Reviewerly/exHarmony huggingface dataset:
Beyond softmax loss: Intra-concentration and inter-separability loss for classification

In the past years, most works have focused on designing an indigenous network architecture to advance progress in classification, but another potential opportunity for improvement, that is, research on classification losses, is underdeveloped. Although some new losses have been proposed, most of them either are variants of softmax loss or should combine with softmax loss. Hence, the inherent deficiencies of softmax loss, such as sensitiveness, class-balanced restriction, closed-set limitation, non-scale-invariance and incoordination between the intraclass distance and interclass distance, cannot be completely overcome. In light of this, we pave a new way to design a loss that has no relation to softmax loss and can avoid its weaknesses. We also propose an efficient algorithm to optimize the new loss that can circumvent computing the complicated gradients of a fraction, and the convergence is theoretically ensured. Extensive experimental results on benchmark datasets demonstrate that the new loss is competitive with state-of-the-art losses for classification. Additionally, other specially designed experiments show that the new loss is also effective at handling class-imbalanced problems, is robust in addressing outliers and can discover samples of unseen classes in open-set cases.
Multi-view Adaptive Graph Convolutions for Graph Classification

In this paper, a novel multi-view methodology for graph-based neural networks is proposed. A systematic and methodological adaptation of the key concepts of classical deep learning methods such as convolution, pooling and multi-view architectures is developed for the context of non-Euclidean manifolds. The aim of the proposed work is to present a novel multi-view graph convolution layer, as well as a new view pooling layer making use of: a) a new hybrid Laplacian that is adjusted based on feature distance metric learning, b) multiple trainable representations of a feature matrix of a graph, using trainable distance matrices, adapting the notion of views to graphs and c) a multi-view graph aggregation scheme called graph view pooling, in order to synthesise information from the multiple generated "views". The aforementioned layers are used in an end-to-end graph neural network architecture for graph classification and show competitive results to other state-of-the-art methods.
Recurrent Neural Networks for Signature Generation

A new technique for producing hash values for text documents is introduced in this report. The method uses Recurrent Neural Networks (RNN). RNNs are functionally and temporally dependent on the input vectors of the neural networks (RNN). RNN 's capacity to integrate current values of inputs with previous values that manipulate the associations and the semanticists of the document constitutes a competitive framework for discovering internal interpretations of document details in a special way. In contrast to conventional approaches, two forms of RNNs are evaluated. Current approaches have been adequately examined and the effects of this study reveal the applicability of this artificial intelligence model to construct hash values for plain text. RNNs are very lightweight , portable and parallel in nature and their abilities are used as a potential professional document hashing technology is presented in this article.
A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting

Risk control has always been a major challenge in finance. Overdue repayment is a frequently encountered discreditable behavior in online lending. Motivated by the powerful capabilities of deep neural networks, we propose a fusion deep learning approach, namely AD-MBLSTM, based on the deep neural network (DNN), multi-layer bi-directional long short-term memory (LSTM) (BiLSTM) and the attention mechanism for overdue repayment behavior forecasting according to historical repayment records. Furthermore, we present a novel feature derivation and selection method for the procedure of data preprocessing. Visualization and interpretability improvement work is also implemented to explore the critical time points and causes of overdue repayment behavior. In addition, we present a new dataset originating from a practical application scenario in online lending. We evaluate our proposed framework on the dataset and compare the performance with various general machine learning models and neural network models. Comparison results and the ablation study demonstrate that our proposed model outperforms many effective general machine learning models by a large margin, and each indispensable sub-component takes an active role.
Alpha-cut based fuzzy cognitive maps with applications in decision-making

Fuzzy cognitive maps (FCMs) are widely used fuzzy modeling tools for handling causal interdependencies in complex systems. In FCMs, system variables (concepts) and degree of interrelationships are quantified by means of fuzzy sets. However, these fuzzy sets are defuzzified and fuzzy singletons are processed in the inference algorithm. Defuzzification of fuzzy numbers before the FCM inference implies information loss which is an undesired situation. Moreover, the resultant concept values are fuzzy singletons that these crisp numbers do not provide any information about the range of possible outcomes. There is a research gap in the literature regarding fuzzy number representation in FCMs that the both of the inputs and outputs of FCMs being fuzzy sets. This study proposes alpha-cut based computational procedures for simulating FCMs in which concepts and degree of relationships are represented via fuzzy numbers. The proposed model is tested on well-known problems adopted from the literature by using type-1 fuzzy numbers and the results are compared with the extension principle-based approach. Moreover, the proposed model is extended to interval type-2 (IT2) fuzzy sets and computational details regarding IT2 FCMs are given. Because relative importance of criteria is usually modeled via fuzzy sets in multiple-attribute decision making problems, a new FCM-based objective weighting method is proposed in order to demonstrate the usefulness of fuzzy number representation in FCMs. The proposed model is implemented in the real-life third-party logistics service provider selection problem.
Cross-domain Design of Blockchain Smart Contract for Library and Healthcare Privacy

The blockchain technology designed by information security experts empowers in library service and healthcare service via smart contracts. Advanced blockchain technology provides a mechanism for smart contracts. Through intelligent threshold design and the open and tamper-proof nature of the blockchain, smart contracts have become a new generation of information security technology. In the next-generation library services, traditional borrowing materials from the library have evolved into a limited-time sharing service for electronic materials. These electronic materials have various loan periods and sharing time-varying by different borrower privileges. This study designs a patron-centric account's smart contract to disseminate library information more effectively and collect accurate library data. The smart contract automatically triggers the loan period and extends this mechanism to designing a personal health privacy sharing protocol for improving the one-time authorization sharing mechanism. As a result, this study may provide blockchain smart contract designs shared by library information and healthcare through cross-domain consideration and scheme.
Solar exergy evaluation and empirical model establishment; case study: Iran

Iran with 300 sunny days in more than two thirds of its land is among the countries with high potential of solar energy. Nevertheless, to date no research has been conducted on status of solar exergy in Iran. In this study, in order to expand the perception of solar energy quality and to compensate the lack of research on solar radiation exergy in Iran, long term meteorological and solar data of eight capital provinces of Iran with five different climatic conditions are utilized. These properly distributed stations include Urmia, Bushehr, Isfahan, Ilam, Kerman, Mashhad, Zahedan and Zanjan. The monthly average daily solar radiation exergy on a horizontal surface for each station is obtained first, then it is recognized that the ratio of exergy to energy is almost independent of the month, the climatic condition and the geographical location; thus, can be considered 0.87 for the whole Iran. For predicting the solar exergy at every station, five empirical models with linear, quadratic, cubic, exponential and power functional forms, all dependent only on relative sunshine duration, are calibrated. Then, eight statistical indicators are utilized to evaluate the performance of the established models for every capital province. The best models recognized for Urmia, Bushehr, Isfahan, Ilam, Kerman, Mashhad, Zahedan and Zanjan have cubic, power, exponential, exponential, linear, quadratic, power and cubic functional forms, respectively. These models are simple and easy to apply and can be also utilized for other places with similar climatic classification and conditions.
Consistent estimation of the max-flow problem: Towards unsupervised image segmentation

Advances in the image-based diagnostics of complex biological and manufacturing processes have brought unsupervised image segmentation to the forefront of enabling automated, on the fly decision making. However, most existing unsupervised segmentation approaches are either computationally complex or require manual parameter selection (e.g., flow capacities in max-flow/min-cut segmentation). In this work, we present a fully unsupervised segmentation approach using a continuous max-flow formulation over the image domain while optimally estimating the flow parameters from the image characteristics. More specifically, we show that the maximum a posteriori estimate of the image labels can be formulated as a continuous max-flow problem given the flow capacities are known. The flow capacities are then iteratively obtained by employing a novel Markov random field prior over the image domain. We present theoretical results to establish the posterior consistency of the flow capacities. We compare the performance of our approach using brain tumor image segmentation, defect identification in additively manufactured components using electron microscopic images, and segmentation of multiple real-world images. Comparative results with several state-of-the-art supervised as well as unsupervised methods suggest that the present method performs statistically similar to the supervised methods, but results in more than 90 percent improvement in the Dice score when compared to the state-of-the-art unsupervised methods.
Hedonic and Utilitarian Motivations of Home Motion-Sensing Game Play Behavior in China: An Empirical Study

As an important branch of video games and the integration of emerging motion-sensing technology, home motion-sensing games cannot only bring hedonic entertainment but also promote utilitarian benefits including exercise and social interaction for people to improve their physical and psychological health. As one of the most populous countries in the world, China has the largest number of households in the world but quite a low home game penetration rate due to the 13 year game industry winter for international enterprises. Whether Chinese customers have the intention of using motion-sensing games to improve their health status in the home environment will directly determine the commercial potential of the relevant industry in the Chinese market. In order to understand the motives of users and explore the market possibility and prospects of the game industry, this study adopts empirical research and structural equation modeling to construct a motivation model of Chinese consumers toward motion-sensing gameplay behavior in the household environment. We distributed 515 questionnaires to conduct a survey; 427 valid responses have been received, and 203 data, which meet the inclusion criteria of the required game experience, have been analyzed by SPSS25.0 and AMOS25.0. A structural equation model for the gameplay motivation has been constructed. The result shows that the three functional motivators, exercise (Path efficient = 0.40, p &lt; 0.01), entertainment (Path efficient = 0.27, p &lt; 0.01), and social interaction (Path efficient = 0.36, p &lt; 0.01) of home motion-sensing games have a significantly positive impact on the user’s intention to play. Furthermore, the diversity and the time-and-place flexibility variables exert an important positive influence on the users’ gameplay behavior through their effects on the three main functional motive variables. To sum up, (1) exercise, (2) entertainment, and (3) social interaction are the main functional motivations of the Chinese consumers’ gameplay behaviors; (4) diversity and (5) time-and-place flexibility are the two main attribute motivators. The acceptance of Chinese users for home motion-sensing games remains positive and high. The motion-sensing game industry has broad market prospects in China through its potential in promoting consumer’s wellness and health in the home environment.
A Lightweight Memory Access Pattern Obfuscation Framework for NVM

Emerging Non-Volatile Memories (NVMs) are entering the mainstream market. With attractive performance, high density, and near-zero idle power, emerging NVMs are promising contenders to build future memory systems. On the other hand, their limited write endurance (106 to 108 write cycles) and enabling data remanence attacks remain as main challenges that could hinder the wide adoption of NVMs in many sectors. With the limited write-endurance of NVMs, implementing major security primitives, such as Oblivious RAM (ORAM) for memory access pattern obfuscation, become more challenging due to a massive extra-write operation demand, which will exacerbate the write endurance problem. Wear-leveling techniques aim at mitigating the endurance problem of NVM by shuffling locations and continuously changing the mappings of memory addresses to the actual memory cells. However, such wear-leveling techniques do not provide the access pattern obfuscation forsecurity. In this letter, we propose a lightweight memory access pattern obfuscation framework for NVM, called O-NVM, a novel design that achieves both wear-leveling and obfuscation with an appealing level of performance and security (obfuscation) guarantees. Our experimental results show that the NVM's lifetime can be improved by 4 - 125 X , and the randomness of address access patterns can achieve the passing rate of statistics tests over 99 percent.
A simple calibration method for line-structured light vision sensor based on planar target of different positions

Accurate, efficient and robust calibration for the line-structured light vision sensor in 3D vision measurement is a significant procedure for that it is the foundation of the subsequent 3D-reconstruction. A simple and high-precision calibration method for the line-structured light vision sensor based on the planar target is proposed in this paper. The presented approach consists of three main steps. In the first step, the camera is calibrated to obtain the intrinsic parameters. In the second step, the camera coordinate of the light plane feature point is acquired through combining the constraint condition between the camera coordinate and pixel coordinate on the perspective projection model of the camera with the special equation of the planar target. In the third step, the light plane is fitted with the obtained light plane feature points based on the least square fitting. Compared with the cross-ratio invariability method, the proposed method achieve calibration with simply extracting the centerline of the light stripe. A calibration accuracy of 0. 080mm is achieved using the proposed method, which is comparable to that of the cross-ratio invariability method. This calibration method satisfies the requirement of high-accuracy for 3D vision measurement.
Intuitive Facial Animation Editing Based On A Generative RNN Framework

Abstract For the last decades, the concern of producing convincing facial animation has garnered great interest, that has only been accelerating with the recent explosion of 3D content in both entertainment and professional activities. The use of motion capture and retargeting has arguably become the dominant solution to address this demand. Yet, despite high level of quality and automation performance‐based animation pipelines still require manual cleaning and editing to refine raw results, which is a time‐ and skill‐demanding process. In this paper, we look to leverage machine learning to make facial animation editing faster and more accessible to non‐experts. Inspired by recent image inpainting methods, we design a generative recurrent neural network that generates realistic motion into designated segments of an existing facial animation, optionally following user‐provided guiding constraints. Our system handles different supervised or unsupervised editing scenarios such as motion filling during occlusions, expression corrections, semantic content modifications, and noise filtering. We demonstrate the usability of our system on several animation editing use cases.
What can Machine Learning do for Radio Spectrum Management?

The opening of the unlicensed radio spectrum creates new opportunities and new challenges for communication technology that can be faced by Machine Learning techniques. In this work, we discuss the potential benefits and the challenges with reference to the recent research developments in this area. Applications go from channel estimation to Signal quality control, and from signal classification to action control. We survey Machine learning and Deep Learning algorithms with possible radio applications and highlight the corresponding challenges.
Detecting Fraudulent Motor Insurance Claims Using Support Vector Machines with Adaptive Synthetic Sampling Method

Classification algorithms suffer from imbalanced training sets. In the area of detecting fraudulent claims in the insurance industry, fraud cases are rare as compared to the genuine ones. Therefore, algorithms of detecting fraud have fewer training samples of positive cases, leading to lower performance metrics compared to when there are equal cases. In this paper, we propose a machine learning method of detecting fraudulent claims. The proposed method uses the adaptive synthetic sampling method (ADASYN) to remove imbalances in the dataset. We then used Support Vector Machines (SVM) to classify the claim cases. The outcome of the algorithm is compared to the imbalanced datasets and other existing methods.
Predicting Real-Time Affective States by Modeling Facial Emotions Captured During Educational Video Game Play

In an attempt to predict the cognitive-affective states of a player during an educational video game session, this study used a self-emote procedure in which participants’ facial expressions and emotions were continuously recorded along with self-reported data about their emotional states. Participants’ facial expressions and emotions were captured using Affdex SDK from Affectiva. The captured data were used for binomial logistic regression to predict the cognitive-affective states of flow, frustration, and boredom. The binomial logistic regression uncovered that expressions and emotions could be used to predict these cognitive-affective states of a player. We discuss these predictors and their potential to adapt an educational video game session with non-intrusive and affect-sensitive personalization capabilities. The current study provides a pathway for the educational play design and suggests that it should be non-intrusive while being adaptive to a player’s capabilities.
In Situ Noise Characterization of the D-Wave Quantum Annealer

Various sources of noise limit the performance of quantum computers based on superconducting flux qubits by altering their states in an uncontrolled manner throughout computations and reducing their coherence time. In quantum annealers, this introduces fluctuations to the linear constants of the original problem Hamiltonian, such that they find the ground states of problems perturbed from those programmed. Here we describe how to turn this drawback into a method to probe the frequency dependence of the noise in situ of the D-Wave 2000Q quantum annealer. The method relates the autocorrelation of the readout-state of a qubit repeatedly collapsed from uniform superposition to that of the noise impingent on the qubit. We show that this leads to an estimate for the noise spectral density affecting D-Wave qubits under normal operating conditions. The method is general and can be used to characterize noise in all architectures for quantum annealing.
Stacked Inverse Probability of Censoring Weighted Bagging: A Case Study In the InfCareHIV Register

Abstract We propose an inverse probability of censoring weighted (IPCW) bagging (bootstrap aggregation) pre-processing that enables the application of any machine learning procedure for classification to be used to predict the cause-specific cumulative incidence, properly accounting for right-censored observations and competing risks. We consider the IPCW area under the time-dependent ROC curve (IPCW-AUC) as a performance evaluation metric. We also suggest a procedure to optimally stack predictions from any set of IPCW bagged methods. We illustrate our proposed method in the Swedish InfCareHIV register by predicting individuals for whom treatment will not maintain an undetectable viral load for at least 2 years following initial suppression. The R package stackBagg that implements our proposed method is available on Github.
Local-binarized very deep residual network for visual categorization

Residual networks usually require more layers to achieve remarkable performance in complex visual categorization tasks, such as pose estimation. However, the increasing number of layers leads to a heavy burden on training and forward inference as well as over-fitting. This paper proposed local binary residual block (LBB) to promote the very deep residual networks on the trainable parameters, FLOPs and accuracy. In each LBB, the 3×3 filters are binarized based on Bernoulli distribution under a sparse constraint, an activation function is prepared to trigger the non-linear response, and the linear 1×1 filters are learned in a real-valued way. After stochastic binarized initialization, the 3×3 filters in LBB need not be updated during training. The above architecture reduces at least 69.2% trainable parameters and 70.5% FLOPs compared to the original model. The LBB is derived from three observations: 1) Activated responses of one standard k×k convolutional layer can be approximated by combining binarized k×k filters with 1×1 filters; 2) Most computation in the very deep residual networks is spent on the 3×3 convolutions; and 3) 1×1 filters play an important role in cross-channel information integration. In addition, the LBB module is suitable for the very deep network framework, including stacked hourglass network and pyramid residual modules. Experiments are conducted on MPII and LSP dataset for pose estimation task; CIFAR-10, CIFAR-100 and ImageNet datasets for object recognition; ECSSD, HKU-IS, PASCAL-S, DUT-OMRON, DUTS for saliency detection. The results show that our model can accelerate the training and inference of the network with only a slight performance degradation.