Reviewerly/exHarmony huggingface dataset:
Joint learning based deep supervised hashing for large-scale image retrieval

Hashing has been widely used for large-scale image retrieval due to its high storage efficiency and fast calculation speed. Recent works have found that deep-supervised hashing methods are superior to non-deep-supervised hashing methods and unsupervised hashing methods in many applications. However, the previous deep-supervised hashing only uses the training set to learn the hash function, which results in that the discrete hash codes corresponding to the retrieval image set and the query image set are derived from the trained hash function. The retrieval image set did not participate in the training and learning of the network. Hence, it is difficult to acquire the real discrete hash code of the retrieval image. In addition, the traditional deep-supervised hashing methods failed to make full use of the supervised information. In this paper, we propose a novel deep supervised hashing method called Joint Learning Based Deep Supervised Hashing (JLDSH). It joints the image classification and hash function learning into the same end-to-end neural network framework. In the training process, we randomly select a subset from the retrieval image set to learn the hash function. The hash code of the entire retrieval image set is directly calculated by the trained hash code of the subset. Meanwhile, JLDSH sets a hyper-parameter on the supervised information to make the output of the network closer to the real discrete hash code. Furthermore, by adding a new loss function, an extended JLDSH-A model is proposed to make the image representation obtained by network training more discrete. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on benchmark datasets.
Highly Accurate Bathroom Activity Recognition Using Infrared Proximity Sensors

Among elderly populations over the world, a high percentage of individuals are affected by physical or mental diseases, greatly influencing their quality of life. As it is a known fact that they wish to remain in their own home for as long as possible, solutions must be designed to detect these diseases automatically, limiting the reliance on human resources. To this end, our team developed a sensors platform based on infrared proximity sensors to accurately recognize basic bathroom activities such as going to the toilet and showering. This article is based on the body of scientific literature which establish evidences that activities relative to corporal hygiene are strongly correlated to health status and can be important signs of the development of eventual disorders. The system is built to be simple, affordable and highly reliable. Our experiments have shown that it can yield an F-Score of 96.94%. Also, the durations collected by our kit are approximately 6 seconds apart from the real ones; those results confirm the reliability of our kit.
The Effect of Social Media to the Sustainability of Short Message Service (SMS) and Phone Call

In the development of increasingly advanced technology, the use of SMS and telephone has been replaced by smartphone users who are more intended to use social media, especially among students. Social media are online media where users can communicate and interact one another for social interactions conducted online through the internet such as WhatsApp, Line, Instagram, Facebook, Twitter, Skype and Telegram. The existence of social media makes SMS and telephone user switch to social media which has more features, capacities and functions. Therefore, it is interesting to investigate the effect of social media to influence the sustainability of SMS and telephone which is seen from the effectiveness of social media in terms of time, quality and quantity, cost, distance, and energy in related to the utilization of SMS and telephone among users. To determine the effect of respected social media, this study use multiple regression, which is analysed using SPSS 17.0 and has passed the validity and reliability test up to 32.7% the influence of all variants. The most dominant influence on Usage and Function variables is Quality and Quantity which has 18% with significant value of test 0,000 smaller than the value set (0.05), the cost of which has 4.7% with significant value of T test at around 0.003 smaller than the value determined (0.05), and Energy which has 2.1% with significant value of T test about 0.013 smaller than the value set (0.05).
Sidechains: Highlights and Challenges

The revolutionary concept of blockchain and its applications are spectacularly drawing more and more attention in today's world. The crypto-currency platforms have been on the forefront of implementing blockchain technology. However, the capabilities of blockchains are not limited to monetary transactions. Introduction of smart contracts allowed development of various distributed applications. As the need for blockchains continues to grow, there is also a strong demand for larger, more scalable and more flexible "multipurpose" chains. Conventional blockchains (even the ones supporting smart contracts) cannot comprehensively cover these requirements. To address these issues, the concept of sidechains has been introduced. This paper provides a brief overview of sidechains, explains how they work, discusses their benefits and drawbacks; moreover, describes how they can be built using relevant software and tools.
zk-AuthFeed: How to Feed Authenticated Data into Smart Contract with Zero Knowledge

The emerging blockchain technology, powered by the smart contract functionality, is expected to revolutionize traditional systems by decentralization and autonomy. When the blockchain technology is applied in real-life systems, a smart contract may need to take in sensitive data to execute its code. For example, a decentralized medical insurance smart contract needs access to personal health data. Therefore, it is crucial to guarantee both privacy and authenticity of data sent to the smart contract, so that everyone can verify data without leaking sensitive information. Currently, only few works have tried to achieve privacy and authenticity at the same time. In this work, we first propose zk-DASNARK, a zero-knowledge SNARK scheme for authenticated data. It is designed by combining the zk-SNARK technique with digital signature in an effective way. Based on zk-DASNARK, we design zk-AuthFeed, a zero-knowledge authenticated data feed scheme to achieve both data privacy and authenticity for smart contract. We implement zk-AuthFeed and conduct comprehensive experiments on Ethereum. The experiments show that zk-AuthFeed is highly efficient: key generation takes about 7 seconds only, proof generation takes less than 2 seconds, and proof verification takes less than 40 ms.
Key Opinion Leaders in Recommendation Systems

Recommendation systems typically rely on the interactions between a crowd of ordinary users and items, ignoring the fact that many real-world communities are notably influenced by a small group of key opinion leaders, whose feedback on items wields outsize influence. With important positions in the community (e.g. have a large number of followers), their elite opinions are able to diffuse to the community and further impact what items we buy, what media we consume, and how we interact with online platforms. Hence, this paper investigates how to develop a novel recommendation system by explicitly capturing the influence from key opinion leaders to the whole community. Centering around opinion elicitation and diffusion, we propose an end-to-end Graph-based neural model - GoRec. Specifically, to preserve the multi-relations between key opinion leaders and items, GoRec elicits the opinions from key opinion leaders with a translation-based embedding method. Moreover, GoRec adopts the idea of Graph Neural Networks to model the elite opinion diffusion process for improved recommendation. Through experiments on Goodreads and Epinions, the proposed model outperforms state-of-the-art approaches by 10.75% and 9.28% on average in Top-K item recommendation.
Deep Multi-Graph Clustering via Attentive Cross-Graph Association

Multi-graph clustering aims to improve clustering accuracy by leveraging information from different domains, which has been shown to be extremely effective for achieving better clustering results than single graph based clustering algorithms. Despite the previous success, existing multi-graph clustering methods mostly use shallow models, which are incapable to capture the highly non-linear structures and the complex cluster associations in multi-graph, thus result in sub-optimal results. Inspired by the powerful representation learning capability of neural networks, in this paper, we propose an end-to-end deep learning model to simultaneously infer cluster assignments and cluster associations in multi-graph. Specifically, we use autoencoding networks to learn node embeddings. Meanwhile, we propose a minimum-entropy based clustering strategy to cluster nodes in the embedding space for each graph. We introduce two regularizers to leverage both within-graph and cross-graph dependencies. An attentive mechanism is further developed to learn cross-graph cluster associations. Through extensive experiments on a variety of datasets, we observe that our method outperforms state-of-the-art baselines by a large margin.
Automatic multiple moving humans detection and tracking in image sequences taken from a stationary thermal infrared camera

Several Particle Filter (PF)-based methods for human tracking in thermal IR image sequences have been proposed in the literature. Unfortunately, the majority of these methods are developed for tracking only a single human. Moreover, this human is manually pre-selected in the first frame of the image sequence, which is not practical for the real case of intelligent and efficient video surveillance system that needs tracking more than one human and without any external operator intervention. To contribute to addressing this need, in this paper, we propose a novel PF-based method that detects and tracks multiple moving humans using a thermal IR camera, without prior knowledge about their number and initial locations in the monitored scene. This method consists of three main parts. In the first one, all the moving objects are extracted from the image sequence by using the Gaussian Mixture Model (GMM) and then, for each extracted object, a combined shape, appearance, spatial and temporal-based similarity function that allows us to detect a human without any prior training of a mathematical model is calculated. The second part consists in tracking the human previously detected by using a PF and an adaptive combination of spatial, intensity, texture and motion velocity cues. In each cue, a model for the detected human is created, and when new observations arrive in the next frames, the similarity distances between each created model and the observed moving regions are calculated. The human tracking is achieved by combining individual similarity distances using adaptive weights, into a PF algorithm. The third part is devoted to detect and handle occlusions by using simple heuristic rules and grayscale Vertical Projection Histogram (VPH). Each part of the proposed method was separately tested on a set of real-world thermal IR image sequences containing background clutters, appearance and disappearance of multiple moving objects, occlusions, illumination and scales changes. A comparative study with several state-of-the-art methods has shown that the proposed method performs consistently better in terms of Center Location Error (CLE) and the Success Rate (SR), and it can also run at speed of about 15 Frame Per Second per human, which is considerably enough for real-time applications.
Edge detection based boundary box construction algorithm for improving the precision of object detection in YOLOv3

In the field of computer vision, object detection is one of the most interesting field, and one major task after object has been detected is to draw boundary boxes around detected object. The proposed work focuses on improving the precision of boundary boxes that had been drawn after objects has being detected on image. When it comes to object detection YOLOv3 is a real time state-of-the-art object detection system. Due to this, the proposed work had taken YOLOv3 as base model and improves the precision of the boundary boxes around the object. The proposed framework uses pretrained COCO dataset for object detection, and with the help of edge detection and pixel values in an area, the proposed work improves the precision of the boundary box around the object. The proposed model gives significantly better precision of boundary boxes when compared with YOLOv3.
An environmental scan of virtual and augmented reality services in academic libraries

Purpose The growing popularity of virtual and augmented reality (VR and AR) technologies, and increased research into their educational uses, has seen them appearing in a significant number of academic libraries. Little is known, however, about how many libraries have actually adopted these technologies or how they have structured library services around them. The purpose of this paper is to answer these questions. Design/methodology/approach The authors surveyed the websites of the Association of Research Libraries (ARL) member libraries to gather information about the availability of VR and AR equipment as well as information about how access is provided. Recorded details about these services included information about staffing, dedicated space, software, what type of technology was offered and whether or not the technology was lent out or only made available for in-library use. Findings Results of the research project showed that a significant number of ARL-member libraries do offer access to VR technology. AR technology was much less widespread. The most common technologies offered were the Oculus Rift and HTC Vive. The technology was most typically offered for in-library use only. There were few details about staff or what software was offered to be used with the technology. Originality/value While there is growing research around how VR and AR is being used in education, little research has been undertaken into how libraries are adopting these technologies. This paper summarizes the research that has been done so far and also takes the next step of providing a larger picture of how widespread the adoption of VR and AR technologies has been within academic libraries, as well as how access to these technologies is being provided.
Heuristic Approach for Real-Time Multi-Agent Trajectory Planning Under Uncertainty

Motion planning of agents is one of the fundamental research problems in autonomous systems. An important aspect of motion planning is collision avoidance of the agents with other agents and obstacles that are present in the agent's environment. Typically, the collision avoidance constraints are non-linear and non-convex. Thus, the mathematical formulation of motion planning of multiple agents, in the presence of other agents and obstacles, is NP-Hard. In this paper, a novel heuristic approach for motion planning in multi-agent dynamic environment is proposed. The approach is computationally cheap, and can be launched locally on each agent for the trajectory planning. The applicability of the proposed approach is illustrated by numerical examples considering uncertainty in the environment. Detailed discussions on the performance of the proposed approach are presented. Finally, the observations on the key characteristics of the proposed approach are summarized.
Automatic assessment of English proficiency for Japanese learners without reference sentences based on deep neural network acoustic models

Speech-based computer-assisted language learning (CALL) systems should recognize the utterances of the learner with high accuracy and evaluate the language proficiency of the specific speaker with appropriate methods. In this paper, we discuss the automatic assessment of the second language (L2) for non-native speakers. There are many existing works on pronunciation evaluation by applying the goodness of pronunciation (GOP) method. This paper introduces an automatic proficiency evaluation system that combines various kinds of non-native acoustic models and native ones, such as Gaussian mixture model (GMM)-hidden Markov model (HMM) and deep neural network (DNN)-HMM. Most of existing works assume that we know the transcription of an utterance (the reference sentence) when evaluating the utterance, especially in reading and repeating tasks. To realize a reference-free proficiency evaluation, we propose a novel machine score named as the reference-free error rate (RER) to evaluate English proficiency. In our experiments, the DNN-based non-native acoustic models outperformed the traditional acoustic models on non-native speech recognition. Thus, we calculated the RER by regarding the recognition result from the DNN-based non-native acoustic model as “reference” and the result from the native acoustic model as “recognition result”. The proposed RER has high correlation with human proficiency scores, which indicates the effectiveness of RER for automatically estimating the proficiency. By combining the RER with other machine scores such as the log-likelihood scores, we obtained high correlation (reading aloud task: r=0.826,p<0.001,N=190; constrained interactive dialogue task: r=0.803,p<0.001,N=26; spontaneous English conversation task: r=0.799,p<0.001,N=28) to the human scores.
Physicochemical Parameters and Geochemical Features of Ore-Forming Fluids for Orogenic Gold Deposits Throughout Geological Time

This paper reviews data from numerous publications focused on the physicochemical parameters and chemical composition of ore-forming fluids from orogenic gold deposits formed during various geological epochs. The paper presents analysis of the distribution of the principal parameters of mineralizing fluids depending on the age of the mineralization. Some parameters of the fluids (their salinity and pressure) at orogenic gold deposits are demonstrated to systematically vary from older (median salinity 6.1 wt.%, median pressure 1680 bar) to younger (median salinity 3.6 wt.%, median pressure 1305 bar) deposits. The detected statistically significant differences between some parameters of mineralizing fluids at orogenic gold deposits are principally new information. The parameters at which mineralization of various age was formed are demonstrated to pertain to different depth levels of similar mineralization-forming systems. The fluid parameters of the most ancient deposits (which are mostly deeply eroded) correspond to the deepest levels of orogenic fluid systems. Hence, the detected differences in the salinity and pressure of the mineralizing fluids at orogenic deposits of different age reflect the vertical zoning of the mineralizing fluid systems.
Transfer Learning of a Neural Network Using Deep Learning to Perform Face Recognition

Deep Learning can be used to solve different problems in image analysis and pattern recognition. Face recognition is it's one of the applications. The use of face recognition to solve different social problems like personal authentication and security is increasing rapidly. In past different face recognition techniques have been introduced like Fisherfaces, Eigenfaces, and LBPH. These methods have low accuracy, so neural networks are used. The deep learning based neural networks are the most accurate. The parameters of the network can be tuned to achieve high accuracy. Face recognition requires a data base of individuals to train a neural network using deep learning. The trained network is then capable of recognition. The framework of the facial recognition process using transfer learning of the pre-trained neural network is described in this paper. We have used AlexNet for recognition which is a pre-trained convolutional neural network. Transfer learning of this pre-trained network has given accuracy of 97.95%. It can be used to classify 1000 different people. For recognition purpose, it requires a large database at least one thousand images per class. The accuracy is although high as compared with techniques mentioned above.
General solution of the time evolution of two interacting harmonic oscillators

We study the time evolution of an ideal system composed of two harmonic oscillators coupled through a quadratic Hamiltonian with arbitrary interaction strength. We solve its dynamics analytically by employing tools from symplectic geometry. In particular, we use this result to completely characterize the dynamics of the two oscillators interacting in the ultrastrong-coupling regime with additional single-mode squeezing on both oscillators, as well as higher-order terms. Furthermore, we compute quantities of interest, such as the average number of excitations and the correlations that are established between the two subsystems due to the evolution. We find that this model predicts a second-order phase transition and we compute the critical exponents and the critical value. We also provide an exact decoupling of the time evolution in terms of simple quantum optical operations, which can be used for practical implementations and studies. Finally, we show how our techniques can be extended to include more oscillators and higher-order interactions.
Metrics, User Models, and Satisfaction

User satisfaction is an important factor when evaluating search systems, and hence a good metric should give rise to scores that have a strong positive correlation with user satisfaction ratings. A metric should also correspond to a plausible user model, and hence provide a tangible manifestation of how users interact with search rankings. Recent work has focused on metrics whose user models accurately portray the behavior of search engine users. Here we investigate whether those same metrics then also correlate with user satisfaction. We carry out experiments using various classes of metrics, and confirm through the lens of the C/W/L framework that the metrics with user models that reflect typical behavior also tend to be the metrics that correlate well with user satisfaction ratings.
Decentralized Attribute-Based Encryption and Signatures

This paper presents decentralized multi-authority attribute-based encryption and signature (DMA-ABE and DMA-ABS) schemes, in which no central authority exists and no global coordination is required except for the setting of a parameter for a prime order bilinear group and a hash function, which can be available from public documents, e.g., ISO and FIPS official documents. In the proposed DMA-ABE and DMA-ABS schemes, every process can be executed in a fully decentralized manner; any party can become an authority and issue a piece for a secret key to a user without interacting with any other party, and each user obtains a piece of his/her secret key from the associated authority without interacting with any other party. While enjoying such fully decentralized processes, the proposed schemes are still secure against collusion attacks, i.e., multiple pieces issued to a user by different authorities can form a collusion resistant secret key, composed of these pieces, of the user. The proposed ABE scheme is the first DMA-ABE for non-monotone relations (and more general relations), which is adaptively secure under the decisional linear (DLIN) assumption in the random oracle model. This paper also proposes the first DMA-ABS scheme for non-monotone relations (and more general relations), which is fully secure, adaptive-predicate unforgeable and perfect private, under the DLIN assumption in the random oracle model. DMA-ABS is a generalized notion of ring signatures. The efficiency of the proposed DMA-ABE and DMA-ABS schemes is comparable to those of the existing practical ABE and ABS schemes with comparable relations and security.