Reviewerly/exHarmony huggingface dataset:
Designing CBR-BDI Agent for Implementing Reverse Logistics System

Over a few last decades, the enterprises make a great effort to avoid the risk of losing competition and strive to respond precisely to the customer return demands by improving their flexibility and agility, while maintaining productivity and quality. Reverse logistical (RL) activities including all operations that seize products from their final destination and bring them back to their origin in the purpose of recapturing value, recycling or disposing the return can be quite complex to manage due to dynamicity and uncertainty in demand. Thus, to deal with these issues we propose a Belief-Desire-intention (BDI) agent modeling which performs the reverse logistics decision process. Nevertheless, the use of intentional or (BDI) agent is uncomplicated and its decision process is easy to understand, its inability of learning from the environment remains the main problem faced in the development of this kind of intelligent agent. To solve this problem our (BDI) agents utilize the features of case-based reasoning (CBR) in order to improve the agent capabilities of learning from environment and to take the best decision about returned product based on the previous cases. The agent system design was implemented in Jason open source agent programming platform and the CBR system was designed using jCOLIBRI design tools.
A Hybrid MapReduce Implementation of PCA on Tianhe-2

"Big Data" has been a popular word anywhere. Researchers want the data processing more efficient. PCA algorithm is an effective data reduction algorithm applied to almost all big data fields. Meanwhile, there are many Machine Learning Algorithm Library applied to provide commonly-used algorithm, but these algorithms do not make good use of the resources of the supercomputer system. This paper uses MapReduce Model to design and implement PCA algorithm using MPI + OpenMP + SIMD hybrid accelerator programming tools on Tianhe-2 and get a significant speedup.
An Integrated Vision in Fundamental Physics

Physics explains almost all properties and behaviours of everything in the universe; starting from the smallest size, microscopic, to the largest, macrocosms. As a natural science, Physics tries to answer any question about ‘what’, ‘why’ and ‘how’ from all of the properties, the behaviours, the interaction and the event of everything. The behaviour and the effect of influencing one to another, giving one to the other, making all things formed and also destruction or annihilation. In another side, Physics show the role as a communicating tool between human and everything in the universe. Physics has a role as a language among men and other things or how human should have to interacting with all of things in the universe. Physics gives advices how to exploit the inhabitants of the nature in the best way possible. It is known, in the conventional way, that basic physics approached the large topics such as: mechanic, electricity, and magnetic as separate rules. The problems on mechanic, electricity, and magnetic was comprehended as a separate subject event in one object. Moreover, the formulations in each topic are seen different between one and other. A new vision has been proposed as an integrated approach in fundamental of Physics. It explained physics simpler, more adaptive in any fields and domain of sciences. The integration that was shown is not only break down the boundaries of difference formulation of mechanic, electricity and magnetic that became one concept and one formulation, but also have to add a new concept completed what we knew before. Thus, the process of the Physics study we have nowadays could be reviewed, or restructured.
The cultural politics of using technology to support the aesthetic in jazz record production.

This paper discusses the attitudes some contemporary jazz musicians have toward the use of technology and the thresholds of studio ‘intervention’ they are willing to cross in order to achieve their preferred studio recording aesthetic. The discussion draws upon personal experiences of mainly free jazz production, and also the returns of a pilot survey of jazz musicians, who were polled on the subject of the use of technology in the production of jazz recordings. Grounded theory was utilised as a methodology to code the survey responses into two main categories of ‘idealistic’ and ‘pragmatic’ responses. This coding enabled a discussion of the themes which were evident in the responses. This limited study found that clear consensus on the extent to which studio techniques should be used in the production of jazz recordings was not apparent in the data. There were instances where some studio techniques were considered acceptable, whilst other which were as similarly technically invasive as the acceptable, were not acceptable. It also appeared that it was generally acceptable to edit pre-composed elements of the musical performances, but not acceptable to edit improvisations. This perhaps reinforces the notion that for the jazz performer the ephemeral ‘moment’, which many studio productions aim to represent, occurs when they are improvising. Finally, it appears for these participants, whether a studio intervention such as an edit is audible in the final recording is irrelevant. The fact that a studio intervention was required or occurred due to perceived deficiencies in the recorded performance, crosses the boundary of acceptability in terms of their own interpretation of ‘authenticity’ in jazz recordings.
GUARDIAML: Machine Learning-Assisted Dynamic Information Flow Control

Developing JavaScript and web applications with confidentiality and integrity guarantees is challenging. Information flow control enables the enforcement of such guarantees. However, the integration of this technique into software tools used by developers in their workflow is missing. In this paper we present GUARDIAML, a machine learning-assisted dynamic information flow control tool for JavaScript web applications. GUARDIAML enables developers to detect unwanted information flow from sensitive sources to public sinks. It can handle the DOM and interaction with internal and external libraries and services. Because the specification of sources and sinks can be tedious, GUARDIAML assists in this process by suggesting the tagging of sources and sinks via a machine learning component.
Improving tourism experience in open data environment with mobile augmented reality: needs and challenges

Tourism industry plays a significant role in the economic growth of every country. It is one of the fastest growing sectors that provides continuous growth and deepening diversification that leads to socio-economic progress. This study gives an overview of the needs and challenges of incorporating Mobile Augmented Reality in an open data environment to improve tourism experience. The use of open data in tourism and mobile augmented reality was presented to have an overview of its impact on tourism. Mobile Augmented Reality applications are one of the best suitable applications to boost tourism activities. However, there are still existing needs and challenges that need to be addressed, these are a. technical requirement of the application, b. acceptability of users to utilize MAR, c. availability of data specifically on the tourism site.
Cocluster hypothesis and ranking consistency for relevance ranking in web search

Conventional approaches to relevance ranking typically optimize ranking models by each query separately. The traditional cluster hypothesis also does not consider the dependency between related queries. The goal of this paper is to leverage similar search intents to perform ranking consistency so that the search performance can be improved accordingly. Different from the previous supervised approach, which learns relevance by click‐through data, we propose a novel cocluster hypothesis to bridge the gap between relevance ranking and ranking consistency. A nearest‐neighbors test is also designed to measure the extent to which the cocluster hypothesis holds. Based on the hypothesis, we further propose a two‐stage unsupervised approach, in which two ranking heuristics and a cost function are developed to optimize the combination of consistency and uniqueness (or inconsistency). Extensive experiments have been conducted on a real and large‐scale search engine log. The experimental results not only verify the applicability of the proposed cocluster hypothesis but also show that our approach is effective in boosting the retrieval performance of the commercial search engine and reaches a comparable performance to the supervised approach.
Assessing Social Media Use in Instruction and Marketing

Assessment is increasingly important for libraries to address, as it speaks to justification for funding and support from the larger university. Many university budget models now require departments and colleges to be self-funding, whereas the library does not have traditional revenue sources. Statistics, including retention impact, encourage faculty and staff to promote library use to their students and encourage departments and colleges to support funding the library. This chapter explores best practices for assessing social media use in the context of instruction and marketing. It outlines the reasons for implementing an assessment plan as well as the steps necessary to successfully assess social media use, starting with outlining the specific goals for social media use, all the way through review and modification of the social media plan.
A New Intelligent Video Surveillance Architecture

Aimed at the old mode of traditional video surveillance for manual operation and forensics, and the fact that massive amounts of video data occupy a large amount of storage and transmission resources, an intelligent video surveillance architecture based on network and big data is proposed. The architecture includes three core modules: face real-time detecting, face intelligent coding, and face fast retrieval, which can automatically detect human faces, and then intelligently encode them and compare them with databases quickly. It greatly reduces the space occupied by video data storage and transmission, improves the intelligent level of video surveillance, and transforms "after-the-fact analysis" to "in-the-fact analysis" to meet modern criminal investigation requirements.
Anomaly detection in production facility network using ant agents

With the development of the information and communication technologies, the concept of Internet of Things has been popular. Production facilities in factories are connected to the Internet to instruct productions and to manage performance of them. Resulting from Internet connection of production facilities, it is increasing the risk of cyber-attacks targeting to the facilities. This paper proposes a method to detect anomaly behavior of production facilities influenced by cyber-attacks in which artificial ant agents are employed. Computer experiments are conducted to confirm the effectiveness of the proposed method.
A new coding-based algorithm for finding closest pair of vectors

Given n vectors x0,x1,…,xn−1 in {0,1}m, how to find two vectors whose pairwise Hamming distance is minimum? This problem is known as the Closest Pair Problem. If these vectors are generated uniformly at random except two of them are correlated with Pearson-correlation coefficient ρ, then the problem is called the Light Bulb Problem. In this work, we propose a novel coding-based scheme for the Closest Pair Problem. We design both randomized and deterministic algorithms, which achieve the best-known running time when the length of input vectors m is small and the minimum distance is very small compared to m. Specifically, the running time of our randomized algorithm is O(nlog2⁡n⋅2cm⋅poly(m)) and the running time of our deterministic algorithm is O(nlog⁡n⋅2c′m⋅poly(m)), where c and c′ are constants depending only on the (relative) distance of the closest pair. When applied to the Light Bulb Problem, our result yields state-of-the-art deterministic running time when the Pearson-correlation coefficient ρ is very large. Specifically, when ρ≥0.9933, our deterministic algorithm runs faster than the previously best deterministic algorithm (Alman, SOSA 2019).
Automatic Generating Vocabulary File in Myanmar Information Retrieval

Construction of vocabulary file for Myanmar information retrieval (MIR) is an important task during index retrieval operation. Although generating vocabulary file using dictionary and orthography have been existed for Myanmar language, the performance depends on the coverage of the dictionary and training dataset and can cause out of vocabulary (OOV) problem, leading to lower precision and recall in information retrieval. Resources are sparse for Myanmar language and it is difficult to find a comprehensive dictionary for any domain. In this paper, we propose a new method for similarity percentage measurement based automatic construction of vocabulary file, a variant of Levenshtein distance without the use of any additional data e.g., training corpus. The development of the automatic generation of vocabulary files from raw Myanmar texts to use in indexing of MIR task. The experimental results showed that the method is highly effective for Myanmar language government domain.
Semantic-Based Search Engine System for Graph Images in Academic Literature

It is well known that information retrieval is an essential aspect of search engine systems because there is a very large amount of data published on the internet that cannot be manually searched. However, search engine systems should not only present relevant results but also obtain new knowledge from the user’s searches. For example, new knowledge in academic research areas may be present in images that include graphs. In this study, we utilize methods to extract graphical and textual information from graph images and store this new knowledge in an ontology. We also propose a search engine system that is applicable to an ontology that contains this extractable information, which is extracted from images with graphs. The developed ontology is useful because users can acquire considerable amount of knowledge that is discovered from the semantic relations in the ontology. To evaluate the search engine system, we conducted four simulations to address four main issues. The results indicate that the proposed system provides accurate and relevant results; moreover, as indicated by the high F-measure values, the performance of the system is highly acceptable. However, we also found some limitations, which will be mitigated in a future study.
PROSPECTS OF INVOLVEMENT OF MODERN STUDENTS IN RESEARCH ACTIVITIES (ON THE MATERIALS OF THE TULA REGION)

The article deals with the reasons of the low degree of involvement of young people in scientific activities. It is noted that the popularization of this direction helps to improve not only economic, technological and other indicators, but also the whole social life. The involvement of young people, and in particular students, in research activities plays a huge role, as implemented developments often carry an innovative approach and allow to develop several possible variations not only to solve urgent problems, but also for their further integration into other priority areas. This paper describes the reasons why there is a fairly low level of student activity in scientific, research and project activities. Studying the aspects of this problem, the above is confirmed by the sociological research in the form of a questionnaire, which allows to identify the degree of motivation among students. It is fixed that at this stage it is difficult to use the means of motivation in full on the basis of the lack of a resource that can contribute to this and bring information to the audience. It is concluded that the basic source is necessary for the actualization of a particular direction. In the age of information and technological progress communication in society, the development and promotion of various ideas and directions is achieved through the use of the media component [3]. Various ways of solution are considered and as the most optimal it was revealed that the source, which will serve as a tool for the dissemination of information about research activities, is a website on the Internet, and the means of its actualization in the appropriate target audience can be designated pages in social networks. It is also noted that the development of the above resources in the social network is the most effective, as young people devote an impressive amount of time to social networks and they are the main source of information for them. The author believes that the introduction and distribution of content about various competitions, conferences, exhibitions and competitions actualizes scientific activities.
Active shape dictionary for automatic segmentation of pathological lung in low-dose CT image

Accurate lung segmentation is of great significance in clinical application. However, it is still a challenging task due to its complex structures, pathological changes, individual differences and low image quality. In the paper, a novel shape dictionary-based approach, named active shape dictionary, is introduced to automatically delineate pathological lungs from clinical 3D CT images. The active shape dictionary improves sparse shape composition in eigenvector space to effectively reduce local shape reconstruction error. The proposed framework makes the shape model to be iteratively deformed to target boundary with discriminative appearance dictionary learning and gradient vector flow to drive the landmarks. The proposed algorithm is tested on 40 3D low-dose CT images with lung tumors. Compared to state-of-the-art methods, the proposed approach can robustly and accurately detect pathological lung surface.
Collaborative Information Seeking in Tourism

People often travel in groups where information seeking occurs throughout the whole course of travel before various decisions can be made. This qualitative study investigates how small groups of young Chinese leisure tourists conduct collaborative information seeking (CIS) to support their joint decision-making as travelling to Australia through a grounded theory approach. Most of existing literature in CIS are limited to workplace contexts. In addition, previous studies often failed to include the outcome of information seeking to better understand collaboration as a process of joint decision-making. This study aims to develop new models and theories of tourist CIS, propose appropriate methods to study CIS in leisure contexts and provide practical implication regarding the design of CIS tools and systems for tourists. This research contributes to existing understandings of CIS by exploring the understudied leisure context, investigating it in a broader framework of joint decision-making, and looking at a comprehensive project where CIS occurs instead of individual information seeking tasks.
Virtual Adornments

The Internet of Things (IoT) [3, 16, 35] is a physical-digital ecosystem of compliant technologies and heterogeneous parts, enabling vast transmissions of data and candid, pervasive presence of things [40]. Fashion, on the other hand, is an embodied practice, an information medium of material, social, cultural, economic and political forces. Many wearables are outfitted to actuate data input sources as a visualised display. However, the impact and rich possibilities of fashion adornment practices for embodied data engagement in IoT wearables design have been overlooked. Introducing computational materials of the IoT to physical properties pushes this virtual system into the physical realm. In this research, an aesthetic criterion of haute couture practices considers the material turn [34, 39]. Design cases of fashion-led adornment style are a promising path to follow in the context of designing wearables for an Internet of Worn Things.
Hand gesture recognition using image segmentation and deep neural network

Sign language is a medium of communication for a person with an auditory and verbal disability or deficiency. Therefore, it is essential to understand their hand gestures without difficulty in order to have effortless and improved communication. Hand gesture detection is a challenging task. In this paper, we proposed an efficient method to recognize and classify images that contains hand gesture, using image Segmentation and the Bottleneck feature from a pre-trained model of Deep Neural Network. Our model achieved a descent accuracy over 96% therefore can be used to build an efficient system which can work as an interpreter between the disabled person and the other party. A comparison between conventional CNN (Convolutional Neural Network) model and our model is also shown to measure the effectiveness of our proposed method.
Stereographic Visualization of 5-Dimensional Regular Polytopes

Regular polytopes (RPs) are an extension of 2D (two-dimensional) regular polygons and 3D regular polyhedra in n-dimensional ( n ≥ 4 ) space. The high abstraction and perfect symmetry are their most prominent features. The traditional projections only show vertex and edge information. Although such projections can preserve the highest degree of symmetry of the RPs, they can not transmit their metric or topological information. Based on the generalized stereographic projection, this paper establishes visualization methods for 5D RPs, which can preserve symmetries and convey general metric and topological data. It is a general strategy that can be extended to visualize n-dimensional RPs ( n &gt; 5 ).
Interval Type-2 Fuzzy System Based on NT Method and Its Probability Representation

In this paper, we study the interval type-2 fuzzy system based on NT method. We establish an interval type-2 fuzzy system based on a set of input-output data and reveal the probability significance of the interval type-2 fuzzy system. We prove that the crisp output of the interval type-2 fuzzy system based on NT method can be regarded as the conditional mathematical expectation of certain random vectors. And we provide the expression of the joint probability density function and this interval type-2 fuzzy system.
Saliency Detection via Objectness Transferring

In this paper, we present a novel framework to incorporate top-down guidance to identify salient objects. The salient regions/objects are predicted by transferring objectness prior without the requirement of center-biased assumption. The proposed framework consists of the following two basic steps: In the top-down process, we create a location saliency map (LSM), which can be identified by a set of overlapping windows likely to cover salient objects. The corresponding binary segmentation masks of training windows are treated as high-level knowledge to be transferred to the test image windows, which may share visual similarity with training windows. In the bottom-up process, a multi-layer segmentation framework is employed, providing local shape information that is used to delineate accurate object boundaries. Through integrating top-down objectness priors and bottom-up image representation, our approach is able to produce an accurate pixel-wise saliency map. Extensive experiments show that our approach achieves the state-of-the-art results over MSRA 1000 dataset.