evaluated using a state-of-the-art recognition model on a subset of the well known NTU RGB+D dataset. Results reported in the experimental section demonstrate the interest of the proposed approach.
Large Scale Graph Based Network Forensics Analysis

In this paper we tackle the problem of performing graph based network forensics analysis at a large scale. To this end, we propose a novel distributed version of a popular network forensics analysis algorithm, the one by Wang and Daniels [].Our version of the Wang and Daniels algorithm has been formulated according to the MapReduce paradigm and implemented using the Apache Spark framework. The resulting code is able to analyze in a scalable way graphs of arbitrary size thanks to its distributed nature. We also present the results of an experimental study where we assessed both the time performance and the scalability of our algorithm when run on a distributed system of increasing size.
Speeding Up VBGMM By Using Logsumexp With the Approximate Exp-function

Gaussian Mixture Models (GMM) are a representative method to realize clustering and are used in many applications such as probability density modeling and soft clustering. There are several methods for parameter estimation of the GMM, among which the Variational Bayesian Gaussian Mixture Model (VBGMM) is known that they are hard to overfitting. In the VBGMM, the logsumexp takes up most of the computation time. This is due to the heavy use of the exponential function, which is computationally intensive. In this paper, we explain a method to speed up logsumexp by using an approximate exponential function. As a result, logsumexp is accelerated 1.991 times faster, and VBGMM is accelerated 1.229 times faster.
Obstacle Avoidance Algorithm Based on Human Experience Knowledge

This paper studies the obstacle avoidance problem of mobile vehicles. Most of the existing algorithms for obstacle avoidance are proposed for specific environments, lacking adaptability for various environments. Motivated by mimicking the obstacle-avoidance actions of humans in different environments, this paper proposes a human experience knowledge (HEK) based obstacle avoidance algorithm. The proposed algorithm provides reliable support for mobile vehicles to implement tasks in a complex environment. Specifically, we first characterize the related experience knowledge of humans by converting to categorical variables. Then, a logistic regression method is utilized to model the corresponding knowledge based on categorical variables. Finally, the HEK-based algorithm, which dynamically adapts to different situations, is designed according to the corresponding knowledge model. Extensive comparative simulation results are conducted to demonstrate that the proposed algorithm has better adaptability and flexibility for various static obstacle environments and a low computational cost.
Towards Context-aware Distributed Learning for CNN in Mobile Applications

Intelligent mobile applications have been ubiquitous on mobile devices. These applications keep collecting new and sensitive data from different users while being expected to have the ability to continually adapt the embedded machine learning model to these newly collected data. To improve the quality of service while protecting users' privacy, distributed mobile learning (e.g., Federated Learning (FedAvg) [1]) has been proposed to offload model training from the cloud to the mobile devices, which enables multiple devices collaboratively train a shared model without leaking the data to the cloud. However, this design becomes impracticable when training the machine learning model (e.g., Convolutional Neural Network (CNN)) on mobile devices with diverse application context. For example, in conventional distributed training schemes, different devices are assumed to have integrated training datasets and train identical CNN model structures. Distributed collaboration between devices is implemented by a straightforward weight average of each identical local models. While, in mobile image classification tasks, different mobile applications have dedicated classification targets depending on individual users' preference and application specificity. Therefore, directly averaging the model weight of each local model will result in a significant reduction of the test accuracy. To solve this problem, we proposed CAD: a context-aware distributed learning framework for mobile applications, where each mobile device is deployed with a context-adaptive submodel structure instead of the entire global model structure.
YAITS

An educational, personalized electronic book called You Are in the Story (YAITS) is evaluated to assess its effectiveness on reading comprehension and motivation on sixth grade EFL students in a state secondary school in Turkey. The study was conducted with 48 students (aged 11-12) that included reading comprehension scores and reading motivation scores. Results indicated that reading comprehension scores showed no significant difference between groups. YAITS considerably led to superior reading motivation scores compared to the printed guided reading control group. Personalized e-book reading had a positive influence on the internal reading motivation of the learners. Participant students claimed that they preferred to read in their free time the printed version of the books because of the sense of ownership that the printed text storybooks offered.
Fishy labeling: Inter‐regional inequality of truthful product information

Abstract Over the past few years, food fraud has become a matter of great concern. We investigate how economic factors are related to the misdeeds of food retailers. Our model predicts that mislabeling is less likely in regions where (i) there are more sellers and (ii) the average income level is higher. The empirical patterns found in the DNA test data are in line with the predictions. Our findings can inform regulators of which regions and fish species to focus on when auditing.
VGG-Based Side Channel Attack on RSA Implementation

Profiling attack based on smaller and deeper neural network VGGNet is performed on a smart-card CRT-RSA implementation. CRT-RSA implementation uses security countermeasures including masking and time jittering. An ad-hoc method is applied to extract points from traces to perform an effective deep learning profiling attack. State-of-the-art convolutional networks are trained and compared on our data set. Experiment results show that the VGG13 network achieves the best performance with less training time, which can be a start point balancing performance and computational resources.
Research on the Transformation and Development of EGP Teachers to ESP Teachers in Post-University English Age Based on Computer

Abstract The popularization of calculation broadens the use of English in social activities, thus forming a driving force for the transformation of college English teaching from EGP to ESP. By analyzing the present situation, necessity, obstacles and strategic approaches of college English ESP teachers’ development and transformation, this paper provides theoretical support for the transformation of college English teachers in the computer age.
Digital Technologies in Education: History, Problems and Opportunities

The universal expansion of digital technologies had an impact on the educational system, too. Schools and higher education institutions have a vital need to reorganize themselves and apply new technologies efficiently. At the same time, this process entails a number of challenges. This paper is devoted to studying the influence of digital technologies on the educational process. It dwells on two main issues: 1) what the features of educational process changes under the influence of technologies and the main stages of its evolution are; 2) what opportunities and problems are generated by active implementation of digital technologies in education. The methodology foundation of the research is comprised of a series of methods (analysis, synthesis and more) and approaches (systemic, conceptual and discursive). Three periods of technological integration are identified: 1) pre-digital, 2) implementation of personal computers, 3) Internet era; their specific features are analyzed. It is established that inflated social expectations were not always met with the implementation of each new technology. Actual use of devices was low, while teachers did not fully master the new tools. Technological changes outlined in the paper led to generation gaps and a change of philosophical and pedagogical approaches, whereas connectivism is gaining popularity nowadays. It is established that despite a number of opportunities provided by digital technologies in education (flexibility, inclusion and personalization), a series of problems emerges (inequality, confidentiality, safety), as well as some technical and socio-psychological challenges. A conclusion is made that the teacher’s figure cannot yet become a thing of the past and digital technologies are best used as a supplement to regular learning, not its replacement.
Virtual Step PIN Pad: Towards Foot-input Authentication Using Geophones

The use of personal identification numbers (PINs) for authentication is ubiquitous due to their simplicity and flexibility. In this work, we present virtual step PIN pad, a novel and practical PIN entry scheme that allows a user to enter a PIN through foot tapping on the ground. The virtual step PIN pad utilizes geophones to collect structural vibration signals caused by foot tapping. When a user generates the activation signals by performing a predetermined sequence of foot taps within the target area, the virtual step PIN pad will be launched, and takes the foot tapping input by the user. The system then demodulates the corresponding structural vibration signals into a PIN. We have developed a prototype of the virtual step PIN pad and conduct a suite of experiments to evaluate its practicality and security. Experimental results show that the virtual step PIN pad can achieve an average success rate of 96.5% for inputting a human-chosen 4-digit PIN. Meanwhile, the success rate for an adversary at a distance of more than 2.5 meters away from the PIN pad to infer the target PIN decreases to below 3%.