Reviewerly/exHarmony huggingface dataset:
Learning Representations by Graphical Mutual Information Estimation and Maximization

The rich content in various real-world networks such as social networks, biological networks, and communication networks provides unprecedented opportunities for unsupervised machine learning on graphs. This paper investigates the fundamental problem of preserving and extracting abundant information from graph-structured data into embedding space without external supervision. To this end, we generalize conventional mutual information computation from vector space to graph domain and present a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graph and hidden representation. Except for standard GMI which considers graph structures from a local perspective, our further proposed GMI++ additionally captures global topological properties by analyzing the co-occurrence relationship of nodes. GMI and its extension exhibit several benefits: First, they are invariant to the isomorphic transformation of input graphs-an inevitable constraint in many existing methods; Second, they can be efficiently estimated and maximized by current mutual information estimation methods; Lastly, our theoretical analysis confirms their correctness and rationality. With the aid of GMI, we develop an unsupervised embedding model and adapt it to the specific anomaly detection task. Extensive experiments indicate that our GMI methods achieve promising performance in various downstream tasks, such as node classification, link prediction, and anomaly detection.
Research on the Privacy Security of Face Recognition Technology

To solve the problem that privacy data are easy to leak in the application of face recognition technology in apps, a method which is based on differential privacy for privacy security protection is proposed. Firstly, Bayesian GAN is conducted to obtain the training data with the same distribution as the privacy data, and the algorithm of differential privacy is conducted to train the training data to obtain these labels with privacy protection. Then, based on the proposed lightface lightweight face recognition model, the tag with noise is generated, and the gradient descent is conducted on the recovered face feature vector from the attack. Finally, through the analysis of privacy loss, an accurate privacy protection boundary is provided. From the results of experiments, it could be known that the proposed privacy security protection method can effectively protect the parameter information of the face recognition model under the face recognition technology and reduce the recognition accuracy of the image recovered by the attacker. Compared with the privacy protection methods such as DPSGD and PATE, it has strong privacy protection ability and can be applied to the privacy protection of practical APP.
Dense Face Detection via High-level Context Mining

The appearance degradation caused by low resolution is the core problem of small face detection. Therefore, a natural approach is to assemble information from the context. This paper focuses on how to use high-level contextual information to improve the abilities of anchor-based detectors to detect dense and degenerate faces. We tap the spatial contextual information on the overall view based on the density map, and propose the prior of face co-occurrence for inferred bounding-boxes coordination. We also propose score-size-specific non-maximum suppression to replace the traditional non-maximum suppression at the end of anchor-based detectors. According to the inferred face boxes' quantity, score and size, the proposed synthetical solution reduces false positives and increases true positives. Our method does not require additional training, which is model-independent and can be embedded into existing face detectors. We also propose a dataset - Crowd Face for face detection, which is full of challenges. We expect to supply enough samples to highlight the difficulties of detecting dense and degenerate faces. We embed our proposed methods into state-of-the-art face detectors on massively benchmarked face datasets. Compared with the prior art on the WIDER FACE hard set, our method increase an Average Precision of 0.1 %-1.3%. On Crowd Face, it increases an Average Precision of 1 % – 6%. Dataset is available on: https://github.com/QxGeng/Crowd-Face.
An Attention-Aided Deep Neural Network Design for Channel Estimation in Massive MIMO Systems

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning-based designs have exhibited great potential in terms of both performance and complexity. In this paper, an attention-aided deep neural network is proposed for channel estimation in hybrid analog-digital massive MIMO systems. Specifically, the integrated attention mechanism automatically realizes the &#x201C;divide-and-conquer&#x201D; policy to exploit the distribution characteristics of highly separable channels with narrow angular spread. Simulation results show that the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead, and the strong robustness further strengthens the practical value of the proposed approach. Moreover, the distributions of learned attention maps are also investigated to reveal the role of attention and endow the proposed approach with a certain degree of interpretability.
A Parallel Multi-Deque Sorting Algorithm

Parallel or multithreaded sorting algorithms have been proposed and researched for multicore and manycore CPU and GPU systems. Some of them are based on divide and conquer concept to exploit data parallelism as well as task parallelism at the same time. In this paper, a Multi-Deque Sort (MDQSort) is proposed and developed on top of our previous algorithm called Multi-Stack (MSP) Sort. Therefore, the MDQSort can easily enhance the performance while getting rid of weaknesses of MSPSort. As a result, MDQSort can be as competitive as the Standard Template Library parallel mode sorting algorithms on a 4-core ARM Cortex A72 Linux system in terms of run time, stability, CPU utilization and memory (RAM) utilization.
Load Balancing with In-Protocol/Wallet-Level Account Assignment in Sharded Blockchains

Sharding is a solution to the blockchain scalability problem. A sharded blockchain divides consensus nodes (validators) into groups called shards and processes transactions separately to improve throughput and latency. In this paper, we analyze the rational behavior of users in account/balance model-based sharded blockchains and identify a phenomenon in which accounts (users' wallets and smart contracts) eventually get concentrated in a few shards, making shard loads unfair. This phenomenon leads to bad user experiences, such as delays in transaction inclusions and increased transaction fees. To solve this problem, we propose two load balancing methods in account/balance model-based sharded blockchains. Both methods perform load balancing by periodically reassigning accounts: in the first method, the blockchain protocol itself performs load balancing and in the second method, wallets perform load balancing. We discuss the pros and cons of the two protocols, and apply the protocols to the execution sharding in Ethereum 2.0, an existing sharding design. Further, we analyze by simulation how the protocols behave to confirm that we can observe smaller transaction delays and fees. As a result, we released the simulation program as “Shargri-La,” a simulator designed for general-purpose user behavior analysis on the execution sharding in Ethereum 2.0.
Digitization of Handwritten Chess Scoresheets with a BiLSTM Network

During an Over-the-Board (OTB) chess event, all players are required to record their moves strictly by hand, and later the event organizers are required to digitize these sheets for official records. This is a very time-consuming process, and in this paper we present an alternate workflow of digitizing scoresheets using a BiLSTM network. Starting with a pretrained network for standard Latin handwriting recognition, we imposed chess-specific restrictions and trained with our Handwritten Chess Scoresheet (HCS) dataset. We developed two post-processing strategies utilizing the facts that we have two copies of each scoresheet (both players are required to write the entire game), and we can easily check if a move is valid. The autonomous post-processing requires no human interaction and achieves a Move Recognition Accuracy (MRA) around 95%. The semi-autonomous approach, which requires requesting user input on unsettling cases, increases the MRA to around 99% while interrupting only on 4% moves. This is a major extension of the very first handwritten chess move recognition work reported by us in September 2021, and we believe this has the potential to revolutionize the scoresheet digitization process for the thousands of chess events that happen every day.
Enhancement algorithm for separability of inland water body in synthetic aperture radar image via sparse representation and image fusion

In synthetic aperture radar (SAR) images, inland water bodies generally appear as dark textures, but due to the interference of thermal noise, speckle noise, and azimuth blur, inland water bodies can be easily confused with weakly scattered areas such as roads, wetlands, and sand. Most of the existing denoising methods have been proposed for removing speckle noise. However, the presence of thermal noise and azimuth blur still makes it difficult to distinguish inland water bodies. The denoising method based on the Doppler spectrum can effectively remove thermal noise, speckle noise, and azimuth blur, but it reduces the resolution, which blurs the contour of the water body, and it is difficult to apply to the detection and processing of small inland water bodies. Fusing a noisy high-resolution image with a low-resolution image with less noise makes it possible to remove the speckle noise and thermal noise of the SAR image while maintaining the edge texture of the water body. Based on the idea of image fusion, sparse representation is used to fuse the original noisy SAR image (HR) and the low-precision image (LR) processed by the denoising algorithm to maximise the use of all the information in HR and LR. According to this idea, an enhancement algorithm for separability of inland water body based on sparse representation is proposed. The experimental results show that the proposed model significantly improves the separability of inland water bodies in SAR images.
A Hybrid Whale Optimization and Particle Swarm Optimization Algorithm

In the field of optimization algorithms, hybrid algorithms are increasingly valued by researchers for their effectiveness in improving algorithmic capabilities.In recent years, a new type of natural meta-heuristic algorithm called whale optimization algorithm has been proposed. The algorithm refers to whales in nature and imitates their three different feeding methods to solve realistic optimization problems. The particle swarm algorithm, on the other hand, is an algorithm proposed by imitating the way a flock of birds transmits information. As population intelligence algorithms, the accuracy of these two algorithms are not high enough in the convergence process. At the same time, they tend to fall into the local optimum. In this paper, a hybrid algorithm based on whale optimization algorithm and particle swarm algorithm is proposed to update the population by a kind of selection iteration. The experimental results confirm that the algorithm has excellent superiority in convergence accuracy and convergence speed.
Entanglement concentration protocols for quantum entangled states

Abstract Noise in the channel affects the security of cryptography protocols. The obvious solution for quantum protocols is to estimate the transformation of quantum states caused by the noise. Cluster states are the basic structures of quantum cryptography protocols, which are transmitted through the lossy quantum channel during the procedure of the protocols. The entanglement of the cluster states is extremely fragile and easy to be inflected by inevitable interaction with the environment. To ensure the fidelity of cluster states, the entanglement of the states must be concentrated after the transmission. We propose four protocols to concentrate the entanglement of five-photon linear cluster states by linear optics, in two of which only three parties should perform the local POVM operations. All the protocols reach the maximum of successful probability, and furthermore they are implemented by the linear optics alone. The simulation results depict that lots of five-photon linear cluster states could be concentrated via our protocols, and those protocols are robust against the accuracy of the linear optics. Hence, our protocols are valuable in many fields of quantum information and quantum cryptography.
SPA Bot: Smart Price-Action Trading Bot for Cryptocurrency Market

Cryptocurrencies can be traded 24 hours a day over the globe where crypto market never stop working. This market is known for being highly volatile and prices fluctuate rigorously in a minute. Even seasoned traders are unable to react quickly enough to this volatility. This is why automated trading bots get into the picture. On the other hand, crypto trading bots need more improvement to mimic expert traders' activities. In this paper, we propose a bot which is able to buy and sell using a dynamic price-action technique. Experimental results from back-testing show that the proposed bot effectively utilized the classical price-action technique for trading in cryptocurrency markets.
World First: An Australian Court Opens the Door to Inventor Recognition for Artificial Intelligence Systems

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Detect Network Water Army based on Large-scale E-commerce Reviews

The e-commerce water army research is to detect those water army in the major e-commerce platforms. Most of the existing studies use manual identification of water army to construct datasets, and the models used are too single. This paper proposes an NSR model based on the official labeled real dataset. This model aggregates the three algorithms of Naive Bayes, Support Vector Machine and Random Forest through a certain combination strategy, and finally forms a strong learner. Subsequently, a characteristic experiment is carried out on 7 types of personal behavior characteristics of the water army, and the corresponding CDF diagram is drawn to show the ability of each behavior feature to detect water army, thus comparing the performance of the water army and normal users in different dimensions. Finally, the experimental results based on the Yelp dataset show that the NSR model outperforms the other three algorithms in the three indicators of Accuracy, Precision and F1-score.
Low Resolution Face Recognition using Enhanced SRGAN Generated Images

Face recognition has attracted enormous interest from researchers due to its widescale adaptation. However, most of the work in the literature is based on face recognition in controlled environments i.e., high-resolution face images with slight variation in pose, illumination and expression. CNN is used as backbone architecture in the face recognition models. Low-resolution face images having large variations in pose, illumination and expressions make a face recognition problem more challenging. It is difficult to extract discriminative features from low-resolution images. That’s why the performance of face recognition models trained on high-resolution images deteriorate on low-resolution images. In this work, quality of low-resolution images is enhanced through multiple super-resolution GANs in order to improve the identification accuracy. Experiments are performed on SCface dataset and the improvement in the identification accuracy shows the effectiveness of using enhanced images generated through super-resolution GANs.
UI Method to Support Knowledge Creation in Hybrid Museum Experience

A user interaction method to support knowledge creation in a hybrid museum experience is proposed and evaluated. The method incorporates a knowledge creation process of visitor experiences to the interaction scheme on the user interface based on two intentions. The first intention is to invoke user actions required for an effective knowledge experience, including individual learning. The second intention is to document the knowledge with sufficient information for sharing and reuse. The method is designed as part of an application for a hybrid museum experience such that the digital device does not distract the visitor from the museum exhibit. This paper presents the proposed UI interaction method, its implementation in the application, and an evaluation study of its effects. The evaluation was conducted with a group of curators to obtain professional feedback on the method's effect on observation behavior and knowledge creation. As a result, we found that the user interface of expressing one's own impressions and seeing the impressions of others helped to deepen the understanding of the exhibits.
Does the emotional tendency of COVID-19 news affect financial markets?

The influence of internet financial news on financial markets has been verified from multiple perspectives. This paper examines whether the emotional tendency of COVID-19 news affect financial markets. Based on constructing the sentiment dictionary of COVID-19 news, we measure the sentiment indicators of COVID-19 news, and then study the dynamic relationship between the emotional tendency of COVID-19 news and the rate of return in financial markets. Compared with the news before the outbreak of COVID-19, our results show that the emotional tendency of COVID-19 news does affect the rate of return in the stock and bond markets.
FLZip: An Efficient and Privacy-Preserving Framework for Cross-Silo Federated Learning

Cross-silo federated learning (FL) allows participants to collaboratively train a machine learning model while ensuring no data leaves their premises. As such, it has great potentials in many sensitive data-driven scenarios, such as finance and intelligent medicine. To further preserve the privacy of the participants' local models, existing industrial FL frameworks use additively homomorphic encryption (HE) to mask the local gradients during model aggregation. However, the enormous computation and communication overhead caused by HE is infeasible in practice. In this paper, we develop a framework called FLZip, which substantially reduces the overhead caused by HE based on gradient aware compression. In FLZip, instead of encrypting individual gradients, each client first filters insignificant gradients by considering the magnitude of the gradients in each layer independently. To allow aggregation to be performed on ciphertexts of the sparsified gradients, FLZip uses a key-value pair encoding scheme. Next, to counter the accuracy loss as a result of sparsification, FLZip also utilizes an error accumulation mechanism. Compared with BatchCrypt, a state-of-the-art FL framework with HE, extensive experiments show that FLZip dramatically reduces the encryption and decryption operations by 6.4× and 13.1 × and shrinks the network footprints to and from the server by 5.9× and 12.5× respectively, without damaging the model accuracy.
BuiltNet: Graph based Spatio-Temporal Indoor Thermal Variation Detection

Monitoring thermal condition with thermal cameras is a potential non-intrusive way to supervise the structural well-being of buildings. Thermal variation can infer various structural damages or construction deficiencies including air leakages through inside and outside surfaces of buildings. Frequent monitoring with thermal images can track the thermal characteristics of different places of built environments which helps to prevent damages beforehand. Previous literature studied thermal conditions in buildings with thermal images are limited to specific regions with constrained environmental settings. In this work, we propose an automated scalable framework BuiltNet for analyzing spatial and temporal temperature variation over various building elements i.e., walls, windows, doors, etc. using longitudinal thermal images. We collected thermal images from a residential apartment home for 10 minutes in consecutive 4-5 hours on different days. The spatial and temporal relations among different spots in a region from sequential thermal images of the corresponding region are represented by graph. We propose an unsupervised deep clustering algorithm based on graph neural network, considering both spatial and temporal features from longitudinal thermal images. Our analysis on the spatial and temporal features of regions in the collected thermal images (from both day and night of different weather conditions) identifies the thermal variation and characterizes the spatiotemporal dynamics over different places in the built environment.
Indonesian Digital Wallet Sentiment Analysis Using CNN And LSTM Method

In this paper, we present a deep learning based approach to sentiment analysis on Indonesian digital wallet review. We collected the reviews of the following digital wallets: DANA, OVO, Link Aja, and Sakuku by obtaining the data from Google Play Store. Sentiment analysis from comments on Google Play Store is used to extract user opinions on various aspects: accessibility, transaction, account, service, and performance. We applied the CNN LSTM architecture on performing the sentiment analysis. The experimental results show that the training accuracy is 87% and the validation accuracy is 83%. Based on the data and analysis results of the four digital wallet applications, namely DANA, OVO, LinkAja, and Sakuku, the sentiments tend to be negative towards the categories we have created, including transactions and access, services, accounts, and performance.
A Digital Random Number Generator Based on Regular Sampling of Double Scroll Chaos

This paper presents the design of a novel digital random number generator (RNG) based on the distribution of regular samples from a double scroll chaotic system. In this study, a double scroll chaotic system is designed by using the Euler approximation and the nonlinearity feature of sawtooth wave in a 3<sup>rd</sup> order ordinary differential equation (ODE). By examining the distribution of the values of chaotic state variables, it is demonstrated that the distribution has at least two regions and a binary sequence can be obtained by exploiting these two regions. The XOR is used to combine the resulting binary sequences obtained from these two regions, and thus, the RNG is designed. The RNG output satisfies all statistical randomness tests in the NIST 800&#x2013;22 package without post-processing. The proposed RNG is implemented using Verilog hardware description language and prototyped on a KCU105 Evaluation Board Xilinx Ultrascale Field Programmable Gate Array (FPGA) with less than 1&#x0025; LUT and Flip-Flop utilization and data throughput of up to 1.3125 Mbps.