Reviewerly/exHarmony huggingface dataset:
Design of Online Variable Data Printing Jobs Production System Based on Cloud-Platform

According to the basic principle of variable data printing and the common production methods of variable data printing, an innovative technical solution for network service platform based on XML technology to realize multi-data format file creation of variable data printing jobs is proposed. The XML format input file is synchronously converted into a PPML format and a PDF format output file by the XML parsing technology DOM4J and the formatting object processor XML-FO. Tencent cloud platform, J2EE development platform to build B/S network architecture and SSM integration framework, MySQL database for data storage and management is used to design and implement cloud-based online variable data print job production system.
Vectors of Pairwise Item Preferences

Neural embedding has been widely applied as an effective category of vectorization methods in real-world recommender systems. However, its exploration of users' explicit feedback on items, to create good quality user and item vectors is still limited. Existing neural embedding methods only consider the items that are accessed by the users, but neglect the scenario when a user gives high or low rating to a particular item. In this paper, we propose Pref2Vec, a method to generate vector representations of pairwise item preferences, users and items, which can be directly utilized for machine learning tasks. Specifically, Pref2Vec considers users' pairwise item preferences as elementary units. It vectorizes users' pairwise preferences by maximizing the likelihood estimation of the conditional probability of each pairwise item preference given another one. With the pairwise preference matrix and the generated preference vectors, the vectors of users are yielded by minimizing the difference between users' observed preferences and the product of the user and preference vectors. Similarly, the vectorization of items can be achieved with the user-item rating matrix and the users vectors. We conducted extensive experiments on three benchmark datasets to assess the quality of item vectors and the initialization independence of the user and item vectors. The utility of our vectorization results is shown by the recommendation performance achieved using them. Our experimental results show significant improvement over state-of-the-art baselines.
Brief Paper: A Study on the Possibility of Introducing Korean Technologies into Vietnam for Monitoring and Prevention of Solitary Deaths of Elderly

The Socialist Republic of Vietnam has become one of the top ten nations with the highest aging rate. The proportion of their aging population increased from 7.2% to 10.95% from 1989 to 2017 and entered into the aging society six years earlier than what had been anticipated in 2011. The main issues in such a society are the problems associated with the elderly living by themselves and their solitary deaths. This study attempts to find a solution which would mitigate the burdens of aging or aged population who are living in a lonely and solitary living condition focusing on the system used for the purpose of managing or monitoring of their daily lives to prevent any undesirable outcomes including solitary deaths. The study also discusses the possibility of introducing the system into Vietnam.
Quaternion Watershed Transform in Segmentation of Motion Capture Data

The novel approach for segmentation of motion capture data is proposed. It utilizes hierarchical watershed transform for time series containing unit quaternions which describe rotations of human skeleton joints as well as their angular velocities. To approximate gradient magnitudes of subsequent time instants, aggregated geodesic distance on hypersphere $$S^3$$ for preceding and following quaternions is computed. The introduced segmentation method was applied to gait analysis. The highly precise motion capture data, registered in a human motion lab (HML), were used. A fully automatic watershed transform with detection of catchment basins as well as a marker controlled one were investigated. The obtained results are promising. By selecting proper hierarchy of a segmentation or by specifying adequate markers, it is even feasible to divide gait cycle into consecutive steps. The segmentation can be improved in respect to a considered problem, if only selected joints are taken into account by watershed transform.
Interpretation of count and mass NPs by L2-learners from generalized classifier L1s

This study investigates whether L2-learners from generalized classifier (GC) languages (Korean and Chinese) can acquire the count/mass distinction of English, in the domain of singular NP interpretation. We hypothesize that L1-Korean/Chinese L2-English learners transfer the properties of NP interpretation from their L1 to their L2. This hypothesis was tested using a Picture-Matching Task (PMT). To confirm the L1-transfer effect, Korean and Chinese versions of the experiment were also administered. The results of the English study show that L1-Korean/Chinese L2-English learners were target-like with count NPs, but allowed object-readings of mass NPs more than English NSs. The results of the Korean and Chinese experiment versions provide possible evidence that the pattern of results obtained for L2-English is due to L1-transfer from Korean/Chinese.
EDUCATIONAL ASPECT OF I. A. KRYLOV’S COMEDIES

The article provides an analysis of the perception and use of the dramaturgy of the XVIII-XIX centuries and, in particular, the dramaturgy of the great Russian fabulist I. A. Krylov with educational and teaching purposes in senior classes of secondary school. It is suggested to use the study and reference to classical dramaturgy as a method of pedagogical educational impact in Literature classes. In this regard, the paper contains a thorough analysis of one-act Krylov’s plays, highlighting the theme and problems of each work, focuses on the genre of dramaturgy and the principles of the educational aspect of the writer’s creative work as a whole.
ORGANIZATION OF CLASSES ON ROBOTICS FOR PRESCHOOLERS USING LEGO WEDO

The article analyzes the need for the development of technical skills and algorithmic literacy starting from preschool age. Considerable attention is paid to the popularization of educational robotics for children of senior preschool age, the formation of their primary ideas about technology, its properties, and its purpose in human life. The program "School of Young Engineers" is proposed. The focus of the program is to develop technical creativity of children of senior preschool age by means of robotics. The main idea of the program is to wide and deep the content of the educational activities of preschoolers using LEGO.
Interactive User Interface to Advent HCI Artefact

HCI is the future for research. Much has been done in this area and a lot more needs to be explored. One of the prominent unexplored areas involves the previous HCI (human–computer interaction) design process and the two point the existing phases namely reliability of product and feedback and evaluation process are identified. Author proposes a novel approach for HCI design process, integration of previous design process that extends two more phases from previous as: testing for reliability of product and feedback and evaluation for user’s response and reality about them. The novelty in research is urged by the previously proposed modules in software engineering and streamlining the design process is them. Author contribute the new paradigm of design philosophy for human-centric design policies and introduce novel design models for exploitable design taking into account the concept of usability engineering. The new HCI waterfall model covers all stages of development for a human–computer interaction project. Each phase of the model has been well derived with graphical representation and shows their real existence. Similar parameters have been taken to design an HCI Interactive Incremental Model. The interactive incremental model of HCI involves the valued part for their verification of each phases, i.e. (what do you think to propose, Extended Version), known as evaluation. These two models open up the new era of HCI research to design few more models for future work and extend their importance in the development phase.
Design of Experiments in Computational Linguistics

As we mentioned earlier in the book, in the automatic analysis of natural language (natural language processing, NLP) and in computational linguistics, machine learning methods are becoming more and more popular. Applying these methods increasingly gives better results. In this chapter, we describe the design of experiments in computational lingusitics: problem – corpus – gold standard – feature selection – dimensionality reduction – classification – evaluation (k-fold cross validation).
Development of Intelligent English Multimedia Teaching Resources Using Data Mining

With the emergence of the “Internet+”, all walks of life have undergone tremendous changes. The “Internet + Education” has become a trend of development, bringing new opportunities and challenges for the English teaching and learning. Through the online English learning platform, we can carry out the teaching and learning, analyze the students’ learning data and establish the relevant models using the data mining technology, and explore the correlation between the English examinations and various elements, which has important values for the students’ learning and the teachers’ teaching. The author has made a thorough study of it. Practical teaching has proved that the multimedia teaching system is easier for students to understand, train their thinking abilities, improve their imagination and enrich the amount of the teaching information. This paper constructs a network multimedia teaching system, which enriches the teaching means.
Minimality and Simplicity of Rules for the Internet-of-Things

Rule-based systems have been increasing in popularity in recent years. They allow for easier handling of both simple and complicated problems utilising a set of rules created in various ways (e.g., manually, or (semi-) automatically, via, say, machine learning or decision trees) depending on the situation. Despite their usefulness however, there are still improvements to be made. Knowledge representation technologies have been available for a long time and provide the means to represent domains formally and correlate entities in those domains. They also allow for ontological reasoning that can take advantage of such connections between entities. These techniques can be useful when applied on rule-based systems in order to improve the quality of rules and, hence, overall system performance. We describe and implement an approach to refine rules used in Internet-of-Things scenarios using knowledge representation and reasoning. The proposed solution uses ontological reasoning on the preconditions and postconditions of rules as it aims to reduce the total amount of rules in a system and simplify them.
Working Abroad in a Research Laboratory in the U.S.

Many computer science students dream of the possibility of working abroad, usually at one of the big companies that define the course in technology, innovating and creating the most significant technology breakthroughs. I wasn't the exception. As a student at the Facultad de Informática, Universidad Nacional de La Plata, I always thought about working as a Software Developer in Silicon Valley for a couple of years. In my mind, accomplishing such a goal would provide me with sufficient insights to better perform in a company (or even start a new one) when returning to my home country. I figured I needed some time until I managed to achieve my goal, but I didn't actively pursue it. Looking back, it was a pretty straightforward path; however, at the moment, I did not know what the result would be.
The Fault Diagnosis Model Established Based on RVM

This paper introduces the application of Relevance Vector Machine (RVM) in fault diagnosis. First, the theoretical contents of RVM including the algorithm characteristics, the derivation of mathematical models, the characteristics of kernel functions and multimode classification are introduced. Then, the multi-classification fault diagnosis model of building electrical system is established by using RVM. Finally, the experimental results show that the RVM model has good classification effect on small sample data.
Exploring Visual Analytics to Measure Reliability for IoT Oriented Pollution Detection Software Perspectives

The measurement of the reliability of such IoT based application requires an embedded analysis. The parameters are the number of imprecise or faulty measures as well as the identification of core modules. This article investigates that how far visual introspection can assist in troubleshooting of IoT-based software bugs. This specific requirement improvises a new idea, where the shape of the plots with actual data can indicate the cause of the error and further they can be patched if the software repairing strategies are implemented adjudging the visual analytics. It is quite indifferent to analyze faults for existing applications as a variation of topological and practicing parameters which takes substantial numbers of iterations and observations. Categorically, the present use-case establishes the fact to analyze and infer concerning the shape of the visual plots derived from embedded modules.
Method of agents’ state estimation in multiresolution multiagent simulation

The paper proposes the multiagent techniques for approximation of agent’s state in the multiresolution multiagent simulation. The key methods we have used for state aggregation and disaggregation are: consensus algorithm and formation control. The idea of the coordination of multiple agents has emerged from both observation and simulation of a collective behavior of biological entities. The consensus algorithms are commonly used for the cooperative control problems in the multiagent systems, whilst the formation control is the most popular and fundamental motion coordination problem in the multiagent systems, where agents converge to predefined geometric shapes. The presented approach shows that multiagent methods seem to be very promising in multiresolution simulation. Consensus and formation control algorithms remove necessity to specify the much more complex algorithms for the aggregation and disaggregation needs.
MVMM: Data Center Scheduler Algorithm for Virtual Machine Migration

Migration algorithms and conception of data centers have significant impact on cost and service qualities. Researchers are looking to find effective solutions for Virtual Machine (VM) migration in data centers to reduce power consumption under the given quality of service constraints. This paper presents a model for the consistency of VM migration in a data center by Dynamic Bayesian Networks (DBN). The novelty of our work is to construct a DBN according to the probabilistic dependencies between the parameters in order to make decisions about the corresponding placement of VM in the distributed data centers. In addition, to evaluate the proposed model, we add a new scheduler algorithm inspiring from the DBN model into GreenCloud simulator, called MVMM (Minimization of Virtual Machine Migration), which provides a VM score to make decisions about the matching placement of VMs in order to allow a minimization of future VMs migrations. The results reveal that the use of the proposed approach can reduce energy consumption until 8% compared to other scheduler algorithms.
An efficient test method for noise robustness of deep neural networks

A pattern recognition system is trained by using a training data set composed of input data and corresponding desired output data. After the training, the performance of the system is evaluated from certain perspectives. One is the misclassification rate (MCR) for a test data set, which is a data set separated from the training data set used in the training. The strength against noise, i.e., the noise robustness, is also an important performance measure. The noise robustness of a system is estimated by testing the MCR for a data set in which the inputs are corrupted by artificial noise. However, this test procedure can be computationally expensive, because a large number of corrupt inputs have to be created in order to cover the variability of the noise and the classification procedure has to be run for all of them. In this paper, based on a perturbative approximation method, we derive an effective test method for the noise robustness of pattern recognition systems based on deep neural networks. We demonstrate the validity of our method through numerical experiments using the MNIST data set and show that our method is much faster than the conventional expensive test method.
A Preference-Learning Framework for Modeling Relational Data

Nowadays large scale Knowledge Bases (KBs) represent very important resources when it comes to develop expert systems. However, despite their huge sizes, KBs often suffer from incompleteness. Recently, much effort has been devoted in developing learning models to reduce the aforementioned issue. In this work, we show how relational learning tasks, such as link prediction, can be cast into a preference learning tasks. In particular, we propose a preference learning method, called REC-PLM, for learning low-dimensional representations of entities and relations in a KB. Being highly parallelizable, REC-PLM is a powerful resource to deal with high-dimensional modern KBs. Experiments against state-of-the-art methods on a large scale KB show the potential of the proposed approach.
Angular Histogram-Based Visualisation of Network Traffic Flow Measurement Data

Knowledge of the traffic that is being carried within a network is critical for ensuring the network’s smooth operation, and network traffic measurement has provided an effective means to achieve this. However, network traffic volume has substantially increased over the last decades. Combine that with the traffic heterogeneity from a diverse range of new, connected devices, we have reached a point where the response to any outage or anomalous event is simply beyond human ability. Network information visualisation is a useful tool to help network administrators deal with this problem. While this approach is not new, traditional approaches do not scale well with the increasing volume and heterogeneity of network traffic. In this paper, we propose the application of angular histogram visualisation to provide an information-rich overview of large network traffic data sets to improve the interpretation and understanding of network traffic flow measurement data. We evaluate our approach experimentally using live network traffic to demonstrate its efficacy and provide suggestions on how it can be further improved.
Artificial Intelligence: Achievements and Postponed Risks

The article considers the problem of the development of artificial intelligence, its ambivalent impact on society and humanity. There are the description and definition of artificial intelligence, the problem field was defined. We conducted a study of the manifest and latent functions of artificial intelligence, which can provoke postponed risks. We considered the vision of artificial intelligence as a complex socio-biotechnological hybrid and a qualitatively new mechanism of social control. The achievements and risk-based role of artificial intelligence in modern complex society was estimated. Particular attention is paid to the nature of knowledge about the phenomenon of artificial intelligence, acquiring an interdisciplinary character. Some deferred risks related to the functioning of artificial intelligence are indicated. The factors contributing to the creation of a socio-biotechnological hybrid in the form of artificial intelligence are considered in the context of the riskological turn. The risks of a new type of irrational rationality are considered, the cause of which, according to the author, is the introduction of IT and artificial intelligence, the consequences of rationality are given. The problem of the risk of deintimization, depriving a person of opportunities and the right to personal information is noted. That problem may entail new manifestations of alienation in the form of the dehumanization of social and natural realities. due to the introduction in many programs of artificial intelligence. The article deals with the problem of the pluralization of knowledge about artificial intelligence, the use of performance mechanisms that produce fakes, absolutizing positive or negative consequences of the functioning of artificial intelligence. The article summarizes the state of the risks of artificial intelligence at the moment. Recommendations for further interdisciplinary study of artificial intelligence are presented. The article substantiates the importance of the implementation of the risk assessment of the emerging programs based on artificial intelligence.
Poster Paper Impact Of Software Stack Version On Micro-architecture

Open source Big Data frameworks such as Spark have been evolvingquite rapidly. Many of the changes have addressed improvement inperformance mainly focusing on the performance of the entire job executing on a distributed system. Past studies have reported micro-architectural performance characteristics of benchmarks based onthese Big Data frameworks. Given the rapid changes to these frame-works, it is expected that some of these code changes will also have amicro-architectural impact. In this paper, we present a comparativestudy of performance of Apache Spark across two major revisions and demonstrate that there are micro-architectural differences in the way the applications use the hardware.
Aggregating Web Search Results

In this paper a method for aggregating Web search results is proposed. The aggregator results are compared with the results of most popular search engines: Google, Bing and Yandex. There are 3 stages of the comparison, one for each of the languages: English, Polish and Russian. The quality of the aggregator search results is tested based on user preferences and measured with normalized discounted cumulative gain (nDCG).
Blockchain’s Unsuitability for Real Property Transactions

This paper attempts to bypass the hyperbole that invariably attends any discussion of the blockchain concept, and focus on its suitability for transfer of high-net-worth items which have a unique identity, or a long shelf life. Real property falls within this classification. As recognised by the World Bank, a stable land tenure system is a necessary ingredient for economic development. Clearly, such a system should not be introduced for such a significant transactional area as land without a proper understanding of the ‘real life’ implications of how it will work.This article concludes that the blockchain concept is, at heart, no more than a medium for transfer of value without third-party intervention. It undoubtedly has its uses, but it is unsuited for transactions of many high-value or unique assets that do not have a limited shelf life. The blockchain construct (at least in terms of what is called its ‘coloured coin’ application) fails to address issues such as the need for independent third-party verification and control of transactions.For ease of reference, the following discussion is separated into sections. Section 1 introduces the blockchain construct, and discusses what makes a system a ‘blockchain’ system. Section 2 applies the blockchain construct to transfer of tangible and intangible property rights at common law, enabling us to understand issues that emerge in terms of introduction of a credible system. Section 3 develops this discussion in terms of a registry for transfer of real property rights under a common law system. This enables us in Section 4 to better understand the verification and control problems that emerge. Finally, in Section 5, the issues of risk and liability through implementation of a blockchain system are raised for discussion.