Reviewerly/exHarmony huggingface dataset:
The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation

One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restricted domains, or are low quality because they are constructed using semi-automatic procedures. In this work, we introduce the FLORES-101 evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia and covering a variety of different topics and domains. These sentences have been translated in 101 languages by professional translators through a carefully controlled process. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all translations are multilingually aligned. By publicly releasing such a high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond.
Analysis of recurrent neural networks via property-directed verification of surrogate models

Abstract This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a surrogate model from a given RNN using active automata learning. This model may then be analyzed using model checking as a verification technique. The term property-directed reflects the idea that our procedure is guided and controlled by the given property rather than performing the two steps separately. We show that this not only allows us to discover small counterexamples fast, but also to generalize them by pumping toward faulty flows hinting at the underlying error in the RNN. We also show that our method can be efficiently used for adversarial robustness certification of RNNs.
Quality Management Implementation Among Commercial Printing Companies in Malaysia

Providing experienced and skilled people at all levels in the printing business are essential elements to building a business model that can help business performance towards global standards. The global printing industry issue is always related to print quality and productivity, moreover as a priority in achieving customer satisfaction. The issues of quality and productivity are relevant to be addressed to determine the survival of commercial printers. This study explored the Senior Managers and the owner of the companies in Malaysia Small and Medium Printing enterprises' perspective on quality management implementation. This research focuses on SME commercial printers in Malaysia and ten Senior Managers and the owner of the companies have been interviewed. This empirical study derived on three main categories, including the advantages and challenges of implementing quality management and the strategies adopted in sustaining it. Keywords: Management; commercial printing; challenge and strategy eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3830
Privacy in blockchain systems

Privacy of blockchains has been a matter of discussion since the inception of Bitcoin.Various techniques with a varying degree of privacy protection and complexity have been proposed over the past decade.In this survey, we present a systematic analysis of these proposals in four categories: (i) identity, (ii) transaction, (iii) consensus, and (iv) smart contract privacy.Each of these categories have privacy requirements of its own, and various solutions have been proposed to meet these requirements.Almost every technique in the literature of privacy enhancing technologies have been applied to blockchains: mix networks, zero-knowledge proofs, blind signatures, ring signatures, secure MPC, homomorphic encryption, to name just a few.We analyze each category separately in the paper.We first define the related privacy issues, and then review the proposed solutions.The limitations of each solution and the attacks discovered are also discussed along with the proposals.For each category, we first define the relevant privacy issues, and then review the proposed solutions along with their features and limitations.
Design and User Experience Evaluation of Bersii Android-based Mobile Application User Interface

The user interface has a substantial and influential role because it becomes a direct liaison between the application system and its users and shapes each user's perception of the Bersii application. Bersii application is created to reduce single-use plastic waste in Indonesia to build and increase public awareness of the importance of protecting the environment. The Bersii mobile application has features such as product refills to reduce the use of plastic waste. This paper designs the user interface (UI) of the Bersii mobile application for buying refill products. The user interface (UI) design results will be tested using usability and User Experience (UX). The usability test uses the System Usability Scale (SUS), and the UX test uses the User Experience Questionnaire (UEQ). The results of usability testing obtained a score of 79, which was included in the category “good". In each aspect of the UEQ test, it obtained the following scores: attractiveness 2,11, perspicuity 1,71, efficiency 1,98, dependability 1,89, stimulation 2,01, and novelty 1,45. Overall, the results of the UEQ testing were included in the category "good”.
The Effects of Connected Speech Instruction on Second or Foreign Language Learners’ Perceptive Skills and Connected Speech Production: A Systematic Review of the Literature (2000-2021)

This systematic review paper attempts to present the current database on the effects of explicit connected speech instruction on English as a Second Language (ESL) or English as a Foreign Language (EFL) learners’ perceptive skills and connected speech production. A total of 22 studies were gathered across the digital database through selective searching of key terms. Using the format of the review by Thomson and Derwing (2014), the following information was tabulated: participants’ demographics, theoretical paradigm, scope of training, training input, duration of instruction, nature of assessments, and whether or not there was a significant improvement in learners’ perceptive skills and connected speech production. The findings have generally shown a consensus among the studies that explicit instruction of connected speech was effective and had positively contributed to the improvement of learners’ perceptive skills and connected speech production. This paper adds to the corpora of literature on connected speech in ESL and EFL contexts and raises awareness of the significance of connected speech instruction in ESL or EFL learning contexts.
Refining micro-seismic catalogs around Seydişehir, Turkey

In Turkey, two official seismology centers, Bogazici University Kandilli Observatory and Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center (KOERI-RETMC) and Republic of Turkey Prime Ministry Disaster Emergency Management Authority (AFAD) announce and share seismic catalogs with the public and international services. According to their seismic network distribution, together with different crustal models and calculation algorithms, obtaining earthquake parameters (location, depth, etc.) could differ between these two centers, especially, affecting source types of the low-magnitude events. Both low-magnitude tectonic events and artificial quakes that originating from open-pit quarry are catalogued by the seismology centers. These two centers announce different source types for the seismic events; therefore, this study focuses on the reliable identification of the micro-seismic events. Magnitude Ml&lt;2.4 177 seismic events commonly identified in KOERI-RETMC and AFAD catalogs are used. Due to the differences between KOERI-RETMC and AFAD seismic catalogs, the “first determination” (FD) was needed to start the discrimination analyses. Then, waveforms of the station SEYD and SEDI are operated by KOERI-RETMC and AFAD were analyzed with four methods (Amplitude Ratio, Complexity, Short Time Fourier Transform and Corner Frequency-Power Spectrum) and two statistical approaches (linear and quadratic discriminant functions) with the use and comparison with FD. Finally, station-based weightings are obtained with all techniques, and the source types of all events are calculated in percent. Generally, the success rates of the methods are calculated over 90%. The reliability increases with the co-usage of many analyses and the application of method-based weighting. Hence, many methods should be used to reliably determine the source types of micro-seismic events. Both centers should make more detailed analyses to identify micro-seismic events and share their reliable and revised catalogs.
Introduction to Cloud Computing Computing

AbstractThis chapter is an overview of cloud computing, including common cloud computing scenarios in life, the characteristics, definitions, origins and development of cloud computing, the advantages and classification of cloud computing, various supporting technologies of cloud computing. The business model, computing model, and implementation of cloud computing are three perspectives for understanding, as well as open source methods that are currently very popular in the cloud computing field. Through the study of this chapter, I hope that readers have a clearer understanding of the general picture of cloud computing and lay the foundation for the in-depth study of the following chapters.
Professional Competency for working LIS Professionals in Engineering College Libraries: A Study

Information and Communication Technology (ICT) has changed society as an Information & Knowledge Society and our way of life. It has integrated tremendously into almost all fields. The library and information scienceprofession is no exception.ICT has merged all library's operations and services; without ICT reaching and communicating with the clients is very difficult in the current scenario.The Library staff must involve and upgrade their skill set to fulfil the changing needs and expectations of the clients.They must be flexible in adopting new skills and levels of Information literacy awareness to meet the user expectations in the current pandemic situation.This paper has analysed ICT skills and the necessity and professional competencies of the library staff based on the librarian's opinion that they strongly agreed the library staff needs to update their skillset by attending ICT training (100 %) and participating in professional seminars, conferences, and workshops. .
Construction of sequences with high nonlinear complexity from a generalization of the Hermitian function field

For [Formula: see text] an odd integer, we provide a sequence from the function field [Formula: see text] of the maximal curve over [Formula: see text] defined by the affine equation [Formula: see text]. This sequence has high nonlinear complexity, and this fact comes from the existence of a rational function on [Formula: see text] with pole divisor of small degree, and support in certain [Formula: see text] rational places.
A Decade of Knowledge Graphs in Natural Language Processing: A Survey

In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption within recent years. Given the increasing amount of research work in this area, several KG-related approaches have been surveyed in the NLP research community. However, a comprehensive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contributing to closing this gap, we systematically analyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contributions. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work.
Chip-Level GHz Capable Balanced Quantum Homodyne Receivers

We present a design of balanced homodyne receivers suitable for ultra-low noise quantum applications such as continuous-variable quantum key distribution and quantum random number generation.For best noise and bandwidth performance a die-level low-parasitic photodiode together with a low-noise high-speed transimpedance amplifier are explored together with a planar lightwave circuit splitter chip serving as a photomixer.Bandwidths of 750 MHz and 2.6 GHz were accomplished while maintaining optimum noise performance, as evidenced by very high quantum-to-classical noise ratios of 26.8 and 18.6 dB, respectively.A common-mode rejection ratio of at least 40 dB was achieved for a frequency range of up to 1 GHz.Its application for continuous-variable quantum key distribution was evaluated by means of estimations under a strict untrusted receiver assumption, showing its potential for generating up to 43 Mbit/s secure-key rate over a reach of 10 km, whereas up to 100 Mbit/s could be supported at shorter reaches.Moreover, the high quantum-to-classical clearance values can enable high quality quantum random number generation at Gb/s rates.The multipurpose operation of the designed balanced receivers for classical communications was examined showing reception sensitivities of -55.8 and -52.6 dBm at 500 Mbit/s and 1 Gbit/s quadrature phase shift keying transmission, respectively, using the 750 MHz receiver.The faster 2.6 GHz receiver enabled 10 Gbit/s duobinary transmission at -14.8 dBm sensitivity.
Creation of Photonic Cat and GKP States Using Modulated Electrons

We show how the coherent shaping of free-electron wavefunctions enables the construction of coherent grid states. We present a scheme to create Schrödinger cat and GKP states, which are sought-after for fault-tolerant photonic quantum computation.
pMPL: A Robust Multi-Party Learning Framework with a Privileged Party

In order to perform machine learning among multiple parties while protecting the privacy of raw data, privacy-preserving machine learning based on secure multi-party computation (MPL for short) has been a hot spot in recent. The configuration of MPL usually follows the peer-to-peer architecture, where each party has the same chance to reveal the output result. However, typical business scenarios often follow a hierarchical architecture where a powerful, usually privileged party, leads the tasks of machine learning. Only the privileged party can reveal the final model even if other assistant parties collude with each other. It is even required to avoid the abort of machine learning to ensure the scheduled deadlines and/or save used computing resources when part of assistant parties drop out. Motivated by the above scenarios, we propose pMPL, a robust MPL framework with a privileged part}. pMPL supports three-party training in the semi-honest setting. By setting alternate shares for the privileged party, pMPL is robust to tolerate one of the rest two parties dropping out during the training. With the above settings, we design a series of efficient protocols based on vector space secret sharing for pMPL to bridge the gap between vector space secret sharing and machine learning. Finally, the experimental results show that the performance of pMPL is promising when we compare it with the state-of-the-art MPL frameworks. Especially, in the LAN setting, pMPL is around $16\times$ and $5\times$ faster than TF-encrypted (with ABY3 as the back-end framework) for the linear regression, and logistic regression, respectively. Besides, the accuracy of trained models of linear regression, logistic regression, and BP neural networks can reach around 97%, 99%, and 96% on MNIST dataset respectively.
Read before Generate! Faithful Long Form Question Answering with Machine Reading

Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question. While current work on LFQA using large pre-trained model for generation are effective at producing fluent and somewhat relevant content, one primary challenge lies in how to generate a faithful answer that has less hallucinated content. We propose a new end-to-end framework that jointly models answer generation and machine reading. The key idea is to augment the generation model with fine-grained, answer-related salient information which can be viewed as an emphasis on faithful facts. State-of-the-art results on two LFQA datasets, ELI5 and MS MARCO, demonstrate the effectiveness of our method, in comparison with strong baselines on automatic and human evaluation metrics. A detailed analysis further proves the competency of our methods in generating fluent, relevant, and more faithful answers.
User-Item Correlation in Hybrid Neighborhood-Based Recommendation System with Synthetic User Data

Recommendation systems have been widely adopted to help users with the information overload from the large volume of online multimedia content by providing them with appropriate options. While modern hybrid recommendation systems require an immense amount of data, several existing online privacy issues make users skeptical about sharing their personal information with service providers. This work introduces various novel methods utilizing the baseline estimate to learn user interests from their interactions. Subsequently, synthetic user feature vectors are implemented to estimate the user-item correlations, providing an additional fine-tuning factor for neighborhood-based collaborative filtering systems. Comprehensive experiments show that utilizing the user-item similarity can boost the accuracy of hybrid neighborhood-based systems by at least 2.11% while minimizing the need for tracking users’ digital footprints.
CD-ABSE: Attribute-Based Searchable Encryption Scheme Supporting Cross-Domain Sharing on Blockchain

The network security situation is grim, and the problem of “information isolated island” is becoming increasingly prominent. In view of the low efficiency and insufficient security of data cross-domain sharing in the open network environment, a searchable data sharing scheme supporting cross-domain is proposed based on attribute encryption technology. Firstly, different types of nodes on the blockchain are used to realize the data sharing of users in different domains. Secondly, the flexible ciphertext-search function is realized through the search form of keyword strategy. Moreover, the scheme adopts the mode of storage under the chain, which reduces the operation pressure of the blockchain. At the same time, according to the characteristics of the blockchain, the traceability and tamper-proof of the access process can be realized. Finally, the analysis shows that the scheme can resist quantum attack and collusive attack while avoiding complex bilinear operation and meet the security of trapdoor search and indistinguishability under chosen-plaintext attack. Compared with other searchable attribute-based encryption schemes, the scheme has certain advantages in function and performance.
TracInAD: Measuring Influence for Anomaly Detection

As with many other tasks, neural networks prove very effective for anomaly detection purposes. However, very few deep-learning models are suited for detecting anomalies on tabular datasets. This paper proposes a novel methodology to flag anomalies based on TracIn, an influence measure initially introduced for explicability purposes. The proposed methods can serve to augment any unsupervised deep anomaly detection method. We test our approach using Variational Autoencoders and show that the average influence of a subsample of training points on a test point can serve as a proxy for abnormality. Our model proves to be competitive in comparison with state-of-the-art approaches: it achieves comparable or better performance in terms of detection accuracy on medical and cyber-security tabular benchmark data.
Dict-BERT: Enhancing Language Model Pre-training with Dictionary

Pre-trained language models (PLMs) aim to learn universal language representations by conducting self-supervised training tasks on large-scale corpora. Since PLMs capture word semantics in different contexts, the quality of word representations highly depends on word frequency, which usually follows a heavy-tailed distributions in the pre-training corpus. Therefore, the embeddings of rare words on the tail are usually poorly optimized. In this work, we focus on enhancing language model pre-training by leveraging definitions of the rare words in dictionaries (e.g., Wiktionary). To incorporate a rare word definition as a part of input, we fetch its definition from the dictionary and append it to the end of the input text sequence. In addition to training with the masked language modeling objective, we propose two novel self-supervised pre-training tasks on word and sentence-level alignment between input text sequence and rare word definitions to enhance language modeling representation with dictionary. We evaluate the proposed Dict-BERT model on the language understanding benchmark GLUE and eight specialized domain benchmark datasets. Extensive experiments demonstrate that Dict-BERT can significantly improve the understanding of rare words and boost model performance on various NLP downstream tasks.
Conditional Generative Adversarial Networks with Total Variation and Color Correction for Generating Indonesian Face Photo from Sketch

Historically, hand-drawn face sketches have been commonly used by Indonesia’s police force, especially to quickly describe a person’s facial features in searching for fugitives based on eyewitness testimony. Several studies have been performed, aiming to increase the effectiveness of the method, such as comparing the facial sketch with the all-points bulletin (DPO in Indonesian terminology) or generating a facial composite. However, making facial composites using an application takes quite a long time. Moreover, when these composites are directly compared to the DPO, the accuracy is insufficient, and thus, the technique requires further development. This study applies a conditional generative adversarial network (cGAN) to convert a face sketch image into a color face photo with an additional Total Variation (TV) term in the loss function to improve the visual quality of the resulting image. Furthermore, we apply a color correction to adjust the resulting skin tone similar to that of the ground truth. The face image dataset was collected from various sources matching Indonesian skin tone and facial features. We aim to provide a method for Indonesian face sketch-to-photo generation to visualize the facial features more accurately than the conventional method. This approach produces visually realistic photos from face sketches, as well as true skin tones.