Reviewerly/exHarmony huggingface dataset:
Locating the Perpetrator: Industry Perspectives of Cellebrite Education and Roles of GIS Data in Cybersecurity and Digital Forensics

Geographic Information Systems (GIS) data is incorporated into cybersecurity and digital forensics at many levels from the development of secure code, to the metadata stored by systems and employed during civil and criminal cases. This paper reports on the role of GIS Data in the development of both systems and the development of legal probable cause. Specifically, we report on how GIS data is incorporated into three advanced courses: Wireless Security, Secure Software Development, and Forensic Investigation of Wireless Network and Mobile Devices. GIS data needs to be accurate for many reasons including probable cause. We report on IRB-approved student surveys about their experience in the three courses. We find that overall students liked the courses and offered insights into future course improvements.
A Research on Sensing Localization and Orientation of Objects in VR with Facial Vibrotactile Display

Tactile display technology has been widely proved to be effective to human-computer interaction. In multiple quantitative research methods to evaluate VR user experience (such as presence, immersion, and usability), multi-sensory factors are significant proportion. Therefore, the integration of VR-HMD and tactile display is a possible application and innovation trend of VR. The BIP (Break in Presence) phenomenon affects the user's spatial awareness when entering or leaving VR environments. We extracted orientation and localization tasks to discuss the influence of facial vibrotactile display on these tasks. Correlational researches are mainly focused on the parts of human body such as torso, limb, and head regions. We chose face region and to carry out the experiment, a VR-based wearable prototype “VibroMask” was built to implement facial vibrotactile perception. Firstly, the behavioral data of subjects' discrimination of vibrotactile information were tested to obtain the appropriate display paradigm. It was found that the discrimination accuracy of low-frequency vibration was higher with loose wearing status, and the delay offset of one-point vibration could better adapt to the orientation task. Secondly, the effect of facial vibrotactile display on objects’ localization and orientation discriminating task in VR was discussed. Finally, subjects’ feedback was collected by using open-ended questionnaire, it is found that users have a higher subjective evaluation of VR experience with facial vibrotactile display.
La collaborazione di Alfredo Trombetti all’Enciclopedia Italiana: le lettere a Giulio Bertoni e Giovanni Gentile

This paper reconstructs Alfredo Trombetti’s collaboration on the first edition of the Enciclopedia Italiana (1929-1937) by considering his correspondence with Giulio Bertoni, director of the linguistics department, and Giovanni Gentile, scientific director of the entire publication. The letters to Bertoni clarify the role played by Trombetti in the initial phase of the work (1925-1926) and in particular his contribution to the drafting of the list of linguistic headwords and collaborators. The letters to Gentile show that in the following years (until his death in 1929) he remained a point of reference within the department: in addition to writing several entries, he analysed the ethnography headword lists and supplemented them with linguistic paragraphs. He also tried to initiate a cartographic project for the linguistics department, which ultimately did not come to fruition.
Blockchain: A Method to Improve Voting System

In order to overcome the disadvantages of poor security and lack of trust in the traditional electronic voting system and improve the reliability of the election system, this paper proposes an electronic voting system based on blockchain.This system is divided into voting module and blockchain management module, which mainly aims at the credibility of voting data and the protection of voters' privacy.The voting module begins with the verification of voter's identity, uses Zero Knowledge Proof algorithm to prove that the entrant is the legitimate owner of part of the rights and interests, uses ECDSA to encrypt the data, and uses Digital Signature to verify the security and integrity of the data.The main purpose of the blockchain management module is to update the data in real time, make the newly added nodes complete data synchronization, check the consistency of the data, and provide the user with historical records.
The Path and Practice of the Integration of ZCC Technology and Digital Archives for Science and Technology

Based on the analysis of the status quo of ZCC technology and digital archives for science and technology, this paper analyzes the need for integration between ZCC technology and digital archives and proposes a path for the synthesis of high-fidelity compression and decompression of big image files, full-text retrieval of text information, encryption technology of image files, and quick data browsing. This integration solves technical problems of digital archives that may be met during the use of massive data storage, rapid transmission, retrieval and utilization of large drawings and images, and assurance of data security.
Mobile robot navigation method based on improved Q-learning algorithm

In recent years, autonomous navigation of mobile robots has become one of the hot topics in research. An improved Q-learning method (IQL) combining action selection strategy and Q function update method is proposed. A new exploration strategy of staged ε greedy exploration is used in IQL. In addition, the idea of backtracking is used to update the Q value to speed up the convergence speed. Experiments conducted in a grid environment show that compared with the classic algorithm, the algorithm converges faster and the program execution time is shorter.
Deep-based Self-refined Face-top Coordination

Face-top coordination, which exists in most clothes-fitting scenarios, is challenging due to varieties of attributes, implicit correlations, and tradeoffs between general preferences and individual preferences. We present a Deep-Based Self-Refined (DBSR) system to simulate face-top coordination based on intuition evaluation. To this end, we first establish a well-coordinated face-top (WCFT) dataset from fashion databases and communities. Then, we use a jointly trained CNN Deep Canonical Correlation Analysis (DCCA) method to bridge the semantic face-top gap based on the WCFT dataset to deal with general preferences. Subsequently, an irrelevance-based Optimum-path Forest (OPF) method is developed to adapt the results to individual preferences iteratively. Experimental results and user study demonstrate the effectiveness of our method.
A Novel Risk Mitigation &amp; Cloud-Based Disaster Recovery Framework for Small to Medium Size Businesses

Current guidelines and frameworks for disaster recovery and business continuity focus on helping corporations and large enterprises. With a rising number of vendors offering cloud computing solutions, most small to medium-size businesses do not count on the minimum number of users to qualify for any tier package. Moreover, the cost of purchasing a solution that does not scale to fit the needs of a small business is a consistent issue. Our solution presents a novel framework granting medium to small-size businesses the opportunity to mitigate risk and resume activities after a disaster has taken place. Furthermore, we present several recovery alternatives using a cloud-based storage solution. The results show that an optimal recovery time objective can be achieved by allowing users to retrieve backups from any platform or device with web-browsing capabilities. Also, a high level of integrity on each client can be reached, which lowers the chances of losing data or exposing financial records to an attacker.
Design of Indoor Path Planning Algorithm for Complex Environment

With the rise and rapid development of technologies such as mobile communications, smart terminals and geographic information systems, we are entering the era of mobile Internet. The connection with the environment is gradually strengthening. People’s demand for location-based location information services is increasing day by day, and many of these service products have entered people’s lives. This article mainly introduces the design research of indoor path planning algorithm for complex environment. This paper uses the design and research of indoor path planning algorithm for complex environment, uses A* algorithm for indoor path planning and design, and reasonably analyzes the feasibility of Dijkstra algorithm and adjacency list model. The adjacency list is a data structure that uses a linked list to store the information of the graph. Each node in the graph has a singly linked list corresponding to it in this data structure. The experimental results of this paper show that the design and research of indoor path planning algorithms for complex environments increases the efficiency of the algorithm by 16%. The limitations of the design and research of indoor path planning algorithms for complex environments provide good applications for ant colony algorithm. The methods and approaches of indoor path planning are analyzed, discussed and summarized, thereby enriching the academic research results.
Zero-Cost Constructor Subtyping

Constructor subtyping is a form of subtyping where two inductive types can be related as long as the inductive signature of one is a subsignature of the other. To be a subsignature requires every constructor of the smaller datatype be present in the larger datatype (modulo subtyping of the constructors’ types). In this paper, we describe a method for impredicatively encoding datatype signatures in Cedille (a dependently typed programming language) that supports highly flexible constructor subtyping, with the subtyping relation given by a derived notion of type inclusion witnessed by a heterogeneously typed identity function. Specifically, the conditions under which constructor subtyping is possible between datatypes are fully independent of the order in which constructors are listed in their declarations. After examining some extended case studies, we formulate generically a sufficient condition for constructor subtyping in Cedille using our technique.
Efficacy of Bayesian Neural Networks in Active Learning

Obtaining labeled data for machine learning tasks can be prohibitively expensive. Active learning mitigates this issue by exploring the unlabeled data space and prioritizing the selection of data that can best improve the model performance. A common approach to active learning is to pick a small sample of data for which the model is most uncertain. In this paper, we explore the efficacy of Bayesian neural networks for active learning, which naturally models uncertainty by learning distribution over the weights of neural networks. By performing a comprehensive set of experiments, we show that Bayesian neural networks are more efficient than ensemble based techniques in capturing uncertainty. Our findings also reveal some key drawbacks of the ensemble techniques, which was recently shown to be more effective than Monte Carlo dropouts.
Detect Fraud Bank Checks with a Simplified Image Multi-threshold Decision Processing Algorithm

This research is to design and build an image processing algorithm to quickly and easily detect a fraud or altered bank check in real time. A MATLAB Image Processing Toolbox is used to assist this study to quickly detect any fraud or altered bank check with the help of an ATM scanner when a fraud or altered check is deposited via an ATM. The testing and validation processes have been performed to confirm the effectiveness and correctness of this detecting algorithm. The current correct detecting rate is about 92.5% for checks deposited via ATMs.
Marginalia in Parisian Historical Publications of the Second Half of the 16th Century in Russian and Franch Libraries

The article is devoted to the study of reader’s practices related to the several centuries of existence of French historical books of the second half of the 16th century, the books’ place in people’s lives and attitude to them. The study’s source base is represented by a body of identified book marginalia from the copies of Parisian publications stored in the National Library of France and the Rare Books Department (Book Museum) of the Russian State Library. The handwritten notes (396 marginalia have been identified and are being introduced into scientific circulation) that are dated belong to the second half of the 16th—20th centuries inclusive. In the second half of the 16th century, printed books in France became the determinant source of intellectual thought, and their role in the lives of educated people increased significantly. The spread of printed books, their influence on the minds of ordinary people and their authority in society contributed to the appearance of a large number of handwritten notes made by readers at different times and on different occasions. The author offers his own content-based classification of the body of marginalia identified by him and gives a detailed description of the studied material. There are three main groups of the marginalia: the records related to the content of the book, the records related to the book itself, and the records not related to the book. The first group consists of 2 types of records: textual records and comments. The second group is divided into 3 types: library records, owner’s records, and other informational records related to the content of the book. A separate group consists of the informational records that are not related to the book. The analysis made it possible to assess the information value and potential of marginalia as historical and psychological sources that allow (in combination with other sources) to study, in a historical context, the cultural and mental characteristics of people of different eras, the existence of books in society, and the book and library traditions of different regions of Europe.
Authenticated Key Exchange Protocol in the Standard Model under Weaker Assumptions

A two-party authenticated key exchange (AKE) protocol allows each of the two parties to share a common secret key over insecure channels, even in the presence of active adversaries who can actively control and modify the exchanged messages. To capture the malicious behaviors of the adversaries, there have been many efforts to define security models. Amongst them, the extended Canetti–Krawczyk (eCK) security model is considered one of the strongest security models and has been widely adopted. In this paper, we present a simple construction of a pairing-based eCK-secure AKE protocol in the standard model. Our protocol can be instantiated with a suitable signature scheme (i.e., an existentially unforgeable signature scheme against adaptive chosen message attacks). The underlying assumptions of our construction are the decisional bilinear Diffie–Hellman assumption and the existence of a pseudorandom function. Note that the previous eCK-secure protocol constructions either relied on random oracles for their security or used somewhat strong assumptions, such as the existence of strong-pseudorandom functions, target collision-resistant functions, etc., while our protocol construction uses fewer and more-standard assumptions in the standard model. Furthermore, preserving the same security argument, our protocol can be instantiated with any appropriate signature scheme that comes in the future with better efficiency.
Denoising in Monte Carlo rendering based on Clustered-Residual block network

Monte Carlo rendering image denoising is different from natural image denoising. The auxiliary information can be used to reconstruct the image. However, ordinary convolutional networks cannot handle MC noise well, and it is difficult to establish a suitable model between noise, auxiliary information and the original image. In this paper we addresses the above issues, propose a Monte Carlo image denoising network based on deep neural network, construct a residual aggregation module, add dense connections, and extract image detail features. Combining the three most commonly used auxiliary information (depth, normal, and reflectivity) helps the network accurately restore the image. The experimental results show that the algorithm proposed in this paper can adapt to various typical scenes and effectively reduce the noise of rendered images.
A Single-Stage Tree-Structure-Based Approach to Determine Fuzzy Average-Utility Itemsets

Fuzzy utility mining (FUM) techniques are used to mine high fuzzy utility itemsets for market analysis. They consider items that include purchase quantities, unit profits, and linguistic terms representing quantity information. Although FUM facilitates market analysis, it has the measurement problem in which the fuzzy utility value for an itemset may be higher than that for its subset. In the past, a tree-based mining method was proposed to find fuzzy average-utility itemsets using a two-stage strategy tree-based method with an average-utility measure. It was, however, computationally expensive because two-stage processing was needed. To handle this, we propose a single-stage tree-structure-based method that uses an external list for each node in the tree to find fuzzy average-utility itemsets efficiently. Experimental results show that the proposed method outperforms the former approach in terms of execution time.
Consistency in Multi-device Service Including VR: A Case Study

Nowadays, we have entered a world of multi-device experiences. This phenomenon poses a significant challenge because we have to deploy applications to different platforms, with a consistent UX for each targeted device preferably. Consistency provides users with a stable framework in similar contexts and helps to improve multi-device usability. Many previous studies focus on maintaining consistency among traditional platforms like smartphones apps and websites. However, with the rapid development of VR, it is crucial to add it to the spectrum because the design for VR differs from that for traditional 2D screens a lot. Therefore, this paper proposes a series of design principles to ensure the consistency across multiple devices including HMD-VR, along with four dimensions of consistency worth considering. We use Virtual Experiment of Film Language, a multi-device serious game as an example. Twelve participants were recruited to experience the VR and WebGL versions of the game to spot inconsistency issues. After that, the game was modified according to the survey results and evaluated by the participants again. The evaluation result showed that consistency was improved. We proposed three consistency design principles based on our findings. They can help multi-device applications improve consistency across devices so as to enhance inter-usability and user experience.
User Interface Design of Virtual Conferencing Tools – Towards a Method for Considering Cultural Impact – Exemplified by Zoom

For designing user interfaces, it is essential to know the influence of the task complexity and its impact in the intercultural context. First, a model for task complexity evaluation will be presented in this paper. This method considers a task to be completed at the UI. In the second step, cultural interaction indicators and their characteristics are considered regarding the complexity level of a task. The matching of task evaluation characteristics, HCI dimensions and cultural dimensions/variables allows aligning the development of different UIs for different cultural environments. Consequently, the relationship model between culture and HCI will be proposed and discussed for five intended cultural environments (China, Germany, India, Poland, and Russia).
Cultivating marine big data and information industry around supercomputing project --Based on the analysis of Shandong Province

As a major province of marine economy and marine science and technology, Shandong has always attached great importance to the development of the marine industry, and has made remarkable and outstanding achievements in recent years, but there is still a certain gap between the goal of building a strong marine province. This paper investigated and analyzed how did supercomputing technology promote the development of ocean big data and information industry in Shandong Province. Finally, according to the problems facing today, we proposed corresponding strategies with a view to providing a valuable reference for relevant departments in making scientific decisions.
Unsubscribe rate of chatbots users received broadcast messages for France, Germany, Russia and the Global market

This paper shows the chatbot users unsubscribe rate when they receive a weekly broadcast message. This research tracks the unsubscribe rate for a Viber application in four different locations - France, Germany, Russia, and Global (all users in countries that are not part of the first three). Quantitative data regarding broadcast messages has been analyzed. We have similar user groups based on location, platform, first interaction, and sent messages.
The Application of Statistical Analysis in Financial Management

This paper first introduces the statistical analysis of related knowledge and the commonly-used methods of financial analysis. By using the centralized mode, the financial information of enterprises is centralized processed. Financial model uses the basic data statistics. As a whole, according to solid management, sectors,other companies, subsidiaries are passed data acquisition according to the plan, with the relevant valuable data as an independent unit in the chain, finally on the basis of return on net assets, with other financial index as input, to make data analysis model. Based on the statistical analysis method of SVM algorithm, this paper maps the statistical analysis into the design of enterprise financial efficiency and data core value, discusses the system architecture and various functional requirements, and designs and realizes the enterprise financial efficiency analysis system based on statistical analysis, which has good adaptability and expansion ability.
Technological Efficiency of Computer Science Education Results in a Secondary School as a Factor of the Quality of Education in the Distance Learning System

Considered an algorithmic approach to achieving students' educational results in the school course of computer science in the distance learning system, which is relevant in modern conditions. The main characteristics of the technological effectiveness of learning outcomes are given in the example of a computer science course.
Integration of Dynamic Multi-Atlas and Deep Learning Techniques to Improve Segmentation of the Prostate in MR Images

Accurate delineation of the prostate in MR images is an essential step for treatment planning and volume estimation of the organ. Prostate segmentation is a challenging task due to its variable size and shape. Moreover, neighboring tissues have a low-contrast with the prostate. We propose a robust and precise automatic algorithm to define the prostate’s boundaries in MR images in this paper. First, we find the prostate’s ROI by a deep neural network and decrease the input image’s size. Next, a dynamic multi-atlas-based approach obtains the initial segmentation of the prostate. A watershed algorithm improves the initial segmentation at the next stage. Finally, an SSM algorithm keeps the result in the domain of allowable prostate shapes. The quantitative evaluation of 74 prostate volumes demonstrated that the proposed method yields a mean Dice coefficient of [Formula: see text]. In comparison with recent researches, our algorithm is robust against shape and size variations.