Reviewerly/exHarmony huggingface dataset:
Workshop Manajemen Organisasi dan Penyusunan Modul Dolanan Anak Bagi Forum Taman Baca Masyarakat (FTBM) di Kabupaten Jepara

Authors:
raw_author_name: Yushinta Eka Farida
raw_affiliation_string: Universitas Islam Nahdlatul Ulama Jepara
raw_author_name: Ahmad Saefudin
raw_affiliation_string: Universitas Islam Nahdlatul Ulama Jepara

Abstract: This community service aims to enhance knowledge and expertise in organizational management and children's play module preparation for the Forum Taman Baca Masyarakat (FTBM) in Jepara Regency. The service used the Participatory Action Research (PAR) method, with the participants being representatives of TBM managers who are members of FTBM. A questionnaire was used as the instrument for evaluating the activity, and the results of which were analyzed descriptively. The service results indicate a significant increase in workshop participants' understanding. A total of 9 participants (30%) who were initially in the low understanding category succeeded in increasing to the high category, including 18 participants (60%). The workshop activity on preparing children's play modules also made a positive contribution to the participants, particularly in terms of module preparation. There was a significant increase in understanding, where 10 participants (33.33%) who were initially in the low understanding category succeeded in increasing to the high category, involving 20 participants (66.67%). This activity was successful in achieving positive results and supporting the development of participants’ skills and understanding of organizational management and module preparation.
Quantitative Skills Training for SNBT Preparation at SMA Negeri 2 Parepare South Sulawesi

Authors:
raw_author_name: Nur Rahmi
raw_affiliation_string: Mathematics, Department of Science, Institut Teknologi Bacharuddin Jusuf Habibie

Abstract: This service activity aims to improve students' quantitative abilities, especially in the SNBT test. The method of implementing this service used training, test simulations and discussions, as well as tips and strategies for solving quantitative ability questions. Assessment tools used during the activity included a pretest, posttest, and questionnaire. The analytical technique to assess the activity's success involved comparing the outcomes of the pre-test and post-test administered. The Results showed that there was an average increase in the scores of the participants' Quantitative Skills test. Overall, the average percentage increase in the participants' Quantitative Skills test scores was 13% from the pre-test scores. It can be concluded that the quantitative skills training provided positively enhanced the student's knowledge and skills, particularly in solving scholastic problems related to the Quantitative Skills test for the upcoming SNBT. Based on the survey, 53.1% of participants were highly satisfied with the conducted training activities and agree that such activities should be continued.
Edukasi Literasi Digital : Pendampingan Transformasi Digital bagi Generasi Muda di Desa Nepo Kabupaten Polewali Mandar

Authors:
raw_author_name: Ruslan Ruslan
raw_affiliation_string: Universitas Negeri Makassar
raw_author_name: Muhammad Ilham S
raw_affiliation_string: Universitas Sulawesi Barat
raw_author_name: Irmawati M
raw_affiliation_string: Universitas Sulawesi Barat
raw_author_name: Umar Umar
raw_affiliation_string: Universitas Sulawesi Barat

Abstract: This service activity aims to increase the understanding of young people in Nepo village regarding digital literacy and digital transformation assistance. The method of implementing this service uses socialization and training with a participatory approach. The evaluation instrument for this activity uses a questionnaire and is analyzed descriptively. The results of this service show that there has been a significant increase in participants' understanding of the concepts of digital literacy, digital transformation and social media. Digital literacy understanding increased from 50% to 79%, while benefits of digital literacy understanding rose from 50% to 87%. Participants also showed an increase in understanding the era of digital transformation (from 38% to 79%) and the impact of social media (from 43% to 93%). They also understand ethics and cyber security better, with significant improvements. The digital literacy education program in Nepo village has successfully achieved its goals, empowering the younger generation in digital literacy, digital transformation and social media ethics.
Hebrew nonverbal sentences wear reconstruction on their sleeve

Authors:
raw_author_name: Omri Doron
raw_affiliation_string: MIT

Abstract: In this paper, I discuss a particle in Hebrew which has been termed Pron by Doron (1983). While its surface form is that of a pronoun, its distribution resembles that of a copula – it appears between the subject and the predicate in nonverbal present-tensed sentences. However, its distribution is limited in unexpected ways for a copula, which gained it some attention in the literature. Contra the standard line of analysis, I argue that Pron is in fact a resumptive pronoun left by the subject as it raises to a higher position. I show that this analysis ties together many of Pron’s prima facie-surprising distribution patterns.
Contextual Complexities and Second Language Acquisition in Cameroon

Authors:
raw_author_name: Louis Mbibeh
raw_affiliation_string: The University of Bamenda, Cameroon.

Abstract: The complexity of the linguistic environment in Cameroon raises the question of context and its role in the acquisition of another language. While examining progress in English language learning in Cameroon, this chapter draws a dichotomy between learners in such contexts considered rural&nbsp;and those regarded as urban or cosmopolitan&nbsp;with its inherent complexities. Using the irregular verb as a yardstick, an evaluation of the acquisition of irregular verb patterns by 80 final year primary school learners from two contexts in the Northwest Region of Cameroon serves as a guide. Oral and written tests are used to check learners’ acquisition of verb inflectional categories, verb tenses and general written and oral productions within the mixed method design. The findings reveal similar trends in the acquisition of inflectional categories and verb tenses by learners in both contexts and divergent trends in general oral and written productions. For instance, learners in both contexts exhibit similar challenges using the Vs, Ved and Ven inflections with a very low average frequency of 26% and with a high frequency of 67.2% for the Ving and Vo inflections. Though learners in the urban centres have higher degrees of efficiency in oral productions, their counterparts in the rural areas exhibit more challenges in verbal as against written productions. The chapter concludes that second language acquisition is not a consequence of a unilateral context but a result of a plethora of other factors both within and without the learning environment with evident pedagogic implications for stakeholders in the second language acquisition industry. While context is noted to have impending implications in language learning as highlighted in the chapter, the finality of SLA is also dependent on other linguistic factors like innate capabilities inherent in all learners.
Perancangan Sistem Informasi Akuntansi Penggajian Pegawai Instansi Pemerintah di BKPSDM Kab. Cianjur

Authors:
raw_author_name: None Perwito
raw_affiliation_string: Politeknik Piksi Ganesha Bandung
raw_author_name: Falaah Abdussalaam
raw_affiliation_string: Politeknik Piksi Ganesha Bandung

Abstract: The process of employee payroll system at BKPSDM Kab. Cianjur Agency already uses a computer but it is not optimal because it still uses Microsoft Excel in data collection and calculation salaries. The purpose of this research is to create a computerized payroll information system that make it easier for agency in the payroll process for employees and minimize the problems that often occur. This information system designed using PHP Myadmin programming language and MySQL as a database. The approach to developing this system uses the waterfall method and the tools involved include Flowmap, Context Diagram, Data Flow Diagrams (DFD), and Entity Relationship Diagrams (ERD). The results of this employee payroll application can make it easier to collect employee data and calculation of employee salaries accurately and can streamline time in making reports so as to reduce errors in the employee payroll system.
Deep Learning Methods for Diagnosing Thyroid Cancer

Authors:
raw_author_name: Gurmanik Kaur Mann
raw_affiliation_string: Department of Electrical Engineering, Sant Baba Bhag Singh University, Jalandhar

Abstract: Abstract One of the prevalent, life-threatening disorders that have been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present applications of deep learning in thyroid cancer imaging and discussed the study's goals. We provided an overview of latest developments in the diagnosis of thyroid cancer using deep learning techniques and addressed about numerous difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.
FPGA-based multipliers for elliptic curve cryptography

Authors:
raw_author_name: Hongfei Ke
raw_affiliation_string: Zhejiang University (China)
raw_author_name: Peiyong Zhang
raw_affiliation_string: Zhejiang University (China)

Abstract: This paper presents an approach to build large-integer multipliers for Elliptic Curve Cryptography (ECC). Compared to the traditional divide and conquer method, our method introduces the 𝑛-term Karatsuba-like algorithm and adopts a cascaded structure for integer multiplications, which simplifies the complexity of the whole structure and contributes to a fine-grained multiplier design. Target at the required width of random elliptic curves over prime fields, we implement the design on virtex-7 FPGA and utilize internal DSPs as the fundamental multiplier resources. The synthesis results are compared quantitatively in terms of designs with different parameters and demonstrate the flexibility of our method.
Expandable residual attention-based high-performance embedded gesture recognition

Authors:
raw_author_name: Shuyu Chen
raw_affiliation_string: Hong Kong Polytechnic University (Hong Kong, China)

Abstract: In embedded human-computer interaction systems, the development of high-performance gesture recognition technology is crucial due to its demand for low power consumption and efficient processing. Addressing the challenge of highprecision gesture recognition in complex backgrounds, a high-performance embedded gesture recognition method based on the Expandable Residual Attention mechanism is proposed. This method enhances the capability of extracting differentscale gesture features by introducing the Expandable Residual Attention mechanism into YOLOv7. Additionally, to address the characteristics of high degrees of freedom and self-occlusion in hand gestures, SoftNMS is introduced with a penalty term to effectively reduce the probability of target omissions. Finally, the gesture recognition model is compressed and accelerated with TensorRT. Experimental results on the Jochen Triesch Static Hand Posture Database demonstrate that the proposed method significantly improves gesture recognition accuracy while maintaining high inference efficiency.
Rules as code and the rule of law: ensuring effective judicial review of administration by software

Authors:
raw_author_name: Rónán Kennedy
raw_affiliation_string: Lero and School of Law, University of Galway, Galway, Ireland

Abstract: This paper considers the possible benefits and substantial risks of ‘Rules as Code’, the parallel drafting of legislation and codification in software, which has been the subject of attention from policy-makers and pilot studies in some jurisdictions. It highlights the long history of these approaches, and the challenges of ossification, mis-translation of rules, and separation of powers problems. It also examines in the detail the Australian Pintarich case, which demonstrates the inadequacy of conventional judicial review of automated decision-making. It outlines some possible solutions to these issues — two ‘internal’ to development processes (greater transparency, and literate pair programming) and two ‘external’ (expanding the capacity of judicial review to look beyond a specific citizen/state interaction and consider the design and development of the controlling software system, and greater cross-disciplinary awareness by lawyers).
Hierarchical Perceptual Graph Attention Network for Knowledge Graph Completion

Authors:
raw_author_name: Wenhao Han
raw_affiliation_string: School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
raw_author_name: Xuemei Liu
raw_affiliation_string: School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China; School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
raw_author_name: Jianhao Zhang
raw_affiliation_string: School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
raw_author_name: Hairui Li
raw_affiliation_string: School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China

Abstract: Knowledge graph completion (KGC), the process of predicting missing knowledge through known triples, is a primary focus of research in the field of knowledge graphs. As an important graph representation technique in deep learning, graph neural networks (GNNs) perform well in knowledge graph completion, but most existing graph neural network-based knowledge graph completion methods tend to aggregate neighborhood information directly and individually, ignoring the rich hierarchical semantic structure of KGs. As a result, how to effectively deal with multi-level complex relations is still not well resolved. In this study, we present a hierarchical knowledge graph completion technique that combines both relation-level and entity-level attention and incorporates a weight matrix to enhance the significance of the embedded information under different semantic conditions. Furthermore, it updates neighborhood information to the central entity using a hierarchical aggregation approach. The proposed model enhances the capacity to capture hierarchical semantic feature information and is adaptable to various scoring functions as decoders, thus yielding robust results. We conducted experiments on a public benchmark dataset and compared it with several state-of-the-art models, and the experimental results indicate that our proposed model outperforms existing models in several aspects, proving its superior performance and validating the effectiveness of the model.
Time-based sampling: Theory and an application to video reconstruction from events

Authors:
raw_author_name: Pier Luigi Dragotti
raw_affiliation_string: Imperial College London

Abstract: Traditional signal processing is based on the idea that an analogue waveform should be converted in digital form by recording its amplitude information at specific time instants. Nearly all data acquisition, processing and communication methods have progressed by relying on this fundamental sampling paradigm. Interestingly, we know that the brain operates differently and represents signals using networks of spiking neurons where the timing of the spikes encodes the signal's information. This form of processing by spikes is more efficient and is inspiring a new generation of event-based audio-visual sensing and processing architectures. In this talk, we investigate time encoding as an alternative method to classical sampling, and address the problem of reconstructing classes of sparse non-bandlimited signals from time-based samples. We consider a sampling mechanism based on first filtering the input, before obtaining the timing information using a time encoding machine. Leveraging specific properties of these filters, we derive sufficient conditions and propose novel algorithms for perfect reconstruction of classes of sparse signals. We then extend our analysis to multi-dimensional signals and introduce a deep neural networks for the reconstruction of intensity videos from events generated by event-driven cameras. The network is obtained by embedding prior-models in its architecture. We show that this approach leads to simple and yet effective neural networks.
Weighted median autoregressive graph filters

Authors:
raw_author_name: Wenxu Yan
raw_affiliation_string: Jiangnan University (China)
raw_author_name: Wei Shuai
raw_affiliation_string: Jiangnan University (China)
raw_author_name: Wenyuan Wang
raw_affiliation_string: Jiangnan University (China)

Abstract: The graph filter can extract the desired features from the graph signal and filter out the noise signal. Most of the graph filters proposed in the literature are linear. Autoregressive moving average (ARMA) filter is a polynomial filter. Compared to finite-impulse response (FIR) graph filters, ARMA graph filters are robust to changes in the signal and/or graph, but are still linear. In this work, we propose a weighted median autoregressive graph filter (WMAF) based on a first-order ARMA graph filter. The proposed filter is a combination of weighted median filter in the traditional signal processing field and median autoregressive filters (MAF), and can be implemented in a distributed way. Compared with linear filter and MAF, the proposed WMAF filter has better filtering effect on pulse noise. In the denoising application of real sensor network data set, the filtered signal has a better signal-to-noise ratio.
Job resource prediction and completion status prediction on large-scale computing platforms

Authors:
raw_author_name: Tao Zhang
raw_affiliation_string: Dawning Information Industry (Beijing) Co., Ltd. (China)
raw_author_name: Yu Zheng
raw_affiliation_string: Dawning Information Industry (Beijing) Co., Ltd. (China)

Abstract: Predicting the resource consumption and completion status of jobs is beneficial to improve the scheduling performance of the system. Many studies have shown that job name can effectively improve the accuracy of prediction. Therefore, by mining the structural semantic information of job name, this paper introduces new features of job name habit, including job name length, number of job name elements, editing distance, and analyzes each substructure of job name, adding classification features after clustering. The introduced new features can better characterize the similarity between jobs and provide strong support for model prediction. Based on the model trained by the new feature data set, the prediction accuracy is significantly improved compared with the model that only introduces the job name.
Supervised and unsupervised learning of (1+1)-dimensional even-offspring branching annihilating random walks

Authors:
raw_author_name: Wei Li
raw_affiliation_string: Central China Normal University, 152 Luoyu Road,Hongshan District,Wuhan City, Hubei Province, China, Wuhan, 430079, CHINA
raw_author_name: Feiyi Liu
raw_affiliation_string: Central China Normal University, 152 Luoyu Road,Hongshan District,Wuhan City, Hubei Province, China, Wuhan, 430079, CHINA
raw_author_name: Jianmin Shen
raw_affiliation_string: Central China Normal University, 455 Building No.9, No.152 Luoyu Road, Wuhan, Hubei 430079, P.R.China, Wuhan, 430079, CHINA

Abstract: Abstract Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this study, ML is applied to (1+1)-d, even offspring branching annihilating random walks (BAW), whose universality class is not DP-like. The supervised learning of (1+1)-d BAW via convolutional neural networks (CNN) results in a more accurate prediction of the critical point than the Monte Carlo (MC) simulation for the same system sizes. The dynamic exponent \;$z$\; and spatial correlation length correlation exponent \;$\nu_{\perp}$\ were also measured and found to be consistent with their respective theoretical values. Furthermore, the unsupervised learning of (1+1)-d BAW via an autoencoder (AE) gives rise to a transition point, which is the same as the critical point. The latent layer of AE, through a single neuron, can be regarded as the order parameter of the system being properly re-scaled. Therefore, we believe that ML has exciting application prospects in reaction-diffusion systems such as BAW and DP.
Using high-precision travel time extraction to detect time deviation of seismic instruments

Authors:
raw_author_name: Han Zhang
raw_affiliation_string: China Jiliang University, Hangzhou, Hangzhou, 310018, CHINA

Abstract: Abstract The method of seismic ambient noise cross-correlations (NCFs) has been demonstrated to be applicable for time offset measurement, especially in evaluating instrument clocks. Continuous recording of seismic ambient noise data makes it possible to analyze the performance of seismic instruments online. However, long-term cross-correlation and stacking calculations are required to obtain accurate travel time from ambient noise, which greatly reduces the time resolution of instrument performance detection. Therefore, we propose a travel time extraction method based on time–frequency analysis, which could obtain the travel time accurately even though the NCF has a low signal-to-noise ratio (SNR), and it is applied to the measurement of clock offsets in seismic instruments. The method combines S Transform and dictionary learning to improve the SNR of NCFs, and uses a peak extraction algorithm based on short-time Fourier transform to obtain accurate travel times. Additionally, the travel time drift of the station pair can be obtained by calculating the travel time difference between causal and acausal parts of NCFs. Using the data from ambient noise observations and the real data with time errors to verify that the proposed method can obtain accurate travel time for NCF with SNR lower than 6 and it can pick up a time offset as low as one sampling point, the detectable change is 0.046% of travel time, which is crucial for detecting weak changes in the performance of seismic instruments.