Reviewerly/exHarmony huggingface dataset:
Remote Check Truncation Systems: Vulnerability Analysis and Countermeasures

All major banks in the USA and around the world offer remote check deposit services. Consumers can use their smart phones to deposit checks remotely. This new online check truncation system is vulnerable to a wide range of attacks, including digital check forgery. Shifting trust from a human teller or an automated teller machine (ATM) to a smart device (cell phone) provides new attack surfaces. This paper exploits security vulnerabilities in the existing remote check deposit system and presents an attack vector for existing remote check truncation systems. The proposed attack vector exploits vulnerabilities in the untrusted client-side check-deposit system that enables an attacker to instrument the check deposit application library. The instrumented library allows the attacker to induce digital check forgery with minimized tampering artifacts. It has been observed through this investigation that digital check forgery-based attacks are more powerful than conventional paper-based check forgery attacks. The effectiveness of these attacks is evaluated by targeting three leading banks in United States, finding that all three of the targeted banks are vulnerable to the proposed attacks. A set of countermeasures based on digital check verification is also proposed to combat digital check forgery attacks on existing remote check deposit systems. The proposed countermeasures rely on tamper detection in digital images and expert-system based decision fusion. The effectiveness of the proposed framework is evaluated using tampered check images. The tampered images used for performance evaluation also include set of tampered images used for successfully attacking the remote check deposit systems(being using by leading banks around the world today). Experimental results show that the proposed expert system-based framework is capable of detecting digital check forgery attacks.
Prevention of Unintended Appearance in Photos Based on Human Behavior Analysis

Nowadays, with smartphones, people can easily take photos, post photos to any social networks, and use the photos for various purposes. This leads to a social problem that unintended appearance in photos may threaten the facial privacy of photographed people. Some solutions to protect facial privacy in photos have already been proposed. However, most of them rely on different techniques to de-identify photos which can be done only by photographers, giving no choice to photographed person. To deal with that, we propose an approach that allows a photographed person to proactively detect whether someone is intentionally/unintentionally trying to take pictures of him. Thereby, he can have appropriate reaction to protect the facial privacy. In this approach, we assume that the photographed person uses a wearable camera to record the surrounding environment in real-time. The skeleton information of likely photographers who are captured in the monitoring video is then extracted and put into the calculation of dynamic programming score which is eventually compared with a threshold for recognition of photo-taking behavior. Experimental results demonstrate that by using the proposed approach, the photo-taking behavior is precisely recognized with high accuracy of 92.5%.
RANCANG BANGUN WEBSITE TRACER ALUMNI AKN PUTRA SANG FAJAR BLITAR

Tracer study is a method used to get feedback from alumni to tertiary institutions. Tracer alumni can be used to determine the absorption of PSN Blitar AKN alumni in the business world and industry. In addition, it also serves as a reference for adjusting competencies that must be owned by prospective alumni of AKN PSF Blitar in accordance with the needs of the business world and industry. System development uses the waterfall method which has several stages, including planning, analysis, design, implementation, and testing. The technology used in this system is web-based which aims to facilitate users in filling out the questionnaire and can be wider in scope. The result of this system design is a system design that uses a use case, the system flow uses a flowchart and the design of the physic data model. The results obtained are a web-based system that can reach a wider user, this system reduces operational costs, restrictions on access rights and individual alumni reports.&#x0D; Conclusion: This system can help universities in conducting tracer studies easily. The system used is web-based which can reach alumni wherever they are so that alumni do not need to come to campus or send questionnaire data so that the data will be faster to process.
A Quantum Assisted Secure Client-Centric Polyvalent Blockchain Architecture for Smart Cities

We propose a quantum assisted secure distributed ledger system that is client centric and polyvalent. The multiple attribute lock encryption (MALE) provides asymmetric key encryption to protect the data. The keys are of high randomness as they are generated via the simulation of a quantum walk. Quantum random walkers on a circle structure have been shown to be an efficient pseudo-random number generator. On top of the quantum random walker, we apply the quantum key distribution (QKD) protocol to avoid the double spending problem and to serve as a seed provider for the quantum walk. We further design the key chain update protocol with the help of MALE mechanism after each data access. Both QKD and quantum random walker can be efficiently simulated with current classical computation and near-term quantum technologies. Potential applications of proposed work in smart-city context are briefly discussed as well.
Application of Cloud Computing in Geological Exploration

Abstract The wide application of information technology in geological exploration will greatly improve the modernization level of geological exploration. At present, geological survey information work has entered the era of big data. How to break the limitation of independent management of geological exploration database and effectively integrate and share the accumulated massive geological exploration data has become a problem that the geological exploration information workers are eager to solve. The emergence and maturity of cloud computing technology provides a feasible solution to this problem. Firstly, from the perspective of public welfare and social service of geological exploration data, this paper summarizes and analyzes the application requirements of cloud computing in geological exploration work by studying the theoretical basis and relevant examples of cloud computing technology. Use Microsoft azure, SQL azure and other related products of Microsoft cloud computing platform azure to achieve the corresponding needs, research and clarify the relationship between geological exploration data and specific applications in geological cloud computing; use VMware vSphere virtualization platform to share existing and available hardware resources, and at the same time, research and build geoscience cloud storage mode based on virtualization technology to realize the sea Reasonable storage of quantitative geological exploration data; establishment of development environment with SDK provided by azure as the core for the development of application program of geological exploration data cloud computing; realization of data storage in the cloud for the purpose of integrating and sharing the achievement data of national mineral resource potential evaluation, and virtualization application of geoscience software.
An Adaptive Stereoscopic Video Streaming Mechanism Using MV-HEVC and 3D-HEVC Technologies

In this paper, we propose an adaptive stereoscopic video streaming mechanism using MV-HEVC and 3D-HEVC technologies. When the current available bandwidth is insufficient for delivering all video layers, the mechanism selects the MV-HEVC and 3D-HEVC video pictures near the current playing point with the proposed rate control algorithm. Simulation results show that the proposed adaptive streaming mechanism can provide tolerable video quality under packet loss conditions in wireless networking environment.
Existential Graphs on nonplanar surfaces

Existential graphs on the plane constitute a two-dimensional representation of classical logic, in which a Jordan curve stands for the negation of its inside. In this paper we propose a program to develop existential Alpha graphs, which correspond to propositional logic, on various surfaces. The geometry of each manifold determines the possible Jordan curves on it, leading to diverse interpretations of negation. This may open a way for appointing a "natural" logic to any surface.
Fast and Slow: Using Spritz for Academic Study?

In an age of increasing digital reading it is interesting that University students’ are not adapting innovative technologies for academic study. This is even more surprising because today’s university students are purportedly digitally native keen to use new technologies in comfortable personal spaces but adhere to traditional paper when studying. There is a distinct paucity of research with respect to how students use technology for academic purposes, particularly independent study. This small-scale qualitative evaluation investigates how undergraduate students respond to a refined Rapid Serial Visual Presentation speed reading application called Spritz, which claims to significantly increase users’ ability to skim read and comprehend content effectively. We evaluated the tool and asked students to express which affordances they would forego to make the technology acceptable. The sample of students focused on those enrolled on a module about academic reading were introduced to Spritz (N = 55). Nine students agreed to take part in the trials. Participants used the Sprtizlet App which enables a reading speed of up to four hundred words a minute to perform reading tasks. The findings suggest the technology is acceptable for certain types of skim reading and scanning, but Spritz did not meet the varied requirements of the participants’’ academic study practices.
Space-occupying lesions identification in mammary glands using a mixed computational strategy

Abstract The mammary pathology can manifest itself in multiple ways and originates space-occupying lesions. The breast cancer is a space-occupying lesion, which is highly prevalent, especially in women, and worldwide it is one of the leading causes of morbidity and mortality in this population. The main image modality for breast cancer detection is the magnetic resonance but this kind of image modality introduces several imperfections that affect the image quality. Some of these imperfections or problems are: inhomogeneity in the anatomical structures, riccian noise and artifacts. These problems make the analysis of the image information a real challenge. To address these problems, in this paper, we propose a computational technique able to extract a space-occupying lesion linked to breast cancer, present in magnetic resonance images. For this, the original image is processed with statistical-arithmetic filters and segmentation algorithms based on thresholding and multi-seed region growing techniques. The results, based on Dice score, show that the proposed technique is suitable for segmenting the breast cancer due high correlation between semi-automatic and manual segmentations. This technique can be useful in the detection, characterization and monitoring of this type of cancer and it can let to medical doctors to realize their work more efficiently.
Cryptography Security Services

This chapter gives an overview about the need for computer security and different types of network securities. Different types of attacks like active and passive are discussed that can harm system resources. This chapter also provides the basics of the cryptography system and basic terms used in cryptography. Security services provided by cryptography are also discussed such as data integrity, privacy/confidentiality, user authentication, message authentication, authorization, digital signatures, validation, access control, and non-repudiation along with their mechanisms.
Solution Complexity of Local Variants of Sabotage Game

The study of graph games serves as a way to analyze an existing logic, as well as an inspiration for designing new logics. Given the fact that game-theoretic analysis is reviving in AI study, a new stress in the study of graph games should be the performance of standard algorithmic tasks that are conducted on graphs. In this paper, we carry out a case study on the respective graph game for three main local variants of sabotage modal logic, which have a broad range of applications in various other fields. We analyze the solution complexity for each game and show the implications these results have on their corresponding logic. This work is a first attempt to understand why similar-looking variants of a graph game and their corresponding logics can have drastically different computational complexities, with the goal to bring up a more general topic that requires further studies, namely to identify the parameters of games and logic that crucially affect complexity.
A Video Frame Resolution and Frame Rate Amplification Method with Optical Flow Method and ESPCN Model

Super-resolution reconstruction for video sequences includes two expansions: the video frame resolution and frame rate, which enhances the resolution of the video frame while increasing the frame rate (frame expansion). Three problems of superresolution to solve include: a) how to use adjacent frames to achieve high quality reconstruction; b) how to generate motion compensation frames to supplement the video; and c) how to improve the efficiency of reconstruction calculation and control the running time. Although many methods have been successfully applied to video super-resolution, these methods still face great challenges in balancing motion compensation accuracy, computational complexity, reconstruction quality and running time. In this paper, an optical flow approach combined with an efficient sub-pixel convolutional neural network (ESPCN) model is proposed for frame resolution and frame rate amplification. By adopting the technical strategy of hyper-splitting before frame insertion (enhancement before frame expansion), the motion compensation frame is generated through the image optical flow to improve the frame rate. The super resolution of video frame is realized by constructing the model combining the motion estimation (ME) between adjacent frames with ESPCN (named as ME+ESPCN). The strategy of first superresolution reconstruction and then inserting frame (enhancement before frame expansion) is adopted to generate motion compensation frame through image optical flow to improve the frame rate. The simulation results show that compared with Sparse Dictionary Learning (SDL) and Super-Resolution Convolutional Neural Network (SRCNN), the proposed method based on ME+ESPCN model accelerates the reconstruction operation significantly, takes about 18ms on average, has higher real-time performance, and the average PSNR value for evaluating the frame restoration quality is about 0.12dB higher than ESPCN (no motion estimation). In addition, compared with the SRCNN method, the PSNR of the amplification technology strategy designed in this paper is improved by about 0.32dB on average.
Verifying the Correctness of Disjoint-Set Forests with Kleene Relation Algebras

We give a simple relation-algebraic semantics of read and write operations on associative arrays. The array operations seamlessly integrate with assignments in computation models supporting while-programs. As a result, relation algebras can be used for verifying programs with associative arrays. We verify the correctness of an array-based implementation of disjoint-set forests with a naive union operation and a find operation with path compression. All results are formally proved in Isabelle/HOL.
The reference resources in Russia’s regional studies (based on the author’s research)

The operating features of the RF regional reference resources are examined; their advancement and place within the flow of sci-tech publications are discussed. During the recent two decades, the new trends emerged: stable interest toward science reference publications in regional studies; publishing of proprietary, subject, thematic and digital reference works in the regions. Initially, these works were prepared by large (republican, regional, municipal) libraries. AS the most recent trend, these publications has been covering the minor territories (district, small town, village). Today many minor Russian libraries promote regional sci-tech information (primarily collapsed, generalized, reference information). The author participates in the project “The regional encyclopedias of Russia” promoted by the Russian National Library. In fact, this is the bibliographic index within the segment of reference resources, namely, science encyclopedias in regional studies ( http://nlr.ru/res/epubl/rue/content.html ). The resource is supplemented with additional lists comprising other types of reference literature (toponymical, administrative-territorial, biographical and other regional scientific reference publications). It is interesting to analyze these publications from the viewpoint of current innovative processes. The author attempts to formulate the concept of “reference resources in regional studies”, to divide them into a segment within the array of the regional reference sources, to specify the generation principles and problems of functioning, to demonstrate the demand for these publications in modern communities.
Classification of Imbalanced Data Using Decision Tree and Bayesian Classifier

The Bayesian classification is a method based on the Bayes theorem which gives best result when attributes are independent of each other and data is normalized. In this paper, a two-step approach is proposed to classify the data attributes which are locally normalized but not globally. The first step involves finding value for each attribute where the gain ratio is maximum. The classification occurs in the second step on the two separate parts of data using the Bayesian classifier. The experimental results show that the accuracy of the proposed method is better than the Bayesian classification and Decision tree.
Modeling the Chatbot Quality of Services (CQoS) Using Word Embedding to Intelligently Detect Inappropriate Responses

The rapid growth of intelligent chatbots as conversational agents with the assistance of artificial intelligence has recently attracted much research attention. The major role of a chatbot is to generate appropriate responses to the user, however sometimes the chatbot fails to understand the user’s meaning. Therefore, detecting inappropriate responses from a chatbot is a critical issue. Several studies based on annotated datasets have investigated the problem of inappropriate responses from a chatbots perspective without considering the user’s perspective. Understanding the context of the conversation is an important point in determining whether a response is appropriate or inappropriate. Sentiment analysis is a natural language processing task that supports mining in user behavior. Therefore, we propose an intelligent framework that combines automated sentiment scoring and a word embedding model to detect the quality of chatbot responses considering the end-user’s point of view. We find our model achieves higher quality results than logistic regression.
Vector Quantization based Image Transmission Using Power Adaptation Algorithms

Power Adaptation Algorithms play an vital role in Wireless communication. These algorithms include applications like lossy data compression, lossy data correction, pattern recognition, density estimation and clustering. They can also be applied in Military as well as Defense applications. Power Adaptation algorithms are very significant concern in Wireless Channels, since they can advance higher Capacities for longer time. On hand Power Adaptation Methods essentially make use of different techniques to put up better quality constraints that satisfy certain criteria. In this Paper Vector Quantization method is used and the quality of the image is also improved compared to further methods in terms of PSNR, RMSE and BER.
A Robust Adversarial Reinforcement Framework for Reading Comprehension

Deep neural networks with attention mechanism have led to recent success in the field of reading comprehension (RC). However, most current RC models perform unsatisfactorily on adversarial examples, that is, their effectiveness will drastically decrease when distracting sentences are inserted in contexts. Motivated by Robust Adversarial Reinforcement Learning in the field of reinforcement learning, we propose a Robust Adversarial Reinforcement Framework (RARF) to solve this problem. In this framework, we train a RC model in the presence of extra disturbances which are operated by an adversary agent. After the joint training, the adversary agent can learn an optimal destabilization adversarial policy and the robustness of the RC model can be improved. In our experiments, we test our framework integrated with the classical model and the state-of-the-art model, i.e., BiDAF and BERT. The experimental results demonstrate that the framework can effectively improve the evaluation metrics about 15-36 on four adversarial evaluations (AddSent, AddOneSent, AddAny and AddCommon).
Unsupervised Multilingual Ontology Learning

Multilinguality poses a big challenge to the growth of semantic web. In order to develop multilingual applications we need to develop ontologies which can be shared across languages. In this paper, we propose an unsupervised learning algorithm to automatically learn multilingual ontology from unstructured text. We propose three different approaches for multilingual ontology learning, Dictionary based method, parallel corpus based method and Latent Dirichlet Allocation (LDA) based method. While the first two approaches require availability of dictionary and parallel corpus, the LDA based approach does not require any special resource. We have conducted our experiments for two languages, English and Hindi, however the proposed method can be adopted for other languages also.
Pervasive Games as Web-applications: a Case Study based on a Laser Game

With the amazing success of PokemonGo, pervasive games have definitely caught the attention of the entire world. Their game experience is characterized by the fusion of real and virtual elements. In order to be able to extend the virtual world into the real one, such games have to access the mobile devices sensors like GPS and camera. For this reason, they are implemented as native or hybrid mobile applications. Contrary to this trend, in this paper we present Shoot Them All, a pervasive game implemented as a web application. In Shoot Them All, players have to hit the opponents, trying, at the same time, not to get hit. The battlefield is the real world, while the guns are virtual, as an extension of players' mobile devices. We were able to successfully developed such a game thanks to the availability of Javascript APIs that allow to access and manage device sensors. The success of this case study highlight how these technologies are mature enough, opening the door to web pervasive games.