Reviewerly/exHarmony huggingface dataset:
Kombinasi Transaksi Penjualan (Merk Beras) Menggunakan Algoritma Apriori (Studi Kasus di UD. SRD)

UD. SRD is an individual company that produces and sells rice with various brands. UD. SRD is an individual company that produces and sells rice with various brands. Rice brands sold include C4 Kelapa, C4 Raja, C4 Merak, Nogo Hitam, Nogo Merah, Nogo Hijau, Pandan Wangi, Mentik Wangi and Rojo Lele. In processing sales data is still done manually that is only using notes or receipts, so the data only serves as an archive. In this study, the data amounted to 404 rice sales transactions from January to September 2016. The purpose of this research is the creation of Apriori algorithm system to predict combination of rice brand in sales transaction in UD. SRD so as to help the company know the sales of the most frequently purchased rice brands simultaneously. The result of combination rice brand is getting using Apriori Algorithm which supported minimum 30% and confidence minimum 70%. It appears from thus statements "If buy C4R, it will buy C3M with 51.54% support and 77.31% confidence", "If buy MW , It will buy C4M with the value of support 48.27% and the value of confidence 79.92% "," If buy C45 and C4M, it will buy MW with the value of support 32.43% and the value of confidence 71.20% "and" If buy MW and C4R , It will buy C4M with the value of support 32.43% and the value of confidence 82.91%".Keywords: Rice Transaction, Selling, Data mining, Association Rule, Apriori Algorithm
Deep Multilevel Graph Partitioning

Partitioning a graph into blocks of "roughly equal" weight while cutting only few edges is a fundamental problem in computer science with a wide range of applications. In particular, the problem is a building block in applications that require parallel processing. While the amount of available cores in parallel architectures has significantly increased in recent years, state-of-the-art graph partitioning algorithms do not work well if the input needs to be partitioned into a large number of blocks. Often currently available algorithms compute highly imbalanced solutions, solutions of low quality, or have excessive running time for this case. This is because most high-quality general-purpose graph partitioners are multilevel algorithms which perform graph coarsening to build a hierarchy of graphs, initial partitioning to compute an initial solution, and local improvement to improve the solution throughout the hierarchy. However, for large number of blocks, the smallest graph in the hierarchy that is used for initial partitioning still has to be large. In this work, we substantially mitigate these problems by introducing deep multilevel graph partitioning and a shared-memory implementation thereof. Our scheme continues the multilevel approach deep into initial partitioning -- integrating it into a framework where recursive bipartitioning and direct k-way partitioning are combined such that they can operate with high performance and quality. Our approach is stronger, more flexible, arguably more elegant, and reduces bottlenecks for parallelization compared to other multilevel approaches. For example, for large number of blocks our algorithm is on average an order of magnitude faster than competing algorithms while computing balanced partitions with comparable solution quality. For small number of blocks, our algorithms are the fastest among competing systems with comparable quality.
Creating a digital learning ecosystem to facilitate authentic place-based learning and international collaboration – a coastal case study

Authentic, place-based experiential learning is essential for students of ecology, whilst an understanding of broader human impacts is necessary for effective conservation efforts. Creating future environmental leaders requires fostering such understanding whilst building transferable skills in collaboration, communication and cultural competence. Mobile technologies and collaborative digital tools can connect students across broad geographic locations, allowing them to share experiences and build a common understanding of global environmental challenges. Within this concise paper, we report on the initial stages and proposed next steps in building a learning ecosystem, consisting of a digital platform and embedded tools, to facilitate undergraduate learning in coastal ecology across universities in Australia, Hong Kong and South Africa. Using here a framework guided by design-based research (DBR), we discuss the design and development of these digital tools in context, and their proposed integration into upper undergraduate science curricula across locations.
Intelligent Conversational Android ERICA Applied to Attentive Listening and Job Interview

Following the success of spoken dialogue systems (SDS) in smartphone assistants and smart speakers, a number of communicative robots are developed and commercialized. Compared with the conventional SDSs designed as a human-machine interface, interaction with robots is expected to be in a closer manner to talking to a human because of the anthropomorphism and physical presence. The goal or task of dialogue may not be information retrieval, but the conversation itself. In order to realize human-level "long and deep" conversation, we have developed an intelligent conversational android ERICA. We set up several social interaction tasks for ERICA, including attentive listening, job interview, and speed dating. To allow for spontaneous, incremental multiple utterances, a robust turn-taking model is implemented based on TRP (transition-relevance place) prediction, and a variety of backchannels are generated based on time frame-wise prediction instead of IPU-based prediction. We have realized an open-domain attentive listening system with partial repeats and elaborating questions on focus words as well as assessment responses. It has been evaluated with 40 senior people, engaged in conversation of 5-7 minutes without a conversation breakdown. It was also compared against the WOZ setting. We have also realized a job interview system with a set of base questions followed by dynamic generation of elaborating questions. It has also been evaluated with student subjects, showing promising results.
SISTEM PEMBAYARAN NON-TUNAI DENGAN MENGGUNAKAN QR-CODE STUDI KASUS KANTIN STT-PLN

Transaction inside canteen of PLN Institute of Technology (STT PLN) is still used manual method, where Buyer and Seller will do their payment system with cash in hand, therefore some of problem may be happened, such as miscalculation when giving a change, unavailable report of income, and miss-calculation of food price are often occur. This research based on Web and Android technology, purposed for giving an easy transaction process from Buyer to Seller, minimizing a miscalculation, and speed up the transaction time. QR-Code is used for a payment process, where Buyer able to do payment process , with scanning canteen barcode on corresponding Seller, and insert amount of price according to the total price that had been specified. With this application Seller and Buyer will be more convenient to do a transaction process inside canteen of PLN Institute of Technology.
RESEARCH ON THE EFFECT OF CHINESE FINANCIAL ACTIVITIES ON SCIENTIFIC AND TECHNOLOGICAL INNOVATION

With the continuous development of China's economic level, accelerating industrial upgrading and transformation, building an innovative society has become a new development goal. Financial activities are closely related to economic development. Therefore, studying the impact of China's financial activities on technological innovation is of great significance. This article uses the data indicators in the field of financial activities and technological innovation in China from 2002 to 2018 to construct a vector error correction model (VEC), analyze the implicit correlation between financial activities and technological innovation, and study the promotion of technological innovation by financial activities and put forward policy recommendations to promote the development of China’s financial industry and enhance technological innovation.
TEACHING DESCRIPTIVE TEXT BY USING FRESH TECHNIQUE (AN EXPERIMENTAL RESEARCH OF THE FIRST GRADE STUDENTS AT SMAN 12 BANDA ACEH)

Abstract Name: Dini Zulva Rusli Student No.: 1306102020066 Department/program: English Education/Undergraduate Title:Teaching Descriptive Text by Using FRESH Technique (An Experimental Research of the First Grade Students at SMA Negeri 12 Banda Aceh) Keywords: FRESH Technique, Descriptive Text, Writing The objective of conducting this research was to find out whether the use of FRESH technique can improve the students’ descriptive writing ability or not. This is an experimental research that involved one group pretest-posttest design. The population of this research was the first grade students of SMA Negeri 12 Banda Aceh. The sample of this research was XMIA2 students which selected by using simple random sampling. The instrument of data collection was written test. The result of data analysis showed that the students’ mean score in pretest was 24.5 and mean score in posttest was 71.3. Moreover, the analysis of t-test showed that t_score > t_table or 20.95 > 1.72 at the level of significance 0.05 with the degree of freedom (df) 19. The most improvement of writing aspect was in organization and content. For other aspects, such as grammar, vocabulary and mechanic were also improved, but not as much as those two aspects. It means that the use of FRESH technique improve students descriptive writing ability especially in organization and content aspects. In brief, the application of FRESH technique can be used to improve the students’ descriptive writing ability.
Exploring Explicit and Implicit Visual Relationships for Image Captioning

Image captioning is one of the most challenging tasks in AI, which aims to automatically generate textual sentences for an image. Recent methods for image captioning follow encoder-decoder framework that transforms the sequence of salient regions in an image into natural language descriptions. However, these models usually lack the comprehensive understanding of the contextual interactions reflected on various visual relationships between objects. In this paper, we explore explicit and implicit visual relationships to enrich region-level representations for image captioning. Explicitly, we build semantic graph over object pairs and exploit gated graph convolutional networks (Gated GCN) to selectively aggregate local neighbors' information. Implicitly, we draw global interactions among the detected objects through region-based bidirectional encoder representations from transformers (Region BERT) without extra relational annotations. To evaluate the effectiveness and superiority of our proposed method, we conduct extensive experiments on Microsoft COCO benchmark and achieve remarkable improvements compared with strong baselines.
RL-IoT: Towards IoT Interoperability via Reinforcement Learning

Our life is getting filled by Internet of Things (IoT) devices. These devicesoften rely on closed or poorly documented protocols, with unknown formats andsemantics. Learning how to interact with such devices in an autonomous manneris key for interoperability and automatic verification of their capabilities.In this paper, we propose RL-IoT -- a system that explores how to automaticallyinteract with possibly unknown IoT devices. We leverage reinforcement learning(RL) to understand the semantics of protocol messages and to control the deviceto reach a given goal, while minimizing the number of interactions. We assumeonly to know a database of possible IoT protocol messages, whose semantics arehowever unknown. RL-IoT exchanges messages with the target IoT device, learningthose commands that are useful to reach the given goal. Our results show thatRL-IoT is able to solve simple and complex tasks. With properly tunedparameters, RL-IoT learns how to perform actions with the target device, aYeelight smart bulb for our case study, completing non-trivial patterns with asfew as 400 interactions. RL-IoT opens the opportunity to use RL toautomatically explore how to interact with IoT protocols with limitedinformation, and paving the road for interoperable systems.
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain

We propose an evaluation framework for class probability estimates (CPEs) in the presence of label uncertainty, which is commonly observed as diagnosis disagreement between experts in the medical domain. We also formalize evaluation metrics for higher-order statistics, including inter-rater disagreement, to assess predictions on label uncertainty. Moreover, we propose a novel post-hoc method called $alpha$-calibration, that equips neural network classifiers with calibrated distributions over CPEs. Using synthetic experiments and a large-scale medical imaging application, we show that our approach significantly enhances the reliability of uncertainty estimates: disagreement probabilities and posterior CPEs.
AN ANALYSIS OF TEACHER’S TALK IN CLASSROOM INTERACTION AT SMPN 5 KOTA SERANG IN ACADEMIC YEAR 2018-2019

The main objective of this research is to shed light on the the categories of teacher’s talk that occur in English teaching and learning process at grade VIII-B of SMPN 5 Kota Serang based the framework FIACS (Flander’s Interaction Analysis Categories System). The students of VIII-B and an English teacher of VIII-B class at SMPN 5 Kota Serang were chosen as participants of this research. In this research, the researchers used qualitative descriptive method in collecting the data. The instruments of this research were observation sheet and video recording. Moreover, the teacher’s talk during the classroom activity was the main data of this research. The findings reveals that the most dominant type used by the teacher is lecturing (29.8%), followed by asking questions (22.8%), giving directions (21.1%) and the last category is criticizing or justifying authority (3.5%). In the classroom activity, the indirect talk is lower than direct talk. The teacher tended to choose direct talk, so called: lecturing, giving direction, criticizing or justifying authority in delivering the materials to the students
Development Of Community Research & Information System Application For Web-Based National University Lecturers: Development Of Community Research & Information System Application For Web-Based National University Lecturers

(LPPM) National University Research and Community Service Institute in general is research and community service aimed at improving the quality of human resources by conducting targeted research so that research results can be used according to community demands. In addition, this institution also facilitates and implements research results as a service work that is beneficial to the community. During this time the administrative process in reporting the results of research carried out by accessing the website contained in the url http://sippm.unas.ac.id/, but the existing website is no longer managed and used. Therefore it will be made Research & Community Service Applications for Web-Based National University Lecturers, where this application is the development of the previous website. With this system, it is expected to facilitate lecturers in conducting administrative processes related to reporting research results. The results of the research that have been carried out, is the implementation of the application using the PHP MySQL programming language, and also using the SDLC Waterfall Method in its development.
Unique Cloud Storage for Privacy Preserving using MD5

In current days, the cloud technology gets a rapid increase of user attention by several small scale and large scale companies which include Software, Business Process Outsourcing (BPO), Healthcare, educational institutions and a lot more fields. It became popular to mange personal data for the economic savings. But for the privacy, the sensitive data in the cloud is must be encrypted before uploading it into the cloud server. Moreover the cloud server is dishonest and it cannot give accurate search results for user who wants to access the actual files. To solve this problem, we proposed and analysed the importance of bloom filter over encrypted data in cloud to provide the accurate searching of data for the cloud users. Here we designed a mechanism called fine-grained query results verification. In this mechanism, over a set of given encrypted query results set, the query user verifies the quality of each data file in the set and authorize the data by using Message Digest (MD5) algorithm. A short signature key is generated in this MD5 algorithm which is used to verify the data authentication.
Smart University: The Integration of Artificial Intelligence in Higher Education to Resolve the Training/Employment Inadequacy

In Morocco and according to HCP data, the unemployment rate increases proportionally to education level.This paradox confirms the inadequacy of training with the job market needs and the permanence development that does not achieve much progress in a world increasingly laborious and demanding [1].In front of company transformations that obviously influence the job market, there is a growing gap between training offered in higher education and the real needs expressed by companies [2].A situation that aims to limit employment opportunities for young people, and thus increase the unemployment rate among this section of the population.In order to ensure a transparent integration of its graduates in the job market, industries and organizations, universities confront many challenges.Which requires a multidisciplinary approach: education combined with complementary skills [3].Artificial intelligence represents a huge opportunity for improving education quality, especially through personalized training paths based on needs, filling gaps or overcoming difficulties.The fast rhythm of technological innovation and the ensuing job movements prove that higher education requires a reexamination of the roles and pedagogy of teachers [4].According to [5], this revolution must link education to employment.Employers need to collaborate with schools and universities to develop curricula and share their practical knowledge of the marketplace.By this work, we try to propose a new approach to ensure both the adequacy of university training and the integration of students into labor market.The aim is to resolve the problem by updating the existing curricula according to the most demanded skills on the local, national and international markets.Consequently, reducing the gap between the offer of higher education and the labor market, through the integration of artificial intelligence techniques and methods, in order to focus on the improvement and adequacy of the training offered, which is still too rare in the continent with regard to its potentialities.
Separate Your Domains: NIST PQC KEMs, Oracle Cloning and Read-Only Indifferentiability.

It is convenient and common for schemes in the random oracle model to assume access to multiple random oracles (ROs), leaving to implementations the task—we call it oracle cloning—of constructing them from a single RO. The first part of the paper is a case study of oracle cloning in KEM submissions to the NIST Post-Quantum Cryptography standardization process. We give key-recovery attacks on some submissions arising from mistakes in oracle cloning, and find other submissions using oracle cloning methods whose validity is unclear. Motivated by this, the second part of the paper gives a theoretical treatment of oracle cloning. We give a definition of what is an “oracle cloning method” and what it means for such a method to “work,” in a framework we call read-only indifferentiability, a simple variant of classical indifferentiability that yields security not only for usage in single-stage games but also in multi-stage ones. We formalize domain separation, and specify and study many oracle cloning methods, including common domain-separating ones, giving some general results to justify (prove read-only indifferentiability of) certain classes of methods. We are not only able to validate the oracle cloning methods used in many of the unbroken NIST PQC KEMs, but also able to specify and validate oracle cloning methods that may be useful beyond that.
The impact of market orientation on organizational performance: the moderating role of innovation in medium enterprises

The purpose of this study aims mainly to identify the impact of Market Orientation in its dimensions (Customer orientation, Competitor orientation, Inter-functional coordination, and Supplier orientation) on organization performance in light of the presence of Innovation as a moderating variable in Jordanian Medium Enterprises. The study population consisted of workers in the upper and middle management in Jordanian Medium Enterprises.A proportional stratified random sample consisted of (269) individuals. The descriptive-analytical method was used via a questionnaire developed as a tool to collect data, it consisted of (52) items. Several statistical methods were used, including multiple linear correlation test, Pearson correlation coefficient, the allowed variance and contrast variation coefficient, the self-correlation test using the Durban-Watson correlation coefficient, simple and multi-step and gradient regression analysis tests, as well as hierarchical regression analysis. The study outcomes showed that; there is an effect of Market Orientation on organization performance in Jordanian Medium Enterprises, and there is a difference in the moral impact of the dimensions of Market Orientation on organization performance depending on Innovation differences.
An Evaluation Study of the Uses of One-Way ANOVA and "T-Test" in Psychological and Educational Studies in Light of Sample Size, Test Power and Effect Size

The aim of the current research is to get acquainted with the reality of using the "T" test and the one-Way ANOVA test as inferential parametric statistical tests considering the concepts of test power and the effect size of according to the sample size.The sample of research published in the Journal of Psychological and Educational Research issued by the Bahrain University in the period from 2008 to 2018, in which the "T" test and the one-way ANOVA were used, which reached 94 research.The research concluded in some of its results that the power of the statistical test increases with the increase of the sample size, while the effect size does not differ according to the sample size, and that the effect sizes were weak and very weak, and the rate of the first type error in this research was approximately 21.4%, while the rate of the second type error contained approximately 21.7%.
Bounding the benefit of adaptivity in quantum metrology using the relative fidelity

Protocols for discriminating between a pair of channels or for estimating a channel parameter can often be aided by adaptivity or by entanglement between the probe states. For large numbers of channel uses, this can make it difficult to bound the best possible performance for such protocols. In this paper, we introduce a quantity that we call the relative of a given pair of channels and a pair of states to those channels. Constraining the allowed states to all pairs of states whose is greater than some minimum input fidelity and minimising this quantity over the valid pairs of states, we get the minimum relative for that constraint. We are then able to lower bound the between the possible output states of any protocol acting on one of two possible channels in terms of the minimum relative fidelity. This allows us to bound the performance of discrimination and parameter estimation protocols, as well as to rule out perfect discrimination for certain pairs of channels, even using the most general, adaptive protocols. By finding a continuity bound for the relative fidelity, we are also able to provide a simple proof that the quantum Fisher information (QFI) of the output of an $N$-use protocol is no more than $N^2$ times the one-shot QFI.