Reviewerly/exHarmony huggingface dataset:
Hand Gesture Recognition via Radar Sensors and Convolutional Neural Networks

In this communication, a low-cost radar-sensor-based apparatus for contactless hand gesture recognition via Doppler signature analysis is proposed. The raw reflected signal, after some pre-processing, is analysed via its time-frequency representation, known as spectrogram. This information is then exploited to train a convolutional neural network (CNN) to perform the classification step. The whole procedure was tested on an in-house experimental data set composed of four different hand gestures, showing good performance and reaching an accuracy of approximately 97%. Finally, the classification performance was tested also in a cluttered environment which includes the presence of a strong echo close to the target.
Software Module Clustering: An In-Depth Literature Analysis

Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities’ structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field.
Similarity Based Block Sparse Subset Selection for Video Summarization

Video summarization (VS) is generally formulated as a subset selection problem where a set of representative keyframes or key segments is selected from an entire video frame set. Though many sparse subset selection based VS algorithms have been proposed in the past decade, most of them adopt linear sparse formulation in the explicit feature vector space of video frames, and don’t consider the local or global relationships among frames. In this paper, we first extend the conventional sparse subset selection for VS into kernel block sparse subset selection (KBS3) to utilize the advantage of kernel sparse coding and introduce a local inter-frame relationship through packing of frame blocks. Going a step further, we propose a similarity based block sparse subset selection (SB2S3) model by applying a specially designed transformation matrix on the KBS3 model in order to introduce a kind of global inter-frame relationship through the similarity. Finally, a greedy pursuit based algorithm is devised for the proposed NP-hard model optimization. The proposed SB2S3 has the following advantages: 1) through the similarity between each frame and any other frame, the global relationship among all frames can be considered; 2) through block sparse coding, the local relationship of adjacent frames is further considered; and 3) it has a wider application, since features can derive similarity, but not vice versa. It is believed that the effect of modeling such global and local relationships among frames in this paper, is similar to that of modeling the long-range and short-range dependencies among frames in deep learning based methods. Experimental results on three benchmark datasets have demonstrated that the proposed approach is superior to not only other sparse subset selection based VS methods but also most unsupervised deep-learning based VS methods.
Correlation Discrepancy Insight Network for Video Re-identification

Video-based person re-identification (ReID) aims at re-identifying a specified person sequence from videos that were captured by disjoint cameras. Most existing works on this task ignore the quality discrepancy across frames by using all video frames to develop a ReID method. Additionally, they adopt only the person self-characteristic as the representation, which cannot adapt to cross-camera variation effectively. To that end, we propose a novel correlation discrepancy insight network for video-based person ReID, which consists of an unsupervised correlation insight model (CIM) for video purification and a discrepancy description network (DDN) for person representation. Concretely, CIM is constructed by using kernelized correlation filters to encode person half-parts, which evaluates the frame quality by the cross correlation across frames for selecting discriminative video fragments. Furthermore, DDN exploits the selected video fragments to generate a discrepancy descriptor using a compression network, which aims at employing the discrepancies with other persons’ to facilitate the representation of the target person rather than only using the self-characteristic. Due to the advantage in handling cross-domain variation, the discrepancy descriptor is expected to provide a new pattern for the object representation in cross-camera tasks. Experimental results on three public benchmarks demonstrate that the proposed method outperforms several state-of-the-art methods.
Structure-Texture Image Decomposition Using Discriminative Patch Recurrence

Morphology component analysis provides an effective framework for structure-texture image decomposition, which characterizes the structure and texture components by sparsifying them with certain transforms respectively. Due to the complexity and randomness of texture, it is challenging to design effective sparsifying transforms for texture components. This paper aims at exploiting the recurrence of texture patterns, one important property of texture, to develop a nonlocal transform for texture component sparsification. Since the plain patch recurrence holds for both cartoon contours and texture regions, the nonlocal sparsifying transform constructed based on such patch recurrence sparsifies both the structure and texture components well. As a result, cartoon contours could be wrongly assigned to the texture component, yielding ambiguity in decomposition. To address this issue, we introduce a discriminative prior on patch recurrence, that the spatial arrangement of recurrent patches in texture regions exhibits isotropic structure which differs from that of cartoon contours. Based on the prior, a nonlocal transform is constructed which only sparsifies texture regions well. Incorporating the constructed transform into morphology component analysis, we propose an effective approach for structure-texture decomposition. Extensive experiments have demonstrated the superior performance of our approach over existing ones.
Hardness and Ease of Curing the Sign Problem for Two-Local Qubit Hamiltonians

We examine the problem of determining whether a multiqubit two-local Hamiltonian can be made stoquastic by single-qubit unitary transformations. We prove that when such a Hamiltonian contains one-local terms, then this task can be NP-hard. This is shown by constructing a class of Hamiltonians for which performing this task is equivalent to deciding 3-SAT. In contrast, we show that when such a Hamiltonian contains no one-local terms then this task is easy; namely, we present an algorithm which decides, in a number of arithmetic operations over $\mathbb{R}$ which is polynomial in the number of qubits, whether the sign problem of the Hamiltonian can be cured by single-qubit rotations.
Deep Learning Based Data Fusion for Sensor Fault Diagnosis and Tolerance in Autonomous Vehicles

Abstract Environmental perception is one of the key technologies to realize autonomous vehicles. Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system. Those sensors are very sensitive to light or background conditions, which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running. In this paper, a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed. By introducing prior features to realize the lightweight network, the features of the input data can be extracted in real time. A new sensor reliability evaluation method is proposed by calculating the global and local confidence of sensors. Through the temporal and spatial correlation between sensor data, the sensor redundancy is utilized to diagnose the local and global confidence level of sensor data in real time, eliminate the fault data, and ensure the accuracy and reliability of data fusion. Experiments show that the network achieves state-of-the-art results in speed and accuracy, and can accurately detect the location of the target when some sensors are out of focus or out of order. The fusion framework proposed in this paper is proved to be effective for intelligent vehicles in terms of real-time performance and reliability.
Genetic programming for development of cost-sensitive classifiers for binary high-dimensional unbalanced classification

Genetic programming (GP) has the built-in ability for feature selection when developing classifiers for classification with high-dimensional data. However, due to the problem of class imbalance, the developed classifiers by GP are prone to be biased towards the majority class. Cost-sensitive learning has shown to be effective in addressing the problem of class imbalance. In cost-sensitive learning, cost matrices are often manually designed and then considered by classification algorithms to treat different mistakes differently. However, in many real-world applications, cost matrices are unknown because of the limited domain knowledge in complex situations. Therefore, in this paper, we propose a novel GP method to develop cost-sensitive classifiers, where a cost matrix is automatically learned, instead of requiring it from domain experts. The proposed method is examined and compared with existing methods on ten high-dimensional unbalanced datasets. Experimental results show that the proposed method outperforms the compared GP methods in most cases.
Densely connected convolutional extreme learning machine for hyperspectral image classification

Extreme Learning Machine (ELM) has gained lots of research interests due to its universal approximation capability and fast learning speed. Although several prior works have focused on developing deep ELM, it is still an open problem to design effective deep ELM. Stacking random layers will result in overfitting and accumulation of random errors. To address this issue, this paper presents a simple yet effective deep ELM called Densely Connected Convolutional ELM (DC2ELM) for hyperspectral image spectral-spatial classification. First, we introduce dense connection into ELM to make full use of intermediate feature maps produced by randomized convolutional layers, which is beneficial to reduce the random error. Secondly, stacked ELM auto-encoders are employed to generate reduced representation, leading to a deeper architecture. The proposed approach consists of fewer trainable parameters than traditional convolutional neural networks and can easily be trained without any iterative parameters tuning, making it easier to implement and apply in practice. We compare the proposed approach with many prior arts over three real hyperspectral images, showing that the proposed approach can achieve superior performance using limited training data and with a reduced risk of overfitting the training data.
RETRACTED: Simulation of physical education teaching based on FPGA and wearable VR equipment

With the development of science and technology, computer technology has been widely used in all aspects around us. It can not only enrich the content of our classroom teaching, improve the quality of teaching, but also be used in sports activities.However, in the process of practical application of computer technology, there are still some problems to be solved.FPGA is the flexibility of programmable hardware and software. FPGA has been optimized for efficient network and security features with a highly pipelined architecture and its parallel processing capability. A better function for very high throughput with high bandwidth and scale applications. FPGA is essential for the next generation network.
Hierarchical Metadata-Aware Document Categorization under Weak Supervision

Categorizing documents into a given label hierarchy is intuitively appealing due to the ubiquity of hierarchical topic structures in massive text corpora. Although related studies have achieved satisfying performance in fully supervised hierarchical document classification, they usually require massive human-annotated training data and only utilize text information. However, in many domains, (1) annotations are quite expensive where very few training samples can be acquired; (2) documents are accompanied by metadata information. Hence, this paper studies how to integrate the label hierarchy, metadata, and text signals for document categorization under weak supervision. We develop HiMeCat, an embedding-based generative framework for our task. Specifically, we propose a novel joint representation learning module that allows simultaneous modeling of category dependencies, metadata information and textual semantics, and we introduce a data augmentation module that hierarchically synthesizes training documents to complement the original, small-scale training set. Our experiments demonstrate a consistent improvement of HiMeCat over competitive baselines and validate the contribution of our representation learning and data augmentation modules.
Postdigital Perspectives on the McPolicy of Measuring Excellence

This is an accepted manuscript of an article published by Springer in Postdigital Science & Education on 19/12/2020, available online: https://doi.org/10.1007/s42438-020-00208-2 The accepted version of the publication may differ from the final published version.
Augmented Queue-Based Transmission and Transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd Integration

Nowadays, amateur broadcasters can massively generate video contents and stream them across the Internet. For this reason, crowdsourced livecast services (CLS) are attracting millions of users around the world. To provide a smooth and high-quality playback experience to viewers with diversified device configurations in dynamic network conditions, CLS providers have to find a way to deploy cost-effective transcoding operations by distributing the computation-intensive workload among Cloud, Edge, and Crowd. In addition, it is necessary to control transcoded streams from million broadcasters to worldwide viewers. To address these challenges, we propose a novel stochastic approach that jointly optimizes the usage of transmission resources ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , bandwidth), and transcoding resources ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , CPU) in CLS systems that leverage the cooperation of Cloud, Edge, and Crowd technologies. In particular, we first design an augmented queue structure that can jointly capture the dynamic features of data transmission and online transcoding, based on the virtual queue technology. Then, we formulate a joint resource allocation problem, using stochastic optimization arguments, and devise an Accelerated Gradient Optimization (AGO) algorithm to solve the optimization problem in a scalable way. Moreover, we provide four main theoretical results that characterize the algorithm’s steady-state queue-length, optimality, and fast-convergence. By conducting both numerical simulations and system-level evaluations based on our prototype, we demonstrate that our solution provides lower system costs and higher QoE performance against state-of-the-art solutions.
Lambdata: Optimizing Serverless Computing by Making Data Intents Explicit

Serverless computing emerges as a new paradigm to build cloud applications, in which developers write small functions that react to cloud infrastructure events, and cloud providers maintain all resources and schedule the functions in containers. Serverless computing thus enables developers to focus on their core business logic and leave server management and scaling to cloud providers. Unfortunately, existing serverless computing systems suffer from a key limitation that deprives them of enjoying significant speedups. Specifically, they treat each cloud function as a black box and are blind to which data the function reads or writes, therefore missing potentially huge optimization opportunities, such as caching data and colocating functions. We present Lambdata, a novel serverless computing system that enables developers to declare a cloud function's data intents, including both data read and data written. Once data intents are made explicit, Lambdata performs a variety of optimizations to improve speed, including caching data locally and scheduling functions based on code and data locality. Our evaluation of Lambdata shows that it achieves an average speedup of 1.51x on the turnaround time of practical workloads and reduces monetary cost by 16.5%.
A survey on regional level set image segmentation models based on the energy functional similarity measure

Image segmentation is an important field of computer vision and has attracted significant research attention in the recent years. In this paper, we provide a survey of regional level set image segmentation models based on the energy functional similarity measure. Our survey begins with an introduction to region-based level set image segmentation and an overview of its general steps. Then the different segmentation models are summarized. We define and survey six categories of regional level set image segmentation models based on energy functional similarity measures. For every category, we present the mainstream approaches from the literature as examples. Experimental analyses are conducted to compare the segmentation performance of various methods, which allow us to draw meaningful conclusions about their mutual advantages and disadvantages. Finally, we conclude this survey by highlighting several promising directions which need to be further explored by the research community in the future.
Automatically detecting bregma and lambda points in rodent skull anatomy images

Currently, injection sites of probes, cannula, and optic fibers in stereotactic neurosurgery are typically located manually. This step involves location estimations based on human experiences and thus introduces errors. In order to reduce localization error and improve repeatability of experiments and treatments, we investigate an automated method to locate injection sites. This paper proposes a localization framework, which integrates a region-based convolutional network and a fully convolutional network, to locate specific anatomical points on skulls of rodents. Experiment results show that the proposed localization framework is capable of identifying and locatin bregma and lambda in rodent skull anatomy images with mean errors less than 300 μm . This method is robust to different lighting conditions and mouse orientations, and has the potential to simplify the procedure of locating injection sites.
Prototype for Integration of Face Mask Detection and Person Identification Model – COVID-19

As people across the globe are combating the widespread COVID-19 pandemic and it becomes very essential to develop new technologies to analyse and fight against the disease spread.The most essential protection against corona virus is Face Mask and as the day surpasses scientist and Doctors have recommended everyone to wear the mask.Therefore, to distinguish the individuals wearing Face Mask, various identification procedures are available.Veils are prescribed as a straightforward obstruction to protect the respiratory beads from going into the air and onto others, when the individual is found to be wearing the cover hacks, wheezes, talks, or raises their voice.Moreover, this is called source control.This proposal depends on the present idea about the job respiratory beads that play a main role in the spread of the COVID-19 infection, matched with developing proof from clinical and research center examinations that show covers and decrease the splash of drops, when worn over the nose and mouth.Coronavirus spreads essentially among individuals who are in close contact with each other (inside around 6 feet), so the utilization of veils is especially significant in settings where individuals are near one another or where social removing is hard to keep up.CDC's suggestions for masks will be updated as new logical proof.Our project is more of a real-world application, the proposed face mask detection platform utilizes artificial network to identify the person with and without mask.If a person is not wearing a mask, then the proposed platform will send a notification to the person if he or she is in the database of the platform.MobileNet_V2 neural networks are used as our classification algorithm and the face recognition module is also used for the person identification model
The Hybridization of Street Offending in the Netherlands

Abstract Based on the results of two research projects from the Netherlands, this paper explores how street-oriented persons adapt and use digital technologies by focussing on the changing commission of instrumental, economically motivated, street crime. Our findings show how social media are used by street offenders to facilitate or improve parts of the crime script of already existing criminal activities but also how street offenders are engaging in criminal activities not typically associated with the street, like phishing and fraud. Taken together, this paper documents how technology has permeated street life and contributed to the ‘hybridization’ of street offending in the Netherlands—i.e. offending that takes place in person and online, often at the same time.
Automatic modulation classification using KELM with joint features of CNN and LBP

Signal automatic modulation classification refers to modulation methods which can automatically classify and identify different communication signals. As the middle part of the signal detection and demodulation, modulation classification plays an important part in the area of military and civilian fields, such as software radio and electronic countermeasures. In recent years, modulation classification technology has made great achievements, but as the engineering requirements improving and the wireless communication channel environment become increasingly complex, there are still many problems to be solved. In order to solve the problems which many modulation methods failed to consider the relationship between different characters, an algorithm based on the feature fusion of the convolutional neural network (CNN) is proposed. In this paper, a smooth pseudo Wigner–Ville distribution is used to make the one-dimensional signal into an image, a model of CNN is used to extract image features, and the image features extracted by CNN and the artificial features are put together to make the feature fusion together. The fusion feature further improves the modulation classification performance. The simulation results show that the algorithm proposed in this paper improves the performance under low signal-to-noise ratio, indicating that this algorithm of constructing fusion features is more effective.