Reviewerly/exHarmony huggingface dataset:
Integrated Feature Pyramid Network With Feature Aggregation for Traffic Sign Detection

Traffic sign detection is a critical task in the visual system of the Advanced Driver Assistance System (ADAS) and the Automated Driving System (ADS). Although the general object detection has achieved promising results by using Feature Pyramid Network (FPN) in recent years, we still observed that FPN cannot obtain satisfactory results in traffic sign detection because the size and class distribution of traffic signs are extremely unbalanced. To overcome this problem, a novel Plug-and-Play neck network Integrated Feature Pyramid Network with Feature Aggregation (IFA-FPN) is proposed in this paper based on the statistical characteristics of traffic signs. First, a lightweight operation is introduced to fully utilize the model and improve the inference speed of the model. Second, an Integrated Operation (IO) is introduced to solve the imbalance problem of Region-of-Interests (RoIs) in pyramid levels. Third, we introduce a Feature Aggregation (FA) structure to strengthen the feature representation capacity of feature maps, thereby enhancing the network robustness against the size discrepancy of traffic signs. The experiments are performed on three mainstream datasets, i.e., the German Traffic Sign Detection Benchmark (GTSDB), Swedish Traffic Sign Dataset (STSD), and Tsinghua-Tencent 100k dataset (TT100k). The experimental results demonstrate the superiority of the proposed IFA-FPN in the traffic sign detection tasks. Specifically, when the proposed IFA-FPN is applied to the Cascade RCNN, it achieves 80.3% mAP in GTSDB which surpasses FPN by 9.9%, 65.2% in mAP in STSD which surpasses FPN by 3.5%, and 93.6% in mAP in TT100k which surpasses FPN by 1.6%.
Dependency Smells in JavaScript Projects

Dependency management in modern software development poses many challenges for developers who wish to stay up to date with the latest features and fixes whilst ensuring backwards compatibility. Project maintainers have opted for varied, and sometimes conflicting, approaches for maintaining their dependencies. Opting for unsuitable approaches can introduce bugs and vulnerabilities into the project, introduce breaking changes, cause extraneous installations, and reduce dependency understandability, making it harder for others to contribute effectively. In this paper, we empirically examine evidence of recurring dependency management issues (dependency smells). We look at the commit data for a dataset of 1,146 active JavaScript repositories to catalog, quantify and understand dependency smells. Through a series of surveys with practitioners, we identify and quantify seven dependency smells with varying degrees of popularity and investigate why they are introduced throughout project history. Our findings indicate that dependency smells are prevalent in JavaScript projects with two or more distinct smells appearing in 80% of the projects, but they generally infect a minority of a project's dependencies. Our observations show that the number of dependency smells tend to increase over time. Practitioners agree that dependency smells bring about many problems including security threats, bugs, dependency breakage, runtime errors, and other maintenance issues. These smells are generally introduced as developers react to dependency misbehaviour and the shortcomings of the npm ecosystem.
Time-Correlated Sparsification for Communication-Efficient Federated Learning

Federated learning (FL) enables multiple clients to collaboratively train a shared model, with the help of a parameter server (PS), without disclosing their local datasets. However, due to the increasing size of the trained models, the communication load due to the iterative exchanges between the clients and the PS often becomes a bottleneck in the performance. Sparse communication is often employed to reduce the communication load, where only a small subset of the model updates are communicated from the clients to the PS. In this paper, we introduce a novel time-correlated sparsification (TCS) scheme, which builds upon the notion that sparse communication framework can be considered as identifying the most significant elements of the underlying model. Hence, TCS exploits the correlation between the sparse representations at consecutive iterations in FL, so that the overhead due to encoding of the sparse representation can be significantly reduced without compromising the test accuracy. Through extensive simulations on the CIFAR-10 dataset, we show that TCS can achieve centralized training accuracy with 100 times sparsification, and up to 2000 times reduction in the communication load when employed with quantization.
Efficient Deep Learning for Reforming Authentic Content Searching on Big Data

With the advancement of search engines, a major change has occurred in the way people are accessing data on the net. Search engines have made access to data efficient and easier as billions of pages on the net (or called big data) are suggested at once. The pages with the most significant rank generally have a higher visibility rate to people and hence every webmaster wants to push their page to higher rank. As a result, Search Engine Optimization (SEO) has become a massive business which strives in enhancing the ranking of clients’ webpage. But there are many myths and misconceptions about the ranking algorithms due to inadequate knowledge about SEO’s methods. Still there is a need to propose a verified algorithm that is efficient in retrieving pages from Google. The link analysis algorithm is in accordance with the link structure of any document as the page which has many links also has many connections to it and hence can increase retrieval capacity. There is another approach called the integrated ranking approach which comes under personalized web research. In the integrated ranking approach, both content and link are used as parameters to improve retrieval efficiency. This approach is used by Google using Deep Learning to increase retrieval efficiency of web pages as per the user’s requirement from the big data.
Multi-Factor Authentication for Secured Financial Transactions in Cloud Environment

The rise of the digital economy and the comfort of accessing by way of user mobile devices expedite human endeavors in financial transactions over the Virtual Private Network (VPN) backbone. This prominent application of VPN evades the hurdles involved in physical money exchange. The VPN acts as a gateway for the authorized user in accessing the banking server to provide mutual authentication between the user and the server. The security in the cloud authentication server remains vulnerable to the results of threat in JP Morgan Data breach in 2014, Capital One Data Breach in 2019, and many more cloud server attacks over and over again. These attacks necessitate the demand for a strong framework for authentication to secure from any class of threat. This research paper, propose a framework with a base of Elliptical Curve Cryptography (ECC) to perform secure financial transactions through Virtual Private Network (VPN) by implementing strong Multi-Factor Authentication (MFA) using authentication credentials and biometric identity. The research results prove that the proposed model is to be an ideal scheme for real-time implementation. The security analysis reports that the proposed model exhibits high level of security with a minimal response time of 12 s on an average of 1000 users.
Photon control and coherent interactions via lattice dark states in atomic arrays

Ordered atomic arrays with subwavelength spacing have emerged as an efficient and versatile light-matter interface, where collective interactions give rise to sets of super- and subradiant lattice states. Here, we demonstrate that highly subradiant states, so-called lattice dark states, can be individually addressed and manipulated by applying a spatial modulation of the atomic detuning. More specifically, we show that lattice dark states can be used to store and retrieve single photons with near-unit efficiency, as well as to control the temporal, frequency, and spatial degrees of freedom of the emitted electromagnetic field. Furthermore, we demonstrate how to engineer arbitrary coherent interactions between multiple dark states and thereby manipulate information stored in the lattice. These results pave the way towards quantum optics and information processing using atomic arrays.
Two Dimensions of Opacity and the Deep Learning Predicament

Abstract Deep neural networks (DNNs) have become increasingly successful in applications from biology to cosmology to social science. Trained DNNs, moreover, correspond to models that ideally allow the prediction of new phenomena. Building in part on the literature on ‘eXplainable AI’ (XAI), I here argue that these models are instrumental in a sense that makes them non-explanatory, and that their automated generation is opaque in a unique way. This combination implies the possibility of an unprecedented gap between discovery and explanation: When unsupervised models are successfully used in exploratory contexts, scientists face a whole new challenge in forming the concepts required for understanding underlying mechanisms.
Task-Oriented Communication for Multidevice Cooperative Edge Inference

This paper investigates task-oriented communication for multi-device cooperative edge inference, where a group of distributed low-end edge devices transmit the extracted features of local samples to a powerful edge server for inference. While cooperative edge inference can overcome the limited sensing capability of a single device, it substantially increases the communication overhead and may incur excessive latency. To enable low-latency cooperative inference, we propose a learning-based communication scheme that optimizes local feature extraction and distributed feature encoding in a task-oriented manner, i.e., to remove data redundancy and transmit information that is essential for the downstream inference task rather than reconstructing the data samples at the edge server. Specifically, we leverage Tishby’s information bottleneck (IB) principle (Tishby et al., 2000) to extract the task-relevant feature at each edge device, and adopt the distributed information bottleneck (DIB) framework of Aguerri and Zaidi, 2021, to formalize a single-letter characterization of the optimal rate-relevance tradeoff for distributed feature encoding. To admit flexible control of the communication overhead, we extend the DIB framework to a distributed deterministic information bottleneck (DDIB) objective that explicitly incorporates the representational costs of the encoded features. As the IB-based objectives are computationally prohibitive for high-dimensional data, we adopt variational approximations to make the optimization problems tractable. To compensate for the potential performance loss due to the variational approximations, we also develop a selective retransmission (SR) mechanism to identify the redundancy in the encoded features among multiple edge devices to attain additional communication overhead reduction. Extensive experiments on multi-view image classification and multi-view object recognition tasks evidence that the proposed task-oriented communication scheme achieves a better rate-relevance tradeoff than existing methods.
The Bitcoin gold correlation puzzle

Bitcoin is regularly referred to as new gold, digital gold or gold 2.0. If Bitcoin is indeed gold-like the correlation of Bitcoin and gold returns should be positive. We estimate the correlation of the two assets across time, across different return frequencies and across quantiles and find a near-zero correlation inconsistent with the claimed similarity. We offer two explanations for this puzzle: either the similarity is only a narrative and not accepted by investors or there are other forces at play that depress the true correlation. Such forces could be a substitution effect, investors sell gold and buy Bitcoin, and a catching up effect, investors buy Bitcoin to catch up with the market weight of gold.
A secure and lightweight certificateless hybrid signcryption scheme for Internet of Things

The encryption and signature schemes based on traditional public key are no longer applicable for devices with limited hardware and software resources in the Internet of Things (IoT), since those schemes consume too much hardware and software. To ensure the security and efficiency of data transmission, we construct a secure and lightweight certificateless hybrid signcryption scheme for IoT. Compared with the existing schemes, this scheme only needs one bilinear calculation for one communication, which is computationally efficient for protecting the secure communication between nodes with limited resources in IoT. To meet the security requirements of the complex IoT, based on the difficult problem assumption of bilinear pairings, this scheme can simultaneously meet security requirements of the IoT such as confidentiality, unforgability, semipublic verification, forward security and known session-specific temporary information security. In addition, we prove the security and correctness of this scheme in Random Oracle Model (ROM).
Real-Time Action Recognition System for Elderly People Using Stereo Depth Camera

Smart technologies are necessary for ambient assisted living (AAL) to help family members, caregivers, and health-care professionals in providing care for elderly people independently. Among these technologies, the current work is proposed as a computer vision-based solution that can monitor the elderly by recognizing actions using a stereo depth camera. In this work, we introduce a system that fuses together feature extraction methods from previous works in a novel combination of action recognition. Using depth frame sequences provided by the depth camera, the system localizes people by extracting different regions of interest (ROI) from UV-disparity maps. As for feature vectors, the spatial-temporal features of two action representation maps (depth motion appearance (DMA) and depth motion history (DMH) with a histogram of oriented gradients (HOG) descriptor) are used in combination with the distance-based features, and fused together with the automatic rounding method for action recognition of continuous long frame sequences. The experimental results are tested using random frame sequences from a dataset that was collected at an elder care center, demonstrating that the proposed system can detect various actions in real-time with reasonable recognition rates, regardless of the length of the image sequences.
A two-stage embedding model for recommendation with multimodal auxiliary information

Recommender system has recently received a lot of attention in the information service community. In many application scenarios, such as Internet of Things (IoTs) environments, item multimodal auxiliary information (such as text and image) can be obtained to expand their feature representation and to increase user satisfaction with recommendations. Motivated by this fact, this paper introduces a novel two-stage embedding model (TSEM), which adequately leverage item multimodal auxiliary information to substantially improve recommendation performance. Specifically, it encompasses two sequential stages: graph convolutional embedding (GCE) and multimodal joint fuzzy embedding (MJFE). In the former, we first generate a bipartite graph for user-item interactions, and then utilize it to construct user and item backbone features via a spatial-based graph convolutional network (SGCN). While in the latter, by employing item multimodal auxiliary information, we integrate multi-task deep learning, deterministic Softmax, and fuzzy Softmax into a convolutional neural network (CNN)-based learning framework, which is optimized to obtain user backbone features and item semantic-enhanced fuzzy (SEF) features accurately. After TSEM converges, user backbone features and item SEF features can be utilized to calculate user preferences on items via Euclidean distance. Extensive experiments over two real-world datasets show that the proposed TSEM model significantly outperforms the state-of-the-art baselines in terms of various evaluation metrics.
Over-the-Air Decentralized Federated Learning

In this paper, we consider decentralized federated learning (FL) over wireless networks, where over-the-air computation (AirComp) is adopted to facilitate the local model consensus in a device-to-device (D2D) communication manner. However, the AirComp-based consensus phase brings the additive noise in each algorithm iterate and the consensus needs to be robust to wireless network topology changes, which introduce a coupled and novel challenge of establishing the convergence for wireless decentralized FL algorithm. To facilitate consensus phase, we propose an AirComp-based DSGD with gradient tracking and variance reduction (DSGT-VR) algorithm, where both precoding and decoding strategies are developed for D2D communication. Furthermore, we prove that the proposed algorithm converges linearly and establish the optimality gap for strongly convex and smooth loss functions, taking into account the channel fading and noise. The theoretical result shows that the additional error bound in the optimality gap depends on the number of devices. Extensive simulations verify the theoretical results and show that the proposed algorithm outperforms other benchmark decentralized FL algorithms over wireless networks.
Remote Sensing Data Augmentation Through Adversarial Training

The lack of remote sensing images and poor quality limit the performance improvement of follow-up research such as remote sensing interpretation. In this paper, a Generative Adversarial Network(GAN) is proposed for data augmentation of remote sensing images abstracted from Jiangxi and Anhui Provinces in China, i.e., Deeply-supervised GAN(D-sGAN). D-sGAN can generate high-quality images that are rich in changes, greatly shorten the generation time, and provide data support for applications such as semantic interpretation of remote sensing images. At First, to modulate the layer activations, a down-sampling scheme is designed based on the segmentation map. Then, the architecture of the generator is Unet++ with the proposed down-sampling module. Next, the generator of this net is deeply supervised by the discriminator using deep Convolutional Neural Network(CNN). This paper further proved that the proposed down-sampling module and the dense connection characteristics of UNet++ are significantly beneficial to the retention of semantic information of remote sensing images. Numerical results demonstrated that the images generated by D-sGAN could be used to improve accuracy of the segmentation network, with the faster generation speed compared to the CoGAN, SimGAN and CycleGAN models. Furthermore, the remote sensing data generated by the model helped the interpretation network to increase the accuracy by 9\%, meeting actual generation requirements.
Yet another textual variability language?

Variability models are commonly used to model commonalities and variability in a product line. There is a large variety of textual formats to represent and store variability models. This variety causes overhead to researchers and practitioners as they frequently need to translate models. The MODEVAR initiative consists of dozens of researchers and aims to find a unified language for variability modeling. In this work, we describe the cooperative development of a textual variability language. We evaluate preferences of the community regarding properties of existing formats and applications for an initial design of a unified variability language. Then, we examine the acceptance of the community for our proposal. The results indicate that our proposal is a promising start towards a unified variability language instead of yet another language. We envision that the community applies our language proposal in teaching, research prototypes, and industrial applications to further evolve the design and then ultimately reach a unified language.
A Voting-Mechanism-Based Ensemble Framework for Constraint Handling Techniques

Effective constraint handling techniques (CHTs) are of great significance for evolutionary algorithms (EAs) dealing with constrained optimization problems (COPs). To date, many CHTs, such as penalty function, superiority of feasible solutions, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -constraint (EC), have been designed. However, different CHTs are usually suited to different problems, even the most appropriate technique changes along with the stages of the optimization process. Motivated by this phenomenon, we propose a voting-mechanism-based ensemble framework, named voting mechanism for constraint handling (VMCH), to integrate multiple CHTs for solving various COPs. In this framework, each CHT acts as a voter, all voters vote for each pair of solutions, and the solution in each pair with the highest weighted votes is considered better. In addition, an adaptive strategy is developed to adjust the voter weights according to their historical voting performance. To investigate the performance of VMCH in improving existing algorithms, the proposed VMCH is embedded into the three best algorithms in the competition on constrained single objective real-parameter optimization at CEC 2018, namely, MAgES, iLSHADE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> , and IUDE, to form three new algorithm versions, i.e., MAgES-VMCH, iLSHADE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -VMCH, and IUDE-VMCH. They are compared with seven state-of-the-art peer algorithms. Extensive experiments are conducted on 57 real-world COPs. The ranking results show that the new algorithm version MAgES-VMCH takes first place among the ten comparison algorithms. Moreover, all the new VMCH-enhanced versions of the three best algorithms are superior to their original versions. Therefore, the proposed VMCH framework can achieve competitive performance in solving COPs.
API recommendation for Mashup creation based on neural graph collaborative filtering

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