Reviewerly/exHarmony huggingface dataset:
End-user composition of graphical user interfaces by composite pattern

We present ComPat, an open source visual editor enabling end users to compose graphical user interfaces based on the composite pattern, a common software engineering design pattern: any widget or group of widgets is treated the same way as a single instance of the same type of widget. ComPat exploits a grid of rows and columns, where each cell, regulated by layout constraints, is populated either by direct import of widgets from a palette or by pattern application. In order to compose graphical user interfaces, any portion could be cut, copied, pasted, and treated as a single object thanks to the composite pattern, thus facilitating reusability. Any portion becomes a pattern that can be applied either by direct instantiation or by rewriting. ComPat automatically generates a Java Swing graphical user interface corresponding to the composition and stores its definition in a User Interface Description Language based on a XML Schema.
Trust-Based Neural Collaborative Filtering

Abstract In order to maintain the strong representational learning of the deep neural network model to learn the interaction function between any users and items, combining the trust relationship between users as a local relationship to enhance the ability to supplement interactive data, to achieve a better recommendation effect. This paper proposes a Trust-based Neural Collaborative Filtering model (TNCF). Firstly, trust information and scoring information are merged through the Generalized Matrix Factorization model (GMF) to obtain recommendations based on trust friend preferences. Then, using the Multi-Layer Perceptron model (MLP), the nonlinear kernel is utilized to learn the interaction function from the data to obtain the recommendation based on the user’s personal taste. Finally, all the interaction results are aggregated for implicit prediction. Compared with the three different baselines on the FilmTrust and Epinions datasets, the experimental consequences reveal that the proposed model improves the recommendation and also performs well on sparse statistics.
Evaluating Methods for Building Arabic Semantic Resources with Big Corpora

This paper presents detailed data on the workings of a system extracting semantic clusters from a large general Arabic corpus which has been presented in a previous work [1], and proposes some bases for best evaluation using Arabic WordNet. In the first experiments, using an evaluation corpus of about 8 millions words and GraPaVec, a method for word vectorization based on automaticallyLebboss, Georges generated frequency patterns, our system clustered word vectors in a Self OrganizingBernard, Gilles Map neural network model and evaluated themAliane, Noureddine with Arabic WordNet existing synsets. We compared the resultsAbdallah, Adelle with state-of-the-art Word2Vec and Glove methods. As our resultsHajjar, Mohammad were astonishingly high, without clear explanations, we present here a more thorough testing protocol, evaluating with a much larger corpus (1.4 billion words), introducing more refined measures, a refined definition of multiclass recall and precision, taking better into account the specifics of wordnet classification and using NLTK tools. Observations on the corpus are given in order to help researchers interested in our approach to assess methods of implementation and evaluation.
Mixed Reality in Action - Exploring Applications for Professional Practice

Mixed reality technologies has been emerging rapidly in the recent years, not only in terms of improving the equipment and software, but also in employing the current state into multiple practical applications. One of the most successful endeavours of introducing a commercial product for mixed reality is the HoloLens by Microsoft. In this paper, we describe the technology and discuss multiple practical applications among various branches of medicine, science and industry. We present the results of our desk research among various ways to employ the mixed reality into practice and discuss the new openings that arise along the development of the technology. The openings described consist of various interaction methods and applications for CSCW.
Evaluating Internet Security Awareness and Practices of BulSU-SC Students

Not everyone is aware of the risks of cyber attacks on the internet once data travels through physical connections. In academic institutions, sometimes students take for granted the risks involved in accessing the internet. They may be excited and enjoy using the social media, communicate through email, and download or upload files without being aware of the cyber security risks these actions may entail. There are a lot of risk possibilities that may occur and are unknown to the students. This paper evaluates the internet security awareness and practices of students enrolled at Bulacan State University Sarmiento Campus during the 1st semester of AY 2017-2018. A total of 289 students from different departments were requested to answer the survey using Google Forms. The survey suggests that most students are still unaware of the vulnerabilities and risks that come with accessing the internet. Students are not fully aware of the existence of security threats and how to identify them, and they are, therefore, unable to execute appropriate defense mechanisms to defeat threats. The researchers, then recommend that internet security practices be integrated in the technology courses which form part of their program curriculum.
Open Set Subject Classification of Text Documents in Polish by Doc-to-Vec and Local Outlier Factor

In this work, we expand the emerging fastText method capabilities to open set classification. It is done by utilization of the Local Outlier Factor (LOF) algorithm. It allows extending the closed set classifier with an additional class that identifies outliers. The analyzed text documents are represented by averaged word embeddings calculated using the fastText method on training data. We evaluate these approach in the task of categorization of Polish language Wikipedia articles with 34 subject areas. Conducting the experiment with two different outlier corpora we show how the LOF parameter (contamination) and the dimension of the feature space (vector representation of documents) influence the open set classification results. The results show that the proposed extension of fastText is capable to work effectively for the open set classification task. Moreover, experiments for different dimensions of word embedding show that even the dimension as low as 5 is sufficient to achieve good results.
How to Manage Privacy in Photos after Publication

Photos and videos once published may stay available for people to view it unless they are deleted by the publisher of the photograph. If the content is downloaded and uploaded by others then they lose all the privacy settings once afforded by the publisher of the photograph or video via social media settings. This means that they could be modified or in some cases misused by others. Photos also contain tacit information, which cannot be completely interpreted at the time of their publication. Sensitive information may be revealed to others as the information is coded as tacit information. Tacit information allows different interpretations and creates difficulty in understanding loss of privacy. Free flow and availability of tacit information embedded in a photograph could have serious privacy problems. Our solution discussed in this paper illuminates the difficulty of managing privacy due the tacit information embedded in a photo. It also provides an offline solution for the photograph such that it cannot be modified or altered and gets automatically deleted over a period of time. By extending the Exif data of a photograph by incorporating an in-built feature of automatic deletion, and the access to an image by scrambling the image via adding a hash value. Only a customized application can unscramble the image therefore making it available. This intends to provide a novel offline solution to manage the availability of the image post publication.
A Schema Matching Approach for Integrated Mobility Service

We propose a schema matching method of transport service data in different schema from multiple data sources. Nowadays, open data about transport services are increasing. However, there are some challenges to use them, because they have complicated data structure such as deeply nested objects or linked small objects. And their disparate data structures make data mathcing more difficult. So we present a method to make match trasnport services data by exploit the data strucutre, terminology and domain knowledge. The proposed method gradually add linked objects to matching target in mathcing process to find corresponding attributes. Generally, wider matching target space raises false positive rate. So our proposed method caluculates similarity score of attributes taking into account reference relationships between linked objects. We experimentaly evaluate the method in real-world transport service data and comfirm the method can improve mathcing accuracy.
High Speed Parallel RC4 Key Searching Brute Force Attack Based on FPGA

A parallel brute force attack on RC4 algorithm based on FPGA (Field Programmable Gate Array) with an efficient style has been presented. The main idea of this design is to use number of forecast keying methods to reduce the overall clock pulses required depended to key searching operation by utilizes on-chip BRAMs (block RAMs) of FPGA for maximizing the total number of key searching unit with taking into account the highest clock rate. Depending on scheme, 32 key searching units and main controller will be used in one Xilinx XC3S1600E-4 FPGA device, all these units working in parallel and each unit will be searching in a specific range of keys, by comparing the current result with the well-known cipher text if its match the found flag signal will change from 0 to 1 and the main controller will receive this signal and stop the searching operation. This scheme operating at 128-MHz clock frequency and gives us key searching speed of 7.7 × 106 keys/sec. Testing all possible keys (40-bits length), requires only around 39.5h.
68‐1: A Machine Learning Approach to Objective Image Quality Evaluation

Subjective Test Scoring is the gold standard for evaluating visual lossless image compression, but its high cost and long lead time prohibits testing more than a few images. Here, we model the display and human eye and use Machine Learning models of increased complexity, achieving a Pearson correlation of 0.95.
Strong Leakage and Tamper-Resilient PKE from Refined Hash Proof System

We revisit the problem of constructing public key encryption (PKE) secure against both key-leakage and tampering attacks. First, we present an enhanced security against both kinds of attacks, namely strong leakage and tamper-resilient chosen-ciphertext (sLTR-CCA) security, which imposes only minimal restrictions on the adversary's queries and thus captures the capability of the adversary in a more reasonable way. Then, we propose a generic paradigm achieving this security on the basis of a refined hash proof system (HPS) called public-key-malleable HPS. The paradigm can not only tolerate a large amount of bounded key-leakage, but also resist an arbitrary polynomial of restricted tampering attacks, even depending on the challenge phase. Moreover, the paradigm with slight adaptations can also be proven sLTR-CCA secure with respect to subexponentially hard auxiliary-input leakage. In addition, we instantiate our paradigm under certain standard number-theoretic assumptions, and thus, to our best knowledge, obtain the first efficient PKE schemes possessing the strong bounded/auxiliary-input leakage and tamper-resilient chosen-ciphertext security in the standard model.
Collecting Design Knowledge through Voice Notes

We introduce KnoCap, a tool that we are developing to collect design knowledge from conversations. It is well-known that design meetings create knowledge, not all of which is typically captured. KnoCap addresses this problem by relying on Echo buttons for designers to indicate when important knowledge is being talked about, so that that knowledge is captured as a voice note that can be played at a future time. In this paper, we motivate our work, present KnoCap's features, and discuss where we want to take KnoCap so it is not just useful in capturing knowledge, but also relaying it when so needed.
Attack on Students’ Passwords, Findings and Recommendations

Passwords are still the most widespread method of authentication. It is well known and very common for users to create weak passwords. We decided to check the strength of passwords of real systems by cracking MD5 hashes. The results have dismayed us given that 94,94% of passwords were cracked within just a few days. In order to understand the results of cracking better, we asked students about their password conventions, and the strength of selected passwords. We report herein on the most interesting findings as well as their recommendations.
Relevance Feedback Utilizing Secure Evaluation with Content-based Image Retrieval in Cloud Computing

Content-based image retrieval (CBIR) is the integrated system of the photograph fetching trouble for instance difficulty of chasing down pictures on a cloud in big datasets. To recognize request semantics and client’s needs if you want to grant submitted consequences with reference to exactness, relevance feedback is combined into CBIR shape. Important evaluation shape will manufacture the precision of yield and will pass at hugest yield. In the watermark-primarily based tradition, a singular watermark is explicitly inserted in blended photos by means of the cloud environment earlier than photos, transmitted towards inquiry mortal. In this way, when an illicit photograph reproduction is located, the illicit inquiry mortal, where appropriates can trail the pictures with the aid of the watermark extraction. Characteristics vectors get assured by using the secure hashing algorithm, analyzing and making ready age are used at image user’s aspect for confirmation motive. TPA (third party auditor) is used to understand enforcement or malevolent activities achieved in cloud circumstances. In our proposed framework, we are including the approach of misrepresentation recognition by generating trapdoor using a hashing calculation, as a document is made with the unique identifier and the client pictures with the names after the link are simplest, a trapdoor is generated.
Benefits and Outcomes of Library Collections on Scholarly Reading in Finland

Abstract Results of an online survey distributed to faculty, PhD students, and researchers throughout Finland in 2016 show that library resources, journal articles, and books are important parts of scholars’ research and work life. The survey was disseminated through FinELib to heads of libraries, who were then asked to distribute it to their academic staff, researchers, and PhD students. The purpose of this study was to examine the value of readings obtained from library collections. To help answer this question, participants were asked about the amount and value of their reading and where they obtained readings, which allowed comparison of how the value of the library collection differs from other sources. The study found that researchers use many ways to identify and obtain articles, and the library collection is identified as one important source. Scholars use the library’s collection mostly to obtain articles for research and those articles help to inspire new ideas. Overall, this study demonstrates the importance of access to scholarly publications for scholars, from the library and elsewhere, such as open access sites and research sharing platforms.
Research on a Privacy Preserving Clustering Method for Social Network

With the continuous application of social network, data mining methods that can effectively and accurately analyze the structure of network communities and their potential features have become a research hotspot. Aiming at the risk of social user privacy leakage in the clustering process, a Markov clustering algorithm (DP-MCL) under differential privacy for social network is proposed. The algorithm firstly uses the improved singular value decomposition technique to pre-process the social network graph, then calculates the average degree of the nodes in the graph, and adopts it as the criterion for Laplace noise perturbation in the initial weight matrix and the probability conversion matrix respecting different privacy budget. Algorithm analysis proves that the DP-MCL algorithm satisfies ε-differential privacy. Finally, the experimental results show that the DP-MCL algorithm achieves a good clustering accuracy for the social network while satisfying the privacy protection requirements.
Real-Time Static Hand Gesture Recognition Using MATLAB

This paper proposes static hand gesture recognition that generally consists of recognizing the pre-defined hand signs. Since it is hard for people with disabilities to communicate and providing special training for learning hand gestures communication is a tedious process, hand gesture recognition will decrease the labour. The algorithm is based on segmentation of the image using background subtraction. The aim of this paper is to yield a system of recognition that is efficient and reliable along being cost-effective.
The Development of the Virtual Testing System of the Self-Driving Cleaning Truck Based on Virtual Reality Technology

Abstract With the development of autonomous driving technology, it is possible self-driving cleaning truck being driving and working automatically. However, a lot of field trials are timeconsuming and wasteful during the investigation of self-driving truck. In order to reduce lots of on-site and real vehicle tests, we construct a 3D virtual pilotless vehicle testing system for the cleaning truck based on virtual reality technology. 3DMax software is used to build virtual 3D scene static model and cleaning truck model. The established static models are imported into Unity3D to simulate different 3D scenes during cleaning truck self-driving. We designed a UI interface to build a virtual testing system of self-driving cleaning truck which is easy to realize human-computer interaction. we have achieved successfully the avoidance of obstacles and round-the-island driving. And we have successfully completed the identification of traffic lights and the pilotless driving test on crossroad virtual scene. In the virtual testing system, we can quickly experience the virtual testing scenes of the self-driving cleaning truck in various scenarios and get some kinematics information. The virtual testing system can partly replace the field tests and provide methods and theoretical guidance for field test, which can shorten the development cycle and save money, and is also unrestricted and repeatable. At the same time, the researching results provide new ideas and methods for the investigation of vehicle tests and pilotless driving.
Using Voters May Lead to Secret Leakage

The security of many digital devices strongly depends on a secret value stored in them. To mitigate security threats, high protection of such a value must be provided. Many attacks against (cryptographic) hardware as well as attack countermeasures were presented recently. As new attacks are invented continuously, it is important to analyze even potential threats to mitigate device vulnerability during its lifetime. In this paper, we report a novel voter-related vulnerability, which can be potentially misused to compromise the secret value stored in an embedded device.
Website Information Push Channel Analysis

By using the idea of the neighborhood system, this paper quantifies the relationship between people in the social circle and the process of information dissemination in the social circle. The topological potential is used to measure the influence of people in the process of information dissemination. A model of trust relationship between people is suggested to measure the strength of trust between people. Combined topological potential, trust strength and heterogeneity, a concept of energy resistance is presented. Based on the principle that minimization of information transmission resistance, the push channel with the least energy resistance can be selected. If the push channel with the least resistance is not unique, a final push channel is determined according to the mean value of trust between objects in the push channels. Finally, a general method of searching and calculating the best channel is given. And the validity of the method is verified by experiment. Experimental results show that the method can effectively improve the accuracy of information push, and the influence of human can intensely affect the process of information dissemination. The greater the influence of objects which consist of the channel of transmit information, the smaller the energy resistance of the push channel is.
Foundations of a DPLL-Based Solver for Fuzzy Answer Set Programs

Recent years have witnessed the effort to extend answer set programming (ASP) with properties of fuzzy logic. The result of this combination is fuzzy answer set programming (FASP), a powerful framework for knowledge representation and non-monotonic reasoning with graded levels of truth. The various results in solving FASP make use of transformations into fuzzy satisfiability (SAT) problems, optimization programs, satisfiability modulo theories (SMT), or classical ASP, each of which comes with limitations or scaling problems. Moreover, most of the research revolves around Gödel and Łukasiewicz semantics. The former approach is elegant in its attempt to generalize well-known methods in classical ASP to the fuzzy case. In our work we seek to extend this approach under the product semantics, utilizing the fuzzy generalization of the DPLL algorithm. As such, we design the inner works of a DPLL-based fuzzy SAT solver for propositional product logic, which should provideUhliarik, Ivor foundations for the technical implementation of the solver.
Role of Cloud Computing in On-Line Social Networking and In-Depth Analysis of Cloud-Sim Simulator

In this chapter, the authors introduced cloudsim simulator and cloud computing role in online social networking. The communication incurred by other activities such as management jobs is negligible. Social relationships can be established for numerous reasons. For example, family members, colleagues, or classmates often have strong social interactions resulting in large communication load. Cloud computing as well as social network-based applications will become dominant in many aspects of life in the next few decades. The performance of such large-scale systems is characterized by system capacity in terms of number of users/clients, flexibility, scalability, and effective cost of operation, etc. Popular social networks have hundreds of millions of users and continue to grow.
Benchmarking Comparison of Swish vs. Other Activation Functions on CIFAR-10 Imageset

The choice of the most appropriate activation functions for artificial neural networks has a significant effect on the training time and task performance. Nowadays the most widely-used activation function is the Rectified Linear Unit (ReLU). Despite its “dying ReLU problem” and many attempts to replace it with something better, it is still considered as default choice to begin with creation of network. Two years ago a new, promising function has been described formulated by Google Brain Team. The proposed function - named Swish - was obtained using a combination of exhaustive and reinforcement learning-based search. According to the authors, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2. This paper describes an experiment on CIFAR-10 image set where Swish appears not to outperform ReLU.