Reviewerly/exHarmony huggingface dataset:
Evaluating possible classifications of websites by design type in electronic commerce

This article has covered e-commerce websites. The history of the websites has been examined. We looked at the types and classification of websites, such as business card sites, corporate sites, directory sites, online stores. The advantages and disadvantages of these classifications are compared. The work of the server side of websites is considered. A diagram of the architecture of a static and dynamic website has been built. The tasks of the server side were considered, such as: using databases for storing and retrieving data; control of sent data and requests; processing of the received data and their transformation; sending processed data to the client. The main stages of the backend, as well as its advantages and benefits, are examined. Two main technologies of website development are studied and analyzed, such as web frameworks and CMS (ContentManagementSystem). Based on our analysis, we compiled a comparative table of the advantages and disadvantages of these technologies. Based on this table, conclusions were drawn about which technology is the most convenient and profitable.
Tableau-Based Decision Procedure for Logic of Knowing-How via Simple Plans

Recently, there has been an increasing interest in studying logic of knowing-how based on the idea of planning. In literature, it is shown that knowing-how logics based on 10 different types of plans, including simple plans, share the same proof system. In this paper, we present a tableau system for the knowing-how logic via simple plans, and we show that there is an algorithm that runs in polynomial space for deciding whether a formula of the knowing-how logic is satisfiable. Since the knowing-how logic is an extension of epistemic logic which is PSPACE-hard for multi-agent version, it follows that the multi-agent knowing-how logic via simple plans is PSPACE-complete.
Deep Marginal Fisher Analysis based CNN for Image Representation and Classification

Deep Convolutional Neural Networks (CNNs) have achieved great success in image classification. While conventional CNNs optimized with iterative gradient descent algorithms with large data have been widely used and investigated, there is also research focusing on learning CNNs with non-iterative optimization methods such as the principle component analysis network (PCANet). It is very simple and efficient but achieves competitive performance for some image classification tasks especially on tasks with only a small amount of data available. This paper further extends this line of research and proposes a deep Marginal Fisher Analysis (MFA) based CNN, termed as DMNet. It addresses the limitation of PCANet like CNNs when the samples do not follow Gaussian distribution, by using a local MFA for CNN filter optimization. It uses a graph embedding framework for convolution filter optimization by maximizing the inter-class discriminability among marginal points while minimizing intra-class distance. Cascaded MFA convolution layers can be used to construct a deep network. Moreover, a binary stochastic hashing is developed by randomly selecting features with a probability based on the importance of feature maps for binary hashing. Experimental results demonstrate that the proposed method achieves state-of-the-art result in non-iterative optimized CNN methods, and ablation studies have been conducted to verify the effectiveness of the proposed modules in our DMNet.
Chronological Sailfish Optimizer for Preserving Privacy in Cloud Based on Khatri-Rao Product

Abstract The innovative trends of cloud computing acquired the interest of several individuals or enterprises that started outsourcing data to the cloud servers. Recently, numerous techniques are introduced for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and incur huge information loss. This paper introduces the efficient technique, named the chronological sailfish optimizer (CSFO) algorithm for privacy preservation in cloud computing. The proposed CSFO is devised by integrating the chronological concept in SailFish optimizer. The input data are fed to a privacy-preservation process wherein hamming weight-based RSA and Khatri-Rao products are utilized for data privacy. Here, the hamming weighted-based RSA is determined by combining the sha256 algorithm with the hamming weight with Rivest–Shamir–Adleman (HRSA) system. Hence, an optimization-driven algorithm is utilized to evaluate optimal matrix generation to handle both the utility and the sensitive information. Here, the fitness function is newly devised considering, realism, privacy and fitness. The experimentation is performed using four datasets, like Pathway Interaction Database, Hungarian, Cleveland and Switzerland. The proposed CSFO provided superior performance with maximal privacy of 0.2173, maximal realism 0.9456 and maximal fitness of 0.5416.
The application of Blockchain technology in Copyright field: formalities and “smart contracts”

Blockchain technology is being successfully applied in the cultural sector to enable the lawful distribution of works. This paper studies copyright related issues regarding the two basic characteristics of the specific technology: its nature as a ledger where information related to ownership is registered and the fact that it provides smart contracts functionality. We are addressing questions related to the legitimacy of the existence of a registry (ledger) and the significance of smart contracts for copyright law.
Computing with Magnetic Thin Films: Using Film Geometry to Improve Dynamics

Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged under the name of physical reservoir computing. In this paradigm, an input-driven dynamical system (the reservoir) is exploited and trained to perform computational tasks. Recent spintronic thin-film reservoirs show state-of-the-art performances despite simplicity in their design. Here, we explore film geometry and show that simple changes to film shape and input location can lead to greater memory and improved performance across various time-series tasks.
Face-based Voice Conversion: Learning the Voice behind a Face

Zero-shot voice conversion (VC) trained by non-parallel data has gained a lot of attention in recent years. Previous methods usually extract speaker embeddings from audios and use them for converting the voices into different voice styles. Since there is a strong relationship between human faces and voices, a promising approach would be to synthesize various voice characteristics from face representation. Therefore, we introduce a novel idea of generating different voice styles from different human face photos, which can facilitate new applications, e.g., personalized voice assistants. However, the audio-visual relationship is implicit. Moreover, the existing VCs are trained on laboratory-collected datasets without speaker photos, while the datasets with both photos and audios are in-the-wild datasets. Directly replacing the target audio with the target photo and training on the in-the-wild dataset leads to noisy results. To address these issues, we propose a novel many-to-many voice conversion network, namely Face-based Voice Conversion (FaceVC), with a 3-stage training strategy. Quantitative and qualitative experiments on the LRS3-Ted dataset show that the proposed FaceVC successfully performs voice conversion according to the target face photos. Audio samples can be found on the demo website at https://facevc.github.io/.
Extractive-Abstractive Summarization of Judgment Documents Using Multiple Attention Networks

Judgment documents contain rich legal information, they are simultaneously lengthy with complex structure. This requires summarizing judgment documents in an effective way. By analyzing the structural features of Chinese judgment documents, we propose an automatic summarization method, which consists of an extraction model and an abstraction model. In the extraction model, all the sentences are encoded by a Self-Attention network and are classified into key sentences and non-key sentences. In the abstraction model, the initial summarization is refined into a final summarization by a unidirectional-bidirectional attention network. Such a summarization could help improve the efficiency in case handling and make judgment documents more accessible to the general readers. The experimental results on CAIL2020 dataset are satisfactory.
Recognition of radar emitter signal images using encoding signal methods

We innovatively apply three different methods of encoding signal as 2-D plots to radar emitter signal recognition: Recurrence Plots (RP), Gramian Angular Field (GAF) and Markov Transition Field (MTF), thus the radar emitter signal recognition problem is converted into image processing problem. We build a 2-stage convolutional neural network (CNN) model to make use of its mature technology in the field of computer vision and image processing for signal recognition. These pipeline offers the following advantages: i) Encoding signal methods enable us to visualize certain aspects of the radar emitter signals through 2D images, and ii) CNN can automatically learn different levels of radar signal features and achieve a high recognition rate. In order to get a best encoding method, we compare the recognition accuracy of the three methods mentioned above, and combine the images encoded by the first two methods (RP and GAF) with high recognition accuracy into 2-channel RP-GAF images and get a better recognition rate. At last, we analyze the reasons why three encoding methods achieve different recognition rates.
Task Allocation for Multi-robot Task and Motion Planning: A Case for Object Picking in Cluttered Workspaces

We present an AND/OR graph-based, integrated multi-robot task and motion planning approach which (i) performs task allocation coordinating the activity of a given number of robots, and (ii) is capable of handling tasks which involve an a priori unknown number of object re-arrangements, such as those involved in retrieving objects from cluttered workspaces. Such situations may arise, for example, in search and rescue scenarios, while locating/picking a cluttered object of interest. The corresponding problem falls under the category of planning in clutter. One of the challenges while planning in clutter is that the number of object re-arrangements required to pick the target object is not known beforehand, in general. Moreover, such tasks can be decomposed in a variety of ways, since different cluttering object re-arrangements are possible to reach the target object. In our approach, task allocation and decomposition is achieved by maximizing a combined utility function. The allocated tasks are performed by an integrated task and motion planner, which is robust to the requirement of an unknown number of re-arrangement tasks. We demonstrate our results with experiments in simulation on two Franka Emika manipulators.
Artificial Intelligence in Toxicological Pathology: Quantitative Evaluation of Compound-Induced Follicular Cell Hypertrophy in Rat Thyroid Gland Using Deep Learning Models

Digital pathology has recently been more broadly deployed, fueling artificial intelligence (AI) application development and more systematic use of image analysis. Here, two different AI models were developed to evaluate follicular cell hypertrophy in hematoxylin and eosin-stained whole-slide-images of rat thyroid gland, using commercial AI-based-software. In the first, mean cytoplasmic area measuring approach (MCA approach), mean cytoplasmic area was calculated via several sequential deep learning (DL)-based algorithms including segmentation in microanatomical structures (separation of colloid and stroma from thyroid follicular epithelium), nuclear detection, and area measurements. With our additional second, hypertrophy area fraction predicting approach (HAF approach), we present for the first time DL-based direct detection of the histopathological change follicular cell hypertrophy in the thyroid gland with similar results. For multiple studies, increased output parameters (mean cytoplasmic area and hypertrophic area fraction) were shown in groups given different hypertrophy-inducing reference compounds in comparison to control groups. Quantitative results correlated with the gold standard of board-certified veterinary pathologists' diagnoses and gradings as well as thyroid hormone dependent gene expressions. Accuracy and repeatability of diagnoses and grading by pathologists are expected to be improved by additional evaluation of mean cytoplasmic area or direct detection of hypertrophy, combined with standard histopathological observations.
High-Speed Planning in Unknown Environments for Multirotors Considering Drag

In this paper, we propose a new planning scheme for high-speed flight in an unknown environment while taking into account drag forces. Drag forces become non-negligible at high speeds and may lead to unfeasible trajectories. The method leverages a new Mixed-Integer Quadratic Program/Model Predictive Control formulation that allows to easily account for drag forces. This formulation makes use of a state-of-the-art Safe Corridors generator to guarantee safety. It uses state-of-the-art mapping algorithms and solvers to achieve a higher computational efficiency than similar state-of-the-art methods. To the best of our knowledge, our method is the first high-speed planner that generates safe trajectories while accounting for drag. The proposed method is tested in simulation and compared to similar state-of-the-art methods for planning in unknown environments in terms of quality and computation time.
Variance and Distribution Models for Steering Tasks

Steering law reveals a linear relationship between the movement time (MT) and the index of difficulty (ID) in trajectory-based steering tasks. However, it does not relate the variance or distribution of MT to ID. In this paper, we propose and evaluate models that predict the variance and distribution of MT based on ID for steering tasks. We first propose a quadratic variance model which reveals that the variance of MT is quadratically related to ID with the linear coefficient being 0. Empirical evaluation on a new and a previously collected dataset show that the quadratic variance model accounts for between 78% and 97% of variance of observed MT variances; it outperforms other model candidates such as linear and constant models; adding the linear coefficient leads to no improvement on the model fitness. The variance model enables predicting the distribution of MT given ID: we can use the variance model to predict the variance (or scale) parameter and Steering law to predict the mean (or location) parameter of a distribution. We have evaluated six types of distributions for predicting the distribution of MT. Our investigation also shows that positively skewed distribution such as Gamma, Lognormal, Exponentially Modified Gaussian (ExGaussian), and Extreme value distributions outperformed the symmetric distribution such as Gaussian and truncated Gaussian distribution in predicting the MT distribution, and Gamma distribution performed slightly better than other positively skewed distributions. Overall, our research advances the MT prediction of steering tasks from a point estimate to variance and distribution estimates, which provides a more complete understanding of steering behavior and quantifies the uncertainty of MT prediction.
OsGG-Net: One-step Graph Generation Network for Unbiased Head Pose Estimation

Head pose estimation is a crucial problem that involves the prediction of the Euler angles of a human head in an image. Previous approaches predict head poses through landmarks detection, which can be applied to multiple downstream tasks. However, previous landmark-based methods can not achieve comparable performance to the current landmark-free methods due to lack of modeling the complex nonlinear relationships between the geometric distribution of landmarks and head poses. Another reason for the performance bottleneck is that there exists biased underlying distribution of the 3D pose angles in the current head pose benchmarks. In this work, we propose OsGG-Net, a One-step Graph Generation Network for estimating head poses from a single image by generating a landmark-connection graph to model the 3D angle associated with the landmark distribution robustly. To further ease the angle-biased issues caused by the biased data distribution in learning the graph structure, we propose the UnBiased Head Pose Dataset, called UBHPD, and a new unbiased metric, namely UBMAE, for unbiased head pose estimation. We conduct extensive experiments on various benchmarks and UBHPD where our method achieves the state-of-the-art results in terms of the commonly-used MAE metric and our proposed UBMAE. Comprehensive ablation studies also demonstrate the effectiveness of each part in our approach.
Reddit use in the LIS community

This study examined online communications within library and information science (LIS)-related subreddits through the perspectives of social identity theory and social media engagement. A total of 17,049 Reddit posts from three major LIS-related subreddits were harvested through Pushshift by Python. Manual and computational coding methods were used to identify linguistic characteristics of the posts as well as the topicality of the content. Multiple logistic regressions were used to analyze the relationship between psychological and emotional characteristics of the posts and user engagement. Results showed that the majority of posts did not mention a specific type of LIS organization; of the subset that did, public libraries and academic libraries were most frequently referenced. “Library services and usage” was the topic most mentioned within LIS-related subreddits, followed by “librarians and jobs.” Various psychological features of posts including emotional tone score, authentic score, clout score, analytic score, and usage of words related to affective processes, anxiety, social processes, causation, and time were significantly related to user engagement. This study contributes to further understanding of social identity theory and social media engagement within the online communication context of LIS community. Key topics and issues in the LIS field and practical suggestions for strategies to improve social media engagement across LIS community were discussed.
Mind the Source Data! Translation Equivalents and Translation Stimuli from Parallel Corpora

Statements like ‘Word X of language A is translated with word Y of language B’ are incorrect, although they are quite common: words cannot be translated, as translation takes place on the level of sentences or higher. A better term for the correspondence between lexical items of source texts and their matches in target texts would be translation equivalence (Teq). In addition to Teq, there exists a reverse relation—translation stimulation (Tst), which is a correspondence between the lexical items of target texts and their matches (=stimuli) in source texts. Translation equivalents and translation stimuli must be studied separately and based on natural direct translations. It is not advisable to use pseudo-parallel texts, i.e. aligned pairs of translations from a ‘hub’ language, because such data do not reflect real translation processes. Both Teq and Tst are lexical functions, and they are not applicable to function words like prepositions, conjunctions, or particles, although it is technically possible to find Teq and Tst candidates for such words as well. The process of choosing function words when translating does not proceed in the same way as choosing lexical units: first, a relevant construction is chosen, and next, it is filled with relevant function words. In this chapter, the difference between Teq and Tst will be shown in examples from Russian–Finnish and Finnish–Russian parallel corpora. The use of Teq and Tst for translation studies and contrastive semantic research will be discussed, along with the importance of paying attention to the nature of the texts when analysing corpus findings.
RecipeLog

Diet management is usually conducted by recording the name of foods eaten, but in fact, the nutritional value of food in the same name varies greatly from recipe to recipe. To know accurate nutritional values of the foods, recording personal recipes is effective but time-consuming. Therefore, we are developing a mobile application "RecipeLog", that assists users to write their own recipes by modifying prepared ones. In our experiments, we show that with RecipeLog users create personal recipes with 45% less edit distance compared to writing from scratch.
Integrated Multiscale Appearance Features and Motion Information Prediction Network for Anomaly Detection

The rise of video-prediction algorithms has largely promoted the development of anomaly detection in video surveillance for smart cities and public security. However, most current methods relied on single-scale information to extract appearance (spatial) features and lacked motion (temporal) continuity between video frames. This can cause a loss of partial spatiotemporal information that has great potential to predict future frames, affecting the accuracy of abnormality detection. Thus, we propose a novel prediction network to improve the performance of anomaly detection. Due to the objects of various scales in each video, we use different receptive fields to extract detailed appearance features by the hybrid dilated convolution (HDC) module. Meanwhile, the deeper bidirectional convolutional long short-term memory (DB-ConvLSTM) module can remember the motion information between consecutive frames. Furthermore, we use RGB difference loss to replace optical flow loss as temporal constraint, which greatly reduces the time for optical flow extraction. Compared with the state-of-the-art methods in the anomaly-detection task, experiments prove that our method can more accurately detect abnormalities in various video surveillance scenes.
A Novel Recruitment Policy to Defend against Sybils in Vehicular Crowdsourcing

Vehicular Social Networks (VSNs) is an emerging communication paradigm, derived by merging the concepts of Online Social Networks (OSNs) and Vehicular Ad-hoc Networks (VANETs). Due to the lack of robust authentication mechanisms, social-based vehicular applications are vulnerable to numerous attacks including the generation of sybil entities in the networks. We address this important issue in vehicular crowdsourcing campaigns where sybils are usually employed to increase their influence and worsen the functioning of the system. In particular, we propose a novel User Recruitment Policy (URP) that, after extracting the participants within the event radius of a crowdsourcing campaign, detects and filters out the sybil vehicles by using a novel sybil detection approach, called SybilDriver. This technique combines the advantages of VANETs and OSNs by means of an innovative concept of proximity graph obtained from the physical vehicular network, in conjunction with a community detection and Random Forest techniques adopted in the OSN domain. Detailed experimental evaluations demonstrate the effectiveness of our approach and also show that it outperforms existing state-of-the-art methods typically used in the OSNs. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>