Reviewerly/exHarmony huggingface dataset:
PREDICTING SAFETY INFORMATION OF DRUGS USING DATA MINING TECHNIQUE

Data Classification is the application of data mining techniques to discover patterns from the micro array and biological datasets.This research entitled "PREDICTING SAFTEY INFORMATION OF DRUGS USING DATA MINING TECHNIQUE" incorporates information theory, which is the process of deriving the information from the unsupervised dataset through feature selection.Finding the best features that are similar to a test data is challenging task in current data era.This research presents a framework for discovering best feature selection from unsupervised datasets.The proposed research work presents a new approach to measure the features (attributes) in drug prediction dataset using the methodologies namely, data cleaning, Adaptive Relevance Feature Discovery and Random Forest Classification.There are number of pharmacy companies are available in the market with multiple medicines for same problem.This prediction of drugs is used to prescribe the medicines for the patient's disease by analyzing the history of the patient's health.Feature selection and dimensionality reduction is characterized by a regularity analysis where the feature values correspond to the number times that term appears in the dataset.The relevance feature discovery method gives a useful measure is used to find the similarity features between data points are likely to be in terms of their features property.Some of the challenges faced in finding the best feature selection include positive, negative and inconsistency.This Proposed work proposes an enhanced Drug prediction based on Random Forest classification method to estimate the feature searching that is measured using minimal redundancy optimization method corresponding to drug prediction dataset.
Pengelompokan Data Kriminal Pada Poldasu Menentukan Pola Daerah Rawan Tindak Kriminal Menggunakan Data Mining Algoritma K-Means Clustering

Crime is all forms of actions and actions that are economically and psychologically harmful that violate the applicable laws in the Indonesian state and social and religious norms. Can be interpreted that, crime is anything that violates the law and violates social norms, so that the public opposes it. This study aims to facilitate and assist law enforcement authorities in anticipating criminal acts in vulnerable areas. The method used in this research is the k-means algorithm method using rapidminer 7.3 software. Where the grouping is done to determine the level of vulnerable areas. The establishment of this system is expected to assist the police in determining areas prone to crime. And from the results of the study stated groups of areas prone to criminal acts, namely MEDAN POLRESTA and LABUHAN BATU POLRES.
The Use of Vocabulary Learning Strategies by EFL and EAP Undergraduate University Learners’ in the Iraqi Context

Vocabulary learning is an essential part of foreign or second language learning.This study aims at identifying the most and least common strategies that are used by Iraqi English as a foreign language (EFL) majors and English for academic purposes (EAP) learners.Also, determine the differences that are in EFL and EAP students' vocabulary learning strategy (VLS) use as well as exploring EFL and EAP students' views and difficulties.The study samples were 100 undergraduate learners (50 EAP learners and 50 EFL majors studying at Al-iraqia University, Iraq).There were two methods adopted; a validated Likert-scale questionnaire based on a developed version of Schmitt (1997) and further selected four of them for a follow-up semistructured interview.The results of the survey indicated that EFL and EAP learners' most common strategy was determination strategy, whereas, the least common strategy was metacognitive.The finding of the independent sample t-test of the five identified categories: metacognitive, determination, cognitive, memory as well as social, indicated that there was no significant difference between EFL and EAP learners' in the frequency of the use of VLS.The results of the interview indicated that the majority of EFL and EAP learners' valued the significance role of VLS.
A infocomunicação ou a convergência das Ciências da Informação e da Comunicação para um objeto comum

Resulting from the closing conference of the VI Post-Graduate Workshop in Information Science, in 2019, the text presents a position in defense of the existence of the operational concept of infocommunication.Information has been an aggregating element and a central object of both Information Science and Communication Sciences, as well as Information Technology and Computer Science.Despite this reality, its treatment and perspective diverge countless times and on many occasions it generates conflicts.The development and greater maturity of these areas and fields of knowledge have sought to respond to the growing need of understanding phenomena associated with information, communication and the digital.In this context, human activity is generated, with a new generation of information, whose increasing complexity and impact on multiple areas of human activity requires new forms of understanding.
Cost and Effects of Data Breaches, Precautions, and Disclosure Laws

In recent times the breach of security systems or cyber-attacks leading to unauthorized acquisitions of computerized data that compromises the security, confidentiality, and integrity of personally identifiable information by many organizations has grown. There is a general belief that data breaches and today’s organizational practices are axiomatically regarded as cause and effect. This paper addresses the cost of data breaches, disclosure laws, and precautions that have been instituted for many organizations and concludes that cybersecurity and data breach question is not “if” but “when” it might happen. Data has grown as one of the critical assets, and the absence of security protocols creates a vulnerability that can be misused by bad actors engaged in hacking and other forms of the data breach. This paper documents that the last decade experienced a phenomenal rise in the number of data breaches caused by hacking and the efficacy of disclosure laws that have been instituted by 48 states in the US. The frequency of data breach incidents has been alarming as billions of records have been breached and billions of dollars have been spent to mitigate those breaches, which could have been allocated for other projects. It is recommended that all organizations, big or small, have cybersecurity policies and a business continuity plan in place to deal with data breaches.
Robust Text Line Detection in Equipment Nameplate Images

Scene text detection for equipment nameplates in the wild is important for equipment inspection robot since it enables inspection robot to take specific actions for different equipment’s. Although text detection in images has achieved great progress in recent years, the detection for equipment nameplates faces several challenges such as extreme illumination and distortion which significantly decrease the detection performance. In this paper, we propose a deep text detection model Robust Text Line Detection (RTLD) for locating word level text instances in equipment cards. Specifically, the proposed model first employs a corner detection module to determine the four corner points of each nameplate, and then a carefully designed image transformed module transforms the irregular nameplate region into a rectangular region. Finally, text detection module is introduced to locate every word level text instance in the transformed images. We conduct extensive experiments to examine our proposed methods on real equipment nameplate images. Our model achieves 91 <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</inf> 2% precision and 92 <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</inf> 6% recall on Equipment Nameplate Dataset. The experimental results demonstrate the effectiveness of our models.
Gestational Diabetes Mellitus Prediction Based on Two Classification Algorithms

Gestational diabetes mellitus (GDM) is a disease with normal glucose tolerance before pregnancy and only diabetes during pregnancy. The discovery of GDM has a long history. The specific causes and mechanisms of its occurrence are still unclear. There is a lack of research on intelligent diagnosis of GDM. GDM has many adverse effects on pregnant women and fetuses, which is of great significance for the early diagnosis of GDM. Based on the measured data of the hospital, this paper realizes the intelligent diagnosis of GDM by using a improved KNN algorithm and a improved BP neural network.
Learning Mandarin Stroke Writing Among Non-Chinese Learners: Mobile Learning Versus Traditional Learning

Mobile assisted learning is said to compensate and facilitate numerous learning difficulties among language learners especially in learning a language which embraced a totally different set of metalinguistics with their first language. This preliminary study was aimed to compare the impact of mobile assisted learning and traditional learning on Mandarin stroke writing among non-Chinese learners in Universiti Teknologi MARA, Mukah, Sarawak, Malaysia. All 100 Level 2 Mandarin undergraduates were requested to sit for a Pre-test before they were divided equally into treatment group (TG) and control group (CG). Students in TG were asked to practice their stroke writing via three predetermined Mobile Applications (MA) while students in CG were requested to practice their stroke writing via traditional rote learning method. Both groups practiced their assigned method for three weeks before their Post-test. Later, the scores of both pre and post tests were compared and interviews were conducted to cross check their views on both methods used. Results suggested positive impacts on both methods used improved stroke writing. However, the differences of improvement rate among these two methods were significant but small, 4.2 %. The findings suggested a need to further study participants’ preferences in the learning of writing in order to cater individual needs in the learning of a new language effectively.
Anaemia in Patients on Haemodialysis Therapy in Dar es Salaam, Tanzania

Moreover, anaemia has been linked with worse clinical outcomes such as increased morbidity and mortality due to cardiovascular disease [2].This complication increases the risk of hospitalization and length of hospital stay and therefore dramatically reducing the quality of life of these patients [3][4][5].End Stage Renal Disease is characterized by nutritional impairment, anaemia, hypertension, renal bone disease, neuropathy, nutritional impairment, and reduced life expectancy [6,7].Anaemia worsens with progressive kidney disease implicating multiple factors being related to its etiology including inability of the failing kidneys to secrete the hormone erythropoietin, uremic toxins and excess aluminum accumulation on bone marrow of patients on long term dialysis, blood loss, increased red blood cell destruction and altered platelet function due to uremia among others [8].
The scale-invariant space for attention layer in neural network

Recently, the Transformer architecture has achieved state-of-the-art performance in many natural language processing tasks. One key component in the Transformer architecture is the attention layer, which captures the relation between tokens. In this paper, we show that the weight of the attention layer has scale-invariant property, i.e. the output is invariant to a rescaling of weights. However, optimization algorithms in the vector space of weight such as SGD are not scaling invariant. This mismatch will potentially hurt the optimization process. To solve the mismatch, we seek a new parameter space for attention layer that is both scale-invariant and can sufficiently represent the output of attention, so that we can employ optimization algorithms in the scale-invariant parameter space. To achieve this goal, we first show that the output of the attention layer can be represented using scale-invariant variables, which is called paths. Then, we define basis paths which are an independent subset of all paths and are sufficient to represent all other paths. We prove that the Scale-Invariant (SI) space for the attention layer is composed of the basis path. Finally, we design an Attention Basis Path Identification(ABPI) Method to identify the basis paths and propose optimizing the attention layer directly in its SI space. Several experiments on benchmark datasets show that we can obtain more effective neural networks with the attention layer by optimizing the attention layer directly in its SI space.
A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images

The increasing use of smartphones and social media apps for communication results in a massive number of screenshot images. These images enrich the written language through text and emojis. In this regard, several studies in the image analysis field have considered text. However, they ignored the use of emojis. In this study, a robust two-stage algorithm for detecting emojis in screenshot images is proposed. The first stage localizes the regions of candidate emojis by using the proposed RGB-channel analysis method followed by a connected component method with a set of proposed rules. In the second verification stage, each of the emojis and non-emojis are classified by using proposed features with a decision tree classifier. Experiments were conducted to evaluate each stage independently and assess the performance of the proposed algorithm completely by using a self-collected dataset. The results showed that the proposed RGB-channel analysis method achieved better performance than the Niblack and Sauvola methods. Moreover, the proposed feature extraction method with decision tree classifier achieved more satisfactory performance than the LBP feature extraction method with all Bayesian network, perceptron neural network, and decision table rules. Overall, the proposed algorithm exhibited high efficiency in detecting emojis in screenshot images.
Distributed Traffic Flow Consolidation for Power Efficiency of Large-Scale Data Center Network

Power optimization for data center networks (DCNs) has recently received increasing research attention, since a DCN can account for 10 to 20 percent of the total power consumption of a data center. An effective power-saving approach for DCNs is traffic consolidation, which consolidates traffic flows onto a small set of links and switches such that unused network devices can be shut down dynamically for power savings. While this approach has shown great promise, existing solutions are mostly centralized and do not scale well for large-scale DCNs. In this article, we propose DISCO, a <b><u>DIS</u></b> tributed traffic flow <b><u>CO</u></b> nsolidation framework, with correlation analysis and delay constraints, for large-scale data center network. DISCO features two distributed traffic consolidation algorithms that provide different trade-offs (as desired by different DCN architectures) between scalability, power savings, and network performance. First, a flow-based algorithm is proposed to conduct consolidation for each flow individually, with greatly improved scalability. Second, an even more scalable switch-based algorithm is proposed to consolidate flows on each individual switch in a distributed fashion. We evaluate the DISCO algorithms both on a hardware testbed and in large-scale simulations with real DCN traces. The results show that, compared with state-of-the-art centralized solutions, DISCO can achieve nearly the same power savings while decomposing the global problem into sub-problems that are three orders of magnitude smaller. As a result, DISCO can run <inline-formula><tex-math notation="LaTeX">$\mathbf {10^4}$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\mathbf {10^6}$</tex-math></inline-formula> times faster for a DCN at the scale of 10K servers. The convergence of DISCO has also been proven theoretically and examined experimentally.
Aplikasi Pembelajaran Interaktif Augmented Reality Tata Surya Sekolah Dasar Menggunakan Metode Marker Based Tracking

Along with the times, the learning media also continues to grow. In solar system subjects usually, the media is used is books, where students can only see 2D forms. The purpose of this research is to build an application that can create a 3D object accompanied by an explanation, so as to make learning more interactive and easily understood using Augmented Reality (AR) technology. In previous journals, a solar system learning application has been made using AR technology, and this research will be developed by creating a quiz menu and true or false to find out how far students understand the material being taught. To ensure the application runs well, testing images, distance and angle markers, application features, and questionnaires using a black box. Marker test results can be distinguished well, markers can be detected well at a distance of 30-90 cm with an angle of 45ᴼ-90ᴼ, then questionnaires to 10 elementary school students related to this application 100% like the solar system AR application as a learning medium.
Continuous-time quantum walk on an extended star graph: Disorder-enhanced trapping process

Using a tight binding model, we investigate the dynamics of an exciton on a disordered extended star graph whose central site acts as an energy trap. When compared with what happens in an ordered network, our results reveal that the disorder drastically improves the excitonic absorption that becomes complete. Moreover, we show the occurrence of an optimal disorder for which the absorption time is strongly minimized, a surprising effect that originates in a disorder-induced restructuring process of the exciton eigenstates. Finally, we also show the existence of an optimal value of the absorption rate that reduces even more the absorption time. The resulting superoptimized trapping process is interpreted as a positive interplay between both the disorder and the so-called superradiance transition.
Interaction method based on visual gesture recognition

In order to enrich the diversity of interactive recognition methods, an interactive method of gesture recognition based on static vision is proposed. The static gesture images are captured by color camera in real time. The gesture is extracted based on FHOG features. The extracted eigenvalues are used as input of SVM multi-class classifier to recognize gesture actions. The gesture features are used to locate feature points to achieve the segmentation of gesture recognition and gesture recognition. The experimental results show that the system can recognize six common static gestures. The system has good robustness, with an average recognition rate of 95.31%, a rejection recognition rate of 9.37%, and an overall recognition efficiency of 90.63%.
Examination of University Students’ Opinions on Use of Augmented Reality Technology in Archeology Field

In this study, determination of students’ opinions on use of augmented reality(AR) in archeology field is aimed. Qualitative methods were utilized in this research for examination of a state university Archeology 1st grade students’ opinions on applications of AR. In the research, semi-structured interview form developed by the researches was utilized as data collection tool. Base on analysis results, students remarked that the most important difference created by AR in education of Archeology is elimination of the limitation in usage of 3-dimension objects and the limitation in seeing/reaching. The opinion is common that AR applications’ field of usage put to the fore front by the students in education of Archeology is animation of antic period ruins’/architecture’s real life images and provision of detailed information on sculptures. There is common opinion that applications of AR under education of Archeology can be used in an integrated way with Ceramic and Sculpture field.
Combination of B-mode and color Doppler mode using mutual information including canonical correlation analysis for breast cancer diagnosis

Aim: This study proposes the combination of B-mode and color Doppler mode using Mutual Information including Canonical Correlation Analysis (MI-CCA) to improve breast cancer diagnosis.Materials and methods: The dataset consisted of 53 benign lesions and 202 malignant lesions including B-mode, and color Doppler mode. Convolutional Neuron Networks (CNNs) was applied to automatically extract the features from breast ultrasound images. Then, MI-CCA was performed to fuse with maximized correlation. Finally, the classification model was built via the support vector machine technique to distinguish breast tumors. Diagnosis performances of single modes, combination modes, and other fusion strategies were compared.Results: The single B-mode obtained 90.92% accuracy, while the color Doppler mode obtained 97.16% accuracy. The MI-CCA fusion reveals a significant improvement with 98.80% accuracy. The results indicated that the fusion of two modes tended to offer a more accurate diagnosis than the single mode. In addition, the unsupervised-PCA was high (AUC 0.91, 95% CI [0.90, 0.91]) and no significant difference was observed with the unsupervised-CCA (AUC 0.90, 95% CI [0.84, 0.90]). The supervised-PCA was the lowest (AUC 0.93, 95% CI [0.91, 0.93] and no significant difference was observed with the supervised-CCA (AUC 0.95, 95% CI [0.91, 0.94]). The proposed MI-CCA was the highest performance (AUC 0.99, 95% CI [0.93, 0.99]). These results indicated that the supervised strategies tended to give a more accurate diagnosis than unsupervised strategies.Conclusion: By using the combination of ultrasound modes, this approach achieves high performance compared with the single mode and other fusion strategies. Our methodology may be a beneficial tool for the early detection and diagnosis of breast cancer
Towards a usable serious game app to support children's language therapy

Speech and language disorders in children account for about 6% of the population, and if not treated adequately they persist. Thus, affected children may experience long-term problems such as both diminished socialization and literacy in later life. Assistive technologies in the form of serious games have been successfully used to support language and speech therapies, but their developers have the challenge of improving their human-computer interfaces. This paper describes the design and development of a mobile software (a serious game app) that runs on a tablet. Its main objective is to support children's speech and language therapy of English language tenses. Important usability testing and design guidelines regarding mobile interfaces are addressed. Future work will include usability testing based on the user-centered design (UCD) paradigm and prototyping to improve its interface design.