Reviewerly/exHarmony huggingface dataset:
A More General Theory of Static Approximations for Conjunctive Queries

Conjunctive query (CQ) evaluation is NP-complete, but becomes tractable for fragments of bounded hypertreewidth. Approximating a hard CQ by a query from such a fragment can thus allow for an efficient approximate evaluation. While underapproximations (i.e., approximations that return correct answers only) are well-understood, the dual notion of overapproximations (i.e, approximations that return complete – but not necessarily sound – answers), and also a more general notion of approximation based on the symmetric difference of query results, are almost unexplored. In fact, the decidability of the basic problems of evaluation, identification, and existence of those approximations has been open. This article establishes a connection between overapproximations and existential pebble games that allows for studying such problems systematically. Building on this connection, it is shown that the evaluation and identification problem for overapproximations can be solved in polynomial time. While the general existence problem remains open, the problem is shown to be decidable in 2EXPTIME over the class of acyclic CQs and in PTIME for Boolean CQs over binary schemata. Additionally we propose a more liberal notion of overapproximations to remedy the known shortcoming that queries might not have an overapproximation, and study how queries can be overapproximated in the presence of tuple generating and equality generating dependencies. The techniques are then extended to symmetric difference approximations and used to provide several complexity results for the identification, existence, and evaluation problem for this type of approximations.
TIGER

Optimal mapping of a parallel code's communication graph is increasingly important as both system size and heterogeneity increase. However, the topology-aware task assignment problem is an NP-complete graph isomorphism problem. Existing task scheduling approaches are either heuristic or based on physical optimization algorithms, providing different speed and solution quality tradeoffs. Ising machines such as quantum and digital annealers have recently become available offering an alternative hardware solution to solve certain types of optimization problems. We propose an algorithm that allows expressing the problem for such machines and a domain specific partition strategy that enables to solve larger scale problems. TIGER - topology-aware task assignment mapper tool - implements the proposed algorithm and automatically integrates task - communication graph and an architecture graph into the quantum software environment. We use D-Wave's quantum annealer to demonstrate the solving algorithm and evaluate the proposed tool flow in terms of performance, partition efficiency and solution quality. Results show significant speed-up of the tool flow and reliable solution quality while using TIGER together with the proposed partition.
Journal Packages: Another Look at Predicting Use

This study uses linear regression analysis to determine if interlibrary loan (ILL) requests and/or denials are predictors of subsequent usage of journals acquired in a package subscription. In 2016, Auburn University Libraries acquired the SAGE Premier package and gained access to 572 previously unsubscribed titles. The study used interlibrary loan request data and denial data from 2015 to determine if these two metrics were predictors of subsequent use for the unsubscribed titles from this package. A strong correlation was found between ILL requests and later usage but almost no correlation between denials and later usage.
A Novel Approach for Objectness Estimation Based on Saliency Segmentation and Superpixels Clustering

Object detection is an important and challenging vision task. It is a critical part in many applications such as image search, robot navigation, and scene understanding. In this paper, we propose a novel objectness measure method, which uses both saliency segmentation and superpixels clustering together. First, we use the single skeleton refinement and fuzzy C-means method to segment the image. Then, the candidate regions are selected by combining the saliency map. At the same time, we used the superpixels clustering and straddling method to filter the windows. The final candidate object windows are obtained based on a fusion of the two results. The experimental results from PASCAL VOC 2007 validate the efficacy of the proposed method, and we get a result of 40.1% on mean average precision which was the best of the tested methods.
A joint mechanism algorithm for object detection

The main goal of object detection is to recognize and locate the object of interest from the static image or video sequence. It is one of the key tasks in the field of computer vision. However, there are many factors in brightness, shape, color and occlusion of targets, and they are disturbed by complex environmental factors, which make the research opportunities and challenges of object detection algorithms coexist. In this paper, two main frameworks of object detection algorithm based on convolutional neural network are researched, which are based on region proposals and regression idea respectively. Then we present a joint mechanism algorithm for object detection. This algorithm makes a balance between detection efficiency and accuracy to make it more meet the actual needs. The internal of the algorithm is adjusted and optimized, so that the two detectors can make their own judgments according to the characteristics of the image, and decide whether to detect the object to classify and locate it, so that the efficiency is higher and the accuracy is also improved.
Efficient Implementation of Recurrent Neural Network Accelerators

In this paper we propose an accelerator for the implementation of Long Short-Term Memory layer in Recurrent Neural Networks. We analyze the effect of quantization on the accuracy of the network and we derive an architecture that improves the throughput and latency of the accelerator. The proposed technique only requires one training process, hence reducing the design time. We present implementation results of the proposed accelerator. The performance compares favorably with other solutions presented in Literature.
Lookback-Guess-Next Optimizer: Feedback-Guided Random Search Technique with Biased Mapping for Solving Unconstrained Optimization Problems

Abstract Finding global optima for functions is a very important problem. Although a large number of methods have been proposed for solving this problem, more effective and efficient methods are greatly required. This paper proposes an innovative method that combines different effective techniques for speeding up the convergence to the solution and greatly improving its precision. In particular, the method uses feedback-guided random search technique to identify the promising regions of the domains and uses the biased mapping technique to focus the search on these promising regions, without ignoring the other regions of the domains. Therefore, at any point of time, the domain of each variable is entirely covered with much more emphasis on the promising regions. Experiments with our prototype implementation showed that our method is efficient, effective, and outperformed the state-of-art techniques.
A Robust Modeling of Impainting System to Eliminate Textual Attributes While Restoring the Visual Texture from Image and Video

The area of video analytics in the context of collaborative networking has gained a lot of attention from the research community owing to its potential applicability in the real life aspects. However, although image and video content which mostly get exchanged in the networking pipelines consist of several significant textual information from the application view-point which often display various confidential textual credentials of a corresponding individual. The realization of this fact that this textual attributes has to be removed for various image forensic requirements, has led to image impainting. The study has addressed this problem and come up with a novel analytical solution which imposes two different methods and further combines this two. In the 1st stage it applies a robust mechanism to detect the region of an image and video frame sequence where textual data representation can be localized and perform extraction of those data it introduces artifact and visual anomalies. On the completion of this stage in the 2nd phase, to eliminate the artifacts from the respective locations, it introduces a novel impainting technique which is computationally efficient and attain higher degree of textual data eliminated recovered image or video sequence which is almost similar like the original image or video sequence, can be visually perceived. The comparative performance analysis show that the proposed technique attain better outcome in terms of textual attributes detection accuracy (%) from specific region of interest (ROI) and also consume very less processing time (Sec) in contrast with the existing system.
At the Origins of the University Education System in Russia

The article examines the history of the formation and development of University education in Russia during the reign of Alexander I. The author analyzes the key legislative acts and statutes of the Russian universities created in 1802–1805, as well as the reorganized in 1819 University of St. Petersburg. The article dwells on the history of Dorpat, Vilnius, Kharkiv, Kazan, and St. Petersburg Universities during the period of Alexander I’s reign. It is shown that as a result of the activities of the Emperor and his associates, a progressive harmonious system of education and higher education was created. The author traces how the educational policy began to change due to radically changed views of Alexander I in the last decade of his reign, which resulted in the clericalization of higher education, public education in general. The traditions initiated by M.V. Lomonosov were halted by the obscurantists and mystics. The universities were treated as hotbeds of freethinking, moral depravity, and philosophy was demonized as a source of spiritual contagion.
Secure delegation of group exponentiations: Efficient and fully verifiable schemes with two servers†

Abstract Recent interactions between cloud service providers and mobile resource‐constrained devices have increased the interest on the delegation of expensive computations to powerful external server providers. At the same time, security and verifiability of the delegated computation are challenging though highly demanded requirements for various application scenarios. Many security solutions using advanced cryptographic technologies require a large number of highly expensive group exponentiations (GEs). In this paper, we address the problems regarding secure and verifiable delegation of GEs. We first analyze two delegation schemes for GEs both introduced recently in Cluster Computing by Fu et al with the claim that they both provide full verifiability. Particularly, we show that the first scheme is only verifiable with a probability at most 2/3, and the second scheme is totally unverifiable. Then, we propose a secure, efficient and fully verifiable delegation scheme InvDel for a GE using two servers, where one of which is malicious. With InvDel the delegator does not require computations of any group inversions (GIs) while providing full verifiability. Then, we extend InvDel to SimInvDel which is the first, efficient and secure delegation scheme for n ‐simultaneous GEs achieving full verifiability for n &gt; 1. Secondly, we give implementation results of InvDel and SimInvDel using an Android application together with a comprehensive efficiency analysis of computation and communication with the previous schemes. For instance, when the required CPU costs for a single GE are compared with a 4092‐bit modulus, InvDel is more than 220‐times (resp. more than two times) more efficient than the local computation (resp. using a previous fully verifiable scheme proposed in the literature). Finally, we also utilize InvDel and SimInvDel to speed‐up the verification of Schnorr's signature and Cramer‐Shoup encryption schemes.
Theoretical analysis quantum tunnelling three potential barriers to the Schrodinger equation in <i>grapheme</i>

Quantum mechanics is a branch of physics that can answer problems involving atomic nuclei, atoms, molecules, and matter in solids rather than classical mechanics.The phenomenon of three barrier tunnelling effects is the development of a double barrier tunnelling effect. This study aims to determine the transmission coefficient on three barriers. This study uses a barrier with the semiconductor material, graphene. Graphene material has a band gap energy close to 0 eV with a lattice distance of 0.246 nm. Analysis of tunnelling effects using the Schrodinger equation does not depend on time with the matrix propagation method. The results shown get the largest transmission coefficient is 1.00 of energy electron 0.9225 eV, meaning the chance of electrons in breaking through the barrier to 100%. However, after reaching the largest coefficient point on the three electron barriers the transmission coefficient decreases even though the energy is getting higher. But on one and two electron energy barriers and the transmission coefficient increases and does not decrease. The properties of this very special graphene can create a new opportunity in the utilization to produce better electronic field products such as supercapacitors and transistors.
ROLE AND PLACE OF INFORMATION AND ANALYTICAL SYSTEMS IN IMPROVING QUALIFICATION OF TEACHERS OF INFORMATICS

In the article types of information-analytical systems are considered, their classification is made. The analysis of researches on the problem of introduction of IAS in business and educational processes of the educational institution is carried out. The components of these systems and the main content technological lines, which should be considered when improving the qualification of teachers of informatics, are determined. The kinds of IAS that are used by the teachers of informatics in their professional activity and perspectives from which IAS should be considered in the educational activity are considered: database management systems, Internet information resources. Their analysis, criteria and indicators influencing efficiency and effectiveness are presented. The place of information-analytical systems in the professional activity of the teacher of informatics is determined. The problems faced by the system of postgraduate pedagogical education for the support of informatics teachers at advanced training courses are outlined with the aim of teaching designing of information-analytical systems, development of informational competences and raising the level of information culture of all subjects of educational process. The design and development of IAS by pupils and teachers ensures the objectivity of learning activities, the acquisition of academic achievements by students (participation and victories in competitions, tournaments, competitions), the release of their creative potential, the continuous professional growth of the teacher, the orientation of the educational process to the development of the individual and the satisfaction of his educational needs, development of software and hardware ICT infrastructure of an educational institution. Understanding the ability of the teacher to use IAS in their professional activities can significantly improve management organization and conduct the educational process, develop information culture of all subjects of the educational process.
The Design and Creation of an Interactive E-Book: “Book of Answer”

Interactive E-book is a digital book which the reader can direct the storyline and interact with it. 'The book of answer' is a kind of popular book that reader can interact with. In this project, we aim to develop an PVE game to simulating the process that the reader interact with the computer with the content of the book saved in. We use Python to realize the interactive dialogue process in GUI (Graphical User Interface).
Depth Map Estimation Model with Efficient Feature Extraction Module

This paper proposes a depth image generation algorithm of stereo images using a deep learning model composed of a CNN. The proposed algorithm consists of a feature extraction unit which extracts the main features of each parallax image and a depth learning unit which learns the parallax information using a 3d cost volume of extracted features. The proposed algorithm uses local region extraction modules for feature extraction and estimates the depth of object region more accurately than existing CNN algorithms.
On Black-Box Complexity of Universally Composable Security in the CRS Model

In this work, we study the intrinsic complexity of black-box Universally Composable (UC) secure computation based on general assumptions. We present a thorough study in various corruption modelings while focusing on achieving security in the common reference string (CRS) model. Our results involve the following:
Reversible data hiding for block truncation coding compressed color images

In this paper, a reversible data hiding method aims at block truncation coding compressed color images is proposed, which can reconstruct the original compressed image effectively after extracting the embedded secret data. In order to improve the compression quality, a mean absolute deviation based clustering is applied to find an approximate optimal common bitmap. The common bitmap is used to get the quantization levels of each block in each color channel. The secret data are then embedded in the quantization levels of each block by the histogram shifting method and quantization level patterns based method. Experimental results show that the proposed method outperforms the existing reversible data hiding methods for BTC-compressed color images in terms of hiding capacity and visual quality.
A Study of Multi-space Search Optimization

Traditionally search algorithms have explored a solution space to solve optimization problems. However, as the field has advanced, in order to overcome the challenges posed by searching the solution space directly such as premature convergence, search has been applied to different spaces. These include genetic programming which explores the program space, cultural learning and semantic genetic programming which work in the belief and behavioural spaces respectively and hyper-heuristics which explores a heuristic space. In solving a problem one of these spaces is usually explored. The research presented in this paper forms part of an initiative aimed at deriving n-space search algorithms that explore more than one space to solve a problem. As a starting point this paper focuses on 2-space search algorithms. The paper presents two models for exploring search across spaces, namely, the concurrent model and the sequential model. The application of these models is then illustrated, the concurrent model is applied to solving the one-dimensional packing problem and the sequential model is applied to classification, namely, network instrusion detection and weather prediction. The models have produced good results for both problem domains, illustrating the potential of multi-space search. Future work will examine extending these algorithms to n-space search algorithms with n greater than 2 as well as additional models for combining the search across spaces.
A Pedagogical Approach Towards Curating Mobile Apps in an Educational Context

Mobile apps vary in functionalities. The more apps are developed, the process of determining whether an app is effective in an educational environment can become next to impossible. The purpose of this chapter is to propose a curation rubric with a specific focus on evaluating mobile apps by utilizing three pedagogical frameworks as guidelines. Existing pedagogical frameworks for categorizing apps have been proposed by Allan Carrington based on Bloom's taxonomy for learning and the SAMR model by Puentedura. In this study the researchers are widening the lens by proposing the inclusion of the levels of instruction by Gagne as a third pedagogical framework as part of a curation rubric for mobile apps. The rational for adding the third framework is supported by the notion that technology should be regarded as “mindtools” which do not only focus on learning with technology, but also rely on effective facilitation and implementation thereof in an educational context.
Combining Data-Driven and Domain Knowledge Components in an Intelligent Assistant to Build Personalized Menus

In this paper, some new components that have been integrated in the Diet4You system for the generation of nutritional plans are introduced. Negative user preferences have been modelled and introduced in the system. Furthermore, the cultural eating styles originated from the location where the user lives have been taken into account dividing the original menu plan in sub-plans. Each sub-plan is in charge to optimize one of the meals of one day in the personal menu of the user. The main latent reasoning mechanism used is case-based reasoning, which reuses previous menu configurations according to the nutritional plan and the corresponding hard constraints and the user preferences to meet a personalized recommendation menu for a given user. It uses the cognitive analogical reasoning technique in addition to ontologies, nutritional databases and expert knowledge. The preliminary results with some examples of application to test the new contextual components have been very satisfactory according to the evaluation of the experts.
The Rational Checks Amount Selection for the Information Management Systems Troubleshooting

The questions of creating a rational set of controlled parameters of the information management system in order to maintain it ready to perform tasks for functional purposes. The main attention is paid to the study of the range of changes in the parameter and its impact on the performance of individual devices and the system as a whole.
Intelligence-Software Cost Estimation Model for Optimizing Project Management

With the evolution of pervasive and ubiquitous application, the rise of web-based application as well as its components is quite rising as such applications are used both for development and analysis of the web component by developers. The estimation of software cost is controlled by multiple factors right from human-driven to process driven. Most importantly, some of the factors are never even can be guessed. At present, there are no records of literature to offer a robust cost estimation model to address this problem. Therefore, the proposed system introduces an intellectual model of software cost model that is mainly targets to perform optimization of entire cost estimation modeling by incorporating predictive approach. Powered by deep learning approach, the outcome of the proposed model is found to be cost effective in comparison to existing cost estimation modeling.
To the question of data-driven identification of author’s age for Russian texts with age deceptions using machine learning

In this work we compare data-driven approaches to the task of author's age identification for Russian texts with age deception. The data corpus has been specially gathered with crowdsourcing for this task. Two ways to determine age deception in author texts are considered and compared: the first is a traditional task of identification of age group of a text author, the second is identification of the occurrence of age imitation in the text with its type (imitating higher age or imitating lower age). The best results obtained by LinearSVC model with vector of TF-IDF features of character n-grams as input data demonstrate the F1-score of about 80% for the second task, and for the first one it is about 44%.