Reviewerly/exHarmony huggingface dataset:
Application Study of AI-powered Non-translatables Function in Computer-aided Translation

In the era of artificial intelligence, computer-aided translation (CAT) tools are also constantly integrating and adding new functions based on artificial intelligence technology. The purpose is to further improve translation efficiency, shorten project cycle and reduce translation cost through the adoption of new technology. With the wide application of computer-aided translation tools in translation teaching and other fields, people put forward higher requirements for the human-machine interactive translation system based on artificial intelligence. Artificial intelligence technology, which can also be used as an educational technology, has been paid more attention to its application in teaching fields. AI-powered non-translatables function can efficiently assist automatic pre-translation and post-editing. This thesis analyzes the processing and application of AI-powered non-translatables function in computer-aided translation, and expects to provide reference for the development of computer-aided translation industry and education based on artificial intelligence and help for the research of artificial intelligence in natural language processing and computer-aided translation.
The Reordering of Everyday Life through Digital technologies During the Covid-19 Pandemic

This paper offers an exploration of everyday life during the Covid-19 pandemic in Bangladesh with a focus on how it has been reordered through commonplace digital technologies and services. We present findings from a qualitative field study. Themes emerging from the findings suggest that digital technologies have been important to the reordering of everyday life during the pandemic: videoconferencing have been instrumental in allowing urban residents to work from home, digital money has enabled workers in rural areas to continue financial transactions, and social media has been a source of news and rumours during the pandemic. However, the benefits of the digital reordering of everyday life are unevenly distributed. The themes emerging are used to frame a discussion of the notion of reordering compared to the concept of resilience within ICTD.
Rethinking Safe Mobility: The Case of Safetipin in India

How do we make sense of the promises of digital technologies for safe mobility? Drawing on a case analysis of Safetipin, a mobile app that has garnered much praise in India, this paper examines the assumptions and promises of technologies designed for safe mobility. The contribution of this paper is twofold. First, we draw on feminist criticisms of safety technologies, defined broadly, to identify the main issues in their framing and efficacy. Second, we build upon initial interviews with users and makers of Safetipin to examine how the app addresses, or fails to address, the criticisms received by other safety technologies. We conclude by foregrounding key questions that ICTD designers can consider when designing for safe mobility in India and beyond.
Mobile Mandi: Towards an Accessible Agricultural Market Information Service for Low-Literate Users

The provision of agricultural market information has demonstrated potential to improve the incomes of smallholder farmers who comprise over 80% of India’s agricultural population. The scarcity of accurate price information, however, leads farmers to accept suboptimal economic outcomes. By eliminating information asymmetries, the provision of actionable information theoretically reduces costs of price discovery and increases farmers’ bargaining power. The resulting price convergence increases market efficiency and reduces volatility. Evaluations of market information services, however, show mixed results. In addition to market barriers, technical and language illiteracy undermine the success of market information services in low-income countries. In this paper, we present Mobile Mandi – a mobile application that delivers daily retail prices of 22 essential commodities at 157 markets sourced from the Government of India’s Department of Consumer Affairs. We present next-day price forecasts by training long short-term memory (LSTM) models on a univariate time series dataset of daily crop prices over ten years and benchmark performance against a baseline persistence model. We design the user interface for low-literate users and provide a conversational agent to facilitate information dissemination in English, Hindi, Punjabi and its transliterations. Noting the prevalent use of the WhatsApp messaging service in India, we integrate the service for seamless interoperability.
The World is in My Hand Now: Smartphones for Empowering Rural Women in Developing Countries

Smartphones are important for international development in the Global South. This paper explores how access to smartphone technologies promote women's agency for social change in these contexts by creating learning and livelihood opportunities and increasing connection with local market and service providers. The paper presents findings from a project which provided 200 Bangladeshi rural women farmers with smartphone technologies, training and support, and qualitatively investigated their ability to cope with geographic vulnerabilities, challenging market access, and social barriers. Reporting on a subset of these women's experiences, smartphone technologies helped these women gain better access to public agricultural extension and financial services, along with better acceptance from family and society. This, in turn, improved their dignity, confidence and sense of wellbeing. Insights will help ICT4D designers and development practitioners understand the importance of low-cost, bottom-up design inputs and social factors in future projects in similar developing country contexts.
Commissioning Development: Grantmaking, Community Voices, and their Implications for ICTD

While information and communication technology for development (ICTD) researchers have prioritized advocating for community voices in innovation design and development, we have limited insights into how community voices are incorporated by the high-level decision-makers who fund and initiate development projects and programs in the Global South. Indeed, understanding local communities’ voices (expressions of needs, challenges, and priorities) in tailoring effective development projects for sustainable development is widely considered an unmet goal. Using a qualitative survey of eight decision-makers (including grantmaking donors, central governments and INGOs) we explored a number of key factors, including national and global political climates, insider-outsider interactions, and evidence-based approaches that influence the high-level decision making process, workflows, and perceptions of community voice in project commissioning within Bangladesh’s public health nutrition development arena. Our findings reveal the tensions that arise among high-level decision-makers, and highlight the challenges associated with connecting with communities during development project design and implementation. We suggest broader implications and design opportunities for inventive project commissioning approaches to bridge the gap between communities and decision-makers. Our findings are of potential value for ICTD and HCI4D researchers interested in sustainable innovation and understanding and participating in the complex workflows of the project commissioning process in sustainable global development.
Unpacking Tensions in Designing Annotation System for Public Toilets to Support Menstrual Mobilities

Safe menstrual hygiene practices are central to maintaining good menstrual health. In addition to menstrual literacy, practicing safe menstrual hygiene requires access to menstrual products and a private, safe, and hygienic space for changing and cleaning. Access to such infrastructure, specifically functional public toilets, becomes even more crucial for menstruators when navigating public spaces while having periods. This work presents the learnings from the prototyping of MenstruSPACE, a toilet locator application offering an annotation system for public toilets towards supporting menstrual mobilities. Our design journey revealed a set of design tensions and open challenges to be considered when designing public infrastructure annotation systems to facilitate safe menstrual mobilities.
Typical meteorological year data analysis for optimal usage of energy systems at six selected locations in Nigeria

ABSTRACT In this study, the typical meteorological year (TMY) data for six locations representing the six geopolitical zones in Nigeria were generated and analyzed using the Sandia method. The analysis shows that seasonal variations exist in all the selected locations indicating two distinct seasons: the dry and wet seasons with varying lengths from north to south of the country. Due to its high global radiation levels (21–25 MJ/m2/d), the North is a desirable location for solar-thermal systems. In addition, the high monthly mean temperature variations (~18°C), low relative humidity (RHM) (15%) and constant wind speeds (4 m/s) experienced in the first 3 months of the year aid the installation of wind energy systems and the application of evaporative cooling techniques that reduce the thermal load and energy consumption of buildings. On the other side, the high RHM (80%) and mediocre radiation values derived almost throughout the year in the Southwest, Southeast and South–south regions discourages the extensive application of evaporative cooling and solar energy-based systems in such locations, but the moderate wind speeds (2.9 m/s) and monthly mean temperature variations associated with these regions between the first 3 months of the year allow for the application of natural ventilation and some passive cooling systems so as to reduce the thermal load of buildings in the regions. The information presented in this work can serve as a guide for design and selection of energy systems and application of energy-related projects in Nigeria.
Contextual Perception in Internet of Mobile Things: A categorical structure

A new perception model in the Internet of Mobile Things (IoMT) is now underway: Contextual Perception (CP), which is encouraged by the mankind autonomic neural system functioning. CP’s general goal is to understand clearly IoMT, which can be self-managed without direct mankind intervention. To address this huge challenge of the CP expects a fundamental solution of the CP notion. For this purpose, in this article, a categorical method is used to construct a robust formal foundation for the CP model in order to obtain the CP formal semantics. Furthermore, the categorical structure of CP in IoMT is formally specified in which IoMT products and co-products, finite limits and colimits in IoMT are formally modeled and argued.
SGAT: Snapshot-guided adversarial training of neural networks

It is well known that ensembles of different models can achieve better performance. Unfortunately, training multiple deep neural networks is time-consuming and expensive. Also, they require more space for storage and more computation for inference, making them unsuitable for applications with limited resources, such as mobile and embedded devices. To address these two fundamental problems in ensemble learning, we propose a novel method called Snapshot-Guided Adversarial Training (SGAT) to automatically accumulate and distill knowledge from the training, where knowledge from the earlier training iterations of the network is distilled, and transferred into its late training iterations via an adversarial learning strategy. To accumulate knowledge from the training process, we employ a cyclic annealing schedule and take a model snapshot at the end of each training interval. Furthermore, we use a shared discriminator to encourage the distillation process. The main advantages of our method are: (1) We can directly train a network with its free snapshots for knowledge distillation, instead of heavily depending on pre-trained models; (2) the inference cost remains the same as using a single model after finishing knowledge transferring; (3) SGAT is a general method and can be applied to the existing network architectures. Our extensive experiments show that SGAT consistently outperforms the standing training method with a clear margin. For example, with the same training budget, it achieves 2.86% more accuracy on average than the baseline when training MobileNet-V2 from scratch on CIFAR-100. Meanwhile, SGAT also achieves better performance in most cases than the existing ensemble method and knowledge distillation. For example, to train MobileNet-V2 from scratch on ImageNet, it gets 0.7% more accuracy than snapshot ensembles, and 0.93% more accuracy than snapshot distillation. More importantly, our accumulated learning strategy makes SGAT achieve much better performance when we increase training time. For example, compared with the standard training method for MobileNet-V2, it gets 3.13% more accuracy on ImageNet.
Framework for formation control of jet-propelled unmanned surface vehicles

This study focuses on the research of spray-pump type unmanned surface vehicles (USVs). Based on the analysis of the dynamics and motion characteristics of USVs, a method combining Dubins curves and the particle swarm optimization algorithm is proposed to find the optimal or suboptimal solution for the formation path. The research goal is to switch multiple USVs from an unordered state to a formation state, taking the speed of USVs and the formation endpoint as dynamic variables and integrating Dubins curve theory into the particle swarm optimization algorithm. A theoretical framework for the formation of spray-pump type USVs is proposed. Finally, the formation of three USVs is realized in a simulation platform.
Applications of Teaching Based on Virtual Reality in Agro-Pastoralism Areas

This article first put forward the educational application of immersive virtual laboratories for the problems of poor spatial thinking ability and weak hands-on operation ability. Second, aiming at the problems of a lack of excellent backbone teachers and insufficient educational information equipment, a Tibetan-Chinese bilingual immersion virtual basic teaching system suitable for students in pastoral areas has been developed to complement the teaching of teachers in remote areas. Third, the application of virtual gymnasiums is proposed to reduce the physical discomfort of students under harsh conditions. And then the application of virtual vocational technical training is proposed for poor employment situations in agro-pastoralism areas. Finally, the Tibetan-Chinese bilingual immersive virtual basic teaching system developed for pastoral students was put into use for a short period of time in the farming and pastoral schools and was well received by the pastoral students, and the teachers also gave feedback that the system has now improved at least the concentration of the student.
An image decryption technology based on machine learning in an irreversible encryption system

In this paper, a hidden image decryption system based on neural network is proposed. Hidden images converted into phase information are encoded and weighted to overlay the host image by the principles of double random phase encryption. Because the hidden image is phase information, it will be converted into intensity image after being encrypted and received by the image sensor, which makes the system unable to retrieve the image information using the phase recovery algorithm and the conventional decryption method, so this is an irreversible coding system. However, by using a convolution neural network to process a large number of ciphertext and its corresponding plaintext, a neural network model can be trained, by which ciphertext can be directly converted to the corresponding plaintext, and the successful decryption of conventional irreversible encryption systems can be achieved. Moreover, the decryption process of the system does not need to provide the host image, so it is a blind watermarking technology. Computer simulation results show that the encryption system proposed in this paper based on machine learning decryption has high security and accuracy.
Economic Growth and Inclusive Development in the Era of Disruptive Technology

We are living in a highly dynamic world where changes happen at an ever- increasing pace. Such changes are driven by new technology and innovation which are affecting many economies and societies.
Sniffer for Detecting Lost Mobiles

The main goal of this Endeavour is to use sniffer technology to track down a misplaced cell phone. Regularly thousands of adaptable get misplaced. Security for mobile devices is necessary since portable companies are now building mobile devices with IMEI to locate the mobile device, but this system is not very effective. Hence, sniffer technology is applied. This device, which combines a Sniffer Base Station, an Unidirectional Antenna, and Tracking Software, can be referred to as a Base Station. To maintain the recurrence, the Sniffer Base Station is used. Recurrence is a VHF extension at 900 MHzTo exchange the flag relating to the desired recurrence for receiving and transmitting signals, unidirectional radio wire is employed. The most important component of cell phone monitoring is the programming. Because of the way this programming is laid out, the IMEI of the device is read from its Memory. It checks for a signal from a device that responds to a flag sent by sniffer technology. The IMEI number, which stands for “International Mobile Station Equipment Identity,” identifies a device that uses GSM technology.
Optimized Face Mask Recognition with Edge Deployment Framework in Guard Bot using Deep Learning

The most recent outbreak to prompt an international health emergency is coronavirus illness. It primarily spreads via airborne transmission from person to person. The number of cases worldwide has increased as a result of community transmission. Several machine learning-based techniques have been used in the health field. Lack of data is one problem preventing machine learning techniques from discovering COVID-19 cases. A Mask-RCNN that can accurately identify face masks can alert users to wear one. To do this, Mask-RCCN employs two cutting-edge techniques. First, we present a unique residual context attention module (RCAM) to extract rich context data, focus on key face mask related regions, and detect mask region from the face using RPN. To develop more distinguishing traits for faces with and without masks, second. This method can distinguish between masked and uncovered faces, making it easier to utilize face masks and keeping an eye out for safety violations.
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent

Quantum federated learning (QFL) is a quantum extension of the classical federated learning model across multiple local quantum devices. An efficient optimization algorithm is always expected to minimize the communication overhead among different quantum participants. In this work, we propose an efficient optimization algorithm, namely federated quantum natural gradient descent (FQNGD), and further, apply it to a QFL framework that is com-posed of a variational quantum circuit (VQC)-based quantum neural networks (QNN). Compared with stochastic gradient descent methods like Adam and Adagrad, the FQNGD algorithm admits much fewer training iterations for the QFL to get converged. Moreover, it can significantly reduce the total communication overhead among local quantum devices. Our experiments on a handwritten digit classification dataset justify the effectiveness of the FQNGD for the QFL framework in terms of a faster convergence rate on the training set and higher accuracy on the test set.
Direction Aware Positional and Structural Encoding for Directed Graph Neural Networks

We propose a novel method for computing joint 2-node structural representations for link prediction in directed graphs. Existing approaches can be grouped into two families. The first group of methods learn structural embeddings of individual nodes in the entire graph through a directed Graph Neural Network (GNNs), and then combine pairs of the encodings to get a representation for the respective node pairs. Methods in the second group compute a representation of the subgraph enclosing the two nodes by employing GNNs initialized with positional encodings and consider these as their potential edge embeddings. Both families of link prediction techniques suffer from considerable shortcomings: The former fail to differentiate two distant nodes with similar neighborhoods; The latter, although provably appropriate for learning edge representations, adopt undirected GNNs, positional encodings, and subgraphs, so the edge direction signal is inevitably lost. Our proposal is also based on the idea of enclosing subgraphs, but the subgraphs are assumed directed, and directed Graph Neural Networks (GNNs) are used to learn their node encodings and initial positional embeddings are direction-aware. Our emphasis on capturing the direction of edges is reflected in superior performance in the link prediction task against baselines with undirected GNNs on symmetrized enclosing subgraphs and existing directed GNNs over a collection of benchmark graph datasets. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Vision Transformer-Based Feature Extraction for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes using the image attribute. In this paper, we put forth a new GZSL technique exploiting Vision Transformer (ViT) to maximize the attribute-related information contained in the image feature. In ViT, the entire image region is processed without the degradation of the image resolution and the local image information is preserved in patch features. To fully enjoy the benefits of ViT, we exploit patch features as well as the CLS feature in the extraction of the attribute-related image feature. In particular, we propose a novel attention-based module, called attribute attention module (AAM), to aggregate the attribute-related information in the patch features. From extensive experiments on benchmark datasets, we demonstrate that the proposed technique outperforms the state-of-the-art GZSL approaches by a large margin.
Effectiveness of Text, Acoustic, and Lattice-Based Representations in Spoken Language Understanding Tasks

In this paper, we perform an exhaustive evaluation of different representations to address the intent classification problem in a Spoken Language Understanding (SLU) setup. We benchmark three types of systems to perform the SLU intent detection task: 1) text-based, 2) lattice-based, and a novel 3) multimodal approach. Our work provides a comprehensive analysis of what could be the achievable performance of different state-of-the-art SLU systems under different circumstances, e.g., automatically- vs. manually-generated transcripts. We evaluate the systems on the publicly available SLURP spoken language resource corpus. Our results indicate that using richer forms of Automatic Speech Recognition (ASR) outputs, namely word-consensus-networks, allows the SLU system to improve in comparison to the 1-best setup (5.5% relative improvement). However, crossmodal approaches, i.e., learning from acoustic and text embeddings, obtains performance similar to the oracle setup, a relative improvement of 17.8% over the 1-best configuration, being a recommended alternative to overcome the limitations of working with automatically generated transcripts.