Reviewerly/exHarmony huggingface dataset:
YOLOrs-lite: A Lightweight CNN For Real-Time Object Detection in Remote-Sensing

Detection CNN architectures often exhibit over-parameterization which results in excessive computational and storage overhead, but also undesired overfitting and reduced performance. In this work we focus on YOLOrs, a state-of-the-art CNN for target detection in remote sensing imagery, and counteract over-parameterization by enforcing Tensor-Train (TT) structure to its convolutional kernels. While TT has been successfully used before for compressing classification CNNs, this work is the first one that uses it to compress a detection CNN. We refer to the resulting network as YOLOrs-lite and compare its performance against standard YOLOrs as well as other state-of-the-art detection networks. Our numerical studies show that the proposed network attains superior detection performance, with storage savings as high as 70%. The proposed network combines light storage with real-time inference, making it quite promising for edge deployment.
Route Tapestries: Navigating 360° Virtual Tour Videos Using Slit-Scan Visualizations

An increasingly popular way of experiencing remote places is by viewing 360° virtual tour videos, which show the surrounding view while traveling through an environment. However, finding particular locations in these videos can be difficult because current interfaces rely on distorted frame previews for navigation. To alleviate this usability issue, we propose Route Tapestries, continuous orthographic-perspective projection of scenes along camera routes. We first introduce an algorithm for automatically constructing Route Tapestries from a 360° video, inspired by the slit-scan photography technique. We then present a desktop video player interface using a Route Tapestry timeline for navigation. An online evaluation using a target-seeking task showed that Route Tapestries allowed users to locate targets 22% faster than with YouTube-style equirectangular previews and reduced the failure rate by 75% compared to a more conventional row-of-thumbnail strip preview. Our results highlight the value of reducing visual distortion and providing continuous visual contexts in previews for navigating 360°virtual tour videos.
AFEC

With the rapid development of various multimedia applications, research on image compression technology has become particularly important. Learning-based compression methods have developed rapidly and achieved excellent rate-distortion performance. Most existing researches have focused on designing a better entropy model to facilitate the probability estimation without attaching importance to how to extract features from images more effectively. However, information extracted by image compression networks is often not realistic and complete enough, especially when the fixed-shape receptive field of the compression network crosses the texture boundary of an image. In this paper, we propose to extract high-fidelity image features adaptively with local textures as the basic unit, which significantly improves the quality of the extracted information and enhances the compactness of the latent representation of the image. Besides, a cross-information-fusion gate is proposed to fuse the two features extracted from the adaptive image feature extraction branch and the main compression branch for reducing spatial redundancy in the latent representation. Experimental results demonstrate our proposed method achieves superior performance compared to existing learned image compression methods and traditional codecs and produces visually pleasing reconstructed images with high-fidelity details.
Safer exceptions for Scala

We describe a scheme for reflecting exceptions as capabilities in the Scala type system that keeps notational overhead to a minimum and avoids well-known problems with Java's checked exceptions framework. The scheme makes exceptions safer but not fully safe since the capability for throwing an exception may still yet escape its enclosing try block. To address this limitation, we also propose a type system which prevents capabilities from escaping.
Pandemic protests: Creatively using the freedom of assembly during COVID-19

It is a new truism that the COVID-19 pandemic has exacerbated an already dire human rights situation across the globe. The waves of protest that swept across the world in the year before the pandemic seemed to have been brought to a sudden halt due to lockdowns and restrictive laws. But at the same time, people everywhere have availed themselves of the wide protective scope of the freedom of assembly, newly re-emphasized in the Human Rights Committee's General Comment of 2020, to come together, protest, and make their voices heard in numerous creative ways. Amid the restrictions, there has been resilience.
Improving Compositional Generalization with Self-Training for Data-to-Text Generation

Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs).Such representations are compositional and it is costly to collect responses for all possible combinations of atomic meaning schemata, thereby necessitating few-shot generalization to novel MRs.In this work, we systematically study the compositional generalization of the state-of-the-art T5 models in few-shot data-totext tasks.We show that T5 models fail to generalize to unseen MRs, and we propose a template-based input representation that considerably improves the model's generalization capability.To further improve the model's performance, we propose an approach based on self-training using fine-tuned BLEURT for pseudo-response selection.On the commonlyused SGD and Weather benchmarks, the proposed self-training approach improves tree accuracy by 46%+ and reduces the slot error rates by 73%+ over the strong T5 baselines in few-shot settings. 1
Automatic Learning in Agriculture: A Survey

Agriculture plays a pivotal role in the growth of any nation.Nowadays, with the advancements of information technologies, big data are generated and executed using faster computing techniques.machine learning techniques have been used to automate and improve agricultural activities for a long time.This paper presents a review of existing applications of machine learning in agriculture with a focus on the applications of Deep Reinforcement Learning techniques in agriculture.The conventional solutions for agricultural machine learning decision-making problems are using supervised approaches.In supervised learning, the machine needs to be trained on samples of inputs and outputs to support decision making.While, in reinforcement learning, sequential decision making happens and the next input depends on the decision of the machine.In this paper, we perform a survey of 49 publications of which 10 were secondary research efforts that discussed a variety of machine learning approaches applications in agriculture and 39 research efforts that used machine learning approaches to support the automation of agricultural activities.
Cryptanalysis of Efficient Masked Ciphers: Applications to Low Latency

This work introduces second-order masked implementation of LED, Midori, Skinny, and Prince ciphers which do not require fresh masks to be updated at every clock cycle. The main idea lies on a combination of the constructions given by Shahmirzadi and Moradi at CHES 2021, and the theory presented by Beyne et al. at Asiacrypt 2020. The presented masked designs only use a minimal number of shares, i.e., three to achieve second-order security, and we make use of a trick to pair a couple of S-boxes to reduce their latency. The theoretical security analyses of our constructions are based on the linear-cryptanalytic properties of the underlying masked primitive as well as SILVER, the leakage verification tool presented at Asiacrypt 2020. To improve this cryptanalytic analysis, we use the noisy probing model which allows for the inclusion of noise in the framework of Beyne et al. We further provide FPGA-based experimental security analysis confirming second-order protection of our masked implementations.
An Action Recognition Method for Volleyball Players Using Deep Learning

This paper investigates the extraction of volleyball players’ skeleton information and provides a deep learning-based solution for recognizing the players’ actions. For this purpose, the convolutional neural network-based approach for recognizing volleyball players’ actions is used. The Lie group skeleton has a large data dimension since it is used to represent the features retrieved from the model. The convolutional neural network is used for feature learning and classification in order to process high-dimensional data, minimize the complexity of the recognition process, and speed up the calculation. This paper uses the Lie group skeleton representation model to extract the geometric feature of the skeleton information in the feature extraction stage and the geometric transformation (rotation and translation) between different limbs to represent the volleyball players’ movements in the feature representation stage. The approach is evaluated using the datasets Florence3D actions, MSR action pairs, and UTKinect action. The average recognition rate of our method is 93.00%, which is higher than that of the existing literature with high attention and reflects better accuracy and robustness.
Real-time Instance Detection with Fast Incremental Learning

Object instance detection is a highly relevant task to several robotic applications such as automated order picking, or household and hospital assistance robots. In these applications, a holistic scene labeling is often not required whereas it is sufficient to find a certain object type of interest, e.g. for picking it up. At the same time, large and continuously changing object sets are characteristic in such applications, requiring efficient model update capabilities from the object detector. Today’s monolithic multi-class detectors do not fulfill this criterion for fast and flexible model updates.This paper introduces InstanceNet, an ensemble of efficient single-class instance detectors capable of fast and incremental adaptation to new object sets. Due to a dynamic sampling-based training strategy, accurate detection models for new objects can be obtained within less than 40 minutes on a consumer GPU while only a small percentage of the existing detection models needs to be updated in a very efficient manner. The new detector has been thoroughly evaluated on the basis of a novel dataset of 100 grocery store objects.
VCMaker: Content-aware configuration adaptation for video streaming and analysis in live augmented reality

The emergence of edge computing has enabled mobile Augmented Reality (AR) on edge servers. We notice that the video configurations, i.e., frames per second (fps) and resolution, significantly affect the key metrics such as detection accuracy, data transmission latency and energy consumption in real AR application. Besides the time-varying bandwidth, we observe that the video contents, such as moving velocities of target objects, have remarkable impacts on the configuration selection. In addition, we take the energy consumption on data transmission into consideration. In this paper, we propose VCMaker, a system that generates video configuration decisions using reinforcement learning (RL). VCMaker trains a neural network model that selects configuration for future video chunks based on the collected observations. Rather than rely on any pre-programmed models, VCMaker learns to make configuration decisions solely through empirical observations of the resulting performances of historical decisions. In addition, we leverage the dynamic Region of Interest (RoI) encoding and motion vector-based object detection mechanisms to advance VCMaker. We implemented VCMaker and conducted extensive evaluations. The results show that VCMaker achieves a 20.5%–32.8% higher detection accuracy, and 25.2%–45.7% lower energy consumption than several state-of-the-art schemes.
End-to-End Segmentation-based News Summarization

In this paper, we bring a new way of digesting news content by introducing the task of segmenting a news article into multiple sections and generating the corresponding summary to each section.We make two contributions towards this new task.First, we create and make available a dataset, SEGNEWS, consisting of 27k news articles with sections and aligned heading-style section summaries.Second, we propose a novel segmentation-based language generation model adapted from pretrained language models that can jointly segment a document and produce the summary for each section.Experimental results on SEG-NEWS demonstrate that our model can outperform several state-of-the-art sequence-tosequence generation models for this new task.
Impact of Factors of Online Deceptive Reviews on Customer Purchase Decision Based on Machine Learning

Online deceptive reviews widely exist in the online shopping environment. Numerous studies have investigated the impact of online product reviews on customer behaviour and sales. However, the existing literature is mainly based on real product reviews; only a few studies have investigated deceptive reviews. Based on the results of deceptive reviews, this article explores the factors that affect customer purchase decision in online review systems, which is flooded by deceptive reviews. Therefore, a deceptive review influence model is proposed based on three influential factors of online review system, sentiment characteristics, review length, and online seller characteristics. Based on them, text mining is used to quantify the indicators of the three influential factors. Through principal component analysis and linear regression, the experimental results of electronic appliances on Tmall show that the three influential factors are positively related to customers' purchase intention and decision making.
IBchain: Internet of Things and Blockchain Integration Approach for Secure Communication in Smart Cities

Introducing IBchain, a new blockchain architecture with the Internet of Things (IoT) could be an attractive framework regarding improvements in connectivity implementation through the smart cities. Instead of meriting innovation and security, the IoT links people, sites, products and consequently, also provides opportunities. Everything that transfers information to the IoT system is integrated by advanced microchips, sensors, and actuators in actual things. The analytical ability of the IoT converts observations into actions, impacting business advancements and significant ways of activity. IoT enables connected objects to transmit information to personal blockchain systems to create tamper-resistant transaction records. The information from sensors and microchips is progressing rapidly with blockchain ledgers, making them more portable and relevant for immediate conversations. In IBchain methodology, the smart objects are permissible to connect securely with other smart objects in diverse situations. IBchain creates an innovative blockchain-based processing configuration through the IoT. The IBchain could analyze blockchain to the main expertise or supports the IoT validation and trustworthiness. It reinforces blockchain and cloud to build an empowering IoT ubiquitous situation for secure communication among the smart devices.
Detection of Phishing Websites Using Ensemble Machine Learning Approach

In this paper, we propose the use of Ensemble Machine Learning Methods such as Random Forest Algorithm and Extreme Gradient Boosting (XGBOOST) Algorithm for efficient and accurate phishing website detection based on its Uniform Resource Locator. Phishing is one of the most widely executed cybercrimes in the modern digital sphere where an attacker imitates an existing - and often trusted - person or entity in an attempt to capture a victim’s login credentials, account information, and other sensitive data. Phishing websites are visually and semantically similar to real ones. The rise in online trading activities has resulted in a rise in the number of phishing scams. Cybersecurity jobs are the most difficult to fill, and the development of an automated system for phishing website detection is the need of the hour. Machine Learning is one of the most feasible methods to approach this situation, as it is capable of handling the dynamic nature of phishing techniques, in addition to providing an accurate method of classification.
Improved performance of machine learning algorithms for prognosis of cervical cancer

With the progression of artificial intelligence in medical services, the world has achieved many benefits. The constant improvement of existing artificial intelligence techniques becomes a boon in the medical field for assisting healthcare providers. In current years, the diagnosis of cancers using machine learning techniques for timely decisions has gained popularity. Cancer is preventable and can be cured with early and timely diagnosis. Cervical cancer is one of the foremost cancers in other female cancers which ranked at the fourth position. The objective of this study to develop a model that provides a timely and cost-effective cervical cancer risk prediction score by using supervised machine learning techniques in amalgamation with dimensionality reduction techniques. The dimensionality reduction techniques help in providing the prediction with a minimum number of features. The experimental investigation on cervical cancer risk factor reveals that Random Forest classifier using recursive feature elimination with cross-validation technique gives 93%.
A guide to design uncertainty-aware self-adaptive components in Cyber–Physical Systems

Cyber–physical systems (CPS) need to be designed to deal with various forms of uncertainties associated with data contributing to the system's knowledge and its belief of the environment. Dealing with uncertainties requires adopting appropriate methods, which then allow for making the right decisions and carrying out the right actions based on imperfect information. However, CPS design is difficult and requires attention to many variables during the design process. For instance, choosing and incorporating methods for dealing with uncertainties into CPS design requires identifying the kinds of uncertainties at hand as well as knowledge of suitable methods and their application. While inspiration can be found in other CPS designs, the details of dealing with uncertainties in other CPSs can be confounded by domain-specific terminology, context, and requirements. To make this aspect of CPS design less daunting, we aim at providing an overview of approaches dealing with uncertainties in the design of CPS targeting collective behavior. To this end, we present a systematic review of relevant scientific projects with industrial leadership and synthesis of relations between system features, the kinds of uncertainties, and methods used to deal with them. The results provide an overview of uncertainties and challenges across different domains, and reason about a guide for designing uncertainty-aware self-adaptive components in CPS.
Android-Based Network Services Application Learning Media for Vocational High Schools

&lt;p class="Abstract"&gt;Digital and communication technology has developed in combination with globalization to ensure quick connectivity and distribution of information. In particular, the quality of education is one of the education challenges that have become a target for urgent solutions. From the various conditions and potentials that exist, efforts that can be made concerning quality improvement in schools are to develop a student-oriented learning system (children center) and facilitate student’s needs for learning needs that are challenging, active, creative, innovative, practical, and fun by developing and apply learning based on Information and Communication Technology. This research was generated using the media development method, which refers to Four-D development, which consists of four stages: Define, Design, Develop, and Disseminate, carried out in high school. Vocational class XI Computer and Network Engineering. In this study, the test subjects were students of class XI Computer and Network Engineering at SMK Negeri 8 Padang. The three validator’s assessment as a whole, the validator test assessment of Android-Based Learning Media is 94.28%. The validity level can be interpreted as being Very Valid to use. Overall, the practicality assessment of Android-Based Learning Media as a learning resource is 88.46%, so that the level of practicality can be interpreted as Very Practical to use. The evaluation of Android-Based Learning Media's effectiveness of 90.86%, so that the level of significance can be interpreted as Very Good for use.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Together We can Figure It out: Groups Find Hospitality Robots Easier to Use and Interact With Them More than Individuals

As robots are becoming more prevalent and entering hospitality settings, understanding how different configurations of individuals and groups interact with them becomes increasingly important for catering to various people. This is especially important because group dynamics can affect people's perceptions of situations and behavior in them. We present research examining how individuals and groups interact with and accept a humanoid robot greeter at a real-world café (Study 1) and in an online study (Study 2). In each study, we separately examine interactions of individuals, groups that participants formed after they arrived at the café (new-formed groups), and groups that participants arrived with at the café (pre-formed groups). Results support prior findings that groups are more likely to interact with a public robot than individuals (Study 1). We also report novel findings that new-formed groups interacted more with the robot than pre-formed groups (Study 1). We link this with groups perceiving the robot as more positive and easier to use (Study 2). Future research should examine perceptions of the robot immediately after interaction and in different hospitality contexts.
A Novel (2, 3)-Threshold Reversible Secret Image Sharing Scheme Based on Optimized Crystal-Lattice Matrix

The (k, n)-threshold reversible secret image sharing (RSIS) is technology that conceals the secret data in a cover image and produces n shadow versions. While k (k ≤ n) or more shadows are gathered, the embedded secret data and the cover image can be retrieved without any error. This article proposes an optimal (2, 3) RSIS algorithm based on a crystal-lattice matrix. Sized by the assigned embedding capacity, a crystal-lattice model is first generated by simulating the crystal growth phenomenon with a greedy algorithm. A three-dimensional (3D) reference matrix based on translationally symmetric alignment of crystal-lattice models is constructed to guide production of the three secret image shadows. Any two of the three different shares can cooperate to restore the secret data and the cover image. When all three image shares are available, the third share can be applied to authenticate the obtained image shares. Experimental results prove that the proposed scheme can produce secret image shares with a better visual quality than other related works.