Reviewerly/exHarmony huggingface dataset:
Unlock A Device with Pressure and Rhythm Based Password

When people open their doors, they usually use the keys they carry in their pockets. As an alternative to these used keys, it is possible to open the doors which are authorized with RFID-enabled key holders or ID cards. But it seems that different alternatives have not been developed much. Moreover, none of them carries the sensory understanding dimension of the person and it is not possible to determine whether or not the entrance request has been made by that person. In this study, both the identification of the person and the opening of the door are ensured by considering both the stroking rhythm and the stroke force of a buttoner placed in the door. Thus, it is possible to open the door according to the person's identity and authority without carrying any object beside it, and it will have a unique original value. The milliseconds between each stroke for the stroke of the quill are recorded in the generated database. Comparison is made with the data obtained in the next stroke and the person is used for verification. In addition, the pressure data of the person is also kept in the database for each stroke and compared. In the study, the data of two different people were recorded in the system and the information of the people using the subjective button was recorded. Then, in addition to these two different people, a total of 15 people, including 13 different people, were allowed to use the system. In total, 50 door opening requests data were recorded and 87.5% success rate was obtained.
Hierarchical multi-agent deep reinforcement learning to develop long-term coordination

Multi-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions to coordinate lie outside of the communication range. We also show that a single RNN is unable to do this if there is an independent action sequence necessary before the coordinated action can be executed. We propose a hierarchical deep reinforcement learning model for multi-agent systems that separates the communication and coordination task from the action picking through a hierarchical policy. As a testbed, we propose the Dungeon Lever Game and we extend the Differentiable Inter-Agent Learning (DIAL) framework [3]. First we prove that the agents need a hierarchical policy to learn communication and actions and, second, we present results from our successful model of the Dungeon Lever Game.
Software Cost Estimation

Software cost estimation (SCE) is a critical phase in software development projects. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. There are several techniques for handling missing data in the context of SCE. The purpose of this article is to show a state-of-art statistical and visualization approach of evaluating and comparing the effect of missing data on the accuracy of cost estimation models. Five missing data techniques were used: multinomial logistic regression, listwise deletion, mean imputation, expectation maximization and regression imputation; and compared with respect to their effect on the prediction accuracy of a least squares regression cost model. The evaluation is based on various expressions of the prediction error. The comparisons are conducted using statistical tests, resampling techniques and visualization tools like the regression error characteristic curves.
Gabor Wavelet Transformation on the Sphere and Its Related Topic

We studied the Gabor wavelet transform of analytic functionals on the sphere in general dimension. Then, we studied the Gabor wavelet transformation on the two-dimensional sphere and its inverse transformation. In this note, following our previous results, to understand the Gabor wavelet transformation on the sphere, we consider some Gabor wavelet transforms of analytic functionals and square integrable functions on the sphere.
How to implement a big data clustering algorithm

The current era of Big Data [7] has forced both researchers and industries to rethink the computational solutions for analyzing massive data. In fact, a great deal of attention has been devoted to the design of new algorithms for analyzing information available from Twitter, Google, Facebook, and Wikipedia, just to cite a few of the main big data producers. Although this massive volume of data can be quite useful for people and companies, it makes analytical and retrieval operations really time consuming due to their high computational cost. A possible solution relies upon the possibility to cluster big data in a compact but still informative version of the entire data set. Obviously, such clustering techniques should produce clusters (or summaries) having high accuracy. Clustering algorithms could be beneficial in several application scenarios such as cybersecurity, user profiling and recommendation systems, to cite a few.
Effects of Skip-Connection in ResNet and Batch-Normalization on Fisher Information Matrix

Deep neural networks such as multi-layer perceptron (MLP) have intensively been studied and new techniques have been introduced for better generalization ability and faster convergence. One of the techniques is skip-connections between layers in the ResNet and another is the batch normalization (BN). To clarify effects of these techniques, we carried out the landscape analysis of the loss function for these networks. The landscape affects the convergence properties where the eigenvalues of the Fisher Information Matrix (FIM) plays an important role. Thus, we calculated the eigenvalues of the FIMs of the MLP, ResNet, and ResNet with BN by applying functional analysis to the networks with random weights, which of MLP was analyzed before in asymptotic case using the central limit theorem. Our results show that the MLP has eigenvalues that are independent of its depth, that the ResNet has eigenvalues that grow exponentially with its depth, and that the ResNet with BN has eigenvalues that grow sub-linear with its depth. These imply that the BN allows the ResNet to use larger learning rate and hence converges faster than the vanilla ResNet.
Trends in CASHL's document delivery service in China

This paper aims to discuss the features and development trends of China Academic Social Sciences and Humanities Library's (CASHL) internet-based document delivery service, examine the journal usage patterns of CASHL member libraries and determine the time range trends of documents requested by users. Ten years of the CASHL's document delivery service transaction data (about 860,000 items) were extracted, cleaned, integrated, and analysed. Journal use pattern is more decentralised and individualised. Request rates for older papers are continuing to increase. The different types of member libraries have large differences in terms of research requests and use.
Digitally Augmenting the Physical Ground Space with Timed Visual Cues for Crutch-Assisted Walking

This late-breaking work presents initial results regarding a novel mobile-projection system, aimed at helping people to learn how to walk with crutches. The existing projection-based solutions for gait training disorders are based on walking over a fixed surface (usually a treadmill). In contrast, our solution projects visual cues (footprints and crutch icons) directly into the floor, augmenting the physical space surrounding the crutches, in a portable way. Walking with crutches is a learning skill that requires continuous repetition and constant attention to detail to make sure they are being used correctly, avoiding negative consequences, such as falls or injuries. We conducted expert consultation sessions, and we identified the main issues that patients face when walking with crutches. This informed the design of Augmented Crutches. We performed a qualitative evaluation and conclude with design implications: the importance of timing, self-assurance and awareness.
Go for GOLD

Building adaptive support systems requires a deep understanding of why users get stuck or face problems during a goal-oriented task and how they perceive such situations. To investigate this, we first chart a problem space, comprising different problem characteristics (complexity, time, available means, and consequences). Secondly, we map them to LEGO assembly tasks. We apply these in a lab study (N=22) equipped with several tracking technologies (i.e., smartwatch sensors and an OptiTrack setup) to assess which problem characteristics lead to measurable consequences in user behaviour. Participants rated occurred problems after each task. With this work, we suggest first steps towards a) understanding user behaviour in problem situations and b) building upon this knowledge to inform the design of adaptive support systems. As a result, we provide the GOLD dataset (Goal-Oriented Lego Dataset) for further analysis.
Eye Tracking Methodology in Screen-based Usability Testing

Eye tracking is an important tool in usability testing of a screen-based user interface. Though eye tracking has been used in usability testing for quite a while, challenges remain. For example, how to accurately calibrate gaze point? How to interpret a scan pattern? In this tutorial, we will introduce the basics of the human oculomotor system, the role of eye tracking in cognition, eye tracking recording techniques, and data analysis methods. Upon the completion of this tutorial, students will have a basic understanding of physiological and psychological mechanisms underlying eye tracking, data collection techniques, and data analysis methods.
Performance Analysis of Multi-Level HAAR in Background Removal for Object Detection

Objective: This study proposes performance improvement in speed of multi-level HAAR processed images for object detection.Method: Background subtraction algorithm is implemented using phase as a feature to reduce illumination variation.The algorithm is implemented on level 2 and level 3 HAAR compressed images.Simulation results are obtained on kit ware database.Findings: Simulation results show that object detection is faster in level 3 HAAR compressed images as compared to level 2 and level 1 HAAR compressed images.Average time required for processing single frame is in range of 6.53 to 29.22 ms in level 3 while that in level 2 is 6.65 to 36.46 ms.Improvement: Using this approach saving of 5% to 22% of processing time is observed at level 2 of HAAR while a saving of 9% to 48% of time is observed at level 3 of HAAR.
Physical Burden on Caregivers Pushing Wheelchairs

Population aging is progressing rapidly in Japan. Amid this climate, while the numbers of older people are increasing, the population of people at a physically productive age and capable of providing nursing care is decreasing, bringing about a shortage of caregivers. Wheelchairs remain indispensable devices for supporting the mobility of older people who have weakened muscular strength. Research on improving the main body of wheelchairs and supporting passengers has been conducted for many years; however, there has been little research on caring for the burden of caregivers pushing wheelchairs. In the present study, we quantitatively evaluated this burden in terms of the posture assumed in pushing a wheelchair. Through this, we propose a method of reducing this burden.
Adaptive Resonance Theory (ART) for Social Media Analytics

ThisTheory chapter presents the ART-based clusteringClustering algorithmsAdaptive resonance theory forAlgorithm social mediaSocial media analyticsResonance in detail. Sections 3.1 and 3.2 introduce Fuzzy ARTFuzzy adaptive resonance theory|( and its clusteringClustering mechanisms, respectively, which provides a deep understanding of the base model that is used and extended for handling theSocial media social mediaMedia clusteringClustering challenges. Important concepts such as vigilanceVigilance|( regionVigilance Regions (VRs) (VR)Region and its properties are explained and proven. Subsequently, Sects. 3.3–3.7 illustrate five types of ARTART adaptive resonance theory variantsVariants, each of which addresses the challenges in oneSocial media social media analyticalAnalytics scenario, including automated parameterParameter adaptationParameter adaptation, userUser preference preferencePreference incorporation, short textText clustering clusteringClustering, heterogeneousHeterogeneous dataData co-clusteringCo-clustering and online streaming dataData indexingStreaming data. The content of this chapter is several priorPrior studies, including Probabilistic ARTProbabilistic fusion adaptive resonance theory [15] (©2012 IEEE. Reprinted, with permission, from [15]), GeneralizedGeneralized HeterogeneousGeneralized heterogeneous fusion adaptive resonance theory FusionFusion ART [20] (©2014 IEEE. Reprinted, with permission, from [20]), Vigilance AdaptationVigilance adaptation ARTVigilance adaptation ART [19] (©2016 IEEE. Reprinted, with permission, from [19]), andOnline multimodal co-indexing OnlineMultimodal co-indexing Multimodal Co-indexingCo-indexing ARTOnline multimodal co-indexing adaptive resonance theory [17] ( http://dx.doi.org/10.1145/2671188.2749362 ).
A hierarchically trained generative network for robust facial symmetrization

Face symmetrization has extensive applications in both medical and academic fields, such as facial disorder diagnosis. Human face possesses an important characteristic, which is as known as symmetry. However, in many scenarios, the perfect symmetry doesn't exist in human faces, which yields a large number of studies around this topic. For example, facial palsy evaluation, facial beauty evaluation based on facial symmetry analysis, and many among others. Currently, there are still very limited researches dedicated for automatic facial symmetrization. Most of the existing studies only utilized their own implantations for facial symmetrization to assist their interdisciplinary academic researches. Limitations thus can be noticed in their methods, such as the requirements for manual interventions. Furthermore, most existing methods utilize facial landmark detection algorithms for automatic facial symmetrization. Though accuracies of the landmark detection algorithms are promising, the uncontrolled conditions in the facial images can still negatively impact the performance of the symmetrical face production. To this end, this paper presents a joint-loss enhanced deep generative network model for automatic facial symmetrization, which is achieved by a full facial image analysis. The joint-loss consists of a pair of adversarial losses and an identity loss. The adversarial losses try to make the generated symmetrical face as realistic as possible, while the identity loss helps to constrain the output to have the same identity of the person in the original input as much as possible. Rather than an end-to-end learning strategy, the proposed model is constructed by a multi-stage training process, which avoids the demand for a large size of the symmetrical face as training data. Experiments are conducted with comparisons with several existing methods based on some of the most popular facial landmark detection algorithms. Competitive results of the proposed method are demonstrated.
Efficient VHDL Implementation of an Upscaling Function for Real Time Video Applications

This paper describes the design and the implementation of a high-performance area-efficient upscaling function on a FPGA. The proposed function performs the resizing of real time video images, from M × N pixels to 2M × 2N pixels, using a bilinear interpolation method. The hardware implementation has been carried out using an RTL structural description in VHDL, to maximize the use of the specific resources of the FPGA, especially its memory resources (BRAM). For this purpose, an optimized memory access schema, that allows the best performance of the architecture with the least use of hardware resources, is proposed. Details of the design and the implementation on a Zynq UltraScale+ MPSoC are given, as well as speed-performance and area utilization. The proposed solution significantly improves the results achieved using this schema compared with those obtained using the Resize function from the Xilinx OpenCV library, when both are configured to obtain the same spatial transformation.
A Hardware Implementation of Colorectal Tumor Classification for Endoscopic Video on Customizable DSP Toward Real-Time Computer-Aided Diagnosis System

This paper presents a hardware implementation of a computer-aided diagnosis system that can classify lesions in real time for endoscopic video image. Unlike still images, video images contain many color shifts, blurs, reflections, and so on, a classification method that takes these noises into account is indispensable. In this paper, in the framework of Bag-of-Feature (BoF), we use a pre-learned convolutional Neural Network (CNN) with general images as feature extraction and Support Vector Machine (SVM) as classification. A robust classification method for endoscopic video is proposed by relearning SVM with several sets of endoscopic images. In addition, since CNN and SVM need to perform many multiplication and accumulation (MAC) operations, we implement the hardware system on a customizable DSP, which can be realize at high speed MAC operations and parallel processing with Very Long Instruction Word (VLIW). The prototyped system can be achieved real-time processing on video frame rate (30 fps @ 200 MHz). The proposed system enables not only to classify the presence or absence of cancer in the lesion but also to identify the progress of cancer and shows the clinical doctor as an objective indicator of the lesion in real time during the endoscopy examination.
On the Self-organizing Migrating Algorithm Comparison by Means of Centrality Measures

In this article we continue in our research which combines three different areas - swarm and evolutionary algorithms, networks and coupled map lattices control. Main aim of this article is to compare networks obtained from best and worst self-organizing migrating algorithm runs. All experiments were done on well known CEC 2014 benchmark functions. For each selected function we picked 30 best and 30 worst runs, converted each run into a network, counted selected properties and compared the results. All obtained results are reported in this article.
Compression of Nanopore FASTQ Files

The research and development of tools for genomic data compression has focused so far on data generated by second-generation sequencing technologies, while third-generation technologies, such as nanopore technologies, have received little attention in the data compression research community. In this paper, we investigate compression schemes for nanopore FASTQ files. We propose a nanopore quality scores compressor, called DualCtx, which yields significant improvements in compression performance with respect to the state-of-the-art. We also extend DualCtx to a full FASTQ compressor, termed DualFqz, by substituting DualCtx for the quality score compression module in a variant of Fqzcomp. We tested DualFqz and various existing compressors on a large nanopore data set. The results show that DualFqz achieves the best compression performance. The experiments also show that most current implementations of compressors fail to execute correctly on files with long variable length reads. DualCtx and DualFqz are freely available for download at: https://github.com/guidufort/DualFqz .
Privacy-Preserving Lightweight Image Encryption in Mobile Cloud

With the rapid development of mobile cloud, multimedia data like image is a major challenge to maintain security and privacy for mobile users. Encryption is one of the best solutions to protect data. However, traditional encryption algorithms are not suitable for images which they were proposed for text data. In this paper, we propose an image encryption technique called privacy-preserving lightweight image encryption (PPLiIE) to make an encryption in a simple way, suitable for images and to maintain privacy. PPLiIE algorithm proceeds with a three-step process to secure the image data in mobile before storing to the cloud. We implement in Python language and analyze our results with various file images to conclude that the encryption time of the PPLiIE is reduced to 50% approximately than the encryption time of AES. In addition, the measurement of key sensitivity and file with variation of chunk size have expressed superior performance of PPLiIE. Finally, we review various security attacks against PPLiIE to express the security level.
Learning from Public Toilet Doors

Writing on walls in public spaces has been a way for people to communicate and express themselves. In this paper, we present the design of a participatory feedback gathering system inspired by this practice. By engaging the campus community in sharing their feedback on the use of spaces and facilities, we aim to encourage them to participate in co-creating the campus space. Our prototype combines a physical object's affordance to attract attention with an internet forum to gather feedback. We document some key findings from our exploratory study and share ideas about future work.
Character recognition algorithms

The article discusses various algorithms for character recognition, describes their advantages and disadvantages. The modified algorithm of character recognition based on the template method is proposed, its block diagram is given, the results of testing are presented, as well as the possibility of its application in the system of automated test forms verification. The article presents experimental data on the probability of correct recognition of a symbol of an arbitrary number, depending on the number of loaded characters in the pattern obtained using this algorithm.. The algorithm developed by the authors is planned to be used in the development of a testing forms verification system, both for entrance tests and for various disciplines studied at the University. This system will allow to automate the test results verification, create a test results database and on its basis to create various reports types.
Cybercrime Countermeasure of Insider Threat Investigation

The threat of cybercrime is becoming increasingly complex and diverse on putting citizen's data or money in danger. Cybercrime threats are often originating from trusted, malicious, or negligent insiders, who have excessive access privileges to sensitive data. The analysis of cybercrime insider investigation presents many opportunities for actionable intelligence on improving the quality and value of digital evidence. There are several advantages of applying Deep Packet Inspection (DPI) methods in cybercrime insider investigation. This paper introduces DPI method that can help investigators in developing new techniques and performing digital investigation process in forensically sound and timely fashion manner. This paper provides a survey of the packet inspection, which can be applied to cybercrime insider investigation.