Reviewerly/exHarmony huggingface dataset:
The Realization of Information Focus in Catalan

Answers to wh-questions are the most widespread method to elicit information focus. When studying the syntax of focus, however, this method is problematic because the most natural answer to a wh-question is often a fragment that only includes the focus. This problem has led to considerable controversy in the literature about the position in which information focus is realized, particularly for Spanish, but also for Catalan. In order to enhance the naturalness and reliability of the question-answer test, we designed an experiment with a new elicitation technique (i.e., questions with a delayed answer) in which some material is inserted between the question and the point in which the participant is asked to answer the question, so that the Catalan participants would spontaneously utter a full sentence instead of a fragment, without being explicitly instructed to do so. The material of this production experiment was then adapted in a rating experiment on the acceptability of preverbal and postverbal information foci in Catalan. The results of this second study confirm the findings of the production experiment: postverbal focus is always preferred over preverbal focus, both in the case of subjects and objects.
Machine Learning for Strategic Trade Analysis

Machine learning techniques are supporting advances in more effectively gaining knowledge from data in many fields—strategic trade analysis should be no exception. This chapter introduces some potential uses of machine learning approaches to support identifying illicit strategic trade from within international trade data. First, an overview is provided of three case studies that utilized various machine learning algorithms for similar purposes, demonstrating the effectiveness of different approaches that could be tailored to strategic trade analysis. The majority of the chapter focuses on three types of machine learning techniques: supervised classification, unsupervised learning, and natural language processing. Within each of these, multiple methods are introduced with a focus of how analysts can specifically use international trade data and other information sources to investigate strategic trade. This discussion represents attempts to introduce machine learning into this field and serve as a starting point for future research and applications in this area.
The Bridge: On the Role of Historical Societies, Archives, and Research Libraries

This article reviews the role of historical societies and libraries in supporting scholarly researcher. It considers pandemic era innovations and options for future support.
A two-stage gap safe screening rule for multi-label optimal margin distribution machine

Multi-label optimal margin distribution machine (mlODM) is an efficient algorithm for multi-label classification. Although it can achieve great generalization performance, it is inefficient for large-scale datasets due to the huge number of label pairs. Motivated by its sparse solution, in this paper, we propose a two-stage gap safe screening rule for accelerating mlODM, termed as TSSR. First, a sequential safe screening rule (SSSR) based on gap is designed to screen out part of redundant label pairs prior to training, which reduces the scale of mlODM. Compared with the previous DVI rule, our method ensures absolute safety without destroying efficiency. In the second stage, to further speed up the solving process, a dynamic safe screening rule (DSSR) is embedded into the solving algorithm DCDM when training the simplified mlODM. More importantly, the feasible solution generated in the first stage can promote the efficiency of DSSR. To the best of our knowledge, this is the first attempt to create a hybrid screening rule for multi-label model. Our TSSR can greatly reduce the cost and achieve exactly the same accuracy. Experimental results on seven multi-label benchmark datasets and two real-world learning problems including movie genres classification and hypoglycemic drugs prediction verify the superiority of the proposed methods.
Software-based Rotation Sensor Using High-Speed Video Analysis

In this study, we proposed a novel concept of real-time software rotation sensor that can simultaneously detect the angles of multiple rotational objects in a high-speed video sequence. Our software sensor can be executed at 500 fps using parallel-implementing digital image correlation processes to inspect the similarities between the input image and 360 reference images at different angles on a GPU-based high-speed vision system. Its performance in measurement accuracy was verified using several experiments for multiple rotating gears monitored with partial occlusion in 500-fps videos, including multiple gears fast-rotating at 2400 rpm.
A Joint Perception Scheme For Connected Vehicles

Currently visual sensing systems, used in autonomous vehicle's research, typically perceive the surrounding environment up to 250m ahead of the vehicle. However, the detection reliability drops when the object's position is more than 50m, due to objects being sparse or unclear for the detection model to make a confident detection. Cooperative perception extends the visual horizon of the onboard sensing system, by expanding the sensing range which improves the detection precision. This paper explores early distributed visual data fusion by creating a multi-vehicle dataset using the Carla simulator to create a shared driving scenario, equipping every spawned vehicle with LiDAR, GNSS, and IMU sensors to emulate a real-driving scenario. Furthermore, we investigate the usage of ZeroMQ-based communication system to distribute visual and meta- data across relevant neighboring vehicles. Since the proposed method distributes raw LiDAR data, we utilize point cloud compression to reduce the size of the published data between relevant connected vehicles to satisfy communication bandwidth requirements. Subsequently, we transform and fuse the received data, and apply a deep learning object detection model to detect the objects in the scene. Our experiments prove that our proposed framework improves the detection average precision while satisfying bandwidth requirements.
DENSE-DEFENSE: Diversity Promoting Ensemble Adversarial Training Towards Effective Defense

Data-driven predictive maintenance utilizes machine learning (ML) to map input sensor data to a desired output. However, security of ML models is vulnerable to adversarial examples which can impact their prediction performance significantly. En-semble adversarial training (EAT) is one approach to defending ML models against adversarial examples where training data is augmented with perturbations transferred from different pre-trained methods. Diversity among the ensemble learners plays a crucial role in reaching overall ensemble robustness. In this work, we propose a diversity promoting ensemble adversarial training approach as a defense mechanism for data-driven predictive maintenance applications. In our framework, we first measure the loss gradient similarity among pre-trained ML models and select the most dissimilar ones to promote diversity. Then, we create perturbed training instances using the selected diverse base learners and augment those examples into our training data. In testing, we measure the performance change after adversarial attacks are introduced. Our experiments on NASA C-MAPSS dataset show that we can improve the resiliency by up to 97% (43% on average) compared to state-of-the-art training settings.
Reference Modulation for High-Quality Scan Images of 2-D Laser Scanner Systems

For laser scanner systems, the most critical issue for high-quality images is the linearity of the slow axis. The laser scanner systems have various disturbances and uncertainties, such as manufacturing tolerances, structure vibrations, and parameter uncertainties. Further, there are vertical and horizontal resonance modes that hinder controller design. In this letter, we propose a reference modulation technique to enhance the vertical angle linearity of the slow axis for two-dimensional laser scanner systems. First, the laser scanner system is mathematically modeled in the state-space form. Then, the reference modulation is designed to boost the loop gain over the desired frequency range. The reference modulation can guarantee high-quality scan images by redesigning the desired reference based on the output feedback. The current tracking controller was then implemented with virtual references for the states. We investigated its tracking performance via sensitivity function analysis against the disturbances. Experiments were performed to evaluate the effectiveness of the proposed method using four different laser scanners. We confirmed that the proposed method achieved up to 48% improvement in the angle tracking error from the experimental results.
Resolving Amharic Lexical Ambiguity using Neural Word Embedding

Despite the fact that there are numerous information sources available online, the search for the exact match with the user’s needs has been hampered for a variety of reasons. One issue is linguistic ambiguity, which exists at various levels such as semantic, syntactic, lexical/word, and so on. This paper focuses on Amharic word-level ambiguity and proposes a method for resolving it. Words can have more than one distinct meaning, and many words can be interpreted in different ways depending on the context. Automatically determining the correct sense of an ambiguous word is critical for many applications such as machine translation and information retrieval. To date, the majority of Amharic sense disambiguation work has been unsupervised/ knowledge base and based on WordNet. However, this approach has limitations in that the full Amharic WordNet is not available and manually preparing takes time and resources. To address the aforementioned limitations, this paper proposes a novel joint supervised and unsupervised sense disambiguation approach based on distributional semantic space. Each approach, as well as the combined approach, is assessed using a total of 10 ambiguous words. The supervised approach achieved 82.3 % of the F1-score and 70% accuracy, while the unsupervised approach achieved 85.7 % of the F1-score and 60% accuracy. The joint approach achieved an F1-score of 92.5 % and an accuracy of 86 %.
Developing Amharic Question Answering Model Over Unstructured Data Source Using Deep Learning Approach

Low-resource languages like Amharic are lagging while many technological advancements are taking place in Question Answering (QA) research. From improving search engine results to enabling conversational agents or chatbots, QA systems have many applications. But previous works on Amharic QA haven’t made any leap towards employing current techniques, and these researches are limited. Although the volume of Amharic texts generated and written electronic documents are increasing, no modern Amharic QA models take advantage of these data sources. In this study, a factoid textual Amharic QA model using deep learning approach has been designed and developed. Given the three components of a textual QA, question analysis is modeled as a classification problem, document processing or passage retrieval is modeled as an answer selection problem, and the answer extraction is done using a NER tool. A word2vec model has been developed from 2.3 GB of free Amharic text. 10,785 questions and answers with five attributes have been prepared. In the question classification (QC) component, an experiment has been made with various deep neural network models like CNN, SimpleRNN, LSTM, GRU, Bi-LSTM, stacked Bi-LSTM, and attention-based Bi-LSTM. The attention-based Bi-LSTM has shown a better performance. A hybrid Bi-LSTM/CNN model has been used in the answer selection component, and cosine similarity metrics have been applied. The result of the answer selection component is a sentence housing an answer. A publicly available NER tool which is based on FLAIR has been used regarding the answer extraction component. At this stage, results from the previous components have been used to extract the final answer as a named entity. With a reasonably large Amharic QA dataset compared to datasets used in previous studies and without handcrafted rules, linguistic tools, and frameworks, this study’s end-to-end deep neural network models have outperformed previous Amharic QA systems.
Use of General Repair Tool for Fixing Security Vulnerabilities

Automated patch generation approaches have been shown to address defects in real-world programs, including security vulnerabilities. On the one hand, general repair tools are designed to fix common bugs. On the other hand, specific repair tools targeted security-related vulnerabilities, such as integer or buffer overflow. However, fewer works focus on assessing general repair tools' capabilities to fix security vulnerabilities. The assessment will be helpful to find out if general repair tools can fix security vulnerabilities without knowing specific characterizing patterns of such vulnerabilities in advance, thus not being subjected to overfitting. In this paper, we present a detailed analysis of a case study using the semantic general repair tool, F1X, to fix security-related vulnerabilities found by the OSS-Fuzz framework. OSS-Fuzz framework is an automated continuous testing platform run on Google's cloud infrastructure, which can pinpoint source code containing security-related vulnerability and generate its failing test case. Using a dataset of 240 security vulnerabilities found in five open source programs from OSS-Fuzz, we compared and analyzed fix patterns generated by F1X with fixing patterns in OSS-Fuzz Github repositories. We believe that the result of this case study will be insightful for developers to strengthen and optimize their repair tools and security analysts to consider integrating automated repair tools into production systems.
Offline Signature Verification System using CNN Algorithm Combined with Histogram of Oriented Gradients

One way of stealing someone's identity is through their biometrics such as their signature, wherein it is a measurement of the characterization of an individual based on their behavioral specifications. One, however, can imitate and forge another person's signature which can result in fraud and a huge loss. Thus, this paper explored offline signature verification by using a feature extractor with a neural network that classifies the signatures as forged or genuine. The research aimed to compare the results to the previously made system that used Artificial Neural Network in classifying the signatures. The Histogram of Oriented Gradients (HoG) extracted features and Convolutional Neural Networks classified the images and it was found that removing PCA and Skeletonization as a preprocessing step was the optimal model for the system. The developed system using CNN with HoG achieved an accuracy of 98.07% without skeletonization and PCA.
Design, Development and Control of a Low Cost Vacuuming Robot (Robo Vac) for Classroom

With the overgrowing increase in technology, robots are being used for navigation, industries and household chores due to their ability to finish the work efficiently. One of these tasks is vacuuming. Conventional vacuum cleaners require manual labor and are bulky in size producing noise. In this study, a low cost Vacuuming robot is developed. RoboVac utilizes IMU along with rotary encoders to track the traveled distance and PID controller implementation for close loop control for straight path motion at the desired constant speed. IMU is used for calculating drift in yaw angle to take turn at a precise angle of 90 degrees. The combination of IR sensors with Ultrasonic sensors detect obstacles and get as near to them as possible for better cleaning. The study highlights the design parameters, motor selection, and algorithm for the autonomous motion of the RoboVac. The proposed algorithm was first simulated using WeBots robot simulator and then implemented on actual hardware. The technique developed along with the hardware was tested in the premises of Institute of Space Technology under different environments (with and without obstacles) and showed satisfactory results. Algorithm can be used and modified for indoor navigation of similar robots.
Implementation of the Mask-R Convolutional Neural Network on Airplane Object Detection

One benefit of the widely used deep learning (DL) is its ability to detect objects on image sensing which itself is an important part of computer vision. By utilizing satellite images containing airplane object, an airplane object detector that can potentially help airports count the number of airplanes at a time can be created. In this research, the DL architecture that is used to detect airplane satellite images is Mask-region-based convolutional neural network (Mask RCNN) with the residual network (ResNet) model. Comparator parameters that are used on the data training process are backbones, pre-trained models, and data augmentation. The models are trained using 800 airplane satellite images and tested using 100 airplane satellite images. The best model in this research is obtained while using the ResNet-101 backbone, COCO pretrained model, and data augmentation, with a mean average precision (mAP)@0.50 value of 0.9857, mAP@[0.5, 0.95] value of 0.5002, and F1-score value of 0.9924, which result in an average scoring value of 0.8261. Compared with another research that used the Faster RCNN architecture, the obtained results of the present study are an mAP@0.5 value of 0.717, mAP@[0.5, 0.95] value of 0.451, and F1-score value of 0.93, which result in an average scoring value of 0.6993.
Sentiment Correlation in News Network and Associated Market Movements Based on Co-occurrence Network

The rapid development of media allows anyone to disseminate information quickly and efficiently. In an increasingly connected global market, the sentiment of news on companies not only shows the performance of the market itself but also influences the movement of the broader market. Through this information, investors make an appropriate investment decision to maximize profits. Therefore, all investment-related information is needed to assist investors in making decisions. This work, we apply NLP techniques to learn the news sentiment of 22 company based on CNN news for 6 years. We conduct the research to show the sentiment correlation to the market movements based on the news co-occurrence network. The financial market is a system with complex inter-dependencies between companies. The co-occurrence of companies in the news may reveal the underlying relations between the companies. Sentiment analysis annals each news's positive, neutral, and negative classes and evaluates the company's sentiment based on their co-occurrence in the same news article. We find groups of highly related companies generally align with the sectoral classification. The network uncovers more interesting information when the groups and sectors different, potentially containing insights above sector classification offers.
Predicting Software Maintenance Type, Change Impact, and Maintenance Time Using Machine Learning Algorithms

Software change requests can arise at any time and changes that are accepted by the change approval board goes to maintenance. Change impact analysis and implementation requires high cost because modules and artifacts in previous versions may not be the same as current versions. Changes usually cause impacts on other modules and artifacts; the time required to implement changes varied and becomes high. The type of maintenance, the impact of changes, and maintenance time can be determined by analyzing software repository data. But the selection of the important software repositories and extracting the relevant information from software repositories is challenging. Some studies were conducted on maintenance type prediction and change impact analysis but these studies focus on single maintenance tasks and specific software types, so the generality of the studies is in question mark both in maintenance task and type of software. In addition to this limited amount of data and only version history data is used. In this study, a method for predicting maintenance type, impacts of changes, and maintenance time is proposed so that relevant maintenance information is extracted from software repositories. Linear Support Vector Classifier, Random Forest, Logistic Regression, Multinomial Naïve Bayes, LSTM, and Bi-LSTM are used to predict maintenance tasks and their change impact. An Artificial Neural Network (ANN) is applied to estimate the time needed to resolve the changes. The experiments results show that Random Forest and LSTM have an accuracy of 94% and 95% respectively, and have better results compared with other models. ANN provides a Mean Square Error of 0.0028. Based on Pearson and Spearman correlation analysis, maintenance type and maintenance time show a positive correlation while change impact shows a negative correlation both with maintenance type and maintenance time.
Moving Horizon path planning for intelligent vehicle oriented to dynamic obstacle avoidance

In urban conditions, the road environment is very complex and the vehicle itself has strong nonlinearity, which makes the decision and control of obstacle avoidance path planning very challenging. To solve the problem of difficult vehicle path planning, this paper proposes a method for path planning that combines artificial potential fields with model predictive control. Drawing on the concept of virtual force field, the virtual force field and model prediction are organically combined by abstracting the force field function as the optimization objective, which can realize the modeling of dynamic environment through virtual force field and consider the multi-task and multi-objective problems in path planning through model prediction. At the same time, the state changes of both the obstacle vehicle and the main vehicle are predicted, and a good path planning effect can be achieved in the low-speed dynamic environment. The simulation results indicate that the method can complete the path planning for static and dynamic obstacles and ensure the safety and stability of the vehicle.
Influence of Fog Weather on Automotive Vision Target Detection

The target detection based on vision will be seriously affected by bad weather (rain, snow, fog, etc.), which causes safety of the intended functionality (SOTIF) problems. The implementation of autonomous driving systems still faces huge challenges. It is of great significance to quantify and evaluate the influence of bad weather on automotive vision target detection. This study focuses on the analysis of the influence of fog weather on automotive vision target detection. The fog weather simulation platform is built in 51 Sim-one simulation software, with which the image data of different targets, different distances, and different scenarios are collected. The numbers of the scale-invariant feature transform (SIFT) feature points and the oriented FAST and rotated BRIEF (ORB) feature points in the rectangular region where the target is located are taken as the indicators to quantify the influence of fog weather on target detection. The results show that the SIFT and ORB feature points decrease by more than 50% under all fog visibilities when the distance is from 5m to 15m. The extraction of SIFT feature points has better adaptability to fog weather. While the overall number of ORB feature points is much higher than the number of SIFT feature points, which is more conducive to target feature matching. The proposed evaluation scheme can reliably quantify the influence of fog weather on automotive vision target detection, which is of great significance to solving the SOTIF problem of the autonomous driving system and improving the safety of autonomous driving in fog weather.