Reviewerly/exHarmony huggingface dataset:
An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study

Background Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. Objective Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. Methods We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). Results We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. Conclusions Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.
Round-Efficient Anonymous Password-Authenticated Key Exchange Protocol in the Standard Model

Anonymous password-authenticated key exchange (APAKE) protocols allow for authenticating legitimate users via low-entropy passwords while keeping their actual identities private. They are important cryptographic primitives for privacy protection, which have attracted much attention recently and have been standardized in the international standard ISO/IEC 20009-4. However, most of the existing APAKE schemes (especially including all the APAKE schemes in the storage-extra setting) are developed in the random oracle model. In this paper, we present the first storage-extra APAKE protocol in the standard model by combing the technique of algebraic MAC with oblivious designated-verifier non-interactive zero-knowledge (DVNIZK) proof. Toward our aim, we first give out a new construction of the oblivious DVNIZK proof system, which is compatible with a new class of algebraic MAC schemes. As a consequence, our APAKE protocol needs only 2 flows of messages in the authentication phase, which is very efficient in terms of rounds. Moreover, we show that this protocol enjoys stronger security guarantees while achieves considerably computational performance.
A CP-ABE Algorithm Based HDFS Distributed Storage Data Security Solution

Abstract With the continuous development of technology, traditional data encryption technology has been cracked, existing data security measures can no longer meet the user’s data security needs, and the problem of data security remains the primary issue facing cloud storage. Based on the analysis of cloud storage data security, this paper proposes a CP-ABE-based HDFS distributed storage data security solution to the problems of traditional attribute-based data encryption technology (CP-ABE). After analysis of the algorithm, the algorithm can ensure the security of the data stored on the cloud platform users through the attribute-based encryption algorithm.
A Novel Controllable Image Segmentation Method Using the Inverse GAMMA Distribution Function Based on Histogram Specification

Image segmentation(IMSEG) is defined as a top of the basic and most significant process of digital image handling which refers to the techniques that used to partitioning and dividing an image into useful and meaningful parts, called segments. It's so important for many applications which difficult and inefficient to process the entire image, such as object recognition or image compression. So, IMSEG aims to segment the image into districts for further operations and processing. There are several techniques for image segmentation which divide the image into many parts regarding to some specific image properties such as the intensity value of pixels, relation between pixels, color, texture and so on. However, most image segmentation techniques have deficient ability to plainly tune the segment sensitivity of the segmented image. In our manuscript we will propose a new technique for image segmentations using the invers GAMMA distribution function based on histogram matching, the proposed technique provides a method to manage the segment sensitivity of segmented image via setting one parameter. Experimental outcomes pretend a superior execution of this created technique.
Signal processing via sampled-data control theory

Digital sounds and images are used everywhere today, and they are all generated originally by analogue signals. On the other hand, in digital signal processing, the storage or transmission of digital data, such as music, videos or image files, necessitates converting such analogue signals into digital signals via sampling. When these data are sampled, the values from the discrete, sampled points are kept while the information between the sampled points is lost. Various techniques have been developed over the years to recover this lost data, but the results remain incomplete. Professor Yutaka Yamamoto's research is focused on improving how we can recover or reconstruct the original analogue data.
Speech Synthesis Method Based on Tacotron + WaveNet

In view of the Tacotron Griffin-Lim algorithm in speech synthesis system recovery phase information of the obvious effect of the synthetic speech artificial processing, low protect boomed, this paper proposes a speech synthesis method based on Tacotron + WaveNet network architecture, the method is based on the sequence mapping Seq2Seq structure, first of all, the input text into one—hot vector, and introduces attention mechanism for MEL spectrograms, finally using WaveNet vocoder back-end processing network reconstruct the phase information of speech signal, so as to convert the input text into waveform. The test language of the experiment was LJ-Speech, and the experiment was conducted for English language. The experimental results showed that the average subjective opinion score MOS was 4.23, which was higher than Tacotron end-to-end speech synthesis method in terms of synthesis naturalness.
Obscure: Information-Theoretically Secure, Oblivious, and Verifiable Aggregation Queries on Secret-Shared Outsourced Data

Despite exciting progress on cryptography, secure and efficient query processing over outsourced data remains an open challenge. We develop a communication-efficient and information-theoretically secure system, entitled <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Obscure</small> for aggregation queries with conjunctive or disjunctive predicates, using secret-sharing. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Obscure</small> is strongly secure (i.e., secure regardless of the computational-capabilities of an adversary) and prevents the network, as well as, the (adversarial) servers to learn the user’s queries, results, or the database. In addition, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Obscure</small> provides additional security features, such as hiding access-patterns (i.e., hiding the identity of the tuple satisfying a query) and hiding query-patterns (i.e., hiding which two queries are identical). Also, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Obscure</small> does not require any communication between any two servers that store the secret-shared data before/during/after the query execution. Moreover, our techniques deal with the secret-shared data that is outsourced by a single or multiple database owners, as well as, allows a user, which may not be the database owner, to execute the query over secret-shared data. We further develop (non-mandatory) privacy-preserving result verification algorithms that detect malicious behaviors, and experimentally validate the efficiency of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Obscure</small> on large datasets, the size of which prior approaches of secret-sharing or multi-party computation systems have not scaled to.
QoS-enhanced bandwidth reallocation mechanism based on auction model in mobile clouds

In recent years, mobile cloud has become a promising platform to offer elastic and scalable service for our daily life. Even so, the features of mobile communication introduce a series of new challenges to conventional bandwidth allocation service. In this paper, we propose an auction-based bandwidth reallocation mechanism to handle the bandwidth shifting issue which may significantly degrade user's QoS satisfactory. To take both resource provider's revenue and user's QoS requirement into consideration, we formulate the bandwidth reallocation problem as constrained optimisation problem, and then design an auction model which introduces a trade-off parameter to adjust the auction price with aiming at improving the user QoS satisfactory in terms of resource cost. Theoretical analysis indicates that the proposed auction model is capable of balancing the benefits between cloud providers and mobile users. Performance evaluation in a realistic mobile cloud shows that the proposed bandwidth reallocation mechanism outperforms existing approaches in terms of various performance metrics.
Cyber Crime Regulation, Challenges, and Response

The escalation of cybersecurity threats around the world transcends boundaries. Various non-state actors, criminal and terrorist organizations, and state entities exploit it for offensive means. Encryption and dark web are effective tools for conducting malicious activities while maintaining anonymity online. However, the definition and an elaborate understanding of cybersecurity and cybercrime remain contested at the global level. Multiple factors, including the legal, technical, and political issues, play an important part in the formation of diverse policy responses by states and international organizations. Despite limitations, various forms of collaboration have developed between the states, non-state actors, and investigation agencies. This article analyzes the meaning, nature of cybercrime legislation, and the scope of policy formation in the global narrative of cybersecurity. The article concludes with the proposed balanced multi-stakeholder model with equal inclusion of the developing countries in policy framework and capacity building initiatives for combatting cybercrime.
Design of Fine Operation and Maintenance Management and Control System of Distribution Equipment Based on Cloud Platform

This paper studies the refined operation and maintenance management of distribution grid equipment from the current situation and management methods. Starting from the positioning and business requirement analysis of the cloud-based intelligent operation and maintenance management platform for distribution grid equipment, this paper discusses the functions of equipment operation status management, operation and maintenance management and its KPI management, mobile operation and maintenance behavior management, as well as the business management content and information presentation form of each sub-module. The purpose is to support intelligent decision-making for the scientific planning of distribution grid, for accurate construction, for efficient operation inspection, and lean management ensures safe, reliable and economic operation of distribution grid, and provides better and convenient power supply and service. The research results have been applied to the construction of operation, maintenance and control of distribution grid in Guangzhou Baiyun Power Supply Company of CSG, and can provide reference for the design of big data application scenarios.
Neuro-fuzzy Knowledge Processing for Smart Emergency Management: Advances in Computational Intelligence and Seismic Community Resilience

A fuzzy-neural knowledge processing model is put forth to support an intelligent emergency management learning environment with reasoning capabilities that can enhance the disaster response and recovery process. Disasters give rise to self-organizing and ill-structured social behaviors and complex response and recovery efforts. After a disaster strikes, the resulting multi-faceted actions and decision processes are characterized by extreme instability, large-scale uncertainty, severe time constraints, high decision stakes, value-laden trade-offs and dynamic negotiations. A neuro-fuzzy-based knowledge processing implementation of the disaster management process is proposed as a means to foster community disaster resilience. By imitating and learning from emergency managers, the herein proposed neuro-fuzzy synergy encodes both structured and non-structured knowledge: when the reasoning of experts is well structured, the knowledge is encoded in the form of fuzzy rules; whereas, when the reasoning of experts is only available through practical examples illustrating their experience, then fuzzy-neural networks are trained to represent this experience. The proposed approach has been tested in supporting the disaster recovery process. The recommendations of the model have been compared against the professional advice of a group of ten experienced emergency managers. It is shown that fuzzy-neural discrete knowledge processing constitutes a robust and efficient soft systems solution to the most vexing disaster management challenges. In particular, the neuro-fuzzy model successfully manages the inherent uncertainty and complexity of the disaster management process, especially for poorly defined cases, i.e., in situations where it is very difficult for professional emergency managers to predict and accurately evaluate disaster content by directly synthesizing judgments, opinions and values that are subjective and, sometimes, conflicting.
PENGARUH PENGELOLAAN SISTEM INFORMASI MANAJEMEN TERHADAP TUGAS GURU SMK NEGERI SE-KABUPATEN MALANG

The existence of the role and function of teachers is one of the most important factors in education, both formal and informal education. Problems faced by teachers in Indonesia are: (1) the problem of teacher quality; (2) the problem of the number of teachers who are still lacking; (3) the problem of teacher distribution; (4) the problem of teacher welfare. Data from PSG Rayon 115 of 2012 and Teacher Competency Test (UKG) from 2012 to 2015 shows that there are still many teachers in Malang Regency who have not been able to develop their productivity in teaching. Education in the global era requires the management of modern and professional education with an educational nuance. Educational institutions are expected to be able to realize their role effectively with excellence in leadership, staff, teaching and learning processes, staff development, curriculum, goals and expectations, school climate, self-assessment, communication, and parent / community involvement. Management Information Systems have a role in improving the work of teachers because it can accelerate the work of teachers to deliver material to students making learning tools and student reports. Management information system cannot be separated from the support of facilities and infrastructure used in conveying information and the process of delivering information. The application of a MIS, makes school management neat so as to improve the performance of employees and teachers. In addition the school also cooperates with the company by providing laboratory equipment, instructors, and teacher training for work standards and standards of learning materials needed by the company. So that it can improve the task of the teacher in providing subject matter that has been adapted between the national curriculum and the company.
Comparative Study on III-V MOSFET and Si-MOSFET Model Parameters based on BP Neural Networks Algorithm

Abstract MOSFET is the basic unit of modern integrated circuit, and main basic characteristics are: current-voltage (IV) characteristics, capacitor-voltage (CV) characteristics and source-drain contact characteristics. The introduction of group III-V semi-conductor, high k- metal date and SBSD allows MOSFET device characteristic device may keep downsizing based on Moore’s Law. However, as II-V MOSFET devices keep downsizing, the short-channel effect and quantum effect are more obvious so that it is more complicate to calculate and extract characteristic parameters of III-V MOSFET. This paper proposes a kind of III-V MOSFET characteristic parameter modeling method based on BP neutral networks algorithm. Compared to other semi-empirical models, this method needs not to calculate characteristic parameters of devices. In stead, it calculates current and voltage output characteristics and transfer characteristics of devices through BP neutral network models according to test data. Through verification, the trained and predicted output relative error is within 5%. The model has short operation time, high calculation precision and good stability. The models established may be applied extensively to other types of transistor, and feasible for practical engineering application.
The planning and production of a corpus of Yilumbu: Progress of the building stage

In this article, an account is given of the planning and production of a corpus of Yilumbu. The primary focus of the article is on the presentation of the project background. Then the article also gives an account of the dictionary conceptualisation plan, i.e. the general preparation phase, the material acquisition phase, the material preparation phase, the material processing phase and the publishing preparation phase. The present research will only give an account of the dictionary conceptualisation plan as well as future work.
Ivan Krypiakevych as a collector and researcher of ex libris

For centuries, the ex libris (a label of an individual or institution, especially their libraries) had an important socio-cultural mission; its text identified the owner of the library or collection. The image was performed in graphic techniques and had an associative or decorative purpose, revealing the life and preferences of the individual, the priorities of the institution, organization. Book labels carried information on the formation, composition and rotation of books in private and public libraries. The purpose of the article is to analyze the works of the Ukrainian historian, bibliologist and collector Ivan Krypiakevych (1886–1967) about the peculiarities of the appearance of handwritten and printed Ex Libris — a label of an individual or institution, their libraries on Ukrainian lands. Methodological principles of work are the principles of historicism, systematicity and objectivity. The study was carried out using the following methods — comparative-historical, structural-systemic, generalization and synthesis of historical data. Based on the publications of the scientist, his epistolary from the archive of the manuscripts department of the V. Stefanyk Lviv National Scientific Library, the thematic characteristics of the genre are revealed in the context of social, political and artistic processes of the Middle Ages, new and modern times. Letters from recipients from Ukraine and abroad were analyzed, which made it possible to reproduce the operation of personal or corporate book label. The analysis of the works and the epistolary of I. Krypiakevych, testifies to the progressive development (with breaks and difficulties) of the bibliophile movement in Ukraine in the modern day. The scientist restored information about the owners and authors of exlibris, monitored their timeline and technique, distinguished the artistic and stylistic features, gave the classification of book labels. The scientist believed that the exlibris in Ukraine reflected the state of the general culture and were worthy representatives of the international bibliophile movement. Keywords: Ivan Krypiakevych, ex libris, libraries, culture, art.
Person Re-identification Based on Camera Style Adaptation

Person re-identification is a popular topic in computer vision, aiming to retrieve a given pedestrian image across the camera. In this paper, a new Person re-identification based on camera style (CamStyle) adaptation is proposed to solve the problem of lack of data and lack of information in pedestrian feature extraction. In the stage of image preprocessing, CamStyle can serve as a data augmentation approach that transforms the camera style of image. In the training stage, using ID loss and Triplet loss to supervise training of eigenvectors. The experimental results show that the recognition accuracy of the method is improved greatly on Market1501, and the validity of the method is verified.
Research on Data Protection Architecture Based on Block Chain

In recent years, blockchain has emerged as a new decentralized infrastructure and distributed computing paradigm with the increasing popularity of digital cryptocurrencies such as Bitcoin. Blockchain technology is a decentralized, de-trusted, open and transparent distributed data storage technology that can reduce the costs of trust and achieve secure and reliable data interaction. Big data has been widely concerned and studied because it contains great value, however, because big data sets usually contain a lot of privacy-sensitive information, extensive and in-depth research on privacy protection of big data has not been realized. Under the premise, reasonable storage of big data is an urgent problem to be solved. Under the premise of analyzing blockchain structure and access control, this paper proposes data security storage protection architecture based on blockchain technology and access control technology, and analyzes the architecture in detail, and finally summarizes the paper.