Reviewerly/exHarmony huggingface dataset:
Evaluation of a Legally Binding Smart-Contract Language for Blockchain Applications

Blockchain governs inter-organizational business processes and enables decentralized autonomous organizations (DAO) with governance capabilities via smart contracts (SC). Due to the programmer&amp;rsquo;s lack of prior knowledge of the contract domain, SCs are ambiguous and error-prone. Several works, i.e., SPESC, Symboleo, and SmaCoNat, exist to support the legally-binding SCs. The aforementioned SCLs present intriguing approaches to building legally-binding SCs but either lack domain completeness, or are intended for non-collaborative business processes. In our previous work, we address the above-mentioned shortcomings of the XML-based smart-legal-contract markup language (SLCML), in which blockchain developers focus on the contractual workflow rather than the syntax specifics. However, SLCML, as a blockchain-independent formal specification language, is not evaluated to determine its applicability, usefulness, and usability for establishing legally-binding SCs for workflow enactment services (WES) to automate and streamline the business processes within connected organizations. In accordance with this, we formally implement the SLCML and propose evaluation approaches, such as running case and lab experiments, to demonstrate the SLCML&amp;rsquo;s generality and applicability for developing legally-binding SCs. Overall, the results of this work ascertain the applicability, usefulness, and usability of the proposed SLCML for establishing legally-binding SCs for WES.
PENGENALAN APLIKASI GREENFOOT UNTUK PENGEMBANGAN GAME STUDI KASUS SMK AN-NUR DEPOK

Community service activities are activities that include efforts to improve the quality of human resources, among others in terms of broadening insight, knowledge and increasing skills carried out by academic activities for the realization of dharma devotion and a form of concern to play an active role in improving welfare and empowering the wider community, especially for students PPLG An-Nur Depok. The lack of understanding of PPLG An-Nur Depok students about how to make Java-based games using the greenfoot application can be constrained in learning at the An-Nur Depok school which is expected to minimize the understanding of game making. With these community service activities can provide solutions for PPLG students An-Nur in overcoming the programming process in making a game from the Greenfoot application. The output target of this community service is to be published in a national journal, besides that the hope is to add insight to PPLG An-Nur Depok students who are capable in the field of game creation, especially programming The activity plan that will be carried out in this community service activity is to conduct an introduction to programming education to PPLG An-Nur students directly, and also directly provide practice on the flow process of making games with the greenfoot application.
A framework for the metrification of input image quality in deep networks

Deep Neural Networks (DNNs) are critical for real-time embedded imaging applications including autonomous vehicles.DNNs are often trained and validated with images that originate from a limited number of cameras, each of which has its own hardware and image signal processing (ISP) characteristics.However, in most real-time embedded systems, the input images come from a variety of cameras with different optical components, sensors and ISP pipelines, and often include perturbations due to a variety of scene conditions.Data augmentation methods are commonly exploited to enhance the robustness of such systems.Alternatively, methods are employed to detect input images that are unfamiliar to the trained networks, including out of distribution detection.Despite these efforts DNNs remain widely systems with operational boundaries that cannot be easily defined.One reason is that, while training and benchmark image datasets include samples with a variety of perturbations, there is a lack of research in the areas of metrification of input image quality suitable to DNNs and a universal method to relate camera system performance to DNN robustness using appropriate quality metrics.This paper addresses this lack of metrification specific to DNNs systems and introduces a framework for systematic modification of camera system performance parameters that relate input image quality attributes to DNN performance.
16 RSA security implementation in quantum computing for a higher resilience

The public key cryptosystem is widely used in the current scenario. The possibility of breaking this system by a quantum algorithm would pose a significant threat to the confidentiality of the data and assets held by all participants in the digital economy. This is because the public key cryptosystem was designed specifically for the purpose of encrypting information. The chapter discusses the security implementation in quantum computing using RSA to improve the security against vulnerabilities. The RSA is designed in such a way that it mitigates all possible attacks in the system, and provides better resilience against attacks. The results shows reduced computational time in detecting attacks and mitigating them than with other methods.
RANCANGAN SISTEM INFORMASI LAPORAN HASIL PENGUJIAN QUALITY CONTROL BERBASIS WEB

Quality control (QC) is one of the most dynamic activities and one that is needed to provide good product quality. PT. You currently have a place or place to check product quality called Quality Control. Where for each food ingredient make product, the QC Admin takes a few samples to check the quality of the food. Then it is made into a report to the Auditor as a supervisory body and analysis the feasibility of food to consumed healthily. The process of making a report on the results of this test still uses records in the form of book documents and formats in the data collection process. So in the delivery process, it often experiences many delays not according to the report target. The purpose of this research is to design a web-based quality control test report information system to make it easier to make reports so that they are faster and more precise. This research conducted using qualitative methods by conducting field studies. The collection technique used was observation and interviews. The results of the research are in the form of web-based application prototypes so that Admins or users can make data input in the form of material data, media data, retrieval data, and testing data that have been carried out as a result of products that have been tested properly, by making input reports as real data in a report which will be given to the head of QC as the person in charge. Reports given to the Head of QC include request data, data collection, and product testing data that has been input by the admin.
PELATIHAN MANAJEMEN PERJALANAN DINAS BAGI SISWA SMK LINK &amp; MATCH KOTA TANGERANG SELATAN

Good business travel management will have a positive effect on the smooth running of a company's business. Business travel is one of the tasks given by superiors to their subordinates to perform in activities such as work visits, education, and training activities which are funded directly by the company. In arranging and preparing for business trips, it must include the documents needed for the travel, confirmation of tickets and hotels, making travel schedules, besides everything that can support the success and comfort of employees on official trips. The Objective is for Link and Match Vocational High School students to understand how to manage a good business trip. The activity performed using the training method on campus 2 of the ITB Swadharma which was attended by 65 SMK Link and Match students. The result of the activity is the increased knowledge and skills of the attendents regarding the management of official travel with the help of information technology.
The Metaverse for Enterprises

The metaverse is expected to change business models and shape consumer behaviors in the coming years. Although research in the field remains in its infancy, the metaverse ecosystem can be defined as a convergence of emerging technologies leading to a shared virtual three-dimensional (3D) world that facilitates interactive, immersive, and collaborative experiences. Most large companies have begun investing in technologies enabling the metaverse. Because of its complexity, no clear consensus has been established on how the metaverse should evolve. Despite this uncertainty, enterprises are expected to take actions to prepare for, and benefit from, technological changes that can improve their competitiveness and sustainability. By reflecting on key topics of metaverse, this study identifies the potential applications and fundamental challenges to be faced in the next years to enable enterprises to keep pace with the latest developments and future trends in the metaverse.
Large Language Models are Extremely Bad at Creating Anagrams

&lt;p&gt;Much has been made of the remarkable abilities of generative artificial intelligence (AI) and large language models (LLMs) such as ChatGPT, and their ability to rapidly produce convincingly human-like text on demand. I set out to evaluate the ability of three popular LLMs (ChatGPT version GPT-4; ChatGPT version GPT-3.5; Google Bard) to construct anagrams, that is, pairs of words or phrases that use all of the same letters exactly once, rearranged into new meaningful words and phrases. These models have been previously demonstrated to perform well on text-based portions of general intelligence tests, successfully solving various word and mathematical puzzles in a manner that resembles elements of human general intelligence. Surprisingly, all three LLMs performed quite badly when prompted to generate anagrams related to a specific theme, succeeding in only 2.5% of anagram attempts overall, with only the GPT-4 version of ChatGPT producing any valid anagrams at all. All three LLMs successfully returned the correct definition of an anagram, along with providing one or more valid examples when queried. In summary, the failure of current LLMs to generate anagrams related to a specific theme provides a curious example of a “cognitive blind spot” in the performance of these otherwise impressive tools.&lt;/p&gt;
Offline Nepali Numeral Character Recognition using Character Specific Features

&lt;p&gt; An off-line Nepali numeral handwritten character recognition, based on the neural network, is described in this paper. Nepali numeral specific features are extracted from character images. Connection of feature vector to the hidden neurons and hidden neurons to the output neurons is chosen to give optimum result. Very few number of samples are used to train the neural network. Recognition accuracy of 81.14 per cent is obtained. &lt;/p&gt;
Personalized Font Generation using Deep Learning Neural Networks

Abstract: Personalized font generation is an emerging technology that aims to create unique and customized fonts based on an individual’s preferences and characteristics. This process involves designing a font from scratch, including selecting the appropriate style, weight, and size. Personalized features such as letter shapes, ligatures, and kerning are then implemented to create a font that is unique to the individual. Personalized font generation has the potential to revolutionize the way we think about typography. By creating fonts that are tailored to an individual’s preferences, we can enhance the user experience and create more engaging and personalized content. This technology is particularly relevant in today’s digital age, where the use of typography is increasingly important in everything from social media posts to marketing materials. In summary, personalized font generation is a promising new technology that offers a unique and tailored typography experience. By combining traditional font design techniques with advanced technologies such as machine learning and artificial intelligence, we can create fonts that are truly one-of-a-kind and help to elevate our communication efforts.
Towards Efficient Video-Based Action Recognition: Context-Aware Memory Attention Network

Abstract Human action recognition from videos has huge applications in daily life given the proliferation of surveillance cameras everywhere. The continuing challenge is to design more efficient models so that that they can approach real-time recognition and yet with good accuracy for ubiquitous deployment. In this paper, we propose a human action recognition model sans optical flow extraction and 3D convolution, called Context-Aware Memory Attention Network (CAMA-Net). The absence of optical flow extraction and 3D convolution makes CAMA-Net more efficient than many existing works. Key to CAMA-Net is an attention module called Context-Aware Memory Attention Module which is used to compute the relevance score between the key and value pairs from the 2D ResNet backbone thereby establishing the correspondences between the video frames. The proposed method is tested on four popular action recognition datasets, namely, UCF101, HMDB51, ActivityNet and Diving48. The experimental results demonstrate the strength of the proposed model as it outperforms existing baseline 2D CNN based models.
The Influence of Organizational Learning Capability on Organizational Commitment of Private Schools’ Library Personnel

Organizations continually face the challenges to compete effectively with global environments and in line with its success are the sincere commitment of employees. To cope up with the emerging needs of organizations, this study looked into the influence of organizational learning capability on the organizational commitment of library personnel. Quantitative research design was employed in this study using multiple regression analysis to determine the influence of organizational learning capability on organizational commitment. A survey questionnaire was administered to the 400 library personnel in private schools in Region XI, Philippines using total population sampling method. Findings show that organizational learning capability significantly influence organizational commitment. Further, results revealed that among the indicators of organizational learning capability, experimentation and openness was the strongest predictor of organizational commitment followed by dialogue and participative decision-making.
The Effect of Learning Interest on The Results of Learning to Write Poetry Text using Problem Based Learning Method

This study aims to prove the effect of interest in learning with the results of learning to write poetry texts using the problem based learning method. The hypothesis tested is that there is an influence of interest in learning on writing poetry texts. The research subjects were 29 students of class VIII F MTs Negeri 2 Subang, West Java, in the 2020/2021 school year. The data comes from the results of learning to write poetry texts and the results of a learning interest questionnaire. Data were analyzed using SPSS 25 with product moment person correlation test to determine the relationship and regression test to determine the effect. Based on the results of statistical analysis, the results of the correlation test of 89.2% showed a very strong relationship between the results of learning to write poetry texts with interest in learning. The results of the regression test also prove that interest in learning has an effect of 79.5% on learning outcomes to write poetry texts. Meanwhile, 20.5% was influenced by other factors.Â Â ABSTRAKPenelitian ini bertujuan untuk membuktikan pengaruh minat belajar dengan hasil belajar menulis teks puisi menggunakan metode problem based learning. Hipotesis yang diuji yaitu terdapat pengaruh minat belajar terhadap menulis teks puisi. Subjek penelitian adalah 29 orang siswa kelas VIII F MTs Negeri 2 Subang, Jawa Barat, tahun ajaran 2020/2021. Data bersumber dari hasil belajar menulis teks puisi dan hasil angket minat belajar. Data dianalisis menggunakan SPSS 25 dengan uji korelasi product moment person untuk mengetahui hubungan dan uji regresi untuk mengetahui pengaruh. Berdasarkan hasil analisis statsitik, hasil uji korelasi sebesar 89,2% menunjukan hubungan yang sangat kuat antara hasil belajar menulis teks puisi dengan minat belajar. Hasil uji regresi juga membuktikan minat belajar berpengaruh sebesar 79,5% terhadap hasil belajar menulis teks puisi. Sementara 20,5% itu dipengaruhi oleh faktor lainnya.Â
RANCANGAN WEBSITE SISTEM E-GOVERNMENT PELAYANAN DAN PERMOHONAN SURAT ONLINE “SIPADU”

Public service is the fulfillment of the desires and needs of society by state administrators. Public service activities at RW 04 Kapuk Poglar West Jakarta include citizen data collection, monitoring, community services, and activity information. Until now, the process of public services implementation still uses records through books and paper, so that the information provided is not conveyed to all residents of RW 04. A website-based information system is required to meet these needs. The application of e-government plays a critical role in improving the quality of public services and can overcome existing problems. The purpose of this study was to design a website-based service information system and application letter "sipadu" at RW 04 Kapuk Poglar. The type of research used is qualitative research, while data collection is carried out by field studies using observational data collection techniques, interviews, and literature study. The result is a system that can simplify and speed up the process of making letters and processing community administrations.
Model-based Offline Reinforcement Learning with Count-based Conservatism

In this paper, we propose a model-based offline reinforcement learning method that integrates count-based conservatism, named $\texttt{Count-MORL}$. Our method utilizes the count estimates of state-action pairs to quantify model estimation error, marking the first algorithm of demonstrating the efficacy of count-based conservatism in model-based offline deep RL to the best of our knowledge. For our proposed method, we first show that the estimation error is inversely proportional to the frequency of state-action pairs. Secondly, we demonstrate that the learned policy under the count-based conservative model offers near-optimality performance guarantees. Through extensive numerical experiments, we validate that $\texttt{Count-MORL}$ with hash code implementation significantly outperforms existing offline RL algorithms on the D4RL benchmark datasets. The code is accessible at $\href{https://github.com/oh-lab/Count-MORL}{https://github.com/oh-lab/Count-MORL}$.
Semisupervised Speech Data Extraction from Basque Parliament Sessions and Validation on Fully Bilingual Basque–Spanish ASR

In this paper, a semisupervised speech data extraction method is presented and applied to create a new dataset designed for the development of fully bilingual Automatic Speech Recognition (ASR) systems for Basque and Spanish. The dataset is drawn from an extensive collection of Basque Parliament plenary sessions containing frequent code switchings. Since session minutes are not exact, only the most reliable speech segments are kept for training. To that end, we use phonetic similarity scores between nominal and recognized phone sequences. The process starts with baseline acoustic models trained on generic out-of-domain data, then iteratively updates the models with the extracted data and applies the updated models to refine the training dataset until the observed improvement between two iterations becomes small enough. A development dataset, involving five plenary sessions not used for training, has been manually audited for tuning and evaluation purposes. Cross-validation experiments (with 20 random partitions) have been carried out on the development dataset, using the baseline and the iteratively updated models. On average, Word Error Rate (WER) reduces from 16.57% (baseline) to 4.41% (first iteration) and further to 4.02% (second iteration), which corresponds to relative WER reductions of 73.4% and 8.8%, respectively. When considering only Basque segments, WER reduces on average from 16.57% (baseline) to 5.51% (first iteration) and further to 5.13% (second iteration), which corresponds to relative WER reductions of 66.7% and 6.9%, respectively. As a result of this work, a new bilingual Basque–Spanish resource has been produced based on Basque Parliament sessions, including 998 h of training data (audio segments + transcriptions), a development set (17 h long) designed for tuning and evaluation under a cross-validation scheme and a fully bilingual trigram language model.
PERANCANGAN APLIKASI KAS BERBASIS DEKSTOP DENGAN METODE SCRAPING PADA RUMAH YATIM SUNTER MUARA

The treasurer or finance department is a crucial part of the organization. The task of the treasurer is to receive, record and make financial reports of the organization. Financial information must be detailed and transparent so that the financial information conveyed is clear to address interested parties regarding the amount of cash reported each month. At the Sunter Muara Orphanage, the financial reports still use a manual system where each income and expenditure of cash funds is recorded in the ledger, which can cause mistakes and errors in preparing financial statements. This study aims to create a computerized cash application using web scraping methods. The research method used is field research with data collection techniques in the form of observation and interviews. The results are in the form of an application prototype that helps the processing of financial cash receipts and disbursements data at orphanages to be faster and more effective and to produce financial reports that are easier and more structured.
MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems

MeetEval is an open-source toolkit to evaluate all kinds of meeting transcription systems. It provides a unified interface for the computation of commonly used Word Error Rates (WERs), specifically cpWER, ORC-WER and MIMO-WER along other WER definitions. We extend the cpWER computation by a temporal constraint to ensure that only words are identified as correct when the temporal alignment is plausible. This leads to a better quality of the matching of the hypothesis string to the reference string that more closely resembles the actual transcription quality, and a system is penalized if it provides poor time annotations. Since word-level timing information is often not available, we present a way to approximate exact word-level timings from segment-level timings (e.g., a sentence) and show that the approximation leads to a similar WER as a matching with exact word-level annotations. At the same time, the time constraint leads to a speedup of the matching algorithm, which outweighs the additional overhead caused by processing the time stamps.
New Associate Editor

IT IS with great pleasure that I welcome Dr. Ben Keller to the editorial board of the IEEE Journal of Solidstate Circuits as a new Associate Editor. Dr. Keller is an expert on digital circuits, processor design, and machine learning accelerators.