Reviewerly/exHarmony huggingface dataset:
Improved Low-Resource Somali Speech Recognition by Semi-Supervised Acoustic and Language Model Training

We present improvements in automatic speech recognition (ASR) for Somali, a currently extremely under-resourced language. This forms part of a continuing United Nations (UN) effort to employ ASR-based keyword spotting systems to support humanitarian relief programmes in rural Africa. Using just 1.57 hours of annotated speech data as a seed corpus, we increase the pool of training data by applying semi-supervised training to 17.55 hours of untranscribed speech. We make use of factorised time-delay neural networks (TDNN-F) for acoustic modelling, since these have recently been shown to be effective in resource-scarce situations. Three semi-supervised training passes were performed, where the decoded output from each pass was used for acoustic model training in the subsequent pass. The automatic transcriptions from the best performing pass were used for language model augmentation. To ensure the quality of automatic transcriptions, decoder confidence is used as a threshold. The acoustic and language models obtained from the semi-supervised approach show significant improvement in terms of WER and perplexity compared to the baseline. Incorporating the automatically generated transcriptions yields a 6.55\% improvement in language model perplexity. The use of 17.55 hour of Somali acoustic data in semi-supervised training shows an improvement of 7.74\% relative over the baseline.
Invisible QR Code Hijacking Using Smart LED

Quick response (QR) codes have found versatile usage in numerous applications, but have also posed severe security threats such as privacy leakage, phishing and even payment inception if the codes are hijacked. The hijacking is often assumed to be preventable by physically isolating the codes from possible attackers, e.g., putting the QR code inside a glass cabinet distant to outsiders. In this paper, we explore a new QR code hijacking attack, named Li-Man, that can subvert such protection using smart LED. The key idea is to illuminate a target victim QR code from afar using specialized flickering light waveforms, which can transform the code to be any other predefined malicious ones when being captured by smart-phone cameras, while keeping the attack invisible to human visual perception. Li-Man builds on a modeling framework that harnesses the disparity between camera and human imaging mechanisms. We develop a Li-Man simulator and also implement a prototype to verify the feasibility and threat level of Li-Man. Experiments demonstrate that Li-Man can successfully realize the invisible hijacking of QR codes from multiple hidden positions in constrained space. On the other hand, we propose and verify a primary countermeasure that is promising to defeat the Li-Man attack.
Explaining Sentiment Classification

This paper presents a novel 1-D sentiment classifier trained on the benchmark IMDB dataset.The classifier is a 1-D convolutional neural network with repeated convolution and max pooling layers.The main contribution of this work is the demonstration of a deconvolution technique for 1-D convolutional neural networks that is agnostic to specific architecture types.This deconvolution technique enables text classification to be explained, a feature that is important for NLP-based decision support systems, as well as being an invaluable diagnostic tool.
ASSV

As one kind of biological characteristics of people, handwritten signature has been widely used in the banking industry, government and education. Verifying handwritten signatures manually causes too much human cost, and its high probability of errors can threaten the property safety and even society stability. Therefore, the need for an automatic verification system is emphasized. This paper proposes a device-free on-line handwritten signature verification system ASSV, providing paper-based handwritten signature verification service. As far as we know, ASSV is the first system which uses the changes of acoustic signals to realize signature verification. ASSV differs from previous on-line signature verification work in two aspects: 1. It requires neither a special sensor-instrumented pen nor a tablet; 2. People do not need to wear a device such as a smartwatch on the dominant hand for hand tracking. Differing from previous acoustic-based sensing systems, ASSV uses a novel chord-based method to estimate phase-related changes caused by tiny actions. Then based on the estimation, frequency-domain features are extracted by a discrete cosine transform (DCT). Moreover, a deep convolutional neural network (CNN) model fed with distance matrices is designed to verify signatures. Extensive experiments show that ASSV is a robust, efficient and secure system achieving an AUC of 98.7% and an EER of 5.5% with a low latency.
On the Use/Misuse of the Term ‘Phoneme’

The term ‘phoneme’ lies at the heart of speech science and technology, and yet it is not clear that the research community fully appreciates its meaning and implications. In particular, it is suspected that many researchers use the term in a casual sense to refer to the sounds of speech, rather than as a well defined abstract concept. If true, this means that some sections of the community may be missing an opportunity to understand and exploit the implications of this important psychological phenomenon. Here we review the correct meaning of the term ‘phoneme’ and report the results of an investigation into its use/misuse in the accepted papers at INTERSPEECH-2018. It is confirmed that a significant proportion of the community (i) may not be aware of the critical difference between ‘phonetic’ and ‘phonemic’ levels of description, (ii) may not fully understand the significance of ‘phonemic contrast’, and as a consequence, (iii) consistently misuse the term ‘phoneme’. These findings are discussed, and recommendations are made as to how this situation might be mitigated.
Development of Automatic Badminton Playing Robot with Distance Image Sensor

We developed a full automatic badminton playing robot "MRO Brothers". The developed robot can receive and serve in the same environment as human badminton player. In this system, the shuttle is detected by Kinect sensor and its orbit is estimated based on the distance image of the shuttle. Moreover, the robot can automatically move to the spot where a shuttle will fall and hit it back by normal badminton racket.
ClearTAC: Verb Tense, Aspect, and Form Classification Using Neural Nets

This paper proposes using a Bidirectional LSTM-CRF model in order to identify the tense and aspect of verbs.The information that this classifier outputs can be useful for ordering events and can provide a pre-processing step to improve efficiency of annotating this type of information.This neural network architecture has been successfully employed for other sequential labeling tasks, and we show that it significantly outperforms the rule-based tool TMV-annotator on the Propbank I dataset.
Lesion Detection in Breast Ultrasound Images Using a Machine Learning Approach and Genetic Optimization

Breast ultrasound has become one of the most important and effective modalities for early detection of breast cancer and it is most suitable for large scale breast cancer screening and diagnosis in low-resource countries. Breast lesion detection is a crucial step in the development of Computer Aided Diagnosis and Surgery systems based on ultrasound images, since it can be used as a seed point to subsequently initialize segmentation methods such as region growing, snakes or level-sets. Because of inherent artifacts of the ultrasound images, such as speckle, acoustic shadows and blurry edges, the detection of lesions is not an easy task. In this work we propose a machine learning based approach to locate lesions in breast ultrasound images. This approach consists on the classification of image pixels as lesion or background with a Random Forest optimized with genetic algorithms to generate candidate regions. After pixel classification the method chooses the correct lesion region by discriminating false positives using a new proposed probability approach. The pixel classification and region discrimination steps are compared with other methods, showing better results in the detection of lesions. The lesion detection was evaluated using the True Positive Fraction and the False Positives per image, having results of 84.4% and 15.6% respectively.
Semantic Web mining for Content-Based Online Shopping Recommender Systems

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.
Biological landmark Vs quasi-landmarks for 3D face recognition and gender classification

Face recognition and gender classification are vital topics in the field of computer graphic and pattern recognition. We utilized ideas from two growing ideas in computer vision, which are biological landmarks and quasi-landmarks (dense mesh) to propose a novel approach to compare their performance in face recognition and gender classification. The experimental work is conducted on FRRGv2 dataset and acquired 98% and 94% face recognition accuracies using the quasi and biological landmarks respectively. The gender classification accuracies are 92% for quasi-landmarks and 90% for biological landmarks.
Implementing Support Vector Machine Sentiment Analysis to Students' Opinion toward Lecturer in an Indonesian Public University

Student feedback is an important evaluation tool for quality improvement. Moreover, in Indonesia's higher education system there is an assessment regulation that puts special attention to the availability of the student feedback system. However, parts of the questionnaire are in the form of descriptive text that requires more effort for analysis. This situation leads to a very tiresome work in case of the number of documents reaches several hundred or even thousands. There were some efforts to apply computer-assisted classification by utilizing machine learning, however, most of them only analyzed English documents. Only a handful that studied the classification of documents in Bahasa Indonesia. In reality, we found some cases where the students used mixed languages while filling the evaluation forms. Therefore, in this study, we expand the application of text classification by using Support Vector Machne (SVM) to cases of student feedback in mixed languages. The model was built computationally and from the test, we get 74% accuracy and 0.46 Kappa value.
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation

U-Net has achieved huge success in various medical image segmentation challenges.Kinds of new architectures with bells and whistles might succeed in certain dataset when employed with optimal hyperparameter, but their generalization always can't be guaranteed.Here, we focused on the basic U-Net architecture and proposed a multi scale supervised 3D U-Net for the segmentation task in KiTS19 challenge.To enhance the performance, our work can be summarized as three folds: first, we used multi scale supervision in the decoder pathway, which could encourage the network to predict right results from the deep layers; second, with the aim to alleviate the bad effect from the sample imbalance of kidney and tumor, we adopted exponential logarithmic loss; third, a connectedcomponent based post processing method was designed to remove the obviously wrong voxels.In the published KiTS19 training dataset (totally 210 patients), we divided 42 patients to be test dataset and finally obtained DICE scores of 0.969 and 0.805 for the kidney and tumor respectively.In the challenge, we finally achieved the 7 th place among 106 teams with the Composite Dice of 0.8961, namely 0.9741 for kidney and 0.8181 for tumor.
Fault severity recognition based on self-adaptive particle swarm optimisation using wavelet kernel extreme learning machine

Gear fault levels are an important issue and can show the operating status of gears. In this paper, the extreme learning machine (ELM) learning method is regarded as a basic algorithm for classifying gear fault levels. In order to improve the generalisation of the ELM and obtain a better classification performance, the wavelet kernel concept was introduced to the ELM and thereby a structure called a wavelet kernel extreme learning machine (WK-ELM) was constructed. In terms of the WK-ELM, because the dilation factor of the wavelet kernel and the number of hidden neurons of the ELM are important for the classification performance, particle swarm optimisation (PSO) was used to search for the most appropriate values of the solved problem. What is more, PSO was also optimised by a self-adaptive inertia weight that formed a novel structure, that is, self-adaptive inertia weight particle swarm optimisation (SAPSO). So far, an automatic fault diagnosis system for gear fault levels based on the SAPSO-WK-ELM has been proposed in this paper. Aimed at recognising different gear fault levels at different rotational speeds, ranking mutual information (RMI) and standardised mutual information (SMI) techniques were applied to select sensitive features for gear fault levels. Compared with other relational algorithms previously used, the results of this study, including simulation and experimental data, have demonstrated that the SAPSO-WK-ELM can achieve an outstanding classification performance.
Sort Elements Based on Priority, in order to Improve the Quality of E-Learning in Health Using Difficulty-Usefulness Pyramid with Weighting (DUP-We)

Many health education institutions in Indonesia are starting the implementation of e-learning, but there are still many technical obstacles. The elements of e-learning that must be improved are relatively many, so it is neces-sary to choose which elements will be prioritized to handle first. This study proposes a new method for selecting elements that will be prioritized to be im-proved using Difficultness-Usefulness Pyramid with Weighting (DUP-We). The subjects were 200 students from the Environmental Health Department of Magetan, Health Polytechnic of Surabaya. The elements chosen were learning design, handouts, books, links to resources, discussion forums, chat, assignment, feedback, quizzes and surveys. The attributes that were used as the basis for determining priorities are difficultness and usefulness. Weighting (1 to 10) is based on the importance of each attribute. Data about attributes and weighting are obtained through filling in questionnaires. For each element a determination is made: 1) weightless difficultness (difficultness x weight) and weighted usefulness (usefullness x weight); 2) weighted average difficultness and weighted usefulness mean; 3) Range (mean difficultness average weighted up to weighted usefulness mean). Furthermore, the range is sorted from the smallest and described in the form of a pyramid. The pyramid shows that the order of elements based on priority is assignment, quiz, feedback, discussion, link to resources, book, survey, learning design, handout and chat.
Calibration of a 3D laser rangefinder and a camera based on optimization solution

<p style='text-indent:20px;'>The calibration of a 3D laser rangefinder (LRF) and a camera is a key technique in the field of computer vision and intelligent robots. This paper proposes a new method for the calibration of a 3D LRF and a camera based on optimization solution. The calibration is achieved by freely moving a checkerboard pattern in front of the camera and the 3D LRF. The images and the 3D point clouds of the checkerboard pattern in various poses are collected by the camera and the 3D LRF respectively. By using the images, the intrinsic parameters and the poses of the checkerboard pattern are obtained. Then, two kinds of geometric constraints, line-to-plane constraints and plane-to-plane constraints, are constructed to solve the extrinsic parameters by linear optimization. Finally, the intrinsic and extrinsic parameters are further refined by global optimization, and are used to compute the geometric mapping relationship between the 3D LRF and the camera. The proposed calibration method is evaluated with both synthetic data and real data. The experimental results show that the proposed calibration method is accurate and robust to noise.
Creating a new “reality” for medical education: the Nexus Reality Lab for virtual reality

The Technology Team at the Ruth Lilly Medical Library, Indiana University (IU), first started exploring virtual reality (VR) in 2016. In 2017, we began offering weekly sessions dubbed VRidays ("VR Fridays") to give students an opportunity to experience the technology. We also purchased a portable VR setup that allowed us to demonstrate VR at our regional campuses.To lower the entry barrier to VR, the Technology Team collaborated with the IU Advanced Visualization Lab to establish a reality lab in our collaborative learning space. The lab opened in the fall of 2018 and consists of four high-end VR stations that are accessible to students at any time, but they can also make an appointment for a more guided experience. Information and instructions are available on a LibGuide.We are currently collecting data on the number of unique users and evaluating application usage. We are working on a feedback mechanism and looking to develop collaborative partnerships across the university.
THE MODERATING ROLE OF TRAINING ON THE RELATIONSHIP BETWEEN STRATEGY MANAGEMENT, INFORMATION TECHNOLOGY MANAGEMENT AND ORGANIZATIONAL PERFORMANCE OF SHARJAH POLICE

This study’s primary objective is to examine the role of training on the relationship between strategy management, information technology (IT) management, and organizational performance. The study proposed the study model based on theoretical basis and a review of literature with the underpinning model being the Resource-Based View of the Firm (RBV), Knowledge–Based View (KBV) and innovation theories. The analysis of data was conducted on the effects of IT management and strategy management on the organizational performance of three hundred and forty-one (341) Sharjah Police in Emirate of Sharjah, UAE, to which the questionnaire survey copies were distributed to. A total of two hundred and forty-five (245) questionnaires were retrieved and tested with the help of SPSS. The analysis results indicated the positive and significant effects of both IT management and strategy management on the performance of Sharjah Police departments. The study enumerated recommendations for the effective IT management and strategy management implementation in the Sharjah Police and confirmed the underpinning theories applicability to the context.&nbsp
News Graph: An Enhanced Knowledge Graph for News Recommendation.

Knowledge graph, which contains rich knowledge facts and well structured relations, is an ideal auxiliary data source for alleviating the data sparsity issue and improving the explainability of recommender systems. However, preliminary studies usually simply leverage a generic knowledge graph which is not specially designed for particular tasks. In this paper, we consider the scenario of news recommendations. We observe that both collaborative relations of entities (\eg entities frequently appear in same news articles or clicked by same users) and the topic context of news article can be well utilized to construct a more powerful graph for news recommendations. Thus we propose an enhanced knowledge graph called \textbf{news graph}. Compared with a generic knowledge graph, the news graph is enhanced from three aspects: (1) adding a new group of entities for recording topic context information;  (2) adding collaborative edges between entities based on users’ click behaviors and co-occurrence in news articles; and (3) removing news-irrelevant relations. To the best of our knowledge, it is the first time that a domain specific graph is constructed for news recommendations. Extensive experiments on a real-world news reading dataset demonstrate that our news graph can greatly benefit a wide range of news recommendation tasks, including personalized article recommendation, article category classification, article popularity prediction, and local news detection.
Scoring Interactional Aspects of Human-Machine Dialog for Language Learning and Assessment using Text Features

While there has been much work in the language learning and assessment literature on human and automated scoring of essays and short constructed responses, there is little to no work examining text features for scoring of dialog data, particularly interactional aspects thereof, to assess conversational proficiency over and above constructed response skills.Our work bridges this gap by investigating both human and automated approaches towards scoring human-machine text dialog in the context of a real-world language learning application.We collected conversational data of human learners interacting with a cloud-based standards-compliant dialog system, triple-scored these data along multiple dimensions of conversational proficiency, and then analyzed the performance trends.We further examined two different approaches to automated scoring of such data and show that these approaches are able to perform at or above par with human agreement for a majority of dimensions of the scoring rubric.
A new model for intra- and inter-institutional soil data sharing

Abstract. Data sharing and collaboration are critical to solving large-scale problems. The prevailing soil data-sharing model is based on different groups sending their data to a lead party. This model is of a centralised nature and, consequently, results in the participants ceding control and governance over their data to the lead party. Here we explore the use of a distributed ledger (blockchain) to solve the aforementioned issues. We explain what a blockchain is and some of its characteristics to then describe some features of a blockchain that make it an interesting candidate for an inter-institutional database. Finally, we describe the potential use case of developing a global soil spectral library with multiple, independent international institutions constituting the network.