Reviewerly/exHarmony huggingface dataset:
Consolidation of Usability Problems With Novice Evaluators Re-Examined in Individual vs. Collaborative Settings

Abstract Usability testing can involve multiple users and evaluators. In such cases, consolidating usability problems (UPs) constitutes an essential part of data analysis. In a between-subjects design, this study aims to re-examine a previous study by comparing the results of novice evaluators merging UPs individually vs. collaboratively and to assess the quality of the final UP lists, by computing the merging rate and the accuracy rate, respectively. Law and Hvannberg compared the results of evaluators merging UPs individually vs. collaboratively in a within-subjects design, revealing a tendency towards merging UPs in collaborative settings. In the present study, 45 novice evaluators consolidated four UP lists into a single UP master list while working alone or with a partner. The results showed no significant difference between evaluators in the two settings, suggesting that the UP consolidation process does not benefit from positive group decision effects.
Risk-Based and Dependency-Aware Criteria Specification for Cloud Services Security Evaluation

A key problem with much of the literature on cloud services evaluation is that it fails to consider the context and the dependencies between the evaluation criteria spanning different services and the various, and often conflicting, customers' requirements. Different aspects are important for different applications, and the choice of evaluation criteria needs to reflect this. This paper proposes a method for selecting the core security evaluation criteria based on the cause-effect relationships between the services, threats, vulnerabilities, and security controls using multiple evaluation factors including the importance, likelihood, and impact. The Analytic Network Process (ANP) is used to estimate the direct influence between the attributes, and the Decision Making Trial and Evaluation Laboratory (DEMATEL) is used to obtain the total influences (direct and indirect) between those attributes. The goal is not to blindly use all the criteria that exist in the literature, but instead to identify those that are most relevant to the context of the evaluation considering the characteristics of cloud service models, deployment models, and the overall evaluation context. This allows to focus on the situation and eliminate unnecessary tasks.
Distributed Multi-class Road User Tracking in Multi-camera Network For Smart Traffic Applications

Reliable tracking of road users is one of the important tasks in smart traffic applications. In these applications, a network of cameras is often used to extend the coverage. However, efficient usage of information from cameras which observe the same road user from different view points is seldom explored. In this paper, we present a distributed multi-camera tracker which efficiently uses information from all cameras with overlapping views to accurately track various classes of road users. Our method is designed for deployment on smart camera networks so that most computer vision tasks are executed locally on smart cameras and only concise high-level information is sent to a fusion node for global joint tracking. We evaluate the performance of our tracker on a challenging real-world traffic dataset in an aspect of Turn Movement Count (TMC) application and achieves high accuracy of 93% and 83% on vehicles and cyclist respectively. Moreover, performance testing in anomaly detection shows that the proposed method provides reliable detection of abnormal vehicle and pedestrian trajectories.
Research on the Resource Shortage Scheme Based on High Availability Cluster

With the rapid development of big data and artificial intelligence, universities, enterprises, and governments have invested a lot of manpower and material resources to conduct related research. Especially in the transition period of colleges and universities in the cultivation of applied-type skilled talents, the lack of hardware resources led to the study of college teachers staying at the theoretical level. This paper provides how to build a high-availability cluster based on Hadoop environment under the existing conditions of colleges and universities, which effectively solves the problem of shortage of hardware resources for college teachers and provides a simple and convenient way for everyone to obtain a safe high-availability cluster environment of colleges and universities.
Exploring Community Security Interventions Using Design Thinking Approach

This study uses design thinking to explore the creation and usage of community security intervention tools. Four communities in the Asokore Mampong Municipality in Ghana were selected purposively for the study. Data were gathered through focus group discussions and interviews using the design thinking approach. The findings of the study revealed that design thinking is an appropriate tool for in-depth data gathering. On the security level, it was revealed that neighborhood watch-dog and volunteer groups were the two major crime prevention tools used in the communities.
Reduced Kernel Extreme Learning Machine for Traffic Sign Recognition

Traffic Sign Recognition (TSR) is an important application that must be incorporated in autonomous vehicles. However, machine learning methods, used normally for TSR, demand high computational resources, which is in conflict with a system that is to be incorporated into a vehicle where size, cost, power consumption and real-time response are important requirements. In this paper, we propose a TSR system based on a Reduced Kernel Extreme Learning machine (RK-ELM) which is efficiently implemented in a Graphic Processing Unit (GPU). On the one hand, the inherent simplicity of ELM-based models makes possible the recognition process to be realized in a very fast and direct way. On the other hand, the computations involved in RK-ELM can be easily implemented in a GPUs so the recognition process is clearly boosted. Experiments carried out with a commonly used dataset benchmark validate our proposal.
PXNOR: Perturbative Binary Neural Network

Research into deep neural networks has brought about architectures and models that solve problems we once thought could not be approached by machine learning. Year after year, performance improves, to the point that it is becoming difficult to differentiate between the strengths of deep neural network models given our current data sets. However, due to their significant requirements in terms of hardware resources, all but few architectures are dependent on cloud environments. Yet, there are many use cases for neural networks in a variety of areas, many of which require consumer-grade hardware or highly resource constrained embedded devices. This paper offers a comparison of selected state-of-the-art neural network miniaturization methods, and proposes a new approach, PXNOR, that achieves a noteworthy accuracy, remarkable inference speed and significant memory savings. PXNOR seeks to fully replace traditional convolutional filters with approximate operations, while replacing all multiplications and additions with simpler, much faster versions such as XNOR and bitcounting, which are implemented at hardware level on all existing platforms.
Bayesian Prediction on PM Modi’s Future in 2019

Electing a Prime Minister (PM) is a process that occurs every 5 years in India, through conducting an election. Who will win the election is one of the most asked questions on everyone's tongue, as the election is just few months away. The objective of this research is to predict the likelihood of PM Narendra Modi's chances to continue as the Prime Minister of India using Bayesian network approach. The aim of this research is not to develop a new predictive algorithm, but to use the existing approach for making predictions on a real-life scenario. Our Bayesian modeling is based on public responses available on social media, India statistics, and news articles on the key policies undertaken by PM Narendra Modi during his current tenure. We explore using causal and diagnostic reasoning to find new insights on the factors shaping his win or no-win, verdict on his government strength and weakness. Our Bayesian model reveals that the current Prime Minister Modi has 61.4% chances of winning the upcoming 2019 elections.
BERT for Sequence-to-Sequence Multi-label Text Classification

We study the BERT language representation model and the sequence generation model with BERT encoder for the multi-label text classification task. We show that the Sequence Generating BERT model achieves decent results in significantly fewer training epochs compared to the standard BERT. We also introduce and experimentally examine a mixed model, an ensemble of BERT and Sequence Generating BERT models. Our experiments demonstrate that the proposed model outperforms current baselines in several metrics on three well-studied multi-label classification datasets with English texts and two private Yandex Taxi datasets with Russian texts.
The (Persistent) Threat of Weak Passwords: Implementation of a Semi-automatic Password-Cracking Algorithm

Password-based authentication remains the main method of user authentication in computer systems. In case of a leak of the user database, the obfuscated storage of passwords is the last remaining protection of credentials. The strength of a password determines how hard it is to crack a password hash for uncovering the plain text password. Internet users often ignore recommended password guidelines and choose weak passwords that are easy to guess. In addition, service providers do not warn users that their chosen passwords are not secure enough. In this work we present a semi-automatic password cracking algorithm that orders and executes user-chosen password cracking attacks based on their efficiency. With our new approach, we are able to accelerate the cracking of password hashes and to demonstrate that weak passwords are still a serious security risk. The intention of this work is to point out that the usage of weak passwords holds great dangers for both the user and the service provider.
Multiple Object Detection in $$360^{\circ }$$ Videos for Robust Tracking

In this paper, we propose an efficient way to detect objects in 360$$^{\circ }$$ videos in order to boost the performance of tracking on the same. Though extensive work has been done in the field of 2D video processing, the domain of 360$$^{\circ }$$ video processing has not been explored much yet, as it poses difficulties such as (1) unavailability of the annotated dataset (2) severe geometric distortions at panoramic poles of the image and (3) high resolution of the media which requires high computation capable machinery. The State-of-the-art detection algorithm involves the use of CNN (Convolution Neural Networks) trained on a large dataset. Faster RCNN, SSD, YOLO, YOLO9000, YOLOv3 etc. are some of the detection algorithms that use CNN. Among these, though YOLOv3 might not be the most accurate, it is the fastest, and this trade-off between speed and accuracy is acceptable. We improvise upon this algorithm, to make it suitable for the 360$$^{\circ }$$ dataset. We propose YOLO360, a CNN network to detect objects in 360$$^{\circ }$$ videos and thus increase the tracking precision and accuracy. This is achieved by performing transfer learning on YOLOv3 with the manually annotated dataset.
Securing Air-Gapped Systems

A security measure which involves isolation of a computer or a network in order to prevent it from establishing an external connection, such a security measure is called air gapping. If we are able to physically segregate a computer in such a way that it becomes incapable of connecting wirelessly or physically with other computers network or devices, then that computer is an example of an air-gapped computer. Since air-gapped computers are neither connected to the Internet nor other networks that are connected to the Internet, this makes hacking such computers quite difficult. If we are able to cut off any connection to the computer, then only we can guarantee that no third party would be able to access the client. The object of this paper is to verify the feasibility of air gapping.
Research on Performance of Terrain Matching Algorithm for Underwater Autonomous Vehicle Based on Particle Filter

Abstract AUV is a powerful tool for exploration and development of marine resources. Underwater terrain matching navigation is an emerging passive autonomous navigation method, which improves the long-term and high-precision navigation capability of AUV. The underwater terrain matching algorithm based on particle filter (PF) use a large number of particle sampling to approximate the system state, which can avoid the Taylor expansion truncation error caused by the terrain linearization method, and has good matching precision and terrain adaptability. However, due to the strong nonlinearity and the uneven distribution of the underwater terrain features, it is difficult to achieve the full-range terrain matching optimize with a single configuration parameter of PF underwater terrain matching algorithm. For this reason, this paper has comprehensively studied the PF terrain matching algorithm (referred to as PF matching algorithm). The principle, accuracy and implementation steps of PF algorithm are analyzed. With the UKF algorithm as a reference, the advantages and disadvantages of PF-based algorithm are studied through computer simulation of terrain matching navigation. The matching precision and terrain adaptability of PF-based algorithm has been tested. From the simulation results, it is concluded that the particle diversity decline is one of the main factors affecting the matching performance, which provides a basis for algorithm performance improvement and engineering adaptability improvement.
Scene Recognition with Comprehensive Regions Graph Modeling

Learning the regional contents of scenes comprehensively is key to scene recognition. Due to semantic diversity and spatial complexity in scene images, modeling based on these regional contents is challenging. The current works mainly focus on some small and partial regions of the scene, while ignoring the majority region of the scene. In contrast, we propose the Semantic Regional Graph modeling framework for the comprehensive selection of discriminative semantic regions in scenes. To explore the relations of these regions, we propose to model these regions in geometric aspect based on the graph model, and generate the discriminative representations for scene recognition. Experimental results demonstrate the effectiveness of our method, which achieves state-of-the-art performances on MIT67 and SUN397 datasets.
Considering User Distribution and Cost Awareness to Optimize Server Deployment

In edge computing systems, it is crucial issue to select suitable placement sites and quantity of servers so as to realize the low latency of Internet of Things (IoT) applications and balance the sever utilization. Hence, this paper proposes a cost-aware edge server optimization deployment method. Firstly, we model the edge server placement problem as a Mixed Integer Nonlinear Programming problem (MNIP), which comprehensively considers the resource allocation ratio, regional average load, and access delay. And then, the Benders decomposition algorithm is employed to solve it. The simulation results show that the proposed method can find better solution to place the edge micro datacenter (MDC) compared with the state-of-art server deployment strategies in terms of latency for applications and utilization of resources.
The technology of forming adaptive recovery filters in mobile devices

Abstract This paper deals with the technology of forming adaptive filters to eliminate distortions such as defocus on images recorded using mobile devices. The main purpose of the development is to ensure a sufficiently high quality of the distortion correction with minimal computational costs. The blind parametric identification of the model with a radially symmetric quadratic-exponential frequency response is used to adjust the filter parameters. The algorithm, the pseudo-code of the program and the results of experiments demonstrating the effectiveness of the proposed approach are given.
Bidirectional adaptive compression

A new dynamic Huffman encoding has been proposed recently (Shmuel et al., 2021), which instead of basing itself on the information gathered from the already processed portion of the file, as traditional adaptive codings do, uses rather the information that is still to come. The current work extends this idea to bidirectional adaptive compression, taking both past and future into account, and not only performs at least as good as static Huffman, but also provably improves on the future-only based variant. We extend the technique to arithmetic coding and give both theoretical and empirical results that support the enhancement of the new compression algorithm.
Cryptodemocratic Governance of Special Economic Zones

Special jurisdictions are testbeds for institutional experimentation that require voting mechanisms to make complex and evolving policy decisions. In this paper we explore how blockchain technology—as a new infrastructure for voting rights—might be applied in special jurisdictions. In a cryptodemocracy, voters are given property rights in their own votes that are recorded on decentralized blockchain ledgers (Allen et al. 2019). Votes in a cryptodemocracy can be given different bundles of property rights—such as the ability to delegate votes—that changes the structure of the democratic system. Our aim in this paper is to explore the underappreciated collective choice problems underpinning special jurisdictions, outline the potential for cryptodemocractic voting systems as new infrastructure for special jurisdictions, and propose a new interdisciplinary research program at the boundaries of institutional economics, political science, and innovation economics.
Liquid slider

We investigate an unconventional user interface (UI) material, water, and how applying it to a finger-touch based input affects the user experience (UX). We compare the use of touchscreen, mechanical and liquid covered sliders, and three different approaches of how water is applied to the surface of the slider. The salient findings of our user study (n=25) show that liquid changes the perception of the slider control making it more hedonic and experiental. When interacting with water, users prefer larger and more free-form gestures, and factors such as depth and temperature of the liquid affect the experience. Our work provides information for interaction designers focusing on user experience, unconventional materials for interactive systems, aesthetics, and calm UIs.
Analysis and Application of Distributed Navigation Spoofers

Abstract As a typical aerospace technology application, Global Satellite Navigation System (GNSS) affects the national Positioning Navigation Timing (PNT) service. However, not all users are making good use of GNSS. In recent years, the most prominent phenomenon is the Unmanned Aerial Vehicle (UAV) “black flight”, which seriously affects the normal operation of civil aviation. To solve such problem, navigation spoofing can be used as an effective method to disrupt the flight of malicious UAVs. Currently, a centralized antenna is the most common way to transmit false navigation signals, but when the target receiver contains two receiving antennas or spatial filtering technology, this spoofing method is ineffective. Therefore, we aim at the distributed spoofers that each antenna emits a false navigation signal, and point out that this sophisticated attack will become the development trend of navigation spoofing in the future.
Prototype of a Security Model to Mitigate Risks in the Management of Electronic Money in Ecuador

It was determined a risk point in the payment model with electronic money, which is the time of the consumer's withdrawal and time of the provider's deposit. The problem is the damage caused to the information by the lack of a model to mitigate the risks to which the transactions of the payment system are exposed. The objective is to generate a prototype of a security model for the management of electronic money in Ecuador through the adoption of a cryptographic algorithm and improve the transactions security. It was used exploratory and deductive research as a method to analyze the reference information and the sequence of steps for the development of a prototype. It turned out a conceptual model of the participants in Ecuador; prototype electronic money security model; a prototype algorithm applied to the security of electronic money; a flow chart of the payment with electronic money; a mathematical formula to calculate the load of the model. It was concluded that the adoption of the AES algorithm in a security model for transactions generated in financial inclusion is robust and effective; this research has an alternative for the management of electronic money.
Counting People Using Images from Two Low Cost Webcams

The number of people entering an exhibition, a fair or a booth, or the number of people getting on and off the bus, etc. based on time-based statistics is very meaningful for the manager. There have been many studies and solutions to implement this problem. Each solution is applied in several different situations, depending on accuracy requirements, deployment location, deployment environment, product costs. This paper proposes a solution to count people with low-cost hardware, countable for both in and out directions, and the accuracy rate of over 92%. The solution proposes using two webcams of the same type, the process of classification and processing is done on Raspberry PI.
Real-Time Lightweight CNN in Robots with Very Limited Computational Resources: Detecting Ball in NAO

This paper proposed a lightweight CNN architecture called Binary-8 for ball detection on NAO robots together with a labelled dataset of 1000+ images containing balls in various scenarios to address the most basic and key issue in robot soccer games: detecting the ball. In contrast to the existing ball detection methods base on traditional machine learning and image processing, this paper presents a lightweight CNN object detection approach for CPU. In order to deal with the problems of tiny size, blurred image, occlusion and many other similar objects during detection, the paper designed a network structure with strong enough feature extraction ability. In order to achieve real time performance, the paper uses the ideas of depthwise separable convolution and binary weights. Besides, we also use SIMD (Single Instruction Multiple Data) to accelerate the operations. Full procedure and net structure have been given in this paper. Experimental results show that the proposed CNN architecture can run at full frame rate (140 Fps on CPU) with an accurate percentage of 97.13%.