Reviewerly/exHarmony huggingface dataset:
Simulation of the Automated Control System for Monitoring Ponds with the Aid of a Miniature Unmanned Underwater Vehicle

This paper contains the description and results of modeling a miniature autonomous underwater robot (MUUV) with an intelligent control system for monitoring reservoirs. The article shows a structural diagram of a prototype robot equipped with a number of sensors. Human-machine interaction described in combined mode when the operator monitors and controls the underwater robot periodically at certain times, while the robot carries out the movements set by the operator, but otherwise the operator does not control the position. Diagrams of the robot's movement when monitoring reservoirs are shown and optimal control algorithms are synthesized. A method for monitoring reservoirs using a mobile underwater robot has been formalized.
Financial Crime on Investigation in Industrial Revolution 4.0 Era

The advancement of digital technology in the industrial 4.0 era has impacted the growth of economic and financial crime, especially financial technology, or Fintech. It was not followed by legal development to overcome these impacts; therefore, to overcome a gap in financial crime that uses digital technology as a tool in committing crimes, new effective and efficient concepts and methods are needed. One fitting theory is the notion of following the money utilizing the method of a financial crime investigation. This approach uses investigative audit and forensic accounting to trace assets over the profits of the crime. However, to implement the method, it is necessary to modify the substance of the legal system to shift the orientation from the orientation of proof of error to proving the proceeds of crime. The article finds that in the aspect of structure, a synergistic and harmonious coordination system is needed between law enforcers and all related parties. While in the aspect of legal culture, the development of community economic infrastructure is needed, especially business transactions that support data-based systems.
Towards Multi-criteria Volunteer Cloud Service Selection

Volunteer cloud computing (VCC) have recently been introduced to provide low-cost computational resources to support the demands of the next generation IoT applications. The vital process of VCC is to provide on demand resource provisioning and allocation in response to resource failures, behavior of volunteers (donors, users) and dynamically changing workloads. Most existing work addresses each of these factors (reliability, trust, and load) independently. Finding the most reliable machine (e.g., the lowest hardware failure rate) does not guarantee that the machine is trustworthy or not loaded, and vice versa. To address these problems, this research proposed a model to select volunteer node (VN) based on three criteria: the trustworthiness of the volunteer, the reliability of the node, and the resource load. We use three different models to estimate the three factors. We used exponential distribution reliability to estimate the reliability of VN and neural network to predict VN resource usages. In addition, we propose a new version of the beta function to estimate trustworthiness. Then we apply multiple regression to weigh each factor and decide which factor will be most effective for preventing task failure. Finally, a VN is selected based on multiple criteria decision analysis.
Broad Recognition of Fake Faces

Deep learning system (DLS) always require a time-consuming process because of too many hyperparameters and complex structure. When we update some data in DLS, a complete retraining process was encountered which spending too much time. To get over these shortcomings, the Broad Learning System (BLS) is established by C.L.Philip Chen, which aims to make a different way of artificial intelligence. By the BLS, the origin inputs are transformed and the structure of the system expanded in the feature nodes (The following is expressed as fn) and enhancement nodes (The following is expressed as en) in the wide sense. In this paper, we apply the BLS to the recognition of fake faces. Differing from previous studies, we further validate the anti-interference ability of BLS by involving a perturbation matrix P, which does not change incremental learning processes of fn and en. According to the performance on the considered dataset, BLS is more effective than DLS in recognition of fake faces. Further validation on other datasets are still necessary.
Conventional and Structure Based Sentiment Analysis: A Survey

Sentiment Analysis is a strand of Natural Language Processing that deals with the emotional polarity a given piece of text has. To gain this understanding from just a string of words, we must first consider a suitable way to break down the text to further classify what each part means. This can be done in a plethora of ways, which mostly stem from the understanding of a classifier. We believe that there is a large amount of information stored in structure-based features within the text, for instance, where the writer may place negation-terms not, neither and how this affects the overall polarity. Similarly, how words in sentences or sections, remote to the current-analysed section, may affect the polarity of said section. A combination of features from both a conventional and a structure-based understanding may also provide us with a larger accuracy in polarity. Therefore, this paper aims to explain both conventional sentiment analysis methods with structure-based methods as well as their practices, advantages and disadvantages concluding with how sentiment analysis can move forward with the appropriation of hybrid methods (methods involving motifs, practices and understandings) from conventional and structure-based methods, for classification.
Optical Chaotic Cryptosystem for Phase Images Using Random Amplitude and Phase Masks with Lorenz Map in Fresnel Domain

In this paper, a novel scheme for the secure transmission of phase images is presented. A phase image is bonded with a random amplitude mask and subjected to the Fresnel transform followed by Lorenz map. Thereafter, a random phase mask is bonded with the image obtained in previous steps. The Fresnel transform is again applied to the intermediate image resulting in an encrypted image. The decryption process is the reverse of the encryption process. The scheme is validated for a grayscale image in MATLAB environment, and a faithful recovery of input images has been recorded. The cryptosystem is exposed to statistical attacks in the form of histogram, 3-D plot, and information entropy, and results reveal that the scheme endures the statistical attacks. Simulations show that the Fresnel parameters and Lorenz map parameters are very sensitive to their original value. Results also established that the scheme is robust to noise and occlusion attacks.
Coding Of Non-Rectangular Signals With Block-Based Transforms

This paper presents a transform coding technique for non-rectangular 2-D signals by extending the signal into a rectangular block in order to enable conventional block-based transform coding. The technique could be suitable for coding residuals of prediction blocks using geometric partitioning which has been adopted into the draft Versatile Video Coding standard. The extension of the non-rectangular signal is found using a sparse solution set generated by applying Orthogonal Matching Pursuits using partitioned transform bases. The method developed in this paper is based on Discrete Cosine Transform. Results achieved in an experimental setup outside of the video coding loop are presented for signals of triangular and trapezoidal shape in comparison to the shape-adaptive DCT. Encouraging gains are observed specifically for larger block sizes and in dependency of the quantization parameter and the partitioning shape.
Convolutional Attention Model For Restaurant Recommendation With Multi-View Visual Features

Current recommendation systems usually use image as a type of side information to enhance recommendation systems. However, there are few models to analyze and apply the effectiveness of different categories of images. In order to effectively combine multi-category image information in the recommendation system, we propose a novel deep network model with different categories of images as side information for recommendation, in which we use convolutional attention to analyze the importance of images within different image categories. The convolutional layers of the attention module are used to evaluate the user's visual preferences for images in different categories. The prediction model uses the cosine similarity of user factors and restaurant factors incorporating visual information. Visual features of the restaurants are extracted from multi-category images by a pre-trained neural network. We apply our model to two real-world restaurant recommendation data sets. Experimental results show that the performance of our model is better than models without or with only one category visual information.
TSCNN: A 3D Convolutional Activity Recognition Network Based on RFID RSSI

Human activity recognition has a wide range of applications, especially for the care of elderly people living alone and the monitoring of abnormal behaviors of key personnel. Although conventional video surveillance technology has made many research advances in this field, this technology destroys people's privacy. Activity recognition technology based on RFID avoids damage to people's privacy, and is being widely studied and applied. This paper uses RFID Received Signal Strength Indicator (RSSI) to identify and classify human behaviors. Predecessors employed CNN and LSTM for human activity identification, but there were still some shortcomings: 1) The 2D convolution loses the temporal information of continuous actions and reduces the classification accuracy. 2) LSTM network has a series of training difficulties. 3) No available public dataset for the current mission. To solve these problems, this paper proposes a convolutional neural network called temporal spatial convolutional neural network (TSCNN). Taking the continuous frame sequence as input, the network is designed using 3D convolution to realize realtime activities recognition. The average classification accuracy of our network is 94.6%, 15.6% higher than the state-of-the- art - Tagfree. Our lowest accuracy is 81.8%, and Tagfree is 35.4%. Besides, the ablation experiment proves the necessity of the design in the TSCNN network. Furthermore, we collect more than 60000 RFID signal data and transform them into corresponding pixel maps to form a new dataset. We present and expose the dataset called RF-men.
Automated Machine Learning: Prospects and Challenges

The State of the Art of the young field of Automated Machine Learning (AutoML) is held by the connectionist approach. Several techniques of such an inspiration have recently shown promising results in automatically designing neural network architectures. However, apart from back-propagation, only a few applications of other learning techniques are used for these purposes. The back-propagation process takes advantage of specific optimization techniques that are best suited to specific application domains (e.g., Computer Vision and Natural Language Processing). Hence, the need for a more general learning approach, namely, a basic algorithm able to make inference in different contexts with distinct properties. In this paper, we deal with the problem from a scientific and epistemological point of view. We believe that this is needed to fully understand the mechanisms and dynamics underlying human learning. To this aim, we define some elementary inference operations and show how modern architectures can be built by a combination of those elementary methods. We analyze each method in different settings and find the best-suited application context for each learning algorithm. Furthermore, we discuss experimental findings and compare them with human learning. The discrepancy is particularly evident between supervised and unsupervised learning. Then, we determine which elementary learning rules are best suited for unsupervised systems, and, finally, we propose some improvements in reinforcement learning architectures.
Joint Extraction of Entity and Relation Based on Pre-trained Language Model

Extracting entities and relations from unstructured text is the key to building a large-scale knowledge graph. In recent years, the relation extraction approaches based on the neural network has achieved good results. However, the existing methods cannot accurately extract overlapping entities (i.e., one entity is shared by multiple relations). In this paper, we propose a simple Electra-based joint model for relation extraction. We use the subject extractor to identify the subject first, and then use the predicate-object extractor to predict its corresponding predicates and objects based on the encoded representation vector of the detected subject. Experiments on DuIE2.0 show good performance on the extraction of Chinese dataset with overlapping triples.
utruchirp

This paper presents the utruchirp software, a tool for measuring impulse responses and modelling room acoustics in real time through auralisation based on convolution using those responses. utruchirp is the result of concerns and needs emerging from the authors' ongoing artistic practice, exploring room scale acoustic feedback as material for live performance, installations, and fixed media pieces as utrumque.
Digital Competences for Online Students

This chapter presents the development of a model for digital competences for online students in Brazil called MCompDigEAD. This study addresses educational competences, digital competences, and online students. The model was developed between 2014 and 2018 based on theoretical references and the mapping of competences in 2 classes. One class was a strictly distance learning undergraduate course and another a hybrid graduate course, both designed to list the digital competences of students in this modality. As a result, a set of competences was obtained. It was analyzed by the online students and specialists. The model was then created with six general competence areas: introduction to digital technologies, digital communication, information management, digital health and security, attendance and digital citizenship, and creation and development of digital content. The competence levels were functional digital literacy, critical digital literacy, and digital fluency as well as fourteen specific competences and three proficiency levels: initial, intermediate, and advanced. The aim is that research results will support both online students and professors in the construction of digital competences.
A Study of a Method to Understand the Intention of Taste Expressions through Text Mining

The purpose of this study is to evidence a method of understanding the intentions of taste expressions from word-of-mouth data of cooking recipe websites using text mining. This study aims to clarify the use of the word “KOKU” as an example to verify the method. KOKU is one of the taste expressions used like richness experienced in various dishes such as in the taste of wine. In order to clarify the relationship between the features of KOKU and cooking categories, they were clustered using the latent Dirichlet allocation. The categories were classified into groups of foods using similar ingredients, sweetness, oils, and seasonings. Through the analysis mentioned above, the features of KOKU were defined. In the past, there has been no attempt to clarify the features of KOKU using word-of-mouth data from cooking recipe websites. The success in defining “KOKU” is evidence that this method has potential to be extended and applied to expressions other than KOKU.
Assessing the Reliability of Visual Explanations of Deep Models with Adversarial Perturbations

The interest in complex deep neural networks for computer vision applications is increasing. This leads to the need for improving the interpretable capabilities of these models. Recent explanation methods present visualizations of the relevance of pixels from input images, thus enabling the direct interpretation of properties of the input that lead to a specific output. These methods produce maps of pixel importance, which are commonly evaluated by visual inspection. This means that the effectiveness of an explanation method is assessed based on human expectation instead of actual feature importance. Thus, in this work we propose an objective measure to evaluate the reliability of explanations of deep models. Specifically, our approach is based on changes in the network's outcome resulting from the perturbation of input images in an adversarial way. We present a comparison between widely-known explanation methods using our proposed approach. Finally, we also propose a straightforward application of our approach to clean relevance maps, creating more interpretable maps without any loss in essential explanation (as per our proposed measure).
Adaptive Transfer Learning for Heterogeneous One-Class Collaborative Filtering

In this paper, we study a recent and important recommendation problem called heterogeneous one-class collaborative filtering (HOCCF), where we have two different types of one-class feedback, i.e., a set of browses and a set of purchases, for preference learning. Previous methods exploit the browses and purchases by extending some existing methods on modeling homogeneous one-class feedback in an integrative or sequential manner. However, an integrative method may be of high complexity in model training due to the expanded prediction rule, and a sequential method is of inconvenience in deployment because more than one parametric models have to be maintained. In this paper, we convert the HOCCF problem to an adaptive transfer learning task, where we first model browses via a factorization model from a perspective of the role of browser, and then adaptively refine the learned model parameters via purchases from a perspective of the dependent role of purchaser. Based on this conversion, we design a novel solution called role-based adaptive factorization (ROAF), and then derive two specific variants with pairwise preference learning and pointwise preference learning. Finally, we conduct extensive empirical studies on two large datasets, and find that our ROAF is a very promising solution in terms of recommendation accuracy, besides its convenience of one single parametric model in deployment.
Augmenting Public Bookcases to Support Book Sharing

Public bookcases offer the opportunity to serendipitously discover books and to anonymously share books with others. The set of available books as well as the sharing patterns are highly dynamic, as anybody can freely take or donate books. This makes it difficult for users to see what is available or of interest to them. To support book sharing via public bookcases we developed a mobile AR application that highlights relevant books in the camera viewfinder and that facilitates searching for specific books. The application recognizes books via text and color features on the spine. In a lab study with 15 participants we evaluated our book recognition algorithm and found that it outperforms unaided visual search. We interviewed users of public bookcases and analyzed the bookcases' setup and rate of change. A subsequent field evaluation of the AR application on nine public bookcases found a recognition accuracy of 80 % for 450 books under different conditions. The proposed approach provides the basis for effectively sharing books via public bookcases.
Assessing quality of decision reducts

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution.
Automatic Prediction of Word Form Reduction in Russian Spontaneous Speech

The aim of this paper is to determine the main features of word forms that undergo reduction in Russian spontaneous speech using machine learning algorithms. We examined the factors proposed in the previous corpus-based studies, namely, the number of syllables, word frequency, part of speech, reduction of the preceding word. We used the texts from the Corpus of Russian Oral Speech as the data. The following machine learning algorithms were applied: extremely randomized tree, random forest and logistic regression. The results show that the higher the frequency of a word form is, the higher the chances are that it will be reduced; the more syllables a word form has, the higher probability of its reduction is. But we did not find the influence of the factor whether the previous word is reduced or not. Regarding the part-of-speech feature, the results are not that straightforward since we received different lists using different algorithms, but the adjective and parenthetical word were in both of them. Thus, we can conclude that adjectives and parenthetical words are likely to be reduced more often than other parts of speech.
Google Service-Based CbITS Authoring Tool to Support Collaboration

The intelligent tutoring system (ITS) is one of the typical Adaptive Instructional Systems (AIS) applications. ITS has been one of the cutting-edge research fields over the past three decades. Despite the many successes of ITSs, there are a few challenges for implementing them on a large scale. One significant challenge is content authoring [, ]. Many authoring components of ITSs are inaccessible to the average user due to their complexity and time-consuming nature. This paper introduces a potential cloud-based solution that will make authoring ITS content faster and relatively easier than existing methods. We believe that our approach can be utilized by other similar systems.
Planning to Score a Goal in Robotic Football with Heuristic Search

This paper considers a problem of planning an attack in robotic football (RoboCup). The problem is reduced to finding a trajectory of the ball from its current position to the opponents goals. Heuristic search algorithm, i.e. A*, is used to find such a trajectory. For this algorithm to be applicable we introduce a discretized model of the environment, i.e. a graph, as well as the core search components: cost function and heuristic function. Both are designed to take into account all the available information of the game state. We extensively evaluate the suggested approach in simulation comparing it to a range of baselines. The result of the conducted evaluation clearly shows the benefit of utilizing heuristic search within the RoboCup context.