Reviewerly/exHarmony huggingface dataset:
Person Re-Identification of Cross-Domain Based on Costume Characteristics

Cross-domain person re-identification (ReID) task has always been a research hotspot. The domain transfer technique based on deep learning alleviates the problem caused by the gap among data distributions. However, the generalization of such kinds of person ReID models are not robust. This paper proposes a novel ReID method to optimize the robustness from the perspective of persons' costume. It can extract hierarchical knowledge features from costume information and apply the feature model to the task of ReID. Specifically, the method first constructs hierarchical labels according to the attribute of costume data. It builds the knowledge model by using the multi-task learning, and performs similarity measurement on multiple ReID datasets. The experimental results show that the proposed method outperforms the latest ReID methods. Therefore, it provides a novel technique for practical applications.
An approach for microscopy image restoration

The blind deconvolution algorithms are widely used in microscopy image restoration. However in more of the biological and biomedical experiments due to high level of noise, it is difficult to obtain good results. The blind deconvolution algorithm can be effectively applied when no information about the blurring and noise is given. In addition, a modified homomorphic filter based on wavelet decomposition is a frequently used instrument for noise reduction. In this paper, we propose an integrated approach that combines properties of blind deconvolution and modified homomorphic filter based on adaptive wavelet packet decomposition for noise reduction. As next, for contrast enhancement gamma correction is applied. We have made a quantitative analysis of the quality achieved by the proposed approach over deconvolution schemes, based on classical Richardson-Lucy algorithm and wavelet discrete transformation by experiments with real microscopy images. To prove our approach and theoretical statements results of laboratory experiments are suggested.
Multi-gene Genetic Programming Based Defect-Ranking Software Modules

Most software defect prediction models aim at predicting the number of defects in a given software. However, it is very difficult to predict the precise number of defects in a module because of the presence of noise data. Another type of frequently used approach is ranking the software modules according to the relative number of defects, according to which software defect prediction can guide the testers to allocate the limited resources preferentially to modules with a greater number of defects. Owing to the redundant metrics in software defect data-sets, researchers always need to reduce the dimensions of the metrics before constructing defect prediction models. However a reduction in the number of dimensions may lead to some useful information being deleted too early, and consequently, the performance of the prediction model will decrease. In this paper, we propose an approach using multi-gene genetic programming (MGGP) to build a defect rank model. We compared the MGGP-based model with other optimized methods over 11 publicly available defect data-sets consisting of several software systems. The fault-percentile-average (FPA) is used to evaluate the performance of the MGGP and other methods. The results show that the models for different test objects that are built based on the MGGP approach perform better those based on other nonlinear prediction approaches when constructing the defect rank. In addition, the correlation between the software metrics will not affect the prediction performance. This means that, by using the MGGP method, we can use the original features to construct a prediction model without considering the influence of the correlation between the software module features.
Point-and-Lift

Travel techniques enabling 3 degrees of freedom (DoF) locomotion allow the user to obtain an overview of the virtual environment (VU). As a result spatial information gathering as well as the user's orientation skills in the VU are supported. Existing 3DoF travel techniques are, however, rather inefficient and enhance simulator sickness symptoms which can lead to a reduced sense of presence. We propose a novel method called Point-and-Lift to address these problems. Furthermore, we present a user study concept comparing our method to state-of-the-art travel techniques regarding simulator sickness, presence, orientation skills and performance.
Integrative Acceleration of First-order Boolean Masking for Embedded IoT Devices

Physical attacks, especially side-channel attacks, are threats to IoT devices which are located everywhere in the field; and therefore, protecting such devices against side-channel attacks is one of our emerging issues. Toward that, Coron et al. gave an efficient arithmetic-to-Boolean mask conversion algorithm which enables us to protect cryptographic algorithms including arithmetic operations, such as hash functions, from the attacks. Recently, Biryukov et al. improved it by locally optimizing subroutines of the conversion algorithm. In this paper, we revisit the algorithm. Unlike Biryukov et al., we improve the Coron et al.'s algorithm with integrative optimizations over the subroutines. The gains against these algorithms are about 22.6% and 7.0% in the general setting. We also apply our algorithm to HMAC-SHA-1 and have an experiment to show that the implementation on a test vehicle smartcard leaks no sensitive information, i.e., secure against the first-order side-channel attack, with the ISO/IEC17825 test.
A Prototypical Photo Sorting Study Design for Comparing Interaction Styles

We discuss a first version of a extensible study design for a generic image-sorting task. It allows for comparing qualitative and quantitative properties of different interaction styles (e.g., direct manipulation, CLI, tangible interaction), input modalities (e.g., mouse vs. touch screen), output modalities, and UI implementations. Therefore, the study design may be of use for designing reproducible and replicable studies. Study participants are asked to sort a set of 27 photos into five categories where each of the photos depict one distinct topic belonging to only one of the categories. We conducted a first pilot study using this design, comparing a desktop GUI, an interactive tabletop, and physical photos. Task completion time was significantly lower when sorting physical photos than in the other two conditions. The study results may serve as a baseline and show limitations of the preliminary design.
Weighted Fusion Incremental Kalman Filter

Usually, the unknown system error is widespread and difficult to be verified or corrected, which will yield to relatively large filtering errors. Incremental equation can eliminate the unknown system errors and improve the accuracy of state estimators. On this basis, a kind of optimal weighted fusion incremental Kalman filter is presented in this paper. It has the global optimality, and greatly improves the state estimation accuracy for the multi-sensor systems under poor observation condition. The proposed algorithm is simple in form and small in computational burden so as to be easily applied in engineering practice. The simulation results show its effectiveness and feasibility.
Attentional Residual Dense Factorized Network for Real-Time Semantic Segmentation

Semantic segmentation is a pixel-level image dense labeling task and plays a core role in autonomous driving. In this regard, how to balance between precision and speed is a frequently-studied issue. In this paper, we propose an alternative attentional residual dense factorized network (AttRDFNet) to address this issue. Specifically, we design a residual dense factorized convolution block (RDFB), which reaps the benefits of low-level and high-level layer-wise features through dense connection to boost segmentation precision whilst enjoying efficient computation by factorizing large convolution kernel into the product of two smaller kernels. This reduces computational burdens and makes real time become possible. To further leverage layer-wise features, we explore the graininess-aware channel and spatial attention modules to model different levels of salient features of interest. As a result, AttRDFNet can run with the inputs of the resolution 512 $$ \times $$ 1024 at the speed of 55.6 frames per second on a single Titan X GPU with solid 68.5% Mean IOU on the test set of Cityscapes. Experiments on the Cityscapes dataset show that AttRDFNet has real-time inference whilst achieving competitive precision against well-behaved counterparts.
A method for decreasing negative effects of the robots close to their targets

An improved artificial moment method is proposed for decreasing the negative effects of the robots close to their targets. In the method, a new algorithm is presented for the key companion of a robot close to its target. The algorithm will not make the robot change its key companion frequently and select the same robot as its key companion for a long duration. Second, two new algorithms are presented respectively for attractive points and attractive angles of robots. As such, a robot can decrease its negative effects on others. Finally, the existing artificial moment motion controller is improved. Simulation results indicate that the proposed method can decrease the negative effects in the system and yield better solutions in complex situations.
Sequence Disambiguation with Synaptic Traces in Associative Neural Networks

Among the abilities that a sequence processing network should possess sequence disambiguation, that is, the ability to let temporal context information influence the evolution of the network dynamics, is one of the most important. In this work we propose an instance of the Bayesian Confidence Propagation Neural Network (BCPNN) that learns sequences with probabilistic associative learning and is able to disambiguate sequences with the use of synaptic traces (low pass filtered versions of the activity). We describe first how the BCPNN achieves both sequence recall and sequence learning from temporal input. Our main result is that the BCPNN network equipped with dynamical memory in the form of synaptic traces is capable of solving the sequence disambiguation problem in a reliable way. We characterize the relationship between the sequence disambiguation capabilities of the network and its dynamical parameters. Furthermore, we show that the inclusion of an additional fast synaptic trace greatly increases the network disambiguation capabilities.
Design of Polynomial Fuzzy Neural Network Classifiers Based on Density Fuzzy C-Means and L2-Norm Regularization

In this paper, polynomial fuzzy neural network classifiers (PFNNCs) is proposed by means of density fuzzy c-means and L2-norm regularization. The overall design of PFNNCs was realized by means of fuzzy rules that come in form of three parts, namely premise part, consequence part and aggregation part. The premise part was developed by density fuzzy c-means that helps determine the apex parameters of membership functions, while the consequence part was realized by means of two types of polynomials including linear and quadratic. L2-norm regularization that can alleviate the overfitting problem was exploited to estimate the parameters of polynomials, which constructed the aggregation part. Experimental results of several data sets demonstrate that the proposed classifiers show higher classification accuracy in comparison with some other classifiers reported in the literature.
SUPPRESSION OF CROSS-TERM IN WIGNER DISTRIBUTION USING DIRECTIONAL FILTERING AND IMAGE PROCESSING

Cross-term suppression in the Wigner distribution is a challenging task. Kernel based schemes are most widely used for reducing cross-terms. However, these schemes suffer from the intrinsic give and take between auto-term resolution and cross-term suppression. This paper presents a cross-term suppression scheme by combining directional filtering with image processing. This scheme retains the resolution of auto-term while significantly reducing cross-terms. The performance comparison with other methods demonstrates that the proposed method is superior compared to other methods in terms of cross-term suppression and energy concentration.
Eyes State Detection in Thermal Imaging

The paper presents a problem of analysing facial area captured in thermal spectrum in order to estimate the drowsiness of the observed person. In the developed approach the state of eyes is taken into consideration. During the experiments we trained and applied a contemporary general-purpose object detector based on Haar-like features, known to be accurate when working in visible spectrum. Based on a position of a face, we localize eyes and apply a Gabor filtering-based approach to classify the eyes state. In contrast to the traditional, visible light-based methods, by using thermal image we are able to capture eyes state in very adverse lighting conditions. The experiments performed on video sequences taken in simulated cabin environment have shown that the proposed approach gives a very prospective results.
Android Oriented Image Visualization Exploratory Search

When using traditional image search engines, smartphone users often complain about their poor user interface including poor user experience, and weak interaction. Moreover, users are unable to find a desired picture partly due to the unclear key words. This paper proposes the word-bag co-occurrence scheme by defining the correlation between images. Through exploratory search, the search range can be expanded and help users refine retrieval of the expected images. Firstly, the proposed scheme applied the bag of visual words (BoVW) vector by processing images on Hadoop. Secondly, similarity matrix was constructed to organize the image data. Finally, the images in which users were interested was visually displayed on the android mobile phone via exploratory search. Comparing the proposed method to current methods by testing with image data sets on ImageNet, the experimental results show that the former is superior to the latter on visual representation, and the proposed scheme can provide a better user experience.
T-S Fuzzy Control for Blood Glucose Regulation in Type-1 Diabetes

This paper is concerned with blood glucose control problem for type-1 diabetes whose nonlinear dynamics are explained in the Takagi-Sugeno (T-S) fuzzy model. The proposed T-S fuzzy control method is derived based on quadratic Lyapunov function aiming at attenuating the error as small as possiblin in the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance sense. Conditions for the solvability of the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controller design given here are written in the form of linear matrix inequalities which are easier to be solved by convex optimization techniques. Simulation example is given to demonstrate the validity and applicability of the proposed approaches.
Control Emotion Intensity for LSTM-Based Expressive Speech Synthesis

To improve the performance of human-computer interaction interfaces, emotion is considered to be one of the most important factors. The major objective of expressive speech synthesis is to inject various expressions reflecting different emotions to the synthesized speech. To effectively model and control the emotion, emotion intensity is introduced for expressive speech synthesis model to generate speech conveyed the delicate and complicate emotional states. The system was composed of an emotion analysis module with the goal of extracting control emotion intensity vector and a speech synthesis module responsible for mapping text characters to speech waveform. The proposed continuous variable “perception vector” is a data-driven approach of controlling the model to synthesize speech with different emotion intensities. Compared with the system using a one-hot vector to control emotion intensity, this model using perception vector is able to learn the high-level emotion information from low-level acoustic features. In terms of the model controllability and flexibility, both the objective and subjective evaluations demonstrate perception vector outperforms one-hot vector.
Fast Object Proposal Generation for Weakly Instance Segmentation

Weakly supervised instance segmentation is a difficult visual task in computer vision. Because this task provides fewer annotations, current researchers completed the task based on traditional methods. Many researchers generated object proposals by traditional method of MCG [5] to conduct instance research. But MCG is too slow that it is difficult for this method to get widespread promotion. This paper only uses less proposals in MCG and an acceleration strategy to shorten the time to generate proposal is proposed. This method uses a differential evolution algorithm to improve the attention of foreground object, and guides MCG to generate the target-related proposal as much as possible. In this experiment, clean pictures with uniform features in the PASCAL VOC2012 life scene dataset was selected for experiments. Experiments show that the proposed method can generate better proposal in a relatively short time, and its instance segmentation results are better than baseline.
Determining optimal orientation of straight-vane baffles for stray light mitigation

Stray light mitigation is a critical aspect in many optical systems and can be approached in many ways. One method to reduce stray light is to incorporate baffle vanes into a system. To go from an ideal baffle vane to a manufacturable baffle adds complexity in how to mechanically reduce stray light. This paper discusses the manufacturable aspect of baffle vane fabrication in determining the optimal orientation a bevel should have on a straight-vane baffle. Various configurations for bevel orientation were analyzed to provide a deeper understanding of past research.
Interactive Exploration of Journalistic Video Footage through Multimodal Semantic Matching

This demo presents a system for journalists to explore video footage for broadcasts. Daily news broadcasts contain multiple news items that consist of many video shots and searching for relevant footage is a labor intensive task. Without the need for annotated video shots, our system extracts semantics from footage and automatically matches these semantics to query terms from the journalist. The journalist can then indicate which aspects of the query term need to be emphasized, e.g. the title or its thematic meaning. The goal of this system is to support the journalists in their search process by encouraging interaction and exploration with the system.
Prediction of a Dam’s Hazard Level

South Africa has a vast infrastructure of dams. Since the country receives very little rainfall, these dams assume prime importance in storing water and sustaining agriculture, industry, household, etc. Thus prediction of their multiple hazard levels (in this case, three) is of prime importance. In addition, South Africa lacks skilled personnels to classify these dam’s hazards. Under such a framework, this work is an application of single and ensemble decision trees in a multi-class supervised learning framework to predict the hazard level of a dam. The result obtained is highly promising and at is above 94 $$\%$$ . With the implementation of the algorithm, we expect to address the problem of paucity of skilled personnels.
Building Blocks of an Innovative Approach to Education in the Field of Cyber Operations in Smart Environment

Future cyber operations in an IoT-enabled smart environment will be much more demanding and complex. The purpose of this paper is to present the building blocks of an innovative approach to education in the field of cyber operations. Proposed model of an innovative approach to education in the field of cyber operations includes three building blocks with the set of key elements. The model is focused on the work process of cyber operations modelling, that will allow students with different levels of knowledge and specializations to work efficiently. The objective of the innovative approach to education in the field of cyber operations is to connect students with different knowledge in the field of cyber security and thus contribute to the development of highly secure IoT solutions and better security awareness. The main objective of the proposed model of an innovative approach to education in the field of cyber operations is to allow the students to develop a content-rich, in-depth knowledge in the field of cyber operations, especially in smart environment.
Improved Background-Aware Correlation Filters based on Content-Related Spatial Regularization

Discriminative correlation filters(DCF) have caught much attention in visual tracking. However, DCFs suffer from the boundary effects. To alleviate the boundary effects, the background-aware correlation filter (BACF) approach has been proposed to resolve this issue by enforcing the filter coefficients outside the target bounding box to zeros, which can improve the tracking accuracy and robustness. However, when occlusion occurs, the target may drift and even leave the target bounding box and the correlation coefficients of the true target could be falsely forced to zeros. In that situation, the tracker drifts towards distracters and loses the target. To tackle this limitation, we put forward a content-related spatial regularization map, which can enhance the ability of picking the target from background. By incorporating both contentrelated and spatial regularization map into the BACF objective function, our tracker improves the PR score and the AUC score by 4.5% and 4.1% respectively over BACF on OTB2013 datasets.
Energy Aware Routing Framework for Internet of Things

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Learning Sparse Hidden States in Long Short-Term Memory

Long Short-Term Memory (LSTM) is a powerful recurrent neural network architecture that is successfully used in many sequence modeling applications. Inside an LSTM unit, a vector called “memory cell” is used to memorize the history. Another important vector, which works along with the memory cell, represents hidden states and is used to make a prediction at a specific step. Memory cells record the entire history, while the hidden states at a specific time step in general need to attend only to very limited information thereof. Therefore, there exists an imbalance between the huge information carried by a memory cell and the small amount of information requested by the hidden states at a specific step. We propose to explicitly impose sparsity on the hidden states to adapt them to the required information. Extensive experiments show that sparsity reduces the computational complexity and improves the performance of LSTM networks (The source code is available at https://github.com/feiyuhug/SHS_LSTM/tree/master ).