Reviewerly/exHarmony huggingface dataset:
The Decision Support System in Selecting The Characteristics of Coffee By Using Analytic Hierarcy Process Method

This study is to determine whether the characteristics of coffee flavor can be implemented into a decision support system. In this study, the problem is the difficulty in finding coffee experts with adequate understanding and knowledge of coffee. The purpose is to implement Analytic Hierarchy Process (AHP) method to determine the characteristics of coffee flavor and to transfer the knowledge about the taste of coffee to coffee lovers.&#x0D; The expected results are to assist in making a decision support system application in selecting the characteristics of coffee, so that it can provide the advice to the common consumers who want to enjoy and the knowledge about the coffee itself as well.
Introduction to the Special Section on Trust and AI

editorial Free Access Share on Introduction to the Special Section on Trust and AI Editors: Jie Zhang NTU, Singapore NTU, SingaporeView Profile , Jamal Bentahar Concordia University, Canada Concordia University, CanadaView Profile , Rino Falcone ISTC-CNR, Italy ISTC-CNR, ItalyView Profile , Timothy J. Norman University of Southampton, UK University of Southampton, UKView Profile , Murat Şensoy Blue Prism Labs, UK Blue Prism Labs, UKView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 19Issue 4November 2019 Article No.: 44epp 1–3https://doi.org/10.1145/3365675Online:09 November 2019Publication History 0citation344DownloadsMetricsTotal Citations0Total Downloads344Last 12 Months114Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Factors Affecting Organizational Success: A Case Of Body Glove

This paper aims to investigate how Body Glove, an outdoor water sports brand started in the United States, drives organizational success. This brand specializes in wet suits. Body Glove has good product quality and continues to develop its product via research and development to meets customers’ expectations. Their passion for water sports led them to create the first wet suit to help fight those cold California waters. The price offered by Body Glove is affordable and worth it with its quality. Body Glove also provides online shopping that can ease customers to enjoy online shopping. We will test the hypotheses using survey. This paper contributes to understanding of how driving factors results in organizational success
Human Centered Methodologies for the Development of Multidisciplinary Design Research in the Field of IOT Systems: Project Habitat and Pleinair

PLEINAIR (“Parchi Liberi E Inclusivi in Network per Attività Intergenerazionale Ricreativa e fisica” - Network of Free and Inclusive Parks for Recreational and Intergenerational Physical Activity) is a research project funded under action 1.2.2 POR FESR 2014–2020 and aims to create inclusive contexts to promote the adoption of an active lifestyle contributing to good health for all, across all age groups. The project is aimed at a multi-generational target audience and promotes the adoption of correct and active lifestyles according to the abilities of each, with personalized motivational strategies. PLENAIR proposes an innovative approach to physical activity, extending the user’s experience into unconventional spaces and everyday life. PLEINAIR intends to create a smartly-equipped park that offers new types of urban furniture encompassing distributed-intelligence elements. Indeed, the key elements of PLEINAIR are the OSO (Outdoor Smart Objects): furniture and play equipment featuring sensors and initiating devices that are interoperable thanks to an IoMT (Internet of Medical Things) infrastructure.
Pengamanan Data File Teks (Word) Menggunakan Algoritma RC4

Data security on computers can provide a protection for our data. Cryptographic techniques are very important techniques in securing data. Cryptography is the science of encryption techniques where the "original text" (plaintext) is encrypted using an encryption key into "random text that is difficult to read" (ciphertext) by someone who does not have a decryption key, one of the modern cryptographic methods developed is the RC4 algorithm. RC4 algorithm (Ron’s Code / Rivest’s Cipher) is one algorithm that can be used to encrypt data so that the original data can only be read by someone who has the encryption key.
Tiger Tally: Cross-Domain Scheme for Different Authentication Mechanism

As the most effective way to improve the efficiency of government work, e-government has been built at all levels of China, accompanied by the construction of hundreds of authentication centers, which cause serious isolation of different systems, waste of resources, inconveniences for users who have business requirements across departments and districts. Currently, users need to repeatedly register and manage multiple different accounts, or even multiple different authentication methods. In the context of population migration, cross-departmental and regional business operations are growing, it is of great significance to find trust transfer methods for different government applications. To overcome the issue, in this paper, we first explicitly put forward a trust delivery model named “Tiger tally” that can use consensus of all the participants instead of traditional centralized structure by using blockchain. Then design a cross-domain authentication protocol that is compatible with different authentication mechanism. As our main contribution, our scheme is advanced to resolve the trust delivery issues and it is strictly considered from perspective of security, low cost and unified regulatory requirements. In particular, by integrating “HMAC”, traditionally the purview of message security with token standard, our scheme realized the idea of “Tiger tally” in ancient. It not only overcomes the long-standing trust delivery obstacles in e-government, but also achieves the traceability of responsibility and security guarantee beyond the isolated systems’ security bound.
Communication-Critical Task Duplication for Cloud Workflow Scheduling with Time and Budget Concerns

scientific workflows are suitable models in many applications for recognition and execution of tasks in parallel, especially in the Cloud. Different aspects of resource provisioning and the resource pricing model in the cloud environment cause the scheduling problem of workflow very complex. One of the main aims of the scheduling algorithms is to satisfy the users’ different quality of service requirements. Communication delay between tasks is an important affecting factor in optimal scheduling of workflows. It can also highly increase the cost of workflow scheduling if proper actions are not taken. To solve this problem, we propose a tasks duplication-based list scheduling algorithm called Communication-Critical Task Duplication (CCTD). We first define the concept of communication critical task (CCT) for a workflow. Then, by presenting a ranking-based approach, we identify communication critical tasks in a workflow as well as duplicating candidates. Task duplication in idle time slots of leased virtual machines which their children tasks are mapped to. This idea, while eliminating the cost and time of data transfer between parent-child tasks, reduces the time of execution of tasks and effectively uses leased time intervals of resources. According to the proposed scheduling algorithm, a new heuristic method has been proposed for the budget distribution. This method distribute overall budget to tasks in proportional to the workload and duplication rank of each task. The proposed algorithm was evaluated and verified using four well-known scientific workflows. The simulation results show that the CCTD algorithm, while respecting the user budget constraint, reduces the workflow overall completion time.
A Group Travel Recommender System Based on Collaborative Filtering and Group Approximate Constraint Satisfaction

In this research, a modified CF (Collaborative filtering) procedure is suggested to decide a destination for group travel planning service. Furthermore, a composite travel product includes hotel, and attraction in addition to the selection of a destination. For such a purpose, individual user’s personal requirements are represented by constraints, and a group constraints are constructed based on group member’s constraints. A two-phase group approximate satisfying constraint procedure is suggested to automate group decision making procedure for selecting their trip packages to improve group tourists’ convenience and their satisfaction. A group travel recommendation is suggested with an illustrative example and an experimental design is suggested to validate the suggested procedure.
Reinforcement Learning with Probabilistically Complete Exploration

Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL). State-of-the-art RL algorithms suffer from high sample complexity, particularly in the sparse reward case, where they can do no better than to explore in all directions until the first positive rewards are found. To mitigate this, we propose Rapidly Randomly-exploring Reinforcement Learning (R3L). We formulate exploration as a search problem and leverage widely-used planning algorithms such as Rapidly-exploring Random Tree (RRT) to find initial solutions. These solutions are used as demonstrations to initialize a policy, then refined by a generic RL algorithm, leading to faster and more stable convergence. We provide theoretical guarantees of R3L exploration finding successful solutions, as well as bounds for its sampling complexity. We experimentally demonstrate the method outperforms classic and intrinsic exploration techniques, requiring only a fraction of exploration samples and achieving better asymptotic performance.
Enforcing the General Planar Motion Model: Bundle Adjustment for Planar Scenes

In this paper we consider the case of planar motion, where a mobile platform equipped with two cameras moves freely on a planar surface. The cameras are assumed to be directed towards the floor, as well as being connected by a rigid body motion, which constrains the relative motion of the cameras and introduces new geometric constraints. In the existing literature, there are several algorithms available to obtain planar motion compatible homographies. These methods, however, do not minimise a physically meaningful quantity, which may lead to issues when tracking the mobile platform globally. As a remedy, we propose a bundle adjustment algorithm tailored for the specific problem geometry. Due to the new constrained model, general bundle adjustment frameworks, compatible with the standard six degree of freedom model, are not directly applicable, and we propose an efficient method to reduce the computational complexity, by utilising the sparse structure of the problem. We explore the impact of different polynomial solvers on synthetic data, and highlight various trade-offs between speed and accuracy. Furthermore, on real data, the proposed method shows an improvement compared to generic methods not enforcing the general planar motion model.
Annotation Guideline No. 8: Annotation Guidelines for Narrative Levels

I first became aware of the SANTA project at the Digital Humanities conference in Montreal in the summer of 2017. I had just been assigned a 90-student secondyear undergraduate Digital Humanities undergraduate English Literature class, set to begin in January 2018, and I was looking for a group annotation project for my students. In previous iterations of the course, I had carried out several annotation projects focused on the narrative phenomenon of free indirect discourse (FID) in texts by Virginia Woolf and James Joyce. What made these projects successful, from my perspective, was that FID is a complex phenomenon (by definition, a passage in which it is difficult or impossible to say for certain whether a character or narrator is speaking certain words) which is however relatively easy to represent in machine language (for instance, with the TEI &lt;said&gt; element and a few value-attribute pairs). The challenge in the assignment, in other words, was literary rather than technical: while it was easy to learn the TEI tagging, it was hard to say for certain whether a passage from To the Lighthouse was in direct discourse or FID, or to identify who exactly was speaking. To my mind, this made the assignment a meaningful one for my students, teaching them a technical skill while also bringing them into closer contact with the sometimesirresolvable complexities of literary language.
Understanding the Limitations of Network Online Learning

Studies of networked phenomena, such as interactions in online social media, often rely on incomplete data, either because these phenomena are partially observed, or because the data is too large or expensive to acquire all at once. Analysis of incomplete data leads to skewed or misleading results. In this paper, we investigate limitations of learning to complete partially observed networks via node querying. Concretely, we study the following problem: given (i) a partially observed network, (ii) the ability to query nodes for their connections (e.g., by accessing an API), and (iii) a budget on the number of such queries, sequentially learn which nodes to query in order to maximally increase observability. We call this querying process Network Online Learning and present a family of algorithms called NOL*. These algorithms learn to choose which partially observed node to query next based on a parameterized model that is trained online through a process of exploration and exploitation. Extensive experiments on both synthetic and real world networks show that (i) it is possible to sequentially learn to choose which nodes are best to query in a network and (ii) some macroscopic properties of networks, such as the degree distribution and modular structure, impact the potential for learning and the optimal amount of random exploration.
An Inference and Prediction Study on the Factors Associated with Public Officer’s Turnover Intention

정부 조직의 변화가 계속되면서 조직 안정성이 흔들리고 이에 따라 조직 구성원인 공무원들이 이직의도를 품거나 실제 이직을 결정할 가능성이 높아지고 있다. 이에 따라 공무원들의 이직의도와 연관이 있는 요인들을 찾아 인사관리에 적극 반영하여 공무원들의 이직을 줄일 필요가 있다. 본 연구에서는 한국행정연구원의 ‘2018 공직생활실태조사’ 결과를 이용하여, 공무원의 이직의도와 연관이 있는 요인들을 살펴보고 또 공무원들의 이직의도에 대한 예측을 해보고자 하였다. 로지스틱 회귀분석 모형을 사용한 결과, 공무원들의 이직의도와 통계적으로 유의미한 연관이 있는 요인으로 승진이나 보수, 복지뿐만 아니라 리더십, 조직문화, 직무스트레스, 조직몰입, 공직만족도 등의 26가지 요인들이 제시되었다. 또 로지스틱 회귀분석 모형과 함께, 머신러닝 기법 중 하나인 Lasso 회귀분석 방법을 이용하여 실제로 공무원들의 이직의도를 예측할 수 있는 방법을 살펴보았다. 예측 실행 결과, Lasso 로지스틱 회귀분석 모형이 일반 로지스틱 회귀분석 모형보다 근소하게 예측력이 높았으나, 두 모형 다 약 70%를 약간 상회하는 정도의 정확성을 보였다.The continuing changes occurring in the government organization shake the organization’s stability, increasing the risk of public officers’ turnover. To reduce the potential harm that a high turnover rate of public officers may impose on the organization, an active approach to public personnel management by addressing known turnover-related factors in the personnel system is required. This study attempts to infer the factors associated with public officer’s turnover intention and to predict their actual turnover intention, using a large-sized public officer survey conducted in 2018 and the ordinary and Lasso logistic regression methods. For the association factor inference, we identified 26 factors, including promotion, wages, leadership, organization culture, job stress, and job commitment, that were statistically significantly associated with the public officer’s turnover intention by using the ordinary logistic regression methods. For the turnover intention prediction based on the association factors, both the ordinary and Lasso logistic regression models predicted whether a public officer had a turnover intention correctly for over 70% of the new test sample, but the Lasso logistic regression model had a slightly better prediction power than the ordinary logistic regression model had.
Identifikasi cacat lintasan luar bantalan bola menggunakan Support Vector Machine (SVM) pada fan industri

Air regulation creates a comfortable and healthy environment for activities in the industry. A fan is a rotary machine that functions to regulate and circulate air in a room or work area. The bearing is an important component of a fan that is potentially damaged during operation. Damage to the bearing will interfere with perform the fan and can even disrupt a whole production process. A bearing condition monitoring method is needed that is effective and easy to use. Conventional methods such as spectrum analysis and sound analysis are not easy to use by operators in the field due to spectrum analysis requires spectrum reading experience while sound analysis is highly dependent on personal experience. This study proposes a vibration-based pattern recognition method that is Support Vector Machine (SVM) to detect damage to a bearing. This method effectively classifies bearing conditions and is easy to use. The study aims to obtain a method of detecting defects in single-row bearing outer paths on industrial fans using SVM. The study uses an industrial fan test rig with two bearing conditions, that is normal conditions (no defects) and external track defects with a depth of 1.4 mm defect and 0.4 mm width. Recording vibration signals using a data acquisition module with a sampling speed of 17066 Hz and a motor rotational speed of 2850 rpm. The SVM classifier is trained using 9 selected statistical parameters which are extracted from 700 sets of vibration signal recordings. The results showed the statistical parameters that were effectively used were Root Mean Square (RMS), Standard Deviation, Kurtosis, Variance, Entropy, Standard Error, Median, Signal-to-Noise and Distortion Ratio (SINAD), and Signal to Noise Ratio (SNR). The most optimal SVM model is obtained by applying combine Median-SINAD statistical parameters, with the same testing accuracy for the RBF, Polynomial and Linear kernels at 100%. Keywords: Bearings, external path defects, kernel, statistical parameters, SVM.
CLUE: A Fast Parallel Clustering Algorithm for High Granularity Calorimeters in High Energy Physics.

One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering numerous digitised energy deposits (hits) in the reconstruction stage. In this article, we propose a fast and fully-parallelizable density-based clustering algorithm, optimized for high occupancy scenarios, where the number of clusters is much larger than the average number of hits in a cluster. The algorithm uses a grid spatial index for fast querying of neighbours and its timing scales linearly with the number of hits within the range considered. We also show a comparison of the performance on CPU and GPU implementations, demonstrating the power of algorithmic parallelization in the coming era of heterogeneous computing in high energy physics.
Choosing the library profession: Value system of the young adults and the content of career guidance

The author argues that the library schools lack the applicants who choose “Library and information activities” as specialization due to the underestimation of the prospects of library profession by secondary school students. Repositioning of the librarian’s image and shifting the emphases within the career guidance content are needed. The author analyzes the related digital resources and concludes that the represented image of library professionals is not that attractive. The meaningful professional orientation must be built on the motives and values of the young people choosing their future profession. The sociological studies completed in several Russian regions the dominating motivation of young people was revealed. They correspond with the satisfaction/dissatisfaction factors demonstrated in questioning the young Russian librarians. The positive aspects of the library profession to be promoted among the young are also discussed. The author proposes that for target career orientation we should rely on the key psychological types that differ in values when choosing profession. The Maslow pyramid demonstrates the differences in the professional choice criteria. It is used to structure the library professional orientation arguments.
The 2-adic complexity of Yu-Gong sequences with interleaved structure and optimal autocorrelation magnitude

In 2008, a class of binary sequences of period $N=4(2^k-1)(2^k+1)$ with optimal autocorrelation magnitude has been presented by Yu and Gong based on an $m$-sequence, the perfect sequence $(0,1,1,1)$ of period $4$ and interleaving technique. In this paper, we study the 2-adic complexities of these sequences. Our results show that they are larger than $N-2\lceil\mathrm{log}_2N\rceil+4 $ (which is far larger than $N/2$) and could attain the maximum value $N$ if suitable parameters are chosen, i.e., the 2-adic complexity of this class of interleaved sequences is large enough to resist the Rational Approximation Algorithm.
Information Leaks via Safari's Intelligent Tracking Prevention

Intelligent Tracking Prevention (ITP) is a privacy mechanism implemented by Apple's Safari browser, released in October 2017. ITP aims to reduce the cross-site tracking of web users by limiting the capabilities of cookies and other website data. As part of a routine security review, the Information Security Engineering team at Google has identified multiple security and privacy issues in Safari's ITP design. These issues have a number of unexpected consequences, including the disclosure of the user's web browsing habits, allowing persistent cross-site tracking, and enabling cross-site information leaks (including cross-site search). This report is a modestly expanded version of our original vulnerability submission to Apple (WebKit bug #201319), providing additional context and edited for clarity. A number of the issues discussed here have been addressed in Safari 13.0.4 and iOS 13.3, released in December 2019.
PEMANFAATAN TEKNOLOGI INFORMASI DALAM PENGGUNAAN WEBSITE SEBAGAI MEDIA INFORMASI PADA POLSEK PORSEA KABUPATEN TOBA SAMOSIR

Use of the website at any institution or institution that uses the right technology as a medium to provide the latest information. Community Service Activities use information technology in the use of websites as information media to provide up-to-date and directly obtained information from police institutions for the community. With the existence of a website on the Porsea police station, it can improve the quality of Porsea police services against the Porsea community in receiving information, news about the Porsea area, and finding progress in increasing the entire Porsea police station that can be directly accessed by the Porsea community. The training method used is the indoor training method using the lecture method, discussion and question and answer and direct practice related to the use of the website. By applying the application in practice, it will increase knowledge about Porsea police in using applications to process and post news, information, and forms of service to Porsea police.