Reviewerly/exHarmony huggingface dataset:
Structuring Element for Secure Reversible Watermarking

The ability to embed data into image with reversibility would allow host image can be restored from modification in embedding task. Structuring elements provided an information-rich link of host image before and after embedding data that could be important in the development of new way of exploring secure competence. We show how a structuring element can be designed for a gray images and used in a filter to allocate positions to hide securely secret data. The pixel locations are then collected to offer capacity of hiding data for the host image. We also demonstrate a connection between the structuring elements and the capacity to keep certainly high detecting complexity.
Design and Analysis on a Parallel Chaos-Based Hash Function

The inherent random-like behavior and one-way property of iteration in chaotic systems provide a good basis for designing Hash function. In the era of big data, due to the increasing data capacity in applications, fast Hash functions with parallel mode are highly desirable when authenticating data integrity. We analyze the issue of how to parallelize Hash function with iterative structure. Some security requirements on parallel Hash function are presented. In addition, using chaotic map and block cipher, we construct a keyed parallel Hash function. The message blocks are firstly processed in parallel by a DM-like structure. Furthermore, a tree mode with chaotic map is utilized to combine the outputs of the hash round function in parallel. The proposed Hash function is analyzed by theory and tested by computer simulations. The test results show that the proposed scheme can resist the various common attacks against Hash functions. It satisfies the secure performance requirements of Hash function. Owing to the usage of the parallel mode to process messages, the proposed chaos-based Hash function possess high efficiency and has high potential in applications to guarantee data integrity on a parallel computing platform.
A Scalable K-Nearest Neighbor Algorithm for Recommendation System Problems

Memory-based classification techniques are commonly used for modeling recommendation problems. They rely on the intuition that similar users and/or items behave similarly, facilitating user-to-item, item-to-item, or user-to-user proximities. A significant drawback of memory-based classification techniques is that they perform poorly with large scale data. Thus, using the off-the-shelf classification techniques for recommendation problems generally lead to impractical computational costs.In this study, we propose a recommendation problem-specific enhancement for a widely known memory-based classification algorithm, K-Nearest Neighbor. For this purpose, the movie recommendation problem is selected, and the scalability of the proposed enhancement is evaluated on three publicly available datasets. In the proposed enhancement, user- and item-proximities are pre-calculated during the first offline recommendation, while an auxiliary data structure is constructed for keeping user-to-user proximities. The stored neighborhood information is then facilitated in order to speed up later recommendations. The experiments show that the proposed algorithm has performed superior to both the classical classification technique and the state-of-the-art off-the-shelf toolkits.
Multi-grained Cross-modal Similarity Query with Interpretability

Cross-modal similarity query has become a highlighted research topic for managing multimodal datasets such as images and texts. Existing researches generally focus on query accuracy by designing complex deep neural network models, and hardly consider query efficiency and interpretability simultaneously, which are vital properties of cross-modal semantic query processing system on large-scale datasets. In this work, we investigate multi-grained common semantic embedding representations of images and texts, and integrate interpretable query index into the deep neural network by developing a novel Multi-grained Cross-modal Query with Interpretability (MCQI) framework. The main contributions are as follows: (1) By integrating coarse-grained and fine-grained semantic learning models, a multi-grained cross-modal query processing architecture is proposed to ensure the adaptability and generality of query processing. (2) In order to capture the latent semantic relation between images and texts, the framework combines LSTM and attention mode, which enhances query accuracy for the cross-modal query and constructs the foundation for interpretable query processing. (3) Index structure and corresponding nearest neighbor query algorithm are proposed to boost the efficiency of interpretable queries. Comparing with state-of-the-art methods on widely-used cross-modal datasets, the experimental results show the effectiveness of our MCQI approach.
Eye Movement Classification Algorithms: Effect of Settings on Related Metrics

The basic building block of any eye tracking research is the eye fixations. These eye fixations depend on more fine data gathered by the eye tracker device, the raw gaze data. There are many algorithms that can be used to transform the raw gaze data into eye fixation. However, these algorithms require one or more thresholds to be set. A knowledge of the most appropriate values for these thresholds is necessary in order for these algorithms to generate the desired output. This paper examines the effect of a set of different settings of the two thresholds required for the identification-dispersion threshold type of algorithms: the dispersion and duration thresholds on the generated eye fixations. Since this work is at its infancy, the goal of this paper is to generate and visualize the result of each setting and leave the choice for the readers to decide on which setting fits their future eye tracking research.
Interactive Role Stereotype-Based Visualization To Comprehend Software Architecture

Motivation: Software visualization can be helpful in comprehending the architecture of large software systems. Traditionally, software visualisation focuses on representing the structural perspectives of systems. In this paper we enrich this perspective by adding the notion of role-stereotype. This rolestereotype carries information about the type of functionality that a class has in the system as well as the types of collaborations with other classes that it typically has.Objective: We propose an interactive visualization called RoleViz, that visualizes system architectures in which architectural elements are annotated with their role-stereotypes.Method: We conducted a user-study in which developers use RoleViz and Softagram (a commercial tool for software architecture comprehension) to solve two separate comprehension tasks on a large open source system. We compared RoleViz against Softagram in terms of participant's: (i) perceived cognitive load, (ii) perceived usability, and (iii) understanding of the system. Result: In total, 16 developers participated in our study. Six of the participants explicitly indicated that visualizing roles helped them complete the assigned tasks. Our observations indicate significant differences in terms of participant's perceived usability and understanding scores.Conclusion: The participants achieved better scores on completing software understanding tasks with RoleViz without any cognitive-load penalty.
A Useful Tool to Support the Ontology Alignment Repair

Ontologies are used in Information Systems to manage and share knowledge pertaining to different domains, considering it is common that one particular piece of knowledge is modeled individually into different ontologies. Alignment processes are carried out towards interoperability; however, the results are not effective in most cases, requiring manual repair and review by domain experts. Thus, this work introduces a repair and review tool for the alignment of ontologies with the goal of assisting experts in the alignment repair tasks. Using the technology acceptance model, a proof of concept was made to analyze the perception by domain experts regarding the tool’s usefulness, ease, and intention of use. From statistical analyses in terms of reliability, default path, inference, and correlation, it has been found that study participants had a positive perception of the tool’s usefulness and ease in the tasks of ontology alignment repair. Therefore, the tool and its functionalities proved to be useful towards assisting domain experts in their tasks of reducing the semantic heterogeneity among ontologies pertaining to the same domain through alignment repair.
Formation of national pride in the conditions of regional patriotic education

The article discusses the problems of the formation of national pride in the process of regional patriotic education of students of general educational organizations. The relevance of this issue is due to several factors. Firstly, within the framework of the Federal Targeted Program for the Development of Education for 2016-2020, one of the main areas of educational and upbringing work is the introduction of a regional component. Secondly, the orientation of educational organizations belonging to a region towards its inherent ethnocultural values influences quite a lot the effectiveness of the patriotic education system. At the same time, familiarization with traditional culture contributes to the formation of national pride in the achievements of its people, a constructive attitude to the world and an active civic position of youth. The system of regional patriotic education of students of secondary schools is described by the example of two constituent entities of the Russian Federation - the Krasnodar Territory and the Republic of Dagestan.
Structured light stereo vision system research

Three-dimensional measurement technology is a popular technology in recent years. The traditional binocular vision measurement system has a low image matching accuracy when there are few or many duplicate feature points on the surface of the object to be measured, which affects the work of three-dimensional reconstruction. In this paper, the combination of structured light three-dimensional measurement technology and binocular imaging technology reduces the difficulty of feature point search, increases the accuracy of image matching, and conducts experiments of three-dimensional measurement by building a verification system. The measurement results show that the technology is highly feasible.
Research on the control system of six joint industrial robot based on CPAC

The defects of the existing commercial robot control system are analyzed and the feasibility of the open control system is studied. The robot control system was designed based on the CPAC development platform with the six-joint robot as the research object. Firstly, a kinematic model of the robot was established by the d-h linkage coordinate system method, and its motion control was described in detail from two aspects of forward and inverse kinematics. Secondly, the inverse kinematics of the robot was solved by using the RPY description method and PIPER criterion, and the correctness of the robot kinematics model was verified. Third, the robot control system software design. Finally, the function modules of the control system are verified through experiments. The experiments show that the robot runs with a small error of pose and posture, which can meet the requirements of the control system and meet the expected design objectives.
The Importance of the Three P's in the Investigation

This article introduces the importance of process during the investigation and the acquisition phases of logical/physical artifacts which may be required during the course of such professional engagement. The article then focuses on the necessity to have a robust supportive framework in a state of preparedness to facilitate the First Responders and CSIRT (Computer Security Incident Response Team) with the necessary underpin to support such investigative engagements – considering effective and pragmatic Policies, Case Management, operational Security Protocols (Run-Books) and all other necessary attributes to underpin a professional, prepared posture from which a team may effectively, and robustly engage an investigation/incident. To elaborate on the importance of such an approach, we outline a number of real-world cases where ineffective processes and controls were applied. Finally, we review the essential elements of securely managing case-related data, and the absolute need to apply security mechanisms such as Certified Standards of FIPS-140-2 encryption to secure sensitive case related assets to assure they are robustly protected at all stages of their life cycle when they are in physical transit, or when they are at rest, associated with a desk-bound PC. The end objective to the entire article is to stress an absolute need to apply process to, as far as is practicable, to achieve positive conclusions from any investigation or incident which has been engaged.
Peritus Framework: Towards multimedia evidence analysis uniformization in brazilian distributed forensic model

Multimedia forensic analysts must be able to cope with an increasing volume and diversity of seized multimedia evidence. Therefore, these professionals must understand a variety of multimedia related technologies and are usually obliged to rely on a set of different tools to perform their analyses. In a diverse and distributed scenario such as the one experienced in Brazil, where forensic services are distributed between Federal and States’ jurisdictions and forensic analysts come from different backgrounds, it is difficult to obtain uniform responses from the different forensic units. This non-uniformity in response procedures results in variable quality and reliability of the performed analyses. In this work, we propose the Peritus framework and present its underlying software as instruments to increase efficiency, reinforce reproducibility, strengthen the chain of custody and achieve response uniformization throughout the country. The presented software is designed on a modular architecture. It integrates a large number of useful forensic tools, taking care of most of the non-automated normalization and integration associated with the forensic analyses and documentation while providing multiple specialized tools. Our experience and the feedback received show that the natural flow and the integration provided by the presented system significantly improves efficiency and the robustness of the obtained results. We believe the Peritus framework plays an important role in the Brazilian distributed forensic scenario, working as a knowledge center for developed tools and a methodological guideline.
An Efficient Semi-Algebraic Decoding Algorithm for Golay Code

In this paper, we propose an algorithm for decoding the QR(23,12,7) code knowing by the binary Golay code. This method never calculates unknown syndromes and does not need the error locator polynomial. Indeed, we use simple parameters to locate the error position. So, to validate the proposed method, all possible error patterns are tested and the proposed decoder correct all of them.
Analysis of European Experience in Library Information Services and Cybersecurity of Scientific Library Users

This paper analyzes approaches to improving library information services for users. The issues of internationalization and application of the leading countries experience in the use of the RDA standard have been outlined. Hazardous factors and potential threats to the security of library information systems have been identified and key approaches have been identified to improve integrity, accessibility and reliability.
An HPC-based Prediction on the Practicality of Long-distance Quantum Key Distributions

Practicality of long-distance quantum communications based on a BB84 quantum key distribution (QKD) protocol is examined with Monte Carlo simulations coupled to a parallel computing. Given a quantum channel that is not free from noises, optimal sizes of the shared key information and corresponding chances for detecting the eavesdropper are calculated to present clues that can be utilized to figure out the utility of the protocol. Delivering simple but sound principles that have not been focused quite well, this work serves as a useful case study that shows the need of high performance computing for QKD modeling, and can trigger potential efforts for further modeling studies that involve more realistic and complicated factors.
Incremental Machine Speech Chain Towards Enabling Listening While Speaking in Real-Time

Inspired by a human speech chain mechanism, a machine speech chain framework based on deep learning was recently proposed for the semi-supervised development of automatic speech recognition (ASR) and text-to-speech synthesis TTS) systems. However, the mechanism to listen while speaking can be done only after receiving entire input sequences. Thus, there is a significant delay when encountering long utterances. By contrast, humans can listen to what hey speak in real-time, and if there is a delay in hearing, they won't be able to continue speaking. In this work, we propose an incremental machine speech chain towards enabling machine to listen while speaking in real-time. Specifically, we construct incremental ASR (ISR) and incremental TTS (ITTS) by letting both systems improve together through a short-term loop. Our experimental results reveal that our proposed framework is able to reduce delays due to long utterances while keeping a comparable performance to the non-incremental basic machine speech chain.
A comparative study of system size dependence of the effect of non-unitary channels on different classes of quantum states

We investigate the effect of different types of non-unitary quantum channels on multiqubit quantum systems. For an n-qubit system and a particular channel, in order to draw unbiased conclusions about the system as a whole as opposed to specific states, we evolve a large number of randomly generated states under the given channel. We increase the number of qubits and study the effect of system size on the decoherence processes. The entire scheme is repeated for various types of environments which include dephasing channel, depolarizing channel, collective dephasing channel and zero temperature bath. Non-unitary channels representing the environments are modeled via their Kraus operator decomposition or master equation approach. Further, for a given n we restrict ourselves to the study of particular subclasses of entangled states, namely the GHZ-type and W-type states. We generate random states within these classes and study the class behaviors under different quantum channels for various values of n.
Study on Multi-Scene VR Service Mode of Library in 5G Environment

In order to realize the visual reconstruction and feature extraction of library multi-scene VR service mode, the library multi-scene 3D reconstruction and service mode recognition under 5G environment are required. A library multi-scene VR service reconstruction technology based on virtual reality VR visual feature analysis and service mode simulation is proposed. Firstly, RFID radio frequency tag identification technology is used to collect images of service reliability areas of libraries in 5G environment, and VR visual information simulation technology is used to enhance the collected images. Then, VR technology is used to track and render the contour and texture of the image edge to realize the visual feature reconstruction, service pattern recognition and three-dimensional scene reconstruction of VR service patterns in multiple scenes of the library. Finally, an actual library multi-scene VR service reconstruction simulation experiment is carried out. The results show that the method can realize the integrity detection of library multi-scene features in 5G environment, and the output image quality is high. The library multi-scene VR service mode in 5G environment is improved.
Analysis of Higher Vocational English Teaching Behavior Integrating Network Information Teaching

Abstract With the continuous development of computer technology, we have entered the "Internet +" era. The " Internet +" era provides an advantage platform for vocational English teaching. The network information of English teaching has stimulated students’ interest in learning. Teachers should recognize the important role of English network information teaching and improve the effectiveness of English teaching in higher vocational schools. This paper analyses the important role of "Internet +" era network information teaching in vocational English teaching, and puts forward the network information teaching strategy of higher vocational English in the context of ‘’Internet + era, hoping to give reference and reference for vocational English teaching.
Designing during, and for Pandemics

COVID-19 pandemic may have captured our attention over the last months, but there are several pandemics currently active. Understanding the nature of a pandemic and learning how to design for, and during a pandemic is more than a contemporary need. In this workshop we will introduce the stages of a pandemic using a public health perspective, and together with the participants, we will reflect on designing for each stage and different potential stakeholders. The aim of the workshop is to explore how we design during pandemics such as COVID-19 and how these situations can influence the design of previously developed health and wellbeing related solutions, as well as our practices as designers. The objectives are to: (1) gain understanding regarding infectious outbreaks, (2) explore design opportunities and challenges during rapidly changing contexts, (3) reflect on the effect of a pandemic on the design process. The benefits of this multidisciplinary workshop are multiple, some of which are: understanding of the pandemic, familiarizing with design tools suitable for designing through pandemics, and exploration of ideas on designing for and during pandemics.
Research on Unstructured Electronic Archives Query Based on Visual Retrieval Technology

Digitalization of enterprise archives is the mainstream trend of archive management. This paper proposes a digital archive index management framework based on visual retrieval technology for unstructured digital archive management problems. The framework adopts the current mainstream deep local feature extraction scheme DELF Pipeline to carry out feature extraction for digital archives, and use the distributed inverted indexing framework Lucene to build an efficient indexing and retrieval system for digital archives. Through a large number of simulation experiments, it is proved that the framework can be well used for the management of enterprise unstructured digital archives, which supports dynamic incremental index construction and has high retrieval efficiency.
Vertext

Online communication platforms like Slack and Microsoft teams have become increasingly crucial for a digitized workplace to improve business efficiency and growth. However, these chat platforms can overwhelm the users with unstructured long streams of back and forth discussions scattered in various places. Thus, discussions become challenging to follow, leading to an increased likelihood of missing valuable information. Moreover, with the unsatisfying keyword-based chat search, users spend a significant amount of time to read, digest, and recall information from the conversations at the cost of productivity. In this paper, we present Vertext, an end-to-end AI system that ingests user conversations and automatically extracts information such as announcements, task assignments, and conversation summary. Moreover, Vertext gives a unique search experience to the users by providing search results along with their context, with an improved performance enabled by semantic search. For the ease of user interaction, all the information is consolidated on a single dashboard provided by Vertext.