Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Gigantic Data Examination Utilizing the Apache Spark and Significant Learning

With the spreading certainty of Gigantic Data, particular prompts and advancements are made in this area of Gigantic Data and systems, for example, the Apache Hadoop as well as the Apache Spark are very much widely used and spread in industry and a part of balance over the previous decades and have wrapped up gigantically phenomenal, particularly in affiliations. It is finding the opportunity to be interminably clear that profitable tremendous data evaluation is fundamental to perception artificial experiences issues. All things considered, a diversified-calculation repository executed inside the Apache Spark structure, it is MLlib. Disregarding the way that this library reinforces different AI figurings, there’s still expansion to utilize the Spark course of action ably for out and out time-genuine also, computationally absurd methods like essential acquisition of knowledge. We are trying to put forward an effective structure which consolidations the separative assessment cutoff purposes of the Apache Spark and the pushed AI plan for a fundamental multilayer perceptron (MLP), utilizing pervasive thought of cascade learning. We lead observational evaluation of our structure on two veritable famous datasets. The outcomes around are attracting and show our proposed structure, accordingly sketching out that it is an alter over routine goliath data evaluation strategies that utilization either Spark or Significant learning as individual parts.
Analysis and modeling for modification of speaking to karaoke-style singing

This study considers an analysis of differences in voice modification within the domains of speaking and singing the same text content, resulting in the first attempt in converting speaking to Karaoke-Style singing. To develop this system, we collected a new dataset, UT-Sing dataset, containing more than 23 h of speech from 81 participants across four different languages: English, Farsi, Hindi, and Mandarin. We asked each participant to produce the same text content by first reading and then singing 5 popular songs while listening to the accompanying instrumental music through open-air headphones. This effectively creates a parallel dataset that is suitable for voice modification systems. We first use this dataset to compare different prosodic and spectral characteristics of Karaoke-style singing versus speaking the same text content. We then leverage the knowledge obtained from this comparison to develop a speaking to Karaoke-Style singing modification system. In the training phase of the developed system, we extract acoustic features using the WORLD vocoder; we align the acoustic features through the DTW algorithm. Finally, we train a residual network to model the relationship between the acoustic features. We employ subjective assessments to evaluate the performance of the developed system.
Interactive Evidence Detection: train state-of-the-art model out-of-domain or simple model interactively?

Finding evidence is of vital importance in research as well as fact checking and an evidence detection method would be useful in speeding up this process. However, when addressing a new topic there is no training data and there are two approaches to get started. One could use large amounts of out-of-domain data to train a state-of-the-art method, or to use the small data that a person creates while working on the topic. In this paper, we address this problem in two steps. First, by simulating users who read source documents and label sentences they can use as evidence, thereby creating small amounts of training data for an interactively trained evidence detection model; and second, by comparing such an interactively trained model against a pre-trained model that has been trained on large out-of-domain data. We found that an interactively trained model not only often out-performs a state-of-the-art model but also requires significantly lower amounts of computational resources. Therefore, especially when computational resources are scarce, e.g. no GPU available, training a smaller model on the fly is preferable to training a well generalising but resource hungry out-of-domain model.
Augmented Reality Based Visual Dictionary for Elementary Students

The contemporary world lives on information. Data must be related to the user and its mannerism of presentation should be as such it is easily understood by the user. Developing technologies, like Augmented reality (AR), will be widely accepted rapidly. AR is an outstanding technology that is already used in some original and artistic applications. For example, Snapchat, Pokemon Go, Skymap etc. This paper discusses the new opportunity for improving the quality of education for the elementary students with the assistance of AR technology. We are implementing Visual Dictionary, with two modules namely, alphabets and occupations. These AR applications could lead students through learning process in an improved way, as AR is an advancement of traditional books with a digital touch. With such an innovative learning technique, education process can be made more fun and interesting.
WOMEN FOR GLOBAL GROWTH: NEED FOR WOMEN ECONOMIC EMPOWERMENT

Modern global economic development heavily relies on new technologies and unprecedented development of science creating more and more new innovative products and services. In such reality the demand for knowledge generation and improvement of resource efficiency is critical. More than ever, nations cannot afford to lose the skills, ideas and perspectives of women who are currently under-represented – in order to utilize the promise of a more prosperous and human-centric future that is to be come soon by contemporary innovations and technologies. New challenges for national economic policies generate new approaches which have to face these modern developments and trends. One of the efficient practices represents women promotion for a very simple reason: women yet are not fully engaged into development and their economic potential has to be unlocked to benefit not only economic goals but also social inclusion and human development to meet upmost goal of every state: inclusive growth and high level of welfare. In this regard, women promotion and their empowerment are key policy challenges. Nowadays the need for women advancement in all spheres of the society is clear and generates no doubts for reasonability and rationality of such approach to economic growth. Women contribution to the economy is vital and proved by different studies and researches. As data of different Institutions showed, there are no clear correlations of gender equality with some specific factors, though countries with high level of human development and economic development showed better results in terms of gender equality. The important message to be considered is that gender equality leads to smarts economics and creates sound base for resource efficiency and innovative development which is crucial for reaching higher competitiveness in global markets.
Assessing Business Model For Motorcycle Rental Based Mobile Application Using Balanced Scorecard (Case Study at PT XYZ)

Using a balanced scorecard, authors conduct an in-depth assessment of the business model for motorcycle rental based mobile application.With measurements on four perspectives: financial, customer, internal business process, and learning and growth, business model assessed whether to contribute to the long-term strategy of PT XYZ.The assessment use financial plan, financial projection, financial ratio, and feasibility study in measuring the business models from a financial perspective.Use the marketing mix in measuring the business model from a customer perspective.Design organizational structure, job description, and business process models for measuring aspects of the internal business process.Also, establish a risk management plan and KPIs in a learning and growth perspective.It is concluded that the business model for motorcycle rental based mobile application can contribute to the long-term strategy of PT XYZ.
Development and Validation of a Machine Learning Algorithm for Prediction of Platelet Transfusion Efficiency in Patients with Hematological Diseases

Background: Prophylactic transfusion of platelets provides a good protection for the implementation of invasive procedures and the prevention of bleeding events during chemotherapy and hematopoietic stem cell transplantation in patients who are suffering from hematological diseases. The issues that what is the optimal dose of platelet transfusion and how to monitor platelet transfusion efficiency are important due to the short storage time of platelet products, rising clinical demands, and decreasing donors. It is worthy that there are amount of studies have been conducted in the past to explore factors that may affect the clinical efficacy of platelet transfusion and characteristics of patients under these different transfusion effects. While, previous studies failed to exactly address the problems of clinical needs for platelet transfusion. Methods: The aim of our study is to develop a model to evaluate the efficacy of platelet transfusion and the statistic method of machine learning algorithm (ML) was involved. The differences between this algorithm and traditional methods are that the former one can continuously be learning from the data and form a self-training model, therefore ML is more accurate than traditional artificial models and generally independent of the model and parameters themselves. We further take the multi-layer fully connected layer neural network model (MLNN) into our consideration because it simulates the multi-layer interconnection of human nervous systems, which is suitable for processing inaccurate and fuzzy information. In our study, the establishment of a neural network model was used to make a multiple-dimension analysis between factors affecting platelet transfusion efficacy and platelet count added value correction index (aCCI), and explore the correlation among these influencing factors as well. Results: The study utilized the data relative to 1840 platelet transfusions performed in 460 patients with hematological diseases. The participants ranged in age from 16 to 92, and the median age was 59.5. There were 199 females and 261 men. The whole data was divided to 2 parts, 2/3 of them were analyzed as a training set and the others were used for validating. We selected 30 factors (including patient-related factors and transfusion product-related characteristics) that may affect the efficacy of platelet transfusion except the storage time of platelet (all data &lt; 2 days), and established a model for predicting platelet transfusion efficacy based on the volume of platelet transfusion. After the model was established, it was tested for goodness of fit, and the results showed that the LOSS value tended to be stable. Conclusions: The establishment of this model may not only be used for predicting the platelet count after platelet transfusions in patients and the amount of platelets that need to be transfused, but also provide supports for the solution of related problems. Disclosures No relevant conflicts of interest to declare.
Performances immersed in or interacted with unique environmental contexts

By selecting the 100 soundscapes in 1996 and coordinating follow-up events in 2002, the Ministry of the Environment, Japan drew the attention to rediscover and preserve unique sounds across the country. The diverse soundcapes are associated with wildlife, natural phenomena, or man-made sounds from industrial or cultural activities. In addition to appreciating the sound events, performances at outdoor Noh stages in Japan were immersed in unique environmental context historically. Bird chirping, wind blowing, and sea tide may all become the context of the play. Similar situation could be found for historical plays of Chinese operas at the stage integrated with a Chinese garden. There are modern facilities or events designed to immersed in or interacted with natural or man-made context. Some take advantage of unique acoustic effect such as echoes in valleys while the others create focus by isolating ambient disturbances and integrating soundscapes with visual, functional, or other forms of environmental qualities.
Study on the methods of controlling the fuzzy proportion of the line-following obstacle avoidance of wheeled robots

In the line-following competitions of robots, such as the Asian Competition of Robots Following Black Lines, the robots are expected to follow the designated paths. However, most robots sway when following a straight line at a full speed. Moving forward like this is not so graceful; worse still, robots would derail from the designated lines, making moving inefficient. At present, the full proportion control enables an automatic control system to make different responses to different conditions, a bigger error requires more modification. Of course, the proportion control is not absolutely powerful and cannot control complicated paths, neither can the integral and differential control. It is impossible to make immediate reaction to a right-angled turn. Therefore, this study proposes the methods of controlling the fuzzy proportion of the ling-following obstacle avoidance of wheeled robots, so that robots will be able to achieve line-following obstacle avoidance and quickly reach the destination when taking a complicated path.
Residual Dense Information Distillation Network for Single Image Super-Resolution

In recent years, Convolutional Neural Networks (CNNs) have achieved excellent results in the study of single image super-resolution. However, super-resolution algorithms based on CNNs still face serious challenges, such as poor detail reconstruction, numerous parameters, and difficulty of training. A Residual Dense Information Distillation Network (RD-IDN) is proposed in this paper which uses dense skip connections and residual structure to solve the problems of difficult training and low utilization of features in Information Distillation Network. Experimental results show that the proposed method is superior to many other Super Resolution algorithms in terms of reconstruction performance and computational comsumption.
Recognition and Classifying Sales Flyers Using Semi-Supervised Learning

The detection of objects and texts and the extraction of them from images has been an enormous challenge that we are still trying to perfect from the past three decades. The current progress in Deep Learning and Machine Learning techniques in image classification and object localization particularly for image which has a lot of information as sales flyers, purpose has been a topic of discussion in last half decade and we have seen some brilliant advancements in recent times which try to solve at least one of these sub-tasks text and object localization and recognition from images and digital documents as sale flyers. In this paper we presented an automatic learning algorithm for the classification of sales flyers, which consists of 3 steps: the first is the detection of objects (Products) in the sales flyers inspired by the faster region convolutional neural network (Faster R-CNN) framework and Residual Neural Network as feature map. The second step is the detection and extraction of the text in the sale flyers by Optical character recognition and finally the merge algorithm with the aim of marge each product with its information such as price and description. We presented three different merge algorithms and compared them among them: (1) merge by distances between Object and text with a accuracy performance of 70%, (2) combine by distances between Object, text and text on the Object with a accuracy performance of 78% and our main contribution is the combination with Convolutional Neural Network CNN and Multilayer Perceptron (MLP) with a accuracy performance of 90%.
Attribute-Aware Pedestrian Image Editing

Pedestrian image generation is a very challenging task. Existing generation methods have drawbacks including body distortion, inadequate visual details and large vague areas. In this paper, we propose Attribute-aware Pedestrian Image Editing (APIE) to address these problems based on given visual attributes. Our model denominated as APIE-Net, has three mechanisms including an attribute-aware segmentation network, a multi-scale discriminator and a latent-variable discriminator. Experiments on Market-1501 and DukeMTMC-reID datasets show that APIE-Net can generate satisfying pedestrian images with given attributes. Moreover, the generated images can augment the original datasets thus improve the performance in pedestrian-related tasks such as person re-identification (re-ID) and attribute prediction. Especially in person re-ID tasks our method outperforms state-of-the-art methods by a large margin.
Pattern recognition by pattern inversion

Alternative approach to pattern recognition is discussed that amounts to operations on inverse patterns and resembles working of Google-type search engines. Unlike neural networks that iteratively calculate weights within many a learning cycles, inverse patterns-based paradigm (neural cortex) does not use weights and follows a challenging learning trend that attempts to achieve a human-like generalization from a single example.
AskTheCode: Interactive Call Graph Exploration for Error Fixing and Prevention

In order to prevent and fix errors in program code, developers need to understand its semantics to a significant extent. For this purpose, they use various approaches, such as manual call graph exploration or dynamic analysis with a debugger. However, these techniques tend to be cumbersome in a larger codebase, because they provide either underapproximate or overapproximate results and it is often hard to combine them. Therefore, we present AskTheCode, a Microsoft Visual Studio extension enabling to interactively explore a call graph, ensuring that only feasible execution traces are taken into consideration. AskTheCode is based on control flow analysis and backward symbolic execution. We show its potential to significantly improve developers' experience on a complex code example.
An Overview of Practical Ontology Implementation in Decision Support Systems

Ontology has already a rather long history in the computer science. It helps to represent knowledge in the domain of interest and make it available both for humans and machines. For many years, ontologies were mostly considered as an object for academic research, but nowadays they are getting used in a growing number of applications. The suggested paper provides a brief overview of practical ontology implementation in decision support systems. Typical knowledge-intensive tasks were used to organize the overview: one example of a system was provided for each task.
CALL replication studies: getting to grips with complexity

Calls for replication studies are becoming more frequent, and Computer Assisted Language Learning (CALL) has now reached sufficient maturity to offer numerous studies that lend themselves to replication. Realistic and successful replications rely on transparency in terms of data, results, and methodology. Two published studies in the area of vocabulary CALL will be discussed from the perspective of their suitability for replication: Franciosi, Yagi, Tomoshige, and Ye (2016) and Kim and Kim (2012). Alzahrani (2017) is a replication of Franciosi et al. (2016), confirming the findings with a markedly different learner group. The replication used the same methodology, a slightly modified list of target words, and Saudi participants. Kim and Kim (2012) compared vocabulary learning across three different screen sizes. The flashcard software is not specified any further, nor are the target words. While such an underspecified methodology is less likely to lead to a successful replication that can strengthen the validity and reliability of research results in our field, it can still provide a good training opportunity for students to learn about methodology in CALL.
Application of Bipolar Neutrosophic sets to Incidence Graphs

In this research paper, we apply the idea of bipolar neutrosophic sets to incidence graphs. We present somenotions, including bipolar neutrosophic incidence graphs, bipolar neutrosophic incidence cycle and bipolar neutro-sophic incidence tree. We define strong path, strength and incidence strength of strongest path in bipolar neutrosophicincidence graphs. We investigate some properties of bipolar neutrosophic incidence graphs. We also describe anapplication of bipolar neutrosophic incidence graphs.
An Interactive Timeline Visualization for Patient Cohorts in the Oncological Routine: A Use Case on Multiple Myeloma.

With the growing interdisciplinarity of cancer treatment and increasing amounts of data and patients, it is getting increasingly difficult for physicians to capture a patient's medical history as a basis for adequate treatment and to compare different medical histories of similar patients to each other. Furthermore, in order to tackle the etiological mechanisms of cancer, it is crucial to identify patients exhibiting a different disease course than their corresponding cohort. Several timeline visualizations have already been proposed. However, the functions and design of such visualizations are always use case dependent. We constructed a cohort timeline prototype mock-up for a specific oncological use case involving multiple myeloma, where the chronological monitoring of various parameters is crucial for patient diagnosis and treatment. Our proposed cohort timeline is a synthesis between elements described in the literature and our own approaches regarding function and design.
ARU speech corpus (University of Liverpool)

This corpus comprises single channel recordings of IEEE (Harvard) sentences (IEEE, 1969) spoken by twelve adult native British English speakers in anechoic conditions. IEEE (1969). Recommended practice for speech quality measurements, IEEE Transactions on Audio and Electroacoustics, 17 (3), 227-246.Principal |nvestigator: Professor Carl Hopkins
Prospecting for Structure Capital: Proactive Strategic Innovation and the Performance of Manufacturing SMEs in Yemen

The implications of strategic innovation for the performance of SMEs have attracted major interest among practitioners and academics. However, empirical studies on the relationship between strategic innovation and performance mediating structure capital have not yet been examined, and it is this significant theoretical gap which this paper will fill. The study also contributes by proposing and developing a framework underpinned by Contingency Theory. Our PLS analysis reveals that strategic innovation is significantly associated with SME performance, and that the relationship between strategic innovation and SME performance is significantly mediated by structure capital. In this dynamic business world, SME managers should invest in and fully utilize strategic innovation and structure capital to achieve a high level of performance and competitive advantage.
A Concept for Graph-Based Temporal Similarity of Patient Data.

Computer-based decision support systems are often used for dedicated tasks such as the detection of sepsis. However, positive predictive values for sepsis detection are reported to achieve only around 46%. In this paper we describe a novel approach to use temporal data of electronic patient records based on similarity measures. We apply the concept of case-based reasoning, which is well-established in many fields of medical informatics. Temporal patient data are organized in a time-graph structure. For the quantification of similarity between cases, we exploit graph theory based approaches. For development and evaluation of our time-graph similarity frame we use the open MIMIC III dataset. In a later phase, we envision to transfer our concept from sepsis to other diseases.