Reviewerly/exHarmony huggingface dataset:
Multi-intent Compatible Transformer Network for Recommendation

The core of recommendation systems is to explore users’ preferences from users’ historical records and accordingly recommend items to meet users’ interests. Previous works explore interaction graph to capture multi-order collaborative signals and derive high-quality representations of users and items, which effectively alleviates the interaction sparsity issue. Recent works extend the scope with a fine-grained perspective and achieve a great success in modeling users’ diverse intents. Although these works distinguish intents, they ignore the hidden correlation among users’ intents resulting in suboptimal recommendation performance. We argue that a user’s interest is made up of multiple intents and these intents are compatible on the interest composition of the user. To this point, we propose multi-intent compatible transformer network (MCTN) to explore the correlation between intents on modeling users’ interests for recommendation. Users and items are embedded into multiple intent spaces through disentangled graph convolution network to disentangle users’ intents. MCTN conducts embedding propagation in each intent space to capture the multi-order collaborative signals on the specific intent. We introduce a transformer network to capture the dependence between intents and derive multi-intent compatible embeddings of users and items for recommendation. The experiments achieves state-of-the-art performance, which demonstrates the effectiveness of the proposed MCTN on modeling multi-intent compatibility into embeddings.
MultiCon: An Efficient Timing-based Side Channel Attack on Shared Memory Multicores

Hardware sharing in multicore processors enables massive performance improvements but brings security challenges. Adversaries exploit shared hardware for timing-based side-channel attacks and leak secret information. This paper proposes a novel multi-channel timing-based side-channel attack (called MultiCon) that exploits aggregated timing variations on shared cache, network-on-chip (NoC), and memory controller hardware in a multicore processor. Base-line cache, memory controller and NoC hardware contention attacks show sufficient timing variations, but their overall efficacy in the presence of external noise reduces significantly. De-noising schemes, such as repetition codes increase the efficacy but at the cost of slowing down the attack. MultiCon uses aggregate timing variations of multiple shared hardware resources and is less sensitive to external noise. Our evaluation on a Tile-Gx72 processor shows that MultiCon is able to achieve higher efficacy in the presence of external noise, and is >3× faster than baseline single-channel attacks.
Sparse Peripheral Counter-vection Flow Visualization with Reverse Optical Flow for VR Sickness Reduction

Sickness is one of major obstacles in the wider adoption of virtual reality. Vection is attributed as the main cause, leading to sensory mismatch between the visual and vestibular senses. One possible measure to reduce the extent of such VR sickness is provide low res-olution peripheral “countervection” visualization as was suggested by [7]. In this poster, we present an extension/improvement to this work, where the countervection flow visualization is based on re-verse optical flow of the features in the scene rather than a projection of a moving strip patterns. We evaluated our extension and its effect in a pilot comparative experiment against the original countervection visualization method (simulated), and obtained positive results.
Artificial Fish Swarm Algorithm for the Optimization of a Benchmark Set of Functions

An efficient artificial fish swarm algorithm (AFSA) for the optimization of a benchmark set of functions is developed in this paper. An analysis of the principal parameters to effect on exploration or exploitation is presented. In this case, two important parameters are changed manually with the main idea to analyze the impact that S and V presents on the development of the AFSA algorithm. The performance and efficiency of the AFSA demonstrated that it is a good algorithm to solve benchmark sets of functions. The AFSA has proven to be successful in the implementation of various benchmarking optimization problems. A comparative with other metaheuristics is presented.
Behavioural Biometrics in Virtual Reality: To What Extent Can We Identify a Person Based Solely on How They Watch 360-Degree Videos?

Recent years have seen a growing number of research papers that address the possibility of identifying users in virtual reality (VR) based on behavioural data. The main thrust of these considerations is applied research towards designing elaborate VR user authentication methods that are resistant to “shoulder surfing”. This widespread approach relies heavily on requiring users to perform elaborate tasks, and fails to explore the more fundamental scientific question of whether users can be identified under conditions of unforced behaviour-regardless of whether the purpose of such identification is to aid authentication, or, conversely, to violate user privacy. In our study <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(N=43)$</tex> , we focused on examining the extent to which it is possible to identify the viewer of a previously unseen 360-degree VR video based on eye-tracking and VR headset orientation data that had been collected while the same viewer watched other 360-degree VR videos. We reached out to deep learning and classical statistical methodology and obtained a maximum average identification accuracy of 75%. Our results indicate that it is worthwhile to continue research efforts towards investigating the possibility of VR user identification under conditions of free, unforced behaviour.
Design and Simulated Annealing Algorithm Application Research in Computer Music Creation

Traditional music teaching has certain limitations, in order to better solve this problem, many schools began to introduce Moodle platform of music teaching system, in order to create a dynamic open learning environment for students. The platform is mainly based on Linux and Ubuntu operating systems, and the function Settings of each module are arranged according to the different needs of different users. At the same time, we propose an ARM chip and SA algorithm to solve the problem that it is difficult to evaluate the effect of music teaching, and it is difficult to put forward more specific and quantitative suggestions for the evaluators. The research results show that this system has certain application value in the actual music teaching process.
Interpreting Latent Spaces of Generative Models for Medical Images Using Unsupervised Methods

Generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) play an increasingly important role in medical image analysis. The latent spaces of these models often show semantically meaningful directions corresponding to human-interpretable image transformations. However, until now, their exploration for medical images has been limited due to the requirement of supervised data. Several methods for unsupervised discovery of interpretable directions in GAN latent spaces have shown interesting results on natural images. This work explores the potential of applying these techniques on medical images by training a GAN and a VAE on thoracic CT scans and using an unsupervised method to discover interpretable directions in the resulting latent space. We find several directions corresponding to non-trivial image transformations, such as rotation or breast size. Furthermore, the directions show that the generative models capture 3D structure despite being presented only with 2D data. The results show that unsupervised methods to discover interpretable directions in GANs generalize to VAEs and can be applied to medical images. This opens a wide array of future work using these methods in medical image analysis. The code and animations of the discovered directions are available online at https://github.com/julschoen/Latent-Space-Exploration-CT .
Efficiently and precisely searching for code changes with diffsearch

Version histories of code contain useful information and these data are public, thanks to open source software. However, searching through large repository histories can be complex, because there is no specific tool to search for code changes. This paper presents DiffSearch, the first efficient and scalable search engine for code changes. Given a list of repositories and a query, DiffSearch can retrieve specific code changes in a few seconds. We design a language-agnostic approach that we test on three popular programming languages: Java, JavaScript, and Python, and we design a query language that is an extension of the supported languages. We evaluate DiffSearch in three steps. First, we measure a recall of 81.8%, 89.6%, and 90,4% for Java, Python, and JavaScript, respectively, and an average response time lower than five seconds. Second, we demonstrate its scalability with a large dataset of one million code changes. Last, we perform a case study to show one of its possible applications, where DiffSearch gathers a dataset of 74,903 Java bug fixes.
Comparing Quantum Optimization Solvers for Rebalancing Analysis of Bike Sharing System

Smart Mobility is the key component of Smart City initiative that are being explored throughout the world. The bike-sharing system (BSS) aims to provide an alternative mode of Smart Mobility transportation system, and it is being widely adopted in urban areas. The use of bikes for short-distance travel helps to reduce traffic congestion, reduce carbon emissions, and decrease the risk of overcrowding. Effective bike sharing system operations requires rebalancing analysis, which corresponds to transferal of bikes across various bike stations to ensure the supply meets expected demand. A critical part for a bike sharing system operations is the effective management of rebalancing vehicle carrier operations that ensures bikes are restored in each station to its target value during every pick-up and drop-off operations. In this work, we compare the performance of two types of quantum optimization algorithms (Quantum Approximate Optimization Algorithm and Quantum Annealing) to evaluate performance of bike sharing rebalancing optimization. In this preliminary work, we compared the performance of QAOA on IBM-Qiskit and Quantum Annealing on two D-Wave solvers (QPU and hybrid solvers) for a case study involving rebalancing across three bike stations.
System Development for Analysis of Research Achievements and Publication on Web Site at J-PARC MLF

On 2016, we started a project to correct and analyze research achievements obtained with J-PARC MLF. Though there was a list of the achievement, it was not applicable to data processing for the analysis because the data format was NOT unified and dependent on the person. Hence, we define a sorting policy of the achievements first and re-collect the achievement even though it was already existing. Next, the achievements were compared with those of other facilities available online based on the quantity and quality analysis with the same index. Finally, the list and analysis results of the achievements of the MLF facility is now open in the web site and being updated every half a year.
TMD: Transformed Mesh Decoder for Mesh Animation

Easy and fast animation of 3D characters is attractive for both gaming and entertainment applications. 3D mesh must be properly rigged and skinned in order to create seamless animation. This process can be time consuming and requires deep knowledge of appropriate software based on kinematic animation. In this work, we present a fast and lightweight deep neural model to automate 3D human animation using skeletal representation from 2D image pose, i.e., joints in 2D space. We accomplish this using Transformed Mesh Decoder (TMD), which is a novel layer for convolutional neural networks. To train the network, we generate a large and diverse dataset using Skinned Multi-Person Linear (SMPL) model. Experiment shows that our method is effective when compared to both the ground truth and state-of-the art linear blend skinning that require manually painted skinning weights for accurate result. The animation process is fast and can achieve approximately 10-15fps in practice. The proposed method is simple which opens the possibility for future improvement in real-time application.
Do Additional Auditory Instructions in Smartphone Navigation Con-Tribute to the Road Safety of Cyclists? A Field Study Evaluating the Gaze Behavior of Cyclists

In this paper, we present an eye tracking study evaluating the effects of visual versus visual and auditory navigation instructions on the gaze behavior of cyclists. One group received purely visual navigation instructions via a smartphone on the handlebars to complete the route, while the other group additionally received auditory navigation instructions via headphones. We found that the distribution of attention depends strongly on the traffic situation. Comparing the attention distribution of the two groups over the five situations evaluated, we see that the purely visual group paid 31% less attention to the surroundings. In the complex situation, it turned out that purely visual navigation is preferable, since fewer glances are directed at the smartphone and thus greater attention can be paid to the surroundings. On the other hand, additional auditory instructions in less complex situations enable the cyclists to focus more on the surroundings.
The Multiple Faces of Cultural Heritage: Towards an Integrated Visualization Platform for Tangible and Intangible Cultural Assets

Linking and visualizing multiple types of entities in a DH knowledge graph generates the need to deal with multiple types of data and media modalities both on the designer and the user side. The InTaVia project develops synoptic visual representations for a multimodal historical knowledge graph which draws together transnational data about cultural objects and historical actors. In this paper we reflect on the question how to integrate and mediate the informational and visual affordances of both kinds of cultural data with hybrid designs and show how a user-centered design process can help to ground the required selections and design choices in an empirical procedure.
Towards Overcoming Type Limitations in Semantic Clone Detection

Currently available tools for semantic clone detection have serious type limitations related to objects, in particular related to instantiation and comparison. The trend in the recent research has focused on improving the performance rather than removing these constraints. This paper proposes a test-based approach for semantic clone detection in Java which overcomes these problems. The test-based approach consists in running two methods with the same input and comparing their outputs. For object instantiation, the approach uses EvoSuite, an automatic JUnit test generator, to create the tests with correct instantiations of all the needed classes, which are then modified to make them suitable for semantic clone detection. The output comparison uses the DeepHash function to obtain numerical values for objects, considering the values of each instance variable. The approach was evaluated with SemanticCloneBench. The results show that the proposed approach performs very well in terms of precision, achieving 94.4%, which is about 8% higher than the reported precision of SLACC, a recent semantic clone detector. Furthermore, out of 228 methods used for the evaluation, the approach was not able to perform the detection for only ten methods.
Underwater image imbalance attenuation compensation based on attention and self-attention mechanism

The scattering and absorption of light through water will lead to underwater images suffering from low contrast and color variations. With the difference belong wavelength, RGB channels obtained non-uniform information. Although many works for underwater image restoration through CNNs, the color distortions caused by imbalance attenuation have not been addressed in previous contributions. In this paper, we demonstrate that employing green and blue channels to support the red channel to extract more depth features is helpful for underwater image recovery tasks. Further, we discard the previous CNN-based model by proposing a new model based on attention and a self-attention mechanism called underwater restoration attention self-attention (URAS). Our pipeline has achieved better performance than other baseline models on the EUVP dataset.
Accurate Hippocampus Segmentation Based on Self-supervised Learning with Fewer Labeled Data

Brain MRI-based hippocampus segmentation is considered as an important biomedical method for prevention, early detection, and accurate diagnosis of neurodegenerative disorders like Alzheimer's disease. The recent need for developing accurate as well as robust systems has led to breakthroughs making advantage of deep learning, but requiring significant amounts of labeled data, which, in turn, is costly and hardly obtainable. In this work, we try to address this issue by introducing self-supervised learning for hippocampus segmentation. We devise a new framework, based on the widely known method of Jigsaw puzzle reassembly, in which we first pre-train using one of the unlabeled MRI datasets, and then perform a downstream segmentation training with other labeled datasets. As a result, we found our method to capture local-level features for better learning of anatomical information pertaining to brain MRI images. Experiments with downstream segmentation training show considerable performance gains with self-supervised pre-training over supervised training when compared over multiple label fractions.
Solar panel selection using an integrated analytical hierarchy process and multi-objective optimisation by ratio analysis: an empirical study

A solar panel is a vital element of a photovoltaic (PV) system. Hence, it is essential to study the reduction of material costs of a solar panel without compromising the efficiency of it. Selection of solar panel is a complex problem due to the presence of various quantitative and qualitative parameters. This paper deals with the development of a mathematical model by combining analytical hierarchy process (AHP) and multi-objective optimisation by ratio analysis (MOORA) for the evaluation and ranking of solar panels. The AHP method is utilised to fix compromise solution with incommensurable and contradictory criteria, consisting of five potential solar panels and eight selection parameters. The AHP technique is adopted for calculating the criteria weights based on the relative importance. Consequently, MOORA method is used for ranking and selecting the solar panel alternatives. The proposed AHP and MOORA approach is found to be effective for the evaluation and ranking of solar panels as it produces comparable results with VIKOR method. In the VIKOR method, alternatives are ranked based on closeness to positive ideal solution.
Intelligent Gaming Input Device for Tilt Recognition

Farcana has developed a smart a gaming input device, that, apart from being a tool for the gamer to use in the process of gameplay, is also a suitable tool to collect biomedical information about the gamer, which after analysis by the artificial intelligence (AI) system allows informing the gamer about whether the individual is in a state of tilt.Tilt itself is a poor emotional state of the individual that appears due to the latter's inability to control one's emotions in the process of gameplay.The gamer can be either winning or losing, yet the fact that he/she can neither control nor even acknowledge the emotional state is tilt.The latter is an immense factor of impact on the overall success of the individual in the sphere of gaming and one's rating in cybersport.This paper has analyzed numerous studies and patents on the topic at hand.The available literature has provided the necessary insight on the topic of tilt and why it is important to help the gamer acknowledge one's state, especially given deteriorating results.Also, we have proposed a framework for the AI system for tilt recognition.
Development of the Modified Method Based on Convolutional Neural Network of Cancer Cell Nucleus Image Localization

In Taiwan, health insurance payments for cancer treatment are determined based on the patient's recovery. After medical personnel obtains a patient's cell examination results, they can check the decrease in the number or atrophy of cancer cells in the patient through methods such as flow cytometry. Medical personnel generally use fluorescence microscopes to view and count the number of nuclei. However, this method is time-consuming, has a high error rate, and the inspection results are highly inconsistent. Previous studies used convolutional neural networks for cell nuclei localization, automatic counting, and micronucleus analysis to solve the aforementioned problems. However, convolutional neural networks (YOLOV4) are to mis-positions of small-scale dual-nucleus cell images. In this study, the image geometric analysis algorithm is proposed to solve this problem. Using this method, YOLOV4 is used to perform 20X optical magnification for small-scale cell nuclei image localization, and the proposed algorithm was modified to improve the accuracy of cell nuclei localization. To demonstrate small-scale nucleus image localization problems and verify the efficacy of the proposed modified method, the results of the localization of small-scale nucleus image of the YOLO and Faster R-CNN algorithms were compared. The proposed method is shown to correct cell nucleus localization errors. This paper describes the proposed method structure and process in the following sections.
Medical Fitness Device for Driving Assessment

This project is to check Medical Fitness for accidental Drivers and paralysed patients health condition using pressure and heart beat sensor with RTC unit. This can be helpful to analyze the accident person’s health improvement condition or periodic monitoring of that person. In hospitals, accidental patients’ body conditions (Leg and Hand) must be monitored constantly, which is typically done by doctors or other paramedical staff. And when the conditions go abnormal then we sense those values by Displaying in LCD and buzzer sound. The suitable parameters which should be monitored are first determined and also the respective sensors are connected to the patient`s body and periodically checked by a PIC microcontroller. And these parameters have fixed threshold levels which are monitored and also the parameters are transmitted to a LCD for continues monitoring purpose.
An Anomaly Detection Framework Based on Data Lake for Medical Multivariate Time Series

Anomaly detection of multivariate time series has become a critical task in the medical domain. With the quick development of medical devices, a large amount of multi-source heterogeneous multivariate time series data is produced. Traditional data platforms have difficulties to organize and explore these data. In addition, the high dimensionality of multivariate time series also makes it difficult for the detection process to capture the correlation between different features. In this paper, we propose an anomaly detection framework based on data lake for medical multivariate time series. Firstly, heterogeneous data are fused to provide a big wide table. Then we help the doctors to explore data, filter out related features and get a filtered dataset. To reduce redundant or noisy data in the filtered dataset, we refine it with Relief algorithm. Finally, we identify anomalies through a multi-scale convolutional recurrent encoder-decoder. Experiments on a synthetic dataset and a use case of heart sound recordings confirm the validity of our framework.