Reviewerly/exHarmony huggingface dataset:
Robust Target Tracking Algorithm Based on Superpixel Visual Attention Mechanism

As existing target tracking algorithms are prone to drift under complex environments, the authors propose a tracking algorithm based on superpixel segmentation and visual attention mechanism. The algorithm works on a particle filter framework, by conducting a superpixel segmentation first and then building a model of the visual attention mechanism for saliently mapping. After extracting HOG features of salient regions, the authors compared the HOG feature with the target template at last, in order to locate the target region in the new frame. The proposed algorithm is evaluated on a comprehensive test platform, in which the simulation show that our tracker is more efficacious and more efficient than the previous traditional target tracking algorithms to cope with target drift issue.
An image retrieval algorithm based on improved color histogram

Nowadays, with the rise of the Internet, digital images have been developing quickly. The demand of multimedia based on digital image is more and more urgent. Image retrieval algorithms are an effective and accurate algorithm, which search images in large image database. It is widely used in Internet platform and image database. However, most of the proposed methods are based on text content, which can not accurately retrieve the target image according to the visual features and the needs of users. So we proposed an image retrieval algorithm based on improved color histogram, which extracts features based on HSV non-uniform quantized color. Our approach divided the image into several blocks, and calculate the color features of these regions respectively with different weights to search images with each block of color histogram. Our method makes full use of the advantages of image segmentation and HSV inhomogeneity to extract color features for image retrieval. Compared with existing methods, our approach can achieve a higher accuracy.
Comparison of Performance by Activation Functions on Deep Image Prior

In this paper, we compare the performance of activation functions on a deep image prior. The activation functions considered here are the standard rectified linear unit (ReLU), leaky rectified linear unit (Leaky ReLU), and the randomized leaky rectified linear unit (RReLU). We use these functions for denoising, super-resolution, and inpainting of the deep image prior. Our aim is to observe the effect of differences in the activation functions.
Arabic Sentiment Analysis based on Topic Modeling

Users of social media generate a huge volume of reviews and comments. These reviews and comments express user's opinions about different topics. As a result, there is a great need to understand and classify these reviews. Sentiment Analysis Systems is a good way to overcome this problem. Reviews are considered as short texts and they are different from traditional documents without enough contextual information. To address this issue, we propose an efficient representation for short text based on concepts instead of terms, which transforms the data representation into a shorter, more compact, and more predictive one. However, for the Arabic language, the majority of semantic resources are incomplete projects; this may presents a serious problem about the coverage ratio of the Arabic language compared with other Languages. To overcome this problem and starting with the assumption that terms belonging to same topic share many semantic links in the same dataset, their corresponding concepts will share the same semantics links in the same dataset. We suggest integrating Topic Modeling as a tool to bring together terms with the same semantic links. The proposed method has been tested and evaluated using the Large Scale Arabic Book Reviews Dataset and the obtained results illustrate the interest and efficiency of our contribution.
Human-Object Contour for Action Recognition with Attentional Multi-modal Fusion Network

Human action recognition has great research and application value in intelligent video surveillance, human-computer interaction and other communication fields. In order to improve the accuracy of human action recognition for video understanding, the extraction of human motion features and attentional fusion methods are studied. This paper has two main contributions. Firstly, based on the essence of optical flow validity, a novel dynamic feature expression method called Human-Object Contour(HOC) is presented, which combines object understanding and contextual information. Secondly, referring to the principle of Stacking in ensemble learning, we propose Attentional Multi-modal Fusion Network(AMFN). According to the characteristics of the video, attention is paid to selecting different modalities rather than simple averaging with fixed weight. The experiment shows that HOC is effectively complementary to the static appearance feature, and the accuracy of action recognition with our fusion network improves effectively. Our approach obtains the state-of-the-art performance on the datasets of HMDB51 (72.2%) and UCF101 (96.0%).
Modelling and Predicting Individual Salaries in United Kingdom with Graph Convolutional Network

Job Posting Sites, such as Indeed and Monster, are specifically designed to help users obtain information from the market. However, at the moment, only approximately half of the UK job postings have a salary publicly displayed. Therefore, the aim of this research is to model and predict the salary of a new job, so as to improve the performance of job search and help a vast amount of job seekers better understand the market worth of their desirable positions. In order to effectively estimate the salary of a given job, we construct a graph database based on job profiles of each posting and build a predictive model through machine learning based on both metadata features and relational features. Our results reveal that these two types of features are conditionally independent and each of them is sufficient for prediction. Therefore they can be exploited as two views in graph convolutional network (GCN), a semi-supervised learning framework, to make use of a large amount of unlabelled data, in addition to the set of labelled ones, for enhanced salary classification. The preliminary experimental results show that GCN outperforms the existing ones that simply pool these two types of features together.
Robotic Path Planning Using Flower Pollination Algorithm

Background: Robotic path planning is an important facet of robotics. Its purpose is to make robots move independently in their work environment from a source to a destination whilst satisfying certain constraints. Constraint conditions are as follows: avoiding collision with obstacles, staying as far as possible from the obstacles, traversing the shortest path, taking minimum time, consuming minimum energy and so on. Hence, the robotic path planning problem is a conditional constraint optimization problem. Methods: To overcome this problem, the Flower Pollination Algorithm, which is a metaheuristic approach is employed. The effectiveness of Flower Pollination Algorithm is showcased by using diverse maps. These maps are composed of several fixed obstacles in different positions, a source and a target position. Initially, the pollinators carrying pollen (candidate solutions) are at the source location. Subsequently, the pollinators must pave a way towards the target location while simultaneously averting any obstacles that are encountered enroute. The pollinators should also do so with the minimum cost possible in terms of distance. The performance of the algorithm in terms of CPU time is evaluated. Flower Pollination Algorithm was also compared to the Particle Swarm Optimization algorithm and Ant Colony Optimization algorithm. Results: It was observed that Flower Pollination Algorithm is faster than Particle Swarm Optimization and Ant Colony Optimization in terms of CPU time for the same number of iterations to find an optimized solution for robotic path planning. Conclusion: The Flower Pollination Algorithm can be effectively applied for solving robotic path planning problem with static obstacles.
Large Scale PoC Experiment with 57,000 people to Accumulate Patterns for Lifestyle Authentication

The spread of ICT has made it possible for people to use various online services via the Internet. User authentication technique is important for using online services in order to confirm that the user is legitimate. There are already some authentication methods, but several problems have been pointed out in them. We focused attention on lifestyle authentication as a new individual authentication method, that utilizes human behavior information. Then, we conducted a large scale PoC experiment in order to collect human behavior information, and we succeeded in gathering data of about 57,000 people. By analyzing the collected data, we found that human behavior is patterned. In this paper, we introduce the large scale PoC experiment and the analysis result of human behavior.
Edge detection of internal defects based on the hidden singularity of gradient streamlines obtained by continuous wavelet transform

A method based on the measurement of out-of-plane displacement by the phase-shifting digital speckle pattern interferometry is proposed to detect the edge of internal defects. The out-of-plane displacement is scanned by the scanning method of gradient streamlines first, which can provide paths through the defect edge with the best signal-to-noise ratio everywhere. Then, continuous wavelet transform (CWT) combined with the weighted structural intensity of the wavelet maxima is applied to detect the hidden singularities of the gradient streamlines that form the defect edge later. Moreover, the weighted structural intensity method is optimized to adapt to the detection of the hidden singularity by CWT. The results of theory, simulation, and experiments agree well, and the proposed method is efficient in internal defect detection.
Challenges and Opportunities in High Performance Cloud Computing

The scientist, engineers, and researchers highly need the high-performance computing (HPC) services for executing the energy, engineering, environmental sciences, weather, and life science simulations. The virtual machine (VM) or docker-enabled HPC Cloud service provides the advantages of consolidation and support for multiple users in public cloud environment. Adding the hypervisor on the top of bare metal hardware brings few challenges like the overhead of computation due to virtualization, especially in HPC environment. This chapter discusses the challenges, solutions, and opportunities due to input-output, VMM overheads, interconnection overheads, VM migration problems, and scalability problems in HPC Cloud. This chapter portrays HPC Cloud as highly complex distributed environment consisting of the heterogeneous types of architectures consisting of the different processor architectures, inter-connectivity techniques, the problems of the shared memory, distributed memory, and hybrid architectures in distributed computing like resilience, scalability, check-pointing, and fault tolerance.
Patch-wise Weakly Supervised Learning for Object Localization in Video

Object localization in video is to predict the location and image boundaries of objects of interest in sequential scenes. Despite numerous methods being developed for the task, there are still challenging issues, such as labor-intensive data preparation. In this paper, we propose a patch-wise approach with weak supervision to resolve those issues in the object localization. We first train an patch-wise object classifier based on convolutional neural network with simple labeling about object classes, instead of the bounding box annotation. Then, the object regions are estimated using the class activation maps of the classifier for each patch. The patch-wise classifier can learn more relevant features of objects from the patches containing various parts of them. In addition, background patches for weakly-supervised learning can be easily prepared. Experiments using the visual object tracking challenge data set showed that the patch-wise weakly supervised approach is effective in the object localization in video.
SPAINChain: Security, Privacy, and Ambient Intelligence in Negotiation Between IOT and Blockchain

Vulnerability in Internet-of-Things (IoT) necessitates the existence of a safeguard for both security and privacy without sacrificing “smartness” of the environment, or its Ambient Intelligence in scientific terms. Blockchains, underpinning crypto-currency, could be the remedy for this vulnerability. This paper quickly overviews security, privacy, Ambient Intelligence, and the use of Blockchain as a safeguard for IOT, before proposing and evaluating SPAINChain:Security, Privacy, and Ambient Intelligence in Negotiation between IOT and Blockchain. IOT-Blockchain mapping is proven to be feasible. The gamut of blockchain is based on a new concept called solidus. Solidus is basically the sum of blockchain favorability dimensions, adjusted by preference of blockchain type.
A Generalized Constraint of Privacy: &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$\alpha$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-Mutual Information Security

We study the security of a variety of cryptographic tasks including traditional privacy (e.g., seeded extractors, encryptions, commitments, and secret sharing schemes) and differential privacy from the perspective of α-mutual information. As far as we know, encryption scheme, commitment, and differential privacy have been studied via mutual information based on the Shannon entropy. Though Bellare et al. in CRYPTO 2012 have got some results about encryption schemes, the upper bound of mutual information is not the tightest. Though Cuff and Yu in CCS 2016 mentioned the direction of the Rényi entropy generalization, only a few results about differential privacy were obtained, and even for Shannon entropy, the proof in that paper has some limitations. In this paper, we propose a modular and unified framework to study the relations between statistical security and mutual information security for a series of privacy schemes other than prior work that focused on a special scheme. In addition, we introduce α-mutual information security via the Rényi entropy for a series of privacy schemes and aim to bridge the gap between statistical security and α-mutual information security. By resorting to an improved upper bound on the difference between the Shannon entropy of two distributions, the convexity of a function, useful equality about statistical distance, and the absolutely homogeneous property of α-norm, we obtain rigorous proofs of their essential equivalence. An extra fruit is that the relations between mutual information security and statistical security of encryption and commitment schemes are improved. Hence, two fundamentally different ways of defining privacy security are connected.
Adversarial training for low-complexity convolutional neural networks using in spectrum sensing

Spectrum sensing of orthogonal frequency division multiplex (OFDM) system has always been a challenge in cognitive radios (CR). In this paper on the basis of “classification converted sensing” scheme, the cyclostationary periodogram generated by OFDM pilots is deduced in the form of images. These images are then plugged into the convolutional neural networks (CNNs) for classifications due to CNN's strength in image classification. More importantly, certain of concerns about CNN adoption in CR system is settled. Firstly, to achieve spectrum sensing against severe noise pollution and channel fading, we use the adversarial training where a CR-specific, modified training database is proposed. Then, to settle the serviceability which is constrained by the computing power at the CR user end, the input images and the CNN architecture are refined to guarantee a low-complexity but high-performance sensing scheme. Simulation results proved our method possesses an excellent sensing capability while achieving higher detection accuracy over the conventional way.
Scale embedding shared neural networks for multiscale histological analysis of prostate cancer

In digital pathology, deep learning approaches have been increasingly applied and shown to be effective in analyzing digitized tissue specimen images. Such approaches have, in general, chosen an arbitrary scale or resolution at which the images are analyzed for several reasons, including computational cost and complexity. However, the tissue characteristics, indicative of cancer, tend to present at differing scales. Herein, we propose a framework that enables deep convolutional neural networks to perform multiscale histological analysis of tissue specimen images in an efficient and effective manner. A deep residual neural network is shared across multiple scales, extracting high-level features. The high-level features from multiple scales are aggregated and transformed in a way that the scale information is embedded in the network. The transformed features are utilized to classify tissue images into cancer and benign. The proposed method is compared to other methodologies to combine the feature from different scales. These competing methods combine the multi-scale features via 1) concatenation 2) addition and 3) convolution. Tissue microarrays (TMAs) were employed to evaluate the proposed method and the other competing methods. Three TMAs, including 225 benign and 377 cancer tissue samples, were used as training dataset. Two TMAs with 151 benign and 252 cancer tissue samples was utilized as testing dataset. The proposed method obtained an accuracy of 0.953 and the area under the receiver operating characteristics curve (AUC) of 0.971 (95% CI: 0.955-0.987), outperforming other competing methods. This suggests that the proposed multiscale approaches via a shared neural network and scale embedding scheme, could aid in improving digital pathology analysis and cancer pathology.
Design of Optimized E-Commerce Website using Web Assembly

Web Designing is one the major concern by all the industrialists and business people.There are many technologies used in designing websites.Today, many online shopping portals have come up and one the major issues in the design of such E-commerce websites is to provide high speed usability.The motivation of this paper comes from the desire to learn the increasingly growing field of web designing.The word "design" in web designing means visual and user interface (UI) design of a web site.The main concern of the website app vistor is the visual impressions of the web site and the ease of usability of the web site.Also the visitor is concerned about the secure and the run time of usage of the website based on the technologies used.The web site design if it is not properly architected will become difficult to change.Hence the users are also concerned with the performance of the web site.The user's concerns also to be considered to design the website and therefore many new features has to be included in web designing with effective database design and easy navigation.Hence the objective of this proposed work is to design an optimized electronic commerce website using the new technology of web designing namely Web Assembly or wasm with user interface which will enable the users to browse, search, get and buy the items of their choice with secure and fast run-time.
Bivariate probability plots: A method for delineating different populations in soil geochemical data

Bivariate probability plots (BPP) have been developed as an extension to univariate probability plots (UPP) to characterize and separate geochemical populations. The two approaches have been applied to Pb, Zn, As and Cd in soils collected from the vicinity of the Irankuh Pb and Zn mines to compare their capacity to separate geogenic patterns from anthropogenic contamination in nearby agricultural land. Separating populations by UPP and comparing thresholds with the Iranian regulatory limits for metal concentrations in agricultural land indicates that the mining operations and adjacent open ground areas are heavily contaminated. Separating the samples into different populations using BPP, based on typical element associations of Pb with Zn and As with Cd, demonstrates the extent of anthropogenic contamination is relatively higher for Pb and Zn than the other two elements. This is attributed to dispersion of dust from the mining site. Outside this area elevated metal concentrations appear related to geogenic factors, and which is attributed to weathering and erosion of underlying mineralized lithologies.
The Marriage of Artificial Intelligence and Tax Law: Past, Present, and Future

According to recent research’s prediction, global GDP could be up to 14% higher in 2030 as a result of various artificial intelligence (AI) applications, which is the equivalent of an additional $15.7 trillion. It makes AI oriented sectors the biggest commercial opportunity in the currently supersonic fast changing economy. This contribution, perhaps surprisingly, does not aim to propose how to tax profits generated by AI industries. The author rather takes an attempt to depict a potential of AI technologies to be applied to tax law. Let us see if AI can be happily married with tax law in order to get the best of both worlds.
Research on Privacy-Preserving Methods of Electronic Medical Records

With the development of Internet technology, electronic medical records (EMRs) are gradually replacing the traditional medical records because of their advantage of portability and ease of storage. At the same time, due to the large number of patient data in EMRs, protecting patients' privacy is an increasing concern that should not be overlooked or understated. This paper studies the privacy preservation mechanism of electronic medical records through two aspects: In the hospital information system, it is analyzed from the policy and regulation, management mechanism and technology level; In the aspect of EMR publishing, this paper mainly introduces K-Anonymity mechanism and differential privacy, which are commonly used privacy protection methods for data publishing.
Advanced Artificial Intelligence and Contract

The aim of this article is to inquire whether contract law can operate in a state of affairs in which artificial general intelligence (AGI) exists and has the cognitive abilities to interact with humans to exchange promises or otherwise engage in the sorts of exchanges typically governed by contract law. AGI is a long way off but its emergence may be sudden and come in the lifetimes of some people alive today. How might contract law adapt to a situation in which at least one of the contract parties could, from the standpoint of capacity to engage in promising and exchange, be an AGI? This is not a situation in which AI operates as an agent of a human or a firm, a frequent occurrence right now. Rather, the question is whether an AGI could constitute a principal â€“ a contract party on its own. Contract law is a good place to start a discussion about adapting the law for an AGI future because it already incorporates a version of what is known as weak AI in its objective standard for contract formation and interpretation. Contract law in some limited sense takes on issues of relevance from philosophy of mind. AGI holds the potential to transform a solution to an epistemological problem of how to prove a contract exists into solution to an ontological problem about the capacity to contract. An objection might be that contract law presupposes the existence of a person the law recognizes as possessing the capacity to contract. Contract law itself may not be able to answer the prior question of legally recognized personhood. The answer will be to focus on how AGI cognitive architecture could be designed for compatibility for human interaction. This article focuses on that question as well.