Reviewerly/exHarmony huggingface dataset:
Real-Time Secure/Unsecure Video Latency Measurement/Analysis with FPGA-Based Bump-in-the-Wire Security

With the growth of the internet of things (IoT), many challenges like information security and privacy, interoperability/standard, and regulatory and legal issues are arising. This work focused on the information security issue, which is one of the primary challenges faced by connected systems that needs to be resolved without impairing system behaviour. Information, which is made available on the Internet by the things, varies from insensitive information (e.g., readings from outdoor temperature sensors) to extremely sensitive information (e.g., video stream from a camera) and needs to be secured over the Internet. Things which utilise cameras as a source of information pertain to a subclass of the IoT called IoVT (internet of video things). This paper presents secured and unsecured video latency measurement results over the Internet for a marine ROV (remotely operated vehicle). A LabVIEW field programmable gate arrays (FPGAs)-based bump-in-the-wire (BITW) secure core is used to provide an AES (advanced encryption standard)-enabled security feature on the video stream of an IoVT node (ROV equipped with a live-feed camera). The designed LabVIEW-based software architecture provides an option to enable/disable the AES encryption for the video transmission. The latency effects of embedding encryption on the stream with real-time constraints are measured and presented. It is found that the encryption mechanism used does not greatly influence the video feedback performance of the observed IoVT node, which is critical for real-time secure video communication for ROV remote control and piloting. The video latency measurement results are taken using 128, 256 and 512 bytes block lengths of AES for both H.264 and MJPEG encoding schemes transmitted over both TCP and UDP transmission protocols. The latency measurement is performed in two scenarios (i.e., with matching equipment and different equipment on either end of the transmission).
New Generation Libraries: Modernization Project

The article describes the main characteristics of the project aiming to modernize municipal libraries of Russia within the framework of the national project “Culture”. The modernization is targeted at development of information and communication potential of regions, creation of conditions for further integrated development of the network of municipal libraries in order to provide local urban and rural communities with equitable access to the resources of major libraries, local history and the National Digital Library. The article summarizes the main statistical data of the starting and target stages of the project, including the number of libraries to be modernized in 2019. There are outlined the main activity directions of the project office established in the Russian State Library to provide the methodological assistance to subjects of the Russian Federation at all stages of the project.
An incremental learning approach for the text categorization using hybrid optimization

Purpose Document retrieval has become a hot research topic over the past few years, and has been paid more attention in browsing and synthesizing information from different documents. The purpose of this paper is to develop an effective document retrieval method, which focuses on reducing the time needed for the navigator to evoke the whole document based on contents, themes and concepts of documents. Design/methodology/approach This paper introduces an incremental learning approach for text categorization using Monarch Butterfly optimization–FireFly optimization based Neural Network (MB–FF based NN). Initially, the feature extraction is carried out on the pre-processed data using Term Frequency–Inverse Document Frequency (TF–IDF) and holoentropy to find the keywords of the document. Then, cluster-based indexing is performed using MB–FF algorithm, and finally, by matching process with the modified Bhattacharya distance measure, the document retrieval is done. In MB–FF based NN, the weights in the NN are chosen using MB–FF algorithm. Findings The effectiveness of the proposed MB–FF based NN is proven with an improved precision value of 0.8769, recall value of 0.7957, F -measure of 0.8143 and accuracy of 0.7815, respectively. Originality/value The experimental results show that the proposed MB–FF based NN is useful to companies, which have a large workforce across the country.
EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning

Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others).This paper proposes a participating system (EmoDet) to detect emotions using deep learning architecture.The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features (e.g.from AffectiveTweets Wekapackage).The proposed system (EmoDet) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.67) over the baseline model provided by Task 3 organizers (F1score 0.58).
Variational Context: Exploiting Visual and Textual Context for Grounding Referring Expressions

We focus on grounding (i.e., localizing or linking) referring expressions in images, e.g., ``largest elephant standing behind baby elephant''. This is a general yet challenging vision-language task since it does not only require the localization of objects, but also the multimodal comprehension of context -- visual attributes (e.g., ``largest'', ``baby'') and relationships (e.g., ``behind'') that help to distinguish the referent from other objects, especially those of the same category. Due to the exponential complexity involved in modeling the context associated with multiple image regions, existing work oversimplifies this task to pairwise region modeling by multiple instance learning. In this paper, we propose a variational Bayesian method, called Variational Context, to solve the problem of complex context modeling in referring expression grounding. Specifically, our framework exploits the reciprocal relation between the referent and context, i.e., either of them influences estimation of the posterior distribution of the other, and thereby the search space of context can be greatly reduced. In addition to reciprocity, our framework considers the semantic information of context, i.e., the referring expression can be reproduced based on the estimated context. We also extend the model to unsupervised setting where no annotation for the referent is available. Extensive experiments on various benchmarks show consistent improvement over state-of-the-art methods in both supervised and unsupervised settings.
CaIV: Cast-as-Intended Verifiability in Blockchain-based Voting

Democracy in the digital age has attracted a lot of public attention in recent years. However, bringing the human right of secrecy in voting to electronic systems is difficult. Properties, such as the possibility of verifying universally that any vote counted was indeed carrying the decision made by a voter, are often conflicting and a trade-off must be found. This paper proposes a blockchain-based electronic voting system providing explicitly cast-as-intended verifiability. By using a non-interactive zero-knowledge proof of knowledge any voter can verify that his or her encrypted vote represents the decision voted for while maintaining at the same time the secrecy of the ballot. In addition, any required cryptographic material can be generated in linear time with respect to the number of voters, making the system suitable for large scale elections, thus scalable.
General non-linear Bellman equations

We consider a general class of non-linear Bellman equations. These open up a design space of algorithms that have interesting properties, which has two potential advantages. First, we can perhaps better model natural phenomena. For instance, hyperbolic discounting has been proposed as a mathematical model that matches human and animal data well, and can therefore be used to explain preference orderings. We present a different mathematical model that matches the same data, but that makes very different predictions under other circumstances. Second, the larger design space can perhaps lead to algorithms that perform better, similar to how discount factors are often used in practice even when the true objective is undiscounted. We show that many of the resulting Bellman operators still converge to a fixed point, and therefore that the resulting algorithms are reasonable and inherit many beneficial properties of their linear counterparts.
Singular Value Decomposition and Neural Networks

Singular Value Decomposition (SVD) constitutes a bridge between the linear algebra concepts and multi-layer neural networks—it is their linear analogy. Besides of this insight, it can be used as a good initial guess for the network parameters, leading to substantially better optimization results.
Blockchain Networks – Security Aspects and Consensus Models

Data integration and fast eﬀective data processing are the primary challenges in today’s high-performance computing systems used for Big Data processing and analysis in practical scenarios. Blockchain (BC) is a hot, modern technology that ensures high security of data processes stored in highly distributed networks and ICT infrastructures. BC enables secure data transfers in distributed systems without the need for all operations and processes in the network to be initiated and monitored by any central authority (system manager). This paper presents the background of a generic architectural model of a BC system and explains the concept behind the consensus models used in BC transactions. Security is the main aspect of all deﬁned operations and BC nodes. The paper presents also speciﬁc BC use cases to illustrate the performance of the system in practical scenarios..
KNN-LC: Classification in Unbalanced Datasets using a KNN-Based Algorithm and Local Centralities

Classification is one of the most central topics in machine learning. Yet, most of the algorithms that solve the classification problem operate under the assumption that the training datasets are balanced. While this assumption is reasonable for many classification problems, it is often not valid. For example, application domains such as fraud and spam detection are characterized by highly unbalanced classes where the examples of malicious items are far less numerous then the benign ones. This paper proposes a KNN-based algorithm adapted to unbalanced classes. The algorithm precomputes distances in the training set as well as a centrality score for every training item. It then weights the distances between the items to be classified and their K-nearest training neighbors, accounting for the distribution of distances in every class and the centrality (and outlierness) of neighbors. This reduces the noise from outliers of the majority class and enhances the weights of central data points allowing the proposed algorithm to achieve high accuracy in addition to high TPR in the minority class.
A New Approach: Edge Computing and Blockchain for Industry 4.0

The diversity of devices, equipment, sensor networks connected to the Internet, and the paradigm of Industry 4.0; have increased the challenge for the development of architectures, services and intelligent applications capable of processing the data generated constantly. Edge Computing (EC) has emerged as a decentralized alternative and collaborative work for Internet of Things scenarios. However, the security and protection of data has made organizations doubt the effectiveness of EC as well as the model of centralized computing based on the Cloud. This article proposes the integration of Blockchain and Edge Computing technology to obtain a distributed, flexible, scalable and secure data flow through a new reference architecture.
IUID Method-Mix: Towards a Systematic Approach for Intercultural User Interface Design (IUID)

A method-mix for intercultural user interface design (IUID) is explained and exemplified by application examples based on a hybrid approach covering cultural contexts in human-computer interaction (HCI) design using a model of culturally influenced HCI.Cultural influence on HCI is described using cultural variables for user interface design.Assumptions and empirical results regarding the influence of culture on HCI, considering the path of the information processing and the interaction style between Chinese and German users are explained based on cultural models.Subsequent indicators represent the relationship between culture and HCI (culturally imprinted by the user).Correlations adopted theoretically between cultural dimensions and variables for HCI design were investigated.These correlations represent relevant constituents of a model for culturally influenced HCI.Considerations applying this model and evidence for the proper application of the IUID method-mix are presented elucidating why and how cultural aspects play a role in HCI design and usability/UX engineering.The IUID method-mix serves to inspire HCI engineers in the requirement analysis phase as well as HCI designers in the design phase.The readers are thereby sensitized to the challenges of intercultural usability/UX engineering and intercultural HCI design and will be equipped with methodological knowledge relevant to the derivation of design recommendations for user interface design for and in their desired cultural contexts.Finally, implications for practitioners are shown, including HCI style scores and practical design recommendations, to prognosticate the effort and the expenditures for considering the cultural context in IUID.
From UX to Engagement: Connecting Theory and Practice, Addressing Ethics and Diversity

In the field of Human-Computer Interaction (HCI), engagement bears critical significance, not just for informing a design and implementation of the interface, but also for creating improved and advanced interfaces that can adapt to users. While the idea of user engagement is passively being researched in a range of domains, it has been used to various related but diverse concepts. For instance, engagement is the vital element of an effective HCI design. The primary goal of this paper is to introduce relevant research questions related to the engagement domain. The paper studies engagement from four different perspectives: (i) Theory: identifying key issues that aid in building a pluralism of engagement frameworks, (ii) Practice: developing novel methodologies for user engagement and reliable assessment tools, (iii) Ethics: discussing the ethical aspects of engagement especially for designers and developers of humane technologies, (iv) Diversity: investigating individual differences to develop personalized engaging designs and understanding user diversities to provide equal opportunities for user engagement. The discussion will lead to opportunities for the potential researchers to acquire relevant knowledge, assess the mechanisms of engagement and evaluate the current design frameworks.
Joint Self-Attention Based Neural Networks for Semantic Relation Extraction

Relation extraction is an important task in NLP community. However, some models often fail in capturing Long-distance dependence on semantics, and the interaction between semantics of two entities is ignored. In this paper, we propose a novel neural network model for semantic relation classification called joint self-attention bi-LSTM (SA-Bi-LSTM) to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information, and capture Long-distance dependence on semantics. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrated that the proposed method is effective against relation classification, which can obtain state-of-the-art classification accuracy just with minimal feature engineering.
Alleviating the recommendation bias via rank aggregation

The primary goal of a recommender system is often known as "helping users find relevant items", and a lot of recommendation algorithms are proposed accordingly. However, these accuracy-oriented methods usually suffer the problem of recommendation bias on popular items, which is not welcome to not only users but also item providers. To alleviate the recommendation bias problem, we propose a generic rank aggregation framework for the recommendation results of an existing algorithm, in which the user- and item-oriented ranking results are linearly aggregated together, with a parameter controlling the weight of the latter ranking process. Experiment results of a typical algorithm on two real-world data sets show that, this framework is effective to improve the recommendation fairness of any existing accuracy-oriented algorithms, while avoiding significant accuracy loss.
Enhancement of Risk Prediction With Machine Learning

Bobak J. Mortazavi, PhD; Emily M. Bucholz, MD, PhD, MPH; Nihar R. Desai, MD, MPH; Chenxi Huang, PhD; Jeptha P. Curtis, MD; Frederick A. Masoudi, MD, MSPH; Richard E. Shaw, MA, PhD; Sahand N. Negahban, PhD; Harlan M. Krumholz, MD, SM
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera

We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video - addressing the difficulty of acquiring realistic ground-truth for such tasks. We propose three contributions: 1) we design new loss functions that capture multiple geometric constraints (eg. epipolar geometry) as well as an adaptive photometric loss that supports multiple moving objects, rigid and non-rigid, 2) we extend the model such that it predicts camera intrinsics, making it applicable to uncalibrated video, and 3) we propose several online refinement strategies that rely on the symmetry of our self-supervised loss in training and testing, in particular optimizing model parameters and/or the output of different tasks, thus leveraging their mutual interactions. The idea of jointly optimizing the system output, under all geometric and photometric constraints can be viewed as a dense generalization of classical bundle adjustment. We demonstrate the effectiveness of our method on KITTI and Cityscapes, where we outperform previous self-supervised approaches on multiple tasks. We also show good generalization for transfer learning in YouTube videos.
Splicing Image and Its Localization: A Survey

With the rapid development of information technology, digital images have become an important medium for information transmission. However, manipulating images is becoming a common task with the powerful image editing tools and software, and people can tamper the images content without leaving any visible traces of splicing in order to gain personal goal. Images are easily spliced and distributed, and the situation will be a great threat to social security. The survey covers splicing image and its localization. The present status of splicing image localization approaches is discussed along with a recommendation for future research.
On the Performance of Quantum Key Distribution FSO Systems Under a Generalized Pointing Error Model

In this letter, the performance of a quantum key distribution (QKD) free-space optical (FSO) system is analyzed while taking a generalized pointing error model into account. More specifically, closed-form expressions for the average received powers at both the legitimate receiver and eavesdropper are derived. In addition, their corresponding asymptotic results valid in the high telescope gain regime are also presented. To capture the secure performance, we also investigate the ratio of received powers at the eavesdropper and at the legitimate receiver. Further, in some special cases, we find the optimal telescope gains for the received powers at both the legitimate receiver and eavesdropper, as well as the power ratio, which is important and useful for a secure QKD FSO system design. Finally, some selected numerical results are presented to illustrate the mathematical formalism and validate the accuracy of the derived analytical expressions.
Vehicle Lighting Recognition System Based on Erosion Algorithm and Effective Area Separation in 5G Vehicular Communication Networks

For the safety of meeting vehicles at night, a design scheme of vehicle lighting recognition system for 5G vehicular communication networks is proposed in this paper. Firstly, a camera is used for image acquisition, and then the acquired RGB image is converted to a hue-saturation-lightness (HSL) image in Dedicated cloud. Secondly, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> convolution kemel is constructed, and the threshold of this HSL is used to create a mask. In which we traverse the original image data and perform uitwise operations on the original image, as well as mask by using the Urightest point as the center of calculation. Lastly, the effective area of the image is divided according to the farthest point of the lane line extension, which can shield the sky and street lights. In addition, all of interference points are excluded according to the symmetry of the headlights shape, the number of false detection is reduced effectively. So, the highlight areas can be marked to indicate the lights from the front vehicles. The results of the analysis are issued to the driving assistant system. The experimental results show that the system can identify the front vehicle lighting with an accuracy of 96.8%. With the help of the identified lighting position, the matrix lamp is set to realize the dynamic management of the beam, and it can be applied to traffic at night for preventing traffic accidents.
TypoWriter: A Tool to Prevent Typosquatting

Typosquatting is a form of internet cybersquatting generated from the mistakes (typos) made by internet users while typing a website address. It often leads the user to another unintended website. Sometimes it is exploited by cybersquatters to attract website traffic by redirecting common typos of popular websites to some other sites with malicious contents. A possible solution is defensive registration of similar domains and redirecting requests to the original site. This would be affordable for the owner of the original domain if a short list of such probable typo domain names can be predicted. Existing works on typosquatting mostly try to detect typo sites by analyzing logs. However, to the best of our knowledge, none of them can predict probable typo variations of a given URL at pre-registration phase. In this paper, we present TypoWriter, an RNN based error prediction tool to fill this gap. TypoWriter achieves a good performance in terms of successful predictions that match with the ground-truth. It is compared with five widely used typo generation tools and substantial improvement is observed.