Reviewerly/exHarmony huggingface dataset:
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference

Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e.g., in reinforcement learning based recommender systems. Reward function is crucial for most of reinforcement learning applications as it can provide the guideline about the optimization. However, current reinforcement-learning-based methods rely on manually-defined reward functions, which cannot adapt to dynamic and noisy environments. Besides, they generally use task-specific reward functions that sacrifice generalization ability. We propose a generative inverse reinforcement learning for user behavioral preference modelling, to address the above issues. Instead of using predefined reward functions, our model can automatically learn the rewards from user's actions based on discriminative actor-critic network and Wasserstein GAN. Our model provides a general way of characterizing and explaining underlying behavioral tendencies, and our experiments show our method outperforms state-of-the-art methods in a variety of scenarios, namely traffic signal control, online recommender systems, and scanpath prediction.
Improving DCIS diagnosis and predictive outcome by applying artificial intelligence

Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all DCIS will progress to invasion. Current histopathological classification systems are unable to predict which cases will or will not progress, and therefore many women with DCIS may be overtreated. Artificial intelligence (AI) image-based analysis methods have potential to identify and analyze novel features that may facilitate tumor identification, prediction of disease outcome and response to treatment. Indeed, these methods prove promising for accurately identifying DCIS lesions, and show potential clinical utility in the therapeutic stratification of DCIS patients. Here, we review how AI techniques in histopathology may aid diagnosis and clinical decisions in regards to DCIS, and how such techniques could be incorporated into clinical practice.
White-box Schnorr Signature for Internet of Things Security

For security and privacy, people are increasingly interested in adopting blockchain technology in the Internet of Things (IoT). Blockchain technology may improve privacy by keeping the IoT transactions anonymous, and at the same time establish agreements between untrusted nodes in the network, eliminating the need to use trusted intermediaries. However, as IoT devices frequently send transactions in the blockchain network, the security of the device's private key becomes extremely important, especially when the device is exposed to a white box attack environment. In this article, we introduce a white-box Schnorr signature scheme to protect the private key security of IoT devices, which significantly improves security. Through real experiments, we have evaluated the performance of the scheme, and the results show that the proposed scheme has high efficiency and value.
Implementation of close contact elicitation at the time of COVID-19 testing—Atlanta, GA, October–November 2020

Contact tracing during the Coronavirus Disease 2019 (COVID-19) pandemic in the USA has been met with various challenges. In an attempt to improve the yield of close contact collection, the Fulton County Board of Health implemented a pilot approach to contact elicitation at the time of testing.Between October and November 2020, close contacts were elicited from persons under investigation (PUIs) at one COVID-19 testing site in Fulton County, GA. Secure online data collection forms were used to record PUI demographic data, close contact information and reasons for not providing contacts.Of 1238 PUIs, 48% reported at least one contact. Among the 66 people who tested positive, 16 (24%) reported contacts compared to 578/1165 (50%) who tested negative. PUIs of increasing age were less likely to provide contacts; Black and Hispanic PUIs were also less likely to report any contacts compared to White and Asian PUIs.Our study revealed that PUIs testing positive were less likely to provide contacts compared to PUIs testing negative. Age and racial differences were also noted in the provision of contacts. Further investigation is needed to understand these discrepancies in order to devise more effective strategies for contact elicitation.
Non-Local and Multi-Scale Mechanisms for Image Inpainting

Recently, deep learning-based techniques have shown great power in image inpainting especially dealing with squared holes. However, they fail to generate plausible results inside the missing regions for irregular and large holes as there is a lack of understanding between missing regions and existing counterparts. To overcome this limitation, we combine two non-local mechanisms including a contextual attention module (CAM) and an implicit diversified Markov random fields (ID-MRF) loss with a multi-scale architecture which uses several dense fusion blocks (DFB) based on the dense combination of dilated convolution to guide the generative network to restore discontinuous and continuous large masked areas. To prevent color discrepancies and grid-like artifacts, we apply the ID-MRF loss to improve the visual appearance by comparing similarities of long-distance feature patches. To further capture the long-term relationship of different regions in large missing regions, we introduce the CAM. Although CAM has the ability to create plausible results via reconstructing refined features, it depends on initial predicted results. Hence, we employ the DFB to obtain larger and more effective receptive fields, which benefits to predict more precise and fine-grained information for CAM. Extensive experiments on two widely-used datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art approaches both in quantity and quality.
An Analytical Study of Signs Used in Baby Sign Language Using Mobilenet Framework

A communication via gestures is a method of non-verbal communication which uses body stances and motions pass on the message, thoughts, realities, feeling to the viewers, and in this method of self-expression each body part assumes a significant job is known as Sign Language. Gesture based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The Baby Sign Language uses gestures which clearly expressing the emotions and desires of toddler and serves as a communication medium between parents and toddlers. In this paper we present research work based on a study of the existing literature on various Sign Languages has been carried out and then prepared a data set of static images for 53 odd baby signs, which were classified using transfer learning technique based on Deep Learning by utilizing a MobileNet, a class of Convolutional Neural Network. This model is further fine-tuned which reduces errors and increases classification accuracy results. A classification accuracy of 85.8% has been achieved on the dataset prepared.
A Study on Google Project Loon- Opportunities and Challenges

Google is a company which certainly attracts criticism, but you cannot argue with the creativity which is nurtured. The company has a knack of taking an idea which no-one has much commercial faith in and running with it. Project loon. Loon LLC is an Alphabet Inc. subsidiary working on providing Internet access to rural and remote areas. The company uses high-altitude balloons in the stratosphere at an altitude of 18 km (11 mi) to 25 km (16 mi) to create an aerial wireless network with up to 4G-LTE speeds. A reference to the balloons used, Project Loon began as a research and development project by X (formerly Google X), but later spun out into a separate company in July 2018.
Experiment on a Transformer Model Indonesian-to-Sundanese Neural Machine Translation with Sundanese Speech Level Evaluation

Speech level is one of the essential Sundanese language elements.As Indonesian mixed within Sundanese language use, the usage of speech level is gradually degrading.Indonesian, in order to get correct word choice in Sundanese language, social contexts may refer to many sources such as a dictionary, or thesaurus.However, for better translation in syntax and context, machine translation is offered.Based on the fact, this experiment focuses on the problem when translating Indonesian to Sundanese and the evaluation of Sundanese speech level in the translated texts.Neural machine translation (NMT) was chosen as the current technology in machine translation, which worked by combining recurrent neural network encoder-decoder.The experiment started with building 50.000Sundanese-Indonesian sentences as a parallel corpus to build and train NMT models.The experiment on sentence training in Transformer NMT without outof-vocabulary (OOV) shows 42.72% BLEU Score, and Average Training Loss was 1.77 while for speech level was dominated by 56% basa loma (coarse) of the whole testing result.
Proficient implementation of data analysis and sentimental analysis of FIFA-18

Opinion Analysis is the way toward deciding if a bit of composing is positive, negative or impartial. Supposition investigation helps information examiners inside huge endeavors measure popular assessment, direct nuanced statistical surveying, screen brand and item notoriety, and comprehend client encounters. Web has gotten quite possibly the most impressive specialized instrument for imparting insights in everyday life. Twitter as a blogging service that can be used to discover news in real time. As Twitter posts are being generated constantly, they are well-suited sources of streaming data for sentiment polarity detection. Tweets can reflect public sentiments during events such as the FIFA World Cup. We use machine learning methods for extracting positive or negative sentiment on tweets. This research work we used the sentiments extracted from Twitter to see if they are positive or negative. Experimental results show that people have positive sentiment towards the game. Secondly a dataset of fifa-18 is extracted from sofifa.com on jupyter notebook and different python libraries are used for analysis and the data is pre-processed cleaned analyzed and final results are displayed. These results can be used to improve the performance of the game, to know the best playing squad etc. Many visualization graphs are not using different python libraries such as matplotlib.
High-Risk Deviant Decisions: Does Neutralization Still Play a Role?

Extant research has shown that neutralization processes can enable potential IS security policy violators to justify their behavior and overcome the deterrence effect of sanctions in order to engage in unethical behaviors. However, such sanctions are typically moderate and not career ending. We test the boundary conditions of this theory by evaluating whether neutralization plays a role in overcoming the impact of extreme levels of deterrence. We extend the Siponen and Vance (2010) framework within a professional context that assigns extreme sanctions to violators. Using the scenario-based factorial survey method common in IS security research, we collected data from future auditors who understand these extreme sanctions. We test the reasons that auditors may use to form intentions to falsify information concerning an information security issue with a company’s accounting information system, thereby jeopardizing data integrity and security by modifying working papers to hide irregularities and, by doing so, violating their professional standards, which could result in career-ending sanctions. We empirically validated and tested the theoretical model. Our results show that sanctions play an important role in reducing employees’ intentions to violate policy but that, even under extreme boundary conditions, employees might seek to rationalize their unethical behavior by denying responsibility for their actions through, for example, arguing that their supervisors pressured them into performing the violations. We also establish that messages heightening the awareness and perceptions of the certainty and severity of organizational punishment are likely to attenuate such deviant behaviors. We discuss the implications of these findings and suggest future avenues for research.
Zero-Shot Action Recognition with Three-Stream Graph Convolutional Networks

Large datasets are often used to improve the accuracy of action recognition. However, very large datasets are problematic as, for example, the annotation of large datasets is labor-intensive. This has encouraged research in zero-shot action recognition (ZSAR). Presently, most ZSAR methods recognize actions according to each video frame. These methods are affected by light, camera angle, and background, and most methods are unable to process time series data. The accuracy of the model is reduced owing to these reasons. In this paper, in order to solve these problems, we propose a three-stream graph convolutional network that processes both types of data. Our model has two parts. One part can process RGB data, which contains extensive useful information. The other part can process skeleton data, which is not affected by light and background. By combining these two outputs with a weighted sum, our model predicts the final results for ZSAR. Experiments conducted on three datasets demonstrate that our model has greater accuracy than a baseline model. Moreover, we also prove that our model can learn from human experience, which can make the model more accurate.
Bayesian Reasoning with Trained Neural Networks

We showed how to use trained neural networks to perform Bayesian reasoning in order to solve tasks outside their initial scope. Deep generative models provide prior knowledge, and classification/regression networks impose constraints. The tasks at hand were formulated as Bayesian inference problems, which we approximately solved through variational or sampling techniques. The approach built on top of already trained networks, and the addressable questions grew super-exponentially with the number of available networks. In its simplest form, the approach yielded conditional generative models. However, multiple simultaneous constraints constitute elaborate questions. We compared the approach to specifically trained generators, showed how to solve riddles, and demonstrated its compatibility with state-of-the-art architectures.
CoDiNet: Path Distribution Modeling With Consistency and Diversity for Dynamic Routing

Dynamic routing networks, aimed at finding the best routing paths in the networks, have achieved significant improvements to neural networks in terms of accuracy and efficiency. In this paper, we see dynamic routing networks in a fresh light, formulating a routing method as a mapping from a sample space to a routing space. From the perspective of space mapping, prevalent methods of dynamic routing did not take into account how inference paths would be distributed in the routing space. Thus, we propose a novel method, termed CoDiNet, to model the relationship between a sample space and a routing space by regularizing the distribution of routing paths with the properties of consistency and diversity. Specifically, samples with similar semantics should be mapped into the same area in routing space, while those with dissimilar semantics should be mapped into different areas. Moreover, we design a customizable dynamic routing module, which can strike a balance between accuracy and efficiency. When deployed upon ResNet models, our method achieves higher performance and effectively reduces average computational cost on four widely used datasets.
Analytical Study of Virtual Machine Allocation Techniques in Cloud Computing Using Machine Learning

Appropriate allocation of virtual machine (VM) contributes significantly in the compelling work in data centres. Virtual machine allocation is in fact distribution of collection of supporting devices known as virtual machines to the structure of tactile devices, hosts or the physical machines positioned in the datacentre. And certainly this allocation if blended with novel machine learning techniques will provide splendid results. This paper analyses the intended efficacious machine learning techniques for the foremost concern of data centres which is allocation of virtual machine to the concrete devices known as hosts or physical machines. The core purpose of this research is to explore some of the peculiar machine learning techniques for allocation of virtual machines in efficient data centre architecture.
Image retrieval of MRI brain tumour images based on SVM and FCM approaches

Abstract Objectives The key test in Content-Based Medical Image Retrieval (CBMIR) frameworks for MRI (Magnetic Resonance Imaging) pictures is the semantic hole between the low-level visual data caught by the MRI machine and the elevated level data seen by the human evaluator. Methods The conventional component extraction strategies centre just on low-level or significant level highlights and utilize some handmade highlights to diminish this hole. It is important to plan an element extraction structure to diminish this hole without utilizing handmade highlights by encoding/consolidating low-level and elevated level highlights. The Fleecy gathering is another packing technique, which is applied in plan depiction here and SVM (Support Vector Machine) is applied. Remembering the predefinition of bunching amount and enlistment cross-section is until now a significant theme, a new predefinition advance is extended in this paper, in like manner, and another CBMIR procedure is suggested and endorsed. It is essential to design a part extraction framework to diminish this opening without using painstakingly gathered features by encoding/joining low-level and critical level features. Results SVM and FCM (Fuzzy C Means) are applied to the power structures. Consequently, the incorporate vector contains all the objectives of the image. Recuperation of the image relies upon the detachment among request and database pictures called closeness measure. Conclusions Tests are performed on the 200 Image Database. Finally, exploratory results are evaluated by the audit and precision.
Robustness of Measurement–Induced Correlations Under Decoherence Effect

In this article, we study the dynamics of quantum correlation measures such as entanglement and measurement–induced nonlocality (MIN). Starting from an arbitrary Bell-diagonal mixed state under Markovian local noise such as bit-phase flip, depolarizing and generalized amplitude damping channel, we provide the decays of the entanglement measured by concurrence and quantum correlation captured by different forms of MIN (trace distance, Hilbert–Schmidt norm and relative entropy) as a function of the decoherence parameters. The effect of local noises on the dynamical behaviors of quantum correlation is also observed. We show the existence of specific and important features of MIN such as revival, noise robustness and sudden change with respect to decoherence parameter. It is observed that all the noises cause sudden death of entanglement for partially entangled states. Further, we show the existence of separable quantum states with non-zero quantum correlations in terms of MIN.
Deep Reinforcement Learning With a Stage Incentive Mechanism of Dense Reward for Robotic Trajectory Planning

To improve the efficiency of deep reinforcement learning (DRL)-based methods for robot manipulator trajectory planning in random working environments, we present three dense reward functions. These rewards differ from the traditional sparse reward. First, a posture reward function is proposed to speed up the learning process with a more reasonable trajectory by modeling the distance and direction constraints, which can reduce the blindness of exploration. Second, a stride reward function is proposed to improve the stability of the learning process by modeling the distance and movement distance of joint constraints. Finally, in order to further improve learning efficiency, we are inspired by the cognitive process of human behavior and propose a stage incentive mechanism, including a hard-stage incentive reward function and a soft-stage incentive reward function. Extensive experiments show that the soft-stage incentive reward function is able to improve the convergence rate, get higher mean reward and lower standard deviation after convergence.
Performance Improvement in Cloud Computing Environment by Load Balancing-A Comprehensive Review

With the advent of cloud computing, the affinity between business and technology had increased manifold, allowing users to access IT resources at their convenience through the pay-per-use scheme. With such huge demand surging day to day, the cloud environment must cater to the user requirements flawlessly and also should be rewarding to the providers of cloud service. To maintain its high level of efficiency, there are several challenges that the cloud environment should tackle. One amongst those challenges is the balancing of load. It is one of the primary features of cloud computing that focuses on avoiding the overloading of nodes where there may be idle nodes or nodes with lesser load present at the same juncture. By keeping an effective check on the load several the Quality of Service (QoS) parameters including response time, throughput, resource utilization, energy consumption, cost etc., can be improved, adding to better performance of the entire cloud environment. Even distribution of load among datacenters will contribute to optimal energy consumption and keeps a check on carbon emissions. In this paper we have presented a methodical review on literature pertaining to load balancing strategies that had been proposed in the cloud environment. We had made in-depth analyses of available load balancing techniques and had come up with their advantages, limitations along with the challenges to be addressed by researchers for developing efficient load balancing strategies in the near future. We had also suggested prospective insights about the aspects in load balancing that could be applied in the cloud environment.
Graph Signal Processing: Vertex Multiplication

On the Euclidean domains of classical signal processing, linking of signal samples to the underlying coordinate structure is straightforward. While graph adjacency matrices totally define the quantitative associations among the underlying graph vertices, a major problem in graph signal processing is the lack of explicit association of vertices with an underlying quantitative coordinate structure. To make this link, we propose an operation, called the vertex multiplication, which is defined for graphs and can operate on graph signals. Vertex multiplication, which generalizes the coordinate multiplication operation in time series signals, can be interpreted as an operator which assigns a coordinate structure to a graph. By using the graph domain extension of differentiation and graph Fourier transform (GFT), vertex multiplication is defined such that it shows Fourier duality, which states that differentiation and coordinate multiplication operations are duals of each other under Fourier transformation (FT). The proposed definition is shown to reduce to coordinate multiplication for graphs corresponding to time series. Numerical examples are also presented.