Reviewerly/exHarmony huggingface dataset:
Deep Model-Based Super-Resolution with Non-uniform Blur

We propose a state-of-the-art method for super-resolution with non-uniform blur. Single-image super-resolution methods seek to restore a high-resolution image from blurred, subsampled, and noisy measurements. Despite their impressive performance, existing techniques usually assume a uniform blur kernel. Hence, these techniques do not generalize well to the more general case of non-uniform blur. Instead, in this paper, we address the more realistic and computationally challenging case of spatially-varying blur. To this end, we first propose a fast deep plug-and-play algorithm, based on linearized ADMM splitting techniques, which can solve the super-resolution problem with spatially-varying blur. Second, we unfold our iterative algorithm into a single network and train it end-to-end. In this way, we overcome the intricacy of manually tuning the parameters involved in the optimization scheme. Our algorithm presents remarkable performance and generalizes well after a single training to a large family of spatially-varying blur kernels, noise levels and scale factors.
On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited

Arbitrary-oriented object detection has been a building block for rotation sensitive tasks. We first show that the boundary problem suffered in existing dominant regression-based rotation detectors, is caused by angular periodicity or corner ordering, according to the parameterization protocol. We also show that the root cause is that the ideal predictions can be out of the defined range. Accordingly, we transform the angular prediction task from a regression problem to a classification one. For the resulting circularly distributed angle classification problem, we first devise a Circular Smooth Label technique to handle the periodicity of angle and increase the error tolerance to adjacent angles. To reduce the excessive model parameters by Circular Smooth Label, we further design a Densely Coded Labels, which greatly reduces the length of the encoding. Finally, we further develop an object heading detection module, which can be useful when the exact heading orientation information is needed e.g. for ship and plane heading detection. We release our OHD-SJTU dataset and OHDet detector for heading detection. Extensive experimental results on three large-scale public datasets for aerial images i.e. DOTA, HRSC2016, OHD-SJTU, and face dataset FDDB, as well as scene text dataset ICDAR2015 and MLT, show the effectiveness of our approach.
Don’t Be Spoiled by Your Friends: Spoiler Detection in TV Program Tweets

Providing a convenient mechanism for accessing the Internet, smartphones have led to the rapid growth of Social Networking Services (SNSs) such as Twitter and have served as a major platform for SNSs. Nowadays, people are able to check conveniently the SNS messages posted by their friends and followers via their smartphones. As a consequence, people are exposed to spoilers of TV programs that they follow. So far, there are two previous works that explored the detection of spoilers in texts, not SNS: (1) keyword matching method and (2) machine-learning method based on Latent Dirichlet Allocation (LDA). The keyword matching method evaluates most tweets as spoilers; hence its poor recall performance. The other method based on LDA, although successful on large text, works poorly on short segments of text such as those found on Twitter and evaluates most tweets as non-spoilers. This paper presents four features that are significant in the classification of spoiler tweets. Using those features, we classified spoiler tweets pertaining to a reality TV show (“Dancing with the Stars”). We experimentally compared our method with previous methods, with our method achieving substantially higher precision compared to the keyword matching and LDA-based methods while maintaining comparable recalls.
The complexity of trees, universal grammar and economy conditions

<title xmlns="http://www.ncbi.nlm.nih.gov/JATS1" /> <p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">In this squib, I argue that the child faces a severe computational complexity problem in parsing even the simplest of trees: the number of possible trees consistent with UG grows exponentially as a function of the number of lexical items. Economy conditions have the result of drastically decreasing the complexity of the parsing task. I also discuss the relationship between UG, I-language, economy conditions and explanatory adequacy.
An Improved Potential Field-Based Probabilistic Roadmap Algorithm for Path Planning

Probabilistic Roadmap (PRM) is one of the most important path planning techniques. It has been widely applied in mobile robot navigation for its simplicity. However, when there are narrow passages in the environment, the planning efficiency of PRM is greatly reduced. To solve this problem, this article proposes an improved potential field-based probabilistic roadmap algorithm. Making use of virtual potential field strategy, the targeting environment is represented with a quantifiable potential field map, so that the obstacle information is clearly expressed. Next, a partition-based sampling strategy is developed to improve the number of sampling points in dense obstacle areas while keeping sampling points uniformly distributed. Finally, some critical points are added with a new eight-directional detection method, thus the success rate of path planning is effectively improved. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.
Fundamentally Understanding and Solving RowHammer

We provide an overview of recent developments and future directions in the RowHammer vulnerability that plagues modern DRAM (Dynamic Random Memory Access) chips, which are used in almost all computing systems as main memory. RowHammer is the phenomenon in which repeatedly accessing a row in a real DRAM chip causes bitflips (i.e., data corruption) in physically nearby rows. This phenomenon leads to a serious and widespread system security vulnerability, as many works since the original RowHammer paper in 2014 have shown. Recent analysis of the RowHammer phenomenon reveals that the problem is getting much worse as DRAM technology scaling continues: newer DRAM chips are fundamentally more vulnerable to RowHammer at the device and circuit levels. Deeper analysis of RowHammer shows that there are many dimensions to the problem as the vulnerability is sensitive to many variables, including environmental conditions (temperature \& voltage), process variation, stored data patterns, as well as memory access patterns and memory control policies. As such, it has proven difficult to devise fully-secure and very efficient (i.e., low-overhead in performance, energy, area) protection mechanisms against RowHammer and attempts made by DRAM manufacturers have been shown to lack security guarantees. After reviewing various recent developments in exploiting, understanding, and mitigating RowHammer, we discuss future directions that we believe are critical for solving the RowHammer problem. We argue for two major directions to amplify research and development efforts in: 1) building a much deeper understanding of the problem and its many dimensions, in both cutting-edge DRAM chips and computing systems deployed in the field, and 2) the design and development of extremely efficient and fully-secure solutions via system-memory cooperation.
Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora

Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes. NER in languages with limited resources, like French, is still an open problem due to the lack of large, robust, labelled datasets. In this paper, we propose a transformer-based NER approach for French using adversarial adaptation to similar domain or general corpora for improved feature extraction and better generalization. We evaluate our approach on three labelled datasets and show that our adaptation framework outperforms the corresponding non-adaptive models for various combinations of transformer models, source datasets and target corpora.
Peculiarities of gender disambiguation and ordering of non-English authors’ names for Economic papers beyond core databases<sup>①</sup>

Abstract Purpose To supplement the quantitative portrait of Ukrainian Economics discipline with the results of gender and author ordering analysis at the level of individual authors, special methods of working with bibliographic data with a predominant share of non-English authors are used. The properties of gender mixing, the likelihood of male and female authors occupying the first position in the authorship list, as well as the arrangements of names are studied. Design/methodology/approach A data set containing bibliographic records related to Ukrainian journal publications in the field of Economics is constructed using Crossref metadata. Partial semi-automatic disambiguation of authors’ names is performed. First names, along with gender-specific ethnic surnames, are used for gender disambiguation required for further comparative gender analysis. Random reshuffling of data is used to determine the impact of gender correlations. To assess the level of alphabetization for our data set, both Latin and Cyrillic versions of names are taken into account. Findings The lack of well-structured metadata and the poor use of digital identifiers lead to numerous problems with automatization of bibliographic data pre-processing, especially in the case of publications by non-Western authors. The described stages for working with such specific data help to work at the level of authors and analyse, in particular, gender issues. Despite the larger number of female authors, gender equality is more likely to be reported at the individual level for the discipline of Ukrainian Economics. The tendencies towards collaborative or solo-publications and gender mixing patterns are found to be dependent on the journal: the differences for publications indexed in Scopus and/or Web of Science databases are found. It has also been found that Ukrainian Economics research is characterized by rather a non-alphabetical order of authors. Research limitations Only partial authors’ name disambiguation is performed in a semi-automatic way. Gender labels can be derived only for authors declared by full First names or gender-specific Last names. Practical implications The typical features of Ukrainian Economic discipline can be used to perform a comparison with other countries and disciplines, to develop an informed-based assessment procedure at the national level. The proposed way of processing publication data can be borrowed to enrich metadata about other research disciplines, especially for non-English speaking countries. Originality/value To our knowledge, this is the first large-scale quantitative study of Ukrainian Economic discipline. The results obtained are valuable not only at the national level, but also contribute to general knowledge about Economic research, gender issues, and authors’ names ordering. An example of the use of Crossref data is provided, while this data source is still less used due to a number of drawbacks. Here, for the first time, attention is drawn to the explicit use of the features of the Slavic authors’ names.
Content-Oriented Learned Image Compression

In recent years, with the development of deep neural networks, end-to-end optimized image compression has made significant progress and exceeded the classic methods in terms of rate-distortion performance. However, most learning-based image compression methods are unlabeled and do not consider image semantics or content when optimizing the model. In fact, human eyes have different sensitivities to different content, so the image content also needs to be considered. In this paper, we propose a content-oriented image compression method, which handles different kinds of image contents with different strategies. Extensive experiments show that the proposed method achieves competitive subjective results compared with state-of-the-art end-to-end learned image compression methods or classic methods.
Multi-Scale Human-Object Interaction Detector

Transformers are transforming the landscape of computer vision, especially for image-level recognition and instance-level detection tasks. Human-object interaction detection transformer (HOI-TR) is the first transformer-based end-to-end learning system for human-object interaction (HOI) detection; vision transformers build a simple multi-stage structure for multi-scale representation with single-scale patch and are the first patch-based transformer architecture for image-level recognition and instance-level detection. In this paper, we build a transformer-based multi-scale human-object interaction detector (MHOI), a novel method to integrate Vision and HOI detection Transformer, instead of directly incorporating two types of transformers, since the vision transformer lacks hierarchical architecture to handle the large variations in the scale of visual entities due to the single-scale patch partitioning. Specifically, MHOI embeds features of the same size (i.e., sequence length) with patches of variable scales simultaneously by utilizing overlapping convolutional patch embedding, then introduces an efficient transformer decoder that designs the query based on anchor points and essential auxiliary techniques to boost the HOI detection performance. Numerically, extensive experiments on several benchmarks demonstrate that our proposed framework outperforms prior existing methods coherently and achieves the impressive performance of 29.67 mAP on HICO-DET and 58.7 mAP on V-COCO, respectively.
Metaverse Meets Consumer Electronics

The first year of the Metaverse Era is considered 2021 mainly because the gaming company Roblox was listed on March 10, 2021. Different from the traditional games, in the Roblox metaverse, the game players can construct their own games to let others play together.
Five Common Misconceptions About Privacy-Preserving Internet of Things

Billions of devices in the Internet of Things (IoT) collect sensitive data about people, creating data privacy risks and breach vulnerabilities. Accordingly, data privacy preservation is vital for sustaining the proliferation of IoT services. In particular, privacy-preserving IoT connects devices embedded with sensors and maintains the data privacy of people. However, common misconceptions exist among IoT researchers, service providers, and users about privacy-preserving IoT. This article refutes five common misconceptions about privacy-preserving IoT concerning data sensing and innovation, regulations, and privacy safeguards. For example, IoT users have a common misconception that no data collection is permitted in data privacy regulations. On the other hand, IoT service providers often think data privacy impedes IoT sensing and innovation. Addressing these misconceptions is essential for making progress in privacy-preserving IoT. This article refutes such common misconceptions using real-world experiments and online survey research. First, the experiments indicate that data privacy should not be perceived as an impediment in IoT but as an opportunity to increase customer retention and trust. Second, privacy-preserving IoT is not exclusively a regulatory problem but also a functional necessity that must be incorporated in the early stages of any IoT design. Third, people do not trust services that lack sufficient privacy measures. Fourth, conventional data security principles do not guarantee data privacy protection, and data privacy can be exposed even if data is securely stored. Fifth, IoT decentralization does not attain absolute privacy preservation.
Internet of Things for Digital Forensics Application in Saudi Arabia

Despite the extensive empirical literature relating to the Internet of Things (IoT), surprisingly few attempts have sought to establish the ways in which digital forensics can be applied to undertake detailed examinations regarding IoT frameworks.The existing digital forensic applications have effectively held back efforts to align the IoT with digital forensic strategies.This is because the forensic applications are ill-suited to the highly complex IoT frameworks and would, therefore, struggle to amass, analyze and test the necessary evidence that would be required by a court.As such, there is a need to develop a suitable forensic framework to facilitate forensic investigations in IoT settings.Nor has considerable progress been made in terms of collecting and saving network and server logs from IoT settings to enable examinations.Consequently, this study sets out to develop and test the FB system which is a lightweight forensic framework capable of improving the scope of investigations in IoT environments.The FB system can organize the management of various IoT devices found in a smart apartment, all of which is controlled by the owner's smart watch.This will help to perform useful functions, automate the decision-making process, and ensure that the system remains secure.A Java app is utilized to simulate the FB system, learning the user's requirements and security expectations when installed and employing the MySQL server as a means of logging the communications of the various IoT devices.
A Communication Efficient ADMM-based Distributed Algorithm Using Two-Dimensional Torus Grouping AllReduce

Abstract Large-scale distributed training mainly consists of sub-model parallel training and parameter synchronization. With the expansion of training workers, the efficiency of parameter synchronization will be affected. To tackle this problem, we first propose 2D-TGA, a g rouping A llReduce method based on the two-dimensional t orus topology. This method synchronizes the model parameters by grouping and makes full use of bandwidth. Secondly, we propose a distributed algorithm, 2D-TGA-ADMM, which combines the 2D-TGA with the alternating direction method of multipliers (ADMM). It focuses on sub-model training and reduces the wait time among workers in the synchronization process. Finally, experimental results on the Tianhe-2 supercomputing platform show that compared with the $${\mathtt {MPI\_Allreduce}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>MPI</mml:mi> <mml:mi>_</mml:mi> <mml:mi>Allreduce</mml:mi> </mml:mrow> </mml:math> , the 2D-TGA could shorten the synchronization wait time by $$33\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>33</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> .
Fingertip Detection Algorithm Based on Maximum Discrimination HOG Feature in Complex Background

Gesture fingertip detection plays a vital role in human-computer interaction applications such as VR and robot control. To improve the accuracy of fingertip detection in complex background, this paper proposes a fingertip detection algorithm based on a novel maximum discrimination HOG feature. Firstly, the Holistically-nested edge detection algorithm is used to detect the edge of hand images and non-hand images, and the HOG features of the contour edge are extracted to obtain the training set of positive and negative samples to reduce the influence of illumination, color, and texture on fingertip detection. Secondly, the maximum discrimination features are filtered from the positive sample set by a custom maximum discrimination feature filter and stored as a feature dictionary. The filtered maximum discrimination features and negative sample set are input into the XGBoost classifier for training and the voting rights classifier is obtained. Filtering maximum discrimination features can reduce the interference of irrelevant features and improve the performance of the classifier. Then, the KNN algorithm is used to find the best match in the dictionary, and the final fingertip position is obtained by the Meanshift algorithm. Finally, the algorithm in this paper is tested. The test results show that the accuracy of fingertip detection in this paper can reach 99 %, and the detected RMSE is kept in the range of 5 pixels, which is higher than that based on YCbCr skin color segmentation, YOLO target detection, and YOLO-YCbCr.
Automation of surveillance systems using deep learning and facial recognition

While it's true that humans are skilled at recognizing faces, what about computers? Machinery is transforming conventional human labor in today's technologically sophisticated world. These cutting-edge machines may be programmed to recognize faces in inconceivable ways, a process known as facial recognition or computer vision. In this study, we present a real-time system for detecting and identifying individuals in live or recorded surveillance feeds using deep learning and face recognition algorithms such as Convolution Neural Networks (CNN). The proposed real-time database integrated system is based on the VGGFace deep learning neural architecture. Transfer learning is used to retrain the model on a smaller original tailored dataset of 7500 images of 26 distinct individuals. Details regarding the creation of original tailored datasets and brief tests with various machine learning and deep learning approaches to analyze and improve the recognition accuracy of the proposed system are also reported. The proposed approach shows the highest degree of recognition accuracy by accurately recognizing each of the 26 individuals with a confidence level ranging from 78.54 percent to 100 percent, a mean average of 96 percent on real-time inputs.
Market efficiency assessment for multiple exchanges of cryptocurrencies

Purpose This study aims to analyze the efficient market hypothesis (EMH) of cryptocurrencies on multiple platforms by observing whether there is a discrepancy in the levels of efficiency between different exchanges. Additionally, EMH is tested in a multivariate way: whether the prices of the same cryptocurrencies traded on different exchanges are temporally related to each other. ADF and KPSS tests, whereas the vector autoregression model of order p – VAR(p) – for multivariate system. Findings Both Bitcoin and Ethereum show efficiency in the weak form on the main platforms in each market alone. However, when estimating a VAR(p) between prices among exchanges, there was evidence of Granger causality between cryptocurrencies in all exchanges, suggesting that EMH is not adequate due to cross information. Practical implications It is essential to assess the cryptocurrency market in a multivariate way, not only to favor its maturation process, but also to promote a broad understanding of its inherent risks. Thus, it will be possible to develop financial products that are actively managed in a more sophisticated cryptocurrency market. Social implications There is a possibility of performing arbitrage on different exchanges and market assets through cross-exchanges. Thus, emphasizing the need for regulation of exchanges in the digital asset market, as an eventual price manipulation on a single platform can impact others, which generates various distortions. Originality/value This study is the first to find evidence of cross-information for the same (and other) cryptocurrencies among different exchanges.