Reviewerly/exHarmony huggingface dataset:
An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler

Background: The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. Objectives: This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. Methods: The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Results: Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Conclusion: Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.
Robot Path Planning Using Analog Circuit Phase Delay

A robot's motion planner should find a path from its current location to its goal in an optimum way, where optimality in this paper is the shortest path. This paper proposes a new method of decision making using a phase delay technique. The robot's workspace is converted into a resistive-capacitive grid network, and the path is found by measuring and comparing the propagation times for a signal from the goal to the robot's next possible positions. A reconfigurable AVLSI architecture based on floating-gate transistors is proposed and simulation results with analytical complexity analysis equations are presented to describe the algorithm's performance. Finally, results from a robot hardware-in-the-loop platform are presented.
A No-Reference Video Quality Assessment Model for Underwater Networks

Underwater imagery is increasingly drawing attention from the scientific community, since pictures and videos are invaluable tools in the study of the vast unknown oceanic environment that covers 90% of the planetary biosphere. However, underwater sensor networks must cope with the harsh channel that seawater constitutes. Medium range communication is only possible using acoustic modems that have limited transmission capabilities and peak bitrates of only a few dozens of kilobits per second. These reduced bitrates force heavy compression on videos, yielding much higher levels of distortion than in other video services. Furthermore, underwater video users are ocean researchers, and therefore their quality perception is also different from the generic viewers that typically take part in subjective quality assessment experiments. Computational efficiency is also important since the underwater nodes must run on batteries and their recovery is very expensive. In this paper, we propose a pixel-based no-reference video quality assessment method that addresses the described challenges and achieves good correlations against subjective scores of users of underwater videos.
Towards Predicting Risky Behavior Among Veterans with PTSD by Analyzing Gesture Patterns

Risky behavior including violence and aggression, self-injury, anger outburst, domestic violence along with self-injury, sexual abuse, rule-breaking, use of drugs and alcohol, suicide, etc. are alarming issues among US military veterans who return from combat zone deployment in Iraq and Afghanistan. Veterans are exposed to trauma in war zones which affect most of them with post-traumatic stress disorder (PTSD) or other mental health problems to some degree. Studies have shown that veterans have much higher rates of PTSD than civilians and are more likely to engage in risky behavior. One of the forms of displaying and engaging in risky behaviors is through gestures. We collaborated with veterans and social scientists to find the list of 13 gestures that are often used by veterans engaged in risky behaviors. In this research work, we have collected accelerometer data from subjects performing the gestures mentioned above and have tried to detect them using machine learning techniques. This paper describes identifying gesture clusters from the accelerometer coordinate data and development of a predictive model that can classify the gestures resulting in the prediction of risky behaviors among the veterans who suffer from PTSD.
Consortia from past to future

Purpose The purpose of this paper is to review the global history of library consortia from ancient times until the present and propose further development of consortia to address journal costs and research distribution. Design/methodology/approach The global history of consortia is reviewed, with a concentration on the early years of the American Library Association. Findings Consortia have proved their value in making libraries more efficient and in advancing research through combined efforts in developing indexes and catalogues. Research limitations/implications The paper encourages libraries to build on their history of cooperation and extend it by linking their repositories, using their own strengths to battle excessive subscription fees, and become publishers in their own right. Practical implications Consortia are encouraged to work together to build a global repository, to stand firm against predatory pricing and to take the lead in the dissemination of scholarship. Originality/value This paper proposes that libraries join in broad regional consortia to oppose unjustifiable prices imposed by the major publishers and that libraries, with their institutions, work regionally and internationally to take control of the distribution of research.
CanvasGAN: A Simple Baseline for Text to Image Generation by Incrementally Patching a Canvas

We propose a new recurrent generative model for generating images from text captions while attending on specific parts of text captions. Our model creates images by incrementally adding patches on a “canvas” while attending on words from text caption at each timestep. Finally, the canvas is passed through an upscaling network to generate images. We also introduce a new method for generating visual-semantic sentence embeddings based on self-attention over text. We compare our model’s generated images with those generated by Reed’s model and show that our model is a stronger baseline for text to image generation tasks.
Target Aware Network Adaptation for Efficient Representation Learning

This paper presents an automatic network adaptation method that finds a ConvNet structure well-suited to a given target task, e.g. image classification, for efficiency as well as accuracy in transfer learning. We call the concept target-aware transfer learning. Given only small-scale labeled data, and starting from an ImageNet pre-trained network, we exploit a scheme of removing its potential redundancy for the target task through iterative operations of filter-wise pruning and network optimization. The basic motivation is that compact networks are on one hand more efficient and should also be more tolerant, being less complex, against the risk of overfitting which would hinder the generalization of learned representations in the context of transfer learning. Further, unlike existing methods involving network simplification, we also let the scheme identify redundant portions across the entire network, which automatically results in a network structure adapted to the task at hand. We achieve this with a few novel ideas: (i) cumulative sum of activation statistics for each layer, and (ii) a priority evaluation of pruning across multiple layers. Experimental results by the method on five datasets (Flower102, CUB200-2011, Dog120, MIT67, and Stanford40) show favorable accuracies over the related state-of-the-art techniques while enhancing the computational and storage efficiency of the transferred model.
Embedded healthcare system based on bioimpedance analysis for identification and classification of skin diseases in Indian context

This chapter contributes toward real-time data collection, data management, system design, and analysis related to an embedded U-healthcare system for various skin diseases. The application domains of the developed system include diagnostic services, decision-support systems, and U-healthcare systems in real-world healthcare applications. The chapter explains a system developed for noninvasive identification and classification of skin diseases in the Indian context. The chapter describes the global scenario of skin diseases as well as in developing countries such as India. There are various challenges faced by dermatologists in the identification and classification of skin diseases. These are described with reference to state-of-the-art dermatological practices such as visual inspection, histopathological examination of a biopsy, and dermoscopy. The limitations of these subjective methods are discussed. The need to devise a noninvasive, reliable, simple, safe, and objective technique is described with reference to bioimpedance measurement. The chapter further describes a system developed for the measurement of human skin impedance. Skin impedance is measured with the help of a developed skin electrode. The requirements, specifications, and limitations of the developed skin electrode are described. The generation of a database of Indian skin diseases is described. The impedance of diseased and normal skin is measured with the help of the developed system and various impedance indices have been computed for an individual subject. The need of impedance indices for identification and classification of skin diseases is described. The identification of skin diseases requires discrimination between diseased and normal skin, which is explained with the help of the Wilcoxon signed rank test. The role of the Statistical Package for the Social Sciences (SPSS) is explained for computing the probability of similarity between diseased and normal skin. The possibility of the classification of skin diseases is described with the help of box and whisker plots and statistical measures of central tendency. The classification of skin diseases using a modular fuzzy hypersphere neural network has been explained. The performance of the proposed system for classification of skin diseases is explained with the help of confusion matrix and timing analysis.
A deep generic to specific recognition model for group membership analysis using non-verbal cues

Automatic understanding and analysis of groups has attracted increasing attention in the vision and multimedia communities in recent years. However, little attention has been paid to the automatic analysis of the non-verbal behaviors and how this can be utilized for analysis of group membership, i.e., recognizing which group each individual is part of. This paper presents a novel Support Vector Machine (SVM) based Deep Specific Recognition Model (DeepSRM) that is learned based on a generic recognition model. The generic recognition model refers to the model trained with data across different conditions, i.e., when people are watching movies of different types. Although the generic recognition model can provide a baseline for the recognition model trained for each specific condition, the different behaviors people exhibit in different conditions limit the recognition performance of the generic model. Therefore, the specific recognition model is proposed for each condition separately and built on top of the generic recognition model. A number of experiments are conducted using a database aiming to study group analysis while each group (i.e., four participants together) were watching a number of long movie segments. Our experimental results show that the proposed deep specific recognition model (44%) outperforms the generic recognition model (26%). The recognition of group membership also indicates that the non-verbal behaviors of individuals within a group share commonalities.
Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.
Uniform interpolation for propositional and modal team logics

Abstract In this paper we consider modal team logic, a generalization of classical modal logic in which it is possible to describe dependence phenomena between data. We prove that most known fragments of full modal team logic allow the elimination of the so called ‘existential bisimulation quantifiers’, where the existence of a certain set is required only modulo bisimulation (i.e. not in the model itself but possibly in a bisimilar model). As a consequence, we prove that these fragments enjoy the uniform interpolation property.
Load Balancing of Unbalanced Assignment Problem With Hungarian Method

The cloud computing presents a type of assignments and systems which occupy distributed resources to execute a role in a distributed way. Cloud computing make use of the online systems on the web to assist the implementation of complicated assignments; that need huge-scale computation. It was said with the intention of in our living world; we can find it challenging to balance workloads of cloud computing among assignments (jobs or tasks) and systems (machines or nodes), so the majority of the time we have to promote a condition to unbalanced assignment problems (unequal task allocations). The present article submits a new technique to solve the unequal task allocation problems. The technique is offered in an algorithmic model and put into practice on the several groups of input to investigate the presentation and usefulness of the works. An evaluation is prepared with the presented approach. It makes sure that the proposed approach provides a better outcome by comparing with some other existing algorithms.
Groundwater Quality Detection

In areas with high urban, industrial, and agricultural pressure, groundwater is exposed to the presence of organic contaminants due to transport through soils and leaching into the aquifer, increasing its vulnerability. The communities relying on such sources of water tend to be poor and live in polluted environments with associated high health risks. Polluted groundwater is less visible but more difficult to clean up than pollution in rivers and lakes. Groundwater pollution most often results from improper disposal of wastes on land. Major sources include industrial and household chemicals and garbage landfills, excessive fertilizers and pesticides used in agriculture, industrial waste lagoons, tailings and process wastewater from mines, industrial fracking, oil field brine pits, leaking underground oil storage tanks and pipelines, sewage sludge, and septic systems. Therefore, with current detection methods, some indices are determined for preserving groundwater quality, which is of vital importance to ensure the original water purity. This chapter discusses the situation regarding groundwater pollution and its monitoring and control. It covers groundwater quality detection indices and methods, comparing and contrasting their advantages and disadvantages. It also examines the creation and operation of groundwater monitoring wells.
Based on The Document-Link and Time-Clue Relationships Between Blog Posts to Improve the Performance of Google Blog Search

Both the blog search engine and the general search engine automatically crawl the pages from the web and produce relevant search results based on the user's query. The first difference between the two types is that the blog search engine focuses on dealing with blog posts and filters out other types of pages. This difference allows bloggers only to care about posts rather than all pages that are indexed by general search engines. The second difference is the post, considering more time-related issues compared to the page. The semantic analysis model is widely used to analyze the various semantic relationships that may arise in the document. In this article, the authors propose a new semantic analysis model to find possible time relationships between posts. The main contribution of this paper has two points: first is that this paper builds a high-performance search system that considers the discussion topic and updated time between posts; second, is that the authors consider the time relationships between posts that can rank the relevant blog topics based on the popularity of the posts.
A Reversible Watermarking Algorithm Resistant to Image Geometric Transformation

This article proposes a novel robust reversible watermarking algorithm. The proposed watermarking scheme is reversible because the original image can be recovered after extracting watermarks from the watermarked image, as long as it is not processed by an attacker. The scheme is robust because watermarks can still be extracted from watermarked images, even if it is undergone some malicious or normal operations like rotation and JPEG compression. It first selects two circles, which are centred at the centroid and the centre of image. Then, statistic quantities of these two circles are employed for robust watermark embedding by altering the pixels' value. The side information generated by above embedding process will be embedded as fragile watermarks at another stage to ensure the recovery of original image. Experimental results verify the high performance of the proposed algorithm in resisting various attacks, including JPEG compression and geometric transformation.
DBMiP: A pre-training method for information propagation over deep networks

Deep neural networks (DNNs) have recently been successful in many applications and have become a popular approach for speech recognition. Training a DNN model for speech recognition is computationally expensive due to the model large number of parameters. Pre-training improves DNN modeling. However, DNN learning is challenging if pre-training is inefficient. This paper introduces a new framework for pre-training that utilizes label information in lower layers (layers near input) for better recognition. The proposed pre-training method dynamically inserts discriminative information not only in the last layer but also in other layers. In this algorithm, the lower layers achieve more generative information while the higher layers achieve more discriminative information. In addition, this method uses speaker information by employing the Subspace Gaussian Mixture Model (SGMM), which improves recognition accuracy. Experimental results on TIMIT, MNIST, Switchboard, and English Broadcast News datasets show that this approach significantly outperforms current state-of-the-art methods such as the Deep Belief Network and the Deep Boltzmann Machine. Moreover, the proposed algorithm has minimal memory requirements.
An Inductive Logic Programming Algorithm Based on Artificial Bee Colony

Inductive logic programming (ILP) is a hot research field in machine learning. Although ILP has obtained great success in many domains, in most ILP system, deterministic search are used to search the hypotheses space, and they are easy to trap in local optima. To overcome the shortcomings, an ILP system based on artificial bee colony (ABCILP) is proposed in this article. ABCILP adopts an ABC stochastic search to examine the hypotheses space, the shortcoming of deterministic search is conquered by stochastic search. ABCILP regard each first-order rule as a food source and propose some discrete operations to generate the neighborhood food sources. A new fitness is proposed and an adaptive strategy is adopted to determine the parameter of the new fitness. Experimental results show that: 1) the proposed new fitness function can more precisely measure the quality of hypothesis and can avoid generating an over-specific rule; 2) the performance of ABCILP is better than other systems compared with it.
Spatial–temporal multi-task learning for salient region detection

This paper proposes a novel multi-task learning based salient region detection method by fusing spatial and temporal features. Salient region detection has been widely used in various computer vision tasks, being as a general preprocessor to identify interest objects. Despite the recent successes, existing saliency models still lag behind the performance of human when visually perceives dynamic scenes. Most of the existing models largely rely on various spatial features. However, these spatial feature based methods have several deficiencies: (i) they can hardly adapt to the situation where moving objects are included, and (ii) they cannot model the human vision process in dynamic scenes. Recently, some saliency models introduce temporal features in their detecting process, such as the optical flow and stacking frames. The potential of temporal features for saliency optimization has been demonstrated. However, since temporal features in these models are merely used as a compensation to static features, the advantages of temporal features have not yet been fully explored. Aiming to comprehensively address these issues above, our method fuses spatial and temporal features, and learns the mapping relationship from various features to salient regions using our multi-task learning framework. The final salient region is generated by our unified Bayesian framework. The experimental results demonstrated that our proposed approach outperforms previous methods.
Pre-gen Metrics: Predicting Caption Quality Metrics Without Generating Captions

Image caption generation systems are typically evaluated against reference outputs. We show that it is possible to predict output quality without generating the captions, based on the probability assigned by the neural model to the reference captions. Such pre-gen metrics are strongly correlated to standard evaluation metrics.
Research on Age Estimation Algorithm Based on Structured Sparsity

In order to solve the inaccuracy of age estimation dataset and the imbalance of age distribution, this paper proposes an age estimation model based on the structured sparse learning. Firstly, the Multi-label representation of facial images is performed by age, and the age estimation model is trained by solving the model matrix. Finally, the correlation with all age labels is calculated according to the facial images and age estimation model to be tested, and the most correlated age is taken as the predicted age. This paper sets up a series of verification experiments, and analyzes the structured sparse age estimation model from several perspectives. The proposed algorithm has achieved good results in the evaluation of indexes such as the mean absolute error, accumulation index curve and convergence rate, and has designed the demo system to put the model into use. Facts prove that the age estimation model proposed in this paper may achieve a good estimation effect.