Reviewerly/exHarmony huggingface dataset:
Multi–Modal High–End Visualization System

This paper describes a production-grade software toolkit used for shared multi-model visualization systems developed by the Expanded Perception and Interaction Centre. Our High-End Visualization System (HEVS) can be used as a framework to enable content to be run transparently on a wider range of platforms (Figure 2) with fewer compatibility issues and dependencies on commercial software. Content can be transferred more easily from large screens (including cluster-driven systems) such as CAVE-like platforms, hemispherical domes, and projected cylindrical displays through to multi-wall displays and HMDs such as VRR or AR. This common framework is able to provide a unifying approach to visual analytics and visualizations. In addition to supporting multi-modal displays, multiple platforms can be connected to create multi-user collaborative experiences across remotely located labs. We aim to demonstrate multiple projects developed with HEVS that have been deployed to various multi-modal display devices.
Dataset of Digitized RACs and Their Rarity Score Analysis for Strengthening Shoeprint Evidence

Abstract In recent years, there is a growing demand to fortify the scientific basis of forensic methodology. During 2016, the President’s Council of Advisors on Science and Technology (PCAST) published a report that states there are no appropriate empirical studies that support the foundational validity of footwear analysis to associate shoeprints with particular shoes based on specific identifying marks, which is a basic scientific demand from the field. Furthermore, meaningful databases that can support such studies do not exist. Without such databases, statistical presentation of the comparison results cannot be fulfilled either. In this study, a database of over 13,000 randomly acquired characteristics (RACs) such as scratches, nicks, tears, and holes, as they appear on shoe sole test impressions, from nearly 400 shoe soles was collected semi‐automatically. The location, orientation, and the contour of each RAC were determined for all the RACs on each test impression. The statistical algorithm Statistic Evaluation of Shoeprint Accidentals (SESA) was developed to calculate a score for finding another feature similar to a particular scanned and digitized RAC in the same shape, location, and orientation as the examined one. A correlation was found between the results of SESA and the results of real casework, strengthening our belief in the ability of SESA to assist the expert in reaching a conclusion while performing casework. The score received at the end of the process serves the expert as a guiding number, allowing more objective and accurate results and conclusions.
Dialogue System of Controlling Robot Based on the Theory of Finite-State Automata

The article discusses the system of dialogue control manipulation robots. The analysis of the basic methods of automatic speech recognition, speech understanding, dialogue management, voice response synthesis in dialogue systems has been carried out. Three types of dialogue management are considered as "system initiative", "user initiative" and "combined initiative". A system of object-oriented dialog control of a robot based on the theory of finite state machines with using a deep neural network is proposed. The main difference of the proposed system lies in the separate implementation of the dialogue process and robot’s actions, which is close to the pace of natural dialogue control. This method of constructing a dialogue control robot allows system to automatically correct the result of speech recognition, robot’s actions based on tasks. The necessity of correcting the result of speech recognition and robot’s actions may be caused by the users’ accent, working environment noise or incorrect voice commands. The process of correcting speech recognition results and robot’s actions consists of three stages, respectively, in a special mode and a general mode. The special mode allows users to directly control the manipulator by voice commands. The general mode extends the capabilities of users, allowing them to get additional information in real time. At the first stage, continuous speech recognition is built by using a deep neural network, taking into account the accents and speech speeds of various users. Continuous speech recognition is a real-time voice to text conversion. At the second stage, the correction of the speech recognition result by managing the dialogue based on the theory of finite automata. At the third stage, the actions of the robot are corrected depending on the operating state of the robot and the dialogue management process. In order to realize a natural dialogue between users and robots, the problem is solved in creating a small database of possible dialogues and using various training data. In the experiments, the dialogue system is used to control the KUKA manipulator (KRC4 control) to put the desired block in the specified location, implemented in the Python environment using the RoboDK software. The processes and results of experiments confirming the operability of the interactive robot control system are given. A fairly high accuracy (92 %) and an automatic speech recognition rate close to the rate of natural speech were obtained.
Application of ant colony clustering algorithm in coal mine gas accident analysis under the background of big data research

In order to better solve the problem of gas outburst prediction, based on the in-depth study of ant colony algorithm, the ant colony clustering algorithm is improved, and the population classification and ant sensory perception characteristics are applied to make the ant colony the most likely to f ind. The optimal solution effectively avoids the possibility of local optimization, improves the global optimization performance and convergence speed of the algorithm, and reduces the influence of human subjective factors. Based on the prominent basic speech and actual working conditions, the paper selects five indexes of gas velocity, initial gas velocity, gas content, gas pressure and coal firmness coefficient as clustering attributes, and uses ant colony clustering algorithm to judge outstanding the state of occurrence. The paper uses MATLAB programming language to write a coal and gas outburst prediction program based on improved ant colony clustering algorithm, and predicts a coal mine. The final result is the same as the actual observation.
A Comprehensive Analysis of Privacy-preserving Techniques in Deep learning based Disease Prediction Systems

Abstract With the rise in demand for deep learning models due to its ability to learn features from data, and predict, it is widely used in disease prediction systems. However, as patient medical records are considered to be highly confidential due to them consisting of personal information, its privacy-preservation is of prime importance. Conventional privacy-preserving techniques often tend to hinder the utilitarian aspect of the system. In this paper we carry out a comprehensive analysis of privacy-preserving techniques for disease prediction systems that use deep learning along with a comparison of the different privacy-preserving techniques. This paper also discusses the existing privacy-preserving approaches in deep learning. They are cryptographic approaches, attribute-based encryptions, homomorphic encryptions and other hybrid approaches.
The role of public agencies in blockchain consortia: Learning from the Cardossier

Blockchain technologies enable new forms of data sharing in platforms. This raises questions around how they are jointly developed and managed in blockchain consortia and what role public agencies play in those efforts. Based on an analysis of prior work on data sharing in public-private partnerships and other blockchain projects, we analyze the case of the Cardossier. The Cardossier project and (later) association develops a platform to link the public and private actors in the Swiss car ecosystem. The participating car registration authority has the roles of an actor in interorganizational processes, supplier of data, source of trust, guarantor of data quality, user of data, and incentive for making goods public. We conclude that the public agencies have a very important role in blockchain consortia and propose that they should use this role actively as part of their efforts to create public value.
Medical Protocol Security

DICOM is an international standard for medical images and related information, and is a medical image format that can be used for data exchange. The agreement is widely used in medical fields such as radiology and cardiovascular imaging. However, since DICOM libraries have less security considerations in protocol implementation, they have a large number of security risks. Aiming at the security issue of DICOM libraries, the paper conducts research on vulnerability mining technology for DICOM open source libraries, proposes a vulnerability mining framework based on Fuzzing technology, and implements a prototype system named DICOM-Fuzzer, which includes initialization, test case generation, automatic test, exception monitoring and other modules. Finally, the open source library DCMTK was selected for testing, and it was found that data overflow would occur when the content of the received file was greater than 7080 lines. Found that there is a vulnerability that causes the PACS system to refuse service. In conclusion, the DICOM protocol does have risks, and its information security needs to be further improved.
Synchronization of reservoir computers with applications to communications

We consider different reservoir computers learning and modeling the same chaotic system. We find that by transmitting a common signal, synchronization will be achieved between these superficially distinct reservoir computers. We further show that even when the transmitted signal is contaminated with extensive observational noise, synchronization will still occur. Moreover, utilizing a cascaded synchronization scheme, we can successfully recover a chaotic masking signal hidden in the transmitter. Our findings reveal that synchronization is a common feature of reservoir computers upon learning chaotic systems.
Multi-Scale Ships Detection in High-Resolution Remote Sensing Image Via Saliency-Based Region Convolutional Neural Network

Ship detection is of great significance in both military and civilian application domains. Deep Convolutional Neural Network (DCNN) method with region proposal, e.g. Faster R-CNN, achieves ship detection well. However, for multi-scale target detection in high-resolution remote sensing image, the limitation of accuracy is induced by the region proposal restricted by the training set. Therefore, the mechanism of multi-scale ship detection based on saliency estimation is proposed in our work. Firstly, a saliency estimation algorithm is used to distinguish which image contains large ships and the image pyramid for each input one is established. Then, using a target detection network in different scales of images. The results are merged at the end of network. Finally, accuracy and validity are verified by real data processing.
Tweet Sentiment Classification by Semantic and Frequency Base Features Using Hybrid Classifier

The technique of sentiment analysis is considered as the most powerful tools in the field of natural language processing as it comes up with large number of possibilities to perceive the sentiments of people’s on several distinct topics. The concept of aspect-based sentiment analysis aims to figure out it further and determines what an individual is talking about, and explains whether she/he likes it or not. A practical example of an ideal realm in context to this topic discussed represents millions of possible schemes of Indian welfare planning. The government has launched such type at all the possible levels in schools, center and state level. These schemes work with an association of both the state and the central government. Such welfare-based schemes are generally introduced for various distinct levels on the basis of peoples (individuals) and their behavior or lifestyle. The schemes are launched for the purpose of developing the minority and the weaker section of the society. Whereas some of the welfare schemes are mainly introduced for girls and women only and it helps in empowering the status of the women by providing financial help as well as the basic need and requirements. There are several distinct ways to handle this major issue by the mechanism of machine learning process. In this paper, a labeled form of data is mainly used based on the polarity, preprocessing of the Tweets, which further extracts unigram features after the process of Tweet-based preprocessing methodology. In case of preprocessing, the data with huge noise is removed with the help of tokenization process; stop word removal process and stemming (deriving) these processes to clear the redundant data such as repeat emoji, words, and hashtags. These label and features are usually learned by SVM, KNN and a hybrid of KNN. In proposed experiment Hybrid approach shows improvement in precision and accuracy than other.
Perceptions of Social Roles Across Cultures

In this paper we introduce a data set of social roles and their aspects (descriptors or actions) as emerging from surveys conducted across a sample of over 400 respondents from two different cultures: US and India. The responses show that there are indeed differences of role perceptions across the cultures, with actions showcasing less variability, and descriptors exhibiting stronger differences. In addition, we notice strong shifts in sentiment and emotions across the cultures. We further present a pilot study in predicting social roles based on attributes by leveraging dependency-based corpus statistics and embedding models. Our evaluations show that models trained on the same culture as the test set are better predictors of social role ranking.
A Comparison of MCMC Sampling for Probabilistic Logic Programming

Markov Chain Monte Carlo (MCMC) methods are a class of algorithms used to perform approximate inference in probabilistic models. When direct sampling from a probability distribution is difficult, MCMC algorithms provide accurate results by constructing a Markov chain that gradually approximates the desired distribution. In this paper we describe and compare the performances of two MCMC sampling algorithms, Gibbs sampling and Metropolis Hastings sampling, with rejection sampling for probabilistic logic programs. In particular, we analyse the relation between execution time and number of samples and how fast each algorithm converges.
Securing Internet of Medical Things (IoMT) Using Private Blockchain Network

A key initiative taken by Government of India in response to Prime Minister Narendra Modi’s “Digital India” move is the formation of Internet of Things (IoT) ecosystem for smart healthcare. Any security flaw occurring during authentication with a centralized environment affects the connectivity of devices in the network and the transaction of data in IoT. As a solution, Blockchain technology emerges to use in IoT network for the betterment. The Internet of Medical Things (IoMT) is the collection of medical devices and applications that connect to healthcare IT systems. In IoMT environment, a private Blockchain network is created containing all the participants of a hospital including doctors, lab technicians, patients and clinical laboratories etc., There exists no necessity to carry the report by the patients during every visit, as the data is already available in the network. The data is completely decentralized to avoid failure, data loss and to provide faster recovery. Whenever a change is attempted by a third person in the database, a notification is sent to all the members of the group. Since this is a private Blockchain, it requires an additional level of authentication. Whenever any new person requires being a part of the network a document for proof is provided. The data is transparent, so any modification done by any member of the group will get notified to everyone. An additional level security can be provided for data transfer based upon the mutually agreed security parameter and any modification in database is accepted upon approval of all members. In addition, when a hospital is not able to provide any sophisticated treatment to the patient due to lack of medical facilities, recommendation is sent to the patient; thereby new hospital added to the private network after successful authentication to access patient’s record. To access record, quantum key may be generated and distributed across the network.
Multi-label learning for concept-oriented labels of product image data

In the designing field, designers usually retrieve the images for reference according to product attributes when designing new proposals. To obtain the attributes of the product, the designers take lots of time and effort to collect product images and annotate them with multiple labels. However, the labels of product images represent the concept of subjective perception, which makes the multi-label learning more challenging to imitate the human aesthetic rather than discriminate the appearance. In this paper, a Feature Correlation Learning (FCL) network is proposed to solve this problem by exploiting the potential feature correlations of product images. Given a product image, the FCL network calculates the features of different levels and their correlations via gram matrices. The FCL is aggregated with the DenseNet to predict the labels of the input product image. The proposed method is compared with several outstanding multi-label learning methods, as well as DenseNet. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts for multi-label learning problem of product image data.
The Virtual Hand Illusion in Obesity: Dissociation Between Multisensory Interactions Supporting Illusory Experience and Self-Location Recalibration

The Rubber Hand Illusion (RHI) is used widely to investigate the multisensory integration mechanisms that support bodily self-consciousness and, more specifically, body ownership and self-location. It has been reported that individuals affected by obesity show anomalous multisensory integration processes. We propose that these obesity-induced changes could lead to an unusual susceptibility to the RHI and anomalous bodily self-experience. To test this hypothesis, we administered a modified version of the RHI (using a picture of the participant's hand) to individuals affected by obesity and participants with a healthy weight. During synchronous and asynchronous stimulation, we compared the subjective experience of the illusion (using a questionnaire) and the effect of the illusion on self-location (i.e., proprioceptive drift). In accordance with the illusion phenomenology, both groups had a comparable subjective illusory experience after the synchronous stimulation. Nevertheless, individuals affected by obesity showed less recalibration of self-location than healthy weight participants. In light of a recent interpretation of the multisensory integration mechanisms that underpin the RHI, our findings suggest that in obesity visuo-tactile integration supporting the subjective experience of the illusion is preserved, whereas visuo-proprioceptive integration for self-location is reduced.
On the accuracy of video quality measurement techniques

With the massive growth of Internet video streaming, it is critical to accurately measure video quality subjectively and objectively, especially HD and UHD video which is bandwidth intensive. We summarize the creation of a database of 200 clips, with 20 unique sources tested across a variety of devices. By classifying the test videos into 2 distinct quality regions SD and HD, we show that the high correlation claimed by objective video quality metrics is led mostly by videos in the SD quality region. We perform detailed correlation analysis and statistical hypothesis testing of the HD subjective quality scores, and establish that the commonly used ACR methodology of subjective testing is unable to capture significant quality differences, leading to poor measurement accuracy for both subjective and objective metrics even on large-screen display devices.
Application of fuzzy image restoration in criminal investigation

The advancement of science and technology has a positive effect on the development of law disciplines. The development of algorithms and artificial intelligence also has a certain impact on judicial practice. Image restoration is a significant technique in image processing. It aims to objectively restore the content or quality of the original image from the degraded image. Image degradation is always generated in image transmission, such as distortion, blur. In modern video surveillance system, image restoration is significant for criminal investigation. However, image restoration based on conventional filter algorithms cannot achieve satisfactory performance. Thus, we first introduce the image restoration algorithms based on different degradation model. Then, we propose some applications of fuzzy image restoration in criminal investigation. We conduct experiments on both degraded images and videos and experimental results have shown the effectiveness of fuzzy image restoration applying to the criminal investigation.
Negotiations of language ideology on the Jodel app: Language policy in everyday online interaction

This study explores language policy in micro interaction on the online discussion app Jodel. By analyzing metalinguistic, evaluative comments, this study aims to provide an example of community-based language regulation. In total, 110 discussion threads in which Jodel users make explicit evaluative remarks on others’ language use are analyzed. Through an analysis of (de)legitimation strategies, the metalinguistic remarks are explored as key discourse elements in the reproduction of language ideologies. The results show that Jodel users promote and uphold a hypercorrect local ideology that, to a large extent, promotes uniformity and traditional standard orthography. Prominent strategies for invoking this local language ideology are humor, repetition, and exposed correction. The metalinguistic comments are used for the purpose of creating boundaries for inclusion and exclusion of community members on the app.
When Certificate Transparency Is Too Transparent

Certificate Transparency (CT) is a recent initiative to log all publicly available certificates, thereby adding an extra layer of accountability and auditability to certificate authorities. Unbeknownst to most users and website administrators, CT logs make all data in all certificates available publicly and permanently. Although certificates are ostensibly intended to be public (after all, their main purpose is to convey an entity's public key), administrators may inadvertently include information that can be mined by anyone. For instance, CT logs contain certificates for subdomains, which naturally facilitates subdomain enumeration. This paper asks: is there other, more sensitive information included in certificates?
A Certificateless Proxy Re-encryption Scheme for Cloud-Based Blockchain

Cloud computing is a powerful technology because it provides users with attractive online files sharing services. However, security and privacy are significant challenges since the cloud cannot be fully trusted due the traditional centralized management system. This paper proposes a certificateless proxy re-encryption as an efficient mechanism to secure access over outsourced data. The proposed scheme relies on blockchain technology for decentralized security administration and data protection. Besides, the scheme achieves data confidentiality and efficient revocation mechanism. Moreover, the security analysis proves the confidentiality and integrity of the data stored in the cloud server. Finally, we evaluate the performance of the proposed scheme.