Reviewerly/exHarmony huggingface dataset:
Co-attention dictionary network for weakly-supervised semantic segmentation

In this paper, we propose the co-attention dictionary network (CODNet) for weakly-supervised semantic segmentation using only image-level class labels. The CODNet model exploits extra semantic information by jointly leveraging a pair of samples with common semantics through co-attention rather than processing them independently. The inter-sample similarities of spatially distributed deep features are computed to merge reference features through non-local connections. To discover similar patterns regardless of appearance variations, we propose to extract image representations by equipping the neural networks with dictionary learning which provides the universal basis elements for different images. Based on the CODNet model, we propose a multi-reference class activation map (MR-CAM) algorithm which generates semantic segmentation masks for a target image by jointly merging semantic cues from multiple reference images. Experimental results on the PASCAL VOC 2012 and MSCOCO benchmark datasets for weakly-supervised semantic segmentation show that the proposed algorithm performs favorably against the state-of-the-art methods.
FASSD-Net: Fast and Accurate Real-Time Semantic Segmentation for Embedded Systems

Recent works of real-time semantic segmentation, remove or make use of light decoders from dense deep neural networks to achieve fast inference speed. This strategy helps to achieve real-time performance; however, the accuracy is significantly compromised in comparison to non-real-time methods. In this paper, we introduce two key modules aimed to design a high-performance decoder for real-time semantic segmentation, which also reduces the accuracy gap between real-time and non-real-time networks. The first module, Dilated Asymmetric Pyramidal Fusion (DAPF), is designed to increase the receptive field on the top of the last stage of the encoder, obtaining richer contextual features. The second module, Multi-resolution Dilated Asymmetric (MDA) module, fuses and refines detail and contextual information from multi-scale feature maps coming from early and deeper stages of the network. Both modules are designed to keep a low computational complexity by using asymmetric convolutions. With these modules, we propose a network entitled “FASSD-Net,” which is based on a light-weight CNN backbone. Running on a single Nvidia GTX 1080Ti, our model reaches 77.5% and 69.3% of mIoU, at 41 and 80 FPS on the Cityscapes and CamVid datasets, respectively. We present an extensive analysis of the accuracy-speed tradeoffs of three FASSD-Net variations on different embedded systems, demonstrating that a light version of our network can run on the low-power consumption Jetson Xavier NX, at 32 FPS reaching 74% of mIoU with full resolution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\times 2048$ </tex-math></inline-formula> ). The source code and pre-trained models are available at github.com/GibranBenitez/FASSD-Net.
PRNU-based Source Camera Identification for Multimedia Forensics

With the increased development and reliance on multimedia data, the importance of attributing the device or camera of origin in the form of source camera identification (SCI) has gained traction in cybersecurity, specifically within digital multimedia forensics. Photo-Response Non-Uniformity (PRNU) is a popular and widely used method for extracting a unique and reliable sensor pattern fingerprint for SCI purposes. The usage of PRNU in distinguishing cameras across different manufacturers and models has proven to be successful; however, we demonstrate that current approaches fail to distinguish cameras amongst the same manufacturers and models. As such, in this paper, we propose a new algorithm that focuses on emphasizing the pixels that contribute to the sensor noise in the PRNU pattern to distinguish cameras of the same type. Unlike other similarity metrics used in the process of SCI, we utilize the Jaccard coefficient in order to provide a proportion value of matching pixel locations shared between the noise patterns of two devices. Our experimental results show that our method can successfully determine the device origin of an image from cameras of identical type across Apple iPhone 6, FujiFilm, Panasonic, and Sony cameras.
A Serverless Academic Adviser Chatbot

Academic advising plays a vital role in students' academic success; however, it is time consuming and difficult to maintain. Moreover, due to the COVID-19 outbreak and the sudden shift of education to the cyberspace, it has become even more challenging and time consuming for advisors to handle the drastically increasing numbers of queries received through online communication channels. To advise an enormous number of newly admitted students, the need arises for solutions that can handle the demands of a large number of students effectively without affecting student's academic success. This paper proposes an efficient, fast, scalable, and cost-effective solution using a serverless chatbot. The proposed Academic Advising chatbot, which can be integrated with Microsoft Teams, implements advanced semantic analysis techniques from Natural Language Processing (NLP) and analyzes the context of the student's queries, and responds accordingly.
Recent Developments in Quantum‐Circuit Refrigeration

We review the recent progress in direct active cooling of the quantum-electric degrees freedom in engineered circuits, or quantum-circuit refrigeration. In 2017, the invention of a quantum-circuit refrigerator (QCR) based on photon-assisted tunneling of quasiparticles through a normal-metal--insulator--superconductor junction inspired a series of experimental studies demonstrating the following main properties: (i) the direct-current (dc) bias voltage of the junction can change the QCR-induced damping rate of a superconducting microwave resonator by orders of magnitude and give rise to non-trivial Lamb shifts, (ii) the damping rate can be controlled in nanosecond time scales, and (iii) the dc bias can be replaced by a microwave excitation, the amplitude of which controls the induced damping rate. Theoretically, it is predicted that state-of-the-art superconducting resonators and qubits can be reset with an infidelity lower than $10^{-4}$ in tens of nanoseconds using experimentally feasible parameters. A QCR-equipped resonator has also been demonstrated as an incoherent photon source with an output temperature above one kelvin yet operating at millikelvin. This source has been used to calibrate cryogenic amplification chains. In the future, the QCR may be experimentally used to quickly reset superconducting qubits, and hence assist in the great challenge of building a practical quantum computer.
Learning Super-Resolution Reconstruction for High Temporal Resolution Spike Stream

Spike camera is a new type of bio-inspired vision sensor, each pixel of which perceives the brightness of the scene independently, and finally outputs 3-dimensional spatiotemporal spike streams. To bridge the spike camera and traditional frame-based vision, there is some works to reconstruct spike streams into regular images. However, the low spatial resolution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$400\times 250$ </tex-math></inline-formula> ) of the spike camera limits the quality of the reconstructed images. Thus, it is meaningful to explore a super-resolution reconstruction for spike streams. In this paper, we propose an end-to-end network to reconstruct high-resolution images from low-resolution spike streams. To utilize more spatiotemporal features of spike streams, our network adopts a multi-level features learning mechanism, including intra-stream feature extraction by spike encoder, inter-stream dependencies extraction based on optical flow module, and joint features learning via spike-based iterative projection. Experimental results demonstrate that our network is superior to the combination of state-of-the-art intensity image reconstruction methods and super-resolution networks on simulated and real datasets.
Sentiment Analysis of Users’ Comments on Indian Hindi News Channels Using Mozdeh: An Evaluation Based on YouTube Videos

Sentiment analysis is the channel to pick out the text from the social media dataset to inquire about the positive and negative opinions of the statement and its subjective and objectiveness. The purpose of the study is to manifest the sentiment of the text posted in five Hindi news channels, that is, AajTak, ABP News, India TV, NDTV India and Republic Bharat on YouTube are investigated by adopting the Mozdeh software to highlight the sequential temperament of the viewers by evaluating the positive and negative sentiments. The present study is subsequently limited to the data being extracted and evaluated by the software Mozdeh. The sentiments of each Hindi news channel are analysed along with the top word frequencies and displaying the time-series graph. The investigation presents that the channel with maximum average positive average negative sentiment belongs to India TV and the female category was in the peak compared to male altogether but the unidentified gender was the highest. During the time series analysis, the year 2020 was seen to be the most productive year since all the spikes were precisely detected. The audience of these channels turns out to be more attentive towards the political and entertainment news world. The study also highlights the tendency and interest of the common audiences to watch the news which dedicates that people are usually unsatisfied with the content being displayed in the videos and the common concentration is mostly based on the political news.
Visually Connecting Historical Figures Through Event Knowledge Graphs

Knowledge graphs store information about historical figures and their relationships indirectly through shared events. We developed a visualization system, VisKonnect, for analyzing the intertwined lives of historical figures based on the events they participated in. A user’s query is parsed for identifying named entities, and related data is retrieved from an event knowledge graph. While a short textual answer to the query is generated using the GPT-3 language model, various linked visualizations provide context, display additional information related to the query, and allow exploration.
S $^3$ Net: Self-Supervised Self-Ensembling Network for Semi-Supervised RGB-D Salient Object Detection

RGB-D salient object detection aims to detect visually distinctive objects or regions from a pair of the RGB image and the depth image. State-of-the-art RGB-D saliency detectors are mainly based on convolutional neural networks but almost suffer from an intrinsic limitation relying on the labeled data, thus degrading detection accuracy in complex cases. In this work, we present a self-supervised self-ensembling network (S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> Net) for semi-supervised RGB-D salient object detection by leveraging the unlabeled data and exploring a self-supervised learning mechanism. To be specific, we first build a self-guided convolutional neural network (SG-CNN) as a baseline model by developing a series of three-layer cross-model feature fusion (TCF) modules to leverage complementary information among depth and RGB modalities and formulating an auxiliary task that predicts a self-supervised image rotation angle. After that, to further explore the knowledge from unlabeled data, we assign SG-CNN to a student network and a teacher network, and encourage the saliency predictions and self-supervised rotation predictions from these two networks to be consistent on the unlabeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our network quantitatively and qualitatively outperforms the state-of-the-art methods.
Accelerated RISC-V for SIKE

Software implementations of cryptographic algorithms are slow but highly flexible and relatively easy to implement. On the other hand, hardware implementations are usually faster but provide little flexibility and require a lot of time to implement efficiently. In this paper, we develop a hybrid software-hardware implementation of the third round of Supersingular Isogeny Key Encapsulation (SIKE), a post-quantum cryptography algorithm candidate for NIST. We implement an isogeny field accelerator for the hardware and integrate it with a RISC-V processor which also acts as the main control unit for the field accelerator. The main advantage of this design is the high performance gain from the hardware implementation and the flexibility and fast development the software implementation provides. This is the first hybrid RISC-V and accelerator of SIKE. Furthermore, we provide one implementation for all NIST security levels of SIKE. Our design has the best area-time at NIST security levels 3 and 5 out of all hardware and hybrid designs provided in the literature.
Abnormal Samples Oversampling for Anomaly Detection Based on Uniform Scale Strategy and Closed Area

The samples representing abnormal situation is usually very few in the dataset, which makes it difficult to learn the features of abnormal samples by machine-learning-based methods. To improve the accuracy of anomaly detection, the number of abnormal samples should be expanded to ensure the balance of the dataset. In this paper, a discrete synthetic minority oversampling technique (D-SMOTE) is proposed to generate new samples. A closed area is constructed using the three nearest abnormal samples in the dataset. The new samples are then uniformly interpolated in a closed area. By this means, the problem of the imbalance for the original dataset is handled, thus improving the data quality. Based on the expanded datasets, a two-dimensional convolutional neural network (2D CNN) is constructed to detect abnormal samples. In experiments, three cases and different machine learning methods are considered for comparison. Several indexes including accuracy, precision, confusion matrix, F1-score, and Recall have been used to evaluate the detection effectiveness. The results show that the abnormal samples can be detected accurately using oversampling data obtained from the proposed D-SMOTE method.
UniCon

Non-Fungible Tokens (NFTs) leverage blockchain technology to certify and transfer ownership of digital assets to individuals. NFTs on the Ethereum blockchain have garnered significant attention recently, with a trading volume of over $2 billion in Q1 2021 only. At the same time, established NFT solutions have low flexibility, limited scalability, and high transaction fees. These deficiencies make them impractical to use at a larger scale to manage digital assets.
High Speed Receiver Modeling Using Generative Adversarial Networks

This paper presents a generative approach to modeling a high-speed receiver with a time series input. The model is not built with domain knowledge but learned from a wide range of channel conditions and input bitstreams to generate an eye diagram. The generated eye diagrams are similar to the simulated eye diagrams for the same scenario. We also developed a neural network model to evaluate the generated eye diagram's relevant characteristics, such as eye height and width. The generated eye diagrams are within 7% and 3% error to the ground-truth in eye height and eye width, respectively, based on our evaluation neural network.
Unifying Behavior Driven Development Templates

Behavior Driven Development (BDD) offers a way to express scenarios, written in structured natural language, on how the system should act to fulfill a requirement. Numerous templates have been written to construct BDD scenarios and various practices were born out of usage. Nevertheless, we mostly fail to find documentation on the used templates and understand empirical practices. This paper explores empirically the BDD templates used in practice by evaluating the keywords mostly associated to the GIVEN, WHEN and THEN scenario dimensions. It then studies whether we can use these existing keywords to build a set of non-redundant concepts covering the representational needs and being usable as reference when constructing BDD scenarios. We consolidate these findings in a meta-model.
An EM algorithm for target tracking with an unknown correlation coefficient of measurement noise

Abstract In practical applications, the range rate used in a target tracking algorithm will further increase the strong nonlinearity between the measurement and the state, and the unknown correlation coefficient of measurement noise between the range and the range rate will cause suboptimal gain of the filter, which will seriously affect the performance of the filter. In this paper, an expectation maximization (EM)-based sequential modified unbiased converted measurement Kalman filter is proposed for target tracking with an unknown correlation coefficient of measurement noise between the range and the range rate. Firstly, a pseudo measurement is constructed by multiplying the range and the range rate to reduce the strong nonlinearity. The mean and covariance of converted errors are subsequently derived using modified unbiased converted measurement (MUCM) to weaken the error caused by the linearization of the measurement equation, which will effectively improve the dynamic accuracy of target tracking. Then, the converted errors of the position and the pseudo measurement are decorrelated by Cholesky factorization to facilitate the subsequent identification of the correlation coefficient, and the posterior probability distribution of the state is obtained using sequential filtering within the Bayesian framework. Finally, the EM is introduced in the updating procedure of the pseudo measurement to estimate both the target state and the correlation coefficient. The target tracking scenario with an unknown correlation coefficient is built to demonstrate the validity and feasibility of the proposed algorithm. Simultaneously, the results of the normalized error squared validate the consistency of the MUCM.
Click Fraud in Digital Advertising: A Comprehensive Survey

Recent research has revealed an alarming prevalence of click fraud in online advertising systems. In this article, we present a comprehensive study on the usage and impact of bots in performing click fraud in the realm of digital advertising. Specifically, we first provide an in-depth investigation of different known categories of Web-bots along with their malicious activities and associated threats. We then ask a series of questions to distinguish between the important behavioral characteristics of bots versus humans in conducting click fraud within modern-day ad platforms. Subsequently, we provide an overview of the current detection and threat mitigation strategies pertaining to click fraud as discussed in the literature, and we categorize the surveyed techniques based on which specific actors within a digital advertising system are most likely to deploy them. We also offer insights into some of the best-known real-world click bots and their respective ad fraud campaigns observed to date. According to our knowledge, this paper is the most comprehensive research study of its kind, as it examines the problem of click fraud both from a theoretical as well as practical perspective.
AMR

It is well known that users on open blockchains are tracked by an industry providing services to governments, law enforcement, secret services, and alike. While most blockchains do not protect their users' privacy and allow external observers to link transactions and addresses, a growing research interest attempts to design add-on privacy solutions to help users regain their privacy on non-private blockchains.
Anonymiced Shareable Data: Using mice to Create and Analyze Multiply Imputed Synthetic Datasets

Synthetic datasets simultaneously allow for the dissemination of research data while protecting the privacy and confidentiality of respondents. Generating and analyzing synthetic datasets is straightforward, yet, a synthetic data analysis pipeline is seldom adopted by applied researchers. We outline a simple procedure for generating and analyzing synthetic datasets with the multiple imputation software mice (Version 3.13.15) in R. We demonstrate through simulations that the analysis results obtained on synthetic data yield unbiased and valid inferences and lead to synthetic records that cannot be distinguished from the true data records. The ease of use when synthesizing data with mice along with the validity of inferences obtained through this procedure opens up a wealth of possibilities for data dissemination and further research on initially private data.
Cloud-Based Business Process Security Risk Management: A Systematic Review, Taxonomy, and Future Directions

Despite the attractive benefits of cloud-based business processes, security issues, cloud attacks, and privacy are some of the challenges that prevent many organizations from using this technology. This review seeks to know the level of integration of security risk management process at each phase of the Business Process Life Cycle (BPLC) for securing cloud-based business processes; usage of an existing risk analysis technique as the basis of risk assessment model, usage of security risk standard, and the classification of cloud security risks in a cloud-based business process. In light of these objectives, this study presented an exhaustive review of the current state-of-the-art methodology for managing cloud-based business process security risk. Eleven electronic databases (ACM, IEEE, Science Direct, Google Scholar, Springer, Wiley, Taylor and Francis, IEEE cloud computing Conference, ICSE conference, COMPSAC conference, ICCSA conference, Computer Standards and Interfaces Journal) were used for the selected publications. A total of 1243 articles were found. After using the selection criteria, 93 articles were selected, while 17 articles were found eligible for in-depth evaluation. For the results of the business process lifecycle evaluation, 17% of the approaches integrated security risk management into one of the phases of the business process, while others did not. For the influence of the results of the domain assessment of risk management, three key indicators (domain applicability, use of existing risk management techniques, and integration of risk standards) were used to substantiate our findings. The evaluation result of domain applicability showed that 53% of the approaches had been testing run in real-time, thereby making these works reusable. The result of the usage of existing risk analysis showed that 52.9% of the authors implemented their work using existing risk analysis techniques while 29.4% of the authors partially integrated security risk standards into their work. Based on these findings and results, security risk management, the usage of existing security risk management techniques, and security risk standards should be integrated with business process phases to protect against security issues in cloud services.