Reviewerly/exHarmony huggingface dataset:
Distributed Fast Supervised Discrete Hashing

Hash-based learning has attracted considerable attention due to its fast retrieval speed and low computational cost for the large-scale database. Compared with unsupervised hashing, supervised hashing achieves higher retrieval accuracy generally by leveraging supervised information. Most existing supervised hashing methods, such as supervised discrete hashing (SDH) and fast SDH (FSDH), are concerned more with the centralized setting. SDH regresses the hash code to its corresponding label, rather FSDH regressing each label to its corresponding hash code. However, in many realistic applications, large amounts of data are usually distributed across different sites. Thus, supervised distributed hashing (SupDisH), which is based on the distributed framework and supervised learning, has been proposed and liberates the limitations of centralized hashing. In this paper, based on FSDH, we propose the distributed fast supervised discrete hashing (DFSDH), which both inherits the excellent retrieval performance of SupDisH and gets significant enhancement in efficiency. Specifically, FSDH is introduced into a distributed framework, in which the centralized hash learning model is shared by all agents. Meanwhile, consistency constraints are introduced to ensure that multiple agents deal with distributed hash learning in parallel. For each agent, an alternate iterative procedure is employed to obtain high-quality binary codes and hashing function. The extensive experiments demonstrate that DFSDH is competitive to most centralized supervised hashing methods and existing distributed hashing methods.
Variational Autoencoder-Based Multiple Image Captioning Using a Caption Attention Map

Image captioning is a promising research topic that is applicable to services that search for desired content in a large amount of video data and a situation explanation service for visually impaired people. Previous research on image captioning has been focused on generating one caption per image. However, to increase usability in applications, it is necessary to generate several different captions that contain various representations for an image. We propose a method to generate multiple captions using a variational autoencoder, which is one of the generative models. Because an image feature plays an important role when generating captions, a method to extract a Caption Attention Map (CAM) of the image is proposed, and CAMs are projected to a latent distribution. In addition, methods for the evaluation of multiple image captioning tasks are proposed that have not yet been actively researched. The proposed model outperforms in the aspect of diversity compared with the base model when the accuracy is comparable. Moreover, it is verified that the model using CAM generates detailed captions describing various content in the image.
Enhancing Microwave System Health Assessment Using Artificial Neural Networks

A novel method is presented for remotely assessing microwave system health using environmental monitoring sensors that employ a low-power random noise radar with artificial neural network-based machine learning processing. The method expands prior stimulated unintended radiated emission (SURE) research in [M. W. Lukacs, A. J. Zeqolari, P. J. Collins, and M. A. Temple, “`RF-DNA fingerprinting' for antenna classification,” IEEE Antennas Wireless Propag. Lett., vol. 14, pp. 1455-1458, 2015] by adding a new hybrid expert-empirical concept-forming technique called matched filter replication (MFR), the outputs of which are used in ensemble learning. Also by comparison with baseline performance, classification improvement is demonstrated using a single iteration of MFR ensemble learning that improves antenna termination state classification by %C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Δ</sub> >19%. A nested ensemble learning architecture is also introduced that enables classification of truly unknown devices with no learner in the ensemble being trained. This exploits the concept of multiple iterations using the MFR process by establishing an enlarged hypothesis space that is subsequently collapsed to form a final classification decision. The new SURE architecture enables the assessment of unknown device capabilities using a network trained on selected primitive traits (in this case, reflectivity).
A new video‐camera‐based visiometer system

Abstract We provide a novel design of atmosphere visibility measurement system with the contrast principle used in the naked eye visibility observation. The new digital photography visiometer system (DPVS), composed of a CCD camera and two identical targets, utilizes the contrast of dual targets to measure the visibility. Two operational modes are designed to enable the DPVS to accommodate the lighting conditions of the day and night. Two passive targets, black bodies, are employed for daytime visibility measurement, and two active lights, LED panels, are utilized for nighttime. The mathematical models of the dual targets for both daytime and nighttime are developed, and the sensitivity analysis shows the requirements of the hardware to achieve desired accuracy. The design of the target including the blackbody and the LED panel is briefly discussed, and the temperature dependence and stability of the LED panels are measured experimentally. Finally, the DPVS is compared with two commercial visibility devices, a forward scatter meter and a transmission meter for field validation. A 5‐day comparative experiment shows that the measurements of the DPVS generally well agree with those of the transmissometer and the forward scatter meter for visibility up to 15 km, and the relative bias of 87% of the DPVS measurements lies between −0.2 and 0.2.
"I'm Done with Cancer. What am I Trying to Improve?"

Research suggests a lifestyle with specific diet, physical activity, and smoking cessation may prevent the progression and recurrence of non-metastatic prostate cancer. Adopting this lifestyle often requires multiple health behavior changes. While health behavior change is well-explored in HCI, the current context differs from past work. It has implications for how technology could support health behavior changes in this specific context. We provided prostate cancer patients with a website, text messages, and activity trackers to understand their experience with health behavior change interventions. Two focus group interviews conducted after the study revealed specific issues regarding these interventions. We found that patients interpreted the recommendations based on their existing understanding of healthy lifestyle and that the inability to measure cancer progression made health behavior change more challenging. Our findings also indicate a gap between the expectation of the researchers and the patients regarding technology. These results have implications for design of technology-enhanced interventions to support health behavior change in other similarly constrained contexts.
Comparative Analysis between Solar and Wind Turbine Energy Sources in IoT Based on Economical and Efficiency Considerations

In this paper the net economic cost and efficiency of two renewable energy systems has been studied and analysis that are useful in IσT applications. The first system consists of 3KW solar energy Source , the second system is consists of a 3KW wind turbine energy source . The study was conducted by using the HOMER software program .The input data entered to the program was calculated form a daily loads required to satisfy a small house in Tikrit city in Iraq. The obtained results show that the net cost of installation of 3KW solar energy system is about 14,792.00 US$, while the net cost of installation of 3KW wind turbine system is about 7,340.00 US$ ,but the monthly mean value of the electrical power generated by solar system in June is about 2.52KW which equal to about 84% of its design capacity and in November is equal to 2.301KW which is equal to about 76% of its design capacity, while the monthly mean value of electrical power generated by wind turbine system in June is about 1.76KW which is equal to about 55% of its design capacity and in November is about 1.301KW which equals to about 43% of its design capacity . That means the wind turbine system is cheaper than solar system, but the output power of solar system is greater than the outputs power of wind turbine generator. So, the solar system is consider as a more feasible system for renewable energy sources option in Tikrit region.
Edge detector based on wavelet transform energy attributes

The paper considers the model of representation of images using the energy characteristics of the wavelet transform. It describes an approach to the detection of edges on images based on the energy features of the wavelet transform. This approach is a modification of the Canny edge detector. It can be applied to the construction of effective information processing and control systems based on computer vision technologies.
GodExpo: An Automated God Structure Detection Tool for Golang

God Class is a class that threatens maintainability and understandability of code by performing most of the work alone. Various tools exist that can detect God Class of Java or C++ programs, however, there is no existing tool for detecting God Class(Structure) in Golang. Although Golang is not an object-oriented language, it offers structures which are similar to classes in OOP as they can contain fields and methods. Unlike OOP, methods of a structure can be defined on any file in the package of Golang. This paper presents a tool entitled GodExpo to detect God Structures in Golang programs by calculating metrics namely Weighted Method Count, Tight Class Cohesion, and Access to Foreign Data. In addition, GodExpo can provide version wise result to observe the evolution of God structures. To evaluate GodExpo, an experiment has been conducted on several versions of two open source Golang projects and the tool successfully found God structures in all versions of those projects.
SReYantra: Automated Software Requirement Inter-Dependencies Elicitation, Analysis and Learning

Requirements elicitation is a cognitively difficult task. Rich semantics in natural language based requirements impose challenges in elicitation, analysis and maintenance of requirement inter-dependencies. The challenges intensify further when dependency types and strengths are considered. Ignoring inter-dependencies can adversely impact the design, development and testing of software products. This PhD research proposal addresses three main challenges. First, Natural Language Processing (NLP) is studied to automatically extract dependencies from textual documents. Further verb classifiers are utilized to automate elicitation and analysis of different types of dependencies (e.g: requires, coupling etc). Second, representation and maintenance of changing requirement dependencies from designing graph theoretic algorithms will be explored. Third, the process of providing recommendations of dependencies will be studied. The results are aimed at assisting project managers to evaluate the impact of inter-dependencies and make effective decisions in software development life cycle.
CUNY-PKU Parser at SemEval-2019 Task 1: Cross-Lingual Semantic Parsing with UCCA

This paper describes the systems of the CUNY-PKU team in "SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA" 1 .We introduce a novel method by applying a cascaded MLP and BiLSTM model.Then, we ensemble multiple system-outputs by reparsing.Our system won the second places in German-20K-Closed track, and third place in English-20K-Closed track.
Robots Liability or Liability for Products?

The article studies the relationship of liability arising during production, acquisition and use of robots and software. It is necessary to create the minimum comfortable conditions from the economic, legal and administrative points of view to develop the robots market. This is first and foremost the creation of an environment open to innovation, which requires legal transparency. The issue of the need to adjust the legal regulation especially in the field of civil and business legislation is to be considered to create conditions for the robots implementation. The study analyzes the existing institutions and concepts of the long-term legal regulation. The issue of the means of communication between a human and machine is also to be considered including the impact on the legal principles of regulation of relations of liability. The definition of liable parties implies the possibility of giving the robot a special legal personality, for example, giving it the status of an electronic person. The current European legislation on liability in tort and damage is studied against the rules of Directive 85/374/EEC. Various mechanisms to ensure and protect the interests of consumers and users of electronic devices are considered. It is assumed the obligation of robots liability insurance, as it has been already implemented for vehicles, the creation of special funds, the establishment of a liability limit, etc. for the practical use of liability mechanisms.
An Adaptive JPEG Double Compression Steganographic Scheme Based on Irregular DCT Coefficients Distribution

Steganography is to embed secret information into digital media and minimize the distortion caused by data embedding.The JPEG is a widely used image compression format, and many JPEG images transmitted on the network are double compressed.Most of the existing steganography algorithms mainly use the single compressed images as the embedded carrier of secret information while rarely taking advantage of the double compressed images.However, the double compressed operation usually leads to changes in the statistical characteristics of images.Therefore, the minor changes caused by steganography can be confused by double compression operation to achieve the purpose of concealing embedding operation.This paper proposes a secure JPEG double compression (DC) steganographic scheme based on irregular discrete cosine transformation (DCT) and coefficients distribution (IDCD-JDS), which can obtain less statistical detectability.The minimum distortion function is designed according to the fact that the statistical distribution of the double compressed images has periodic peaks and valleys as well as multiple irregular intervals.By using the syndrome trellis coding (STC) to embed secret information, the modifications are limited to regions that are difficult to detect.The quality factors (QF) relationship between the first and second compression is further studied to explore the effective range for double compressed images so that the proposed algorithm can achieve better performance under different conditions.The experiments show that this scheme can reduce the embedding change by utilizing the inherent statistical distribution and irregular block complexity in double compressed images, and it performs better than the existing methods.
On Performance of Commodity Single Board Computer-Based Clusters: A Big Data Perspective

In recent times, the commodity Single Board Computers (SBCs) have now become sufficiently powerful that they can run standard operating systems and mainstream workloads. In this chapter, we investigate the design and implementation of Single Board Computer (SBC)-based Hadoop clusters. We provide a compact design layout and build two clusters each using 20 nodes. We extensively test the performance of these clusters using popular performance benchmarks for task execution time, memory/storage utilization, network throughput, and energy consumption. We investigate the cost of operating SBC-based clusters by correlating energy utilization for the execution time of various benchmarks using workloads of different sizes. Although the low-cost benefit of a cluster built with ARM-based SBCs is desirable, these clusters yield low comparable performance and energy efficiency due to limited onboard capabilities. It is, however, possible to tweak Hadoop configuration parameters to ARM-based SBC specifications to efficiently utilize available resources. Finally, a discussion on the design implications of these clusters as a testbed for inexpensive and green cloud computing research is presented.
MegaLight

Screen-camera communication using dynamic barcode streaming has emerged as a convenient and secure method for short-range, impromptu device-to-device communication. Conventional dynamic barcode systems adopt a rule-based approach to recognize the color barcode stream in the receiver, which is empirical, inflexible, and lacks self-adaptiveness. In this paper, we propose a novel solution framework for color barcode stream recognition basing on machine learning techniques. By including a number of training frames into the barcode stream to build a classification model, the proposed framework can achieve high accuracy in color barcode recognition and is adaptive to different ambient lighting conditions (without sudden changes during transmission). A semi-supervised learning approach basing on the Mixture of Experts (MoE) model is further proposed to reduce the start-up time. We implement MegaLight on both black-white and color barcode systems. Extensive experiments demonstrate that MegaLight can significantly reduce the frame demodulation error and reach up to 3x improvement in system goodput comparing to conventional barcode stream recognition approaches.
Digital Assistant for Supporting Bank Customer Service

Digital Assistants are trending in most industries. Despite the fact that it invaded the banking industry a few years ago, the concept is still new in the Sri Lankan banking context. A Chat-bot improves customer satisfaction providing solutions to customers, using the most preferred user interaction method called chat application as a solution to the usually inefficient, time-consuming processes which are followed in call centres. The proposed system is a question answering system which facilitates customers to solve their day-to-day banking related questions. The system provides answers to a set of most Frequently Asked Questions (FAQ) related to the banking domain. Customer inquiries are extracted and converted using natural language processing techniques understood by the system. It formulates and presents an appropriate answer using a set of preconfigured templates referring to the answer saved in a knowledge base. Knowledge is represented in the form of an Ontology.
Usability and Security Analysis of the KeepKey Wallet

This paper presents an investigation of the usability and security of the hardware wallet KeepKey 4.2.10. A Man-in-the-Middle (MITM) attack was implemented to simulate potential attacks and evaluate its implications for the wallet's security. In the usability study, ten participants were observed as they used the wallet to complete various Bitcoin address comparison and confirmation tasks. Furthermore, the study examined users' sentiments towards the transaction process of the KeepKey wallet. The results illustrate that the majority of the participants could detect some but not all MITM attacks. Moreover, there were a considerable number of successful MITM attacks in the experiment. From a usability perspective, our findings show that the Keepkey system has some usability issues that should be tackled to enhance the usability and the security of the wallet.
Help Me Search

In this paper, we address the problem of difficult queries by using a novel strategy of collaborative query construction where the search engine would actively engage users in an iterative process to continuously revise a query. This approach can be implemented in any search engine to provide search support for users via a "Help Me Search" button, which a user can click on as needed. We focus on studying a specific collaboration strategy where the search engine and the user work together to iteratively expand a query. We propose a possible implementation for this strategy in which the system generates candidate terms by utilizing the history of interactions of the user with the system. Evaluation using a simulated user study shows the great promise of the proposed approach. We also perform a case study with three real users which further illustrates the potential effectiveness of the approach.
Eye Tracking-Based Workload and Performance Assessment for Skill Acquisition

The result of training to improve in a given skill is most often demonstrated by an increase in the relevant performance measures. However, a complementary and at times more informative measure is the mental workload imposed on the performer when doing the task. While a number of varied methods exist for measuring workload, we have chosen to explore physiological and neurological correlates for their low amount of impact and interference on subjects during an experiment. In this study, participants trained on a six-task cognitive battery over four weeks while being simultaneously recorded with remote eye tracking and a host of other neurophysiological instruments. In this preliminary analysis, we found that measures of saccades, fixations, and pupil diameters significantly correlated with task performance over time and at different difficulties, indicating the validity of our task battery as well as the specificity of workload-related eye tracking measures.
Responding to customer complaints on English and Polish corporate profiles on Twitter

Abstract The study investigates the strategies used by English and Polish companies in the process of handling customer complaints on Twitter. Since English and Polish are recognized as representatives of negative and positive politeness cultures, respectively, the analysis was to examine if there are differences in politeness conventions in customer-provider interaction on Twitter. The study found that although similar strategies are used by English and Polish companies in responding to complaints, the frequency of the respective strategies is different. The results of the analysis confirm differences in the use of positive and negative politeness strategies as well as differences in the level of directness in interaction between English and Polish profiles.
A progressively essential secret image sharing scheme using hierarchy shadow

This paper presents an improved (t, s, k, n)-scalable essential secret image sharing based on Lagrange interpolation. In the proposed scheme, an image is shared to n participants in which s are essential. Any set of k or more shadows including a least t essential shadows can obtain the low-quality image, any set of k or more shadows including a least t essential shadows can recover the high-quality image. The more essential shadows to participate in the recovery, the more information of the secret image is obtained. While t = s, the entire secret image can be completely recovered. Even if the essential participants do not cooperate to recovery, k non-essential participants can also collaborate to obtain the low-quality image. Moreover, when an essential shadow is lost or destroyed, only the high-quality image recovery is influenced, meanwhile the low-quality image is still obtained. Compared with the previous similar literatures that some parts of the image cannot be recovered if one of the shadows is accidentally lost or maliciously attacked, our scheme is more suitable for the practical application.