Reviewerly/exHarmony huggingface dataset:
Multi-Issue Negotiation Protocol with Pre-Domain Narrowing

Consensus building among agents is crucial in multi-agent systems because each agent acts independently according to its utility function, and conflict among agents can occur. Therefore, automated negotiation is an essential technology for efficiently resolving conflicts and forming consensuses while also maintaining agents’ privacy. As the domain to be negotiated is large, the computational cost of reaching a consensus increases and the agreement rate decreases. Some negotiation protocols have been proposed wherein a mediator collects the utility information of each agent and creates multiple alternatives of agreements to handle large-scale multi-issue negotiations. However, in such protocols, a limitation is placed on agents’ privacy because all agents have to disclose their private information by following the mediator and pre-decided negotiation rules. In this study, we propose a negotiation protocol with a predomain-narrowing phase to enable efficient negotiations in large-scale domains which can maintain the privacy of information that agents should not disclose to their opponents or the mediator. The proposed protocol divides the negotiation process into a predomain-narrowing phase and the main negotiation phase. In the proposed protocol, the parts subject to negotiation are first narrowed in upon, and then the main negotiation is performed. We also propose two narrowing methods: issue- and option-narrowing. Further, we propose naive agent strategies considering the predomain-narrowing phase. We perform comparative simulation experiments between the baseline negotiation protocol without a domain-narrowing phase and the proposed negotiation protocol with the predomain-narrowing phase. The experimental results show that the proposed protocol achieves higher agreement rates in less negotiation time than the baseline.
Formation of the Engineer’s Communicative Competence in Digital Economy

The article explores the change in the educational paradigm of students of engineering training programs in the context of digital transformation of production. The importance of effective proficiency in engineering languages, including mathematics, programming and communication, is substantiated. A comparative analysis of the opinions of Ural and Hungarian students of technical universities made it possible to evaluate the practice of training, introduction of modern technologies for the formation of professional and personal communicative competence of future engineers.
A Novel Personalized Recommendation Model Based on User's Context

With the rapid development of information and the Internet technology, people are gradually entering the age of information-overload. Therefore, how to acquire valuable information from the complicated data has become an urgent challenge. Recently, Different personalized recommendation models and algorithms have been proposed to resolve this problem. However, traditional recommendation methods only focus on how to associate items with user interests in a more effective way, while ignoring the user's contextual information. Therefore, it is imperative to coalesce the user's context with the recommendation algorithm together and promote the performance of the recommendation system. In this study, a new personalized recommendation model is proposed to dig latent preferences of users toward context and items, which efficiently integrates the context-aware information into the system. Then a latent context preference recommendation method (LCP-RM) is designed. At last a Standard Gradient Descent method is used to optimize the recommendation model. The simulation results show that our proposed method has achieved the optimal performance in multiple evaluation indicators compared with other algorithms.
UTAKATA: Floating Bubble Display

We propose UTAKATA, an ephemeral display device that presents information by using floating clusters of bubbles. In our previous study, we implemented an electrolysis bubble display using drinkable beverages. Although clear clusters of bubbles could be created in short times, they did not disappear for a long time; consequently, the refresh time period of the display was considerably long. To overcome this deficiency, we present UTAKATA, a ticker-like bubble display using a running-water channel. To establish this display, a linear array of seven electrodes is fabricated on the bottom of a water channel. By activating the appropriate electrodes among the seven ones, circular bubble clusters are generated above the electrodes and then imposed to float downstream using a water flow. This allows representing an N × 7 dot-matrix display to have a shorter refresh time compared with our previous method, and expanding the range of expressions of ephemeral user interfaces using bubbles.
Proximity Full-Text Searches of Frequently Occurring Words with a Response Time Guarantee

Full-text search engines are important tools for information retrieval. In a proximity full-text search, a document is relevant if it contains query terms near each other, especially if the query terms are frequently occurring words. For each word in the text, we use additional indexes to store information about nearby words at distances from the given word of less than or equal to MaxDistance, which is a parameter. A search algorithm for the case when the query consists of high-frequently used words is discussed. In addition, we present results of experiments with different values of MaxDistance to evaluate the search speed dependence on the value of MaxDistance. These results show that the average time of the query execution with our indexes is 94.7-45.9 times (depending on the value of MaxDistance) less than that with standard inverted files when queries that contain high-frequently occurring words are evaluated. This is a pre-print of a contribution published in Pinelas S., Kim A., Vlasov V. (eds) Mathematical Analysis With Applications. CONCORD-90 2018. Springer Proceedings in Mathematics & Statistics, vol 318, published by Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-42176-2_37
Operations with elements of transferred density matrix via unitary transformations on extended receiver

We combine the long-distance quantum state transfer and simple operations with the elements of the transferred (nor perfectly) density matrix. These operations are turning some matrix elements to zero, rearranging the matrix elements and preparing their linear combinations with required coefficients. The basic tool performing these operations is the unitary transformation on the extended receiver. A system of linear algebraic equations can be solved in this way as well. Such operations are numerically simulated on the basis of 42-node spin-1/2 chain with the two-qubit sender and receiver.
Engineering privacy-aware smart home environments

Smart homes (SmHs) integrate a number of user interaction technologies, which have the potential to cause privacy violations between occupants of the home either through inappropriate information disclosures or disturbances. One approach to protecting SmH occupants' privacy is to develop techniques for dynamically adapting the UI layer according to the context, as well as each occupant's preferences and capabilities. Therefore, the focus of this research is to develop a framework and set of algorithms which can adapt the configuration and behavior of UI layer in order to preserve the privacy of SmH occupants while maintaining usability.
Comment on "Datacenter Congestion Control: Identifying what is essential and making it practical" by Aisha Mushtaq, et al, CCR, July 2019

We dispute the authors' claim that SRPT is the crucial factor in achieving good FCT performance in datacenter networks.
Demonstration of G-ID: Identifying 3D Prints Using Slicing Parameters

We demonstrate G-ID, a method that utilizes the subtle patterns left by the 3D printing process to distinguish and identify objects that otherwise look similar to the human eye. The key idea is to mark different instances of a 3D model by varying slicing parameters that do not change the model geometry but can be detected as machine-readable differences in the print. As a result, G-ID does not add anything to the object but exploits the patterns appearing as a byproduct of slicing, an essential step of the 3D printing pipeline.
Spot size diagnosis of monochromatic X-ray optics system based on an algorithm

A monochromatic X-ray optics system has been developed in this study which would mainly been used to calibrate the satellite-borne detectors. The system consists of four parts, including the X-ray source, the collimating system, the monochromator, and X-ray detectors. In particular, an algorithm has been proposed to diagnose spot size of monochromatic X-ray optics system. As a comparison, two methods were adopted to diagnose the spot size. Experimental results shows that the method and algorithm to diagnose spot size is feasible. This work will be helpful to research on spot size diagnosis of X-ray optics system, and would contribute to the calibration on ground of satellites-borne detectors.
Factors Affecting Academic Achievement Students’of Mathematics Department by Using Probit Regression Analysis

Abstract One of the students’ academic achievement can be seen GPA, the higher the index value, the better student achievement success rate of students who obtained. Many factors affect student achievement index both internal factors and external factors. This paper described at factors that affect student achievement index of Department of Mathematics UNP who enrolled 2016 by using probit regression analysis. The results of this study are students SBMPTN, does not work while in college, and was not active in organizations on campus have the opportunity to get a better grade.
Formalization of scientific and technical sentences in Russian language

Abstract The article presents the research results on the computational theory of semantic interpretation in Russian language. These interpretations represent a generalizing formalization of a scientific and technical style sentences. Furthermore, the concept of a partition system of a word scale, its classes and a method of forming partition classes is defined. We showed that the word scale corresponds to many partition systems that are specified by a special rule for selecting instances from classes. Additionally, we proposed generalized algorithms for constructing a partition system.
MADSA

With the increasing amount of cognitive benefits found to help musicians, it has become increasingly imperative to begin playing music. However, aspiring musicians often have trouble pinpointing and correcting mistakes, particularly in longer and more technical musical scores. Furthermore, tiny differences in notation (e.g., a different direction in a turn, or the absence of a slash in a grace note) can misguide a musician from learning the correct way to play a song. These two factors can lead the musician astray from correctly identifying and improving on the sections the musicians struggle upon, inhibiting their rate of improvement. To help musicians identify miscues in their performance, a sotware solution through spectral analysis and its various algorithms were developed. A Java program was written to implement a Discrete Fourier Transform that visualizes the data from a .wav sound file by its frequency and amplitude. Real-time data is collected from a musician playing a specific song which is laid over the spectrogram and compared to see if the frequencies match a known "perfect" performance of the same song. The effectiveness of the program was tested with students who played various pieces different instruments and were asked to describe where they played incorrectly. The resulting responses were then compared to the data from the program. This project helps train musicians to play more accurately and provides an alternative to a music teacher if the student either cannot afford lessons or is not able to meet the teacher in person.
Neural Network Model Of Message Passing in Team Cooperation

The football match is so wonderful that every moment on the pitch touch the hearts of the spectators. What if two players are in contact on the pitch? To this end, we will build a neural network model to more intuitively reflect the players' interactions in the game by observing the small interactions between the players in the team, thus can more clearly reflect the entire competition condition. The model can model dynamic information as the graph develops. In particular, the proposed framework can update the node information by continuously capturing the sequential information of edges, the time interval between edges, and the information propagation. Experimental results on various dynamic graphs demonstrate the effectiveness of the proposed framework.
HabiTech: Inhabiting Buildings, Data &amp; Technology

As larger parts of our lives are determined in the digital realm, it is critical to reflect on how democratic values can be preserved and cultivated by technology. At the city-scale, this is studied in the field of 'digital civics'; however, there seems to be no corresponding focus at the level of buildings/building inhabitants. The majority of our lives are spent indoors and therefore the impact that 'indoor digital civics' may have, might exceed that of city-scale, digital civics. The digitization of building design and building management creates an opportunity to better identify, protect, and cultivate civic values that, until now, were centralized in the hands of building designers and building owners. By bringing together leading architecture/HCI academics and commercial stakeholders, this workshop builds on previous workshops at CHI. The workshop will provide a forum where a new agenda for research in 'HabiTech' can be defined and new research collaborations formed.
The Utilization of Ontology in Association Rule

Abstract Association rule is one of the data mining techniques to find associative combinations of items. There are several algorithms including Apriori, FP - Growth, and CT-Pro. One of the advantages of the Apriori algorithm is that it produces many rules. To improve its result, one of the methods is by using the semantic web technology. This work proposes how the hierarchical type of ontology can be utilized by the Apriori algorithm to improve the results. The Apriori with ontology implements the Interestingness Rule (IR) which is a parameter to determine the degree of association between combinations of items in a dataset. The series of experiments show that the proposed idea can improve the results compare to the default Apriori algorithm.
Tactics for Proving Separation Logic Assertion in Coq Proof Assistant

The verification of the correctness of large programs, particularly operating systems is an unmanageable but important endeavor. we are interested in verifying C programs with formal methods, the logic is separation logic, a Hoare-style program logic. In this paper, we present a simple extension of the syntax of separation logic assertion on existing verification system in Coq proof assistant to make assertions more versatile and flexible to describe the state of programs. Moreover, we develop several tactics for proving some related assertions to reduce manual proof as much as possible and improve the efficiency of verification.
Review

In response to the explosive growth of the Internet's information volume, how to discover and track changes in social media has gradually become a key issue in the field of data mining. Especially, how to find the popular and hot topics has become an important issue in social network analysis. In this paper, we review the topic models applied into the social network analysis. At this stage, this kind of problem is mainly solved by the method based on evolutionary clustering. The method can be subdivided into two methods: the topic model and the matrix/tensor decomposition model. This article focuses on the analysis of a class of the most important Latent Dirichlet Allocation (LDA) model from the topic model, as well as a variety of topic models derived from the LDA model, and a brief discussion of future research directions.
An Improved Collaborative Filtering Algorithm Based on User Interest Diffusion and Time Correlation

This paper proposes an improved collaborative filtering recommendation algorithm based on user interest diffusion and the time correlation. Firstly, the algorithm improves user synthesis similarity calculation method based on user interest diffusion, calculates the direct similarity of user interest and the similarity of user interest diffusion, and obtains the synthesis similarity of user interest through parameter adjustment. Then, for the user interest change with time, the time correlation function is applied to the similarity calculation between users. Finally, the recommendation weight is divided into the time correlation data weight and the synthesis similarity data weight, so that a more accurate prediction score is obtained.. The comparison experiments showed that the algorithm can reduce the sparseness of the data set effectively when the data is sparse, and improves the precision of the recommendation algorithm.
Towards the Scheduling of Vertex-constrained Multi Subgraph Matching Query

Subgraph matching is one of the most fundamental problems in graph database, which is associated with a wide spectrum of applications. Researchers have primarily devoted their efforts to improving performance for individual query, while we often need to compute multiple queries all at once in practice. In this paper, we study the problem of vertex-constrained multi subgraph matching query (vMSQ), where we propose a novel scheduling algorithm for processing multiple queries in parallel, while taking into considerations of load balance and maximum possible sharing of computation.
Robust IHWT- CRT Image Watermarking using YCbCr Color Space

Abstract This research proposes a watermarking method that combines Integer Haar Wavelet Transform (IHWT) and Chinese Remainder Theorem (CRT) algorithms on RGB images that are converted to YCbCr color space. The goal is to get a watermarking method that is robust, imperceptible and secure. Robust because with IHWT done on component Y as a place to insert a watermark done on the frequency domain that is resistant to attack. Whereas CRT is an insertion method in a spatial domain that is relatively more imperceptible to human vision, CRT also more secure because CRT is a cryptographic algorithm. Based on the results of the test and bookkeeping of the imperceptibility quality, the average PSNR value is above 48dB and the average SSIM is above 0.99. Whereas the robustness test proves that by using the YCbCr color space the increase in resistance occurs in Gaussian noise, JPEG compression and JPEG 2000 compression with an average NC value above 0.8.
Metering Graphical Data Leakage with Snowman

A long-standing technique to interfere with theft of sensitive data by its intended users is permitting these insiders only remote access to the data via a thin client. Even allowing only remote access is inadequate, however, to counter an insider willing to reconstruct the data from the graphical output, in the limit by photographing the data on-screen and applying automatic character recognition to these photographs offline. In this paper we propose and evaluate a system, called Snowman, that accurately monitors the amount of sensitive data output to a client. To conduct this monitoring without slowing the interactive user session, leakage is concurrently tracked in a replica of the application execution. This, in turn, introduces a key technical challenge that Snowman solves, namely identically replicating execution of an unmodified Linux binary while also performing efficient multi-label taint-tracking on it. We show through empirical measurements with a word processor, a spreadsheet program, and a code editor that Snowman induces little overhead on interactive user sessions and easily differentiates data-access patterns induced by normal usage and sufficiently aggressive data theft with reasonable responsiveness.
A Hybrid Long Arabic Text Summarization System Based on Integrated Approach Between Abstractive and Extractive

Inevitably generating a robust summary from a long Arabic document is a challenging task owing to the fact that Arabic is a complex language and has unique attributes. In this paper, we propose an integrated approach between abstractive and extractive for providing an informative and coherent summary from a long document. The extractive method employs a novel formulation for extracting a set of statistical and semantic features by taking into consideration the semantic, importance, and position of the sentence. The combination of statistical and semantic features is used to learn a soft voting classifier to extract the significant sentences. In the abstractive approach, only significant sentences that classified from the extractive approach will be trained with encoder-decoder bidirectional long short-term memory (LSTM) for producing a compose novel summary. We show that the mixed proposed architecture between extractive and abstractive outperforms and provides better results comparing to some existing Arabic summarizing systems.
Implementing a semi-causal domain-specific language for context detection over binary sensors

In spite of the fact that many sensors in use today are binary (i.e. produce only values of 0 and 1), and that useful context-aware applications are built exclusively on top of them, there is currently no development approach specifically targeted to binary sensors. Dealing with notions of state and state combinators, central to binary sensors, is tedious and error-prone in current approaches. For instance, developing such applications in a general programming language requires writing code to process events, maintain state and perform state transitions on events, manage timers and/or event histories. In another paper, we introduced a domain specific language (DSL) called Allen, specifically targeted to binary sensors. Allen natively expresses states and state combinations, and detects contexts on line, on incoming streams of binary events. Expressing state combinations in Allen is natural and intuitive due to a key ingredient: semi-causal operators. That paper focused on the concept of the language and its main operators, but did not address its implementation challenges. Indeed, online evaluation of expressions containing semi-causal operators is difficult, because semi-causal sub-expressions may block waiting for future events, thus generating unknown values, besides 0 and 1. These unknown values may or may not propagate to the containing expressions, depending on the current value of the other arguments. This paper presents a compiler and runtime for the Allen language, and shows how they implement its state combining operators, based on reducing complex expressions to a core subset of operators, which are implemented natively. We define several assisted living applications both in Allen and in a general scripting language. We show that the former are much more concise in Allen, achieve more effective code reuse, and ease the checking of some domain properties.
The Use of Companion Applications to Support Instructor-Led Training

This case study reflects on the use of a mobile training companion application to overcome limitations of supporting a level-three evaluation of the participants following a face-to-face training course. A level-three evaluation determines how well a person that attended training is able to transfer the information they learned to their job. The rapid adoption of smartphones enables the creation of solutions not previously considered viable in this industry, which has typically used either a traditional web-based training strategy or a traditional face-to-face training strategy to meet its training objectives. This solution is especially important because the training attendees work individually and each person covers a different territory than another, making it even more difficult to measure transfer.