Reviewerly/exHarmony huggingface dataset:
FMCW Radar-Based Real-Time Hand Gesture Recognition System Capable of Out-of-Distribution Detection

Real-time hand gesture recognition plays a vital role in human-computer interaction (HCI). Recent radar-based hand gesture recognition methods have focused on achieving high classification accuracy using deep neural network (DNN)-based classifiers. However, the hand gesture recognition system should not only classify the gestures accurately but also detect out-of-distribution (OOD) samples to be used in real-world HCI scenarios with high reliability. Recognition systems without OOD detection capability misclassify unintended gestures in silence, especially in real-time scenarios. To tackle this problem, we propose a real-time hand gesture recognition system that can simultaneously classify hand gestures and detect OOD samples by using a Frequency Modulated ContinuousWave (FMCW) radar sensor. First, we design radar data processing technique and Transformer encoder-based classifier to achieve high classification accuracy. Second, the relative Mahalanobis distance (RMD)-based OOD detection method is adopted to increase the reliability of the proposed system. Finally, one in-distribution dataset and two OOD datasets are collected to verify the proposed system. The proposed system achieves a classification accuracy of 93.95%on the in-distribution dataset.We conduct the OOD detection experiments with two OOD datasets for which the proposed system reports AUROC values of 92.96% and 92.84%, respectively. Furthermore, the feasibility of the proposed system is certified through a real-time experimental demonstration.
Do you kiss when you text? Cross-cultural differences in the use of the kissing emojis in three WhatsApp corpora

Abstract Emojis are pictographs added to messages on social media and websites. Researchers have observed that emojis representing kissing faces are often used to close instant messaging conversations. This has been interpreted as an imitation of cheek kissing, a common behavior in some cultural contexts. We analyze the use of seven types of kissing emojis in three corpora of WhatsApp chats, one from Spain (where cheek kisses in face-to-face interaction are commonplace in many situations), the other from Germany (where kisses are occasionally given), and the third from the German-speaking part of Switzerland (where cheek kisses are a common greeting between relatives and friends). To do so, we systematically categorize and compare the use of a sample of these emojis on WhatsApp. The analysis suggests that there are differences between the three corpora in the use of the kissing emojis. The emoji “face throwing a kiss” is often included in closing messages in the Spanish and Swiss-German data, while in the Federal German corpus kisses do not appear at the end of a conversation; using these emojis in openings is uncommon in all three corpora. This suggests that these emojis can exhibit cultural variation, but they do not clearly mirror face-to-face behavior.
Health-guided recipe recommendation over knowledge graphs

While the availability of large-scale online recipe collections presents opportunities for health consumers to access a wide variety of recipes, it can be challenging for them to discover relevant recipes. Whereas most recommender systems are designed to offer selections consistent with users' past behavior, it remains an open problem to offer selections that can help users' transition from one type of behavior to another, intentionally. In this paper, we introduce health-guided recipe recommendation as a way to incrementally shift users towards healthier recipe options while respecting the preferences reflected in their past choices. Introducing a knowledge graph (KG) into recommender systems as side information has attracted great interest, but its use in recipe recommendation has not been studied. To fill this gap, we consider the task of recipe recommendation over knowledge graphs. In particular, we jointly learn recipe representations via graph neural networks over two graphs extracted from a large-scale Food KG, which capture different semantic relationships, namely, user preferences and recipe healthiness, respectively. To integrate the nutritional aspects into recipe representations and the recommendation task, instead of simple fusion, we utilize a knowledge transfer scheme to enable the transfer of useful semantic information across the preferences and healthiness aspects. Experimental results on two large real-world recipe datasets showcase our model's ability to recommend tasty as well as healthy recipes to users.
GNNIE

Graph neural networks (GNN) inferencing involves weighting vertex feature vectors, followed by aggregating weighted vectors over a vertex neighborhood. High and variable sparsity in the input vertex feature vectors, and high sparsity and power-law degree distributions in the adjacency matrix, can lead to (a) unbalanced loads and (b) inefficient random memory accesses. GNNIE ensures load-balancing by splitting features into blocks, proposing a flexible MAC architecture, and employing load (re)distribution. GNNIE's novel caching scheme bypasses the high costs of random DRAM accesses. GNNIE shows high speedups over CPUs/GPUs; it is faster and runs a broader range of GNNs than existing accelerators.
H2H

The complex nature of real-world problems calls for heterogeneity in both machine learning (ML) models and hardware systems. The heterogeneity in ML models comes from multi-sensor perceiving and multi-task learning, i.e., multi-modality multi-task (MMMT), resulting in diverse deep neural network (DNN) layers and computation patterns. The heterogeneity in systems comes from diverse processing components, as it becomes the prevailing method to integrate multiple dedicated accelerators into one system. Therefore, a new problem emerges: heterogeneous model to heterogeneous system mapping (H2H). While previous mapping algorithms mostly focus on efficient computations, in this work, we argue that it is indispensable to consider computation and communication simultaneously for better system efficiency. We propose a novel H2H mapping algorithm with both computation and communication awareness; by slightly trading computation for communication, the system overall latency and energy consumption can be largely reduced. The superior performance of our work is evaluated based on MAESTRO modeling, demonstrating 15%-74% latency reduction and 23%-64% energy reduction compared with existing computation-prioritized mapping algorithms. Code is publicly available at https://github.com/xyzxinyizhang/H2H.
Hybrid Multiple Filter Embedded Political Optimizer for Feature Selection

DNA microarray data analysis is notorious because it contains a large number of characteristics, asymmetrical class distribution, and it is restricted with less number of samples. We emphasize high-dimensional multi-class unbalanced issues in this work. The high dimensional, multi-class unbalanced problem has found it challenging for traditional classifiers to execute classification assignments effectively on both minority and majority classes. Numerous approaches have been made to handle either high dimensionality datasets or difficulties with class imbalance. Despite this, because of their complex relationships, few techniques have been presented to address the intersection of multi-class unbalanced and high-dimensional issues at the same time. Using multiple filter-based rankers (MF) and a hybrid political optimizer (PO), this study proposes unique hybrid algorithms for feature selection with the high-dimensional multi-class imbalanced issue. To evaluate the performance of the model, we have used four different classifiers named Support vector machine (SVM), Naive Bayes(NB), Decision Tree(DT), and K Nearest Neighbour(KNN). The experimental results show that the proposed methods are more effective than other well-known methods in the case of classification performance improvisation. Performance metrics accuracy indicates that the proposed methods are capable of searching the feature space and identifying very robust and discriminative features that best predict the minority class.
Dual-color blind image watermarking algorithm using the graph-based transform in the stationary wavelet transform domain

Aiming at the problem of color image copyright protection, a dual-color blind image watermarking algorithm using the graph-based transform (GBT) in the stationary wavelet transform domain (SWT) is proposed. First, the color watermark image and the color carrier image are separated into their R, G, and B channel components. Second, the R, G, and B components of the color watermark are scrambled by the Fibonacci–Lucas transform and encrypted with the cosine-transform-based chaotic system (CTBCS). Each component of the encrypted watermark is then converted into a binary sequence. Third, the low-frequency sub-bands from each component of the carrier image performed by the SWT are divided into non-overlapping blocks. Finally, the three binary watermark sequences are embedded into the GBT low-frequency coefficients of selected image blocks of the corresponding component by the parity quantization. Before the watermark extraction, the image to be authenticated is rotationally corrected based on the Fan-beam transform. This operation improves the resistance of the proposed algorithm to rotation geometry attacks. Experimental results show that the algorithm is robust to conventional signal processing attacks and to arbitrary angle rotation attacks while effectively ensuring the invisibility of the watermark.
Controlling strokes in fast neural style transfer using content transforms

Abstract Fast style transfer methods have recently gained popularity in art-related applications as they make a generalized real-time stylization of images practicable. However, they are mostly limited to one-shot stylizations concerning the interactive adjustment of style elements. In particular, the expressive control over stroke sizes or stroke orientations remains an open challenge. To this end, we propose a novel stroke-adjustable fast style transfer network that enables simultaneous control over the stroke size and intensity, and allows a wider range of expressive editing than current approaches by utilizing the scale-variance of convolutional neural networks. Furthermore, we introduce a network-agnostic approach for style-element editing by applying reversible input transformations that can adjust strokes in the stylized output. At this, stroke orientations can be adjusted, and warping-based effects can be applied to stylistic elements, such as swirls or waves. To demonstrate the real-world applicability of our approach, we present StyleTune , a mobile app for interactive editing of neural style transfers at multiple levels of control. Our app allows stroke adjustments on a global and local level. It furthermore implements an on-device patch-based upsampling step that enables users to achieve results with high output fidelity and resolutions of more than 20 megapixels. Our approach allows users to art-direct their creations and achieve results that are not possible with current style transfer applications.
ACCORD: A Scalable Multileader Consensus Protocol for Healthcare Blockchain

Blockchain is an emerging distributed and decentralized technology that promises to revolutionize the healthcare sector by securely storing and maintaining incorruptible electronic health record data. Consensus protocols are at the core of blockchain technology. They establish security and integrity in the system by ensuring that the majority of miners are in agreement on all transactions and blocks added to the distributed ledger. While many consensus protocols have been proposed, most of them require heavy computation and are not scalable. In this work, we propose a novel permissioned consensus protocol, named ACCORD, a multi-leader (quorum-based) protocol that achieves fork-resistance, robustness, and scalability. To achieve this, ACCORD consists of three distinct components: (1) an asynchronous quorum selection procedure to designate the creators of future blocks, (2) a block creation protocol run by the quorum to prevent omissions in the presence of honest quorum members, and (3) a decentralized arbitration protocol to ensure consensus by voting. Additionally, we implemented the protocol and conducted experiments to demonstrate scalability, robustness, and fairness.
Winograd Convolution for Deep Neural Networks: Efficient Point Selection

Convolutional neural networks (CNNs) have dramatically improved the accuracy of image, video, and audio processing for tasks such as object recognition, image segmentation, and interactive speech systems. CNNs require large amounts of computing resources for both training and inference, primarily because the convolution layers are computationally intensive. Fast convolution algorithms such as Winograd convolution can greatly reduce the computational cost of these layers. However, Winograd convolution has poor numeric properties, such that greater savings in computation cause exponentially increasing floating point errors. A defining feature of each Winograd convolution algorithm is a set of real-value points where polynomials are sampled. The choice of points impacts the numeric accuracy of the algorithm, but the optimal set of points for small convolutions remains unknown. Existing work considers only small integers and simple fractions as candidate points. In this work, we propose a novel approach to point selection using points of the form \(\lbrace -\frac{1}{c},-c,c,\frac{1}{c}\rbrace\) using the full range of real-valued numbers for c . We show that groups of this form cause cancellations in the Winograd transform matrices that reduce numeric error. We find empirically that the error for different values of c forms a rough curve across the range of real-value numbers. It is therefore possible to localize the values of c that lead to lower error. We show that it is not necessary to choose integers or simple fractions as evaluation points, and that lower errors can be achieved with non-obvious real-valued points. We study a range of sizes for small convolutions and achieve reduction in error ranging from 2% to around 59% for both 1D and 2D convolution, when compared to state of the art. Furthermore, we identify patterns in cases when we select a subset of our proposed points that will always lead to a lower error. Finally, we implement a complete Winograd convolution layer and use it to run state-of-the-art deep convolution neural networks on real datasets and show that our proposed points achieve reduction in error, ranging from 22% to 63%, while also showing how an increased Winograd output size can result in execution speed-up for some cases.
Data sampling approach using heuristic Learning Vector Quantization (LVQ) classifier for software defect prediction

On the basis of quality estimate, early prediction and identification of software flaws is crucial in the software area. Prediction of Software Defects SDP is defined as the process of exposing software to flaws through the use of prediction models and defect datasets. This study recommended a method for dealing with the class imbalance problem based on Improved Random Synthetic Minority Oversampling Technique (SMOTE), followed by Linear Pearson Correlation Technique to perform feature selection to predict software failure. On the basis of the SMOTE data sampling approach, a strategy for software defect prediction is given in this paper. To address the class imbalance, the defect datasets were initially processed using the Improved Random-SMOTE Oversampling technique. Then, using the Linear Pearson Correlation approach, the features were chosen, and using the k-fold cross validation process, the samples were split into training and testing datasets. Finally, Heuristic Learning Vector Quantization is used to classify data in order to predict software problems. Based on measures like sensitivity, specificity, FPR, and accuracy rate for two separate datasets, the performance of the proposed strategy is contrasted with the approaches to classification that presently exist.
L-Sign: Large-Vocabulary Sign Gestures Recognition System

Understanding sign gestures is an essential step to helping individuals with hearing impaired. The existing works can only identify a small set of gestures accurately and the accuracy rate drops sharply with an increasing number of gestures. Because there are two challenges&#x2014;a large number of similar gestures in sign language and the various signing speed of different people. Based on commercial smart bracelets, this article proposes a large-vocabulary sign language recognition system (which we call L-sign). First, we propose an entropy-based forward and backward matching algorithm to segment each gesture signal. Second, we design a gesture recognizer including a candidate gesture generator and semantic-based voter. The candidate gesture generator is aimed at providing candidate gesture designs based on a 3-branch convolutional neural network. The purpose of a semantic-based voter is to select the target gesture from candidate gestures by scoring, where the semantic distances between the last gesture in the current sentence and any candidate gestures is calculated, and a multilayer k-means algorithm is proposed to obtain a multilayer sign word structure to complete the scores of candidate gestures. Lastly, we deployed L-sign on the MYO bracelet. For 200 commonly used Chinese sign gestures, the experimental results show that the average accuracy rate was greater than 90&#x0025;.
Efficient Light Field Image Compression with Enhanced Random Access

In light field image compression, facilitating random access to individual views plays a significant role in decoding views quickly, reducing memory footprint, and decreasing the bandwidth requirement for transmission. Highly efficient light field image compression methods mainly use inter view prediction. Therefore, they typically do not provide random access to individual views. On the other hand, methods that provide full random access usually reduce compression efficiency. To address this trade-off, a light field image encoding method that favors random access is proposed in this paper. Light field image views are grouped into independent (3× 3) views, which are called Macro View Images (MVIs) . To encode MVIs, the central view is used as a reference to compress its adjacent neighboring views using a hierarchical reference structure. To encode the central view of each MVI, the most central view along with the center of a maximum of three MVIs, are used as reference images for the disparity estimation. In addition, the proposed method allows the use of parallel processing to reduce the maximum encoding/decoding time-complexity in multi-core processors. Tile partitioning can also be used to randomly access different regions of the light field images. The simulation results show that the proposed method outperforms other state-of-the-art methods in terms of compression efficiency while providing random access to both views and regions of interest.
Multi-Level Encoding and Decoding in a Scalable Photonic Tensor Processor With a Photonic General Matrix Multiply (GeMM) Compiler

The resurgence of artificial intelligence enabled by deep learning and high performance computing has seen a dramatic increase of demand in the accuracy of deep learning model which has come at the cost of computational complexity. The fundamental operations in deep learning models are matrix multiplications, and large scale matrix operations and data-centric tasks have experienced bottlenecks from current digital electronic hardware in terms of performance and scalability. Recent research on photonic processors have found solutions to enable applications in machine learning, neuromorphic computing and high performance computing using basic photonic processing elements on integrated silicon photonic platform. However, efficient and scalable photonic computing requires an information encoding/decoding scheme. Here, we propose a multi-level encoding and decoding scheme, and experimentally demonstrate it with a wavelength-multiplexed silicon photonic processor. We also discuss the scalability of our proposed scheme by introducing a photonic general matrix multiply compiler, and consider the effects of speed, bit precision, and noise. Our proposed scheme could be adapted to a variety of photonic information processing architectures for photonic neural networks, photonics tensor cores, and programmable photonic.
Efficient Exploration for Multi-Agent Reinforcement Learning via Transferable Successor Features

In multi-agent reinforcement learning (MARL), the behaviors of each agent can influence the learning of others, and the agents have to search in an exponentially enlarged joint-action space. Hence, it is challenging for the multi-agent teams to explore in the environment. Agents may achieve suboptimal policies and fail to solve some complex tasks. To improve the exploring efficiency as well as the performance of MARL tasks, in this paper, we propose a new approach by transferring the knowledge across tasks. Differently from the traditional MARL algorithms, we first assume that the reward functions can be computed by linear combinations of a shared feature function and a set of task-specific weights. Then, we define a set of basic MARL tasks in the source domain and pre-train them as the basic knowledge for further use. Finally, once the weights for target tasks are available, it will be easier to get a well-performed policy to explore in the target domain. Hence, the learning process of agents for target tasks is speeded up by taking full use of the basic knowledge that was learned previously. We evaluate the proposed algorithm on two challenging MARL tasks: cooperative box-pushing and non-monotonic predator-prey. The experiment results have demonstrated the improved performance compared with state-of-the-art MARL algorithms.
Political Hate Speech Detection and Lexicon Building: A Study in Taiwan

There is the minimal restriction to users' speech in cyberspace.The Internet provides a space where people can freely present their speech, which puts a Utopian sense of freedom of speech into practice.However, the appearance of hate speech is a significant side effect of online freedom of speech.Some users use hate speech to attack others, making the attacked targets uncomfortable.The proliferation of hate speech poses severe challenges to cyber society.Users may hope that social media platforms and online communities promote anti-hate speech.However, hate speech detection is still a developing technology that requires system developers to create a method to detect unacceptable hate speech while maintaining the online freedom of speech environment.No excellence detection approach has yet been proposed, although some literature has focused on it.The current study proposes an approach to build a political hate speech lexicon and train artificial intelligence classifiers to detect hate speech.Our academic and practical contributions include the collection of a Chinese hate speech dataset, creating a Chinese hate speech lexicon, and developing both a deep learning-based and a lexicon-based approach to detect Chinese hate speech.Although we focus on Chinese hate speech detection, our proposed hate speech detection system and hate speech lexicon development approach can also be used for other languages.