Reviewerly/exHarmony huggingface dataset:
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Energy conservation in abussabah library building, Farook college

This study aims to find out the electric energy consumption in the Abussabah library building, Farook College, and to suggest a solution for reducing the high energy consumption. The study is conducted by dividing the Abussabah library building into three sections. Interview schedule with the librarian and observation for collecting general data was the data collection methods used to carry out the study. The findings of the study revealed that the library consumes a considerable amount of electric energy. The total electric energy consumption of the library building for the year 2019 was 8350.33KWh. Among the three sections of the library building, the library itself uses the majority of the total energy demand, followed by Audio Visual Theatre and department respectively. The result of the study proposed installing a solar panel of 15KW for the building, with an initial cost of 2 Lakhs, which will serve the electric needs of the building efficiently.
Differentially Private Generative Models Through Optimal Transport

Although machine learning models trained on massive data have led to breakthroughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricted access to data. Generative models trained with privacy constraints on private data can sidestep this challenge and provide indirect access to the private data instead. We propose DP-Sinkhorn, a novel optimal transport-based generative method for learning data distributions from private data with differential privacy. DP-Sinkhorn relies on minimizing the Sinkhorn divergence---a computationally efficient approximation to the exact optimal transport distance---between the model and the data in a differentially private manner and also uses a novel technique for conditional generation in the Sinkhorn framework. Unlike existing approaches for training differentially private generative models, which are mostly based on generative adversarial networks, we do not rely on adversarial objectives, which are notoriously difficult to optimize, especially in the presence of noise imposed by the privacy constraints. Hence, DP-Sinkhorn is easy to train and deploy. Experimentally, despite our method's simplicity we improve upon the state-of-the-art on multiple image modeling benchmarks. We also show differentially private synthesis of informative RGB images, which has not been demonstrated before by differentially private generative models without the use of auxiliary public data.
Towards Principled Representation Learning for Entity Alignment

Knowledge graph (KG) representation learning for entity alignment has recently received great attention. Compared with conventional methods, these embedding-based ones are considered to be robuster for highly-heterogeneous and cross-lingual entity alignment scenarios as they do not rely on the quality of machine translation or feature extraction. Despite the significant improvement that has been made, there is little understanding of how the embedding-based entity alignment methods actually work. Most existing methods rest on the foundation that a small number of pre-aligned entities can serve as anchors to connect the embedding spaces of two KGs. But no one investigates the rationality of such foundation. In this paper, we define a typical paradigm abstracted from the existing methods, and analyze how the representation discrepancy between two potentially-aligned entities is implicitly bounded by a predefined margin in the scoring function for embedding learning. However, such a margin cannot guarantee to be tight enough for alignment learning. We mitigate this problem by proposing a new approach that explicitly learns KG-invariant and principled entity representations, meanwhile preserves the original infrastructure of existing methods. In this sense, the model not only pursues the closeness of aligned entities on geometric distance, but also aligns the neural ontologies of two KGs to eliminate the discrepancy in feature distribution and underlying ontology knowledge. Our experiments demonstrate consistent and significant improvement in performance against the existing embedding-based entity alignment methods, including several state-of-the-art ones.
Linking Biometric Voice Identity with Self-monitoring Health Data as a Temporal-spatial Event Stored in a Mobile Device

The goal of this research is to investigate a biometric solution that links biometric personal identity to selfmonitoring data, with time and location information, as a temporal-spatial event in a personal health record stored in a mobile device. The proposed biometric solution is based on a secure computation technology that reconstructs a cryptographic key for (un)locking personal health record in real time when a verification sample is sufficiently similar to the enrollment sample - whereas the verification process is based on a secure twoparty security computation that compares the enrollment and verification samples without either party sharing the data with each other, nor relying on a trusted third party. The contribution of this research is to demonstrate the practical feasibility of the approach in a resource constrained mobile computing environment. The significance of this research is its potential application for enabling a safe bubble space for social interaction among individuals who have self-monitoring data showing lack of Covid-19 symptoms at a specific time and location.
Analisa Hidrologi Terhadap Pembangunan Pembangkit Listrik Tenaga Air (Studi Kasus Pembangunan PLTA Malea di Kabupaten Tana Toraja Provinsi Sulawesi Selatan)

Hydropower electric Development (PLTA Malea) Plan in Tana Toraja has a watershed area of 1499 km2, high rainfall and natural topographical conditions. Hydrological analysis is important during pre-construction with the aim of analyzing water availability and determining parameters in designing water structures for hydropower development. The research method used is the Limantara synthetic hydrograph unit and the NRECA method of rainfall analysis. The results show that the Q100 year of 2312 m3 / second is used for the weir plan, while the dependable discharge using the NRECA analysis and the FDC graph is obtained by determining the most optimal discharge at a probability of 60% of 63.76 m3 / second and the energy produced is 246, 95 MW which the value does not exceed the capacity factor of 65%. The assumption is that by raising the water level in the upstream / weir to 1,5 m from the existing condition, it will generate additional energy with an average of 1.58 MW. It shows that there is an annual income earned by the Indenpendet Power Producer of Rp. 6,740,044,740 / year.Keyword : Dependable Discharge, Flood Discharge, NRECA Methode, FDC, Energy
Pengembangan Sistem Informasi Geografis Pemetaan Lokasi Panti Sosial Kabupaten Ngawi

Ngawi is a regency with an area of 1,298.58 km². Ngawi Regency with large area it has a lot enough of social walfare instituations or social homes under Social Service of Ngawi Regency. The existence of social homes will create a continuity of social assistance allocation, both from indiduals or institutions. But in fact, that information distribution about social homes is still limited with look for the the information from local people or directly come to social service. Even Google Maps application, it doesn’t cover all of the social homes there. It will cause unequaly distribution of social assistance itself. Based on that problem, it need a geographic information system. Geographic information system can help to map the social home’s location and distribution of social home’s inforamtion at Ngawi Regency. The development of this system uses waterfall method and will be implemented website-based with the programming language PHP,HTML, and Leaflet Javascript. The outcome of developing a geographic information system is a system that capable to manage data, mapping and look for locations, also disseminate information on social homes in Ngawi Regency. Through blackbox testing, the result obtained reach 100% on the guttman scala so it can be concluded that the system can run well as expected
AEAD Key Usage Limits in OSCORE

Object Security for Constrained RESTful Environments (OSCORE) usesAEAD algorithms to ensure confidentiality and integrity of exchangedmessages. Due to known issues allowing forgery attacks against AEADalgorithms, limits should be followed on the number of times aspecific key is used for encryption or decryption. This documentdefines how two peers using OSCORE must take these limits into accountand what steps they must take to preserve the security of theircommunications. Therefore, this document updates RFC8613.
Hokey Pokey Causal Discovery: Using Deep Learning Model Errors to Learn Causal Structure

While machine learning excels at learning predictive models from observational data, learning the causal mechanisms behind the observed phenomena presents the significant challenge of distinguishing true causal relationships from confounding and other potential sources of spurious correlations. Many existing algorithms for the discovery of causal structure from observational data rely on evaluating the conditional independence relationships among features to account for the effects of confounding. However, the choice of independence tests for these algorithms often rely on assumptions regarding the data distributions and type of causal relationships. To avoid these assumptions, we develop a novel deep learning approach, dubbed the Hokey Pokey model, to indirectly explore the conditional dependencies among a set of variables by rapidly comparing predictive errors given different combinations of input variables. We then use the results of this comparison as a predictive signal for causal relationships among the variables. We conduct rigorous experiments to evaluate model robustness and generalizability using generated datasets with known underlying causal relationships and analyze the capacity of model error comparisons to provide a predictive signal for causal structure. Our model outperforms commonly used baseline models (PC and GES) and is capable of discovering causal relationships of different complexity (graph size, density and structure) in both binary and continuous data.
NMT literacy at the interface of AI and intercultural intelligence

ReferencesBowker, L. & J. Buitrago Ciro. 2019. Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Bingley: Emerald Publishing.Earley, P. C. & S. Ang. 2003. Cultural intelligence: Individual interactions across cultures. Stanford, CA: Stanford Business Books. Federici, F. M. & C. Declercq, eds. 2019. Intercultural Crisis Communication. London: Bloomsbury Press.Forcada, M. L. 2017. Making sense of neural machine translation. Translation Spaces 6 (2): 291–309.Hassan, H., A. Aue, C. Chen, V. Chowdhary, J. Clark, C. Federmann, X. Huang, M. Junczys-Dowmunt, W. Lewis, M. Li, S. Liu, T-Y. Liu, R. Luo, A., Menezes, T., Qin, F., Seide, X., Tan, F., Tian, L., Wu, S., Wu, Y., Xia, D., Zhang, Z., & Zhou, M. 2018. Achieving human parity on automatic Chinese to English news translation. arXiv:1803.05567.Martindale, M. J. & M. Carpuat. 2018. Fluency over accuracy: A pilot study in measuring user trust in imperfect MT. Proceedings of AMTA 2018 1: 13-25. http://aclweb.org/anthology/W18-1803Massey, G. & M. Ehrensberger-Dow. 2017. Machine learning – implications for translator education. Lebende Sprache 62 (2): 300-312.
On the Controversy over Control Constructions Involving the Korean Verb seltukhata ‘To Persuade’

This research aims to address the controversy over word order patterns of control constructions of the Korean verb seltukhata (‘to persuade’) and settle it with evidence from production and corpus studies (Choi 2007; Hong & Yun 2020). The controversy, in essence, was sparked by the question of whether the constructions Cl-Acc and Acc-Cl of the verb seltukhata are syntactically related by scrambling (Cormack & Smith 2004; Polinsky et al. 2007; Hoe 2013). To answer this question, this study adopts the results of Hong & Yun (2020) concerning the word order preference in control constructions and suggests that the Acc-Cl structure is derived from the Cl-Acc through scrambling.
Training By Vanilla SGD with Larger Learning Rates

The stochastic gradient descent (SGD) method, first proposed in 1950's, has been the foundation for deep-neural-network (DNN) training with numerous enhancements including adding a momentum or adaptively selecting learning rates, or using both strategies and more. A common view for SGD is that the learning rate should be eventually made small in order to reach sufficiently good approximate solutions. Another widely held view is that the vanilla SGD is out of fashion in comparison to many of its modern variations. In this work, we provide a contrarian claim that, when training over-parameterized DNNs, the vanilla SGD can still compete well with, and oftentimes outperform, its more recent variations by simply using learning rates significantly larger than commonly used values. We establish theoretical results to explain this local convergence behavior of SGD on nonconvex functions, and also present computational evidence, across multiple tasks including image classification, speech recognition and natural language processing, to support the practice of using larger learning rates.
TextTN: Probabilistic Encoding of Language on Tensor Network

As a novel model that bridges machine learning and quantum theory, tensor network (TN) has recently gained increasing attention and successful applications for processing natural images. However, for natural languages, it is unclear how to design a probabilistic encoding architecture to efficiently and accurately learn and classify texts based on TN. This paper proposes a general two-step scheme of text classification based on Tensor Network, which is named as TextTN. TextTN first encodes the word vectors in a probabilistic space by a generative TN (word-GTN), and then classifies a text sentence using a discriminative TN (sentence-DTN). Moreover, in sentence-DTN, its hyper-parameter (i.e., bond-dimension) can be analyzed and selected by the theoretical property of TextTN's expressive power. In experiments, our TextTN also obtains the state-of-the-art result on SST-5 sentiment classification task.
PENGEMBANGAN MEDIA PEMBELAJARAN MENULIS SURAT BAHASA JERMAN

This study aims to develop a media of letter-writing (Brief). The reference of this research is the design of material development by Jolly and Bolito (in Tomlinson, 1998:96) consisting of: i. Identification of material needs, ii. Exploration needs, iii. Contextual realization, iv. The realization of pedagogy, v. The preparation of the material includes the use of the product by the learners, vi. Evaluation of material in accordance with material development objectives. The research instruments used are: a. Instrument of student response to instructional media, b. Instrument observation of student activity in learning, c. documentation, d. Test and assessment criteria in the ZiDS-exam to find out student learning outcomes. Development of learning media to write letters (Brief) in accordance with the procedures that have been determined and have good quality because it is valid, practical and effective. The application of learning media to write letters proved to improve the learning achievement of student writing skill, from 76,92% to 100% from 26 people able to write reply letter. Thus the learning media to write letters feasible to be used in lectures Schreibfertigkeit subject and to be a supporting medium for students.Kata kunci: pengembangan bahan, media pembelajaran, keterampilan menulis
Χρήση ομαδοποιημένου ταξινομητή για αναγνώριση συναισθημάτων από κείμενο

The object of the work is to analyze and predict emotions from written short comments through the Twitter social network. The extraction of emotions can be achieved by various approach methods, the most popular are the statistical methods, those of the vocabulary approach and machine learning with which we have chosen to deal. The models of machine learning, depending on the way they process the data for their education, are divided into three main categories which are: supervised learning,unsupervised and semi-supervised learning. In supervised learning, which is also the runner-up of the task, the model is trained from the Data Set, that is, the predictionof an unknown element, depends to a large extent on the reliability of the sample. The data set we processed consists of 1,600,000 tweets that have been classified into two main categories of emotions (positive and negative). Various ways of extracting attributes from text data, namely the widely used Bag-of-Words (BOW) method with its two main variants Count Vectorizer and TFIDF Vectorizer, were alsoanalysed. The sorter chosen for the above procedure was that of maximum entropy. Simple sorters used are prone to errors. The basic idea of the methodology of ensemble sorters is to create a forecasting model consisting of a set of models that will be combined with each other through a process. The ultimate goal of grouped sorters is to greatly reduce errors and thus improve the performance of the forecast. The most well-known is the method of voting. However, because they were not sufficient to get the desired results, we created a grouped sorter, consisting of three base sorters (Max Entropy, Multinominal,LSVM) in which their predictions were used as data of a second level sorter (Ridge,SGD) against which they then used the voting technique for the final grouped sorter. Finally, the grouped sorter is more improved than ordinary , of course it has room for improvement.
Design Automation for Side Channel Resistant Lightweight Cryptography

The scaling of devices in loT era has opened doors to broad range of privacy and security concerns making it vulnerable to various side channel attacks (SCA) and differential fault attacks (DFA). The generic EDA flows provide powerful solutions for simulation, verification, power, performance and area (PPA) optimizations. But these flows are not enabled to detect side channel leakages or provide countermeasures or handle huge search space under both lightweightedness and security dimension paradigm. So, we propose to augment classical EDA flow with security aware notion addressing the challenges wrt primitive design, SCA and DFA thereby helping crypto-designers to reduce overall design cycle-time and early detection of security flaws in lightweight design and provide countermeasure.
IoT based Smart Healthcare Monitoring System: A Systematic Review

Among the various IoT applications, research into health care systems is still in its early stages. Health care applications point to interest in IoT devices due to lower cost, easier to understand, and improved patient satisfaction. In this paper we depict an idea of ​​in what way IoT used into health care monitoring machines. It is one of the most important application of IoTwhich includes sensor, patient, surgeon, system, and supplementarykeen devices online. In a non-functional way, the IoT-based smart HMS provided an opportunity for physicians to evaluate their patients at a consistent distance. The Internet of Things is jeopardizing various developments such as frequency bandwidth, smart mobile intelligence, wireless wiring network (WSN) that integrates with Coap, 6LoWPAN, REST, and other protocols.
The Evaluation of User Experience and Functional Workload of a Physically Inter- active Virtual Reality System

In this study, we evaluated the user experience of a physically interactive virtual reality (VR) system, which was developed to provide passive kinesthetic haptics in order to enhance motor learning functions during an occupational virtual training. We compared the user experience (e.g., perceived ease-of-use, ease-of-learning, and usefulness) and the functional workload between real and virtual environments by simulating a pick-and-place lifting task in both environments. Results showed an increase in user experience— such as ease of use and learning and usefulness—and a decrease in the overall functional workload with the progression of successive VR tasks.
AN EVALUATIVE STUDY OF SOCIO-ECONOMIC INFORMATION NEEDS OF RURAL WOMEN IN RIJAU LOCAL GOVERNMEN AREA OF NIGER STATE :

The study examined the evaluative study of socio-economic information needs of rural women in Rijau Local Government area of Niger State. Questionnaire was used as an instrument for data collection and simple percentage distribution was used to analyze the data collected. The study showed the socio-economic information needs of rural women include information on raw materials such as the cost, quality, location, source and dealers/seller, how to obtain loan, cooperative societies and government assistance to artisans, new products, latest/current designs and procedure for production, how to market their products, Information on health such as personal hygiene, food and nutrition, housekeeping, child care and family planning, political issues, government policies, laws and human rights, information on processing and preservation of agricultural produce, information on how to apply improve seeds and information on Mechanization. The study recommended that government should as a matter of emergency provide rural women in Rijau local government with information materials, creating mass literacy programme, improving accessibility and availability of information materials, orientation about the use of library, reducing cost of information materials, increasing capital to finance their business, good storage facilities, construction of good roads and educating them on how to market their goods to get good price.
KEPUASAN BELAJAR DARING DAN PENGARUHNYA TERHADAP KEPRIBADIAN MAHASISWA DI KOTA DUMAI

Implementation of education policies in the emergency period of the spread of the Covid 19 has been going on since March 2020 by conducting online learning. Measurement of student satisfaction in learning is an activity that should be carried out to determine the level of student satisfaction with online learning. In this research will focus on online learning satisfaction of college students in the  Dumai dan the effect of online learning on student personality.  The method used in this research is descriptive statistics where data retrieved through questionnaires distributed to students from 7 universities in the  Dumai. Questionaire aided calculation using SPSS software. The result of the X2 variable, studesnts answered strongly agree about 17,1%. This condition means that students are very satisfied with online learning because that can reduce the spread of Covid 19 in the Dumai. The impact of online learning on the personality of the Y1 variable, students answered strongly agree about 27,4% which means online learning it has a bad effect on students because it makes them addicted to playing gadgets. Asymp.Sig (2-sided) value is 0.000 ˂ 0.05, there is a relationship and influence between online learning satisfaction and student personality in Dumai.