Reviewerly/exHarmony huggingface dataset:
Toward Adapting Metamodeling Approach for Legacy to Cloud Migration

Migration of legacy application to Cloud is a fast-growing area of knowledge. Many IT-based organizations inclined toward empowering their legacy application with cloud computing capabilities. Many researchers, academicians, national, and international bodies are creating knowledge models to allow knowledge sharing and provide effective cloud migration model. This knowledge is scattered and huge, but lack of knowledge management. Our motive is to produce a metamodel, which could be able to generalize the cloud migration domain. Metamodel approach is an approach, to gather all domain concepts and their relationships. Using the metamodel, variety of domain solution models can be built. It can act as a language infrastructure which unifies describing the process model of moving legacy enterprise applications to the cloud environments. The benefits of the metamodel include simplifying the migration process, guidance, reuse specialized migration knowledge and support training and knowledge management activities. Furthermore, it reduces complexity and ambiguity in cloud migration domain.
Investing in Cyber Defense: A Value-Focused Analysis of Investment Decisions for Microgrids

To mitigate disruptions to commercial power grids, and to achieve operational efficiencies by managing energy use, many organizations are fielding smaller, local, self-contained microgrids.The computer control systems that operate the microgrids create new vulnerabilities to a rapidly-escalating array of cyber attacks.This creates a tension between the need to improve energy assurance and efficiency through microgrids, and the need to protect against cyber attacks that can disrupt and damage the organization's energy systems.Through a series of interviews with subject matter experts and end-users, this exploratory study surfaces the decision-makers' important values in this decision space and develops a network of those values to guide decision-makers to make better decisions in balancing these competing needs.
3D Object Detection Using a New Descriptor with RGB-D

Object detection is a very important study area in computer vision. Many research use only RGB images to find objects. In our work, we present new descriptor for object detection using RGB-D’s Depth image data. We combine RGB image with depth image to create new feature vector. The introduced features feeds Bag of Visual Words algorithm to classify images of the objects. Result shows us to RGB-D images are given better accuracy results to comparing with RGB image.
A Fuzzy Inference System as a Tool to Measure Levels of Interaction

The interaction between humans and their environment is a current research area. Technology has been part of the environment with which humans have forged their progress, making this socio-technical phenomenon a field of study of interest in the field of research and technological development. In this paper, we present a FIS as a Tool to Measure Levels of Interaction during human-machine interaction. The proposal describes a system that bases its reasoning on user performance during user-exhibition interaction context in an interactive museum. Thte performance is determined by levels of interaction derived from intercativity actions and situations within the user-exhibition context. The FIS objective is to improve the user experience by managing the interaction process in user-exhibition relationships.
Logical Analogies: Interpretations, Oppositions, and Probabilism

I present two logical systems to show the “analogy of proportionality” common to several interpretations: modality (necessity and possibility), quantification, truth-functional relations, moral attitudes (deontic logic), states of knowledge (epistemic logic), and states of belief (doxastic logic). To display the two underlying analogical relations, I call upon the originally Scholastic convention, recently put to use again, of using squares, hexagons, and octagons “of opposition”. A combined epistemic–deontic logic happens to be found in the traditional “probabilist” theory of the “good conscience”, and I shall then briefly explain how this is so.
Polarized Topic Modeling for User Characteristics in Online Discussion Community

Topic modeling methods, such as latent Dirichlet allocation (LDA), are successfully applied in a number of computational linguistics applications. This paper presents a new approach to topic modeling within a new domain other than linguistic analysis. We present a pilot study where an LDA model is applied to an online community rather than the textual contents they produced using the idea that a user in an article is analogous to a word in a document within the context of the LDA model. We also propose a method for determining polarity using positive (+) and negative (-) signs regarding topics. As a result, each user has a topic score whose absolute value is equal to the topic distribution learned from topic modeling, and its sign indicates the polarity on that specific subject. We demonstrate the effectiveness of our proposed approach with experimental results, which provide opportunities to apply the LDA model to targets other than lexical elements.
Biomedical Signal Classification Methods

Machine learning utilizes the theory of statistics to create mathematical models, because the main task is to produce inference from a sample. When a model is created, its representation and algorithmic solution for inference requires it to be efficient as well. In some applications, the efficiency of the machine learning algorithm might be as significant as its classification accuracy. Machine learning is employed toward many problems in biomedical signal processing as a decision support element. The aim of this chapter is to assist researchers and biomedical engineers in choosing a suitable machine learning algorithm, and then guide them for optimal strategy by using publicly available biomedical signal databases. Hence the fundamental machine learning techniques employed for the classification of the biomedical signals are discussed in this chapter. Toward the end of each section, suitable MATLAB functions with different biomedical signal applications will be illustrated by an example.
Analysis of Pure Consciousness as a Correlated Extended Informative Perspective

Introduction. In this paper, an attempt is made to study the Husserlian philosophy of knowledge in order to identify, on the basis of our own reflections, not just the true fundamental core of pure consciousness, but the dynamic existence within the framework of that stratum to which we fall upon accomplishing the phenomenological reduction.&#x0D; The methodological basis for this work is the position of the phenomenological theory of pure consciousness from "Ideas for Pure Phenomenology and Phenomenological Philosophy. Book One. A General Introduction to Pure Phenomenology" by E. Husserl. According to Husserl, our usual everyday experience may be subject to reduction up to the discovery of a layer of pure, a priori cognitive processes. The very same a priori knowledge can be found in I. Kant’s "Critique of Pure Reason", in particular from his reflections on transcendental aesthetics and foundations of pure reason.&#x0D; Results. Having analyzed the hidden possibilities of pure consciousness, the author declares that, even in such a phenomenological layer, where any volitional arbitrariness is excluded, there is a structure, or, in other words, a kind of intellectualization, shaped by time.&#x0D; Conclusion. The author believes that a half-hearted view of consciousness as merely an intentional being leads to a negative simplification of the subject of knowledge. A new, expanded model of phenomenal-existential consciousness, proposed in this work, showed that the decomposition of the basic attributes of pure consciousness has an expanded cognitive perspective of such phenomena-things that are not understood by simple and one-sided Husserlian intention, but, on the contrary, they reveal even more complex phenomena.
Mitigating Reputational Risk Through Image Repair Strategies

This article examines rhetorical strategies as well as the linguistic construction of those strategies in press releases put out by Cadbury Malaysia in response to accusations that it has failed to comply with halal certification standards. Drawing on image repair theory and the concept of the ideological square, six press releases were analysed to identify the rhetorical strategies as well as semantic structures that were used to repair the organization’s image and minimize reputational risk. The analysis reveals how the organization moved beyond denial to also employ the rhetorical strategies of attacking one’s accuser and bolstering. Despite early media reports naming government agencies as the accusers, the press releases put out by Cadbury Malaysia determined the source of accusations as unnamed individuals within the agencies, thereby avoiding confrontations with the relevant authorities. The analysis shows how language is used in press releases to construct the positive self and negative other as Cadbury Malaysia promoted a discourse of renewal to reassert its position within a highly lucrative halal market. This article provides novel insights into understanding how language works within rhetorical strategies of image repair.
A Feasibility Study for Speed up Data Transformation Process for Pentaho by Auto-Tune Parallelism

On ETL processing using Pentaho, we propose automatic shortening processing time method by tuning each step concurrency level for Pentaho data transformation. It monitors queue length for each processing steps, and tunes number of threads for the step of longest queue length. For certain in-house data transformation target, proposed method can shorten processing time comparable to tune manually by expert.
MedSpecSearch: Medical Specialty Search

MedSpecSearch ( www.medspecsearch.com ) is a search engine for helping users to find the relevant medical specialty for a doctor visit based on users' description of symptoms. This system is useful for users who are not sure of which medical specialty they should consult to. Furthermore, the API of the search engine can be used as part of the online doctor appointment and medical consultation sites to route the patient or question to the right medical specialty. The system returns the top three relevant specialties when the estimated confidence score is high. Otherwise, it asks users to input more data.
Automatic Analysis of Authorship Using Syntactic n-grams

We have conducted various experiments [93] in order to test the usefulness of the concept of syntactic n-grams. Essentially, we consider the task of authorship attribution, i.e., there are texts for which the authors are known and a text for which we have to determine the author (among the considered authors only). In our case, we use a corpus composed of texts written by three different authors.
The Influence of Backstories on Queries with Varying Levels of Intent in Task-Based Specialised Information Retrieval

Various ways of determining ambiguity of search queries exist for general search. Relevancy of result documents for searches however is not determined by the query alone. The current user intent is what drives a search and determines if results are useful. Intent ambiguity describes queries that might have multiple intents. Conventional disambiguation methods might not work in specialised search where a goal is usually similar or the same (e.g. finding job offerings in job search). Research described in this document investigates how to determine single and possible multi-intent queries for job search and how contextual information, especially backstories, affect the job search process. Results will lead to a better understanding of how important backstories and the handling of intent ambiguity are in specialised information retrieval. The importance of test collections with built-in ambiguity to better test performance will also be indicated. The proposed research is conducted with data from a major Australian job search platform.
Towards real-time optical quantum sensors

Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text V. Cimini, L. Ruggiero, I. Gianani, M. Sbroscia, T. Gasperi, E. Roccia, L. Mancino, D. Tofani, F. Bruni, M. A. Ricci, and M. Barbieri, "Towards real-time optical quantum sensors," in Quantum Information and Measurement (QIM) V: Quantum Technologies, OSA Technical Digest (Optica Publishing Group, 2019), paper T5A.33. Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
The Collaborative Filtering Method Based on Social Information Fusion

In the social network, similar users are assumed to prefer similar items, so searching the similar users of a target user plays an important role for most collaborative filtering methods. Existing collaborative filtering methods use user ratings of items to search for similar users. Nowadays, abundant social information is produced by the Internet, such as user profiles, social relationships, behaviors, interests, and so on. Only using user ratings of items is not sufficient to recommend wanted items and search for similar users. In this paper, we propose a new collaborative filtering method using social information fusion. Our method first uses social information fusion to search for similar users and then updates the user rating of items for recommendation using similar users. Experiments show that our method outperforms the existing methods based on user ratings of items and using social information fusion to search similar users is an available way for collaborative filtering methods of recommender systems.
Improving latent variable descriptiveness by modelling rather than ad-hoc factors

Powerful generative models, particularly in natural language modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variable. We discuss an alternative and general approach to latent variable modelling, based on an objective that encourages a perfect reconstruction by tying a stochastic autoencoder with a variational autoencoder (VAE). This ensures by design that the latent variable captures information about the observations, whilst retaining the ability to generate well. Interestingly, although our model is fundamentally different to a VAE, the lower bound attained is identical to the standard VAE bound but with the addition of a simple pre-factor; thus, providing a formal interpretation of the commonly used, ad-hoc pre-factors in training VAEs.
Evolutionary Computation Meets Multiagent Systems for Better Solving Optimization Problems

In this work, we discuss the synergy between Evolutionary Computation (EC) and Multi-Agent Systems (MAS) when both are used together to enhance the process of solving optimization problems. Evolutionary algorithms are inspired by nature and follow Darwin theory of the fittest. They are usually applied where there is no specific algorithm which can solve optimization problems in a reasonable time. Multi-Agent Systems, in their turn, are collections of autonomous entities, named agents, that sense their environment and execute some actions in the environment to meet their individual or common goals. When these two techniques are applied together, one can create powerful approaches to better solve optimization problems. This paper presents an overview of this combined approach, considering both mono-objective and multi-objective approaches. In particular, we stress the importance of hyper-heuristic approaches, i.e., heuristics that help to choose the best EC algorithm among a candidate set.
Where Do HPC and Cognitive Science Meet in Latin America?

In the last few decades there has been a noticeable shift of attention of the high-performance computing (HPC) applications development community from deterministic to heuristic models of problem solving, mainly due to observation that models based on human knowledge and expertise have proven to be good approaches to solving complex problems. Also, a shift of artificial intelligence (AI) to HPC has occurred, as AI researchers now find in HPC the means to build more complex models of human cognition. This is in general the case, and it is also true in the Latin America region. On the other hand, in this region there seems to be an estrangement between the cognitive science (CS) and the AI communities, perhaps due to the shift of AI to HPC and the resulting change of attention of AI researchers. However, there is a noticeable increase in the number of academic programs in the region focusing on CS. In this article we provide evidence of the previous assertions and propose a list of suggestions or recommendations on how to bring the HPC and CogSci communities closer in the region, as well as the potential benefits of such a process.
Strong Convergence of Neuro-Fuzzy Learning With Adaptive Momentum for Complex System

This paper studies a split-complex-valued neuro-fuzzy algorithm for fuzzy inference system, which realizes a frequently used zero-order Takagi–Sugeno–Kang system. Here, adaptive momentum is utilized to speed up the learning convergence. Some strong convergence results are demonstrated based on the weak convergence results, which expresses that the weight sequence of fuzzy parameters converges to a fixed point. Simulation results support the theoretical findings.
Design and Application of the Intelligent Learning Platform for College English Based on the New Engineering Concept

The deep integration of the information technology and the higher education in China has changed the ways of the higher education in China and greatly influenced the implementation of the classroom teaching. Under the impact of the information technologies, college English classroom ecology has been greatly disturbed, resulting in the imbalance of the classroom ecological structures and the dysfunction. The intelligent learning requires the continuous improvement, optimization and adaptation to the environment, and the support and promotion of the personalized, characteristic, comprehensive and innovative development of people in the information age, which represents the direction of the human learning. The construction of the learning environment is the basis of realizing the reform of the teaching and learning methods. It is imperative to construct a wise and ecological college English learning environment.
Automatic Cafe Management System Using Waiter Robot

Advancement in robotic technology has empowered the leeway of robotics from traditional and industrial robotic applications. In recent decades the interest in robotic technology increased in the social, industrial and medical side. In most cafe systems there is a big problem of waiter cleanness and also waiter's monthly salary. In this paper the concept of very low cost automatic café management system using waiter robot is presented in which it has two parts. One is the automatic ordering system in which the customer can place the order using electronic menu system and can verify via LCD display. Once the customer confirms the order. The order wirelessly transferred to the kitchen via wireless module and display on LCD which is placed in the kitchen. The cook prepares the order. Once the order gets prepared the kitchen person call the robot wirelessly to serve the food on the respective table. The robot waiter serves the order automatically and returns to its original position.
The role of L/W in the solar radiation for Saharian cities

The urban fabric of the desert cities is based on the principle of reducing the impact of urban canyons on direct solar radiation. Here comes this research, which is based on a comparative study of the periods of direct solarisation and values of the solar energy of urban canyons via two urban fabrics that have different building densities, where the ratio between L/W is different. In order to obtain the real values of the solar energy (thermal, lighting), the test field was examined every two hours, each three consecutive days. The measurement stations are positioned by the three types of the relationship between L/W, (L≥2w, L=w, L≤0.5w). According to the results, we noticed and recorded the difference in the periods of direct solarization between the types of urban engineering canyons, reaching 6 hours a day, the difference in thermal values of air, reaching 4 °C, and the difference in periods of direct natural lighting, reaching 6 hours. It should be noted that the role of the relationship between L/W is to protect the urban canyons by reducing the impact of direct solar radiation on urban canyons, providing longer hours of shading, and reducing solar energy levels (thermal, lighting) at the urban canyons. This research is classified under the research axis (the studies of external spaces in the urban environment according to the bioclimatic approach and geographic approach). But this research aims to focus on the tracking and studying the distribution of the solar radiation - thermal radiation and lighting radiation - in different types of street canyons by comparing the study of the direct solarization periods of each type and the quantity of solar energy collected during the solarization periods.
Motion blur reduction with triangular waveform emission and optimized enhance filter

Abstract Degradation of moving image quality by motion blur on hold‐type displays is a well‐known issue. In order to reduce motion blur, a number of driving methods, such as a higher frame rate and a shorter temporal aperture, have been proposed. Methods to reduce motion blur by means of signal processing as a precompensation method have also been proposed. In these methods, however, hold emission that emits light at a constant intensity during a frame is used. Since the spatial frequency response of the hold emission in a moving picture has a null point, the effectiveness of precompensation methods is limited to the lower‐spatial frequency domain. A triangular waveform emission has been investigated, and it has a higher spatial frequency response than hold emission. In the present paper, we calculated an optimized enhancement filter for the triangular waveform emission as a precompensation method and demonstrated its effectiveness for reducing motion blur by using the triangular waveform emission and the optimized enhancement filter. As a result, the optimized enhancement filter reduced motion blur and suppressed distortion that emerged when using a conventional enhancement filter.
Discovery of Probable Sentiments in Hypertensive Pregnant Women using Horizontal Fragmentation and Pointwise Mutual Information

Since a decade research over sentiment analysis and opinion mining was evolving slowing and emerging widely with greater perspectives and objectives. Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated on a daily basis. This analysis relies on the opinions made by the individuals. These opinions are text, may be positive or negative or a phrase which gives significance to the context. Also these opinions have the power of expressing the context besides drags the attention of new folks. Expressing such opinions ranges from documents level, to the sentence level, to phrase level, to word level and to special symbol level. All these opinion types are labelled with common name Sentiment Analysis. Sentiment Analysis is health care is evolving narrowly with wider research strings. This paper mainly focuses in identifying Sentiments in health care. These sentiments can be medical test values which may be numeric and nominal; sometimes in text too. Such sentiments are identified with pre-fragmentation of data set and Pointwise Mutual Information measure. To accomplish this data of hypertensive pregnant women is considered.