Reviewerly/exHarmony huggingface dataset:
Robust and Noise-Insensitive Recursive Maximum Correntropy-Based Evolving Fuzzy System

In this article, a novel recursive maximum correntropy-based evolving fuzzy system (RMCEFS) is proposed. The proposed system has the capability of reorganizing the structure and adapting itself in a dynamically changing environment with non-Gaussian noises. The system generates a new rule based on the correntropy criterion which represents a robust nonlinear similarity measure between two random variables and avoids recruiting the noises as the rules. Maximizing the cross-correntropy between the system output and the desired response leads to the maximum correntropy criterion for system self-adaptation. In our article, a recursive solution of the maximum correntropy criterion is derived to update the parameters of the evolving rules. This avoids the convergence problem produced by the learning size in the gradient-based learning. Also, the steady-state convergence performance of the proposed RMCEFS is studied, where the analytical solutions of the steady-state excess mean square error for the Gaussian noise and non-Gaussian noises are derived. The simulation studies show that the proposed RMCEFS using the recursive maximum correntropy converges much faster and is more accurate than the existing evolving fuzzy systems in the case of noise-free and noisy conditions.
Cameras Everywhere Revisited: How Digital Technologies and Social Media Aid and Inhibit Human Rights Documentation and Advocacy

Abstract Pessimism currently prevails around human rights globally, as well as about the impact of digital technology and social media in supporting rights. However, there have been key successes in the use of these tools for documentation and advocacy in the past decade, including greater participation, more documentation, and growth of new fields around citizen evidence and fact-finding. Governments and others antagonistic to human rights have caught up in terms of weaponizing the affordances of the internet and pushing back on rights actors. Key challenges to be grappled with are consistent with ones that have existed for a decade but are exacerbated now—how to protect and enhance safety of vulnerable people and provide agency over visibility and anonymity; how to ensure and improve trust and credibility of human rights documentation and advocacy campaigning; and how to identify and use new strategies that optimize for a climate of volume of media, declining trust in traditional sources, and active strategies of distraction and misinformation. All of these activities take place primarily within a set of platforms that are governed by commercial imperatives and attention-based algorithms, and that increasingly use unaccountable content moderation processes driven by artificial intelligence. The article argues for a pragmatic approach to harm reduction within the platforms and tools that are used by a diverse range of human rights defenders, and for a proactive engagement on ensuring that an inclusive human rights perspective is centred in responses to new challenges at a global level within a multipolar world as well as specific areas of challenge and opportunity such as fake news and authenticity, deepfakes, use of artificial intelligence to find and make sense of information, virtual reality, and how we ensure effective solidarity activism. Solutions and usages in these areas must avoid causing inadvertent as well as deliberate harms to already marginalized people.
Learning A 3D Gaze Estimator with Improved Itracker Combined with Bidirectional LSTM

Free-head 3D gaze estimation which outputs gaze vector in 3D space has wide application in human-computer interaction. In this paper, we propose a novel 3D gaze estimator by improving the Itracker and employing a many-to-one bidirectional LSTM (bi-LSTM). First, we improve the conventional Itracker by removing the face-grid and reducing one network branch via concatenating the two-eye region images to predict the subject's gaze of a single frame. Then, we employ the bi-LSTM to fit the temporal information between frames to estimate gaze vector for video sequence. Experimental results show that our improved Itracker obtains 11.6% significant improvement over the state-of-the-art methods on MPIIGaze dataset (single image frame) and has robust estimation accuracy for different image resolutions. Moreover, experimental results on EyeDiap dataset (video sequence) further bring 3% accuracy improvement by employing the bi-LSTM.
Cloud Computing and Load Balancing in Cloud Computing -Survey

Cloud Computing is widespread to support industrial and academy related applications worldwide. Management of workloads to meet all the resource allocation issues, due to the participation of huge number of users is majorly studied in previous research works. Enterprises, search organization, government department and universities are planning to move on cloud computing spreads globally and many organizations now see the cloud as being an extension of IT resources. It becomes very demanding for a web application to manage large number of users, who accesses the particular web application at one time, that's why Load Balancer come into the picture. In this paper we survey the comparison and contrast of various loads balancing policy.
Robust Median Filtering Forensics by CNN-Based Multiple Residuals Learning

Median filtering (MF), a non-linear smoothing operation, has often been utilized as a means of image denoising to protect image edges and hide the traces of image tampering.Although the existing MF forensics methods have achieved excellent performance without post-processing, there is still a challenge of detecting MF in small image size and JPEG compression scenario.To meet this challenge, a robust MF forensic method using convolutional-neural-network (CNN)-based multiple residuals learning is proposed in this paper.Firstly, to reveal the traces left by MF, we use multiple high-pass filters to initialize the weights of the pre-processing layer, and obtain discriminative residuals to characterize MF artifacts in various aspects.Then the output of the pre-processing layer is employed as the input of CNN, which is elaborately designed to extract rich hierarchical features for further classification.Furthermore, Batch Normalization (BN) is introduced as a regularization method to help accelerate convergence of the entire network.The extensive experimental results on the composite database demonstrate that the proposed method is superior to the state-of-the-art methods when detecting MF in both JPEG compressed and small size images.
Chaotic dynamic weight grey wolf optimizer for numerical function optimization

The grey wolf optimizer (GWO) algorithm is a recently proposed optimization technique based on the social leadership and hunting behavior of grey wolves in nature. Due to its small number of control parameters, ease of implementation and high level of exploration and exploitation, the GWO has attra cted the interest of researchers from different fields. However, the GWO has problems with its position-updated equation, which is good for exploitation but not conducive to exploration because it can prematurely convergence to local optima. To overcome this drawback, a chaotic dynamic weight grey wolf optimizer (CDGWO) is proposed. In the CDGWO algorithm, a new position-updated equation is presented by applying a chaotic map and dynamic weight to guide the search process for potential candidate solutions. In addition, a nonlinear control parameter strategy is designed to balance the exploration and exploitation and accelerate the convergence speed of the GWO algorithm. The search accuracy and performance of the modified position-updated equation and the nonlinear control parameter strategy are verified using 19 well-known classical benchmark functions. The experimental results show that, for almost all benchmark functions, the CDGWO algorithm gives competitive results in terms of convergence, solution quality and local optimal avoidance compared with other nature-inspired optimizations and GWO variants.
Adaptive energy-aware scheduling method in a meteorological cloud

This paper focuses on the energy-aware scheduling problem of moldable non-linear parallel tasks in a meteorological cloud. Such a meteorological Cloud mainly provides computing resources for the execution of meteorological models, such as Weather Research and Forecasting model (WRF). In a meteorological Cloud, the parallelism of tasks (i.e., meteorological models) can only be configured in the beginning, and the assigned resources retained exclusively until all sub-tasks have been finished. For the scheduling of those tasks, one key challenge is how to reduce the average energy consumption while guaranteeing others requirements of such tasks. We address this challenge by considering simultaneously the deadlines of tasks, the energy consumption, the system load, and the non-linear speedup of parallel tasks when we make the scheduling decision. Specifically, we propose an adaptive energy-aware scheduling method called ASSD, that is based on the Dynamic Voltage and Frequency Scaling (DVFS) model of computing resources and the speedup of tasks under different parallelisms. We evaluate our method via simulations on a meteorological cloud. Our results show that the proposed method not only increases the number of completed tasks but also significantly reduces the average energy consumption.
Video analytics-based facial emotion recognition system for smart buildings

Video surveillance, within prisons, monitor the emotional status of inmates, as human emotions provide insight into their intended actions. This work attempts to build an automated system that cognizes human emotion from the pattern of pixels in a facial image. In this paper, a solution based on Iterative Optimization Strategy is proposed to minimize the loss function. The proposed strategy is applied in the Fully Connected layer of Deep ConvNet. To evaluate the performance of the system we use two benchmark datasets named Japanese Female Facial Expression database and Kaggle Facial Expression Recognition dataset respectively. The system was manually tested with captured video, and video from a real documentary on YouTube. From the results, we could see that the proffered system achieves a precision, i.e. (the closeness of agreement among a set of results) of 0.93.Abbreviations: DCNN: Deep Convolutional Neural Network; ER: Error; LR: Learning Rate; FM: Feature Map
Deep Features for Tissue-Fold Detection in Histopathology Images

Whole slide imaging (WSI) refers to the digitization of a tissue specimen which enables pathologists to explore high-resolution images on a monitor rather than through a microscope. The formation of tissue folds occur during tissue processing. Their presence may not only cause out-of-focus digitization but can also negatively affect the diagnosis in some cases. In this paper, we have compared five pre-trained convolutional neural networks (CNNs) of different depths as feature extractors to characterize tissue folds. We have also explored common classifiers to discriminate folded tissue against the normal tissue in hematoxylin and eosin (H&E) stained biopsy samples. In our experiments, we manually select the folded area in roughly 2.5 mm $$\times $$ 2.5 mm patches at 20x magnification level as the training data. The “DenseNet” with 201 layers alongside an SVM classifier outperformed all other configurations. Based on the leave-one-out validation strategy, we achieved $$96.3\%$$ accuracy, whereas with augmentation the accuracy increased to $$97.2\%$$ . We have tested the generalization of our method with five unseen WSIs from the NIH (National Cancer Institute) dataset. The accuracy for patch-wise detection was $$81\%$$ . One folded patch within an image suffices to flag the entire specimen for visual inspection.
Security Engineering for Machine Learning

Artificial intelligence is in the midst of a popular resurgence in the guise of machine learning (ML). Neural networks and deep learning architectures have been shown empirically to solve many real-world problems. We ask what kinds of risks ML systems pose in terms of security engineering and software security.
A structured distributional model of sentence meaning and processing

Abstract Most compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a structured distributional model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations. The semantic representation of a sentence is a formal structure derived from discourse representation theory and containing distributional vectors. This structure is dynamically and incrementally built by integrating knowledge about events and their typical participants, as they are activated by lexical items. Event knowledge is modelled as a graph extracted from parsed corpora and encoding roles and relationships between participants that are represented as distributional vectors. SDM is grounded on extensive psycholinguistic research showing that generalized knowledge about events stored in semantic memory plays a key role in sentence comprehension.We evaluate SDMon two recently introduced compositionality data sets, and our results show that combining a simple compositionalmodel with event knowledge constantly improves performances, even with dif ferent types of word embeddings.
Sentiment Analysis on Twitter Data using R

Nowadays social networking sites are at the blast from where huge amount of information is produced or retrieved. 90% people of the world are sharing their perspectives every day on micro blogging sites, since it contains short and simple expressions. The various devices, mobiles, laptops, tabs and other IoT data gadgets generate huge volume of data and Microservices based web applications running on these have made it simpler for us to get any kind of data at any time and from any place. Social media is also used for expressing our opinions for the products and services. The feedbacks and ratings of millions of the social site users can be collated to extract their attitudes and sentiment towards any products or services and use that information for future market and business improvement or domain analysis. Mining user's opinion from social media is a difficult task; it can be refined into numerous ways. In this paper, an open source approach is presented which we have collected tweets from Twitter API and then pre-processed, analyzed and visualized these tweets using R. To analyze sentiments of tweets we are utilizing a statistical tool, R programming. This sentiment analysis is based on text data retrieval from streamed web and then classifying people perspectives in eight distinct classifications of feeling (disgust, fear, anger, anticipation, sadness, trust, surprise) and two unique sentiments (positive and negative).
Extended Bit-Plane Compression for Convolutional Neural Network Accelerators

After the tremendous success of convolutional neural networks in image classification, object detection, speech recognition, etc., there is now rising demand for deployment of these compute-intensive ML models on tightly power constrained embedded and mobile systems at low cost as well as for pushing the throughput in data centers. This has triggered a wave of research towards specialized hardware accelerators. Their performance is often constrained by I/O bandwidth and the energy consumption is dominated by I/O transfers to off-chip memory. We introduce and evaluate a novel, hardware-friendly compression scheme for the feature maps present within convolutional neural networks. We show that an average compression ratio of 4.4× relative to uncompressed data and a gain of 60% over existing method can be achieved for ResNet-34 with a compression block requiring <;300 bit of sequential cells and minimal combinational logic.
Ghosting-free DCT based multi-exposure image fusion

We propose a novel algorithm for multi-exposure fusion (MEF). This algorithm decomposes image patches with the DCT transform. Coefficients from patches with different exposure are combined. The luminance and chrominance of the different images are fused separately. The algorithm adapts to dynamic sequences in order to avoid ghosting effects. The initial sequence is processed to be made static before applying the fusion procedure. Experiments with several data sets show that the proposed algorithm performs better than state-of-the-art.
Sphere: Simulator of edge infrastructures for the optimization of performance and resources energy consumption

Edge computing constitutes a key paradigm to address the new requirements of areas such as smart cars, industry 4.0, and health care, where massive amounts of heterogeneous data from continuous geographically-distributed sources have to be processed and computed near real-time. To this end, new distributed infrastructures consisting on small computing clusters close to data sources, also known as Cloudlets have emerged. In order to evaluate the performance of these solutions we present Sphere, a simulation tool that enables researchers to establish various scenarios, including: (a) topology and orchestration model of the infrastructure; (b) incoming workload patterns; (c) resource-managing models; and (d) scheduling policies. Moreover, Sphere allows researchers to apply efficiency and performance policies both at infrastructure and cluster levels. The simulator presents the following benefits: (a) Evaluation of various orchestration models; (b) Analysis of resource-efficiency and performance strategies at Edge-infrastructure and cluster (Cloudlet/Cloud) level; (c) Execution of diverse workload generation patterns; (d) Evaluation of strategies for the infrastructure communication, as well as the impact on tasks completion time (makespan); and (e) Simulation of each cluster (Cloudlet/Cloud) independently, including resource-managing, scheduling and resource-efficiency models. Finally, we performed a deep evaluation based on realistic Edge-Computing use cases. The results of the experiments confirm that it is a performant and reliable tool for the analysis of orchestration, graph-resolving, energy-efficiency, resource-managing and scheduling strategies in Edge-computing environments.
It's Not About the Journey; It's About the Destination: Following Soft Paths Under Question-Guidance for Visual Reasoning

Visual Reasoning remains a challenging task, as it has to deal with long-range and multi-step object relationships in the scene. We present a new model for Visual Reasoning, aimed at capturing the interplay among individual objects in the image represented as a scene graph. As not all graph components are relevant for the query, we introduce the concept of a question-based visual guide, which constrains the potential solution space by learning an optimal traversal scheme, where the final destination nodes alone are used to produce the answer. We show, that finding relevant semantic structures facilitates generalization to new tasks by introducing a novel problem of knowledge transfer: training on one question type and answering questions from a different domain without any training data. Furthermore, we report state-of-the-art results for Visual Reasoning on multiple query types and diverse image and video datasets.
A Hybrid Translation System from Turkish Spoken Language to Turkish Sign Language

Sign language is the primary tool of communication for deaf and mute people. It employs hand gestures, facial expressions, and body movements to state a word or a phrase. Like spoken languages, sign languages also vary among the regions and the cultures. The aim of this study is to implement a machine translation system to convert Turkish spoken language into Turkish Sign Language (TID). The advantages of rule-based and statistical machine translation techniques are combined into a hybrid translation system.
Resource Allocation with Automated QoE Assessment in 5G/B5G Wireless Systems

5G and B5G systems will be designed to better utilize the scarce spectrum. This improvement will be realized through enhanced allocation and management schemes, as well the added viability of utilizing unlicensed spectrum. Meanwhile, 5G/ B5G emphasize an ever more personalized communication experience through adapting network and spectrum choice to user requirements and experience. QoE thus becomes a critical basis for resource allocation in future networks. However, current QoE approaches are largely static and offline, making them unfit for the highly dynamic and demanding nature of future communication. To overcome this limitation, we propose a resource allocation architecture with automated QoE assessment. The architecture builds on recent advances in affective computing and sensing, and accounts for allocation considerations in a mixed (licensed/unlicensed) context.
Learning deep features for source color laser printer identification based on cascaded learning

Color laser printers have fast printing speed and high resolution, and forgeries using color laser printers can cause significant harm to society. A source printer identification technique can be employed as a countermeasure to those forgeries. This paper presents a color laser printer identification method based on cascaded learning of deep neural networks. First, the refiner network is trained by adversarial training to refine the synthetic dataset for halftone color decomposition. Then, the halftone color decomposing ConvNet is trained with the refined dataset. The trained knowledge about halftone color decomposition is transferred to the printer identifying ConvNet to enhance the identification accuracy. Training of the printer identifying ConvNet is carried out with real halftone images printed from candidate source printers. The robustness about rotation and scaling is considered in training process, which is not considered in existing methods. Experiments are performed on eight color laser printers, and the performance is compared with several existing methods. The experimental results clearly show that the proposed method outperforms existing source color laser printer identification methods.
Deep Neural Network Assisted Approach for Antenna Selection in Untrusted Relay Networks

This letter mainly studies the transmit antenna selection (TAS) scheme based on deep neural network (DNN) in untrusted relay networks. In our previous work, we revealed that machine learning (ML)-based TAS schemes have performance degradation caused by complicated coupling relationship between the achievable secrecy rate and channel gains. To solve this issue, we here introduce DNN to decouple the above complicated relationship. The simulation results show that the DNN scheme can achieve better decoupling and, thus, accomplish almost the same performance as the exhaustive searching scheme.