Reviewerly/exHarmony huggingface dataset:
Keypoint Message Passing for Video-Based Person Re-identification

Video-based person re-identification~(re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras. Existing methods are mostly based on convolutional neural networks~(CNNs), whose building blocks either process local neighbor pixels at a time, or, when 3D convolutions are used to model temporal information, suffer from the misalignment problem caused by person movement. In this paper, we propose to overcome the limitations of normal convolutions with a human-oriented graph method. Specifically, features located at person joint keypoints are extracted and connected as a spatial-temporal graph. These keypoint features are then updated by message passing from their connected nodes with a graph convolutional network~(GCN). During training, the GCN can be attached to any CNN-based person re-ID model to assist representation learning on feature maps, whilst it can be dropped after training for better inference speed. Our method brings significant improvements over the CNN-based baseline model on the MARS dataset with generated person keypoints and a newly annotated dataset: PoseTrackReID. It also defines a new state-of-the-art method in terms of top-1 accuracy and mean average precision in comparison to prior works.
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision

Recent state-of-the-art (SOTA) effective neural network methods and fine-tuning methods based on pre-trained models (PTM) have been used in Chinese word segmentation (CWS), and they achieve great results.However, previous works focus on training the models with the fixed corpus at every iteration.The intermediate generated information is also valuable.Besides, the robustness of the previous neural methods is limited by the large-scale annotated data.There are a few noises in the annotated corpus.Limited efforts have been made by previous studies to deal with such problems.In this work, we propose a self-supervised CWS approach with a straightforward and effective architecture.First, we train a word segmentation model and use it to generate the segmentation results.Then, we use a revised masked language model (MLM) to evaluate the quality of the segmentation results based on the predictions of the MLM.Finally, we leverage the evaluations to aid the training of the segmenter by improved minimum risk training.Experimental results show that our approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness 1 .
Two component quantum walk in one-dimensional lattice with hopping imbalance

We investigate the two-component quantum walk in one-dimensional lattice. We show that the inter-component interaction strength together with the hopping imbalance between the components exhibit distinct features in the quantum walk for different initial states. When the walkers are initially on the same site, both the slow and fast particles perform independent particle quantum walks when the interaction between them is weak. However, stronger inter-particle interactions result in quantum walks by the repulsively bound pair formed between the two particles. For different initial states when the walkers are on different sites initially, the quantum walk performed by the slow particle is almost independent of that of the fast particle, which exhibits reflected and transmitted components across the particle with large hopping strength for weak interactions. Beyond a critical value of the interaction strength, the wave function of the fast particle ceases to penetrate through the slow particle signalling a spatial phase separation. However, when the two particles are initially at the two opposite edges of the lattice, then the interaction facilitates the complete reflection of both of them from each other. We analyze the above mentioned features by examining various physical quantities such as the on-site density evolution, two-particle correlation functions and transmission coefficients.
A Comparison of Image Compression Techniques Over Wireless Fading Channel

In today’s life, the multimedia transmission is becoming an integral part of wireless communication. However, the transmission of a multimedia file requires sending a large amount of data which consume a huge amount of bandwidth. Due to the limitations in bandwidth utilization in wireless mobile communication, different compression techniques are used, and they reduce the amount of information to be transmitted. However, the compression techniques very often reduce the quality of a multimedia file because of their inherent nature. In addition, the quality may further be deteriorated after wireless transmission due to the presence of fading. In this work, we compared two compression techniques JPEG and JPEG2000 that are widely used in still image compression. From the study, it has been observed that the JPEG2000 performs better in low SNR environment, while JPEG performs better in high SNR environment. In addition, the performance of both the techniques improves, while using maximal ratio diversity combining technique as compared to selection combining technique.
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks

Anomaly detection is important in many real-life applications. Recently, self-supervised learning has greatly helped deep anomaly detection by recognizing several geometric transformations. However these methods lack finer features, usually highly depend on the anomaly type, and do not perform well on fine-grained problems. To address these issues, we first introduce in this work three novel and efficient discriminative and generative tasks which have complementary strength: (i) a piece-wise jigsaw puzzle task focuses on structure cues; (ii) a tint rotation recognition is used within each piece, taking into account the colorimetry information; (iii) and a partial re-colorization task considers the image texture. In order to make the re-colorization task more object-oriented than background-oriented, we propose to include the contextual color information of the image border via an attention mechanism.We then present a new out-of-distribution detection function and highlight its better stability compared to existing methods. Along with it, we also experiment different score fusion functions. Finally, we evaluate our method on an extensive protocol composed of various anomaly types, from object anomalies, style anomalies with fine-grained classification to local anomalies with face anti-spoofing datasets. Our model significantly outperforms state-of-the-art with up to 36% relative error improvement on object anomalies and 40% on face anti-spoofing problems.
Collaborative Filtering Recommendation Algorithm Based on Improved BiasSVD

In the era of information explosion, the internet platform with massive information provides personalized services for users by using recommendation system. With users and projects growing ever more, the data sparsity problem may affect the cold start of projects. To solve the above problem, an improved collaborative filtering recommendation algorithm is proposed in this paper. Firstly, the biasedSVD algorithm is used to reduced dimension of user rating matrix, which brings the less complexity and alleviates the problem of data of sparsity. Secondly, the user's behaviors are analyzed and a novel weight named the time-preference weighting factor is proposed to describe the user's rating preference for items. Experimental results based on MovieLens data set show that the proposed method outperforms the state-of-the-atrs.
A Multi-Microgrid Thermal Game Model Based on Quantum Blockchain

The electricity transactions of microgrids face several problems: the high platform management cost, the low security, and the untimely consumption of scattered electricity. To solve these problems, this paper presents a multi-microgrid thermal game model based on quantum blockchain. Specifically, a dynamic model was established for the noncooperative game between aggregators, microgrids, and large users to maximize the benefit of each party, and to realize the timely consumption of scattered electricity. Next, a transaction platform was constructed based on the two-round password based authenticated key exchange (PAKE) protocol, which eliminates non-interactive zero-knowledge (NIZK), aiming to substantially enhance the post-quantum security of transactions. Then, the quantum signature using two-particle entangled Bell states was adopted to safeguard the quantum communication of electricity transactions, and authenticate the nodes. Example analysis shows that our model can realize the timely consumption of scattered electricity and thermal energy, improve the security of transaction data and users, and achieve Pareto optimality. The research provides theoretical support and decision-making basis for electricity transactions in the post-quantum age.
Transitional care of nurses to older adults with artificial pacemaker

ABSTRACT Objective: to understand how the transitional care of nurses to olde adults with artificial pacemaker occurs. Method: a qualitative research, carried out in a philanthropic hospital in the city of Salvador, Bahia, Brazil. Fourteen nurses working in surgical and cardiac inpatient units participated. Data were collected between August and November 2020, through semi-structured interviews, and analyzed using the methodological framework Discourse of the Collective Subject and discussed in the light of Afaf Meleis’ theory of transitions. Results: nurses’ incipient knowledge about transitional care was identified. It was observed that the nursing discharge report is a facilitating instrument for transitional care. Final considerations: the study pointed out that the transitional care of nurses to older adults with artificial pacemakers does not have a theoretical foundation, reporting as a priority the care with the pacemaker identification card and with the surgical wound.
Comparative Analysis of Bio-Inspired Optimization Algorithms in Neural Network-Based Data Mining Classification

It always helps to determine optimal solutions for stochastic problems thereby maintaining good balance between its key elements. Nature inspired algorithms are meta-heuristics that mimic the natural activities for solving optimization issues in the era of computation. In the past decades, several research works have been presented for optimization especially in the field of data mining. This paper addresses the implementation of bio-inspired optimization techniques for machine learning based data mining classification by four different optimization algorithms. The stochastic problems are overcome by training the neural network model with techniques such as barnacles mating , black widow optimization, cuckoo algorithm and elephant herd optimization. The experiments are performed on five different datasets, and the outcomes are compared with existing methods with respect to runtime, mean square error and classification rate. From the experimental analysis, the proposed bio-inspired optimization algorithms are found to be effective for classification with neural network training.
Yield-Adjusted Operation for Convolution Filter Denoising

A perfect denoising for measurement shall remove noise, while keeping signal truth, so it is a dual-objective optimization of the signal yield and the noise residue. The frequency difference between the noise and signal is the basis of band-limited filter denoising. The root cause for the sharp peak denoise distortion is the insufficient spectrum sampling because of the scattered frequency distribution, which makes it hard to achieve dual-objective optimization. Thus, this article proposes a four-step operation of the signal yield adjustment for beyond the band-limited system. The first step is identifying the signal and noise levels in raw data, then adjusting the sampling density of high-signal level areas and enriching it by linear interpolation, then smoothing the reshaped profile, which is friendly to the filter, and finally, restoring the deformed one to its original form. An executable script function has fully achieved the whole operation. Some actual sharp spectra (Raman, NMR, laser-induced breakdown spectroscopy, and X-ray diffraction) make a comparison between the way with the Savitzky-Golay (SG) method and wavelet (multi-scale) denoising. The results show that all the effects are better than those of the SG filter, all estimations of the yield of signals are more than 99%, and the residue of noise is less than 10%. With multi-scale denoising, this operation is more targeted and gets more rational spectrum profiles─noise reduction without spectrum distortion.
Scanflow

Machine Learning (ML) is more than just training models, the whole life-cycle must be considered. Once deployed, a ML model needs to be constantly managed, supervised and debugged to guarantee its availability, validity and robustness in dynamic contexts. This demonstration presents an agent-based ML workflow manager so-called Scanflow1, which enables autonomic management and supervision of the end-to-end life-cycle of ML workflows on distributed clusters. The case study on a MNIST project2 shows that different teams can collaborate using Scanflow within a ML project at different phases, and the effectiveness of agents to maintain the model accuracy and throughput of the model serving while running in production.
Completeness of Graphical Languages for Mixed State Quantum Mechanics

There exist several graphical languages for quantum information processing, like quantum circuits, ZX-calculus, ZW-calculus, and so on. Each of these languages forms a †-symmetric monoidal category (†-SMC) and comes with an interpretation functor to the †-SMC of finite-dimensional Hilbert spaces. In recent years, one of the main achievements of the categorical approach to quantum mechanics has been to provide several equational theories for most of these graphical languages, making them complete for various fragments of pure quantum mechanics. We address the question of how to extend these languages beyond pure quantum mechanics to reason about mixed states and general quantum operations, i.e., completely positive maps. Intuitively, such an extension relies on the axiomatisation of a discard map that allows one to get rid of a quantum system, an operation that is not allowed in pure quantum mechanics. We introduce a new construction, the discard construction , which transforms any †-symmetric monoidal category into a symmetric monoidal category equipped with a discard map. Roughly speaking this construction consists in making any isometry causal. Using this construction, we provide an extension for several graphical languages that we prove to be complete for general quantum operations. However, this construction fails for some fringe cases like Clifford+T quantum mechanics, as the category does not have enough isometries.
Optimal solution of engineering design problems through differential gradient evolution plus algorithm: a hybrid approach

Abstract It is very necessary and applicable to optimize all disciplines. In practical engineering problems the optimization has been a significant component. This article presents the hybrid approach named as differential gradient evolution plus <?CDATA $\left(DGE+\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close=")" open="(" separators=""> <mml:mrow> <mml:mi>D</mml:mi> <mml:mi>G</mml:mi> <mml:mi>E</mml:mi> <mml:mo>+</mml:mo> </mml:mrow> </mml:mfenced> </mml:math> algorithm which is the combination of differential evolution gorithm and gradient evolution <?CDATA $(GE)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">(</mml:mo> <mml:mi>G</mml:mi> <mml:mi>E</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> algorithm. <?CDATA $DE$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>D</mml:mi> <mml:mi>E</mml:mi> </mml:math> was used to diversify and <?CDATA $GE$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>G</mml:mi> <mml:mi>E</mml:mi> </mml:math> was used for intensification with a perfect equilibrium between exploration and exploitation with an improvised distribution of dynamic probability and offers a new shake-off approach to prevent premature convergence to local optimum. To describe the success, the proposed algorithm is compared to modern meta-heuristics. To see the accuracy, robustness, and reliability of <?CDATA $DGE+$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>D</mml:mi> <mml:mi>G</mml:mi> <mml:mi>E</mml:mi> <mml:mo>+</mml:mo> </mml:math> it has been implemented on eight complex practical engineering problems named as: pressure vessel, belleville spring, tension/compression spring, three-bar truss, welded beam, speed reducer, gear train and rolling element bearing design problem, the results revealed that <?CDATA $DGE+$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>D</mml:mi> <mml:mi>G</mml:mi> <mml:mi>E</mml:mi> <mml:mo>+</mml:mo> </mml:math> algorithm can deliver highly efficient, competitive and promising results.
Machine‐Learned Light‐Field Camera that Reads Facial Expression from High‐Contrast and Illumination Invariant 3D Facial Images

Facial expression conveys nonverbal communication information to help humans better perceive physical or psychophysical situations. Accurate 3D imaging provides stable topographic changes for reading facial expression. In particular, light‐field cameras (LFCs) have high potential for constructing depth maps, thanks to a simple configuration of microlens arrays and an objective lens. Herein, machine‐learned NIR‐based LFCs (NIR‐LFCs) for facial expression reading by extracting Euclidean distances of 3D facial landmarks in pairwise fashion are reported. The NIR‐LFC contains microlens arrays with asymmetric Fabry−Perot filter and NIR bandpass filter on CMOS image sensor, fully packaged with two vertical‐cavity surface‐emitting lasers. The NIR‐LFC not only increases the image contrast by 2.1 times compared with conventional LFCs, but also reduces the reconstruction errors by up to 54%, regardless of ambient illumination conditions. A multilayer perceptron (MLP) classifies input vectors, consisting of 78 pairwise distances on the facial depth map of happiness, anger, sadness, and disgust, and also exhibits exceptional average accuracy of 0.85 ( p&lt; 0.05). This LFC provides a new platform for quantitatively labeling facial expression and emotion in point‐of‐care biomedical, social perception, or human−machine interaction applications.
Tessian: Spear-Phishing Threat Landscape 2021

Phishing is one of the most pernicious and persistent threats that organisations face. In itself, it is simple to pull off and requires little in the way of resources. But it is also frequently the first stage in sophisticated, multi-vector attacks. And in recent years, phishing has morphed from being the tool of crude spammers to a well-honed weapon in the arsenal of cyber criminals and state-backed threat actors.
A Hybrid Fuzzy based Cross Neighbor Filtering (HF-CNF) for Image Enhancement of fine and coarse powder Scanned Electron Microscopy (SEM) images

Image enhancement is one of the most critical stages towards any image processing application. The outcome of image enhancement determines the accuracy and precise nature of the overall output from the image processing under interest. This research paper has shown specific interests towards enhancement of Scanned Electron Microscopic (SEM) images which are primarily concerned with projection of fine details exist in internal details of surfaces, metals, fine powders, fibers etc. These fine details play a dominant role in detection of minute cracks, artifacts, progressing faults, texture of powders, their coarseness or fineness, internal details of fibers in forensics. However, due to the image capturing process which is through conventional camera-based models, noise tends to be a major source in degrading or blurring the underlying vital information. A cross neighbor fuzzy filter is a hybrid combination called Hybrid Fuzzy Based Cross Neighbor Filtering (HF-CNF) which is proposed in this research paper in order to minimize impulse and random noise to a great extent also to fine tune the further processing stages. The proposed method has been subjected to extensive analysis by comparison with state of art and recent benchmark methods and superior performance justified in terms of several validation metrics.
Neural Attention-Aware Hierarchical Topic Model

Neural topic models (NTMs) apply deep neural networks to topic modelling.Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count information is utilized for the training, while more fine-grained sentence-level information is ignored, and (2) external semantic knowledge regarding documents, sentences and words are not exploited for the training.To address these issues, we propose a variational autoencoder (VAE) NTM model that jointly reconstructs the sentence and document word counts using combinations of bag-of-words (BoW) topical embeddings and pre-trained semantic embeddings.The pre-trained embeddings are first transformed into a common latent topical space to align their semantics with the BoW embeddings.Our model also features hierarchical KL divergence to leverage embeddings of each document to regularize those of their sentences, thereby paying more attention to semantically relevant sentences.Both quantitative and qualitative experiments have shown the efficacy of our model in 1) lowering the reconstruction errors at both the sentence and document levels, and 2) discovering more coherent topics from real-world datasets.
A Framework of Darknet Forensics

The dark web market is full of illegal and criminal activities such as the sale of sensitive personal information, guns, drugs, and terrorist videos. Cybercriminals use The Onion Router(TOR) browser to enter the dark web for information publishing and trading. Because the onion router browser provides privacy protection and anonymity, it is widely used. This privacy protection mode has brought great challenges to network investigators. This article aims to detect the use of the latest Tor browser, compare and analyze the evidence information contained in the registry, memory images, hard disk files, and network data packets through forensic experiments. At the same time, it compares and analyzes the different access modes of the Tor browser, and collects and uses Tor browsing. Evidence of cybercrime committed by a device is helpful to the development of electronic data forensics analysis.