Reviewerly/exHarmony huggingface dataset:
Saliency Detection Based on Weighted Saliency Probability

The key to computer-based image recognition is to distinguish salient objects from the image background. However, it is still challenging to detect salient region when an object significantly touches the image boundaries. In this study, we present a novel salient region detection method based on a color space volume and a novel weighted saliency probability to address the issue. First, we propose a novel color space volume, and it was regarded as the foreground based on the L^*a^*b^* color space. Second, we present a new background measure called background probability to find background regions by exploiting a background prior and centroid distance weights. Moreover, we propose a new foreground measure called foreground probability to detect foreground by utilizing the brightness of color space volume. Finally, we propose a novel weighted saliency probability to obtain a clean and uniform salient map based on the background probability and the foreground probability. Experiments on three benchmark image datasets demonstrated that the proposed method outperformed several well-known saliency detection methods.
A Critical Survey On Privacy Prevelling In Collaborative Filtring Recomender System: Challenges, State-Of-The-Art Methods And Future Directions

Recommender systems have become very popular in recent years and are used in different fields of technology. Memory-based Collaborative Filtering (CF) Recommender System is a quickly progressing study area and proved to be doing well for different types of recommender systems. Memory-based CF recommends items based on the entire collection of items which have been rated by users previously. CF generates recommendations on the basis of neighbors in memory and known as Memory based CF. In this study, we have discussed a major challenge for recommender system that has direct access to user data that leads to privacy risk and may become the cause of attacks and other risks. Leading to the above challenges, we have conducted a comprehensive survey of different risks and analyzed them critically. Moreover, we have investigated the existing state-of-the-art approaches which are being used to address such challenges. Based on findings, the current study will come up with existing approaches that reduce privacy risks for Memory-based CF.
Modeling Variability in Brain Architecture with Deep Feature Learning

The brain has long been divided into distinct areas based upon its local microstructure, or patterned composition of cells, genes, and proteins. While this taxonomy is incredibly useful and provides an essential roadmap for comparing two brains, there is also immense anatomical variability within areas that must be incorporated into models of brain architecture. In this work we leverage the expressive power of deep neural networks to create a data-driven model of intra- and inter-brain area variability. To this end, we train a convolutional neural network that learns relevant microstructural features directly from brain imagery. We then extract features from the network and fit a simple classifier to them, thus creating a simple, robust, and interpretable model of brain architecture. We further propose and show preliminary results for the use of features from deep neural networks in conjunction with unsupervised learning techniques to find fine-grained structure within brain areas. We apply our methods to micron-scale X-ray microtomography images spanning multiple regions in the mouse brain and demonstrate that our deep feature-based model can reliably discriminate between brain areas, is robust to noise, and can be used to reveal anatomically relevant patterns in neural architecture that the network wasn't trained to find.
Empirical Evaluation of Coupling Metrics in Software Fault Prediction

Software Fault Prediction (SFP) techniques are commonly used to determine the fault proneness of software modules to complement the development and testing process. In SFP, fault prediction is based on software metrics that reflect any aspect of the software, where coupling is one of them. Software coupling is one such metric, which is a measure of the interdependency of software modules. Coupling induces complexity in the coupled module and makes it difficult to comprehend. Eventually, more coupled modules are likely to be faultier. This spurs a need to evaluate the impact of coupling exclusively, on fault proneness. Since, coupling by inheritance is harmless, as it promotes reusability, so coupling through inheritance is not considered. We evaluated seven coupling metrics on 87 different publicly available datasets. After pre-processing, selected datasets are split with all possible coupling metrics. Resulting 474 split datasets are used for the experiments. These datasets have only coupling metrics and nominal-binary class labels. Model building and validation are done for Support Vector Machine. Results of entropy-loss shows that coupling metrics are good predictors of faults in software. Furthermore, {Coupling Between Objects, Design Complexity, Fan-in} outperform the rest of the 30 feature set. Finally, coupling metrics are ranked by keeping in view their position achieved and the number of accompanying metrics. This declares Efferent Coupling as the best coupling metric with respect to the other six coupling metrics in 474 datasets. The paper concludes the viability of coupling metrics in software fault prediction. Evaluating the absolute impact of coupling in SFP and then the relative ranking of coupling metrics is the significance of this paper.
Detecting Online Content Deception

The surge of content (such as fake news) in the last few years has made content deception an important area of research. We identify two main types of content deception based on either fake content or misleading content. We present a classification of deception attacks along with their delivery methods. We also discuss defense measures that can detect deception attacks. Finally, we highlight some outstanding challenges in the area of content deception.
An Approach to Detect Sarcasm in Tweets

When academicGodara, Jyoti andAron, Rajni business ventures are discussed, electronic documents form the crucial part of receiving and transferring information. There is no use of online information if we cannot extract it and use it to cater our ventures. In order to frame up any overview, it is required to find the relevant text with complete omission of unnecessary information while keeping the focus on details and compile them into a document. The sentiment analysis is the approach used to evaluate users’ sentiments on websites, forums, comments, feedback as negative, positive or neutral. But, sometimes, people express their negative sentiment in a positive manner. This flips the polarity of the sentence and sentiment analysis performance is affected. Thus, automatic detection of this sarcastic text is an essential aspect of sentiment analysis. Motivation for this paper includes to provide overview of Sentiment Analysis and Sarcasm. Research studies in the area of sentiment analysis and Sarcasm are also discussed. There are multiple existing techniques for automatic detection of Sarcasm, according to the literature studied ensemble learning has performed better than others. In this paper, designing and development of an ensemble classification method is proposed. Base classifiers selected are Decision Tree, Naive Bayes and K-nearest Neighbor. It will thus improve various parametric values for the sarcasm detection.
HEE-Sketch: an Efficient Sketch for Sliding-Window Frequency Estimation over Skewed Data Streams

Frequency estimation over sliding window has become a critical problem in data stream applications. Sketch is a widely used solution for frequency estimation, which can be successfully implement on hardware. The key metrics of sketches are accuracy, speed, and memory usage. However, existing sketches cannot achieve high accuracy, high speed and using limited memory at the same time for sliding window queries over skewed data streams. In this paper, we present the Hierarchical Elastic Exponential Sketch (HEE-sketch) to tackle the problem. For sliding-window frequency estimation, our HEE-sketch can achieve high accuracy and high speed when using limited memory. Extensive experimental results on real-world datasets show that with the same memory footprint, the accuracy of the HEE-sketch is improved up to 12.4 times, while the speed is improved up to 4.47 times, compared with the ECM-sketch, the state-of-the-art sliding-window sketch model.
An Optimizing Algorithm for Deadline Constrained Scheduling of Scientific Workflows in IaaS Clouds Using Spot Instances

In recent years, due the advantages of on-demand resource provisioning and pay-as-you-go billing model, IaaS clouds are becoming more and more popular for performing large-scale scientific applications such as scientific workflows. To increase the utilization of the resources, many cloud vendors provide transient resources with charging price far below their normal counterparts, such as the spot instances provided by Amazon EC2. By using transient resources, the execution cost of scientific workflows can be greatly reduced. The side effect is that transient resources could be revoked at any time and hence may delay the execution of the scientific workflows. To overcome the problem, in this paper we propose an optimizing algorithm for deadline constrained scheduling of scientific workflows in IaaS clouds using spot instances. The proposed algorithm generates initial schedules based existing scheduling algorithms but adopt a partial schedule segmentation method to divide the initial schedules into diverse sections. For different sections, on-demand and spot instances are adaptively used according to the characteristics of the tasks in the sections. The experimental results of real-world workflow applications show that the proposed optimization algorithm can significantly reduce the monetary cost of the initial schedule solutions, while keeping their deadline constraints nearly all the time.
Transfer Learning Based Histopathologic Image Classification for Burns Recognition

Burn is one of the most leading devastating injuries affecting people worldwide with high impact rate in low-and middle-income countries subjecting hundreds of thousands to loss of lives and physical deformities. Both affected individuals and health institutions are faced with challenges such as inadequate experience/well trained workforce and high diagnostics cost. The demand of having efficient, cost-effective and user-friendly technique to aid in addressing the problem is on the rise. Deep neural networks have recently attracted the attention of many researchers and achieved impressive results in many applications. Therefore, this paper proposed the use of off-the-shelf Convolutional Neural Network features from two ImageNet pre-trained models (GoogleNet and ResNet152), VGG-Face. The features are used to train Support Vector Machine (SVM) and Decision Tree (DT). 100% identification accuracy was recorded using ImageNet model and SVM.
Noise Reduction of Speech Signal Based on Wavelet Transform with Improved Threshold Function

The wavelet threshold noise reduction method is widely used in the field of signal noise reduction. Based on the traditional soft and hard threshold function noise reduction, combined with the shortcomings of the current improved wavelet threshold noise reduction algorithm, this paper proposes a new improved speech noise reduction threshold function. The experimental simulation waveform and quantitative indicators show that the threshold function retains effective speech information to a greater extent, and has a better improvement on the continuity and constant error in the process of speech noise reduction. It is suitable for the analysis and processing of noise-containing signals, and has certain practical value.
AutoQP: Genetic Programming for Quantum Programming

Quantum computing is a new era in the field of computation which makes use of quantum mechanical phenomena such as superposition, entanglement, and quantum annealing. It is a very promising field and has given a new paradigm to efficiently solve complex computational problems. However, programming quantum computers is a difficult task. In this research, we have developed a system called AutoQP which can write quantum computer code through genetic programming on a classical computer provided the input and expected output of a quantum program. We have tested AutoQP on two different quantum algorithms: Deutsch Problem and the Bernstein-Vazirani problem. In our experimental analysis, AutoQP was able to generate quantum programs for solving both problems. The code generated by AutoQP was successfully tested on actual IBM quantum computers as well. It is expected that the proposed system can be very useful for the general development of quantum programs based on the IBM gate model. The source code for the proposed system is available at the URL: https://github.com/usamaahsan93/AutoQP.
NESTA: Hamming Weight Compression-Based Neural Proc. EngineAli Mirzaeian

In this paper, we present NESTA, a specialized Neural engine that significantly accelerates the computation of convolution layers in a deep convolutional neural network, while reducing the computational energy. NESTA reformats Convolutions into 3 × 3 batches and uses a hierarchy of Hamming Weight Compressors to process each batch. Besides, when processing the convolution across multiple channels, NESTA, rather than computing the precise result of a convolution per channel, quickly computes an approximation of its partial sum, and a residual value such that if added to the approximate partial sum, generates the accurate output. Then, instead of immediately adding the residual, it uses (consumes) the residual when processing the next batch in the hamming weight compressors with available capacity. This mechanism shortens the critical path by avoiding the need to propagate carry signals during each round of computation and speeds up the convolution of each channel. In the last stage of computation, when the partial sum of the last channel is computed, NESTA terminates by adding the residual bits to the approximate output to generate a correct result.
Using Label Noise Filtering and Ensemble Method for Sentiment Analysis on Thai Social Data

Sentiment analysis is an essential task for social listening, especially in service and product analysis. Prior works on sentiment analysis, especially in Thai language, mostly focus on the improvement of model architecture without considering error propagation from word tokenizers or noisy text from social media. In this paper, three contributions are proposed for implementing social analysis model. First, text pre-processing is used to mitigate noise from input texts. Second, robustness towards word segmentation is enhanced by using an ensemble process with two tokenizers. Lastly, the training process inspired by Co-training method is proposed in order to filter label noise within the data. In the experiments, the model achieves 2.56% improvement on the average macro f-l score when compared with the baseline models in social media data.
An Empirical Study on Accidental Cross-Project Code Clones

Software clones are considered a code smell in software development. While most clones occur due to developers copy - paste behaviour, some of them arise accidentally as a symptom of coding idioms. If such accidental clones occur across projects, then they may indicate a lack of abstraction in the underlying programming language or libraries. In this research, we study accidental cross-project clones from the perspective of missing abstraction. We discuss the six cases of frequent cross-project clones, three of them symptoms of missing language features (which have been resolved with the release of Java 7 and Java 12), and two of them symptoms of missing library features (which have not yet been addressed).
LSB-Based Visual Image Encryption Scheme in Cloud Environment

Preventing multimedia data in cloud environment from leakage is a significant and imperative issue in the modern information-oriented society. Many encryption schemes have been proposed to convert original images into noise-like images. However, the noise-like images are conspicuous visual signs to attract the attention of attackers, and thus lead to large quantities of attacks. In order to solve the problem, this paper propose a visual encryption scheme to convert an original image into another visually meaningful image to elude the attacker's attention and ensure the security of the image. Image data security is implemented by pre-encryption procedure and the meaningful appearance of encrypted image is based on Least significant bit (LSB) substitution, respectively. The experimental results demonstrate the superiority of the proposed scheme in terms of excellent encryption performance. The proposed scheme can protect original images that require a higher level of security compared with existing encryption schemes.
A Re-ID and Tracking-by-detection Framework for Multiple Wildlife Tracking with Artiodactyla Characteristics in Ecological Surveillance

Long-term non-interventional observation of wild animals in the natural environment is very necessary for ecological protection. With the development of artificial intelligence, it is possible to effectively utilize the features of wild artiodactyls and realize multi-target tracking and reidentification. In this paper, a re-identification and tracking-by-detection framework is proposed for real-time tele-observation of Artiodactyla. According to the characteristics of artiodactyla, our algorithm designed a three-direction feature extraction and feature matching method to achieve re-identification. The kalman filter is used in cooperation with the detector to confirm the presence of the target. Our framework integrates detector, kalman tracker and KCF tracker, which alleviates the problem of discontinuous detection results caused by the low detection rate of artiodacods in the field environment. In the KCF tracking process, the tracking bounding boxes are corrected with high confidence detection results. The experiments demonstrate the feasibility and effectiveness of the framework.
Distributed Generative Modelling with Sub-linear Communication Overhead

Pushing machine learning towards the edge, often implies the restriction to ultra-low-power (ULP) devices with rather limited compute capabilities. Generative models estimate the data generating probability mass $$\mathbb {P}^*$$ which can in turn be used for various tasks, including simulation, prediction/forecasting, and novelty detection. Whenever the actual learning task is unknown at learning time or the task is allowed to change over time, learning a generative model is the only viable option. However, learning such models on resource constrained systems raises several challenges. Recent advances in exponential family learning allow us to estimate sophisticated models on highly resource-constrained systems. Nevertheless, the setting in which the training data is distributed among several devices in a network with presumably high communication costs has not yet been investigated. We close this gap by deriving and exploiting a new property of integer models. More precisely, we present a model averaging scheme whose communication complexity is sub-linear w.r.t. the parameter dimension d, and provide an upper bound on the global loss. Experimental results on benchmark data show, that the aggregated model is often on par with the non-distributed global model.
PV System Power Model Application in Smart Cities

The photovoltaic (PV) system energy generation is closely related to climate conditions, installation site, and system design. In this paper, a simple energy generation model is proposed and established by means of module temperature and solar irradiation, module temperature coefficient, module rated power, number of modules, and three correction coefficients including temperature derating coefficient, inverter conversion loss coefficient, and other loss coefficients. The experiment results show that the estimation model proposed in this paper has high accuracy using the Mean Absolute Percentage Error (MAPE) model as the evaluation index, the estimation model MAPE is 6.75%, while the PV SYST software simulation has MAPE of 11.90%. According to international MAPE evaluation standards, the method proposed in this paper has higher accuracy level than PV SYST software. This model can be used for the estimation of energy generation when installing solar PV systems, and can be used for the reasonable assessment of PV system long-term power generation, to predict system power generation anomalies, and reduce system abnormalities caused by downtime losses, and increase investment efficiency.
Introduction to Fuzzy Harmony Search

There are several metaheuristics currently being used to solve many real-life problems, such as problems of mathematical.
Machine Learning Approach for Energy Consumption Prediction in Datacenters

Cloud Computing represents the ideal solution for end-users either small medium enterprises or simple clients. This solution is given as a for clients to go from classic service concept to oriented service concept. Moreover, this paradigm collects a set of operations which made them a complex task to the managers. Since the coming of the Cloud Computing encourages service providers to deploy their services. These enormous services need some infrastructure services that are located in datacenters in order to execute them. Due to this use, Cloud infrastructure owners are concerned by the huge energy consumed during this execution. This problematic will affect the use of costs for the services providers. To tackle this problem, in this work, we present several models presented in machine learning methods in order to predict the energy to be consumed for the next use. These forecasts could help the infrastructure providers to propose a plan and some analytics to eliminate the waste of used resources during the execution of services. The implementation of this model has been provided in order to evaluate our system. The obtained results demonstrate the effectiveness of our proposed system.
Binarizing Weights Wisely for Edge Intelligence: Guide for Partial Binarization of Deconvolution-Based Generators

This article explores the weight binarization of the deconvolution-based generator in a generative adversarial network (GAN) for memory saving and speedup of image construction on the edge. This article suggests that different from convolutional neural networks (including the discriminator) where all layers can be binarized, only some of the layers in the generator can be binarized without significant performance loss. Supported by theoretical analysis and verified by experiments, a direct metric based on the dimension of deconvolution operations is established, which can be used to quickly decide which layers in a generator can be binarized. Our results also indicate that both the generator and the discriminator should be binarized simultaneously for balanced competition and better performance during training. The experimental results on CelebA dataset with DCGAN and original loss functions suggest that directly applying state-of-the-art binarization techniques to all the layers of the generator will lead to 2.83× performance loss measured by sliced Wasserstein distance compared with the original generator, while applying them to selected layers only can yield up to 25.81× saving in memory consumption, and 1.96× and 1.32× speedup in inference and training, respectively, with little performance loss. Similar conclusions can also be drawn on other loss functions for different GANs.