Reviewerly/exHarmony huggingface dataset:
Bacterial Colony Optimization for Data Clustering

Clustering is the process of the grouping object in a given data objects based on their similarity between data objects. The swarm intelligent based clustering algorithms are generally stochastic search techniques that followed by the morality of cooperative behavior and self organization of an insect group. On the other hand, data clustering may be well planned as a complicated global optimization problem. Hence, the bacterial colony optimization is a new optimization algorithm which is followed by the behaviors of bacteria and proposed in this paper for solving the data clustering problem. The experimental results show that the proposed model achieves better clustering efficiency compared with other conventional clustering methods.
Joint remote state preparation of mixed states

Joint remote state preparation (JRSP) has gained much attention since its first appearance. However, almost all the proposals have focused on the JRSP of pure states. In this paper, we present a scheme of JRSP for mixed states via the three-qubit GHZ state. The scheme is deterministic for real coefficient mixed states and probabilistic for complex ones. The corresponding quantum circuits are given for the deterministic JRSP schemes. The model of the deterministic JRSP scheme is introduced in the language of density operators. Furthermore, we investigate the efficiency of deterministic JRSP when some qubits in the protocol are subjected to a noisy environment. Four types of noise usually encountered in quantum communication are considered, i.e. the bit-flip, phase-flip (phase-damping), depolarizing and amplitude-damping noise. We find that the fidelity is affected by both coefficients of the target mixed state and noise parameter of the channel. Specifically, the fidelity decreases with the purity parameter in the presence of noise.
Square-Root Approach for Chandrasekhar-Based Maximum Correntropy Kalman Filtering

The first two Chandrasekhar recursions for the maximum correntropy criterion (MCC) Kalman filter (KF) have been recently derived for constant discrete-time linear systems. Their key feature is a mathematical re-formulation of the underlying MCC-based Riccati-type difference equation in terms of the involved error covariance matrix increment. Thus, the Chandrasekhar recursion-based solution is proved to yield a significant reduction of the computational complexity. This letter discusses the existence of a stable square-root solution for Chandrasekhar-type MCC-KF estimators, i.e. their computational reliability issue in a finite precision arithmetic. Two square-root solutions are proposed in terms of covariance quantities, namely within the Cholesky factorization and singular value decomposition.
Image Classification Using Synchronized Rotation Local Ternary Pattern

In this paper, a synchronized rotation local ternary pattern (SRLTP) operator for image classification is presented. The proposed SRLTP descriptor improves on the local ternary pattern (LTP) method with an additional process on the generated lower and upper LTPs. The lower and upper patterns are encoded to a rotation invariant pattern histogram and a uniform pattern histogram, respectively. Therefore, the feature vector can utilize the advantages offered by the rotation invariant pattern histogram while retaining the original information in the uniform pattern histogram. In addition, a two-dimensional discrete wavelet transform (DWT) and a discrete Fourier transform (DFT) enhanced the robustness of the texture classification are applied in the experiments. The DWT adapts the context variations and reduces noise by applying a high-pass filter and a low-pass filter with different image resolutions. The DFT is mostly used to overcome the rotation variation problem. To verify the performance of proposed SRLTP descriptor, the effectiveness and robustness of the proposed descriptor were compared with those of existing descriptors by using four public texture data sets. The experimental results demonstrate that the performance of the SRLTP descriptor is convincing and outperformed to the existing descriptors.
Survey on Key Technology of Robocup Rescue Robot

In nature, there are many disasters that may threaten the safety of human life, such as house collapse caused by earthquakes, fires, etc. The danger and complexity of the rescue environment often bring great difficulties to the search and rescue work. In the harsh rescue environment, there are often many secondary disasters, such as aftershocks after the earthquake and the second collapse of the house. The emergence of rescue robots has greatly reduced the risk of people being hurt during the rescue process, and improved the efficiency of rescue work. Based on the development status of rescue robots, this paper briefly introduces the Robocup Rescue competition and the hardware and software components of rescue robots. At the same time, it briefly analyzes the SLAM technology and its algorithms of rescue robots, and finally states the future development of rescue robots.
Cell detection on digitized Pap smear images using ensemble of conventional image processing and deep learning techniques

In this paper, we focus on the problem of cell segmentation in digitized Pap smear images, which is a prerequisite of automatically detecting cervical cancer in its early stage. According to the trends, we consider deep learning based approaches in the form of applying fully convolutional neural networks (FCNNs). A common bottleneck of deep learning is that large annotated dataset is required for proper training. As large public datasets are not yet available in this field, we have composed a corresponding manually labeled dataset. Though this dataset is quite large, the manual annotation is less reliable in this domain, so we had to apply such a deep learning framework that is able to overcome this issue. Accordingly, we have applied such an ensemble of FCNN and traditional segmentation approaches that provide sufficiently large diversity according to the most challenging manual annotation-related issues, like the inaccurate selection of cell boundaries. We propose ensembles to merge the outputs of the different segmentation methods, which have been proven superior to any of the ensemble members according to our experimental studies.
Proactive Phishing Sites Detection

Phishing is one of the social engineering techniques to steal users’ sensitive information by disguising a fake Web site as a trustworthy one. Previous research proposed phishing mitigation techniques, such as blacklist, heuristics, visual similarity, and machine learning. However, these kinds of methods have limitation on the detection of a zero-hour phishing site, a phishing site that no one has noticed yet.
Determinants of Software Vulnerability Disclosure Timing

The timing of vulnerability disclosures (by vulnerability discoverers) has significant implications for software producers and users. Immediate disclosure (before a patch becomes available) could result in exploits with subsequent harm to installed systems. Therefore, it is important to understand the determinants of this timing. In this study, we investigate the impacts of (i) the perception of the vulnerability discoverer about the software producer, (ii) the type of vulnerable software, and (iii) the severity of the vulnerability, on a vulnerability discoverer's choice of disclosure timing. We collect data from three different sources and control for the vulnerability discoverer's motivations and beliefs. Our results indicate that those who perceive a software producer to be timely in its patch release, reward it by delaying the disclosure. We also find that it is more likely that the disclosure is delayed for open source software and it is less likely that the disclosure is delayed for more severe vulnerabilities. The findings of this study are relevant to software producers in their decision‐making process on resource allocation for software patches and should also help policy‐makers to devise regulations relevant to the timing of disclosures and patch releases. Furthermore, these findings could be relevant to software consumers searching for a particular software product that they would like to use. This study attempts to provide insights into an ongoing discussion in the operations management community regarding how to allocate and divide resources between software development and software maintenance.
The pottery skills and tacit knowledge of a maser: An analysis using eye-tracking data

An important aspect of Japanese traditional culture is the design and creation of pots, bowls, and other pottery, an artform that has historically been subject to cultural influences from China and the Korean peninsula. It has been suggested that passing down of hand-crafting techniques to the next generation is quite difficult, as the techniques involve considerable tacit knowledge. Thus, this study aimed to explore the creative processes of a master of the craft using an eye-tracking system. We recorded the eye movements of Hakuga Nakamori, a third-generation artisan, as he created a bowl and a pot. The results showed that, in the process of working toward creating a piece of art, Mr. Nakamori first focused his attention on the whole piece, and then on certain elements thereof.
Handling Local State with Global State

Equational reasoning is one of the most important tools of functional programming. To facilitate its application to monadic programs, Gibbons and Hinze have proposed a simple axiomatic approach using laws that characterise the computational effects without exposing their implementation details. At the same time Plotkin and Pretnar have proposed algebraic effects and handlers, a mechanism of layered abstractions by which effects can be implemented in terms of other effects. This paper performs a case study that connects these two strands of research. We consider two ways in which the nondeterminism and state effects can interact: the high-level semantics where every nondeterministic branch has a local copy of the state, and the low-level semantics where a single sequentially threaded state is global to all branches. We give a monadic account of the folklore technique of handling local state in terms of global state, provide a novel axiomatic characterisation of global state and prove that the handler satisfies Gibbons and Hinze’s local state axioms by means of a novel combination of free monads and contextual equivalence. We also provide a model for global state that is necessarily non-monadic.
Rapid-Precision Position Measurement of Linear Motor Mover Based on Joint Spatial Phase Method

In this article, a rapid and precise position measurement method based on joint spatial phase is studied for linear motors. First, a low-cost mover position measurement system is designed on the basis of motion characters of linear motors. The target image is an aperiodic fence stripe. The image sequences with the movement of the mover are recorded quickly by linear charge coupled device (CCD) camera. Second, spatial domain algorithm is introduced for the rough registration, and according to the registration results, the overlapping regions are extracted to form two new images, and then the improved phase difference method is used to get the accurate displacement of the two new images. The actual mover displacement is finally calculated by the calibration coefficient of the system. Simulation and experimental results show that this method proposed takes approximately 2 ms on average, and the measurement accuracy reaches 0.02 mm.
Security Analysis of SAT_Jo Lightweight Block Cipher for Data Security in Healthcare IoT

In this fast moving world, every industry is advanced by a new technological paradigm called Internet of Things (IoT). It offers interconnectivity between the digital and the real world which will swiftly transform the style of doing business. It opens up a wide-ranging new array of dynamic opportunities in all industries and is fuelling innovation in every part of life. Due to the constrained nature of the devices in IoT environment, it is difficult to execute complex data encryption algorithms to enhance the security. Moreover, computation overhead caused by the existing cryptographic security algorithms is heavy and has to be minimized. To overcome these challenges, this paper presents the security analysis of the lightweight block cipher SAT_Jo to ensure the data security in healthcare Internet of Things. It is based on SPN structure and runs for 31 rounds. It encrypts 64-bits of block length with key of 80 bits. Cadence NC-Verilog 5.1 is used for simulation and Cadence Encounter RTL Compiler v10.1 for synthesis. The implementations are synthesized for UMC 90 nm low-leakage Faraday library from technology libraries. Moreover, the proposed SAT_Jo block cipher withstands in various attacks such as differential attack, linear attack and algebraic attack in healthcare IoT environment.
Attention-based LSTM Network for Wearable Human Activity Recognition

Sensor-based human activity recognition (HAR) has become a popular research topic because of its wide applications. Conventional machine learning approaches have made enormous progress in the past years. However, those methods rely on handcrafted features that are incapable of handling complex activities, especially with high dimensional sensor data. Deep learning technology, together with its various models, is one of the most accurate methods of working on activity data. In this paper, we propose an attention-based Long Short Term Memory (LSTM) network for wearable human activity recognition. Specifically, we construct an LSTM network to model the sensor readings, which has been proved to be very effective for time sequences. Then, we introduce the attention mechanism for the base LSTM network to learn which parts of the raw sensor data are more important for determining the overall activities. When tested with the Opportunity data set, the F1-score is increased by 2.6%, compared with baseline LSTM results.
Evaluation of Student Information System (SIS) In Terms of User Emotion, Performance and Perceived Usability: A Turkish University Case (An Empirical Study)

Student Information System (SIS) has abundant functions in order to maintain academic workflow. Besides, user experience on SIS is critical so as to sustain its functions. Although user experience of various information systems (IS) has been studied in the literature related to user emotion, performance and perceived usability, this is not the case for the SIS. In order to fill this gap, this empirical study has been investigated how student's backgrounds affect their experiences with a SIS in terms of their emotion, performance and perceived usability. The study was administered in the one of the universities of Turkey with a sample of 32 participants. Participants were divided technology prone group and other group to measure the differences among the groups. Participants were asked to perform a number of tasks on the system; the tasking being carefully chosen to direct participants to different aspects of the SIS. In addition, to get more insight about participants' experiences, EWPL-TR, SUS-TR and Overall Assessment Questions were directed to them. This study showed significant differences among user's emotion, performances and perceived usability depending on their specific demographic backgrounds. The results of this research will be important for developers of SIS and similar websites who want to take the advantage of developing supportive systems by focusing on users' demographic backgrounds.
Multi-sensing of fragile persons for risk situation detection: devices, methods, challenges

The ageing of the world population has raised ever-increasing demands for measuring physical conditions and assisting the elderly and/or fragile population at their homes. Physiological, motor and even environmental measurements are excellent indicators of their health status. Furthermore, connected wearable technologies in the framework of the Internet of Things allow for risk situations prevention. Recent advances of Artificial Intelligence techniques make possible high-accuracy decision making on multi-sensory data. Nevertheless, to train models and to perform online real-time detection of risk events from heterogeneous taxonomy, robust wearable devices are required. In this paper, existing solutions are reviewed for human sensing for these purposes. Moreover, we present the implementation of a multi-sensor device for the recognition of risk situations with a focus on the data synchronisation.
Joint Rotation-Invariance Face Detection and Alignment with Angle-Sensitivity Cascaded Networks

Due to the angle variations especially in unconstrained scenarios, face detection and alignment have become challenging tasks. In existing methods, face detection and alignment are always conducted separately, which can greatly increase the computation cost. Moreover, this separation will abandon the inherent correlation underlying the two tasks. In this paper, we propose a simple but effective architecture, named Angle-Sensitivity Cascaded Networks (ASCN), for jointly conducting rotation-invariance face detection and alignment. ASCN mainly consists of three consecutive cascaded networks. Specifically, in the first stage, the rotation angle is predicted and candidate bounding boxes are proposed simultaneously. In the second stage, ASCN further refines the candidates and orientations. In the last stage, ASCN jointly learns the accurate bounding boxes and alignment. Besides, for accurately locating landmarks in hard examples, we introduce a pose-equitable loss to balance the faces with large poses. Extensive experiments conducted on benchmark datasets demonstrate the surprising performance of our method. Notably, our method maintains real-time efficiency for both detection and alignment tasks on the ordinary CPU platform.
Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition

Unconstrained video-based face recognition is a challenging problem due to significant within-video variations caused by pose, occlusion and blur. To tackle this problem, an effective idea is to propagate the identity from high-quality faces to low-quality ones through contextual connections, which are constructed based on context such as body appearance. However, previous methods have often propagated erroneous information due to lack of uncertainty modeling of the noisy contextual connections. In this paper, we propose the Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation between tracklets, which are represented by nodes in a graph. UGG explicitly models the uncertainty of the contextual connections by adaptively updating the weights of the edge gates according to the identity distributions of the nodes during inference. UGG is a generic graphical model that can be applied at only inference time or with end-to-end training. We demonstrate the effectiveness of UGG with state-of-the-art results in the recently released challenging Cast Search in Movies and IARPA Janus Surveillance Video Benchmark dataset.
Hiding Video in Audio via Reversible Generative Models

We present a method for hiding video content inside audio files while preserving the perceptual fidelity of the cover audio. This is a form of cross-modal steganography and is particularly challenging due to the high bitrate of video. Our scheme uses recent advances in flow-based generative models, which enable mapping audio to latent codes such that nearby codes correspond to perceptually similar signals. We show that compressed video data can be concealed in the latent codes of audio sequences while preserving the fidelity of both the hidden video and the cover audio. We can embed 128x128 video inside same-duration audio, or higher-resolution video inside longer audio sequences. Quantitative experiments show that our approach outperforms relevant baselines in steganographic capacity and fidelity.
A Low-Latency Multi-Touch Detector Based on Concurrent Processing of Redesigned Overlap Split and Connected Component Analysis

A low-latency multi-touch detector architecture for locating numerous touches in large-panel devices is presented in this paper. Two respective processors for the overlap split and the connected component analysis (CCA) are the key components in a multi-touch detector. Exploiting the simplicity of typical intensity maps in practice, the two processors are first redesigned under a principle that every pixel is processed only once in a raster-scan order. More specifically, the concept of valley point is introduced, and a simple yet effective overlap-split scheme called valley-point division (VPD) is newly developed based on the concept. In addition, equivalent labels in the CCA are handled on the fly instead of being kept to be processed later, and the logics and memories for corner cases that never occur in practice are unloaded. Enabled by the redesign, subsequently, the two processors are integrated into one to concurrently conduct both the VPD and the CCA during the same raster scan. As a result, the proposed detector completes the whole detection procedure with a single raster scan of a map, and is exempted from a large memory required to hold an entire map. Implementation results in a 65-nm CMOS for a large panel of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$400\times250$ </tex-math></inline-formula> sensors demonstrate that the proposed detector takes less than a half latency of the existing ones while occupying only 17% silicon area and consuming 52% power on average.
Retrieving Relevant and Diverse Movie Clips Using the MFVCD-7K Multifaceted Video Clip Dataset

Multimedia search is an emerging area in information retrieval (IR) and recommender systems (RS) research. However, there is a lack of standardized audiovisual datasets that include rich content descriptors, which are a necessity in content-based IR and RS. The contributions of this paper are twofold: First, we present a new multimedia dataset of movie clips, named MFVCD-7K Multifaceted Video Clip Dataset, that comes with low-level and semantic multimodal descriptions of their content (textual, audio, and visual). In addition, we showcase the use of this dataset for a novel content-based video clip retrieval and result diversification task we introduce. We investigate baseline algorithms for retrieval and diversification, and provide experimental results according to relevance and diversity measures. We believe that both dataset and baseline results constitute an important asset for the IR, RS, and multimedia communities.
Towards an Objective Measure of Presence

A large body of literature is concerned with models of presence-the sensory illusion of being part of a virtual scene-but there is still no general agreement on how to measure it objectively and reliably. For the presented study, we applied contemporary theory to measure presence in virtual reality. Thirty-seven participants explored an existing commercial game in order to complete a collection task. Two startle events were naturally embedded in the game progression to evoke physical reactions and head tracking data was collected in response to these events. Subjective presence was recorded using a post-study questionnaire and real-time assessments. Our novel implementation of behavioral measures lead to insights which could inform future presence research: We propose a measure in which startle reflexes are evoked through specific events in the virtual environment, and head tracking data is compared to the range and speed of baseline interactions.