Reviewerly/exHarmony huggingface dataset:
LRDNN: Local-refining based Deep Neural Network for Person Re-Identification with Attribute Discerning

Recently, pose or attribute information has been widely used to solve person re-identification (re-ID) problem. However, the inaccurate output from pose or attribute modules will impair the final person re-ID performance. Since re-ID, pose estimation and attribute recognition are all based on the person appearance information, we propose a Local-refining based Deep Neural Network (LRDNN) to aggregate pose estimation and attribute recognition to improve the re-ID performance. To this end, we add a pose branch to extract the local spatial information and optimize the whole network on both person identity and attribute objectives. To diminish the negative affect from unstable pose estimation, a novel structure called channel parse block (CPB) is introduced to learn weights on different feature channels in pose branch. Then two branches are combined with compact bilinear pooling. Experimental results on Market1501 and DukeMTMC-reid datasets illustrate the effectiveness of the proposed method.
A literature review of scholarly communications metadata

The purpose of this literature review is to identify the challenges, opportunities, and gaps in knowledge with regard to the use of metadata in scholarly communications. This paper compiles and interprets literature in sections based on the professional groups, or stakeholders, within scholarly communications metadata: researchers, funders, publishers, librarians, service providers, and data curators. It then ends with a 'bird's eye view' of the metadata supply chain which presents the network of relationships and interdependencies between stakeholders. This paper seeks to lay the groundwork for new approaches to present problems in scholarly communications metadata.
Dynamically Route Hierarchical Structure Representation to Attentive Capsule for Text Classification

Representation learning and feature aggregation are usually the two key intermediate steps in natural language processing. Despite deep neural networks have shown strong performance in the text classification task, they are unable to learn adaptive structure features automatically and lack of a method for fully utilizing the extracted features. In this paper, we propose a novel architecture that dynamically routes hierarchical structure feature to attentive capsule, named HAC. Specifically, we first adopt intermediate information of a well-designed deep dilated CNN to form hierarchical structure features. Different levels of structure representations are corresponding to various linguistic units such as word, phrase and clause, respectively. Furthermore, we design a capsule module using dynamic routing and equip it with an attention mechanism. The attentive capsule implements an effective aggregation strategy for feature clustering and selection. Extensive results on eleven benchmark datasets demonstrate that the proposed model obtains competitive performance against several state-of-the-art baselines. Our code is available at https://github.com/zhengwsh/HAC.
SPINE: Structural Identity Preserved Inductive Network Embedding

Recent advances in the field of network embedding have shown that low-dimensional network representation is playing a critical role in network analysis. Most existing network embedding methods encode the local proximity of a node, such as the first- and second-order proximities. While being efficient, these methods are short of leveraging the global structural information between nodes distant from each other. In addition, most existing methods learn embeddings on one single fixed network, and thus cannot be generalized to unseen nodes or networks without retraining. In this paper we present SPINE, a method that can jointly capture the local proximity and proximities at any distance, while being inductive to efficiently deal with unseen nodes or networks. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed framework over the state of the art.
DyS: A Framework for Mixture Models in Quantification

Quantification is an expanding research topic in Machine Learning literature. While in classification we are interested in obtaining the class of individual observations, in quantification we want to estimate the total number of instances that belong to each class. This subtle difference allows the development of several algorithms that incur smaller and more consistent errors than counting the classes issued by a classifier. Among such new quantification methods, one particular family stands out due to its accuracy, simplicity, and ability to operate with imbalanced training samples: Mixture Models (MM). Despite these desirable traits, MM, as a class of algorithms, lacks a more in-depth understanding concerning the influence of internal parameters on its performance. In this paper, we generalize MM with a base framework called DyS: Distribution y-Similarity. With this framework, we perform a thorough evaluation of the most critical design decisions of MM models. For instance, we assess 15 dissimilarity functions to compare histograms with varying numbers of bins from 2 to 110 and, for the first time, make a connection between quantification accuracy and test sample size, with experiments covering 24 public benchmark datasets. We conclude that, when tuned, Topsøe is the histogram distance function that consistently leads to smaller quantification errors and, therefore, is recommended to general use, notwithstanding Hellinger Distance’s popularity. To rid MM models of the dependency on a choice for the number of histogram bins, we introduce two dissimilarity functions that can operate directly on observations. We show that SORD, one of such measures, presents performance that is slightly inferior to the tuned Topsøe, while not requiring the sensible parameterization of the number of bins.
A sentiment analysis method of objects by integrating sentiments from tweets

Sentiment analysis has been gaining importance in many applications such as recommendation systems, the decision making support and prediction models. Sentiment analysis helps to understand and evaluate public opinion regarding social events, product services, and political trends, especially the f eelings expressed through comments by users in social networks such as Twitter, Facebook, and Instagram. There have been a lot of research attempts to address the tweets sentiment analysis problem with high accuracy, particularly in case of tweets that express a single sentiment towards a single object. However, the results of the classification are not highly accurate in cases such as the following: a user expresses multiple sentiments towards a single object in a tweet; a user presents multiple sentiments towards multiple objects; and a user indicates a single sentiment towards multiple objects. Furthermore, the previous studies only analyze the sentiment of each tweet without considering the objects and the sentiment towards each object from an entire set of tweets. This study attempts to deal with the limitations of the previous methods; an approach is proposed herein, based on integrating the sentiment towards a particular object from all tweets related to that object. The proposed method focuses on determining the objects and indicating the sentiment towards the specific objects by combining the sentiments related to each object from the entire set of tweets. On experimental evaluation, the proposed method is observed to have achieved a fairly good result in terms of the error ratio and achieved information.
AntProphet: an Intention Mining System behind Alipay's Intelligent Customer Service Bot

We create an intention mining system, named AntProphet, for Alipay's intelligent customer service bot, to alleviate the burden of customer service. Whenever users have any questions, AntProphet is the first stop to help users to answer their questions. Our system gathers users' profile and their historical behavioral trajectories, together with contextual information to predict users' intention, i.e., the potential questions that users want to resolve. AntProphet takes care of more than 90% of the customer service demands in the Alipay APP and resolves most of the users' problems on the spot, thus significantly reduces the burden of manpower. With the help of it, the overall satisfaction rate of our customer service bot exceeds 85%.
METHOD OF FORMING RECOMMENDATIONS USING TEMPORAL CONSTRAINTS IN A SITUATION OF CYCLIC COLD START OF THE RECOMMENDER SYSTEM

The problem of the formation of the recommended list of items in the situation of cyclic cold start of the recommendation system is considered. This problem occurs when building recommendations for occasional users. The interests of such consumers change significantly over time. These users are considered “cold” when accessing the recommendation system. A method for building recommendations in a cyclical cold start situation using temporal constraints is proposed. Temporal constraints are formed on the basis of the selection of repetitive pairs of actions for choosing the same objects at a given level of time granulation. Input data is represented by a set of user choice records. For each entry, a time stamp is indicated. The method includes the phases of the formation of temporal constraints, the addition of source data using these constraints, as well as the formation of recommendations using the collaborative filtering algorithm. The proposed method makes it possible, with the help of temporal constraints, to improve the accuracy of recommendations for “cold” users with periodic changes in their interests.
Human Development and Its Effects on Economic Growth and Development

The focus of the current study is to empirically examine the impact of Human Development on Economic Growth and Development in African countries, between 1990 and 2015. The key findings and results of the study suggested the existence of a positive and significant impact of human development on economic growth and development in Africa. The study employed Human Development Index as the main variable of interest with GDP considered as the dependent variable with inflation, capital, investment and labor as control variables. The study also came out with the findings that Labour and foreign aid also have a positive and significant relationship with growth as recorded by most researchers. It was recommended that more efforts should be placed on developing human capacities in all areas. Keywords : Human development, economic growth, random effect, fixed effect * Department of Management Science & Engineering, School of Finance and Economics, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, 212013, P.R. China ** Department of Marketing, Koforidua Technical University, P. O. Box Kf 98, Koforidua, Ghana https://doi.org/10.21632/irjbs.12.2.101-109
Exploring Human-Like Reading Strategy for Abstractive Text Summarization

The recent artificial intelligence studies have witnessed great interest in abstractive text summarization. Although remarkable progress has been made by deep neural network based methods, generating plausible and high-quality abstractive summaries remains a challenging task. The human-like reading strategy is rarely explored in abstractive text summarization, which however is able to improve the effectiveness of the summarization by considering the process of reading comprehension and logical thinking. Motivated by the humanlike reading strategy that follows a hierarchical routine, we propose a novel Hybrid learning model for Abstractive Text Summarization (HATS). The model consists of three major components, a knowledge-based attention network, a multitask encoder-decoder network, and a generative adversarial network, which are consistent with the different stages of the human-like reading strategy. To verify the effectiveness of HATS, we conduct extensive experiments on two real-life datasets, CNN/Daily Mail and Gigaword datasets. The experimental results demonstrate that HATS achieves impressive results on both datasets.
On Guiding Search in HTN Planning with Classical Planning Heuristics

Planning is the task of finding a sequence of actions that achieves the goal(s) of an agent. It is solved based on a model describing the environment and how to change it. There are several approaches to solve planning tasks, two of the most popular are classical planning and hierarchical planning. Solvers are often based on heuristic search, but especially regarding domain-independent heuristics, techniques in classical planning are more sophisticated. However, due to the different problem classes, it is difficult to use them in hierarchical planning. In this paper we describe how to use arbitrary classical heuristics in hierarchical planning and show that the resulting system outperforms the state of the art in hierarchical planning.
Novel Collaborative Filtering Recommender Friendly to Privacy Protection

Nowadays, recommender system is an indispensable tool in many information services, and a large number of algorithms have been designed and implemented. However, fed with very large datasets, state-of-the-art recommendation algorithms often face an efficiency bottleneck, i.e., it takes huge amount of computing resources to train a recommendation model. In order to satisfy the needs of privacy-savvy users who do not want to disclose their information to the service provider, the complexity of most existing solutions becomes prohibitive. As such, it is an interesting research question to design simple and efficient recommendation algorithms that achieve reasonable accuracy and facilitate privacy protection at the same time. In this paper, we propose an efficient recommendation algorithm, named CryptoRec, which has two nice properties: (1) can estimate a new user's preferences by directly using a model pre-learned from an expert dataset, and the new user's data is not required to train the model; (2) can compute recommendations with only addition and multiplication operations. As to the evaluation, we first test the recommendation accuracy on three real-world datasets and show that CryptoRec is competitive with state-of-the-art recommenders. Then, we evaluate the performance of the privacy-preserving variants of CryptoRec and show that predictions can be computed in seconds on a PC. In contrast, existing solutions will need tens or hundreds of hours on more powerful computers.
Detection of hydrocarbon microseepage-induced anomalies by spectral enhancements of Landsat 7 ETM+ images in part of Assam–Arakan Fold Belt, India

Subsurface hydrocarbon traps are not correctly sealed, and hydrocarbons move vertically from the reservoir as invisible traces in the form of microseepages. Long-term hydrocarbon microseepages cause surface or near-surface alterations such as bleaching of red beds, enrichment of ferrous iron minerals and higher concentrations of clay and carbonate minerals in soils/rocks. Multi- and hyperspectral remote sensing data have successfully been used to detect such alterations in many parts of the world. In India, such studies have not been carried out till now. In this study, Landsat 7 ETM+ images have been used to find out hydrocarbon microseepage-bearing areas in part of Assam–Arakan Fold Belt in the northeastern part of India. Based on the spectral characteristics of the hydrocarbon microseepage-induced altered minerals, two spectral enhancement techniques, viz. principal component analysis (PCA) and band ratio analysis, have been carried out on the Landsat 7 ETM+ images. PCA reveals that three principal component images—1457PC3, 1345PC2 and 3457PC4—show relatively better enhancement for the hydrocarbon-bearing alteration areas. Again, band ratio analysis of the images indicates that ratio images—3/1, (2 + 5)/(3 + 4) and 7/5—show excellent spectral enhancement for the hydrocarbon-induced mineral alterations. The three PC images have been combined with the three band ratio images to find out probable hydrocarbon microseepage areas. The remote sensing-derived prospect areas have been validated with surface geochemical, seismic/geologic and gravity data available in the area.
Decentralized Distribution of PCP Mappings Over Blockchain for End-to-End Secure Direct Communications

Network Address Translation (NAT) is a method that enables devices with private IP addresses to connect to the Internet by sharing a public IP address. Traversing the NAT device remains a challenge for a wide range of applications such as Voice over IP (VoIP) and Internet of Things (IoT). The Port Control Protocol (PCP) is a relatively new protocol standardized by the Internet Engineering Task Force (IETF) to solve the NAT traversal issues. It allows a NATed device to request and manage a mapping between its private IP address and transport-layer port to a public IP address and port. As PCP requires an application-dependent method for distributing the mappings to remote hosts, several attacks can target the distributing server and render the communication channel vulnerable. In this paper, we propose and implement a decentralized Blockchain-based approach for distributing PCP-mappings, enabling secure end-to-end (e2e) direct communications without any trusted third party server. NATed devices register their PCP mappings and public keys into the Blockchain, and other peers can then learn about these mappings to establish end-to-end secure direct communications. The implementation verifies that the system is feasible in terms of transactions fees, can simplify and secure end-to-end direct communications, and can interwork with conventional security methods.
Artificial Bee Colony algorithm used for load balancing in cloud computing: Review

&lt;p&gt;Cloud computing is emerging technology in IT land. But it still faces challenges like load balancing. It is a technique which dynamic distributed workload among various nodes equally in a situation where some nodes are under load and some are overload. Main achievements of load balancing are resource consumption and reduce energy. Swarm intelligence provides an important role in the field of those problems which cannot easily solve and they need classical and mathematical technique. An artificial bee colony is a foraging behavior inspires algorithm it established by karaboga in 2005. It has fast convergence, strong, robustness, and high flexibility. The different researcher used ABC algorithm for improvement in load balancing. This survey paper is a comprehensive study about load balancing in cloud computing using the ABC algorithm. It also defines some basic concept about swarm intelligent and its property&lt;strong&gt;.&lt;/strong&gt;&lt;/p&gt;
Shape Matching Based on Multi-Scale Invariant Features

Shape matching has been extensively used in various fields. The local feature-based or global feature-based algorithms can hardly describe the shape comprehensively due to their inherent defects. Combining the local and global feature to describe the shape has become a trend. In this paper, an improved discrete curve evolution algorithm is proposed which combines the discrete curve evolution with the uniform sampling and achieves a better description of the shape contour. Three simple and intuitive multi-scale features which represent both the global and local features of shapes are designed from aspects of the spatial relationship of contour points, structural information of contour sequence, and shape geometry feature. A cyclic Smith-Waterman algorithm is introduced to solve local contour matching and starting point selection. Experimental results demonstrate that our proposed features are translation, rotation, and scaling invariant, and have good robustness to deformation. Retrieval accuracies of Kimia99, Kimai216, and MPEG-7 indicate that our method can bring out a better performance.
C-RPNs: Promoting object detection in real world via a cascade structure of Region Proposal Networks

Recently, significant progresses have been made in object detection on common benchmarks (i.e., Pascal VOC). However, object detection in real world is still challenging due to the serious data imbalance. Images in real world are dominated by easy samples like the wide range of background and some easily recognizable objects, for example. Although two-stage detectors like Faster R-CNN achieved big successes in object detection due to the strategy of extracting region proposals by Region Proposal Network, they show their poor adaption in real-world object detection as a result of without considering mining hard samples during extracting region proposals. To address this issue, we propose a Cascade framework of Region Proposal Networks, referred to as C-RPNs, which adopts multiple stages to mine hard samples while extracting region proposals and learn stronger classifiers. Meanwhile, a feature chain and a score chain are proposed to help learning more discriminative representations for proposals. Moreover, a loss function of cascade stages is designed to train cascade classifiers through backpropagation. Our proposed method has been evaluated on Pascal VOC and several challenging datasets like BSBDV 2017, CityPersons, etc. Our method achieves competitive results compared with the current state-of-the-arts and attains all-sided improvements in error analysis, validating its efficacy for detection in real world.
Robustness Analysis of 3D Convolutional Neural Network for Human Hand Gesture Recognition

Recently, a number of methods for dynamic hand gesture recognition has been proposed.However, deployment of such methods in a practical application still has to face with many challenges due to the variation of view point, complex background or subject style.In this work, we deeply investigate performance of advanced convolutional neural networks for a specific case of hand gestures and evaluate how robust it is to above variations.To this end, we adopt an existing 3D convolutional neural network which was originally proposed for general human action recognition and obtained very competitive accuracy.We extend it to two-streams architecture (RGB and optical flow) and apply transfer learning on our dataset of hand gestures.To evaluate the robustness of the method, we design carefully a multi-view dataset that composes of five dynamic hand gestures in indoor environment with complex background.Experiments with single or cross view on this dataset show that background and viewpoint has strong impact on recognition robustness.In addition, the network's performances are mostly increased by multi-modality combinations and fine-tuning strategy.This analysis helps to make recommendation for deploying the method in real situation.
Machine learning for tissue diagnostics in oncology: brave new world

Machine learning is an exciting technology with broad application in big data analysis, as well as increasingly in specialised healthcare. As a diagnostic tool in tissue workup and pathology, it has the potential for personalised and stratified approaches, but the limitations and pitfalls need to be better understood and characterised especially in this critical area of medical care.
Minimizing interpretations in fuzzy description logics under the Gödel semantics by using fuzzy bisimulations

We study the problem of minimizing interpretations in fuzzy description logics (DLs) under the Gödel semantics by using fuzzy bisimulations. The considered logics are fuzzy extensions of the DL 𝒜ℒ𝒞reg (a variant of propositional dynamic logic) with additional features among inverse roles, nominals and the universal role. Given a fuzzy interpretation ℐ and for E being the greatest fuzzy auto-bisimulation of ℐ w.r.t. the considered DL, we define the quotient ℐ/E of ℐ w.r.t. E and prove that it is minimum w.r.t. certain criteria. Namely, ℐ/E is a minimum fuzzy interpretation that validates the same set of fuzzy terminological axioms in the considered DL as ℐ. Furthermore, if the considered DL allows the universal role, then ℐ/E is a minimum fuzzy interpretation bisimilar to ℐ, as well as a minimum fuzzy interpretation that validates the same set of fuzzy concept assertions in the considered DL as ℐ.