Reviewerly/exHarmony huggingface dataset:
ISDI: A New Window-Based Framework for Integrating IoT Streaming Data from Multiple Sources

Due to the rapid advancement in Internet of Things (IoT), myriad systems generate data of massive volume, variety and velocity which traditional databases are unable to manage effectively. Many organizations need to deal with these massive datasets that encounter different types of data (e.g., IoT streaming data, static data) in different formats coming from multiple sources. Different data integration mechanisms are designed to process mostly static data. Unfortunately, these techniques are not adequate to integrate IoT streaming data from multiple sources. In this paper, we identify the challenges of IoT streaming data integration (ISDI). A generic window-based ISDI approach is proposed to deal with IoT data in different formats and subsequently introduced the algorithms to integrate IoT streaming data obtained from multiple sources. In particular, we extend the basic windowing algorithm for real-time data integration and to deal with the timing alignment issue. We also introduce a de-duplication algorithm to deal with data redundancies and to demonstrate the useful fragments of the integrated data. We conduct several sets of experiments and quantify the performance of our proposed window-based ISDI approach. The experimental results, performed on several IoT datasets, show the efficiency of our proposed ISDI solution in terms of processing time.
Curricular Foundations for Cybersecurity

Cybersecurity has emerged at the center of several international crises, and the demand for well-educated professionals in this area has rapidly escalated. Given the newness of the area and the lack of curricular consensus about cybersecurity, education providers have struggled to keep up. More engagement is needed from the cybersecurity education community, but recent developments, such as the release of the CSEC2017 curricular guidelines, are promising.
Use of multi-lexicons to analyse semantic features for summarization of touring reviews

Purpose This paper aims to improve a sentiment analysis (SA) system to help users (i.e. customers or hotel managers) understand hotel evaluations. There are three main purposes in this paper: designing an unsupervised method for extracting online Chinese features and opinion pairs, distinguishing different intensities of polarity in opinion words and examining the changes in polarity in the time series. Design/methodology/approach In this paper, a review analysis system is proposed to automatically capture feature opinions experienced by other tourists presented in the review documents. In the system, a feature-level SA is designed to determine the polarity of these features. Moreover, an unsupervised method using a part-of-speech pattern clarification query and multi-lexicons SA to summarize all Chinese reviews is adopted. Findings The authors expect this method to help travellers search for what they want and make decisions more efficiently. The experimental results show the F -measure of the proposed method to be 0.628. It thus outperforms the methods used in previous studies. Originality/value The study is useful for travellers who want to quickly retrieve and summarize helpful information from the pool of messy hotel reviews. Meanwhile, the system will assist hotel managers to comprehensively understand service qualities with which guests are satisfied or dissatisfied.
Internet of Things: A Primer

In the world of communication technology, two of the events that hold special significance are the invention of ARPANET, a computer network allowing computers to exchange data even when being geographically separate, and the rise of the Internet of Things (IoT). The latter, however, was an evolving process instead of a single event. The earliest implementations of the IoT concept occurred when a couple of Carnegie Mellon University students found a way to monitor the number of cans remaining in a vending machine by allowing devices to communicate with the external world. They did this by adding a photosensor to the device that would count every time a can left the vending machine, and thus, the number of remaining cans was calculated. These days IoT devices are capable of monitoring your heart rate, and even controlling it if required in the case of an adverse event. Moreover, some IoT devices can now serve as a source of evidence during trials in court, as seen in late 2015, when the FitBit data of a woman was used in a murder trial. Other incidents include usage of pacemaker data and Amazon Echo recordings in various court trials. The journey of IoT devices from a university dorm room to being present inside human beings is fascinating, to say the least.
Automated Review Analyzing System Using Sentiment Analysis

An integral part of human behavior has always been to find out what others think about what would happen next. With the ongoing trend of e-commerce websites and personal blogs, people make active use of various technologies to understand and classify opinions. This paper introduces a new system to understand the emotions and feelings underlying the reviews provided by users on various e-commerce websites. This system holds an edge over the current rating system of star values by providing the users with a more precise and descriptive result. The main disadvantage of the star system is that it does not provide enough choice to the user. The methodology mentioned in this paper, named ARAS or Automated Review Analyzing System, overcomes this issue by using sentiment analysis which feeds upon each word in the review rather than a separate weighing system.
Contextualized Word Representations for Document Re-Ranking

Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several existing neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models.
Mapping languages analysis of comparative characteristics

RDF generation processes are becoming more interoperable, reusable, and maintainable due to the increased usage of mapping languages: languages used to describe how to generate an RDF graph from (semi-)structured data. This gives rise to new mapping languages, each with different characteristics. However, it is not clear which mapping language is fit for a given task. Thus, a comparative framework is needed. In this paper, we investigate a set of mapping languages that inhibit complementary characteristics, and present an initial set of comparative characteristics based on requirements as put forward by the reference works of those mapping languages. Initial investigation found 9 broad characteristics, classified in 3 categories. To further formalize and complete the set of characteristics, further investigation is needed, requiring a joint effort of the community.
Multi-level Graph Compression for Fast Reachability Detection

Fast reachability detection is one of the key problems in graph applications. Most of the existing works focus on creating an index and answering reachability based on that index. For these approaches, the index construction time and index size can become a concern for large graphs. More recently query-preserving graph compression has been proposed and searching reachability over the compressed graph has been shown to be able to significantly improve query performance as well as reducing the index size. In this paper, we introduce a multilevel compression scheme for DAGs, which builds on existing compression schemes, but can further reduce the graph size for many real-world graphs. We propose an algorithm to answer reachability queries using the compressed graph. Extensive experiments with two existing state-of-the-art reachability algorithms and 10 real-world datasets demonstrate that our approach outperforms the existing methods.
Community-led data collection using Open Data Kit for surveillance of animal African trypanosomiasis in Shimba hills, Kenya

In Sub-Saharan Africa, there is an increase in trypanosome non-susceptibility to multiple trypanocides, but limited information on judicious trypanocide use is accessible to smallholder farmers and agricultural stakeholders in disease endemic regions, resulting in widespread multi-drug resistance. Huge economic expenses and the laborious nature of extensive field studies have hindered collection of the requisite large-scale prospective datasets required to inform disease management. We examined the efficacy of community-led data collection strategies using smartphones by smallholder farmers to acquire robust datasets from the trypanosomiasis endemic Shimba hills region in Kenya. We used Open Data Kit, an open-source smartphone application development software, to create a data collection App.Our study provides proof of concept for the viability of using smartphone Apps to remotely collect reliable large-scale information from smallholder farmers and veterinary health care givers in resource poor settings. We show that these datasets can be reliably collated remotely, analysed, and the findings can inform policies that improve farming practices and economic wellbeing while restricting widespread multi-drug resistance. Moreover, this strategy can be used to monitor and manage other infectious diseases in other rural, resource poor settings.
Generating Behavior Features for Cold-Start Spam Review Detection

Existing studies on spam detection show that behavior features are effective in distinguishing spam and legitimate reviews. However, it usually takes a long time to collect such features and is hard to apply them to cold-start spam review detection tasks. In this paper, we exploit the generative adversarial network for addressing this problem. The key idea is to generate synthetic behavior features (SBFs) for new users from their easily accessible features (EAFs). We conduct extensive experiments on two Yelp datasets. Experimental results demonstrate that our proposed framework significantly outperforms the state-of-the-art methods.
Pairing-Free CP-ABE based Cryptography Combined with Steganography for Multimedia Applications

Technology development has led to rapid increase in demands for multimedia applications. Due to this demand, digital archives are increasingly used to store these multimedia contents. Cloud is the commonly used archive to store, transmit, receive and share multimedia contents. Cloud makes use of internet to perform these tasks due to which data becomes more prone to attacks. Data security and privacy are compromised. This can be avoided by limiting data access to authenticated users and by hiding the data from cloud services that cannot be trusted. Hiding data from the cloud services involves encrypting the data before storing it into the cloud. Data to be shared with other users can be encrypted by utilizing Cipher Text-Policy Attribute Based Encryption (CP-ABE). CP-ABE is used which is a cryptographic technique that controls access to the encrypted data. The pairing-based computation based on bilinearity is used in ABE due to which the requirements for resources like memory and power supply increases rapidly. Most of the devices that we use today have limited memory. Therefore, an efficient pairing free CP- ABE access control scheme using elliptic curve cryptography has been used. Pairing based computation is replaced with scalar product on elliptic curves that reduces the necessary memory and resource requirements for the users. Even though pairing free CP-ABE is used, it is easier to retrieve the plaintext of a secret message if cryptanalysis is used. Therefore, this paper proposes to combine cryptography with steganography in such a way by embedding crypto text into an image to provide increased level of data security and data ownership for sub-optimal multimedia applications. It makes it harder for a cryptanalyst to retrieve the plaintext of a secret message from a stego-object if steganalysis were not used. This scheme significantly improved the data security as well as data privacy.
Classwise Clustering for Classification of Imbalanced Text Data

In this paper, the problem of classification of imbalanced text data is addressed. Initially, imbalanceness present across the classes is reduced by converting each class into multiple smaller subclasses. Further, each document is represented in a lower-dimensional space of size equal to the number of subclasses using term-class relevance (TCR) measure-based transformation technique. Then, each subclass is represented in the form of an interval-valued feature vector to achieve the compactness and stored in a knowledgebase. A symbolic classifier has been effectively used for the classification of unlabeled text documents. Experiments are conducted on Reuters-21578 and TDT2 text datasets. The results reveal that the performance of the proposed method is better than the other existing methods.
Adaptive Active Contour Model Based on Weighted RBPF for SAR Image Segmentation

We propose a new adaptive active contour model (ACM) based on weighted region-based pressure force (RBPF) which is applied to SAR image segmentation. First, the normalized intra-class variances of pixel grayscales of inner and outer areas are used as the new coefficients of the grayscale descriptions of inner and outer areas. Then, the weighted RBPF is constructed to control the curve motion more accurately. Second, when calculating the grayscale descriptions of the inner and outer areas, adaptive weights are introduced to reduce the effect of interference pixels, which improves the accuracy of the grayscale descriptions. Furthermore, some regularized terms are incorporated into the objective functional to ensure the stability of the model. The segmentation results for various kinds of images demonstrate that the proposed model is superior to some state-of-the-art ACMs in segmentation performance and is robust to the initial curve.
Perceptually Equivalent Resolution in Handheld Devices for Streaming Bandwidth Saving

We present the description, results, and analysis of the experiments conducted to find the equivalent resolution associated with handheld devices. That is, the resolution from which users stop perceiving quality improvements if better resolutions are presented to them in such devices. Thus, it is the maximum resolution that it is worth considering for generating and delivering video, as long as sequences are not too intensively compressed. Therefore, the detection of the equivalent resolutions allows for notable savings in bandwidth consumption. Subjective assessments have been carried out on fifty subjects using a set of video sequences of very different nature and four handheld devices with a broad range of screen dimensions. The results prove that the equivalent resolution in current handheld devices is 720p as higher resolutions are not valued by users.
Empirical Study on the Mobile App-Aided College English Vocabulary Teaching

The paper aims at analyzing how to improve College English vocabulary teaching with the aid of a Mobile App.The research objects are 60 freshmen of non-English majors from two classes of a provincial medical university in Mainland China.Based on the contrast of vocabulary tests results between the control group and the experimental group, the author analyzes the influences of a Mobile App aid on College English vocabulary teaching.The main findings of this research can be listed as follows: students' pronunciation performances of required vocabulary in experimental group are much better than those of the control group; vocabulary spelling of students with the Mobile App aid learning is more accurate than that of those without the Mobile App aid; the experimental group students identify the meanings of required vocabulary more effectively than the control group students; the experimental group students tend to apply more newly-learned vocabulary required in the designed task.The results gained are of great methodological significance in the vocabulary teaching and learning.
StringTouch

This paper describes a demo prototype of a tangible user interface (TUI) concept that is derived from the expressive play of musical string instruments. We translated this interaction paradigm to an interactive demo which offers a novel gesture vocabulary (strumming, picking, etc.). In this work we present our interaction concepts, prototype description, technical details and insights on the rapid and low-cost manufacturing and design process. (Video demonstration: https://vimeo.com/309265370)
<i>Retracted on May 21, 2020</i> : Smart contract's interface for user centric business model in blockchain

This article has been removed from the ACM Digital Library because it was determined that the Conflict of Interest Policy was violated by a reviewer, Gwanggil Jeon, who later added to the final submitted version of the paper as an author. The Policy of Authorship Misrepresentation was violated by Awais Ahmad, Murad Khan and Gwanggil Jeon being added to the final submitted version of the paper.
Hyper Typer

In this paper we introduce Hyper Typer, a serious Android game for collecting text entry performance data on a large scale in an unsupervised manner. Publishing the game on the Google Play Store resulted in a total of 1,917 usable transcribed phrases with 58,829 keystrokes over an eleven-week period. By analyzing the data, we demonstrate the feasibility of the method and give preliminary results regarding the overall performance and error rate of players. Moreover, the collected data allows us to compare two of the most commonly used Android soft-keyboards.
Visual recognition memory for scenes in aerial photographs: Exploring the role of expertise

Aerial photographs depict objects from an overhead position, which gives them several unusual visual characteristics that are challenging for viewers to perceive and memorize. However, even for untrained viewers, aerial photographs are still meaningful and rich with contextual information. Such visual stimulus properties are considered appropriate and important when testing for expertise effects in visual recognition memory. The current experiment investigated memory recognition in expert image analysts and untrained viewers using two types of aerial photographs. The experts were better than untrained viewers at recognizing both vertical aerial photographs, which is the domain of their expertise, and oblique aerial photographs. Thus, one notable finding is that the superior memory performance of experts is not limited to a domain of expertise but extends to a broader category of large-scale landscape scenes. Furthermore, the experts' recognition accuracy remained relatively stable throughout the experimental conditions, illustrating the ability to use semantic information over strictly visual information in memory processes.
Composition Context-Based Web Services Similarity Measure

Web services similarity measure is an important problem in service computing area, which is the technological foundation of service substitution, service discovery, service recommendation, and so on. Most of the existing works use a static description of services to measure the similarity between two services. However, the interaction information of Web services recorded in the historical compositions is totally neglected. In this paper, we propose a novel Web services similarity measure approach based on the notion of service composition context. Specifically, we first introduce three types of parameter correlations between service input and output parameters. These correlations can be obtained from existing services compositions. Based on parameter correlations, we propose the service composition context model. Through the composition context of a service, the composition context network is constructed using contexts of all services. Then, we propose to measure the similarity between any two services using the PersonalRank and SimRank++ algorithms by taking the obtained context network as input. By experiments, we analyze the characteristics of our proposed method and demonstrate that its accuracy is much better than the state-of-the-art approaches.
A systematic mapping study of cloud policy languages and programming models

Cloud computing can be considered as a disruptive technology that is making life easier for Cloud users. Determining a focus of research in a specific subject area is sometimes challenging. A systematic map enables a synthesis of a scheme for categorizing data in a field of interest. The goal of this research paper is to carry out a systematic mapping study of policy language and programming models on the cloud. The mapping involved contribution category such as method, research category such as evaluation and major topics extracted from the abstracts of primary studies. The result indicated there are more publications on evaluation research in term of security with 8.9%. There were more papers published on validation research, solution proposal and experience research on the topic of paradigms with 7.53%, 6.85% and 4.11% respectively. Also, there were more publications on philosophical research in terms of privacy with 4.11%. In addition, there were more articles published on opinion research in terms of the survey with 4.11%. On the other hand, there were no articles on metric in terms of framework, paradigms and accountability, and reliability to the best of the researchers’ knowledge. The outcome of this systematic study will be of benefit to cloud users, researchers, practitioners and providers.
DETECTION OF A HUMAN HEAD ON A LOW-QUALITY IMAGE AND ITS SOFTWARE IMPLEMENTATION

Abstract. The paper considers the task solution of detection on two-dimensional images not only face, but head of a human regardless of the turn to the observer. Such task is also complicated by the fact that the image receiving at the input of the recognition algorithm may be noisy or captured in low light conditions. The minimum size of a person’s head in an image to be detected for is 10 × 10 pixels. In the course of development, a dataset was prepared containing over 1000 labelled images of classrooms at BSTU n.a. V.G. Shukhov. The markup was carried out using a segmentation software tool specially developed by the authors. Three architectures of convolutional neural networks were trained for human head detection task: a fully convolutional neural network (FCN) with clustering, the Faster R-CNN architecture and the Mask R-CNN architecture. The third architecture works more than ten times slower than the first one, but it almost does not give false positives and has the precision and recall of head detection over 90% on both test and training samples. The Faster R-CNN architecture gives worse accuracy than Mask R-CNN, but it gives fewer false positives than FCN with clustering. Based on Mask R-CNN authors have developed software for human head detection on a lowquality image. It is two-level web-service with client and server modules. This software is used to detect and count people in the premises. The developed software works with IP cameras, which ensures its scalability for different practical computer vision applications.