Reviewerly/exHarmony huggingface dataset:
Data Virtualization for Analytics and Business Intelligence in Big Data

Data analytics and Business Intelligence (BI) is essential for strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between data offering insights. The major difference between BI and analytics is that analytics has predictive competence whereas Business Intelligence helps in informed decision-making built on the analysis of past data. Business Intelligence solutions are among the most valued data management tools available. Business Intelligence solutions gather and examine current, actionable data with the determination of providing insights into refining business operations. Data needs to be integrated from disparate sources in order to derive insights. Traditionally organizations employ data warehouses and ETL process to obtain integrated data. RecentlyData virtualization has been used to speed up the data integration process.Data virtualization and ETL are often complementary technologies performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store.In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.
A Heuristic-based Task Scheduling Method for Reducing Waiting Time in Cloud Environment

Task scheduling is a well-known NP-Hard problem that requires a near optimal solution for mapping tasks to resources. The waiting time is one of the most significant performance measures in scheduling. Increasing the waiting time may cause starvation problem or it may increase the reject rate. This paper proposed a method which aims to reduce the waiting time by classifying tasks and VMs (virtual machine) into two separate categories. The method considers priority of tasks as well as priority of VMs. It uses a heuristics function for allocating cloudlet to VMs based on priority factors. This method is simulated in CloudSim toolkit and the results show an improvement in terms of waiting time and resource utilization.
Learning Document Embeddings with Crossword Prediction

In this paper, we propose a Document Embedding Network (DEN) to learn document embeddings in an unsupervised manner. Our model uses the encoder-decoder architecture as its backbone, which tries to reconstruct the input document from an encoded document embedding. Unlike the standard decoder for text reconstruction, we randomly block some words in the input document, and use the incomplete context information and the encoded document embedding to predict the blocked words in the document, inspired by the crossword game. Thus, our decoder can keep the balance between the known and unknown information, and consider both global and partial information when decoding the missing words. We evaluate the learned document embeddings on two tasks: document classification and document retrieval. The experimental results show that our model substantially outperforms the compared methods.1.
Modelling Identity Rules with Neural Networks

In this paper, we show that standard feed-forward and recurrent neural networks fail to learn abstract patterns based on identity rules. We propose Repetition Based Pattern (RBP) extensions to neural network structures that solve this problem and answer, as well as raise, questions about integrating structures for inductive bias into neural networks. Examples of abstract patterns are the sequence patterns ABA and ABB where A or B can be any object. These were introduced by Marcus et al (1999) who also found that 7 month old infants recognise these patterns in sequences that use an unfamiliar vocabulary while simple recurrent neural networks do not. This result has been contested in the literature but it is confirmed by our experiments. We also show that the inability to generalise extends to different, previously untested, settings. We propose a new approach to modify standard neural network architectures, called Repetition Based Patterns (RBP) with different variants for classification and prediction. Our experiments show that neural networks with the appropriate RBP structure achieve perfect classification and prediction performance on synthetic data, including mixed concrete and abstract patterns. RBP also improves neural network performance in experiments with real-world sequence prediction tasks. We discuss these finding in terms of challenges for neural network models and identify consequences from this result in terms of developing inductive biases for neural network learning.
Fast Implementation of LSH With SIMD

In this paper, we propose a method of efficient software implementation for the cryptographic hash function LSH with single instruction multiple data (SIMD).The method is based on word-wise permutations of LSH.Using the modified functions Step j = P • Step j • P -1 and MsgExp instead of the original step function Step j and message expansion function MsgExp, where P is a permutation and P -1 is the inverse permutation of P, we show that the number of the SIMD instructions for implementing LSH is reduced.For efficient implementation of LSH in other environments (e.g., MIMD), various types of word permutations are listed.
News Article Classification using Kolmogorov Complexity Distance Measure and Artificial Neural Network

News article classification is a recently growing area of interest in text classification because of its associated multiple matching categories.However, the weak reliability indices and ambiguities associated with state-of-the-art classifiers often employed make success in this domain very limited.Also, the high sensitivity and large disparity in performance results of classifiers to the varying nature of real-world datasets make the need for comparative evaluation inevitable.In this paper, the accuracy and computational time efficiency of the Kolmogorov Complexity Distance Measure (KCDM) and Artificial Neural Network (ANN) were experimentally evaluated for a prototype large dimensional news article classification problem.2000 News articles from a dataset of 2225 British Broadcasting Corporation (BBC) news documents (including examples from sport, politics, entertainment, education and technology, and business) were used for categorical testing purposes.Porter's algorithm was used for word stemming after tokenization and stop-words removal, and a Normalized Term Frequency-Inverse Document Frequency (NTF-IDF) technique was adopted for feature extraction.Experimental results revealed that ANN performs better in terms of accuracy while the KCDM produced better results than ANN in terms of computational time efficiency.
I bisogni linguistici nei corsi di italiano L2 rivolti ad utenti vulnerabili

This paper presents the results of a research on language educational practices related to the teaching of Italian as a second language (ISL), aimed at learners in the situation of vulnerability and disadvantage. In many contexts of welcoming, the need to reconcile the first reception practices relating to housing, sustenance and health, with the dimension of language teaching, is evident. The ISL teachers who work within emergency frameworks often experience a feeling of discomfort connected to the motivational dynamics: if the students receive the necessities of the essentials from those who deal with the reception and expect to stay in Italy for a short period of time, it is possible that there will not be intrinsic motivation for studying and exercising the language. The survey, divided into several operational phases, is based on a questionnaire submitted to the teaching staff that deals with the ISL education within the contexts of reception. The most significant aspect of the data analysis is the absence of communicative needs compared to the daily life of the students.
Differential Game Logic.

Differential game logic (dGL) is a logic for specifying and verifying properties of hybrid games, i.e., games that combine discrete, continuous, and adversarial dynamics. Unlike hybrid systems, hybrid games allow choices in the system dynamics to be resolved adversarially by different players with different objectives. The logic dGL can be used to study the existence of winning strategies for such hybrid games, i.e., ways of resolving the player’s choices in some way so that he wins by achieving his objective for all choices of the opponent. Hybrid games are determined, i.e., from each state, one player has a winning strategy, yet computing their winning regions may take transfinitely many steps. The logic dGL, nevertheless, has a sound and complete axiomatization relative to any expressive logic. Separating axioms are identified that distinguish hybrid games from hybrid systems. Finally, dGL is proved to be strictly more expressive than the corresponding logic of hybrid systems by characterizing the expressiveness of both.
A machine learning approach to the design of customized shoe lasts

Footwear manufacturing begins with the design of a shoe last, a step that has a large impact on the comfort and fit of the resulting shoe. Yet, shoe lasts remain hand-crafted, with experts manually...
Performance comparison of ANN and template matching on English character recognition

the recognition of handwritten documents, which aims at transforming the written text into machine-encoded text, is considered as one of the most challenging problems in the area of pattern recognition and an open research area. This brings the necessity to make research works on character recognition of the English alphabet not only recognizing measure and evaluate the performance of algorithms we used. A number of algorithms have been proposed for English character recognition such as a support vector machine, hidden Markov model, and neural network. In this research, the design and implementation of a character recognition system for English characters using artificial neural networks and template matching are presented. The complete system employs image acquisition, preprocessing, character segmentation, and classiﬁcation and recognition. Finally, compare the performance of ANN and template matching algorithms. A data was an MNIST dataset taken from the NIST database. Overall, a recognition of ANN accuracy of 88 percent was obtained and template matching accuracy was 73 percent.
Argument indexation in Hakhun Tangsa

This paper describes argument indexation in Hakhun Tangsa, a language variety spoken by one of the Tangsa subtribes called Hakhun across the Indo-Myanmar boarder on the Patkai mountain range. Most finite clauses in Hakhun carry an argument index on the verb complex, which code person and number of the argument they cross-index. There are two sets of argument indices in Hakhun Tangsa – one with a sonorous coda or no coda at all and the other with a stop coda. The choice between these two sets depends on the tense/aspect/modality marker in the verb complex. The typical argument indexation pattern in Hakhun Tangsa is hierarchical, i.e. the verb complex indexes the argument which is higher in person hierarchy irrespective of its grammatical relation. The verb complex also marks the argument configuration either as direct or inverse by choosing one set of tense/aspect/modality marker over another. Another indexation pattern found in this language is subject indexation, where the subject is indexed over the object. The choice between these two kinds of indexation patterns is conditioned by semantic/pragmatic factors, such as affectedness of the patient participant.
Significant Patterns of APPRAISAL in Online Debates

This study aims to shed some light on the role of evaluative language in the process of persuasion in the newly emerging genre of ‘online debate’. Drawing on the appraisal framework within Systemic Functional Linguistics, this study investigates the distributional patterns of appraisal choices and co-choices in a corpus of widely viewed online debate texts (ODTs). Based on the voting results of each ODT, textual parts of the corpus were segmented into two main categories: ‘more persuasive’ and ‘less persuasive’ debaters. Supported by two specially designed software tools, the ODT corpus was manually annotated for appraisal features, and frequencies of choices and co-choices were extracted automatically. In line with previous research, the findings of this study revealed significant appraisal patterns associated with the ODT debaters, in addition to unique co-patterns characteristic of the ‘more persuasive’ and ‘less persuasive’ debaters. These findings are discussed in terms of potential implications, limitations and directions for future research.
Deep Learning Enabled Predicting Modeling of Mortality of Diabetes Mellitus Patients

Diabetes mellitus (DM) is a major public health concern that requires continuing medical care. It is also a leading cause of other serious health complications associated with longer hospital stays and increased mortality rates. Fluctuation of blood glucose levels are easy to monitor. Physicians manage patients' blood glucose to prevent or slow the progress of diabetes. In this paper, the MIMIC-III data set is used to develop and train multiple models that aim to predict the mortality of DM patients. Our deep learning model of convolutional neural network produced a 0.885 AUC score, above all baseline models we constructed, which include decision trees, random forests, and fully connected neural networks. The inputs for each model were comprised of admission type, age, Elixhauser comorbidity score, blood glucose measurements, and blood glucose range. The results obtained from these models are valuable for physicians, patients, and insurance companies. By analyzing the features that drive these models, care management for diabetic patients in an ICU setting can be improved resulting in lowered motality rate.
Navigation Based on Fuzzy Cognitive Maps for Needs of Ubiquitous Robotics

Internet of Things (IoT) has become a phenomenon of last five years. There is a number of various applications and its potential is still growing. Robotics is also one of perspective areas for IoT but it has some specific requirements. The concept of the so-called intelligent space (IS) is especially convenient for robotic applications, which can profit from spatially distributed and mutually networked sensors. Firstly, it is not necessary to carry all necessary sensors on board and hereby robots become much cheaper and secondly, such a solution is able to better predict occurrence of specific events, too. A ubiquitous robot, known as Ubibot, is an issue of interconnecting of IoT and IS. In this paper we proposed our own concept of Ubibot, where for navigation purposes the so-called fuzzy cognitive map (FCM) was used. For adjusting FCM parameters our own adaptation method based on evolutionary algorithms was used. The proposed system was experimentally tested and obtained results were analysed.
Software Process Improvement: Requirements Management, Verification and Validation

This paper presents a case study of use of CMMI (Capability Maturity Model Integration) best practices for an organization that want to improve their software development processes. The purpose was to improve the process area Requirements Management (REQM), supporting the level of compliance with the Verification (VER) and Validation (VAL) process areas. IDEAL (Initiating, Diagnosing, Establishing, Acting & Learning) was used to implement best practices. SCAMPI (Standard CMMI Appraisal Method for Process Improvement) was applied to evaluate the current status of the institution's processes. The results show that the management of the requirements was improved, defects were reduced, and customer satisfaction was achieved.
Towards Automatic Semantic Models by Extraction of Relevant Information from Online Text

Monitoring of human activities is an essential capability of many smart systems. In recent years much progress has been achieved. One of the key remaining challenges is the availability of labeled training data, in particular taking into account the degree of variability in human activities. A possible solution is to leverage large scale online data repositories. This has been previously attempted with image and sound data, as both microphones and cameras are widely used sensing modalities. In this paper, we describe a first step towards the use of online, text-based activity descriptions to support general sensor-based activity recognition systems. The idea is to extract semantic information from online texts about the way complex activities are composed of simple ones that have to be performed (e.g. a manual for assembling a furniture piece) and use such a semantic description in conjunction with sensor based, statistical classifiers of basic actions to recognize the complex activities and compose them into semantic trees. Extraction of domain relevant information evaluated in 11 different text-based manuals from different domains reached an average recall of 77%, and precision of 88%. Actual structural error-rate in the construction of respective trees was around 1%.
A Comparative Study of Navigation Techniques and Information Retrieval Algorithms for Web Mining

Search queries on databases, such as Cloud Bigtable(Cloud Bigtable) is a openly accessible form of Bigtable used by Google system) frequently give back countless, just a little subset of which is significant to the client.Ranking and categorization, which can likewise be consolidated, have been proposed to reduce this data over-burden issue.Results order and most applicable data recovery for a given hunt question is the focus of this work.A natural way to organize citations is according to their concept hierarchies.In this framework, we present a dynamic navigation of queries used with Improved Distance Rank Algorithm.The dynamic navigation provides less query results compared with static navigation used by Google.Distance Rank algorithm depends on fortification learning with the distance between pages as punishment and it minimizes the distance values of pages and provides better results compared to the familiar Page Rank algorithm.This algorithm models a real user surfing the web.When users randomly browses the web, they selects the next pages based their background from the last pages and the current status of the web page.By combining these two algorithms we will provide more efficient results to the given search query.
A Services Development Approach for Smart Home Based on Natural Language Instructions

With development of the infrastructures supporting smart home which has entered in a new stage featured by intelligent services.The services based on natural language instructions are aimed at simplifying and improving our lives.To customize and develop these services more efficiently, this paper proposes an approach to model and execute services based on natural language instructions at runtime which introduces the knowledge graph into development process.Firstly, a concept model of knowledge graph is introduced for smart home services.Secondly, we put forward the mechanism to construct the instance of knowledge graph for smart home services.Finally, rules of transformation are provided to achieve mapping from natural language instructions to the services of knowledge graph.We evaluate our model on a prototype system and the experimental results show that our approach can develop services at runtime and effectually reduce the lines of code by 90%.
Learning Approach for Smart Self-Adaptive Cyber-Physical Systems

Modern Cyber-Physical Systems (CPSs) need to be able to operate efficiently and reliably within continually changing, uncertain, and unanticipated environments. Namely, these systems should be capable of learning, automatically reconfiguring themselves, and be able to cooperate and collaborate with other CPSs. In a nutshell, exhibit human-like, smart capabilities in an autonomic manner. However, engineering such systems is all but trivial, primarily because we need to develop systems at design-time that are capable of autonomously coping with the uncertainty and change at runtime. Therefore, not only the importance of self-adaptivity as a system's feature increases, but it becomes a fundamental approach for the systems to continue meeting their functional specifications, fulfilling their business objectives while preserving the performance-despite all the runtime changes that the system may encounter. To tackle these challenges, this paper proposes the initial research vision and agenda with the envisioned contributions towards an approach for self-adaptation of cooperative, smart CPSs through shared knowledge and learning.
Dissecting Pie Charts

Pie charts can be regularly found both in the popular media and research publications. There is evidence that other forms of visualizations make it easier to evaluate the relative order of the data items. Doughnut charts have been suggested as a variation that has advantages over pie charts. We investigated how pie charts and doughnut charts are affected by the number of sectors in the chart, the difference in sector sizes, and the size of the hole. We carried out an eye tracking study to find out how the charts are read. Our study reveals the distribution of visual attention for each type of chart. The results indicate that doughnut charts with a medium size hole have a slight edge over the other chart types we studied. We also show that contrary to common claims, for information extraction also the area and length of sector arc are used in addition to the angles of the sectors.
Inferring Ears

This paper draws attention to two important and fruitful anecdotes from history useful for the development of a cognitive semiotic approach to music. The first is from Peirce’s writings, describing a complete structural change of understanding, perception and listening to music. Peirce describes the invention of a specific cognitive pidgin and the emergence of new social, embodied and cerebral habits. This emergence is shown in the example of Peirce’s friend who allegedly lost his sense of hearing but still enjoys music—no thanks to his ears. The second case study considers the “inferring ear” of Jimi Hendrix and his cooperation with Miles Davis, who taught Hendrix how to codify what he heard. Hence these anecdotes open pathways into the problem of the nature of musical perception, useful for exploring the codification and learning of music in particular. The nature of these abilities may be seen as intersubjective mimetics that are mediated through suprasubjective, triadic, embodied relations (signs). The article analyzes these topics from a point of view of a Peircean framework (with detours into the work of T. Deacon, V. Colapietro and G. Deleuze), aming to show the interconnections between such perspectives and some examples of contemporary neuroscientific research in this field.
Information Competing Process for Learning Diversified Representations

Learning representations with diversified information remains as an open problem. Towards learning diversified representations, a new approach, termed Information Competing Process (ICP), is proposed in this paper. Aiming to enrich the information carried by feature representations, ICP separates a representation into two parts with different mutual information constraints. The separated parts are forced to accomplish the downstream task independently in a competitive environment which prevents the two parts from learning what each other learned for the downstream task. Such competing parts are then combined synergistically to complete the task. By fusing representation parts learned competitively under different conditions, ICP facilitates obtaining diversified representations which contain rich information. Experiments on image classification and image reconstruction tasks demonstrate the great potential of ICP to learn discriminative and disentangled representations in both supervised and self-supervised learning settings.