Reviewerly/exHarmony huggingface dataset:
Exploiting Recommender Systems in Collaborative Healthcare

With the development of new medical auxiliaries such as virtual reality and surgery robotics, recommender systems are emerged to interact with the medical auxiliaries and support doctor’s decisions and operations, especially in collaborative healthcare, recommender systems can interactively take into account the preferences and concerns from both patients and doctors. However, how to apply and integrate recommender systems is still not clear in collaborative healthcare. Therefore, from practical perspective this paper investigates the application of recommender systems in three typical collaborative healthcare domains, which are augmented/virtual reality, medicine and surgery robotics. The results not only provide the insights of how to integrate recommender systems with healthcare auxiliaries but also discuss the practical guidance of how to design recommender systems in collaborative healthcare.
Hybridization Cuckoo Search Algorithm for Extracting the ODF Maxima

BCSA has constantly attracted the interest of investigators from diverse disciplines worldwide since its introduction in 2009. This interest has led to various hybridizations for improving the performance of the basic BCSA. These hybridizations can improve MCSA and achieve favorable results.
Exploring Favorable Positions of Wearable Smart Sensors to Falls Detection: Smart Living for Elderly

With the ageing of the global population, fall detection of elderly become a prominent public health problem. Health service, academia and industry are desirous to develop a robust system for automatic falls detection in elderly's daily life, especially for the elderly who living alone. In this paper, we develop an optimize falls detection algorithm that initially investigates the problems of existing systems, and then collect numerous activities of daily living (ADLs) and fall events dataset by a self-defined method. The algorithm is deployed within a sensor system that uses a triaxial accelerometer and a triaxial gyroscope sensors to detect accidental falls. The aim of this paper is to explore the favorable body location for single-sensor wearable falls detection device to detect the falls for elderly. The developed sensor system is used to experiment on seven different locations of human body. The result is detailed evaluated by measuring sensitivity and specificity of algorithm applied in each experimented sensor locations.
An improved motion pedestrian tracking algorithm based on CamShift

Target detection, recognition and tracking are particularly important in intelligent driving. With the development of artificial neural network research, the recognition and tracking algorithm based on neural network has been greatly improved in recognition speed and accuracy. But its performance depends greatly on the training database, and the amount of calculation is too large to meet the real-time requirements. The tracker based on correlation filtering is fast, but it will lose the target once the target is slightly occluded. In this paper, we proposed an improved algorithm based on CamShift and Kalman filter. Through the prediction function of Kalman filter, reduce the range of search window of CamShift. Then the target information obtained by CamShift algorithm is fed back to Kalman filter for updating and correction. The optimization algorithm not only satisfies the real-time requirement of video target tracking, but also improves the tracking accuracy even if the target is overshadowed.
Generating 3D Person Trajectories from Sparse Image Annotations in an Intelligent Vehicles Setting

This paper presents an approach to generate dense person 3D trajectories from sparse image annotations on-board a moving platform. Our approach leverages the additional information that is typically available in an intelligent vehicle setting, such as LiDAR sensor measurements (to obtain 3D positions from detected 2D image bounding boxes) and inertial sensing (to perform ego-motion compensation). The sparse manual 2D person annotations that are available at regular time intervals (key-frames) are augmented with the output of a state-of-the-art 2D person detector, to obtain frame-wise data. A graph-based batch optimization approach is subsequently performed to find the best 3D trajectories, accounting for erroneous person detector output (false positives, false negatives, imprecise localization) and unknown temporal correspondences. Experiments on the EuroCity Persons dataset show promising results.
Research on Safe Distance Measuring Method of Front Vehicle in Foggy Environment

In order to solve the problem of rear-end collision on the road in foggy environment, this paper constructs an experimental platform to process the vehicle foggy images rapidly and measure distance of front vehicles in real-time. The existing studies on fog removal algorithm is directly applied to road images. This is easy to cause many problems, such as low brightness of the near road surface area and the far sky area, the degree of fog removal in the middle area is low and the real-time performance is poor. So, we first down sample the image based on the dark channel principle to improve the real-time performance of the algorithm. Second, we introduce a tolerance mechanism to deal with the bright regions that do not satisfy the dark channel prior. This tolerance mechanism corrects the misestimated refractive index of such a region and effectively solves problems of the color distortion and the low contrast. Then, we complete the vertical edges detection of the vehicle by using the ED algorithm and the improved Hough transform. Finally, we measure the safe distance of front vehicles by the measuring model. The result shows that the platform constructed in this paper is simple, effective and high real-time performance. And the details of traffic images are clearer. The maximum measured value of the front vehicle distance in foggy environment is increased. The platform we constructed can solve the rear-end problem in foggy environment effectively.
The Designing of Website-Based Learning Media as an Alternative for Online Learning for Student Practicing at SMKN 1 Sawoo Ponorogo

Abstract SMKN 1 Sawoo is a vocational high school that requires each student at a certain class level to do an internship. These activities require students to carry out these activities outside of school and of course the learning process does not stop completely. Therefore, we need a learning media that can help teachers in online learning. So that the learning process can continue. One of them uses website-based learning media. The website-based learning media at SMKN 1 Sawoo aims to help teachers and students in carrying out teaching and learning activities remotely. The research method used is the waterfall method. The media needed is a learning media website that can be used to deliver material and conduct learning evaluations. The material that can be delivered is of course multimedia based, which in addition to text can be in the form of images, videos and audio. In addition to being used for delivering material, this online learning media website can be used as a medium to do assignments given by each subject teacher.
Implementation of Cryptocurrency Trading on Marketplace

Abstract The purpose of this study to identify of trading cryptocurrency on Marketplace with the intention that users can understand the trading marketplace easily and quickly based on the stages of trading that are recommended in the system in this study. The method that used in this research was a descriptive method to find out a complete description of the variables with related situations. The result of this research is to find out about the implementation of trading on the marketplace, profits, risks, security and using features in the marketplace. This research will discuss how to trade cryptocurrency on the x marketplace, profit, risk, security and use the tools in the marketplace. The conclusion in this study is the existence of a system that has such completeness Interface, website simplicity, security features, clear office and team, regulation of legal entities, responsive customer service, speed of deposit or withdrawal of a Marketplace affects Trader to trade in the marketplace.
Activating Audiences: How spatial music can help us to listen

This article discusses the importance of active listening when engaging new audiences with experimental and unfamiliar musical formats. Spatial music is examined as a physically immersive medium in which the audience is implicated as an active component in the execution of its performance. A brief account of the historic context of spatial music will be presented alongside speculation for the trajectory of its future; particularly its potential as a model for audience engagement. This article will first consider how spatially immersive performances have the capacity to activate listeners and how can this help to engage new audiences with new ways of listening. It will also question the notion of inhabiting spatial music, with an investigation of the multiple ways in which spatial music relates to physical space and the terms of its inhabitation. The concept of virtual listening will be discussed in response to trends towards passive hearing , as driven by recent technological developments in acoustic software and hardware, and the resultant abstraction of the spatial and social dynamics of sound in virtual space. The physiological and psychological differences between listening and hearing will also be examined as a means of establishing fundamental differences in the ways that we interact with music, and questioning what our listening habits tell us about audience engagement in the context of experimental music performance. This article will also question the individual roles of the musician, composer, architect/designer and audience in the ongoing responsibility to improve audience engagement in new, or unfamiliar musical works. Importantly, this article will also explicitly examine who we are referring to when we use the term ‘new audiences’. Major developments in acoustic technology during the last few decades have somewhat confused the diagram between music, space and listener. The understanding of which elements are active and which are passive is especially ambiguous at a time when ambisonic and binaural technologies have become developed enough to provide accurate simulations of the abstract, acoustic qualities of spaces, but on virtual terms. Architects, composers, musicians, engineers and audiences are at a crossroads in the development of new music and experimental, spatiosonic practice. ‘Spatiosonic’ is a hybrid term which is used throughout this article to describe work and phenomena which regard space (spatio) and sound (sonic) as equal, interactive partners. This article considers some of the opportunities and limitations at stake in current techniques of composition, performance and listening.
Angry Noise

Critical work in music education has interrogated Eurocentric values and representational systems in music curricula, as well as idealized constructions of musicianship grounded in Western aesthetic practices. In this chapter we argue, further, that inherited Eurocentric music pedagogies enact a “distribution of the sensible,” a self-evident regime of sense perception prefiguring what is hearable and doable and possible, inviting students, through the very processes of music education itself, to ultimately disqualify themselves from vibrant and vital relations to musical worlds and sound-making practices. We argue that dominant forms of music education do not simply mobilize Eurocentric values and genres but are driven, fundamentally, by “talent regimes” – disciplinary systems freighted with aesthetic presuppositions, ableist epistemologies, and pedagogical operations that interpellate students into gendered roles and classist relations to sounds, and that tacitly disqualify bodies based on “self-evident” disabilities and perceived developmental “inadequacies.” At a time when arts education is increasingly enrolled in the service of neoliberalized discourses of “creativity” and “play,” we present counter-models that not only work to liberate music education from the constrictions of bourgeois aesthetics and commodity relations, but help to situate aural/sound/music learning experiences as a fundamental and necessary mode of sensory inquiry for young people’s learning. Drawing on work in critical disability and technology studies, we conclude with an exploration of counter-models for alternative and more equitable pedagogical orientations to materials-centric inquiry, as well as opportunities for learning through soundwork composition – models that can, we argue, maximize the creative capacities of anyone and everyone.
A Computational Investigation of Breast Tumour on Mammogram Based on Pattern of Grey Scale Distribution

Breast cancer is the utmost female tumor and the primary cause of deaths among female. Computer-Aided Detection (CAD) systems are widely used as a tool to detect and classify the abnormalities found in the mammographic images. A detection of breast tumor in a mammogram has been a challenge due to the different intensity distribution which leads to the misdiagnosis of breast cancer. This research proposes a dectection system that is capable to detect the presence of mass tumor from a mammogram image. A total of 160 mammogram images are acquired from Mammographic Image Analysis Society (MIAS) databse, which are 80 normal and 80 abnormal images. The mammogram images are rescaled to 300 x 300 resolution. The noise in the mammogram is suppressed by using a Wiener filter. The images are enhanced by using Power Law (Gamma) Transformation, ɣ = 2 for a better image quality. The greyscale information that contain tumor mass is extracted and used to model the proposed detection system by using 80% or 128 and of the total 160 mammogram images. The rest 20% or 32 mammogram images are used to test the performance of the proposed detection system. The experimental results show that performance of the proposed detection system has 90.93% accuracy.
V-Disparity Based Obstacle Avoidance for Dynamic Path Planning of a Robot-Trailer

Structured space exploration with mobile robots is imperative for autonomous operation in challenging outdoor applications. To this end, robots should be equipped with global path planners that ensure coverage and full exploration of the operational area as well as dynamic local planners that address local obstacle avoidance. The paper at hand proposes a local obstacle detection algorithm based on a fast stereo vision processing step, integrated with a dynamic path planner to avoid the detected obstacles in real-time, while simultaneously keeping track of the global path. It considers a robot-trailer articulated system, based on which the trailer trace should cover the entire operational space in order to perform a dedicated application. This is achieved by exploiting a model predictive controller to keep track of the trailer path while performing stereo vision-based local obstacle detection. A global path is initially posed that ensures full coverage of the operational space and during robot’s motion, the detected obstacles are reported in the robot’s occupancy grid map, which is considered from a hybrid global and local planner approach to avoid them locally. The developed algorithm has been evaluated in a simulation environment and proved adequate performance.
Viability of joined flight for small unmanned aerial vehicles

ABSTRACT The range of small, electrically powered UAVs is still limited by the mass specific energy of batteries. This paper investigates the idea that, in cases where multiple aircraft must transit to the same location, savings in mass or an extension of achievable range are possible when they join wingtip-to-wingtip. The viability of joined flight is investigated by quantifying the relative magnitude of savings resulting from increased aerodynamic efficiency and that of penalties due to the increased structural and component weights. Through a parametric analysis the level of savings achievable is found to be greatly dependent on the proportion of the flight spent in a joined configuration and aircraft design parameters such as wing loading, aspect ratio and the added weight of the joining mechanism. A custom, multidisciplinary UAV sizing algorithm is presented and utilised to design several sample aircraft, featuring two different joining mechanism architectures. The results verify the findings of the parametric study and indicate that mass savings are possible only for moderate to low aspect ratios, with semi-permanent magnetic joining mechanism performing better than rigid structural ones, even when the joined fight segment accounts for only 30% of the total airborne time.
Efficient Entity Authentication Using Modified Guillou–Quisquater Zero-Knowledge Protocol

Fiat and Shamir were the first ones to come up with the idea of proving a zero-knowledge scheme with the concept of knowledge about knowledge. However, their scheme required to be run for a considerable amount of time, so that the probability of a false claimant being correct is less. This meant greater computation cost. Taking inspirations and motivations from Fiat and Shamir, Guillou and Quisquater developed a scheme based on the same concept of knowledge about knowledge. Their scheme was based on the working of RSA, and it required to be run fewer number of times for attaining the same probability of a false claimant being correct. However, some shortcomings have been observed in Guillou–Quisquater’s scheme. In this work, we pointed out those shortcomings and tried to modify their scheme. The modified scheme is developed on the difficulty of Diffie–Hellman key exchange protocol. The proposed scheme also requires to be run for lesser number of rounds. We also performed the security analysis and performance analysis for the proposed scheme.
Mobile literacy among Syrian refugee women teachers

This research project investigates mobile literacy of Syrian refugee women teachers settled in Lebanon and Sweden. Our research provides input into Syrian refugee women teachers’ professional aspirations and their connection to informal mobile learning. In both countries, training programs are used for these newly arrived teachers, enabling them to move forward in their careers, where digital and mobile learning play an important part. The purpose is to investigate how Syrian refugee women teachers are blending their teaching profession and vocational training with mobile literacy and digital technology. A qualitative method approach was applied, interviewing 20 refugee women in Lebanon and Sweden, all teachers from Syria. The outcomes show that the teachers are developing their vocational abilities in getting more career-oriented training in their areas of education by means of enhancing their language skills through mobile technology.
Long distance entity relation extraction with article structure embedding and applied to mining medical knowledge

As a central work in medical knowledge graph construction, relation extraction has gained extensive attention in the fields of natural language processing and artificial intelligence. Conventional works on relation extraction share a common assumption: a sentence can express a relation of an entity pair only if both entities appear in this sentence. Under this assumption, plenty of informative sentences are precluded. In this paper, we break the assumption and propose a new relation extraction model that incorporates article structure information, which not only provide additional information, but also allows extracting long distance relations. We apply the model to online medical relation extraction and demonstrate its advantage over conventional models.
Video-Based Content Extraction Algorithm from Bank Cards for iOS Mobile Devices

This paper proposes an algorithm for information fields detection and recognition of bank cards in video sequences, obtained from the iOS mobile device camera. For this, we use two basic steps. The first step is preprocessing for localization of symbols. The second step is symbol block recognition using OCR system. Preprocessing algorithm includes card edge detection, information fields segmentation, segments enhancement, symbols edge emphasizing and the final step is symbol block recognition using OCR system. Based on our approach and iPhone SDK frameworks, OpenCV and Tesseract library the bank card details recognition software is implemented. For experiments our database of the real static images and video sequences was used.
Challenges in Deep Learning-Based Profiled Side-Channel Analysis

In recent years, profiled side-channel attacks based on machine learning proved to be very successful in breaking cryptographic implementations in various settings. Still, despite successful attacks even in the presence of countermeasures, there are many open questions. A large part of the research concentrates on improving the performance of attacks while little is done to understand them and even more importantly, use that knowledge in the design of more secure implementations. In this paper, we start by briefly recollecting on the state-of-the-art in machine learning-based side-channel analysis. Afterward, we discuss several challenges we believe will play an important role in future research.
A Practical Model for Collaborative Databases: Securely Mixing, Searching and Computing

We introduce the notion of a Functionally Encrypted Datastore which collects data anonymously from multiple data-owners, stores it encrypted on an untrusted server, and allows untrusted clients to make select-and-compute queries on the collected data. Little coordination and no communication is required among the data-owners or the clients. Our notion is general enough to capture many real world scenarios that require controlled computation on encrypted data, such as is required for contact tracing in the wake of a pandemic. Our leakage and performance profile is similar to that of conventional searchable encryption systems, while the functionality we offer is significantly richer. In more detail, the client specifies a query as a pair (Q, f) where Q is a filtering predicate which selects some subset of the dataset and f is a function on some computable values associated with the selected data. We provide efficient protocols for various functionalities of practical relevance. We demonstrate the utility, efficiency and scalability of our protocols via extensive experimentation. In particular, we evaluate the efficiency of our protocols in computations relevant to the Genome Wide Association Studies such as Minor Allele Frequency (MAF), Chi-square analysis and Hamming Distance.
System of Diagnosis of Acute Nazhopharingitis Using Artificial Neural Networks

This paper deals with the task of analyzing data for medical personnel and patients, namely, providing medical care to children and adults with acute respiratory viral infection (acute nasopharyngitis). Using the Python environment and supporting libraries for working with data in the PostgreSQL database management system, medical data analysis was performed. Subsequently, the patients were diagnosed. This process was carried out using medical card of clinical recommendations for providing medical care to children and adults with such disease. Methods of mathematical theory (mathematical statistics and regression analysis) were adopted and applied in specialized software as an experiment on one hand, while data analysis methods and algorithms were carried out in a free software on the other hand. To perform these tasks, a program was developed using the Python environment and the TensorFlow library. The analysis of the experimental studies results was carried out. The basic patterns of influence of patient diagnosis indicators on diagnosis, data structure parameters, graphical diagrams using the matplotlib library were identified, and the result was positive.