Reviewerly/exHarmony huggingface dataset:
Probabilistic Video Prediction From Noisy Data With a Posterior Confidence

We study a new research problem of probabilistic future frames prediction from a sequence of noisy inputs, which is useful because it is difficult to guarantee the quality of input frames in practical spatiotemporal prediction applications. It is also challenging because it involves two levels of uncertainty: the perceptual uncertainty from noisy observations and the dynamics uncertainty in forward modeling. In this paper, we propose to tackle this problem with an end-to-end trainable model named Bayesian Predictive Network (BP-Net). Unlike previous work in stochastic video prediction that assumes spatiotemporal coherence and therefore fails to deal with perceptual uncertainty, BP-Net models both levels of uncertainty in an integrated framework. Furthermore, unlike previous work that can only provide unsorted estimations of future frames, BP-Net leverages a differentiable sequential importance sampling (SIS) approach to make future predictions based on the inference of underlying physical states, thereby providing sorted prediction candidates in accordance with the SIS importance weights, i.e., the confidences. Our experiment results demonstrate that BP-Net remarkably outperforms existing approaches on predicting future frames from noisy data.
Adversarial joint domain adaptation of asymmetric feature mapping based on least squares distance

Joint domain adaptation aims to learn a high-quality classifier for an unlabeled dataset with the help of auxiliary data. Most methods reduce domain shifts through some carefully designed distance measures. Adversarial learning, which is rarely used for joint domain adaptation, can learn more transferable features while avoiding explicit distance measures. However, it usually suffers from a gradient vanishing problem during the training process. In order to solve the above problems, we propose a novel adversarial joint domain adaptation method, namely Asymmetric Feature mapping based on Least Squares Distance (AFLSD), which consists of asymmetric marginal distribution alignment and conditional distribution alignment. The asymmetric feature mapping, which can get closer features with more flexible parameters, is optimized by the least squares distance to reduce the gradient vanishing problem. The results of classification and other comparative experiments show that AFLSD is superior to the most advanced domain adaptation methods.
RVTensor: A Light-Weight Neural Network Inference Framework Based on the RISC-V Architecture

The open-source instruction set architecture RISC-V has developed rapidly in recent years, and its combination mode of multiple sub-instruction sets has attracted the attention of IoT vendors. However, research on the IoT scenario inference framework based on the RISC-V architecture is rare. Popular frame-works such as MXNet, TensorFlow, and Caffe are based on the X86 and ARM architectures, and they are not optimized for the IoT scenarios. We propose RVTensor that a light-weight neural network inference framework based on the RISC-V architecture. RVTensor is based on the SERVE.r platform and is optimized for resource-poor scenarios. Our experiments demonstrate that the accuracy of RVTensor and the Keras is the same.
Diagnosing Rarity in Human-object Interaction Detection

Human-object interaction (HOI) detection is a core task in computer vision. The goal is to localize all human-object pairs and recognize their interactions. An interaction de-fined by a <; verb, noun> tuple leads to a long-tailed visual recognition challenge since many combinations are rarely represented. The performance of the proposed models is limited especially for the tail categories, but little has been done to understand the reason. To that end, in this paper, we propose to diagnose rarity in HOI detection. We propose a three-step strategy, namely Detection, Identification and Recognition where we carefully analyse the limiting factors by studying state-of-the-art models. Our findings indicate that detection and identification steps are altered by the interaction signals like occlusion and relative location, as a result limiting the recognition accuracy.
Analysis of Approaches to Determining Solar Radiation Intensity at Preset Geographical Point

Currently, photovoltaic energy converters are becoming increasingly popular for power supplying various objects, both connected to centralized power grids and operating offline. As a result, a large number of studies are carried out to justify, develop and create new technologies and equipment for the solar energy utilization, which could be effectively used by diverse consumers for different regions. Designing any photovoltaic installation, the selection of the actinometrical data source is the priority issue. There is a large number of approaches to determining the gross potential of solar energy, however, not all of them contain the full array of data that is necessary for designing, while some of them are not very accurate, while others are difficult to use and their accuracy is limited by the observation period. The article presents a methodology for the express assessment of the solar energy potential, designed by means of combining the calculated and experimental data, which allows determining with a high degree of reliability the hourly sums of all components of solar radiation on differently oriented receiving surfaces. Such combined application of various approaches introduced the following features of the method: the proposed approach is based on the data averaged over a period of 20 years, and their array is interpolated to the grid of 0.5x 0.5. Therefore, it is relevant to assess the applicability of the developed method of express assessment of solar energy potential for a preset geographical point, for which experimental studies were carried out. The reliability of the calculated total solar radiation intensity by the presented method is confirmed by deviation of 0.2–8 % for every day of the year. The deviation of 0.6– 8.2% is achieved for the months of the year in the range of calculated values comparing to the actinometrical values.
Anomaly prediction in mobile networks : A data driven approach for machine learning algorithm selection

In this paper, we propose a model for proactive anomaly detection in mobile networks. We show that when Key Performance Indicators (KPIs) are highly correlated, the linear regression gives a good accuracy in anomaly detection for a short prediction horizon. When the prediction horizon is far, KPIs become weakly correlated. We propose to transform discrete measurements into functional data and apply a functional data regression method to perform the prediction. We compare pro-posed models using KPI measurements obtained from a real Long Term Evolution (LTE) network. We show that an improvement in prediction performance can be obtained by using functional data analysis.
Immersive Search: Using Virtual Reality to Examine How a Third Dimension Impacts the Searching Process

In this paper, we present results from an exploratory study to investigate users' behaviors and preferences for three different styles of search results presentation in a virtual reality (VR) head-mounted display (HMD). Prior work in 2D displays has suggested possible benefits of presenting information in ways that exploit users' spatial cognition abilities. We designed a VR system that displays search results in three different spatial arrangements: a list of 8 results, a 4x5 grid, and a 2x10 arc. These spatial display conditions were designed to differ in terms of the number of results displayed per page (8 vs 20) and the amount of head movement required to scan the results (list < grid < arc). Thirty-six participants completed 6 search trials in each display condition (18 total). For each trial, the participant was presented with a display of search results and asked to find a given target result or to indicate that the target was not present. We collected data about users' behaviors with and perceptions about the three display conditions using interaction data, questionnaires, and interviews. We explore the effects of display condition and target presence on behavioral measures (e.g., completion time, head movement, paging events, accuracy) and on users' perceptions (e.g., workload, ease of use, comfort, confidence, difficulty, and lostness). Our results suggest that there was no difference in accuracy among the display conditions, but that users completed tasks more quickly using the arc. However, users also expressed lower preferences for the arc, instead preferring the list and grid displays. Our findings extend prior research on visual search into to the area of 3-dimensional result displays for interactive information retrieval in VR HMD environments.
Reservoir Computing Meets Wi-Fi in Software Radios: Neural Network-based Symbol Detection using Training Sequences and Pilots

In this paper, we introduce a neural network (NN)based symbol detection scheme for Wi-Fi systems and its associated hardware implementation in software radios. To be specific, reservoir computing (RC), a special type of recurrent neural network (RNN), is adopted to conduct the task of symbol detection for Wi-Fi receivers. Instead of introducing extra training overhead/set to facilitate the RC-based symbol detection, a new training framework is introduced to take advantage of the signal structure in existing Wi-Fi protocols (e.g., IEEE 802.11 standards), that is, the introduced RC-based symbol detector will utilize the inherent long/short training sequences and structured pilots sent by the Wi-Fi transmitter to conduct online learning of the transmit symbols. In other words, our introduced NN-based symbol detector does not require any additional training sets compared to existing Wi-Fi systems. The introduced RC-based Wi-Fi symbol detector is implemented on the software defined radio (SDR) platform to further provide realistic and meaningful performance comparison against the traditional Wi-Fi receiver. Over the air experiment results show that the introduced RC-based Wi-Fi symbol detector outperforms conventional Wi-Fi symbol detection methods in various environments indicating the significance and the relevance of our work.
Novel Extreme Learning Machine Using Kalman Filter for Performance Prediction of Aircraft Engine in Dynamic Behavior

In this paper, a novel state-propagation extreme learning machine using a Kalman filter (KF-ELM) is proposed. In comparison with the plain extreme learning machine, the proposed algorithm takes the topological parameters as state variables and minimizes the covariance of state estimates to overcome the state conjunction and transformation dilemma in time series. As a result, its topological stability and prediction accuracy are enhanced, and these merits are further proved theoretically. In addition, the computational effort of KF-ELM is on the same order of magnitude as the plain extreme learning machine, while the former possesses a faster convergent speed. Then, several benchmark datasets are utilized to test the effectiveness and soundness of the proposed algorithm. Finally, it is employed to predict the gas path performance of a turbofan engine. The performance prediction accuracy is better than the plain ELM with different input rules in the dynamic process. Particularly under various flight operation conditions, the proposed algorithm performs well and its stability is sufficiently showcased. In a word, the proposed algorithm provides a candidate technique for predicting aircraft engine performance in dynamic behavior.
Scalable deep asymmetric hashing via unequal-dimensional embeddings for image similarity search

In recent years, Hashing has become a popular technique used to support large-scale image retrieval, due to its significantly reduced storage, high search speed and capability of mapping high dimensional original features into compact similarity-preserving binary codes. Although effectiveness achieved, most existing hashing methods are still some limitations, including: (1) Many supervised hashing methods only transform the label information into pairwise similarities to guide the hash code learning process, which will lead to the loss of rich semantic information between image pairs. (2) Some pioneer hashing methods use a relaxation-based strategy to solve discrete problems, resulting in a large quantization error. (3) Some supervised hashing methods handle the hashing learning procedure based on an asymmetric learning manner, and although this partially solves the problems of low efficiency and accuracy of symmetric learning strategy, they are all based on the embeddings of equal dimension, which leads to the reduction in the models representation ability and an increase in potential noise. To overcome the above limitations, in this paper, we propose a novel yet simple but effective hashing method, named Scalable Deep Asymmetric Hashing (SDAH). Specifically, SDAH is an end-to-end deep hashing method based on a fast iterative optimization strategy, which utilizes two real-valued embeddings of unequal dimensions, i.e., real-valued embeddings of images and labels, to flexibly perform asymmetric similarity computation. It can circumvent the use of the large pairwise similarity matrix by introducing an intermediate label matrix term which results in a remarkable reduction in the memory space cost. By doing this, the learned hash codes are more semantically informative for image retrieval tasks. Experimental results on several benchmark datasets highlight the superiority of SDAH in comparison with many state-of-the-art hashing methods.
The ClassDojo app: training in the art of dividuation

This article emerged in response to the increasing ubiquity of the ClassDojo app, a Silicon Valley-developed digital communication and behavior management platform, in grade schools throughout the world. The author engages critically with ClassDojo by situating it within current scholarship around network governance, describing how the app may reflect a broader transition from Foucauldian disciplinary societies to Deleuzian societies of control. Through a focus on ClassDojo, the author provides an account of how technologically driven processes of 'dividuation' encourage a qualitatively different form of power—what Savat, following Deleuze, referred to as modulation—to augment disciplinary power's longstanding role in producing the 'good student,' the 'good teacher,' and the 'good parent.' The author argues that the modulatory effects of ed-tech platforms like ClassDojo mark an important shift away from schools as sites for the production of neoliberalised individuals and toward schools as networked nodes for the anticipation of dividuals.
PCA-AE: Principal Component Analysis Autoencoder for Organising the Latent Space of Generative Networks

Autoencoders and generative models produce some of the most spectacular deep learning results to date. However, understanding and controlling the latent space of these models presents a considerable challenge. Drawing inspiration from principal component analysis and autoencoders, we propose the principal component analysis autoencoder (PCA-AE). This is a novel autoencoder whose latent space verifies two properties. Firstly, the dimensions are organised in decreasing importance with respect to the data at hand. Secondly, the components of the latent space are statistically independent. We achieve this by progressively increasing the latent space during training, and with a covariance loss applied to the latent codes. The resulting autoencoder produces a latent space which separates the intrinsic attributes of the data into different components of the latent space, in a completely unsupervised manner. We also describe an extension of our approach to the case of powerful, pre-trained GANs. We show results on both synthetic examples of shapes and on a state-of-the-art GAN. For example, we are able to separate the colour shade scale of hair, pose of faces and gender, without accessing any labels. We compare the PCA-AE with other state-of-the-art approaches, in particular with respect to the ability to disentangle attributes in the latent space. We hope that this approach will contribute to better understanding of the intrinsic latent spaces of powerful deep generative models.
MHM: Multi-modal Clinical Data based Hierarchical Multi-label Diagnosis Prediction

Diagnosis prediction aims to forecast diseases that a patient might have in his next hospital visit, which is critical in Clinical Decision Supporting System (CDSS). Existing approaches mainly formulate diagnosis prediction as a multi-label classification problem and use discrete medical codes as major features. While the structural information among medical codes and time series data in clinical data are generally neglected. In this paper, we propose Multi-modal Clinical Data based Hierarchical Multi-label model (MHM) to integrate discrete medical codes, structural information and time series data into the same framework for diagnosis prediction task. Experimental results on two real world datasets demonstrate the superiority of proposed MHM over state-of-the-art approaches.
The consensus equilibria of mining gap games related to the stability of Blockchain Ecosystems

The equity and currency tokens are typically two kinds of initial coin offerings (ICOs) like Bitcoin or Ethereum based on the platform of Blockchains to provide a particular product or service, it is very important to study the mechanism of Blockchain Ecosystems. The goal of this paper is to explain the stable in the sense for the existence of consensus equilibria for mining gap games by using one new concept called 'consensus games (CG)' under the framework of Blockchain Ecosystems which mainly mean the economic activities by taking into the account of three types of different factors which are expenses, reward mechanism and mining power for the work on blockschain by applying consensuses including the 'Proof of Work' due to Nakamoto in 2008 as a special case.
Comparison of SURF and HOG extraction in classifying the blood image of malaria parasites using SVM

Malaria is the deadliest disease caused by a protozoa parasite of the Plasmodium genus that is transmitted through the female Anopheles mosquito. Five Plasmodium species can cause infections and deaths such as P. Ovale, P. Malaria, P. vivax, and P. falciparum, each parasitic malaria pattern in the blood image has a unique and distinct pattern. The introduction of the parasitized pattern and uninfected cells conducted in this study used the extraction of surf and HOG features as well as SVM as methods classification. Research results show that SVM with SURF feature extraction can classify the parasitized pattern and uninfected cells in the blood image better than SVM with the extraction of HOG features. The average accuracy feature extraction of the SURF 85.83% while HOG feature extraction is only 83.50%.
Attitude dynamics aiding for three-dimensional passive target tracking of strap-down seeker based on instrumental variable Kalman filter

The accuracy of three-dimensional (3D) passive target tracking under strap-down system is usually affected by the measurement accuracy of attitude angular rate and attitude angle. In order to save the problem, a novel 3D passive target tracking method based on instrumental variable Kalman filter (IVKF) aided by the attitude dynamic is proposed. At first, the maneuvering target motion model is established based on the “current” statistical model and the filtering equation of MEMS inertial measurement unit (IMU) is also set up. Then, linearize the nonlinear state equations and replace the nonlinear measurement equations with pseudolinear equations. The 3D pseudolinear Kalman filter (PLKF) algorithm is derived according to the linear Kalman filter (KF). To counter the severe bias problems with PLKF, bias compensation and recursive instrumental variable (IV) methods are considered. In order to enhance observability of the system, a 3D motion tracking sliding-mode guidance law is deduced. Finally, some mathematical simulations were made to verify the effectiveness of the proposed method. The simulation results show the effect of the measurement accuracy and complexity of the algorithm are reduced, which proves the validity of the method.
Identification and parameter estimation algorithm of radar signal subtle features

With the rapid development of electronic technology and its widespread application in modern warfare, the modulation methods of individual radar signal sources are more flexible, the parameters are diverse, and the modern battlefield electromagnetic signal environment is complex, so that radar signal recognition based on traditional conventional parameters Technology cannot meet real needs. Among the many radar signal modulation recognition algorithms that have appeared in recent years, neural network theory and intra-pulse analysis algorithms are widely used, but they each have their own shortcomings, and a single method cannot meet the needs of increasingly complex modulation signal recognition and parameter estimation needs. . Based on the above background, the research content of this paper is to identify the subtle characteristics of radar signals and their parameter estimation algorithms. In this paper, two types of typical modulation signals in intentional modulation of radar signals, namely phase-coded (PSK) signals and frequency-modulated (FM) signals, are proposed. A SVEFD algorithm based on classification from coarse to fine. According to the characteristics of the 3 dB bandwidth of the frequency spectrum of the PSK signal and the FM signal, the coarse classification between the classes is performed first. Finally, through experimental simulations, the results show that the correct recognition probability of the SVEFD algorithm at a lower signal-to-noise ratio is much higher than the m algorithm and also higher than the SVD algorithm. When the signal-to-noise ratio is greater than 1 dB, the average recognition rate reaches 94%, which proves that The effectiveness of the proposed SVEFD algorithm. When the signal-to-noise ratio is .1 dB, in addition to maintaining the correct recognition rate of more than 90% for the LFM and COSTAS signals, the other six types of signals have low correct recognition rates, so the SVEFD algorithm has advantages in comparison.
Dynamic Game Model for Ranking Bitcoin Transactions Under GSP Mechanism

Bitcoin is the first and most successful Blockchain system so far. In the Bitcoin system, miners use transaction attached fees as a driving force to mine a new block and package transactions, while users compete by bidding transaction fees for faster confirmation. Considering the particularity of Bitcoin trading system, we take time series into consideration to analyze the transaction rules of Bitcoin system from the perspective of multiple cycles and establish a dynamic game model related to time under Generalised Second Price(GSP) mechanism, and also confirm the model's superiority on saving users' fees, compared with the static game model. Also, we propose the quantification of the user experience quantified by calculating the price difference between the transactions uploaded by the same user within adjacent times, making the transaction process of the Bitcoin system no longer the final say of the transaction price. The dynamic game model shows that there is a perfect Bayesian game equilibrium solution in the payment decision, so there is no incentive for users to change the attached fee, and the whole system is maintained stably. In addition, we verify the dynamic game model from computational experiment. Firstly, it is proved that with the help of revenue discount, the cost saving of the dynamic model is generally higher than that of the static model. Then the user's revenue under the dynamic model is showing an upward trend, and the transactions order under the dynamic model is more stable than that under static model, which can be illustrated mathematically and computationally that the proposed dynamic game model in this paper will help all transactions be processed more efficiently in a uniform pipeline.