Reviewerly/exHarmony huggingface dataset:
A Blockchain Voting System Based on the Feedback Mechanism and Wilson Score

A blockchain voting system based on the feedback mechanism and Wilson score is proposed to solve the problem of the malicious votes behavior. Firstly, the relatively accurate supporting rate and ranking for candidates are obtained using the Wilson score. Secondly, different feedback coefficients are calculated according to the above parameters. Finally, the account points are adjusted according to the feedback coefficients. The feedback mechanism is designed in the voting smart contract, and the smart contract is deployed on the blockchain to ensure the enforcement of the feedback mechanism. A fully functional smart contract is designed and briefly verified in this paper. The experiment is conducted under the K-out-of-L type of weighted voting. Experimental results show that the Wilson score can accurately modify the candidates’ supporting rates, and the feedback mechanism can effectively suppress malicious votes.
Visual Model Fit Estimation in Scatterplots and Distribution of Attention

Abstract. Scatterplots are ubiquitous data graphs and can be used to depict how well data fit to a quantitative theory. We investigated which information is used for such estimates. In Experiment 1 ( N = 25), we tested the influence of slope and noise on perceived fit between a linear model and data points. Additionally, eye tracking was used to analyze the deployment of attention. Visual fit estimation might mimic one or the other statistical estimate: If participants were influenced by noise only, this would suggest that their subjective judgment was similar to root mean square error. If slope was relevant, subjective estimation would mimic variance explained. While the influence of noise on estimated fit was stronger, we also found an influence of slope. As most of the fixations fell into the center of the scatterplot, in Experiment 2 ( N = 51), we tested whether location of noise affects judgment. Indeed, high noise influenced the judgment of fit more strongly if it was located in the middle of the scatterplot. Visual fit estimates seem to be driven by the center of the scatterplot and to mimic variance explained.
Novel Casestudy and Benchmarking of AlexNet for Edge AI: From CPU and GPU to FPGA

Convolutional Neural Networks (CNNs) require massive parallelism due to the high-precision floating-point arithmetic operations they perform. So, demand of processing power in them is significantly higher than what a standard CPU can offer. This has traditionally made CNNs more suited for running on a Graphics Processing Unit (GPU). However, GPUs consume much more power than CPUs, rendering the former impractical for implementing CNNs in Edge AI (Artificial Intelligence), where restraining power consumption is paramount. On the other hand, FPGAs (Field Programmable Gate Arrays) are more suited for AI computing at the edge as they consume much lesser power than GPUs and even CPUs. Additionally, GPUs and CPUs are not suited for real-time AI applications, which require both high throughput and low latency at the same time and FPGAs excel at all these requirements. The purpose of this paper is to provide a study of the performance of FPGA as the most suitable platform for AI-based computing at the edge. To achieve this, we chose AlexNet, a popular CNN image classifier, for which we present a case study on four different platforms: CPU, GPU, embedded RISC core, and FPGA fabric. Then, we quantitatively measure and compare the performance in terms of inference time (time needed to classify an image) on all these platforms. Inference time using FPGA is reduced by almost 64, 1.6, and 1.1 times compared to a dual-core ARM, an i5-6400 CPU, and an Nvidia GPU, respectively.
Offset-Based In-Loop Filtering With a Deep Network in HEVC

With the great flexibility and performance of deep learning technology, there have been many attempts to replace existing functions inside video codecs such as High-Efficiency Video Coding (HEVC) with deep-learning-based solutions. One of the most researched approaches is adopting a deep network as an image restoration filter to recover distorted compressed frames. In this paper, instead, we introduce a novel idea for using a deep network, in which it chooses and transmits the side information according to the type of errors and contents, inspired by the sample adaptive offset filter in HEVC. A part of the network computes the optimal offset values while another part estimates the type of error and contents simultaneously. The combination of two subnetworks can address the estimation of highly nonlinear and complicated errors compared to conventional deep- learning-based schemes. Experimental results show that the proposed system yields an average bit-rate saving of 4.2% and 2.8% for the low-delay P and random access modes, respectively, compared to the conventional HEVC. Moreover, the performance improvement is up to 6.3% and 3.9% for higher-resolution sequences.
Multiscale information fusion-based deep learning framework for campus vehicle detection

Vehicle detection is a hotspot in the field of remote sensing image analysis. In particular, campus vehicle detection can assess the density of traffic in an area and provide security for students. The detection accuracy is low for dense vehicle areas or complex background areas. According to the feature of campus vehicle, we propose a multiscale information fusion strategy to construct a novel deep learning framework for campus vehicle detection. This new method based on Single Shot MultiBox Detector (SSD) combines a lightweight deep neural network MobileNet to extract features. A sub-network composed of multiple convolutional layers is connected to detect and locate the object. This method fuses feature information on multiple levels. When removing overlapped object candidate regions, the threshold value is set based on the non-maximum suppression method to eliminate redundant candidate regions. Therefore, the generated negative samples are reduced, which guarantees the stable effect of the proposed model. Experiments show that the proposed vehicle detection method has a faster detection speed. The robustness and accuracy of the proposed model are better than other related vehicle detection methods.
Breast calcification detection based on multichannel radiofrequency signals via a unified deep learning framework

Breast calcifications in radiographic images suggest a high likelihood of breast lesion malignancy. However, it is difficult to detect calcifications in traditional B-mode ultrasound images due to resolution limits and speckle noise. In this paper, we propose a unified deep learning framework for automatic calcification detection based on multichannel ultrasound radio frequency (RF) signals. First, beamforming is used during preprocessing to merge and blend multichannel signals into one-channel RF signals. Each scan line is converted into a spectrogram by the short-time Fourier transform (STFT) to utilize the frequency domain characteristics. Then, an improved fully convolutional neural network called the RF signal Spectrogram-Calcification-Detection-Net (SCD-Net) is proposed to detect calcifications from spectrograms. This method employs a deep learning architecture based on YOLOv3 and combines features via convolutional long short-term memory (ConvLSTM). Next, a Kalman filter for tracking calcifications between consecutive spectrograms based on SCD-Net detection results is applied since the spatial coherence of calcifications in neighboring frames can be taken into account. Finally, the detected calcification is mapped from the time domain of spectrograms to B-mode images for clinical diagnosis. Experiments were conducted on a database of 337 experienced doctor-marked breast tumors with calcifications. Compared to the state-of-the-art methods for detecting calcifications, the proposed method achieved an average precision (AP) of 88.25%, an accuracy of 84% and an F1 score of 91%. The experimental results demonstrate that the unified framework has great performance for tumor calcification detection. The system can be effectively applied in a portable ultrasound instrument to accurately help radiologists and provide guidance for breast tumor diagnosis. This implies that the proposed approach can be implemented in real practice for analyzing breast RF signals, which have many useful medical applications in clinical breast tumor diagnosis.
Embedding for Anomaly Detection on Health Insurance Claims

Properly analyzing health insurance claims data could lead to significant business insights and benefits for health service providers and insurance companies. Yet, health insurance data is often high dimensional and contains complex interleave sequences of claims. Instead of conducting machine learning tasks directly on the raw data, a better approach is performing the tasks on high-quality embeddings of the raw data. Driven by the real business need of Solution Segic Inc., a Canadian technology company in the group insurance industry, we extract health insurance claims embeddings with neural networks in the context of anomaly detection. We propose and thoroughly examine six embedding components that are customized based on different possible assumptions made on the data. One of our proposed embedding components, EC-ReStepRec, significantly outperforms other candidates on two anomaly detection tasks. This is the first embedding study done on health insurance claims for anomaly detection.
TSI: Temporal Scale Invariant Network for Action Proposal Generation

Despite the great progress in temporal action proposal generation, most state-of-the-art methods ignore the impact of action scales and the performance of short actions is still far from satisfaction. In this paper, we first analyze the sample imbalance issue in action proposal generation, and correspondingly devise a novel scale-invariant loss function to alleviate the insufficient learning of short actions. To further achieve proposal generation task, we adopt the pipeline of boundary evaluation and proposal completeness regression, and propose the Temporal Scale Invariant network. To better leverage the temporal context, boundary evaluation module generates action boundaries with high-precision-assured global branch and high-recall-assured local branch. Simultaneously, the proposal evaluation module is supervised with introduced scale-invariant loss, predicting accurate proposal completeness for different scales of actions. Comprehensive experiments are conducted on ActivityNet-1.3 and THUMOS14 benchmarks, where TSI achieves state-of-the-art performance. Especially, AUC performance of short actions is boosted from 36.53% to 39.63% compared with baseline.
Grey Wolf Optimizer for Multimodal Medical Image Registration

Modern meta-heuristics algorithms have been successfully used to registration of different medical images. In this paper, we present a new technic based on the GWO algorithm for multimodal registration of a section of the human brain using computed tomography (CT) and magnetic resonance (MR) methods. Through the obtained simulation results, it appears that the proposed approach achieves high-resolution and robust registration compared to other methods such as Particle Swarm optimization PSO and sine cosine algorithm (SCA).
Real-time and accurate object detection on edge device with TensorFlow Lite

Abstract Objection detection is of vital importance to many fields, such as autonomous driving, outdoor robotics, and computer vision. Existing approaches on object detection can hardly run on the resource-constrained edge devices. In order to mitigate this dilemma, we propose to apply TensorFlow Lite to convert Float32 neural network model to unit8 neural network with subtle or even no accuracy loss. Two advantages are here for conversion. First, it reduces the model size to a quarter so that it fits for devices with limited storage. Second, it achieves much faster inference time. I conduct an experiment on MSCOCO dataset. Experimental results show that our proposed method achieves mAP 72.1 and FPS 23 on edge device.
When wells run dry

With the recent IPv4 address exhaustion, many networks can no longer rely on requesting additional IPv4 addresses space. They resort to new ways to obtain addresses: buying and leasing. In this paper, we first shed light on the recent economic trends of the IPv4 buying market by augmenting transfer statistics with public and private pricing information from four large IPv4 brokers. We infer the size of the IPv4 leasing market through two different data sources: routing information observed from BGP collectors and RDAP databases operated by the Regional Internet Registries. We find that neither of those sources alone is capable of estimating the full market size. We relate our findings to discussions with 13 IPv4 brokers and summarize how networks handle their demand for obtaining IPv4 addresses in 2020.
Coupling Programs and Visualization for Machine Knitting

To effectively program knitting machines, like any fabrication machine, users must be able to place the code they write in correspondence with the output the machine produces. This mapping is used in the code-to-output direction to understand what their code will produce, and in the output-to-code direction to debug errors in the finished product. In this paper, we describe and demonstrate an interface that provides two-way coupling between high- or low-level knitting code and a topological visualization of the knitted output. Our system allows the user to locate the knitting machine operations generated by any selected code, as well as the code that generates any selected knitting machine operation. This link between the code and visualization has the potential to reduce the time spent in design, implementation, and debugging phases, and save material costs by catching errors before actually knitting the object. We show examples of patterns designed using our tool and describe common errors that the tool catches when used in an academic lab setting and an undergraduate course.
Discrete-time quantum walk with time-correlated noise

We investigate the dynamics of discrete-time quantum walk subject to time correlated noise. Noise is described as an unitary coin-type operator before each step, and attention is focused on the noise generated by a Gaussian Ornstein Uhlenbeck process, going beyond the usual telegraph noise, where the random variables are consist of only -1 and 1. Under the first-order approximation of BCH formula, the master equation of noisy discrete-time quantum walk is derived. The dynamics given by the master equation are in good agreement with those given by numerical simulations within a certain period of steps, which is controlled by noise parameters. Two remarker behaviors of long time noisy dynamics are observed in numerical simulations, corresponding to two opposite noise regimes: in slow noise regime, with the increase of the noise amplitude, the quantum coherence is suppressed, and the dynamics of noisy discrete-time quantum walk gradually transits to that of classical random walk. In fast noise regime, the walker is confined into few lattice sites, and the width of wave packet is much narrower compared with that in slow noise regime.
Automobile Insurance Fraud Detection using Supervised Classifiers

Automobile fraudulent claim leads to several consequences for the company and policyholder. The current detection system is costly and inefficient. This research aims to design a prediction model in detecting automobile insurance fraud using a machine learning approach. The study used realworld data on an automobile insurance company in Indonesia. The dataset has a high imbalanced distribution between the data of policyholders who commit fraud and legitimate data. This research handles the imbalanced dataset problem by using the Synthetic Minority Oversampling Technique (SMOTE) and undersampling methods. The proposed supervised classifiers are Multilayer Perceptron (MLP), Decision Tree C4.5, and Random Forest(RF). The performance of models is evaluated through the confusion matrix, ROC Curve, and parameters such as sensitivity. This research found that Random Forest outperformed the results comparing to other classifiers with 98.5% accuracy.
Incomplete multi-view clustering with partially mapped instances and clusters

Most multi-view clustering methods assume that each view has complete instances and clusters. However, in real world applications, the instances or clusters may be missed in some views. Recently, multi-view clustering on data with partially mapped instances has been studied. In this paper, we study the multi-view clustering on data with partially mapped instances and clusters to extend the application of multi-view clustering. We propose a NMF (Non-negative Matrix Factorization) based algorithm which separately deals with the mapped clusters/instances and the individual clusters/instances, i.e., both the basis matrix and the indicator matrix consist of a mapped part and an individual part. By bounding the mapped instances to reduce to the same indicator vectors, the mapped instances and clusters connect multiple views and guide to find the indicator vectors of all the instances. Furthermore, we improve the algorithm by using locally geometrical information to reduce the negative impact caused by multi-view interaction. Experiments show that the proposed algorithms perform well on data with partially mapped instances and clusters.
Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry

Abstract Machine learning offers promising solutions for high-throughput single-particle analysis in label-free imaging microflow cytomtery. However, the throughput of online operations such as cell sorting is often limited by the large computational cost of the image analysis while offline operations may require the storage of an exceedingly large amount of data. Moreover, the training of machine learning systems can be easily biased by slight drifts of the measurement conditions, giving rise to a significant but difficult to detect degradation of the learned operations. We propose a simple and versatile machine learning approach to perform microparticle classification at an extremely low computational cost, showing good generalization over large variations in particle position. We present proof-of-principle classification of interference patterns projected by flowing transparent PMMA microbeads with diameters of $${15.2}\,\upmu \text {m}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mn>15.2</mml:mn> </mml:mrow> <mml:mspace /> <mml:mi>μ</mml:mi> <mml:mtext>m</mml:mtext> </mml:mrow> </mml:math> and $${18.6}\,\upmu \text {m}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mn>18.6</mml:mn> </mml:mrow> <mml:mspace /> <mml:mi>μ</mml:mi> <mml:mtext>m</mml:mtext> </mml:mrow> </mml:math> . To this end, a simple, cheap and compact label-free microflow cytometer is employed. We also discuss in detail the detection and prevention of machine learning bias in training and testing due to slight drifts of the measurement conditions. Moreover, we investigate the implications of modifying the projected particle pattern by means of a diffraction grating, in the context of optical extreme learning machine implementations.
High-speed all-optical processing for spectrum

Data-processing techniques in spectroscopy are fundamental and powerful analytical tools for lots of practical applications. In the age of big data, high-speed data-processing in spectroscopy is in urgent need, especially for the real-time analysis/feedback of data stream of spectroscopy or the capture of non-repetitive/rare phenomena in fast dynamic process. So far, intensive researches focus on high-speed processing of light signal in time/spatial domain but few people find a way to do it in spectral domain. Here, we report an optical computing technology for high-speed optical spectrum processing with features of real time, multiple functions, all-fiber configuration and immunity to electromagnetic interference. The software-controlled system could perform as, but not limited to, the first-order (or arbitrary fractional-order) differentiator/integrator/Hilbert transformer and tunable band-pass filter, respectively, to handle spectral data rapidly. High-speed processing of optical spectrum at a rate of 10,000,000 times per second is demonstrated.
SesameBERT: Attention for Anywhere

Fine-tuning with pre-trained models has achieved exceptional results for many language tasks. In this study, we focused on one such self-attention network model, namely BERT, which has performed well in terms of stacking layers across diverse language-understanding benchmarks. However, in many downstream tasks, information between layers is ignored by BERT for fine-tuning. In addition, although self-attention networks are well-known for their ability to capture global dependencies, room for improvement remains in terms of emphasizing the importance of local contexts. In light of these advantages and disadvantages, this paper proposes SesameBERT, a generalized fine-tuning method that (1) enables the extraction of global information among all layers through Squeeze and Excitation and (2) enriches local information by capturing neighboring contexts via Gaussian blurring. Furthermore, we demonstrated the effectiveness of our approach in the HANS dataset, which is used to determine whether models have adopted shallow heuristics instead of learning underlying generalizations. The experiments revealed that SesameBERT outperformed BERT with respect to GLUE benchmark and the HANS evaluation set.
Context-based Translation for the Out of Vocabulary Words Applied to Hindi-English Cross-Lingual Information Retrieval

Cross-Lingual Information Retrieval (CLIR) provides flexibility to users to query in their regional (source) languages regardless the target documents languages. CLIR uses trending translation techniques Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). SMT and NMT achieve good results for foreign languages but not for Indian languages due to non-absoluteness of the parallel corpus. Source language user queries may contain the Out Of Vocabulary (OOV) words which are not present in the parallel corpus such words may be skipped without performing translation by SMT. In this paper, a context-based translation algorithm is proposed to translate the OOV words by utilizing two unlabeled & unrelated large raw corpora (in source and target language) and a small bi-lingual parallel corpus. Since SMT performs better than NMT for Hindi to English translation as per the literature, therefore, experimental results are evaluated for FIRE datasets against baseline SMT. The proposed algorithm improves evaluation measures, Recall up to 6.04% (0.8785) for FIRE 2010 and up to 3.96% (0.7365) for FIRE 2011, & Mean Average Precision (MAP) up to 14.37% (0.3239) for FIRE 2010 and up to 5.46% (0.1988) for FIRE 2011, in comparison to the baseline SMT which achieves 0.8284 and 0.7084 Recall for FIRE 2010 and 2011, & 0.2832 and 0.1885 MAP for FIRE 2010 and 2011. An analysis for the number of OOV words shows that the proposed algorithm reduces the number of OOV more effectively, up to 0.81% for FIRE 2010 and 1.73% for FIRE 2011.
Developed JPEG Algorithm Applied in Image Compression

Abstract JPEG is most popular image compression and encoding, this technique is widely used in many applications (images, videos and 3D animations). Meanwhile, researchers are very interested to develop this massive technique to compress images at higher compression ratios with keeping image quality as much as possible. For this reason in this paper we introduce a developed JPEG based on fast DCT and removed most of zeros and keeps their positions in a transformed block. Additionally, arithmetic coding applied rather than Huffman coding. The results showed up, the proposed developed JPEG algorithm has better image quality than traditional JPEG techniques.