Reviewerly/exHarmony huggingface dataset:
Study on Post-editing for Machine Translation of Railway Engineering Texts

With rapid development of China's railways, there are more overseas construction projects and technical exchanges in the field of railway engineering, which have generated widespread demands for translation. To meet the increasingly growing demands for translation of railway engineering texts, the mode of machine translation plus post editing (MTPE) has been frequently applied besides traditional human translation (HT) for the combination of translation quality and efficiency. Through the case study of post editing in the machine translation of China's High-Speed Railway by Google Translate, this paper discusses the common error types of machine translation in railway engineering translation, and puts forward corresponding post editing strategies, so as to provide references for MTPE of railway engineering translation in the future. It is hoped that research on post editing in the mode of machine translation for railway engineering texts may improve the translation quality and efficiency, thus helping speed up the process of China's railway going global.
A Mobile-Based Alphabet Learning Game To Intervene Dyslexia Among Children

Language is the core in human communication which involves reading and writing. At birth, infants begin their linguistic development as they listen, observe, and decipher the rules of language. When a child reaches 4 years they have basic ability to decode and communicate with the spoken language. After 4 or 5 years most of the children begin to learn written language (alphabet) and thereby they learned to read or decode written language. But some children deviate from this normal language acquisition due to different factors. Learning disabilities are one of the key factors which affect language acquisition of the children. Among them, "Dyslexia" is the most common which is characterized by reading difficulties or inability to decode written language (graphemes) into reading (Phonemes) [1] . In Sri Lanka, there has been no study conducted to identify the exact prevalence of dyslexia. Most of the parents and teachers outside the major cities lack a clear awareness about dyslexia, hence most of the children in Sri Lanka face difficulties since the Sri Lankan education system entirely depends on reading and writing. So, treatment of dyslexia is a must. But treatment centers are only limited to a few major cities like Colombo and Kandy due to lack of specialized paediatricians and speech therapists. In Sri Lanka treatment of dyslexia is mostly conducted through traditional methods using pen and paper. In Lady Ridgeway Hospital for children and Ayati-National Center for Children with Disabilities, they are currently using the over-teaching method and multi-sensory method along with rapid naming activities, whiteboard activities, narrative therapy and storytelling. Based on the analysis of data collected through the interviews with paediatricians, speech therapists and parents of the dyslexic children, observations on dyslexic children and findings from literature we suggest the hypothesis that mobile games can be used to intervene dyslexia among children [2] , [5] .
The Dark Social Web: Responsibility, Manipulation, and Participation in Global Digital Spaces

This article offers a definitional note of the dark social web and introduces the remaining articles in this collection.
Solving linear equation systems on noisy intermediate‐scale quantum computers

Abstract Quantum computing promises classically unparalleled benefits for various applications. Its properties are exploited in the Harrow‐Hassidim‐Lloyd (HHL) algorithm that, in conjunction with quantum phase estimation, is capable of constructing quantum states that are proportional to the solution of linear equation systems and does so exponentially faster than the fastest known classical algorithms. We explore this capability by computing the nodal displacements of a 1‐dimensional loaded cantilever, discretized by using the finite element method (FEM).
An Evaluation of Edge Computing Platform for Reliable Automated Drones

This paper evaluates the edge computing platform for the drone backup system, which enhances the reliability of automated drones. The drone backup system is assumed to be alternate to execute the critical applications, which used to be executed on edge or cloud, such as image recognition, path planning, etc. Since the drone is facing severe conditions in terms of computational capability, battery capacity, etc., the performance and energy consumption are key issues to support the operation of automated drones. In this paper, we measure the execution time and energy consumption on Raspberry Pi with Intel Neural Compute Stick 2 accelerator for three practical applications: Single Shot MultiBox Detector, State Lattice Planner, and Pix2Pix. The experimental results show the performance and energy consumption on the practical scenarios for the drone backup system. Based on these knowledge, the design optimization of the drone backup systems will be performed for safer drones.
The Development of Web Based Information System at Universitas Klabat Career Center

A web-based information system is used to collect, process, store existing data, and provide information for decision-making. The use of web-based information systems can help support various business processes and integrate all the processes. Universitas Klabat, with the Career Center that carries out alumni tracer study, still does not have an integrated system in its implementation. An information system that can integrate every tracer study implementation can encourage productivity and improve higher education quality in the accreditation process. This study aims to create a web-based tracer study information system at Universitas Klabat's Career Center. In developing the system, a PHP based programming language framework, namely Laravel, was employed to develop the system. The resulting web-based information system can integrate every process of the tracer study. The information system has four types of users, namely administrators, operators, alumni, and employers. The information system can also generate reports needed by Universitas Klabat and report purposes to the Ministry of Research and Technology.
Compression for Text Detection and Recognition Based on Low Bit-Width Quantization

In recent years, with the development of Neural Network, it has made a significant breakthrough in the field of text detection and recognition. However, large-scale deep Neural Network needs a large amount of storage space and computing resources, which makes it difficult to be effectively applied in various hardware platforms. In order to solve this problem, we quantize the text detection and recognition model from floating-point to low bit-width. Utilizing the low bit-width quantization method in DoReFa-Net, we quantize the text detection network, Connectionist Text Proposal Network (CTPN) and the text recognition network, Convolutional Recurrent Neural Network (CRNN). Comparing the results in different quantized bit-width, we find that after quantizing the weights and activations of CTPN to 2-bit, the F-score only drops by 0.09 on the MTWI 2018 Dataset. When the weights and activations of CRNN is quantized to 4-bits, the accuracy decrease of training with Synthetic Chinese String Dataset is merely 0.1%. After the compression of the text detection and recognition, the size of model is reduced to only 32% of the original, which means that storage space can be saved by 68%.
Stereo Domain Translation for Denoising and Super-Resolution Using Correlation Loss

This paper proposes a GAN-based denoising and super-resolution network for stereo images. The proposed network solves the two problems separately in an end-to-end training fashion. A matchability attention module are introduced to compute matching cost spaces and provide the stereo information between generated stereo images. In addition, the correlation loss is proposed to preserve the correspondence between a stereo pair. We evaluate the proposed network using the KITTI 2012 and KITTI 2015 datasets. In addition, we compare with state-of-the-art denoising and super-resolution methods. Experimental results show that the proposed method significantly outperforms the existing method both in terms of qualitative and quantitative analysis.
An optimized mask-guided mobile pedestrian detection network with millisecond scale

With the tremendous advance of deep learning, pedestrian detection has gradually become the basis of various subsequent advanced applications, such as segmentation, tracking, or high-level object-based reasoning. However, There are few mobile-based video detection networks that can be implemented in practical production applications. Therefore, we proposed a super lightweight network based on video, which made the following two major contributions: (1) For each frame, the detection boxes of the previous frame and the RGB channels of the current frame are jointly used as the input of the network, which can improve the detection accuracy effectively with little increase in the network complexity, at the same time avoiding the need to adopt relatively limited video datasets during the training procedure. (2) In order to make full use of the time-related information of adjacent frames, we adopt the method of birth and death which is commonly used in tracking and post-matching filtering method to reduce the jitter of the detection boxes between adjacent frames and improve the overall visual effect. The final result outperforms other tiny networks at nearly the speed of 5ms, by achieving 45.5% mAP on person category of COCO-2017 dataset on MI 8 GPU with at least 90% in detection accuracy.
Experimental Wafer Carrier Contamination Analysis and Monitoring in Fully Automated 300 mm Power Production Lines

Contamination control is essential in semiconductor manufacturing to ensure high yield and product quality. Latest power electronic devices are manufactured in fully automated 300 mm production lines, which utilize closed wafer containers called Front Opening Unified Pods (FOUPs). It has been observed, that FOUPs capture airborne molecular contaminants (AMC) outgassing from processed wafers or being transferred from the equipment minienvironment. These AMC might be released afterwards and can lead to defects causing yield and/or reliability issues of the power devices. Specific FOUP cleaning and exchange rules are already being utilized in the fab. But so far, these rules are not validated or adapted by actual concentration values in the FOUPs. In this paper, contamination levels in FOUPs are investigated to identify the sources of different AMC. The contamination data is analysed together with FOUP logistics data in order to establish an optimized FOUP management strategy. In the first part, in-line carrier contamination control is explained and a general overview of the AMC detected is given. In the second part, the data-driven FOUP-monitoring is described using the example of the root cause analysis of hydrofluoric acid (HF) contamination.
Social Spammer Detection Based on PSO-CatBoost

With the rapid development of social networks, more and more organizations or individuals use social media to communicate with each other, passing on information and getting information, etc. However, while bringing convenience to people, social media has also become the main target of malicious attackers who try to take advantage of the system vulnerability and cause harm to other normal users, they obtain benefits mainly through sending false information, advertising links, phishing, etc. In this paper, firstly, we collect the features of spammers from the four views (profile, behavior, relationship, and interaction) for a more comprehensive analysis of spammers, secondly, we creatively combine the features of Particle Swarm Optimization (PSO) and CatBoost algorithm, and finally, we propose a novel PSO-CatBoost model based on the CatBoost model for detecting spammers. In order to validate the effectiveness of our proposed model, some ensemble learning algorithms are compared, and the experimental results show that our model outperforms other models.
Real time System Implementation for Stereo 3D Mapping and Visual Odometry

Depth Estimation and localisation is a critical and computation heavy task. Due to its computation complexity, it is hard to achieve the high rate performance on the normal CPU. We here have as purpose compact, low power architecture for real time stereo depth estimation and stereo visual odometry that can be easily used on the UAV (unmanned aerial vehicle) and other autonomous navigation vehicles. A novel implementation of Stereo odometry based on careful feature selection and tracking [2] on GPU is described. It accelerates core computations like feature tracking, RANSAC based non linear solver using the GPU. 3D Mapping using stereo disparity estimation based on More Global Matching (MGM) a variant of SGM is implemented on FPGA. A Pipeline Architecture is introduced to increase throughput of the 3D map by leveraging multiple ARM cores. The programs are tested on Jetson Nano (an embedded GPU) and Ultra96 (ARM-FPGA Soc) which have less form factor and consume less power. Update rate of 20 is achieved for 6 degrees of freedom pose on Jetson Nano (60 on i7 core with Nvidia 1050ti) and an update rate of 16 fps is achieved for 3D point cloud on Ultra96 making the system desirable for above mentioned applications.
Interest Point Recommendation based on Multi Feature Representation and Attention Mechanism

A deep Interest point recommendation model based on multi feature representation and attention mechanism is proposed. On the basis of deep recommendation method, the extraction model of Interest point and user characteristics was constructed. Specifically, the constraint matrix decomposition method was used to obtain the joint representation of geographic location and category features of Interest point, and the semantic features of Interest point were obtained by word embedding model to realize the comprehensive and in-depth multi feature representation of Interest point; At the same time, the generation method of user characteristics was improved, and attention mechanism was used to improve the ability of the model to obtain the key information of user preference; In addition, a deep learning recommendation framework is constructed to model the nonlinear interaction between features to improve the accuracy of recommendation.
Real-time Water Area Segmentation for USV using Enhanced U-Net

As an image segmentation network widely used in the medical community, U-Net has proved the feasibility of networks with encoding-decoding paradigms. However, U-Net is rarely used for more complex RGB images due to its limited parameters. Combined with the scenes of water area segmentation, we make a targeted modification of U-Net to make it more suitable for RGB image segmentation, with only a small increase in parameters. In addition, a general image local gradient penalty is proposed to introduce local gradient information into the loss function to balance punishment for difficult and easy example points in output masks. The penalty is helpful to improve classification performance during training. To further reduce computational burden of unmanned surface vessels, we introduce model channel pruning in a more efficient way. In particular, the channel pruning and fine-tuning are combined into a repeatable step, and multiple small pruning ratios instead of single large pruning ratio are used to obtain a series of models with different accuracy-speed trade-offs. The experimental results show the practicability and applicability of our proposed method for water area segmentation.
Enhanced Comprehensive Learning Particle Swarm Optimization with Dimensional Independent and Adaptive Parameters

Comprehensive learning particle swarm optimization (CLPSO) and enhanced CLPSO (ECLPSO) are two literature metaheuristics for global optimization. ECLPSO significantly improves the exploitation and convergence performance of CLPSO by perturbation-based exploitation and adaptive learning probabilities. However, ECLPSO still cannot locate the global optimum or find a near-optimum solution for a number of problems. In this paper, we study further bettering the exploration performance of ECLPSO. We propose to assign an independent inertia weight and an independent acceleration coefficient corresponding to each dimension of the search space, as well as an independent learning probability for each particle on each dimension. Like ECLPSO, a normative interval bounded by the minimum and maximum personal best positions is determined with respect to each dimension in each generation. The dimensional independent maximum velocities, inertia weights, acceleration coefficients, and learning probabilities are proposed to be adaptively updated based on the dimensional normative intervals in order to facilitate exploration, exploitation, and convergence, particularly exploration. Our proposed metaheuristic, called adaptive CLPSO (ACLPSO), is evaluated on various benchmark functions. Experimental results demonstrate that the dimensional independent and adaptive maximum velocities, inertia weights, acceleration coefficients, and learning probabilities help to significantly mend ECLPSO’s exploration performance, and ACLPSO is able to derive the global optimum or a near-optimum solution on all the benchmark functions for all the runs with parameters appropriately set.
A privacy‐focused approach for anomaly detection in IoT networks

Summary IoT devices present a series of liabilities to administrators that aim for fully secure networks. Owing to their heterogeneity and limited processing power, administrators often implement monitoring mechanisms to prevent unauthorized use of resources and data exfiltration. However, most approaches allow for easy discrimination of private behavioral patterns of its users or nodes by their MAC or IP addresses. Inferring data exchange patterns at the physical layer is a more complex task, as a single signal power indicator may correspond to a mixture of simultaneous data transmissions. This article proposes privacy‐focused mechanisms by resorting to physical layer data analysis and one‐class classification models to perform anomaly detection in IoT networks. We present a full processing pipeline that considers the signal that identifies and models patterns on the channel activity and silence periods. We train our models with data captured from interactions with an Amazon Echo with devices generating background noise and test them against a similar scenario with a tampered network node periodically uploading data. Our data show that the best performing model, kernel density estimation, is able to detect anomalies with a 99% precision rate, even surpassing the tested neural networks approaches. We also propose a framework that aims to deploy validated models into production IoT environments. We designed an end‐to‐end data flow that autonomously extracts data and classifies them at the anomaly detection server. The envisioned components were designed to be horizontally scaled for a myriad of data streams and machine learning algorithms working in parallel.
Epistemic stance in the translations of Chinese medicine classics: a case study of Huang Di Nei Jing

Abstract This article is a contrastive study of epistemic stance in the English translations of the Chinese medical classic Huang Di Nei Jing by clinicians and non-clinicians. Epistemic stance is concerned with a translator’s certainty about the proposition of a statement and is highly consequential to information validity. By drawing on the systemic functional linguistic framework and using two sets of translations of the Chinese medicine classic, Huang Di Nei Jing , by both clinicians and non-clinicians, the study investigates the linguistic choices concerning epistemic stance. The findings show that epistemic stance is closely related to the translators’ domain knowledge and expertise, with clinician-translators more likely to express their epistemic stance in the translations. However, this study also finds a counterintuitive epistemic pattern: non-clinician translators express more certainty in their translations.
Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure

Graph embedding has shown its effectiveness to represent graph information and capture deep relationships in graph data. Most recent graph embedding methods focus on attributed graphs, since they preserve both structure and content information in the network. However, corruption can exist in the graph structure as well as the node content of the graph, and both can lead to inferior embedding results. Unfortunately, few existing graph embedding algorithms have considered the corruption problem, and to the best of our knowledge, none has studied structural corruption in attributed graphs, including missing and redundant edges. This field is difficult for previous methods, mainly due to two challenges: (1) the existence of various corruption causes has made it difficult to recognize corruptions in graphs, and (2) the complexity of graph-structured data has increased the difficulty of handling corruption therein for graph embedding methods. These facts lead us here to propose a novel autoencoder-based graph embedding approach, which is robust against structural corruption. Our idea comes from the recent discovery of memorization effects in deep learning. Namely, deep neural networks prefer to fit clean data first, before they over-fit corrupted data. Specifically, we train two autoencoders simultaneously and let them learn the reliability of the edges in the graph from each other. The two autoencoders would evaluate the edges according to their reconstructed structure and manipulate this by devaluing those distrusted edges to update the structure information. The updated structure would be used further in the next iteration as the ground-truth of its peer-network. Experiments on different versions of real-world graphs show state-of-the-art results and demonstrate the robustness of our model against structural corruption.
Design and Analysis of a System to Read Braille with Remote Cloud Architecture

Recognizing that physical Braille books have a high cost of production and purchase, using a refreshable Braille display is a viable solution. This paper discusses the design and architecture of a system which grants the visually impaired access to a number of books on a refreshable Braille display by using the huge number of existing e-books. Understanding that almost all the books that are available to a person who isn't visually impaired, the design described in the paper takes e-books and converts them into Braille that is directly rendered on the refreshable Braille display's interface with solenoid based actuation, thus eliminating the extra costs incurred in producing and purchasing specially printed Braille books. The design describes a 3D printable physical device which uses a NodeMCU for its computation and has internet connectivity to utilize the support of a web app and cloud servers whose architecture and functionalities are also discussed.