Reviewerly/exHarmony huggingface dataset:
Metapod: Accessible Hardening of Docker Containers for Enhanced Security

Containers have become a popular tool for developers to build scalable applications in today's fast-growing world. They are a lightweight alternative to virtual machines. This benefit, however, can come at the cost of security. Security is an aspect often overlooked by programmers. Unfortunately, an astonishing number of security vulnerabilities have been linked to containers. These can arise from multiple sources including the container runtime, the image, and the host machine. Existing approaches to hardening containers often compel programmers to have prerequisite understanding of concepts and application procedures. Enforcing these concepts in practice demands tedious manual action on the part of the programmer. Our work aims to create an easy-to-use and accessible application that solves this problem thereby facilitating easier incorporation of recommended security practices in Docker containers. These may be practices such as restricting Linux Capabilities, resource allocation, etc. Developers may easily manage security controls using an intuitive interface, giving them more time to focus on building their application.
Creativity in Serials Cataloging: Heresy or Necessity?

The concept of creativity in cataloging was defined. Creativity presupposes thorough knowledge of cataloging rules and standards. The goal of creative cataloging is to adapt records in order to enhance patron access to information in the library database. Using examples from various types of serial records, the workshop leaders discussed creative cataloging methods in the online environment. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
THE EFFECTIVENESS OF LITERACY MAPPING ON STUDENTS’ READING NARRATIVE TEXT

The objective of this research is to decrease students' reading narrative text and effectiveness their reading level using literacy mapping students' from 8G of SMP Negeri 18 Depok. This study used a Quantitative Method with a Pre-Experimental design by implementing the design of pre-test and post-test. In this study, the writer used one class as the sample with a total of the students' are 30 students'. The data were collected by giving pre-test and post-test as the instrument. The result of the study was calculated using a t-test. The findings showed that the students' post-test scores (were was higher than pre-test scores. By using the t-test, the result of the t-table with degrees of freedom (df) 0.01 obtained 2.048. The findings reveal that these research successes to decrease students' reading narrative text and effectiveness their reading level using literacy mapping.
Digital Rights Management (DRM) in the Libraries of Digital-Era: Concepts, IPR Issues &amp;amp; Concerns of LIS Community

A rapid shifting perceived from traditional content to digital media due to increasing advantages in every step of the information life cycle: create, collect, organize, store, access, deliver & use. Along with the advantages, digital content transactions are more likely prone to effects through IPR issues. Copyright and intellectual property rights (IPR) have been established and extended over hundreds of years, but implementing the same in the digital environment faces huge challenges. This article started by introducing digital rights & digital rights management (DRM), its architectural framework & techniques, and associated IPR issues. The perception of the LIS community about the role & responsibility of users & library professionals to solve these problems are the main focus of the latter part. A short survey is conducted using Google form questionnaire (N=238) and targeted all four categories, namely, LIS students (100 or 40%), LIS research scholars (25 or 10%), LIS faculties (18 or 85) & Library professionals (95 or 32%); to get the perception of the LIS community. The targeted experimental documents are categories of digital contents by their origin (i.e. Born, Turn & Gain digital documents). The geographical scope of the study extends beyond India to include some international viewpoints as well. In responses, no of the female respondent (131 or 55%) is slightly higher than male respondents (107 or 45%), while the majority of responses came from the Punjab state (42 or 19.72%). The survey findings suggested that Online materials (113 or 50.90%) Turn digital documents (168 or 75.68%) are more likely prone to IPR issues. Among users’ responsibilities, “Use of proper acknowledgement, referencing & citation” is the most preferred one (221 or 99.55%) and “Arrange orientation & outreach programme regarding IPR issues” is indicated as the major responsibility (212 or 95.50%) of the library professionals.
Oracles and Internet of Things in the Internet of Value

In this chapter, we discuss the importance of trusted information entering into blockchain systems provided by oracles. Whether these are human inputted manual signals, or automatic machine updates from Internet of Things (IoT) devices, updating records is at the heart of any blockchain-based system. Smart contracts that depend on external data rely on these devices to function correctly.
THE RELATIONSHIP BETWEEN TOTAL QUALITY MANAGEMENT AND SERVICE QUALITY IN DEPARTMENT OF MUNICIPAL AFFAIRS AND TRANSPORT OF UAE

Since 1980, Total Quality Management (TQM) has emerged as a management strategy for providing customers with services that match their needs. In the UAE, the department of municipal affairs and transport will introduce quality measuring tools. TQM establishes quality as the guiding force behind leadership, planning, and design, necessitating the assistance of the eight essential aspects. Thus, TQM would include leaders and managers who have earned quality excellence awards, shown superior performance, delivered superior service, and earned public confidence. In public administration, quality improvement and assessment have resulted in the development and implementation of strategic models. Total quality management originated in the manufacturing industry. TQM and excellence awards are critical in the UAE economy due to the demand for high-quality public services. This is unsurprising, given that service organisations continue to face harsh realities equal to those faced by industrial organisations. This is the fundamental rationale for using service quality models. The phases of service quality management may help improve the efficiency and satisfaction of service delivery while also building a strategic framework for technology integration and innovation. The Department of Municipal Affairs and Transportation is obligated to provide value-added services to its customers and must demonstrate a strong commitment to quality service in order to achieve economic success.&#x0D; &#x0D; JEL: H11; H83; L10&#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0945/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Automation Test Tool for the Page Load Time of Mobile Applications

When users use a mobile phone application for the first time, except for functional requirements the more direct experience is the performance of the application on the phone. The main work of this article is designing and implementing load time test methods on the Android application pages. Under the joint action of the Appium automated test framework and Maven package management tool, the test process of the tested application is designed such as the order of specific page jumps, the relationship between package structures and classes, etc. After writing the function of automation code and verifying that the page can be successfully jumped by non-human intervention, continue to modify the code to complete the special automated test through the toolkit developed in this article, and finally get the load time result set of pages.
INTERDISCURSIVENESS IN THE HUMANITARIAN SPHERE AS SCIENTIFIC KNOWLEDGE INTERACTION MANIFESTATION

The article touches the problem of knowledge interaction in humanitarian discourse. Noting the increase of humanitarian knowledge role at the present stage of science development, the author of the article considers the concepts “interaction”, “discourse”, “interdiscursivity” and analyzes the interdiscourse phenomenon is as having cognitive-communicative nature. The research presents the analysis of historical and philosophical texts fragments confirming various fields of science knowledge interaction and identification of episodes of different sciences knowledge attracted in the process of generating knowledge of a particular science. The category «irterdiscursiveness» is acknowledged as the category of knowledge interaction manifestation on the discoursive level; the category “irtertextuality” reveals knowledge interaction on the textual level. It is concluded that the other areas knowledge involvement serves to implement the author's pragmatic attitudes and contributes to the creation of comprehensive knowledge. This interdisciplinary approach would give great opportunities for a deeper and expanded study of the humanities. The discussed phenomenon is widely observed nowadays, activating the multilateral relations of different areas of knowledge.
Performance Predictions Accuracy of a Smart Sun Tracking System for Photovoltaic Applications

Despite technological innovations, solar energy efficiency is still low nowadays. It thus needs to be optimized. To improve solar photovoltaic (PV) modules efficiency, several methods have been designed and implemented. Such methods are the Maximum Power Point Tracking (MPPT) and Solar Tracking (ST). In this decade, the solar tracking method is facing a great challenge: the minimization of the electrical energy consumption of electromechanical systems used to track the sun’s position. Manufacturers must be able to predict the net electrical power output obtained from a tracking PV panel to save energy and time. The purpose of the present work is to simulate the performance of a smart solar tracker and assess the accuracy of predictions. To achieve this objective, the one diode model of solar PV cell has been used. In an innovative approach, the one diode model has been simulated using experimental data of temperature and global solar irradiance which are easily available on the internet. The accuracy of predicted values was analysed using reliable statistical indicators such as the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Coefficient of determination (R-square) and the Pearson Coefficient of Correlation (PCC). Simulation results of the power output of the tracking PV panel is better than the one of the fixed panel with 0.25, 0.32, 0.66, and 0.81 respectively for the MAE, the RMSE, the R-square and the PCC. These results show that predictions are in good agreement with experimental measures.
Experience of using SMART technologies in university education

The article summarizes the experience of using SMART technologies in the educational process of the university. The concept and the main components of SMART technologies (forms, methods, hardware and software) are identified. Specific examples of the use of SMART technologies (mobile learning, Interactive Whiteboard, Prezi and Kahoot! Web services) are considered to support face-to-face classes, improve the efficiency of students’ independent work, and systematically monitor knowledge and skills. The intellectual software used in the university to support active forms and methods of teaching (didactic games, discussions and project method) is presented.
Split delivery scheme for vehicle supported UAV

The traditional demand split logistics distribution schemes are mainly based on vehicle distribution. In recent years, with the rapid development of civil UAVs, the use of UAVs for distribution will be more efficient and economical in some specific conditions. In this paper, through the analysis of the advantages of UAV distribution, we simulate the demands and distribution schemes in real scene, especially in remote mountainous areas. And then, we propose to define a demand-splitting distribution scheme of vehicle-supported UAV, which ensures the customer point will be satisfy demand when the loading capacity of the UAV is exceeded. This scheme aims to provides a realizable distribution scheme for the customers point in mountainous areas with large demand.
Sole possession notes redux, Part 2

Last month, I continued a discussion of sole possession notes that I had started 17 years ago.
Measuring Trust and Automatic Verification in Multi-Agent Systems

Due to the shortage of resources and services, agents are often in competition with each other. Excessive competition will lead to a social dilemma. Under the viewpoint of breaking social dilemma, we present a novel trust-based logic framework called Trust Computation Logic (TCL) for measure method to find the best partners to collaborate and automatically verifying trust in Multi-Agent Systems (MASs). TCL starts from defining trust state in Multi-Agent Systems, which is based on contradistinction between behavior in trust behavior library and in observation. In particular, a set of reasoning postulates along with formal proofs were put forward to support our measure process. Moreover, we introduce symbolic model checking algorithms to formally and automatically verify the system. Finally, the trust measure method and reported experimental results were evaluated by using DeepMind’s Sequential Social Dilemma (SSD) multi-agent game-theoretic environments.
The Sensitive Data Feature Extraction Based on Low-Rank Multimodal Fusion

With the increasing level of information technology in society, personal sensitive data often appears in a variety of modal forms such as text, audio and video. In order to effectively identify and analyse personal sensitive data, feature extraction is required. However, the features of different modalities of personal sensitive data are often heterogeneous, with a high level of noise and redundant information. In order to efficiently and collaboratively characterise personal sensitive data, eliminate redundant and contradictory information, and effectively fuse multimodal features, this paper proposes a novel feature extraction method based on low-rank multimodal fusion for sensitive data, which addresses the problem of difficult extraction of multimodal interaction information. The method uses self-attentive modules, tensor outer product and low-rank decomposition to build a fusion algorithm that can effectively fuse text, audio and video modal information of sensitive data. Tests on MOSI and IEMOCAP datasets show that the proposed algorithm has good feature fusion capability.
Multi-mode Input Siamese Network for Tracking Object in Nighttime

In recent years, there have been many algorithms based on siamese network in the visual object tracking to solve the visual object rapidly move, background clutter, motion blur, deformation, and other complex problems. However, aiming at the problem of visual object tracking in nighttime environment, there is little research on this problem by scholars. Therefore, we propose a multi-mode input fusion module embedded in the siamese network for tracking object in nighttime environment. Since there is no common public dataset for nighttime vision target tracking, OTB-100 dataset is darkened to generate different degrees of simulated nighttime scenes. Experimental results on different degrees of OTB-100 simulated nighttime scenes show that our method can obtain better precision and success rate.
Formulating critical questions to the user in the course of decision-making

In this chapter, we formulate a problem of generating questions to complement a text such as patient complaint with information required to diagnose the patient. We focus on question generation (QG) setting where answering the generated questions is expected to be critical for diagnosis. Instead of generating questions from text, we generate questions for the information outside of this text that is important to classify or make a decision for this text. Automatically identifying a discrepancy between what information is available and what information is needed, the system attempts to formulate questions so that the answers provide the needed information. We evaluate the proposed QG algorithm with respect to the meaningfulness of questions and overall accuracy of the resultant health diagnostics. We conclude that inventing questions for missing information for diagnosis is a critical component of decision-making in medicine.
Inventory Management with Attention-Based Meta Actions

Roguelike games are a challenging environment for Reinforcement Learning (RL) algorithms due to having to restart the game from the beginning when losing, randomized procedural generation, and proper use of in-game items being essential to success. While recent research has proposed roguelike environments for RL algorithms and proposed models to handle this challenging task, to the best of our knowledge, none have dealt with the elephant in the room, i.e., handling of items. Items play a fundamental role in roguelikes and are acquired during gameplay. However, being an unordered set with a non-fixed amount of elements which form part of the action space, it is not straightforward to incorporate them into an RL framework. In this work, we tackle the issue of having unordered sets be part of the action space and propose an attention-based mechanism that can select and deal with item-based actions. We also propose a model that can handle complex actions and items through a meta action framework and evaluate them on the challenging game of NetHack. Experimental results show that our approach is able to significantly outperform existing approaches.
BRUSH: Label Reconstructing and Similarity Preserving Hashing for Cross-modal Retrieval

The hashing technique has recently sparked much attention in information retrieval community due to its high efficiency in terms of storage and query processing. For cross-modal retrieval tasks, existing supervised hashing models either treat the semantic labels as the ground truth and formalize the problem to a classification task, or further add a similarity matrix as supervisory signals to pursue hash codes of high quality to represent coupled data. However, these approaches are incapable of ensuring that the learnt binary codes preserve well the semantics and similarity relationships contained in the supervised information. Moreover, for sophisticated discrete optimization problems, it is always addressed by continuous relaxation or bit-wise solver, which leads to a large quantization error and inefficient computation. To relieve these issues, in this paper, we present a two-step supervised discrete hashing method, i.e., laBel ReconstrUcting and Similarity preserving Hashing (BRUSH). We formulate it as an asymmetric pairwise similarity-preserving problem by using two latent semantic embeddings deducted from decomposing semantics and reconstructing semantics, respectively. Meanwhile, the unified binary codes are jointly generated based on both embeddings with the affinity guarantee, such that the discriminative property of the obtained hash codes can be significantly enhanced alongside preserving semantics well. In addition, by adopting two-step hash learning strategy, our method simplifies the procedure of the hashing function and binary codes learning, thus improving the flexibility and efficiency. The resulting discrete optimization problem is also elegantly solved by the proposed alternating algorithm without any relaxation. Extensive experiments on benchmarks demonstrate that BRUSH outperforms the state-of-the-art methods, in terms of efficiency and effectiveness.
Organic Dye Molecules as Single Photon Sources for Optical Quantum Technologies

We present Hong-Ou-Mandel (HOM) experiments with photons emitted by a single molecule of Dibenzoterrylene in an Anthracene nanocrystal under pulsed excitation, and preliminary results for photons emitted by two spatially-separated molecules on the same sample.
ELR-Net: An Enhancement and Location-Refinement Network for Co-Saliency Detection

Co-salient object detection is a challenging task, which aims to segment the co-occurring salient objects in multiple images at the same time. To address this task, we propose an end-to-end Enhancement Location-Refinement Network (ELR-Net) to capture both salient and repetitive visual patterns from multiple images. For various scenarios, common objects in different images only have the same semantic information, so we first propose a deep co-salient method based on channel and spatial attention module (CASM), which combines the attention mechanism to enhance the common semantic information. Subsequently, we employ a Co-attention and Refinement Module (CARM) to capture the common attributes of co-salient objects by learning the features consensus representation from a group of images using our group affinity module (GAM) and then we develop a self-correlation module (SCM) to further fine-grained information on the co-salient regions. Specifically, SCM can maintain the feature independence upon semantic categories and further help our model to distinguish pixels with similar but different categories. Moreover, single image saliency maps (SISMs) are predicted to extract intra-saliency cues, and then a correlation fusion module (CFM) is employed to extract inter-saliency cues. The proposed ELR-Net is evaluated on three challenging benchmarks, i.e., CoSal2015, CoSOD3k, and CoCA, demonstrate that our ELR-Net outperforms 9 cutting-edge models and achieves state-of-the-art performance.
EDI and the Integrated Library System

The library today relies heavily on the EDI capabilities of its ILS system. Sandra Westall covers the implications, options, and realities of EDI development and implementation for the ILS vendor. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth. corn]
A Novel Time-varying Path Planning Method for Dynamic Obstacles

In the research of snake-like manipulator and mobile robot obstacle avoidance, most researchers only plan the path for static obstacles. However, there are many dynamic obstacles in the real world. Therefore, this paper proposes a general method that can plan the real-time changing path for the dynamic obstacle avoidance of moving objects such as quasi-continuous manipulators. The size, position, and other information of the obstacle are extracted by point cloud or image processing. The bounding box method is selected according to the shape of the obstacle so that the obstacle is equivalent to some basic shapes. For the dynamically updated bounding box, the corresponding continuous time-varying path/function is formulated, so that real-time obstacle avoidance can be realized. By analyzing the constraints that the continuous function that characterizes the time-varying path needs to be satisfied, the ultimate continuous function is selected by formulating the optimization objective. For various types of dynamic obstacles, the corresponding time-varying path can be planned, and the quasi-continuous manipulator moving along this path can realize real-time obstacle avoidance. Simulation results demonstrate the effectiveness of the proposed method.