Reviewerly/exHarmony huggingface dataset:
Anonymous blockchain Based Routing For Moving-target Defense Across Federated Clouds

Cloud federation is the evolution of modern cloud computing. It provides better resource-sharing, perfect resource-utilization, and load-balancing. However, the heterogeneity of security policies and configurations between cloud service providers makes it hard for users to totally trust them. Further, the severe impact of modern cloud attacks such as cross-side channels on federated environments is a major roadblock against such evolution. Securing users' capsules (Virtual Machines and containers) against cross-side channel attacks is considered as a big challenge to cloud service providers. Moving-target Defense (MtD) by live capsule migration was introduced as an effective mechanism to overcome such challenge. However, researchers noted that even with MtD, migrated capsules can still be tracked via routing information. In this paper, we propose a novel Blockchain-based routing mechanism to enable trace-resistant Moving-target Defence (BMtD) to enable anonymous live cross-cloud migrations of running capsules in federated cloud environments. Exploiting the Vulnerable, Exposed, Attacked, Recovered (VEAR) model, simulation results demonstrated the effectiveness of BMtD in minimizing viral attack dispersion.
Using word embeddings for library and information science research

In the research field of library and information science (LIS), pattern recognition and machine learning approaches have often been adopted to analyze massive amounts of textual data in digital libraries. In recent natural language processing, word embeddings have attracted much attention because they can capture semantic meanings and contexts. This paper presents a short survey of how word embeddings have been used in LIS research, especially focusing on articles published in LIS journals between 2009 and 2019. Our simple bibliographic analysis showed that at least 15 LIS journal papers used word embeddings. These 15 papers were briefly described under the following categories: knowledge extraction and visualization from scholarly data, classification in scholarly data, and others that focus on more general corpora.
Strategy Planner: Enhancements to support better defense and pass strategies within an LfD approach

This work presents recent improvements to Strategy Planner (SPlanner) and its corresponding FCPortugal Setplays Framework (FSF) to provide sophisticated setplays. This toolkit is useful to design strategic plans for robotic soccer teams as a particular case of Multi-Agent Systems (MASs). The new enhancements enable more realistic setplays, including, but not limited to, the definition of better passes strategies and defensive setplays. The enhanced tool is used to populate a dataset with demonstrations made by soccer experts and used in a Learning from Demonstration (LfD) approach to allow teams of robotic soccer learning new setplays.
Teaching wave concepts using traditional musical instruments and free software to prepare prospective skillful millennial physics teachers

Abstract This study aims to show the basic concepts of wave through traditional musical instruments integrated with computer-based software and smartphones, as well as to find out the profile and improvement of students’ science process skills. The design of inquiry-oriented learning activities is guided by science process skill worksheets that involve the exploration of traditional musical instruments and software. Traditional musical instruments used are stringed instruments (Gambo Mbojo), wind instruments (Sundanese flute), and membrane instruments (Rebab), while the software used is Adobe Audition, Zelscope, MacScope II, LiteChromatic, Spectrum Advenced, and Spear. The students studied were 32 prospective physics teacher students enrolled in the wave course. Data collection was carried out by means of literature study and tests. The test kits use multiple choice science process skills (SPS-tests). The data was statistically analyzed using the mean difference test (paired sample t -test) with normalized gain determined by the hake factor ( N -gain ). Based on the results of the research, it was concluded that the learning design of the traditional musical instrument and freesoftware can be increase the quality of the process of physics learning which is indicated by the achievement students science process skills which significantly increase between before and after learning activities.
Binary Classification of Cognitive Workload Levels with Oculography Features

Assessment of cognitive workload level is important to understand human mental fatigue, especially in the case of performing intellectual tasks. The paper presents a case study on binary classification of cognitive workload levels. The dataset was received from two versions of the digit symbol substitution test (DSST), conducted on 26 healthy volunteers. A screen-based eye tracker was applied during an examination gathering oculographic data. DSST test results such as total number of matches and error ratio were also applied. Classification was performed with several different machine learning models. The best accuracy (97%) was achieved with linear SVM classifier. The final dataset for classification was based on nine features selected with the Fisher score feature selection method.
Face Key Point Location Method based on Parallel Convolutional Neural Network

Based on convolutional neural network and face detection algorithm, this paper proposes a training sample expansion strategy, and a parallel convolutional network face detection algorithm for face features, occlusion and illumination detection, combined with Relu activation function and Dropout random regularization strategy. Network training not only speeds up the convergence of the network, but also improves the generalization ability. On this basis, the software based on face detection and feature point location is designed to realize the automatic loading of images and the face recognition function, to achieve accurate positioning of the face points, and to locate experiments on the LWF face database. The results show that the method is greatly improved in accuracy and reliability, and it can achieve robust and accurate estimation of key points.
Parthood and part–whole relations in Zulu language and culture

Part–whole relations are pervasive throughout domain ontologies and enjoy interest also in, inter alia, NLP and manufacturing, and by philosophers in the scope of mereology. There exist a stable list of part–whole relations that are assumed to be common, yet for isiZulu, among other languages, ther e were at least linguistic differences. This raises the question whether there are ontological differences, which would imply that the ‘common’ list is not universal across languages and cultures. We investigated this for 18 part–whole terms in the Zulu language that we selected from an initial list of 81 terms collected. They were formalised and aligned to the well-known part–whole relations, and checked against a corpus. While there is a term for general parthood in Zulu, the main difference observed concerns relation proliferation due to very specific relata that are entities typically represented only in domain ontologies. This poses new questions for ontology engineering on how to manage the plurality of relations and for philosophy to possibly extend mereology.
On the Assessment of Nature-Inspired Meta-Heuristic Optimization Techniques to Fine-Tune Deep Belief Networks

Machine learning techniques are capable of talking, interpreting, creating, and even reasoning about virtually any subject. Also, their learning power has grown exponentially throughout the last years due to advances in hardware architecture. Nevertheless, most of these models still struggle regarding their practical usage since they require a proper selection of hyper-parameters, which are often empirically chosen. Such requirements are strengthened when concerning deep learning models, which commonly require a higher number of hyper-parameters. A collection of nature-inspired optimization techniques, known as meta-heuristics, arise as straightforward solutions to tackle such problems since they do not employ derivatives, thus alleviating their computational burden. Therefore, this work proposes a comparison among several meta-heuristic optimization techniques in the context of Deep Belief Networks hyper-parameter fine-tuning. An experimental setup was conducted over three public datasets in the task of binary image reconstruction and demonstrated consistent results, posing meta-heuristic techniques as a suitable alternative to the problem.
A Novel Approach for Detection of Tumors in Mammographic Images Using Fourier Descriptors and KNN

The commonest disease in Indian women is breast cancer (BC). The Mammography Screening is the best solution to predict breast cancer in early stages. Though more research is going to predict BC in the early stages, the death rate is increasing year by year. So, we proposed a method for efficient classification to identify BC in the early stage. Our system uses efficient shape descriptors and is segmented by using Thresholding. Classification is done by using K-Nearest Neighbor (KNN). Our system classified the mammography images into Normal, Benign or Malignant.
From Technical Analysis to Text Analytics: Stock and Index Prediction with GRU

Technical Analysis is a trading practice to identify and observe the changes in stock movements to anticipate future movement. It operates mainly by observing patterns discovered among the historical closing prices in numeric form. Studies have shown that sentiments play a significant role in stocks movement prediction [1]-[3]. This work studies the effect of adding text data into stocks and index prediction. We studied major technological stocks and NASDAQ index movement which are highly volatile in nature. We set up experiments to predict the 10-day forward momentum of stock and index. In this work, we make use of machine learning techniques to establish 10 different models including decision tree, random forest, K-nearest neighbors, support vector machine, recurrent neural network, long-short term memory network, gated recurrent unit network to observe their ability to make prediction. The results illustrate that the non-neural network models experience over-fitting issue despite the good accuracy. On the other hand, neural network models require more data. The addition of news and sentiments can improve deep learning models' performance from accuracy of 50.1% to 78.57%.
Comparison of exponential smoothing method and autoregressive integrated moving average (ARIMA) method in predicting dengue fever cases in the city of Palembang

Abstract Forecasting is the process of making a statement about an event where the event has not been known or observed. For pharmaceutical students, learning about forecasting techniques can help determine the treatment of various diseases, one of which is dengue fever. Dengue fever is an acute disease caused by the dengue virus. Dengue fever is still a public health issue in major cities in Indonesia, one of which is Palembang. Based on the profile done by Palembang City’s Public Health Office in 2017, dengue fever cases in the area from year to year tend to fluctuates. To get the overview of the number of dengue fever cases in the upcoming years, time series forecasting methods are used, namely the Exponential Smoothing method and the Autoregressive Integrated Moving Average (ARIMA) method. Afterward, the results of predictions from the two methods are compared. Forecasting using the ARIMA method gives the smallest MSE and MAE results of 108077.877 and 172.424, respectively, compared to the Exponential Smoothing method. This means that the ARIMA method is better at predicting the number of dengue fever cases in Palembang in the coming years.
Intent Sets

"Chatbot" is a colloquial term used to refer to software components that possess the ability to interact with the end-user using natural language phrases. Many commercial platforms are offering sophisticated dashboards to build these chatbots with no or minimal coding. However, the job of composing the chatbot from real-world scenarios is not a trivial activity and requires a significant understanding of the problem as well as the domain. In this work, we present the concept of Intent Sets - an Architectural choice, that impacts the overall accuracy of the chatbot. We show that the same chatbot can be built choosing one out of many possible Intent Sets. We also present our observations collected through a set of experiments while building the same chatbot over three commercial platforms - Google Dialogflow, IBM Watson Assistant and Amazon Lex.
Text Mining Pre-Processing Using Gata Framework and RapidMiner for Indonesian Sentiment Analysis

Abstract Research in the field of Text Mining in general still uses text in English, Arabic, China or others language, while for text in Indonesian is still very limited, so it requires good tools to help Indonesian researchers to conduct research in the field of text mining in Indonesian. Pre-processing is needed for text mining processes such as deleting notation ‘@’, ‘http’ removal, Indonesian stopwords, normalizing acronym, slang words, emoticons, and Indonesian stemming. The GATA Framework Text Mining provided is one of the options for conducting text mining research in Indonesian and has been used by several researchers. There are several known data mining processing methods, including KKD, CRISP-DM, and SEMMA, all three of which are quite reliable methods. CRISP-DM which consists of; Bussiness Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment is a method that is quite widely used in research in the field of text mining which can be combined with text pre-processing. With so much research in the field of Text Mining in Indonesian, the need for pre-processing in Indonesian is very important. GATA Framework is an option for pre-processing devices that can be combined with Repidminer devices, as seen from the results of the excellent FUPRS.
Chapter 9. Healthy lifestyle, dieting, fitness and bodybuilding

This paper studies compliments that occur in selected Polish online discussion forums devoted to topics related to a healthy lifestyle, dieting, fitness and bodybuilding. The aim of the investigation is to find out which syntactic structures, topics and functions are prevalent, and whether the Corpus Query Language is an adequate tool to analyze compliments in data retrieved from Internet forums. The study has shown that (1) the most frequent structures differ from those identified in previous studies, (2) the predominant topics identified in this study only partially overlap with previous studies, and (3) the most common function is expressing solidarity and establishing rapport. The corpus-based methodology and CQL, with some limitations, proved useful and allowed effective examination of a large database of ca. six million words.
Sabotaging the system boundary: A study of the inter-boundary vulnerability

The hierarchy theory is the foundation of the modern computer system design. However, the interaction part between different system layers is usually the weak point of the system, which tends to have security flaws. When communicating across the system boundary, failure to enforce the required synchronization in the shared memory can cause data inconsistency of the communication partners. Especially when there is a privilege gap between different boundary sides, such data inconsistency can lead to security vulnerability and sabotage the trust boundary. In this paper, we propose the concept of inter-boundary vulnerability and give the first in-depth study of them. We investigate three typical boundaries in the system that inter-boundary vulnerabilities are prone to occur, including the kernel-user boundary, the hardware-OS boundary, and the VMM-guest OS boundary. Then, based on the investigation of 115 real-world vulnerability cases, we extract four vulnerability types and provide analysis for each type to illustrate the principle. Finally, we discuss the state-of-the-art techniques that are relevant to the detection, prevention, and exploitation of such vulnerabilities, aiming to light the future research on this topic.
Enhanced Data Mining Technique to Measure Satisfaction Degree of Social Media Users of Xeljanz Drug

In the recent times, social media has become important in the field of health care as a major resource of valuable health information. Social media can provide massive amounts of data in real-time through user interaction, and this data can be analysed to reflect the harms and benefits of treatment by using the personal health experiences of users to improve health outcomes. In this study, we propose an enhanced data mining framework for analysing user opinions on Twitter and on a health-care forum. The proposed framework measures the degree of satisfaction of consumers regarding the drug Xeljanz, which is used to treat rheumatoid arthritis. The proposed framework is based on seven steps distributed in two phases. The first phase involves aggregating data related to the drug Xeljanz. This data is pre-processed to produce a list of words with a term frequency-inverse document frequency score. The word list is then classified into the following three categories: positive, negative and neutral. The second phase involves modelling social media posts using network analysis, identifying sub-graphs, calculating average opinions and detecting influential users. The results showed 77.3% user satisfaction with Xeljanz. Positive opinions were especially pronounced among users who switched to Xeljanz based on advice from a physician. Negative opinions of Xeljanz typically pertained to the high cost of the drug.
Intelligent detection method for abnormal big data in heterogeneous networks based on Bayesian classification

In order to overcome the existing abnormal big data intelligent detection method, the problem of low detection accuracy and poor convergence is not carried out without abnormal big data classification. A new Bayesian classification based heterogeneous network anomaly big data intelligent detection is proposed in this paper. method. Design an abnormal big data intelligent detection architecture, use TcpDump collection tool to collect and process heterogeneous network traffic data, and build the relationship between bottleneck traffic and abnormal big data based on the processed data, through Fourier transform The method obtains the data frequency information and uses the Bayesian network classification method to realize the intelligent detection of abnormal big data in heterogeneous networks. The experimental results show that compared with the traditional method, the proposed method greatly improves the detection accuracy, convergence and anti-interference, and fully demonstrates that the proposed method has better detection effect.
On the odd cycle game and connected rules

We study the positional game where two players, Maker and Breaker, alternately select respectively 1 and b previously unclaimed edges of Kn. Maker wins if she succeeds in claiming all edges of some odd cycle in Kn and Breaker wins otherwise. Improving on a result of Bednarska and Pikhurko, we show that Maker wins the odd cycle game if b≤((4−6)∕5+o(1))n. We furthermore introduce “connected rules” and study the odd cycle game under them, both in the Maker–Breaker as well as in the Client–Waiter variant.
Conjugate Gradient Polak Ribiere In Improving Performance in Predicting Population Backpropagation

Abstract Perform predictive data in the form of time series required a correct method, one method is now often used is the propagation of this method is a method that is able to minimize the error value of the output of the predicted number of people, but still generate quite a lot of iteration that needs to be optimized by minimize iterations and use of time, then the use of conjugate gradient polak Ribiere are expected to minimize the use of time, the number of the epoch of the results of standard backpropagation.
Object Segmentation with Deep Neural Nets Coupled with a Shape Prior, When Learning From a Training set of Limited Quality and Small Size

Statistical shape priors can be crucial in segmenting objects when the data differentiates poorly between the object and its surroundings. For reliable learning, while some methods need high-quality expert segmentations, other methods need large training sets, both of which can often be difficult to obtain in clinical deployment or scientific studies. We propose to couple deep neural networks with a pointset-based shape prior that can be learned effectively despite training sets having small size and imperfections in expert curation. The prior relies on sparse Riemannian modeling in Kendall shape space. Results on clinical brain magnetic resonance imaging data show that our framework improves over the state of the art in segmenting the thalamus and the caudate.
An Experimental Study of Prediction Methods in Robust optimization Over Time

Robust optimization Over Time (ROOT) is a new method of solving Dynamic optimization Problems in respect to choosing a robust solution, that would last over a number of environment changes, rather than the approach that chooses the optimal solution at every change. ROOT methods currently show that ROOT can be solved by predicting an individual fitness for a number of future environment changes. In this work, a benchmark problem based on the Modified Moving Peaks Benchmark (MMPB) is proposed that includes an attractor heuristic, that guides optima to a determined location in the environment, resulting in a more predictable optimum. We study a number of time series forecasting methods to test different prediction methods of future fitness values in a ROOT method. Four time series regression techniques are considered as the prediction method: Linear and Quadratic Regression, an Autoregressive model, and Support Vector Regression. We find that there is not much difference in choosing a simple Linear Regression to more advanced prediction methods. We also suggest that current benchmark problems that cannot be predicted will deceive the optimizer and ROOT framework as the peaks may move using a random walk. Results show an improvement in comparison with MMPB used in most ROOT studies.
Single Image Reflection Removal via Attention Model and SN-GAN

Single image reflection removal is of great practical importance for various computer vision tasks. Most non-learning methods try to solve this problem through the model-optimization scheme, which fails to produce promising results due to the shortage of suitable priors to model the difference between the reflection layer and the transmission layer. This paper presents an improved generative adversarial network to resolve this problem. First, we suggest that reflection removal is not only a channel-wise separation problem, but also a spatial variational occlusion removal task, which is sensitive to both spatial and channel-wise features. To this end, we integrate the CBAM module into the generator to enhance both spatial and channel-wise feature representation. Second, we consider the reflection layer as a spatial mask with space-relevant reflection intensity information, which can be used to elevate the performance of the discriminator. We then design a novel SNGAN structure with utilize the predicted reflection as a guidance to achieve better adversarial supervision. Specifically, our new generative network has an encoder-decoder structure with skip-connections, where the attention enhancement block is integrated into each skip-connection of the encoder-decoder subnet, and followed by an eight-layer fully convolutional subnet. Furthermore, the SNGAN loss is combined with L2 pixel loss and L1 VGG19 perceptual loss for training. The experimental results with benchmark datasets indicate that our method outperforms several state-of-the-art networks.