Reviewerly/exHarmony huggingface dataset:
A Research of Readers' Borrowing Library Books Management and Optimization Based On Big Data Technology

In the era of Internet, massive data are coming. The big data mining and analysis is vital to guide people in their work. In this paper, we introduce properties of data of the library borrowing big data and distribution of library books. By data mining of the library borrowing big data, we bring forward some opinions in three aspects. Firstly, according to the number of books borrowed from different stacks, we come up with some ideas to optimize the library floor set and make reasonable use of resources. Secondly, Based on the analysis of borrowing time, we make some suggestions to optimize the management of borrowing books. Finally, according to the types of books of the most borrowed, we put forward some suggestions to optimized collection books of library.
A Mechanism to Improve Effectiveness and Privacy Preservation for Review Publication in LBS

Local business service systems (LBSS), as an essential role of location-based service (LBS), have been gaining tremendous popularity in our daily life. Individuals' reviews in these systems are very important as they not only contribute to building reputations for businesses but also play a guiding role for consumers. However, users' privacy disclosure and the effectiveness of reviews are the urgent problems to be solved for the further development of LBSS. This paper proposes a mechanism to improve effectiveness and privacy preservation for review publication. In users' privacy protection, the mechanism firstly formalizes the model of attackers, then focuses on the identification or inference attack caused by reviews. For improving the effectiveness of reviews, the mechanism introduces users' reputation scores to rank the reviews. We evaluate our mechanism thoroughly by extensive experiments, and the results validate that our mechanism can achieve a better performance.
Dataset

The ability to accurately estimate the presence and counts of occupants inside a monitored area has many notable and compelling applications, e.g., efficient space utilization, intelligent building operations, comfortable indoor environment, safety, and evacuation. Significant research on design approaches and methods have been implemented to extrapolate rich information on occupant behavior and actions. However, these methods are evaluated based on a subset of divergent data collected from heterogeneous sensing modalities available in instrumented public buildings. Comprehensive baseline datasets are lacking which can serve for benchmarking and performance comparison despite the potential inconsistencies. In this paper, we present a dataset collected from three 3D Stereo Vision Cameras for 13 full days, with a total of 5,485,350 readings and 1242 estimated occupants in the summer of 2019 from a public building. The dataset contains occupancy presence and trajectory data. The dataset can serve for benchmarking and foster data-driven research in occupant presence and behavior modeling. This dataset would help to extract the inter-relationships of different factors influencing occupant behavior and trajectory patterns. Furthermore, high fidelity information about actions can be extrapolated from the presented dataset.
A SysML Method with Network Dimensioning

Acceptance of the Systems Modeling Language (SysML) among system engineers heavily depends on the method and tool associated with the language.This particularly applies to a family of systems where increasing data exchanges between equipments create high requirements for the networks.The paper therefore revisits the method associated with the free SysML tool TTool in order to take network dimensioning into account in the early steps of the life cycle of distributed systems.TTool is interfaced with WoPANets, a tool based on network calculus theory.An AFDX network serves as case study.
Hyperspectral image restoration by subspace representation with low‐rank constraint and spatial‐spectral total variation

Hyperspectral images (HSIs) restoration is an important preprocessing step. The spectral vectors in HSI can be separated into different classification based on the land-covers, which means the spectral space can be regarded as the union of several low-rank subspaces. Subspace low-rank representation (SLRR) is powerful in exploring the inner low-rank structure and has been applied for HSI restoration. However, the traditional SLRR only seek for the rank-minimum representation under a given dictionary, which may treat the structured sparse noise as inherent low-rank components. In addition, the SLRR framework cannot make full use of the spatial information. In this study, a framework named subspace representation with low-rank constraint and spatial-spectral total variation is proposed for HSI restoration. In which, an artificial rank constraint is introduced to control the rank of the representation result, which can improve the removal of the structured sparse noise and exploit the intrinsic structure of spectral space more effectively. Meanwhile, the spatial-spectral total variation regularisation is applied to enhance the spatial and spectral smoothness. Several experiments conducted in simulated and real HSI datasets demonstrate that the proposed method can achieve a state-of-the-art performance both in visual quality and quantitative assessments.
Legoons: Inflatable Construction Kit for Children

Legoons is a ready-to-play inflatable construction kit that enables children, 6 to 12 year old, to construct characters and artifacts that can be actuated by pumping air. Our set features three types of silicone bricks with distinct behaviors when inflated, as well as various decoration bricks, connectors, and stoppers. With our design, we propose adding soft materials and organic motion to the traditionally rigid and mechanical construction kits. Our unconventional kit celebrates children's imaginations with playful and transforming materials, as well as their desire for personal expression that motivates them to tinker, experiment, and bring their creations to life. We describe our project's various design iterations, fabrication steps and discuss the lessons we learned from our initial playtests with children.
One-dimensional quantum walks with two-step memory

In this paper, we investigate one-dimensional quantum walks with two-step memory, which can be viewed as an extension of quantum walks with one-step memory. We develop a general formula for the amplitudes of the two-step memory walk with Hadamard coin by using path integral approach and numerically simulate its process. The simulation shows that the probability distribution of this new walk is different from that of the Hadamard quantum walk with one-step memory, while it presents some similarities with that of the normal Hadamard quantum walk without memory.
On the Feasibility of Secure Logging for Industrial Control Systems Using Blockchain

With industrial control systems (ICSs) being increasingly networked, the need for sound forensic capabilities for such systems increases. One vital source of information in forensic investigation are log files. Techniques for secure logging aim to protect log files from manipulation. We investigate how a blockchain can enable secure logging for ICSs. We argue that a blockchain fits well both into general models of secure logging and into the Purdue model for ICSs. We report on experiences from connecting the syslog functionality of a Siemens SIMATIC S7-1500 programmable logic controller to the public Ethereum blockchain network. While the level of manipulation protection is comparably high, the transaction time for the public Ethereum blockchain severely limits the usefulness of this type of secure logging for ICSs.
Automatic liver segmentation on CT images combining region-based techniques and convolutional features

Precise automatic liver segmentation plays an important role in computer-aided diagnosis of liver pathology. Despite many years of research, this is still a challenging task, especially when processing heterogeneous volumetric data from different sources. This study focuses on automatic liver segmentation on CT volumes proposing a fusion approach of traditional methods and neural network prediction masks. First, a region growing based method is proposed, which also applies active contour and thresholding based probability density function. Then the obtained binary mask is combined with the results of the 3D U-Net neural network improved by GrowCut approach. Extensive quantitative evaluation is carried out on three different CT datasets, representing varying image characteristics. The proposed fusion method compensates for the drawbacks of the traditional and U-Net based approach, performs uniformly stable for heterogeneous CT data and its performance is comparable to the state-of-the-art, therefore it provides a promising segmentation alternative.
Research of Typical Line Loss Rate in Transformer District Based on Data-Driven Method

Line loss rate plays an important role in evaluating the economic operation of power systems. Line loss management is one of the key management contents of power companies. Based on the existing power information system, marketing system, Production Management System (PMS) and other collection operation data, the mechanism and characteristics of the line loss of massive transformer districts are explored, and the classification criteria of the transformer district are formulated according to the different characteristics. Then, the typical transformer district is selected from the same type of transformer districts, and the line loss rate of the typical transformer district is taken as the benchmark value.
3D Path planning of fixed and mobile environments using potential field algorithm with Genetic algorithm

Path planning is one of the most important techniques responsible for drawing a collision-free path for each robot in a crowded environment. Potential field algorithm is one of most fame algorithms of path planning, it works online. The potential field algorithm deals with any robot as an electron in a potential field. This robot or electron attract to the goal location and repulsion from other robots and obstacles. These forces cause it to move naturally to the target point with a suboptimal path. In this paper, we study and simulate multi scenarios of potential field algorithm in both cases which are: static and dynamic 3D environments. In the static environment, we suppose many static obstacles in random positions then find suboptimal path according to this algorithm. The second scenarios based on the existence of multiple mobile robots. The straight line path of each robot collides at least with the one other path. This algorithm used to solve this problem and find a collision-free path for each robot.
Anti-Predatory NIA for Unconstrained Mathematical Optimization Problems

Nature-Inspired Algorithms (NIAs) are one of the most efficient methods to solve the optimization problems. A recently proposed NIA is the anti-predatory NIA, which is based on the anti-predatory behavior of frogs. This algorithm uses five different types of self-defense mechanisms in order to improve its anti-predatory strength. This paper demonstrates the computation steps of anti-predatory for solving the Rastrigin function and attempts to solve 20 unconstrained minimization problems using anti-predatory NIA. The performance of anti-predatory NIA is compared with the six competing meta-heuristic algorithms. A comparative study reveals that the anti-predatory NIA is a more promising than the other algorithms. To quantify the performance comparison between the algorithms, Friedman rank test and Holm-Sidak test are used as statistical analysis methods. Anti-predatory NIA ranks first in both cases of “Mean Result” and “Standard Deviation.” Result measures the robustness and correctness of the anti-predatory NIA. This signifies the worth of anti-predatory NIA in the domain of mathematical optimization.
Convolutional Neural Network with an Optimized Backpropagation Technique

This paper exhibits an Object recognition technique using the Convolutional Neural Network in Deep Learning. Backpropagation is a redundantly used method to calculate the gradient of a curve. The gradient, in turn, is involved in the weight upgradation while training the deep neural network. Being a repeatedly rediscovered algorithm, Backpropagation still stands out to give better results with its various optimization techniques. An Object Recognition technique in deep learning using backpropagation, optimized with a heuristic optimization technique is implemented and evaluated.
Real-Time Visual Navigation in Huge Image Sets Using Similarity Graphs

Nowadays stock photo agencies often have millions of images. Non-stop viewing of 20 million images at a speed of 10 images per second would take more than three weeks. This demonstrates the impossibility to inspect all images and the difficulty to get an overview of the entire collection. Although there has been a lot of effort to improve visual image search, there is little research and support for visual image exploration. Typically, users start "exploring" an image collection with a keyword search or an example image for a similarity search. Both searches lead to long unstructured lists of result images. In earlier publications, we introduced the idea of graph-based image navigation and proposed an efficient algorithm for building hierarchical image similarity graphs for dynamically changing image collections. In this demo we showcase real-time visual exploration of millions of images with a standard web browser. Subsets of images are successively retrieved from the graph and displayed as a visually sorted 2D image map, which can be zoomed and dragged to explore related concepts. Maintaining the positions of previously shown images creates the impression of an "endless map". This approach allows an easy visual image-based navigation, while preserving the complex image relationships of the graph.
Argument Schemes: Extending the Pragma-Dialectical Approach

In the last two decades various theoretically-oriented publications have appeared about argument schemes.
Doxastic Group Reasoning via Multiple Belief Shadowing

In real world situations an agent may need to switch between distinct roles and/or groups. This calls for a well-controlled and computationally-friendly adjustment of relevant beliefs, especially when groups’ structures and organization evolve dynamically. A need for adaptability may also emerge from the impossibility of fixing agents’ roles or teams at design time. In such changing circumstances reasoning about beliefs is a challenging issue. A concept of belief shadowing, introduced in [8], address these phenomena with a use of $$A {} \texttt {a}{} \texttt {s}B$$ operator expressing that A acts as B. That is, beliefs of $$A {} \texttt {a}{} \texttt {s}B$$ are those of B, unless B does not know the doxastic status of a given belief, in which case the belief of A is binding. This simple construct turns out to be efficient for shallow and transient forms of belief change. Yet, while being convenient in situations when an agent plays a specific role or joins a given group, single shadowing hardly fits cases of multiple roles and/or multiple groups entered simultaneously without prioritizing them. As a remedy we introduce multiple shadowing together with a query language, , where roles and groups are dealt with uniformly. The multiple shadowing operator appears simple yet flexible for reasoning about the associated beliefs, which otherwise are rather complex and onerous to reason about.Importantly, the presented language is tractable. Possible applications of as a lightweight tool for doxastic reasoning are pointed out.
Humanistic trend in education in a global context

The purpose of the research is to study the peculiarities of the realization of the humanistic trend in education in the conditions of electronic informational and educational environment. A comparative approach is used in the research. This approach allowed the author to establish that one of the trends of the development of educational systems of Russia, China and the United States is a humanistic trend in education, which has its own national characteristics and peculiarities associated with the traditions, customs and particularity the development of national educational systems in these countries. The author reveals the general vector of development of the humanistic trend in education in Russia, China and the USA in the conditions of the electronic informational and educational environment, manifested in the following features. They are 1) equality in education for students; 2) the creation of a common scientific and educational space; 3) a teacher or a professor departs from the directive role, he plays the role of a facilitator and a consultant; 4) the students play the active roles, they interact freely with each other in an online environment (the principle of interactivity). Besides, 5) the students are responsible for the results of the independent learning activities, motivated for the process of the independent knowledge acquisition; 6) the students learn to master the “Lifelong Learning” principles. In addition, 7) the students master the competencies of the digital economy and education, learn to build human relationships, to show empathy, learn to form value attitudes towards society and life.
Real-Time Monocular Vision-Based UAV Obstacle Detection and Collision Avoidance in GPS-Denied Outdoor Environments Using CNN MobileNet-SSD

In this paper, we propose a monocular vision-based system that uses a MobileNet-SSD CNN for obstacle detection and collision avoidance in GPS-denied outdoor environments. This framework consists of two processes carried out simultaneously in a frame-to-frame basis: (1) an obstacle detector and classifier using a lightweight convolutional neural network with a UAV monocular onboard camera for real-time mobile systems; (2) a collision avoidance algorithm with a proportional controller responsible for the autonomous flight in GPS-denied outdoor environments. However, because object detection and classification are computationally intensive tasks, the processing is carried out off-board on a ground control station that receives online imagery and data of the UAV during the autonomous flight. The novel aspects in this work are related to the capacity of the system to detect and avoid obstacles in real-time with computationally low range hardware without GPU. We exploit public datasets meant for other purposes and carefully selected images to build a new lightweight dataset to train the CNN. Further, the output imagery data is used by a proportional controller that communicates back to the vehicle to evaluate a possible obstacle avoidance trajectory and execute it if necessary. We carried out evaluations and flights in real scenarios with multiple obstacles such as vehicles, people, bicycles, and trees for autonomous flights in GPS-denied outdoor environments with promising results.
A New Image Enhancement Technique Based on Adaptive Non-linear Contrast Stretching

This paper addresses the problem of creating new methods of image enhancement in automatic mode focused on use in mobile and wireless applications. This work aims to increase the efficiency of image enhancement through intensity transformations. To achieve this aim, we propose a new approach to enhance the images based on an estimate of the cumulative distribution for the sub-ranges of the brightness scale. To demonstrate the capabilities of this approach, proposes a new image enhancement technique by parameter-free intensity transforming. The proposed technique allows to effectively increasing the contrast of complex images without the appearance of unwanted artifacts. The results of experimental research using no-reference metrics and expert estimates of image contrast indicate the effectiveness of the proposed technique.
Sparsity augmented weighted collaborative representation for image classification

Sparse representation-based classification (SRC) and collaborative representation-based classification (CRC) have garnered significant attention recently. In CRC, it is argued that it is the collaborative representation mechanism but not the ℓ<sub>1</sub>-norm sparsity that makes SRC successful for classification tasks. However, recent studies reveal that sparsity does play a critical role in accurate classification, thus it should not be totally overlooked due to relatively high computational cost. Inspired by these findings, we propose a method called sparsity augmented weighted collaborative representation-based classification (SA-WCRC) for image classification. First, the representation coefficients of the test sample are obtained via weighted collaborative representation and sparse representation, respectively. Second, we augment the coefficient obtained by weighted collaborative representation with the sparse representation. Finally, the test sample is classified based on the augmented coefficient and the label matrix of the training samples. Both the augmented coefficient and classification scheme make SA-WCRC efficient for classification. Experiments on three face databases and one scene dataset demonstrate the superiority of SA-WCRC over its counterparts.
Bridging the Gap between Requirements Modeling and Behavior-Driven Development

Acceptance criteria (AC) are implementation agnostic conditions that a system must meet to be consistent with its requirements and be accepted by its stakeholders. Each acceptance criterion is typically expressed as a natural-language statement with a clear pass or fail outcome. Writing AC is a tedious and error-prone activity, especially when the requirements specifications evolve and there are different analysts and testing teams involved. Analysts and testers must iterate multiple times to ensure that AC are understandable and feasible, and accurately address the most important requirements and workflows of the system being developed. In many cases, analysts express requirements through models, along with natural language, typically in some variant of the UML. AC must then be derived by developers and testers from such models. In this paper, we bridge the gap between requirements models and AC by providing a UML-based modeling methodology and an automated solution to generate AC. We target AC in the form of Behavioral Specifications in the context of Behavioral-Driven Development (BDD), a widely used agile practice in many application domains. More specially we target the well-known Gherkin language to express AC, which then can be used to generate executable test cases. We evaluate our modeling methodology and AC generation solution through an industrial case study in the financial domain. Our results suggest that (1) our methodology is feasible to apply in practice, and (2) the additional modeling effort required by our methodology is outweighed by the benefits the methodology brings in terms of automated and systematic AC generation and improved model precision.
Validating 126 million MARC records

The paper describes the method and results of validation of 14 library catalogues. The format of the catalog record is Machine Readable Catalog (MARC21) which is the most popular metadata standards for describing books. The research investigates the structural features of the record and as a result finds and classifies different commonly found issues. The most frequent issue types are usage of undocumented schema elements, then improper values in places where a value should be taken from a dictionary, or should match to other strict requirements.