Reviewerly/exHarmony huggingface dataset:
Towards Detection of Abnormal Vehicle Behavior Using Traffic Cameras

Throughout the world, many surveillance cameras are being installed every month. For example, there are over 18,000 publicly accessible traffic cameras in 200 cities and metropolitan areas in the United States alone. Live video streams provide real-time big data about behavior happening in the present, such as traffic information. However, until now, extracting intelligence from video content has been mostly manual, i.e. through human observation. The development of smart real-time tools that can detect abnormal vehicle behaviors may alert law enforcement and transportation agencies of possible violators and can potentially avoid traffic accidents. In this study, we address this problem by developing an application for detection of abnormal driving behavior using traffic video streams. Evaluation is performed using real videos from traffic cameras to detect stalled vehicles and possible abnormal vehicle behavior.
Image Super-Resolution by Neural Texture Transfer

Due to the significant information loss in low-resolution (LR) images, it has become extremely challenging to further advance the state-of-the-art of single image super-resolution (SISR). Reference-based super-resolution (RefSR), on the other hand, has proven to be promising in recovering high-resolution (HR) details when a reference (Ref) image with similar content as that of the LR input is given. However, the quality of RefSR can degrade severely when Ref is less similar. This paper aims to unleash the potential of RefSR by leveraging more texture details from Ref images with stronger robustness even when irrelevant Ref images are provided. Inspired by the recent work on image stylization, we formulate the RefSR problem as neural texture transfer. We design an end-to-end deep model which enriches HR details by adaptively transferring the texture from Ref images according to their textural similarity. Instead of matching content in the raw pixel space as done by previous methods, our key contribution is a multi-level matching conducted in the neural space. This matching scheme facilitates multi-scale neural transfer that allows the model to benefit more from those semantically related Ref patches, and gracefully degrade to SISR performance on the least relevant Ref inputs. We build a benchmark dataset for the general research of RefSR, which contains Ref images paired with LR inputs with varying levels of similarity. Both quantitative and qualitative evaluations demonstrate the superiority of our method over state-of-the-art.
Detection of breast cancer lymph node metastases in frozen sections with a point-of-care low-cost microscope scanner

Detection of lymph node metastases is essential in breast cancer diagnostics and staging, affecting treatment and prognosis. Intraoperative microscopy analysis of sentinel lymph node frozen sections is standard for detection of axillary metastases but requires access to a pathologist for sample analysis. Remote analysis of digitized samples is an alternative solution but is limited by the requirement for high-end slide scanning equipment.To determine whether the image quality achievable with a low-cost, miniature digital microscope scanner is sufficient for detection of metastases in breast cancer lymph node frozen sections.Lymph node frozen sections from 79 breast cancer patients were digitized using a prototype miniature microscope scanner and a high-end slide scanner. Images were independently reviewed by two pathologists and results compared between devices with conventional light microscopy analysis as ground truth.Detection of metastases in the images acquired with the miniature scanner yielded an overall sensitivity of 91% and specificity of 99% and showed strong agreement when compared to light microscopy (k = 0.91). Strong agreement was also observed when results were compared to results from the high-end slide scanner (k = 0.94). A majority of discrepant cases were micrometastases and sections of which no anticytokeratin staining was available.Accuracy of detection of metastatic cells in breast cancer sentinel lymph node frozen sections by visual analysis of samples digitized using low-cost, point-of-care microscopy is comparable to analysis of digital samples scanned using a high-end, whole slide scanner. This technique could potentially provide a workflow for digital diagnostics in resource-limited settings, facilitate sample analysis at the point-of-care and reduce the need for trained experts on-site during surgical procedures.
High-Speed Area-Efficient Implementation of AES Algorithm on Reconfigurable Platform

Nowadays, digital information is very easy to process, but it allows unauthorized users to access to this information.To protect this information from unauthorized access, cryptography is one of the most powerful and commonly used techniques.There are various cryptographic algorithms out of which advanced encryption standard (AES) is one of the most frequently used symmetric key cryptographic algorithms.The main objective of this chapter is to implement fast, secure, and areaefficient AES algorithm on a reconfigurable platform.In this chapter, AES algorithm is designed using Xilinx system generator, implemented on Nexys-4 DDR FPGA development board and simulated using MATLAB Simulink.Synthesis results show that the implementation consumes 121 slice registers, and its maximum operating frequency is 1102.536MHz.Throughput achieved by this implementation is 14.1125 Gbps.
Lightweight Dynamic Crypto Algorithm for Next Internet Generation

Modern applications, especially real time applications, are hungry for high-speed end-to-end transmission which usually conflicts with the necessary requirements of confidential and secure transmission. In this work, a relatively fast, lightweight and attack-resistant crypto algorithm is proposed. The algorithm is a symmetric block cipher that uses a secure pre-shared secret as the first step. Then, a dynamic length key is generated and inserted inside the cipher text. Upon receiving the cipher text, the receiver extracts the key from the received cipher text to decrypt the message. In this algorithm, ciphering and deciphering are mainly based on simple XoR operations followed by substitutions and transpositions in order to add more confusion and diffusion to the algorithm. Experimental results show faster encryption/decryption time when compared to known encryption standards.
Transmission of images by unmanned underwater vehicles

As an acoustic communications medium, water is characterized by frequency dependent attenuation, short range, very low bandwidth, scattering, and multi-path. It is generally difficult to acoustically communicate even terse messages underwater much less images. For the naval mine counter-measures mission, there is value in transmitting images of mine-like objects, acquired by side-scan sonar on-board unmanned underwater vehicles, to the above-water operator for review. The contribution of this paper is a methodology and implementation, based on vector quantization, to compress and transmit snippets of side-scan sonar images from underway unmanned underwater vehicles to an operator. The work has been validated through controlled indoor tank tests and several at-sea trials. The fidelity of the received images is such that trained operators can recognize targets in the received images as well as they would have in the original images. Future work investigates machine learning to improve the compression basis and psycho-visual studies for the specialized skill of feature recognition in sonar images.
Doing autoethnography of social robots: Ethnographic reflexivity in HRI

Abstract Originating from anthropology, ethnographic reflexivity refers to ethnographers’ understanding and articulation of their own intervention to participants’ activities as innate study opportunities which affect quality of the ethnographic data. Despite of its methodological discordance with scientific methods which minimize researchers’ effects on the data, validity and effectiveness of reflexive ethnography have newly been claimed in technology studies. Inspired by the shift, I suggest potential ways of incorporating ethnographic reflexivity into studies of human-robot social interaction including ethnographic participant observation, collaborative autoethnography and hybrid autoethnography. I presume such approaches would facilitate roboticists’ access to human conditions where robots’ daily operation occurs. A primary aim here is to fill the field’s current methodological gap between needs for better-examining robots’ social functioning and a lack of insights from ethnography, prominent socio-technical methods. Supplementary goals are to yield a nuanced understanding of ethnography in HRI and to suggest embracement of reflexive ethnographies for future innovations.
Robust Image Identification with DC Coefficients for Double-Compressed JPEG Images

In the case that images are shared via social networking services (SNS) and cloud photo storage services (CPSS), it is known that the JPEG images uploaded to the services are mostly re-compressed by the providers. Because of such a situation, a new image identification scheme for double-compressed JPEG images is proposed in this paper. The aim is to detect a single-compressed image that has the same original image as the double-compressed ones. In the proposed scheme, a feature extracted from only DC coefficients in DCT coefficients is used for the identification. The use of the feature allows us not only to robustly avoid errors caused by double-compression but also to perform the identification for different size images. The simulation results demonstrate the effectiveness of the proposed one in terms of the querying performance.
Genetic Algorithm-Based Sensor Allocation With Nonlinear Centralized Fusion Observable Degree

As the main performance self-evaluation index of the Kalman filter, the estimation error covariance (EEC) has been used to design the allocation cost function of task and resources for sensor tracking networks. For nonlinear systems, the sensor allocation method based on the EEC needs to adjust the allocation plans after obtaining the filtering results. Meanwhile, recent investigations have indicated that the self-evaluation function EEC of the Kalman filtering is universally inapplicable in practical applications, for which the estimation models are generally mismatched due to difficulty in accurately training parameters and approximation of nonlinear systems. Thereby, the sensors cannot be properly allocated by using the EEC as a preliminary criterion. Alternatively, observable degree (OD) is a naturally quantitative measure on observability and can be utilized to effectively measure the estimation performance. In this paper, the OD analysis with scale transform invariance for nonlinear systems is studied by using the unscented Kalman filter, the pseudostate transition matrix, and the pseudo observation matrix on the basis of the results of linear systems. Afterward, the OD of nonlinear fusion systems, the sensor utilization efficiency, the priority of tasks, and the sensor performance and sensitivity are jointly considered to formulate the optimization problem for sensor allocation. The genetic algorithm with intelligent learning function is employed to solve the optimization problem. Moreover, extensive simulation demonstrates the feasibility of the proposed approach.
The Titans at SemEval-2019 Task 6: Offensive Language Identification, Categorization and Target Identification

This system paper is a description of the system submitted to "SemEval-2019 Task 6", where we had to detect offensive language in Twitter.There were two specific target audiences, immigrants and women.The language of the tweets was English.We were required to first detect whether a tweet contains offensive content, and then we had to find out whether the tweet was targeted against some individual, group or other entity.Finally we were required to classify the targeted audience.
Mastodon Content Warnings: Inappropriate Contents in a Microblogging Platform

Our social communications and the expression of our beliefs and thoughts are becoming increasingly mediated and diffused by online social media. Beyond countless other advantages, this democratization and freedom of expression is also entailing the transfer of unpleasant offline behaviors to the online life, such as cyberbullying, sexting, hate speech and, in general, any behavior not suitable for the online community people belong to. To mitigate or even remove these threats from their platforms, most of the social media providers are implementing solutions for the automatic detection and filtering of such inappropriate contents. However, the data they use to train their tools are not publicly available.In this context, we release a dataset gathered from Mastodon, a distribute online social network which is formed by communities that impose the rules of publication, and which allows its users to mark their posts inappropriate if they perceived them not suitable for the community they belong to. The dataset consists of all the posts with public visibility published by users hosted on servers which support the English language. These data have been collected by implementing an ad-hoc tool for downloading the public timelines of the servers, namely instances, that form the Mastodon platform, along with the meta-data associated to them. The overall corpus contains over 5 million posts, spanning the entire life of Mastodon. We associate to each post a label indicating whether or not its content is inappropriate, as perceived by the user who wrote it. Moreover, we also provide the full description of each instance. Finally, we present some basic statistics about the production of inappropriate posts and the characteristics of their associated textual content.
[Paper] Systems for Supporting Deaf People in Viewing Sports Programs by Using Sign Language Animation Synthesis

In this paper, we propose display systems for supporting deaf and hard of hearing people in viewing sports programs by using Japanese Sign Language (JSL) animation synthesis. The synthesis can automatically produce JSL CG animation from live sports data during a game. We improved the synthesis to make sports-specific collocated motions by compounding several word motions. Utilizing the improved synthesis, we developed three prototype systems for displaying JSL CG animation and live sports video simultaneously: a web browser-based system, tablet application-based system and a tablet & TV system. We carried out a series of experiments to evaluate these systems by using real-time data from actual games, and the tablet & TV system was most preferred.
Online social network security awareness: mass interpersonal persuasion using a Facebook app

Purpose Online social network (OSN) users have a high propensity to malware threats due to the trust and persuasive factors that underpin OSN models. The escalation of social engineering malware encourages a growing demand for end-user security awareness measures. The purpose of this paper is to take the theoretical cybersecurity awareness model TTAT-MIP and test its feasibility via a Facebook app, namely social network criminal (SNC). Design/methodology/approach The research employs a mixed-methods approach to evaluate the SNC app. A system usability scale measures the usability of SNC. Paired samples t -tests were administered to 40 participants to measure security awareness – before and after the intervention. Finally, 20 semi-structured interviews were deployed to obtain qualitative data about the usefulness of the App itself. Findings Results validate the effectiveness of OSN apps utilising a TTAT-MIP model – specifically the mass interpersonal persuasion (MIP) attributes. Using TTAT-MIP as a guidance, practitioners can develop security awareness systems that better leverage the intra-relationship model of OSNs. Research limitations/implications The primary limitation of this study is the experimental settings. Although the results testing the TTAT-MIP Facebook app are promising, these were set under experimental conditions. Practical implications SNC enable persuasive security behaviour amongst employees and avoid potential malware threats. SNC support consistent security awareness practices by the regular identification of new threats which may inspire the creation of new security awareness videos. Social implications The structure of OSNs is making it easier for malicious users to carry out their activities without the possibility of detection. By building a security awareness programme using the TTAT-MIP model, organisations can proactively manage security awareness. Originality/value Many security systems are cumbersome, inconsistent and non-specific. The outcome of this research provides organisations and security practitioners with a framework for designing and developing proactive and tailored security awareness systems.
Intercambios virtuales en educación superior: desarrollo de las habilidades interculturales a través de la colaboración en línea

Virtual exchange has been defined as a form of virtual mobility which aims to expand the reach and scope of traditional intercultural learning programs. This paper presents an example of a virtual exchange called InterCult - Intercultural Competences - which aimed to give an opportunity for students from Germany, France, and Brazil to explore intercultural aspects through online collaboration, i. e. to learn differences between own culture and other cultures by communicating and working on tasks together by using digital media. The research path was divided into three phases: project design, virtual exchange, and evaluation. The data collected during the virtual exchange involved the analysis of online conversations in international groups, face-to-face discussions during the classes at the end of each activity in national groups, the videos produced and shared in the online community, online meetings between the teachers, and the results of the online survey. Data were analyzed based on three perspectives: technology; engagement and collaborative work; intercultural competences. Results showed this type of experience is extremely important for a generation who will have to work in multicultural teams and contexts. Virtual exchange has been defined as a form of virtual mobility which aims to expand the reach and scope of traditional intercultural learning programs. This paper presents an example of a virtual exchange called InterCult - Intercultural Competences - which aimed to give an opportunity for students from Germany, France, and Brazil to explore intercultural aspects through online collaboration, i. e. to learn differences between own culture and other cultures by communicating and working on tasks together by using digital media. The research path was divided into three phases: project design, virtual exchange, and evaluation. The data collected during the virtual exchange involved the analysis of online conversations in international groups, face-to-face discussions during the classes at the end of each activity in national groups, the videos produced and shared in the online community, online meetings between the teachers, and the results of the online survey. Data were analyzed based on three perspectives: technology; engagement and collaborative work; intercultural competences. Results showed this type of experience is extremely important for a generation who will have to work in multicultural teams and contexts.
Fast-forward approach to adiabatic quantum dynamics of regular spin clusters: Nature of geometry-dependent driving interactions

The fast forward scheme of adiabatic quantum dynamics is applied to finite regular spin clusters with various geometries and the nature of driving interactions is elucidated. The fast forward is the quasi-adiabatic dynamics guaranteed by regularization terms added to the reference Hamiltonian, followed by a rescaling of time with use of a large scaling factor. With help of the regularization terms consisting of pair-wise and 3-body interactions, we apply the proposed formula (Phys. Rev.A 96, 052106(2017)) to regular triangle and open linear chain for N = 3 spin systems, and to triangular pyramid, square, primary star graph and open linear chain for N = 4 spin systems. The geometry-induced symmetry greatly decreases the rank of coefficient matrix of the linear algebraic equation for regularization terms. Choosing a transverse Ising Hamiltonian as a reference, we find: (1) for N = 3 spin clusters, the driving interaction consists of only the geometry-dependent pairwise interactions and there is no need for the 3-body interaction; (2) for N = 4 spin clusters, the geometry-dependent pair-wise interactions again constitute major part of the driving interaction, whereas the universal 3-body interaction free from the geometry is necessary but plays a subsidiary role. Our scheme predicts the practical driving interaction in accelerating the adiabatic quantum dynamics of structured regular spin clusters.
Improvement of the entanglement properties for entangled states using a superposition of number-conserving operations

The non-Gaussian operation, which can be easily implemented by current techniques, is an effective way for the improvement of the continuous-variable entanglement. Here, we theoretically propose a scheme for generating a number-conserving two-mode generalized superposition of products (TM-GSP) state by performing the -order GSP operations i.e. on an input two-mode squeezed vacuum (TMSV) state. Then, the entanglement properties of the TM-GSP state are analyzed detailedly by means of logarithmic negative, Einstein–Podolsky–Rosen (EPR) correlation and two-mode squeezing property. It is shown that, in contrast to the TMSV case, for the optimal choices of s1 and s2, the entanglement properties of the TM-GSP state can be improved by using symmetric GSP operations () and single-side GSP operations . Furthermore, the fidelity effect is also considered when the TM-GSP state is used as the entangled resource under the Braunstein and Kimble scheme. In terms of the optimal teleportation fidelity, it is found that the effect of the single GSP operation is equivalent to that of the case . This phenomenon also exists in logarithmic negative, EPR correlation and two-mode squeezing property. These results show that the prepared TM-GSP state may provide a well application in the fields of quantum information processing.