Reviewerly/exHarmony huggingface dataset:
Face recognition using multitasking cascading convolutional networks

Abstract The face recognition has many applications in such areas like intelligent security and access control, biometrics, safeguard, verification, attendance accounting, machine vision, etc. Identification of a personality by the face has a number of advantages over other methods: the person does not need to be contacted physically, which is the most acceptable way for mass applications and no complicated or expensive equipment is required. This article discusses the problem of recognition and identification of a person’s face using convolutional neural networks that process frames from a camera in real time or from a recorded video file with the subsequent entry of the identified person into the database. Multitasking Cascade Convolutional Neural Network (MTCNN) has three convolutional networks (P-Net, R-Net, and O-Net) and is capable of outperforming many face detection tests while maintaining real-time performance. The proposed method for human face recognition was developed as a software product, tested and showed the probability of correct recognition in real time 96.02%.
Scrambling Code Blind Identification in SDH Signal Intelligent Reception

The blind identification of scrambling codes is an important issue in intelligent reception of unknown synchronous digital hierarchy (SDH) lines. In this paper, the blind identification of scrambling codes is studied in three aspects: extraction of scrambling code sequence with error codes, determination of scrambling code primitive polynomials and initial attack of linear registers. A method for deducing scrambling primitive polynomials based on higher order statistics is proposed. The method based on partial exhaustive search initial state attack method based on Walsh transform is improved to handle the specific problem in unknown SDH lines. The proposed scheme can identify the scrambling code of the unknown SDH line completely, and has better fault-tolerant performance and low computational complexity.
An Auxiliary Industrial Equipment Maintenance System Using Mixed Reality

The intellectualization of modern industrial equipment presents a great challenge to its maintenance. The traditional maintenance of equipment requires the repairer to reference related manual, resulting in low maintenance efficiency. And the attempt based on the Virtual Reality can not produce the real and operable experience. To solve the problems, we proposes an auxiliary equipment maintenance system based on Mixed Reality technology that superposes CAD model and other virtual information to the actual equipment using HoloLens. We take the maintenance of a gasoline engine as an example, mainly including troubleshooting, maintenance guidance and equipment part training, where users only need to follow the steps set by the system, and the experiment about system evaluation shows that it makes equipment maintenance work more simple, intuitive, convenient and efficient.
Fast Anomaly Detection in Micro Data Centers Using Machine Learning Techniques

This paper proposes a new approach to fast detection of abnormal behaviour of cooling and IT systems in micro data centers (MDCs) based on machine learning (ML) techniques. Conventional protection of MDCs focuses on monitoring individual parameters such as temperature at different locations and when these parameters reaches certain high values, then alarm will be triggered. This paper employs ML techniques to extract normal and abnormal behaviour of the cooling and IT systems. Developed data acquisition system together with unsupervised learning methods quickly learns the physical dynamics of normal operation and is able to detect deviations from such behaviours. This provides an efficient way for not only producing health index for the MDC, but also a rich label logging system that will be used for the supervised learning methods. The effectiveness of the proposed detection technique is evaluated on a MDC placed at Computing Infrastructure Research Center (CIRC) in McMaster Innovation Park (MIP), McMaster University.
Design and Implementation of a Robust Convolutional Neural Network-Based Traffic Matrix Estimator for Cloud Networks

Recent research literature shows promising results by convolutional neural network- (CNN-) based approaches for estimation of traffic matrix of cloud networks using different architectures. Although conventionally, convolutional neural network-based approaches yield superior estimation; however, these rely on assumptions of availability of a large training dataset which is completely accurate and nonsparse. In real world, both these assumptions are problematic as training data size may be limited, and it is also prone to missing (or incomplete) measurements as well as may have measurement errors. Similarly, the 2-D training datasets derived from network topology based may be sparse. We investigate these challenges and develop a novel architecture which can cater for these challenges and deliver superior performance. Our approach shows promising results for traffic matrix estimation using convolutional neural network-based techniques in the presence of limited training data and outlier measurements.
Biofeedback in the Dynamic VR Environments:

Contemporary architecture design is featured by human-oriented. Getting the users’ feedback of schemes thus to optimize their positive interactions with spaces is essential to increase designs’ quality and attraction. However, uncovering users’ true thoughts is challenging because it is difficult for laypersons with no professional design background to describe how architectural elements can affect their emotions. This study aims to use digital technology to comprehend architectural elements’ impact on users’ perception. Specifically, it combines virtual reality (VR) with biosensing technology to detect users’ emotions, guiding designers to improve their design schemes. In this context, to propose a method of evaluation, this research adopted the International School of Design of Harbin Institute of Technology's outdoor spaces as the empirical case. VR had been applied as a visualization tool to construct a dynamic and immersion environment that imitates the campus space. Then some key design elements were changed to form contrast schemes. Participants were recruited to experience these contrast spaces and were collected Electrodermal Activity (EDA) and Photoplethysmography (PPG) signals for emotional arousal analysis. Biofeedback data were analyzed using the paired-samples T-test. The results indicated that some of the design elements did affect users’ emotional arousal during roaming; others did not cause a significant difference affection. According to the evaluation result, designers can understand the emotional connection between a design element and users to decide if it needs to be further refined. This research proposed a method that can be used for dynamic and repeatable evaluation of design before the project is completed. Simultaneously, this method can help designers quantitate users’ emotional perception and gain the objective basis for design optimization.
Dynamics of two coupled qubits in a two-mode cavity through four-photon processes: Nonclassical properties under intrinsic decoherence

The impact of the intrinsic decoherence on the dynamics of a system composed of two coupled qubits inside a two-mode cavity with a nondegenerate parametric amplifier is analytically investigated. High-order nonlinearity causes strong entanglement and mixedness, which are analyzed by using entropy, negativity, and log negativity. In the absence of intrinsic decoherence, it is found that qubit-qubit coupling improves the generation of population inversion, entanglement, and mixedness. The coupling acts as external decoherence in the presence of the intrinsic decoherence, and the quantum effects evolve monotonically to non-zero stationary values. The qubit-qubit entanglement is crucially dependent on the nonlinearity and the intrinsic decoherence. The stability of this entanglement is granted for small intrinsic decoherence.
IAB-Net: Informative and Attention Based Person Re-Identification

This paper proposes Informative Attention Based IAB Network, a advance framework that unifieds multiple attention modules by preserving localized and global contextual information so that the model can learn most informative, representative and discriminative features. Specifically, we have also introduced Channel and Spatial Attention (CASA) Network that consists on a pair of attention modules named as Channel Attention Module and Spatial Attention Module. Channel attention module and spatial attention module primarily focusing on channel aggregation, spatial dimension and position awareness, respectively. In our proposed pipeline, we have used this pair after each convolutional block of ResNet-50, that significantly boost the performance and representation power of network. By using this new lightweight backbone with orthogonality constraint to enforce diversity on both hidden activation and weights and along with attention modules, our experiments on different popular benchmarks i.e Market-1501 and DukeMTMC-reID have achieved state-of-the-art performance and we confirm that our framework manifests harmonious refinement in detection and classification. The code is publicly available at this link https://github.com/faize5/IAB-Net.
iPUG: Accelerating Breadth-First Graph Traversals Using Manycore Graphcore IPUs

The Graphcore Intelligence Processing Unit (IPU) is a newly developed processor type whose architecture does not rely on the traditional caching hierarchies. Developed to meet the need for more and more data-centric applications, such as machine learning, IPUs combine a dedicated portion of SRAM with each of its numerous cores, resulting in high memory bandwidth at the price of capacity. The proximity of processor cores and memory makes the IPU a promising field of experimentation for graph algorithms since it is the unpredictable, irregular memory accesses that lead to performance losses in traditional processors with pre-caching. This paper aims to test the IPU’s suitability for algorithms with hard-to-predict memory accesses by implementing a breadth-first search (BFS) that complies with the Graph500 specifications. Precisely because of its apparent simplicity, BFS is an established benchmark that is not only subroutine for a variety of more complex graph algorithms, but also allows comparability across a wide range of architectures. We benchmark our IPU code on a wide range of instances and compare its performance to state-of-the-art CPU and GPU codes. The results indicate that the IPU delivers speedups of up to $$4{\times }$$ over the fastest competing result on an NVIDIA V100 GPU, with typical speedups of about $$1.5{\times }$$ on most test instances.
Parsimonious graphs for the most common trichords and tetrachords

Parsimonious transformations are common patterns in different musical styles and eras.In some cases, they can be represented on the Tonnetz, Cube Dance, Power Towers, or the central region of an orbifold, mainly when they only include the most even trichords and tetrachords.In this paper, two novel graphs, called Cyclopes, are presented, which include more than double the number of chord types in previously published graphs, thus allowing to represent a larger musical repertoire in a practical way.Apart from parsimonious transformations, they are also especially suitable for representing trichords a major third apart, tetrachords a minor third apart, and the cadences V7-I(m) and II Ø -V7-I(m) with major or minor tonic chords.Therefore, they allow to clearly visualize the relationship among the corresponding chords and better understand those composition patterns, as well as being efficient mnemonic resources, all of which make them useful tools both for music analysis and composition.
Charge Based Power Side-Channel Attack Methodology for an Adiabatic Cipher

SIMON is a block cipher developed to provide flexible security options for lightweight hardware applications such as the Internet-of-things (IoT). Safeguarding such resource-constrained hardware from side-channel attacks poses a significant challenge. Adiabatic circuit operation has recently received attention for such applications due to ultra-low power consumption. In this work, a charge-based methodology is developed to mount a correlation power analysis (CPA) based side-channel attack to an adiabatic SIMON core. The charge-based method significantly reduces the attack complexity by reducing the required number of power samples by two orders of magnitude. The CPA results demonstrate that the required measurements-to-disclosure (MTD) to retrieve the secret key of an adiabatic SIMON core is 4× higher compared to a conventional static CMOS based implementation. The effect of increase in the target signal load capacitance on the MTD is also investigated. It is observed that the MTD can be reduced by half if the load driven by the target signal is increased by 2× for an adiabatic SIMON, and by 5× for a static CMOS based SIMON. This sensitivity to target signal capacitance of the adiabatic SIMON can pose a serious concern by facilitating a more efficient CPA attack.
State-of-the-Art in 3D Face Reconstruction from a Single RGB Image

Since diverse and complex emotions need to be expressed by different facial deformation and appearances, facial animation has become a serious and on-going challenge for computer animation industry. Face reconstruction techniques based on 3D morphable face model and deep learning provide one effective solution to reuse existing databases and create believable animation of new characters from images or videos in seconds, which greatly reduce heavy manual operations and a lot of time. In this paper, we review the databases and state-of-the-art methods of 3D face reconstruction from a single RGB image. First, we classify 3D reconstruction methods into three categories and review each of them. These three categories are: Shape-from-Shading (SFS), 3D Morphable Face Model (3DMM), and Deep Learning (DL) based 3D face reconstruction. Next, we introduce existing 2D and 3D facial databases. After that, we review 10 methods of deep learning-based 3D face reconstruction and evaluate four representative ones among them. Finally, we draw conclusions of this paper and discuss future research directions.
Secure Medical Data Using Symmetric Cipher Based Chaotic Logistic Mapping

Now-a-days many industries are moving towards digitalization, the healthcare industry is one of them. The electronic health records contain a voluminous amount of personal information that can be used for fraudulent insurance claims, obtain prescription medications, cause identity theft. In this work, we have proposed an image encryption mechanism to secure medical image data from unauthorized users. The chaotic logistic map and linear feedback shift register are employed for securing medical image data. The chaotic logistic map and the linear feedback shift register (LFSR) generate two pseudo random sequences. Then an XOR operation is performed on these sequences to generate the key used for encryption. The generated key and security related transformations are then applied to generate cipher medical image data. The chaotic maps provide random behaviour to the algorithm and are highly sensitive to initial conditions which provide additional security to the system. The proposed work can efficiently secure several formats of medical images and is also resistant to various security attacks like brute-force, man-in-the-middle, etc.
Auto-encoder—LSTM-Based Outlier Detection Method for WSNs

Wireless sensor networks (WSNs) have got tremendous interest from various real-life appliances, in particular environmental applications. In such long-stand employed sensors, it is difficult to check the features and quality of raw sensed data. After the deployment, there are chances that sensor nodes may expose to unsympathetic circumstances, which result in sensors to stop working or convey them to send inaccurate data. If such things not detected, the quality of the sensor network can be greatly reduced. Outlier detection ensures the quality of the sensor by safe and sound monitoring as well as consistent detection of attractive and important events. In this article, we proposed a novel method called smooth auto-encoder to learn strong plus discriminative feature representations, and reconstruction error of among input–output of smooth auto-encoder is utilized as an activation signal for outlier detection. Moreover, we employed LSTM-bidirectional RNN for maturity voting for collective outlier detection.
Zero-shot domain adaptation for natural language inference by projecting superficial words out

In natural language inference, the semantics of some words do not affect the inference. Such information is considered superficial and brings overfitting. In this paper, we project the superficial information out to learn a more general representation. The generality refer to the adaptation to different domains. The projection is over the dropout framework. To project all information of recurring words out, we propose a parameter-free model (HardDrop). By further noticing that the information of some recurring words needs to be reserved, we propose SoftDrop to learn to cautiously project the information out. Our approaches outperform the competitors over the source domain and over zero-shot target domains. For some target domains (e.g. from RTE to MRPC), the accuracy/f1-score increases from 47.6%/0.501 to 68.1%/0.771.
Data collection of safety accidents in sports training of athletes with Internet of Things technology

The Internet of Things (IoT) technology is an information technology developed in recent years with the development of electronic sensors, intelligence, network transmission and control technologies. This is the third revolution in the development of information technology. This article aims to study the algorithm of the Internet of Things technology, through the investigation of the hazards of athletes’ sports training, scientifically and rationally use the Internet of Things technology to collect data on safety accidents in athletes’ sports training, thereby reducing the risk of athletes’ sports training and making athletes better. In this article, the methods of literature research, analysis and condensing, questionnaire survey, theory and experiment combination, etc., investigate the safety accident data collection of the Internet of Things technology in athletes’ sports training, and provide certain theories and methods for future in-depth research practice basis. The experimental results in this article show that 82% of athletes who are surveyed under the Internet of Things technology will have partial injuries during training, reducing the risk of safety accidents in athletes’ sports training, and better enabling Chinese athletes to achieve a consistent level of competition and performance through a virtuous cycle of development.
Semantic Segmentation with DenseNets for Breast Tumor Detection

Breast cancer is one of the cancers with highest mortality rate, which are impairing the health of millions of women globally. Nowadays, the detection method of breast tumors based on breast ultrasound images (BUI) is semi-automatic. The localization of a region of interest (ROI) and the delineation of breast tumors within ROI need to be manually performed by an experienced doctor. In this work, we propose a fully automatic approach based on semantic segmentation to add contextual information in the complete image interpretation and avoid the ROI definition. The densely connected convolutional neural network is used for this approach to generate the feature maps. Dataset with 200 breast tumor images from Institute of Oncology in Warsaw was used to evaluate the performance of this approach. For DenseNet, the accuracy of our method is 99.2%. For traditional fully convolutional network, this accuracy is 85.60%. For the results of segmentation, the dice coefficient is equal to 0.8329. The results have demonstrated that the proposed method is accurate and objective for the segmentation of breast tumors in BUIs. Moreover, the robustness and generalization capacity of the method have been proven in this dataset.
Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups

It is critical and important for venture investors to find high-potential startups at their early stages. Indeed, many efforts have been made to study the key factors for the success of startups through the topological analysis of the heterogeneous information network of people, startup, and venture firms or representation learning of latent startup profile features. However, the existing topological analysis lacks an in-depth understanding of heterogeneous information. Also, the approach based on representation learning heavily relies on domain-specific knowledge for feature selections. Instead, in this paper, we propose aScalable Heterogeneous Graph Markov Neural Network (SHGMNN) for identifying the high-potential startups. The general idea is to use graph neural networks (GNN) to learn effective startup representations through end-to-end efficient training and model the label dependency among startups through Maximum A Posterior (MAP) inference. Specifically, we first define different metapaths to capture various semantics over the heterogeneous information network (HIN) and aggregate all semantic information into a summated graph structure. To predict the high-potential early-stage startups, we introduce GNN to diffuse the information over the summated graph. We then adopt an MAP inference over Hinge-Loss Markov Random Fields to enforce label dependency. Here, a pseudolikelihood variational expectation-maximization (EM) framework is incorporated to optimize both MAP inference and GNN iteratively: The E-step calculates the inference, and the M-step updates the GNN. For efficiency concerns, we develop a GNN with a lightweight linear diffusion architecture to perform graph propagation over web-scale heterogeneous information networks. Finally, extensive experiments and case studies on real-world datasets demonstrate the superiority of SHGMNN.
Architecture violations detection and visualization in the continuous integration pipeline

Abstract New code in projects can introduce violations that deviate the code implementation from the intended architecture. This process is known as architecture erosion. In this article, we propose an approach for recovering the implemented architecture, and detecting violations when comparing it with the intended architecture. Given a code repository, the continuous integration pipeline calls the solution to detect the incidences of architecture violations as well as some quality and social metrics. This data is presented in metric‐centered views that help development teams to manage architecture erosion. Our approach is based on model‐driven engineering techniques since models serve to represent the code, and a model‐based pattern language helps us to automate the search for violation occurrences and execute corresponding actions (e.g., creation/assignment of issues). We confirm the approach benefits in a real project implemented by a software developing company, in a sample project available on the internet, and in a software development course, including 20 projects, where every single project decreases its architecture violations density through time.