Reviewerly/exHarmony huggingface dataset:
An approach for UAV indoor obstacle avoidance based on AI technique with ensemble of ResNet8 and Res-DQN

Unmanned Aerial Vehicles (UAVs) have many important applications in both civil and military areas. One of the most popular type of UAVs is quad-copter which uses four propellers to carry out its flight process. In this paper, a new control model which helps quad-copter to automatically find path and avoid obstacles indoor is introduced. The challenge of this model for quad-copter is the complex indoor environments with obstacles. Base on Deep Reinforcement Learning and Deep Learning platform, state of the art algorithms in Artificial Intelligence (AI), a new Ensemble model is proposed. The proposed model uses two algorithms to control quad-copter. One is quad-copter path finding algorithm (Deep Learning - ResNet8) and the other algorithm (Deep Reinforcement Learning - Res-DQN) dealing with obstacle avoidance. The output of both two algorithms are combined to change the direction of quad-copter adaptively with indoor environments. The simulation results have been assessed to verify the numerous performance of proposed control model.
Shoplifting Smart Stores using Adversarial Machine Learning

Smart stores cashier-less technology is partially based on camera-equipped object detection systems. Powerful machine learning algorithms are deployed at the back-end for classification. In this paper, we explore the usage of adversarial machine learning techniques to deceive the smart stores' classifiers. In particular, we experiment with printable adversarial patches and target making an expensive item classified as a cheaper one. By sticking patches to the objects and lifting them, a customer can make her customized discounts and alter the machine learning prediction. We discuss experiments, results, and possible countermeasures.
MaCA: a Multi-agent Reinforcement Learning Platform for Collective Intelligence

Heterogeneous multi-agent cooperative decisionmaking is one of the kernel problems in collective intelligence field. Reinforcement learning may be an effective technology to improve this research. An appropriate training environment is a necessary condition for intensive training effectively. In this study, a scalable multi-agent reinforcement training platform called MaCA was built to improve reinforcement learning effectiveness for heterogeneous collective cooperative decision making. First, a kernel environment aimed electromagnetism military combat background was established as the basis of the platform. Second, a set of reinforcement learning interface was designed for reinforcement learning algorithm adapting. Third, a reinforcement learning agent based on MARL algorithm and a rule-based agent were implemented. Finally, an experiment for training and rivalry was conducted to evaluate the effectiveness of the platform. The experimental results show that after trained in MaCA, the MARL agent shows certain cooperation ability and achieved 100% win rate against the rule-based agent after hundreds of thousands of training iteration. The results demonstrate that MaCA is a suitable and effective environment of multi-agent decision training in heterogeneous reinforcement learning.
Tourist Recommender System using Hybrid Filtering

Recommendation has become difficult task to predict the ratings for the new user and new item in the recommender systems. This problem is known as `cold-start'. In this paper a hybrid filtering which uses content-based filtering, collaborative filtering and demographic is proposed to address the `cold-start' problem. To predict the new user rating and hence to find the similar items with neighborhood, hybrid filtering uses demographic details. This proposed approach uses the advantages and overcomes the drawbacks that are in existing recommendation methods like CB and CF. In this paper different dataset are used to predict the ratings for the new user using the demography and different POIs are extracted that satisfy the new user. The results produced by this approach are relatively acceptable. This proposed method can work effectively and efficiently to solve the cold-start problem.
Attention shake siamese network with auxiliary relocation branch for visual object tracking

Siamese network is highly regarded in the visual object tracking filed because of its unique advantages of pairwise input and pairwise training. It can measure the similarity between two image patches, which coincides with the principle of the matching-based tracking algorithm. In this paper, a variant Siamese network based tracker is proposed to introduce attention module into traditional Siamese network, and relocate the object with some auxiliary relocation methods, when the proposed tracker runs under an untrusted state. Firstly, a novel attention shake layer is proposed to replace the max pooling layer in Siamese network. This layer could introduce and train two different kinds of attention modules at the same time, which means the proposed attention shake layer could also help to improve the expression power of Siamese network without increasing the depth of the network. Secondly, an auxiliary relocation branch is proposed to assist in object relocation and tracking. According to the prior assumptions of visual object tracking, some weights are involved in the auxiliary relocation branch, such as structure similarity weight, motion similarity weight, motion smoothness weight and object saliency weight. Thirdly, a novel response map based switch function is proposed to monitor the tracking process and control the effect of auxiliary relocation branch. Furthermore, in order to discuss the effect of pooling layer in Siamese network, 9 pooling and attention architectures are proposed and discussed in this paper. Some empirical results are shown in the experiment part. Comparing with the state-of-the-art trackers, the proposed tracker could achieve comparable performance in multiple benchmarks.
Empirical study on imbalanced learning of Arabic sentiment polarity with neural word embedding

With the proliferation of social media and mobile technology, huge amount of unstructured data is posted daily online. Consequently, sentiment analysis has gained increasing importance as a tool to understand the opinions of certain groups of people on contemporary political, cultural, social or c ommercial issues. Unlike western languages, the research on sentiment analysis for dialectical Arabic language is still in its early stages with several challenges to be addressed. The main goal of this study is twofold. First, it compares the performance of core machine learning algorithms for detecting the polarity in imbalanced Arabic tweet datasets using neural word embedding as a feature extractor rather than hand-crafted or traditional features. Second, it examines the impact of using various oversampling techniques to handle the highly-imbalanced nature of the sentiment data. Intensive empirical analysis of nine machine learning methods and six oversampling methods has been conducted and the results have been discussed in terms of a wide range of performance measures.
Competencies Required for Developing Computer and Information Systems Curriculum

This full paper in the research category investigates the core competencies required in Computer and Information Systems (CIS) education. We define competency, in the context of existing literature, to mean skills, knowledge and disposition required in a given profession. Skills can be hard skills or soft skills. Insight and awareness of competencies are core imperatives in curriculum design and also play a critical role in the development of academic programs, courses, and syllabi. Despite their importance, there are limited studies that investigate and provide the core Information Systems (IS) competencies especially from an industry perspective. The research question at hand is: What core competencies are required in a Computer and Information Systems program from an industry perspective' To answer this question, this paper conducts a thematic analysis to analyze 132 CIS-related-industry job descriptions collected from five currently-popular job portals (indeed.com, monster.com, careerBuilder.com, idealist.org and simplyhired.com). We analyzed this data to contribute a set of competencies that can support academic endeavors, such as curriculum revision in the Computer Information Systems profession. Furthermore, we provide a classification of those competencies into knowledge, skills and disposition. CIS instructors and academic program developers can use the findings of this research to facilitate the development CIS-related academic-program outcomes, learning objectives and curricula. Our findings can also be used to facilitate competency-based learning in the CIS profession.
Impairment Shift Keying: Covert Signaling by Deep Learning of Controlled Radio Imperfections

The broadcast nature of the wireless spectrum necessarily implies the possibility of eavesdropping, as well as malicious modification of waveforms through inexpensive, widely available software-defined radios (SDRs). This paper proposes a method for covert wireless communications that can be used to authenticate a device or exchange private information between devices. Our approach, called Impairment Shift Keying (ISK), introduces small yet controlled modifications to the radio transmitter hardware, which distorts regular standards-compliant waveforms, such as WiFi, with only 1% increase in bit error rate. A deep convolutional neural network (CNN) is trained to learn these overlay signal variations, which serves as a low-overhead classifier returning a binary 0 or 1 per detected impairment pattern. By mapping device-specific injected impairment patterns to signal variations, ISK validates device IDs with only few inphase (I) and quadrature (Q) samples. Furthermore, through an experimental testbed, ISK is shown to be resilient to channel and SNR level variations, allowing a throughput of 93-1500 Kbps on the covert channel that is undetected by other receivers.
A Novel Dynamic Path Re-Planning Algorithm With Heading Constraints for Human Following Robots

Paths will be re-planed timely according the change of obstacles and target position for Human Following Robots (HFRs) so that they can run safely and rapidly.Traditional path planning algorithms are generally focused on how to avoid obstacles or connect with the new target position effectively, however there are rarely studies on the elimination of robot heading fluctuation which is caused by path re-planning.Concerning this issue, we introduce a novel dynamic path re-planning algorithm AT * which is based on A * and walkable area thinning.When robots heading difference value between the current and the last moment which is derived from A * exceeds the threshold, AT * will search for path connection points by walkable area thinning, and then a new path going through optimal connection point and meeting heading constraints will be planned.Thinning algorithm is improved in order to make the passable skeleton can connect path origin and the end.Results from simulation experiments show that AT * algorithm keeps heading stability as a priority at path re-planning and the robot motion stability can be improved effectively.
Social media and counterpublic spheres in an authoritarian state: Exploring online political discussions among Cambodian Facebook users

Social media have been viewed as an important channel through which dissenting and marginalized voices can be disseminated and discussed in authoritarian political settings, such as Cambodia. Drawing on a case study of viral discussions on Facebook concerning a hit-and-run traffic accident that took place in March 2019 in Cambodia, this paper explores (1) the key themes that arose from the online conversations and whether they constituted counterpublic discourse, (2) the discursive strategies used by online discussants, and (3) the political implications of such online phenomena. Based on a thematic and discourse analysis of 5000 Facebook comments generated from 100 widely shared public posts about the incident, the study uncovered a number of recurrent themes emerging from the online interactions, most of which were critical of the authoritarian establishment. The study also identified three key discursive strategies employed by Facebook users to discuss and question the status quo: satire, direct criticism and rumour. In addition, guided by Fraser (1990) and Warner (2002)’s concepts of “counterpublics” and boyd (2010)’s recent notion of “networked publics”, the study argues that, through such online discursive interactions, a new social identity in the form of online counterpublics has been formed.
Evaluate Control Laws Related To Interaction Matrix For Image-Based Visual Servoing

In this paper, the methods for approximating the interaction matrix used in image-based visual servoing are analyzed. The five approximation methods are reviewed and evaluated based on the ability to solve the issues related to the interaction-matrix approximations such as the camera retreat problem, out of camera's field of view, singularity, stability and local minimum. In general, behavior of the methods is different although some methods have similar behavior in a few special case and each method has its own advantages and disadvantages Therefore, depending on each specific case, the appropriate approximation methods are chosen. The simulation results for the cases that cause the issues in image-based visual servoing show the strengths and drawbacks of each method, thereby helping to select the appropriate methods.
An Efficient Polynomial Multiplier Architecture for the Bootstrapping Algorithm in a Fully Homomorphic Encryption Scheme

Bootstrapping algorithm, which is the intermediate refreshing procedure of a processed ciphertext, has been the performance bottleneck among various existing Fully Homomorphic Encryption (FHE) schemes. Specifically, the external product of polynomials is the most computationally expensive step of bootstrapping algorithms that are based on the Ring Learning With Error (RLWE) problem. In this paper, we design a novel and scalable polynomial multiplier architecture for a bootstrapping algorithm along with a conflict-free memory management scheme to reduce the latency, while achieving a full utilization of the processing elements (PEs). Each PE is a modified radix-2 butterfly unit from fast Fourier transform (FFT), which can be reconfigured to use in both the number theoretic transform (NTT) and the basic modular multiplication of polynomial multiplication in the external product step. The experimental results show that our design yields 33% less area-time product than prior designs.
Enhancement of child gross-motor action recognition by motional time-series images conversion

Acquisition of gross motor skills during childhood is quite significant for the physical and psychological developments; hence, early intervention and adequate care are desired if possibility of their developmental disability is discovered. Due to these reasons, several tests were presented and have been used in several countries, but they are not so popular in Japan because of staff shortage and/or time deficit. Therefore, the present authors have been studying a laborsaving AI assessment system based on the gross motor action recognition (GM-AR). In our previous studies, main scheme of the system was proposed, and several IT tools and the GMARs were developed. This paper focuses on an improvement of the GM-AR by expanding the previous result, and the following three methods are newly presented: conversion of the skeleton's time-series data into motional time-series images, the data augmentation, and new CNN-based deep learning. As a result, applying these methods to actual GM assessment including thirteen GM skills at kindergarten, total about 0.15 million data sets were obtained and the classification accuracy was extremely improved to 99.5% by cross validation.
Internet of Things Based Pothole Detection System using Kinect Sensor

One of the approaches to diminish street mishap is distinguishing the mounds and potholes present out and about. The greater part of the mishaps prompting demise caused because of potholes, speed breakers, and alcoholic driving. In this paper. The Internet of things based street checking is proposed to recognize the potholes and protuberances on streets through Kinect Sensor so we can maintain a strategic distance from the accidents. The proposition portrays one such street support utilizes Kinect sensor, Raspberry Pi, with Internet Capabilities that are associated with an Internet-of-Things. The proposed arrangement brings ongoing information about the condition of the street in a specific area which can be sent to nearby specialists to participate in further activities.
Deformation Wave Theory and Application to Optical Interferometry

A method to diagnose the deformation status of solid objects under loading is discussed. The present method is based on a recent field theory of deformation and fracture and optical interferometry known as the Electronic Speckle-Pattern Interferometry (ESPI). Using one of the most fundamental principles of physics referred to as symmetry in physics, this field theory formulates all stages of deformation and fracture on the same theoretical basis. In accordance with the formalism, the theory has defined the criteria for different stages of deformation (linear elastic, plastic and fracturing stages) expressed by certain spatiotemporal features of the differential displacement (the displacement occurring during a small time interval). The ESPI is used to visualize the differential displacement field of a specimen as two-dimensional, full-field interferometric fringe patterns. This paper reports experimental evidence that demonstrates the usefulness of the present method. A tensile load is applied to an aluminum-alloy plate specimen at a constant pulling rate and the resultant in-plane displacement field is visualized with a two-dimensional ESPI setup. The differential displacement field is obtained at each time step and the interferometric fringe patterns are interpreted based on the criterion for each stage of deformation. It has been found that the criteria of linear elastic deformation, plastic deformation and fracturing stage are clearly observed in the corresponding fringe patterns and that the observations are consistent with the loading characteristics.
Cumulative/dynamic ROC curve estimation under interval censorship

The receiver operating characteristic (ROC) curve is a graphical tool commonly used to assess the discriminatory ability of continuous markers in binary classification problems. Different extensions of the ROC curve have been proposed in the prognosis context, where the characteristics in the study are time-dependent events. Perhaps the most direct generalization is the so-called cumulative/dynamic (C/D) ROC curve. The main particularity when dealing with the C/D ROC curve estimation is the presence of incomplete information. Several approximation methods addressing this censoring problem have been suggested in the statistical literature, most of them focused on the right-censored case. Interval censorship arises naturally from those studies where subjects undergo periodical follow-ups. They may miss a scheduled appointment and the exact event times are only known to fall in a certain range. A new approach for estimating the C/D ROC curve under the particular scheme of interval censorship is presented in this work. Its finite-sample behaviour is studied via Monte Carlo simulations on two different scenarios. Results suggest that the proposed approximation is better than the existing one in terms of absolute error. Its direct application is illustrated in the real-world data set which motivated this research. The uniform strong consistency and a suitable R function for its practical implementation are provided as Appendices.
The Performance Evaluation of a Website using Automated Evaluation Tools

The desired qualities of a website should be high performance, usability, mobile-friendliness, accessibility, SEO (Search Engine Optimization), linked with social media, and secured. In order to gain the insights needed for improving the website ranking suggested by the search engine, performance evaluation is essential. Accordingly, this paper evaluates the performance of a herbal cosmetic website by using automated evaluation tools, i.e., SEOptimer, Website Grader, and Qualidator. The result showed that using the SEOptimer tool, the highest level items - usability, mobile-friendliness, obtained a score of 100%. And security - obtained a score of 71%. In the case of the Website Grader tool, it was found that the highest level items were usability, SEO, and security, each of which had a score of 100%. Finally, in the case of the Qualidator tool, it was found the highest level item - accessibility - had a rating of 75.1%, usability had a rating of 70.5%, and SEO had a rating of 67.1%. Therefore, it can indicate that the cosmetics.com website exhibits an excellent performance.
Differential Privacy-preserving Distributed Machine Learning

Distributed machine learning (DML) has received widespread attentions, where a shared prediction model is collaboratively learned by multiple servers. However, since the data used for model training often contains users' sensitive information, DML faces potential risks of privacy disclosure. Particularly, when servers are untrustworthy, it is critical while challenging to guarantee users to obtain privacy preservation that is self-controllable and does not weaken in strength during the whole DML process. In this paper, we propose a privacypreserving solution for DML, where privacy protection is achieved through data randomization at the users' side and a modified alternating direction method of multipliers (ADMM) algorithm is designed for servers to mitigate the effect of data perturbation. We prove that this solution provides differential privacy guarantee and preserves the convergence property of a general ADMM paradigm. Also, we provide extensive theoretical analysis about the performance of the trained model. Numerical experiments using standard classification datasets are finally conducted to validate the theoretical results.
Approaching human vision perception to designing visual graph in data visualization

Summary In a data visualization system, data are converted into information and/or knowledge by human vision perception of graph representing these data. This research studies the characteristics of human vision perception and the relationships between data variables of dataset to constitute cube models representing dataset as a graph on the cube. The research proposes the process designing multidimensional graph considered as structural graph on cube model to represent multivariate data. Structural graph is designed in the light of the relationships between data variables and human vision perception. After mapping structural graph onto computer screen, the graph is integrated into retinal variables to improve objectively visual features and respond subjectively human perception of vision.
The Implementation of LeNet-5 with NVDLA on RISC-V SoC

NVDLA (NVIDIA Deep Learning Accelerator) is an open-source CNN (Convolutional Neural Network) accelerator and has broad application prospects. The implement of Lenet-5 based on NVDLA is presented in this paper, which makes the recognition of handwritten numeral more efficient. Under the condition of no compiler for NVDLA is available so far, the function of registers and the working principles of NVDLA were explored. The LeNet-5 runs on NVDLA successfully through configuring the NVDLA registers. This work could also make a base for the implementation of other deep learning networks, such as AlexNet and VGG. The performance of the accelerator is estimated, and the results of the experiment indicate that Rocket SoC + NVDLA is able to achieve higher CNN processing efficiency, up to 4647x times than RISC-V core.