Reviewerly/exHarmony huggingface dataset:
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems

Data sparsity and data imbalance are practical and challenging issues in cross-domain recommender systems. This paper addresses those problems by leveraging the concepts which derive from representation learning, adversarial learning and transfer learning (particularly, domain adaptation). Although various transfer learning methods have shown promising performance in this context, our proposed novel method RecSys-DAN focuses on alleviating the cross-domain and within-domain data sparsity and data imbalance and learns transferable latent representations for users, items and their interactions. Different from existing approaches, the proposed method transfers the latent representations from a source domain to a target domain in an adversarial way. The mapping functions in the target domain are learned by playing a min-max game with an adversarial loss, aiming to generate domain indistinguishable representations for a discriminator. Four neural architectural instances of ResSys-DAN are proposed and explored. Empirical results on real-world Amazon data show that, even without using labeled data (i.e., ratings) in the target domain, RecSys-DAN achieves competitive performance as compared to the state-of-the-art supervised methods. More importantly, RecSys-DAN is highly flexible to both unimodal and multimodal scenarios, and thus it is more robust to the cold-start recommendation which is difficult for previous methods.
SRDGAN: learning the noise prior for Super Resolution with Dual Generative Adversarial Networks

Single Image Super Resolution (SISR) is the task of producing a high resolution (HR) image from a given low-resolution (LR) image. It is a well researched problem with extensive commercial applications such as digital camera, video compression, medical imaging and so on. Most super resolution works focus on the features learning architecture, which can recover the texture details as close as possible. However, these works suffer from the following challenges: (1) The low-resolution (LR) training images are artificially synthesized using HR images with bicubic downsampling, which have much richer-information than real demosaic-upscaled mobile images. The mismatch between training and inference mobile data heavily blocks the improvement of practical super resolution algorithms. (2) These methods cannot effectively handle the blind distortions during super resolution in practical applications. In this work, an end-to-end novel framework, including high-to-low network and low-to-high network, is proposed to solve the above problems with dual Generative Adversarial Networks (GAN). First, the above mismatch problems are well explored with the high-to-low network, where clear high-resolution image and the corresponding realistic low-resolution image pairs can be generated. Moreover, a large-scale General Mobile Super Resolution Dataset, GMSR, is proposed, which can be utilized for training or as a fair comparison benchmark for super resolution methods. Second, an effective low-to-high network (super resolution network) is proposed in the framework. Benefiting from the GMSR dataset and novel training strategies, the super resolution model can effectively handle detail recovery and denoising at the same time.
Performance Probability Distribution Function for Modelling Solar Radiation in South Southern Nigeria: A Case Study of Yenagoa

This study has attempted to assess the performance of the most suitable statistical distribution function for modelling solar radiation over Yenagoa, Bayelsa State in Nigeria. The probability distribution functions are tested based on eleven years (2007-2017) solar radiation data obtained from National Aeronautics and Space Administration (NASA). Six probability distribution functions are tested to ascertain the most appropriate one based on four different statistical tools and fitting accuracy. The associated parameters of the most appropriate fitted probability distribution function are calculated and the trends in the characteristic of the solar radiation are deduced. The result shows that logistic distribution presents the most suitable probability distribution function for modelling solar radiation over the selected environment with RMSE of 1.500 KWh/m2/day, MAE of 1.260 KWh/m2/day, MAPE of 22.000% and R2 of 0.880.When compared with the other five distribution functions, the same trend could be seen although with different values of RMSE, MAE, MAPE and R2. The estimated distribution location and scale parameters of the model vary with month and season. The overall result will be useful for predicting future solar radiation over the studied environment. It will also be a good reference point for the design of large solar power projects in Yenagoa in particular and south southern Nigerian environments at large.
A recommendation system for allocating video resources in multiple partitions

A recommendation system or recommender aims to deliver meaningful recommendations for items or services to any interested party (e.g., users and applications). Recommenders provide their results on top of the collected data related either to the items' and users' description or ratings defined by users. Recommenders can be adopted in the domain of large-scale data management with significant advantages. Due to huge volumes of data, many techniques consider the separation of data into a number of partitions. Analytics are delivered on top of these data partitions and, accordingly, are aggregated to form the final response into the incoming queries. Data separation techniques can be incorporated to allocate the data into the appropriate partitions, thus, to improve the efficiency in the delivery of analytics. In this chapter, we propose a recommendation system responsible for allocating the data to the most appropriate partition according to their current contents. Our approach facilitates the provision of the analytics for each data partition by collecting “similar” data into the same partition. The aim is to support statistical insights into every partition to efficiently define query execution plans. We adopt a decision-making scheme combined with a naïve Bayesian classifier for deriving the appropriate partition. We focus on the management of streams of video files. The proposed recommender derives the appropriate partition for each incoming video file based on a set of characteristics. We evaluate our scheme through a set of simulations that reveal its strengths and weaknesses.
Rapidly-exploring Random Trees-based Test Generation for Autonomous Vehicles

Autonomous vehicles are in an intensive research and development stage, and the organizations developing these systems are targeting to deploy them on public roads in a very near future. One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. In this paper, an automated test generation approach that utilizes Rapidly-exploring Random Trees is presented. A comparison of the proposed approach with an optimization-guided falsification approach from the literature is provided. Furthermore, a cost function that guides the test generation toward almost-avoidable collisions or near-misses is proposed.
An End-to-End Network for Generating Social Relationship Graphs

Socially-intelligent agents are of growing interest in artificial intelligence. To this end, we need systems that can understand social relationships in diverse social contexts. Inferring the social context in a given visual scene not only involves recognizing objects, but also demands a more in-depth understanding of the relationships and attributes of the people involved. To achieve this, one computational approach for representing human relationships and attributes is to use an explicit knowledge graph, which allows for high-level reasoning. We introduce a novel end-to-end-trainable neural network that is capable of generating a Social Relationship Graph - a structured, unified representation of social relationships and attributes - from a given input image. Our Social Relationship Graph Generation Network (SRG-GN) is the first to use memory cells like Gated Recurrent Units (GRUs) to iteratively update the social relationship states in a graph using scene and attribute context. The neural network exploits the recurrent connections among the GRUs to implement message passing between nodes and edges in the graph, and results in significant improvement over previous methods for social relationship recognition.
Reflections on Educational Issues among Modern Language Students

The following pedagogical proposal seeks to understand what types of reflections Modern Languages students reveal about educational issues when analyzing artwork within their context. We worked with twelve Modern Languages students in their fifth and sixth semester at a public university in Colombia. The students began by analyzing four murals located within the university. Then, they created their own artwork and provided reflections on each other’s pieces of art. The findings showed that the students first began analyzing visual aspects of the murals and artwork, followed by the educational issues represented in each piece. As a common theme, the students reflected on the idea that working together yields a diversity of solutions. Finally, the students demonstrated that they could benefit from reflective practices despite not having started their teaching practicum.
Efficient combined SSIM- and landmark-driven image registration in a variational framework

Image registration methods are used to establish geometrical correspondences between different datasets. Various characteristics of image data can be exploited to drive image registration algorithms. Thus, the currently available schemes can be roughly divided into two classes: intensity-based and feature-based registration schemes. In this paper, we present a mathematical framework, based on the calculus of variations, for combining these two classes in order to benefit from the advantages of both strategies. The goal is to obtain a registration algorithm which achieves a good matching of datasets near landmark locations but also away from them (by matching the corresponding intensities). The proposed approach includes the novel formulation of a disparity term which simultaneously takes into account the structural similarity index (a similarity measure which considers spatial dependencies in the images) and the location of outstanding points. Since the iteration which results of the variational formulation is translated into the frequency domain, the implementation of the proposed algorithm offers a good speed-performance trade-off when compared to other state-of-the-art image registration implementations. Experimental results show the advantages, in the medical setting, of the combined SSIM- and landmark-based approach over well-established registration techniques which use either landmark or intensity information alone. In particular, the registration of triple-phase 3D computed tomographies of the liver under injection of a contrast agent has been chosen for such a comparison. The datasets are acquired at different times depending on the arrival time of the contrast agent in arteries, portal and hepatic veins, so they have to be registered in order to show the liver structures acquired at each phase in a common framework. These multi-phase studies provide tumor enhancement on the arterial and portal venous phases that support differential diagnosis of lesions in the liver.
Transmission Estimation by Complex Assumption with Occlusion Handling

Transmission estimation is a critical step in single-image dehazing. The estimate of each pixel describes the portion of the scene radiance that is degraded by hazing and finally reaches the image sensor. Transmission estimation is an underconstrained problem, and, thus, various assumptions, priors, and models are employed to make it solvable. However, most of the previous methods did not consider the different assumptions simultaneously, which, therefore, did not correctly reflect the previous assumptions in the final result. This paper focuses on this problem and proposes a method using an energy function that clearly defines the optimal transmission map and combines the assumptions from three aspects: fidelity, smoothness, and occlusion handling, simultaneously. Fidelity is measured by a novel principle derived from the dark channel prior, smoothness is described by the assumption of piecewise smoothening, and occlusion handling is achieved based on a new proposed feature. The transmissions are estimated by searching for the optimal solution of the function that can retain all the employed assumptions simultaneously. The proposed method is evaluated on the synthetic images of two datasets and various natural images. The results show that there is remarkable fidelity and smoothness in the transmission and that a good performance is exhibited for haze removal.
The Role and Function of National Bibliographies for Research

In this special issue, the subject of national bibliographies is presented from a user perspective, limited to the role and function of national bibliographies for research. Prior to being guest ed...
A proof of convergence of multi-class logistic regression network

This paper revisits the special type of a neural network known under two names. In the statistics and machine learning community it is known as a multi-class logistic regression neural network. In the neural network community, it is simply the soft-max layer. The importance is underscored by its role in deep learning: as the last layer, whose autput is actually the classification of the input patterns, such as images. Our exposition focuses on mathematically rigorous derivation of the key equation expressing the gradient. The fringe benefit of our approach is a fully vectorized expression, which is a basis of an efficient implementation. The second result of this paper is the positivity of the second derivative of the cross-entropy loss function as function of the weights. This result proves that optimization methods based on convexity may be used to train this network. As a corollary, we demonstrate that no $L^2$-regularizer is needed to guarantee convergence of gradient descent.
Deep Representation Learning for Social Network Analysis

Social network analysis is an important problem in data mining. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e., network embeddings, so that the network topology structure and other attribute information can be effectively preserved. Network representation leaning facilitates further applications such as classification, link prediction, anomaly detection and clustering. In addition, techniques based on deep neural networks have attracted great interests over the past a few years. In this survey, we conduct a comprehensive review of current literature in network representation learning utilizing neural network models. First, we introduce the basic models for learning node representations in homogeneous networks. Meanwhile, we will also introduce some extensions of the base models in tackling more complex scenarios, such as analyzing attributed networks, heterogeneous networks and dynamic networks. Then, we introduce the techniques for embedding subgraphs. After that, we present the applications of network representation learning. At the end, we discuss some promising research directions for future work.
Quantumness and memory of an open qubit under classical control

Hybrid quantum-classical systems constitute a promising architecture for useful control strategies of quantum systems by means of a classical device. Here we provide a comprehensive study of the dynamics of various manifestations of quantumness with memory effects, identified by non-Markovianity, for a qubit controlled by a classical field and embedded in a leaky cavity. We consider both Leggett-Garg inequality and quantum witness as experimentally-friendly indicators of quantumness, also studying the geometric phase of the evolved (noisy) quantum state. We show that, under resonant qubit-classical field interaction, a stronger coupling to the classical control leads to enhancement of quantumness despite a disappearance of non-Markovianity. Differently, increasing the qubit-field detuning (out-of-resonance) reduces the nonclassical behavior of the qubit while recovering non-Markovian features. We then find that the qubit geometric phase can be remarkably preserved irrespective of the cavity spectral width via strong coupling to the classical field. The controllable interaction with the classical field inhibits the effective time-dependent decay rate of the open qubit. These results supply practical insights towards a classical harnessing of quantum properties in a quantum information scenario.
Hate Speeches and Disrespect for the Opposition Parties in Nigeria: Implications for Democratic Consolidation and National Development

A major "display" of party politics especially in this democratic dispensation is characterized with the use of verbal arsenals (hate speeches) against the opposition party.Never in the history of Nigeria has this level of verbal disrespect been experienced.There is no gainsaying the negative effects of hate speeches heating up the polity and sowing seeds of disunity.This paper therefore examines the politics of hate speeches and disrespect for opposition parties and the implication for democratic consolidation and national development in Nigeria.It is therefore the opinion of this work that hate speeches and disrespect for the opposition parties are factors hindering the success of elections in recent times and consequently impeded democratic consolidation and national development/integration.This further threatens the continued existence of the Nigerian state.It is on the above premise this paper recommends a legal check on hate speeches to foster democratic consolidation and national integration.Content analysis was used both as an instrument of data collection and analysis.
PILOT: Password and PIN Information Leakage from Obfuscated Typing Videos

This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters that indicate keystrokes. To this end, we developed an attack called Password and Pin Information Leakage from Obfuscated Typing Videos (PILOT). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM. We conducted several experiments in various attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, PILOT recovers 8-character alphanumeric passwords in as little as 19 attempts. When guessing PINs, PILOT significantly improved on both random guessing and the attack strategy adopted in our prior work [4]. In particular, we were able to guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold improvement compared to random guessing. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper.
Prospects For The Development Of Cryptocurrency In Russia. From Bitcoin To Ethereum

This article presents prospects of the development of cryptocurrency in Russia, examines the principles of the operation of Bitcoin as main currency nowadays and its application in the real economic sector. Identifies and substantiates the advantages and disadvantages of this technology, as a result, the degree of influence of cryptocurrency and the consequences of its usage in the Russian economy. At present, over 1000 types of cryptocurrencies exist. In this paper, two of them are analyzed: the first cryptocurrency, the leader with the stabilized volatility and price: Bitcoin, and rather a new application for cryptocurrency which was skyrocketed as the second world popular: Ethereum. In this research paper we set an objective to compare Bitcoin and Ethereum, determine problems of cryptocurrency market in Russia and the prospects for development of their usage. In addition, authors are describing history of the research subject and trends to develop: from Bitcoin to Ethereum evolution.
A Proxy Voting Scheme Ensuring Participation Privacy and Receipt-Freeness

Proxy voting is a form of voting meant to support the voters who want to delegate their voting right to a trusted entity, the so-called proxy.Depending on the form of proxy voting, the proxy is either authorized to cast a ballot for the voting option that the voter chooses, or to vote according to her own wishes, if the voter is not sure how to vote and wants to delegate the decision making in the election.While the first form of proxy voting has been applied to traditional elections in order to support the voters who are unable to physically get to a polling station, the second form has been a topic of research in Internet voting.Recently, an Internet voting scheme has been proposed, that extends the well-known Helios scheme towards the functionality of proxy voting.This scheme, however, also has the drawbacks of Helios regarding participation privacy and receipt-freeness.As such, the information whether any voter participated in the election either by casting a direct vote or delegating their vote can be deduced from the published information.The scheme furthermore allows both the voters and the proxies to create receipts that prove casting a ballot for a specific candidate, as well as allows the voters to create receipts that prove delegating to a specific proxy.In this work we use the idea of dummy ballots, proposed in another extension of Helios to extend the proxy voting scheme towards participation privacy and receipt-freeness.
RESEARCH OF THE CONTENTS OF EDUCATION AND FORMATION OF DOCUMENTATION COMPETENCES IN THE SPHERE OF SPECIALTIES «INFORMATIONAL, LIBRARY AND ARCHIVE BUSINESS»

Сидоренко А
TEKNIK RESPONSIVE WEB DESIGN BOOTSTRAP 4 SERTA PENERAPANNYA DALAM RANCANG BANGUN LAYOUT WEB

Technology development makes influences the way to access the internet, which generally uses Desktop and Notebook computers, giving to other alternatives, namely accessing the internet using Tablets and Smartphones. The problem is that the majority of web layout designs cannot adjust the layout size automatically for these devices, because the resolution size of each device is different. To overcome the existing problems, we need a responsive web design method and technique in order to produce web layouts that can adjust the size of screen resolution automatically. The method used by applying the concept of Responsive Web Design (RWD), so that web layouts can adapt to screen size. Responsive web design techniques can be done in several ways, including using manual techniques using CSS Media Query and implementing CSS Frameworks such as Bootstrap CSS. The test results showed that the implementation of responsive web layouts using Bootstrap was very effective and efficient because web designers only needed the main library from Bootstrap, which was then combined by modifying according to the wishes of the user.