Reviewerly/exHarmony huggingface dataset:
Web Forensic on Container Services Using Grr Rapid Response Framework

Cybercrime on Internet that keeps increasing does not only take place in the environment that running web applications traditionally under operating system, but also web applications that are running in more advance environment like container service. Docker is a currently popular container service in Linux operating system needs to be secured and implements incident response mechanisme that will investigate web server that was attacked by DDoS in fast, valid, and comprehesive way. This paper discusses the investigation using Grr Rapid Response framework on web server that was attacked by DDoS running in container service on Linux operating system, and the attacker using Windows oprating system that runs DDos script. This research has succesfully investigated digital evidence in the form of log file from web server running on container service and digital evidence through netstat on Windows computer.
Literary Natural Language Generation with Psychological Traits

The area of Computational Creativity has received much attention in recent years. In this paper, within this framework, we propose a model for the generation of literary sentences in Spanish, which is based on statistical algorithms, shallow parsing and the automatic detection of personality features of characters of well known literary texts. We present encouraging results of the analysis of sentences generated by our methods obtained with human inspection.
BURSTS: A bottom-up approach for robust spotting of texts in scenes

In this paper, we present a bottom-up approach for robust spotting of texts in scenes. In the proposed technique, character candidates are first detected using our proposed character detector, which leverages on the strengths of an Extremal Region (ER) detector and an Aggregate Channel Feature (ACF) detector for high character detection recall. The real characters are then identified by using a novel convolutional neural network (CNN) filter for high character detection precision. A hierarchical clustering algorithm is designed which combines multiple visual and geometrical features to group characters into word proposal regions for word recognition. The proposed technique has been evaluated on several scene text spotting datasets and experiments show superior spotting performance.
Artificial Intelligence in Egyptian Tourism Companies: Implementation and Perception

Artificial intelligence (AI) is considered one of the most important innovative technology. AI has been used in several industries and has already accomplished notable impacts. The tourism industry is one of several sectors that is affected by AI tools; chatbots, personalized service recommendations, smart solutions are used in travel agencies. This paper aims to explore the implementation of AI techniques in the Egyptian tourism companies and investigate the employees’ perceptions towards using AI tools in tourism operations. This paper applies the quantitative approach; an online questionnaire was distributed to tourism companies, only 320 responses were valid for statistical analysis. Concerning applying AI tools in tourism operations, the results revealed significant differences among tourism companies which provide full tourism services. Regarding the size of tourism companies, large and medium-scale tourism companies apply AI techniques more than small and micro-scale ones. This research found two fundamental employees’ views of applying AI: advantages of AI (enthusiastic) and disadvantages of AI (suspicious). From a managerial standpoint, this research shows the AI techniques that are applied in tourism as well as identifies the importance of their implementation, which may help managers to draw policies and strategies to improve their technological infrastructure and skills, as well as apply the most beneficial AI tools. This, in turn, enhances their performance and saves time and money.
A Practical Weather Detection Method Built in the Surveillance System Currently Used to Monitor the Large-Scale Freeway in China

Road weather conditions are closed-related to the transportation safety and traffic capacity. With the development of road surveillance systems, weather conditions could be recognized from video. However, it is hard to be detected by machine. To address it, a deeply supervised convolution neural network (DS-CNN) is designed and trained on a self-established dataset. The traffic image dataset includes five groups labeled with “sunny”, “overcast”, “rainy”, “snowy” and “foggy”. Each group has manually labeled and selected more than 2500 images. The DS-CNN, can achieve the precision rate of 0.9681 and the recall rate of 0.9681. This practical weather detection method has been built in the surveillance system covers five freeways. The experimental result used in practice shows much worse detection results at first with disturbance of difference scenarios, worn camera, transmission failure and so on. With further improvement of DS-CNN, we found that it was much more effective than hand-crafted features in this task, and a deeper neural architecture could derive more powerful features. Moreover, the results show that dense weather information has more details in a small scale. In order to fast report the regional weather detection result, a designed visualization method is also proposed in spatiotemporal dimension to fuse with currently-used system. The high accuracy and fast detection speed with friendly visualization would lead to more precise traffic management and prompt the road weather traffic control to more intelligence levels.
On Aggregation in Ensembles of Multilabel Classifiers

While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: “predict then combine” (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and “combine then predict” (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses.
Global fire monitoring products of FY-3D/MERSI-II and their applications

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Effects of Incorporating a Topic-Scanning Guiding Mechanism in E-books on EFL Reading Comprehension, Learning Perceptions, and Reading Behaviors

Reading academic textbooks in English has been an uneasy task for non-native English students. In addition, e-books are gradually popular across educational contexts. Previous studies indicated that students generally could not comprehend texts well on screen, so this study proposed a topic-scanning guiding mechanism with e-books to support EFL students reading comprehension from e-books. This paper aims to investigate the effects of an e-book on EFL students’ reading comprehension, learning perceptions, and reading behaviors in EFL freshman reading the course, incorporating a topic-scanning guiding mechanism with the e-book. A quasi-experiment with a pretest-posttest design was conducted in a university freshmen English course, 50 students were assigned as the experiential group, and 46 students were in the control group. Findings from this study would further contribute to students’ learning perceptions and reading behavior analysis on designing e-book system to improve their reading competence.
Sparse Graph Connectivity for Image Segmentation

It has been demonstrated that the segmentation performance is highly dependent on both subspace preservation and graph connectivity. In the literature, the full connectivity method linearly represents each data point ( e.g., a pixel in one image) by all data points for achieving subspace preservation, while the sparse connectivity method was designed to linearly represent each data point by a set of data points for achieving graph connectivity. However, previous methods only focused on either subspace preservation or graph connectivity. In this article, we propose a Sparse Graph Connectivity (SGC) method for image segmentation to automatically learn the affinity matrix from the low-dimensional space of original data, which aims at simultaneously achieving subspace preservation and graph connectivity. To do this, the proposed SGC simultaneously learns a self-representation affinity matrix for subspace preservation and a sparse affinity matrix for graph connectivity, from the intrinsic low-dimensional feature space of high-dimensional original data. Meanwhile, the self-representation affinity matrix is pushed to be similar to the sparse affinity as well as be the final segmentation results. Experimental result on synthetic and real-image datasets showed that our SGC method achieved the best segmentation performance, compared to state-of-the-art segmentation methods.
The impact of knowledge and effectiveness of educational intervention on readiness for hospital discharge and adherence to therapeutic recommendations in patients with acute coronary syndrome

Introduction: ESC guidelines emphasize the importance of educating patients after acute coronary syndromes aimed at familiarizing the patient with the essence of the disease, the principles of self-control and self-care as well as actions enhancing health maintenance. Aim: The aim of the study was to assess the impact of knowledge and effectiveness of educational intervention on readiness for discharge from the hospital and compliance with therapeutic recommendations six months after discharge. Material and methods: The study group consisted of 218 patients (28.9% women and 71.1% men) aged 31 to 90 years (63.0 ± 11.24) treated with coronary angioplasty for myocardial infarction. The effectiveness of educational intervention was assessed by comparing patients’ knowledge on the day of admission (1KE) and discharge from the hospital (2KE). Knowledge was assessed in 3 aspects: knowledge of symptoms, knowledge of the disease, and knowledge of prevention. Education was provided between 1KE and 2KE based on the brochure entitled “Myocardial Infarction”. The level of knowledge on the day of discharge and the increase in knowledge obtained after education (ΔKE) were referred to the level of Readiness for Hospital Discharge after Myocardial Infarction Scale (RHD-MIS) and to the level of adherence in chronic diseases scale (ACDS). Results: As a result of the education carried out between 1KE and 2KE, a significant increase in knowledge was obtained (p &lt; 0.05). There was no correlation between ΔKE and RHD-MIS. However, it was shown that a higher level of knowledge at discharge (2KE) was associated with a higher RHD-MIS result (R = 0.17; p = 0.01). The highest impact on the RHD-MIS result was due to the level of knowledge of disease symptoms in 2KE: 75.5 ± 19.5%, 76.4 ± 21.1%, 85.8 ± 16.4% for low, medium and high RHD-MIS results (p = 0.002) ; R = 0.15; p = 0.0003. Knowledge of the disease symptoms in 2KE was associated with the patient’s expectations in RHD-MIS - a higher level of knowledge was associated with higher patient expectations, respectively: 73.2 ± 21.7%, 80.0 ± 18.5%, 82.9 ± 19.2 for low, medium and high RHDMIS (p = 0.02); R = 0.19; p = 0.006. There was no correlation between ΔKE or 2KE and ACDS results 6 months after discharge. Conclusions: The educational brochure is an effective tool in improving patients’ level of knowledge. Better knowledge of the symptoms of coronary artery disease and myocardial infarction is associated with a higher degree of readiness for discharge from the hospital but does not affect the observance of therapeutic recommendations in the field of pharmacotherapy after 6 months.
Research on the Value of Inclusive Education Based on Big Data in Promoting the Development of Minority Education

Abstract Compulsory education for ethnic minorities is a weak link in China’s education system, which is mainly reflected in the unfair phenomena in educational opportunities, educational process and educational results. Inclusive education for ethnic minorities in the context of big data refers to the activities to promote the socialization of ethnic minority students in China by means of technological gifts in the era of big data for ethnic minorities whose population is relatively weak. As the largest developing country, promoting inclusive education in China is still the most basic task of the country, and the government will undoubtedly bear the most important responsibility in promoting inclusive education. The content of inclusive education is more and more broad, far beyond the scope of special education, involving all kinds of learning difficulties and students with special needs. In modern China, which has increasingly frequent international contacts, we need to re-examine China’s current education from the perspective of inclusive education based on the background of big data.
Habits Attribution and Digital Evidence Object Models Based Tool for Cybercrime Investigation

The amount of data stored on computers is growing rapidly every year, which makes time-consuming investigation of digital evidence in cybercrime, because of the need to investigate a large amount of data and extract criminal evidence from it.Expert investigation begins with the collection, copying and authentication of each content on the digital medium.The following steps deal with the findings and extract evidence of crime using a variety of methods and tools.Our research deals with the frameworks, methods, models and tools of the search for digital evidence of cybercrime.However, there is as yet no specialized method and tool available to assist an expert in reducing the size of investigated data and to solve the problem of searching for and identifying digital evidence of cybercrime due to the lack of specialized tools and techniques to automate expert investigation.In this paper we propose cybercrime forensic investigation tool based on the digital evidence object model
Physical Distancing, Face Masks, and Eye Protection to Prevent Person-Person COVID-19 Transmission: A Systematic Review and Meta-Analysis

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Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network

Most online stores provide product review facilities that contain responses to a product. The number of reviews makes it difficult for potential customers to make conclusions, so that sentiment analysis is needed to extract information from these reviews. Most sentiment analysis is done at the document level, so the results were still lacking in detail because the classification is based on the entire sentence or document and does not identify the specific aspect discussed. This research aims to classify aspect-based sentiments from online store reviews using the convolutional neural network (CNN) method with the extraction of features using Word2Vec. The dataset used is Indonesian review data from the site bukalapak.com. The test results on the built system showed that CNN's method of Word2Vec feature extraction has a better score than the naive bayes method with an accuracy value of 85.54%, 96.12% precision, 88.39% recall, and f-measure 92.02%. Classification without using stemming preprocessing on the dataset increases the accuracy by 2.77%.
CharTeC-Net: An Efficient and Lightweight Character-Based Convolutional Network for Text Classification

This paper introduces an extremely lightweight (with just over around two hundred thousand parameters) and computationally efficient CNN architecture, named CharTeC-Net (Character-based Text Classification Network), for character-based text classification problems. This new architecture is composed of four building blocks for feature extraction. Each of these building blocks, except the last one, uses 1 × 1 pointwise convolutional layers to add more nonlinearity to the network and to increase the dimensions within each building block. In addition, shortcut connections are used in each building block to facilitate the flow of gradients over the network, but more importantly to ensure that the original signal present in the training data is shared across each building block. Experiments on eight standard large-scale text classification and sentiment analysis datasets demonstrate CharTeC-Net’s superior performance over baseline methods and yields competitive accuracy compared with state-of-the-art methods, although CharTeC-Net has only between 181,427 and 225,323 parameters and weighs less than 1 megabyte.
Selecting and Combining Classifiers Based on Centrality Measures

Centrality measures have been helping to explain the behavior of objects, given their relation, in a wide variety of problems, since sociology to chemistry. This work considers these measures to assess the importance of every classifier belonging to an ensemble of classifiers, aiming to improve a Multiple Classifier System (MCS). Assessing the classifier’s importance by employing centrality measures, inspired two different approaches: one for selecting classifiers and another for fusion. The selection approach, called Centrality Based Selection (CBS), adopts a trade-off between the classifier’s accuracy and their diversity. The sub-optimal selected subset presents good results against selection methods from the literature, being superior in 67.22% of the cases. The second approach, the integration, is named Centrality Based Fusion (CBF). This approach is a weighted combination method, which is superior to literature in 70% of the cases.
Proactively Extracting IoT Device Capabilities: An Application to Smart Homes

Internet of Things (IoT) device adoption is on the rise. Such devices are mostly self-operated and require minimum user interventions. This is achieved by abstracting away their design complexities and functionalities from the users. However, this abstraction significantly limits a user's insights on evaluating the true capabilities (i.e., what actions a device can perform) of a device and hence, its potential security and privacy threats. Most existing works evaluate the security of those devices by analyzing the environment data (e.g., network traffic, sensor data, etc.). However, such approaches entail collecting data from encrypted traffic, relying on the quality of the collected data for their accuracy, and facing difficulties in preserving both utility and privacy of the data. We overcome the above-mentioned challenges and propose a proactive approach to extract IoT device capabilities from their informational specifications to verify their potential threats, even before a device is installed. We apply our approach to the context of a smart home and evaluate its accuracy and efficiency on the devices from three different vendors.
Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection

Object detection in densely packed scenes is a new area where standard object detectors fail to train well [6]. Dense object detectors like RetinaNet [7] trained on large and dense datasets show great performance. We train a standard object detector on a small, normally packed dataset with data augmentation techniques. This dataset is 265 times smaller than the standard dataset, in terms of number of annotations. This low data baseline achieves satisfactory results (mAP = 0.56) at standard IoU of 0.5. We also create a varied benchmark for generic SKU product detection by providing full annotations for multiple public datasets. It can be accessed at this URL . We hope that this benchmark helps in building robust detectors that perform reliably across different settings in the wild.
Applying Support Vector Machines to POS tagging of the Ainu Language

We describe our attempt to apply a state-of-the-art sequential tagger – SVMTool – in the task of automatic part-of-speech annotation of the Ainu language, a critically endangered language isolate spoken by the native inhabitants of northern Japan. Our experiments indicated that it performs better than the custom system proposed in previous research (POST-AL), especially when applied to out-of-domain data. The biggest advantage of the model trained using SVMTool over the POST-AL tagger is its ability to guess part-of-speech tags for OoV words, with the accuracy of up to 63%.
An Optimization of Multi-Class Document Classification with Computational Search Policy

In the era of internet communication, many electronic documents are spread and flow on the platform of website in every splits of seconds. The research interest for the process of knowledge discovery is changed from the traditional data to online data such as online news document classification. Most percentage of the online data is text document and therefore the optimization of multi-class document classification is becoming a challenge for today society. Traditional search policy for feature selection process is degrading with exhaustive search for complex feature in document classification. Therefore, meta-heuristic based computational search is also becoming good solution to overcome the problem of exhaustive search with exploitation process. The search policy of computational algorithm can provide the global optimal solution with random search approach on both exploitation and exploration process, and the selected search results of feature subsets can support the optimal classification results.