Reviewerly/exHarmony huggingface dataset:
Imikode

This research paper presents Imikode, a virtual reality (VR)--based learning game to support the teaching and learning of object-oriented programming (OOP) concepts in computing education. The system was implemented using Google Cardboard SDK and works with any Cardboard compatible headset. The teacher can use Imikode to introduce OOP class in an immersive and engaging way. This early prototype provides basic features to help introduce OOP concepts. We aim at extending it using Artificial Intelligence (AI), by adding a conversational agent able to parse erroneous statements and provide adequate instructions on how to fix them.
On the Use of Word Embeddings for Identifying Domain Specific Ambiguities in Requirements

Software requirements are usually written in common natural language. An important quality criterion for each documented requirement is unambiguity. This simply means that all readers of the requirement must arrive at the same understanding of the requirement. Due to differences in the domain expertise of requirements engineer and other stakeholders of the project, it is possible that requirements contain several words that allow alternative interpretations. Our objective is to identify and detect domain specific ambiguous words in natural language text. This paper applies an NLP technique based on word embeddings to detect such ambiguous words. More specifically, we measure the ambiguity potential of most frequently used computer science (CS) words when they are used in other application areas or subdomains of engineering, e.g., aerospace, civil, petroleum, biomedical and environmental etc. Our extensive and detailed experiments with several different subdomains show that word embedding based techniques are very effective in identifying domain specific ambiguities. Our findings also demonstrate that this technique can be applied to documents of varying sizes. Finally, we provide pointers for future research.
Min–max time efficient inspection of ground vehicles by a UAV team

We present a control design for N unmanned aerial vehicles (UAVs) tasked with an inspection of M ground moving vehicles. The location of each ground vehicle is known to each UAV, but the navigation and intent of each ground vehicle are unknown, therefore, this uncertainty has to be anticipated in each UAV's navigation. We use the minimum time stochastic optimal control to navigate each UAV towards the inspection of each ground vehicle. Based on this control, we formulate assignments of ground vehicles to be inspected by UAVs as an optimization problem to inspect all ground vehicles in the minimum expected time. Accounting for ground vehicle uncertain trajectories, we update the optimal assignment by a Markov inequality rule. The rule prevents the possibility of indefinite updating of assignments without finishing the inspection of all vehicles. On the other hand, it updates an assignment if it leads to a statistically significant improvement of the inspection expected time. The presented approach is illustrated with numerical examples.
Cycle Age-Adversarial Model Based on Identity Preserving Network and Transfer Learning for Cross-Age Face Recognition

Age variations bring a large challenge for face recognition tasks. Existing Cross-Age Face Recognition (CAFR) methods have two limitations. Firstly, many CAFR approaches require both age labels and identity labels for training. However, it is difficult to collect images under a large age span from each individual. Secondly, many works are based on the assumption that age and identity information are independent of each other, which may not satisfy various conditions. In this paper, a Cycle Age-Adversarial Model (CAAM) is proposed for CAFR, which only uses the age labels for training without considering independence hypothesis. CAAM includes two different branch networks. Firstly, the branch of Age-robust Feature Extracting Model (AFEM) is designed to adaptively learn age-invariant features by adversarial learning scheme, which includes an age discriminator network and a feature generator network. The age discriminator network is trained to discriminate the age information, and the generator extracts age-invariant features through adversarial learning with discriminator. Secondly, a branch of the Identity Preserving Network (IPN) is proposed to keep identity information, which introduces Unsupervised Identity Loss (UIL) to enlarge the inter-class distance, and decrease the loss of identity information in the learning process. Finally, the features of the two branches are cyclically optimized through minmizing Feature Consistency Loss (FCL), which integrates age invariance learning and identity discrimination learning into final feature representation. Different from existing CAFR networks, our adversarial learning strategy for age-robust feature learning can be generalized to other attributes including pose and expression. Moreover, we introduce cycle optimization strategy to merge the advantages of two branch networks, which is a novel strategy to fuse multi-task features. Extensive CAFR experiments performed on the benchmark MORPH Album2, CACD-VS and Cross Age LFW databases demonstrate the effectiveness and superiority of CAAM.
Digital Competence of Tourism Students: Explanatory Power of Professional Training

This article aims to analyze the influence of professional training on the digital competence of tourism students at a Mexican public university. For this, a quantitative methodology was used through the application of a survey amongst 400 students. Moreover, the partial least squares structural equation modeling (PLS-SEM) and other multivariate techniques were used to test the hypotheses. The results show that the role of teaching, the curriculum proposal, and the student autonomy as part of professional training all have an impact on digital competence in order to generate and effectively use digital knowledge, manage information in support of their activities and use the media in individual and collective channels, as well as emancipate collaborative learning and maintain leadership in the network. It is discussed how the emphasis of curricular programs and the support of teachers play a significant role as cultivators of digital competence, as well as the initiative and independence of students to better exploit digital media for their personal, academic and professional activities.
Automatic detection of brachytherapy seeds in 3D ultrasound images using a convolutional neural network

A novel approach for automatic localization of brachytherapy seeds in 3D transrectal ultrasound (TRUS) images, using machine learning based algorithm, is presented. 3D radiofrequency ultrasound signals were collected from 13 patients using the linear array of the TRUS probe during the brachytherapy procedure in which needles are used for insertion of stranded seeds. Gold standard for the location of seeds on TRUS data were obtained with the guidance of the complete reconstruction of the seed locations from multiple C-arm fluoroscopy views and used in the creation of the training set. We designed and trained a convolutional neural network (CNN) model that worked on 3D cubical sub-regions of the TRUS images, that will be referred to as patches, representing seed, non-seed within a needle track and non-seed elsewhere in the images. The models were trained with these patches to detect the needle track first and then the individual seeds within the needle track. A leave-one-out cross validation approach was used to test the model on the data from eight of the patients, for whom accurate seed locations were available from fluoroscopic imaging. The total inference time was about 7 min for needle track detection in each patient's image and approximately 1 min for seed detection in each needle, leading to a total seed detection time of less than 15 min. Our seed detection algorithm achieved [Formula: see text] precision, [Formula: see text] recall and [Formula: see text] F1_score. The results from our CNN-based method were compared to manual seed localization performed by an expert. The CNN model yielded higher precision (lower false discovery rate) compared to the manual method. The automated approach requires little modification to the current clinical setups and offers the prospect of application in real time intraoperative dosimetric analysis of the implant.
Reductions for safety proofs

Program reductions are used widely to simplify reasoning about the correctness of concurrent and distributed programs. In this paper, we propose a general approach to proof simplification of concurrent programs based on exploring generic classes of reductions. We introduce two classes of sound program reductions, study their theoretical properties, show how they can be effectively used in algorithmic verification, and demonstrate that they are very effective in producing proofs of a diverse class of programs without targeting specific syntactic properties of these programs. The most novel contribution of this paper is the introduction of the concept of context in the definition of program reductions. We demonstrate how commutativity of program steps in some program contexts can be used to define a generic class of sound reductions which can be used to automatically produce proofs for programs whose complete Floyd-Hoare style proofs are theoretically beyond the reach of automated verification technology of today.
Design and development of an EOG-based simplified Chinese eye-writing system

Eye movement information provides an alternative way for patients with muscular and neurological disorders to communicate with other people or devices. Electrooculography (EOG) is an effective eye movement recording method that has been widely applied to design human–computer interaction systems. In this paper, we propose a simplified Chinese eye-writing system based on EOG (EsCew) by identifying the basic strokes of Chinese characters. Specifically, we first use a bandpass digital filter to preprocess the raw EOG signals to suppress noise interference. Then, we determine the effective eye movement segments according to different strokes by detecting blink signals using the sliding window technique. On this basis, we establish basic stroke templates in terms of the handwriting characteristics of Chinese characters. To reduce the computational complexity, the DTW algorithm is adopted to classify the EOG segments. Finally, we match the stroke sequence with the encoded Chinese characters to obtain the final recognition results. In the lab environment, the recognition experiments are performed on 10 most representative Chinese characters, i.e., . The average accuracies of the basic strokes and Chinese characters are 93.998% and 94.52%, respectively. The experimental results validate the feasibility of the proposed EsCew system. Download : Download high-res image (25KB)Download : Download full-size image
13 years of speaker recognition research at BUT, with longitudinal analysis of NIST SRE

In this paper, we present a brief history and a “longitudinal study” of all important milestone modelling techniques used in text independent speaker recognition since Brno University of Technology (BUT) first participated in the NIST Speaker Recognition Evaluation (SRE) in 2006—GMM MAP, GMM MAP with eigen-channel adaptation, Joint Factor Analysis, i-vector and DNN embedding (x-vector). To emphasize the historical context, the techniques are evaluated on all NIST SRE sets since 2004 on a time-machine principle, i.e. a system is always trained using all data available up till the year of evaluation. Moreover, as user-contributed audiovisual content dominates nowadays’ Internet, we representatively include the Speakers In The Wild (SITW) and VOiCES challenge datasets in the evaluation of our systems. Not only we present a comparison of the modelling techniques, but we also show the effect of sampling frequency.
PACEX: PAtient-Centric EMR eXchange in Healthcare Systems using Blockchain

Electronic Medical Records (EMRs) contain private and sensitive information of the patients and require distribution across multiple stakeholders for sustained and consistent medical care. EMRs are owned and maintained by hospital systems. So, patients often face difficulties in getting their data shared among different stakeholders. Blockchain provides distributed, transparent, and immutable log of records that benefit the design of healthcare systems. There have been several studies done to use Blockchain technology as a mediation tool for sharing sensitive EMRs between multiple stakeholders in the e-health care system. However, these studies lack the requirement of patients to have full control over their EMR data. This work develops PACEX, PAtient-Centric EMR eXchange, that enables patients to share and have complete control over their data using Blockchain. In PACEX, we develop an easy-to-use patient application that interacts with different hospital systems and handles EMR exchanges with integrity. We developed a proof of concept implementation of PACEX using Ethereum Blockchain and Smart Contracts to achieve access control and maintain a history of record exchanges among different stakeholders. The evaluation results provide the quantitative analysis that gives time estimates for various transactions involved in PACEX and the qualitative analysis that describes the security properties of the system. PACEX achieves essential security requirements such as Authentication, Integrity, Access Control, and Traceability over the EMR data exchange.
Perceived Control or Haptic Sensation? Exploring the Effect of Image Interactivity on Consumer Responses to Online Product Displays

The diffusion of image interactivity technology in online advertising has changed how people interact with mediated information. This study explores how interactive images and touch-screen interfaces influence consumer responses toward products in online shopping. Findings from two experiments showed that interactive images, compared to static images, increased consumers' attitudes toward online retail stores (Study 1), perceived informativeness, and willingness to pay (Study 2). The effect of image interactivity was mediated by consumers' perceived control, but not haptic sensation as implied in prior research. Theoretical and managerial implications of these findings are discussed.
Abstractive Text Summarization Using Pointer-Generator Networks With Pre-trained Word Embedding

Abstractive text summarization is the task of generating a summary that captures the main content of a text document. As a state-of-the-art method for abstractive summarization, the pointer-generator network produces more fluent summaries and solves two shortcomings of reproducing factual details inaccurately and phrase repetition. Though this network can generate Out-Of-Vocabulary (OOV) words, it cannot completely represent them in the context and may face the information loss problem. This paper aims to improve the quality of abstractive summarization with an extra pretrained layer of word embedding for the pointer-generator network. This mechanism helps to maintain the meaning of words in more various contexts. This assures that every word has its own representation, even though it does not exist in the vocabulary. We modify the network with the two latest word embedding mechanisms, i.e. Word2vec and Fasttext, to represent the semantic information of words more accurately. Some OOV words which are marked as unknown tokens now can have their right embeddings and be well considered in summary generation. The experiments on the CNN/Daily Mail corpus shows that the new mechanism outperforms the only pointer-generator network in all 3 ROUGE scores (R1, R2, RL).
Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches

Text mining and natural language processing of clinical text, such as notes from electronic health records, requires specific consideration of the specialized characteristics of these texts. Deep learning methods could potentially mitigate domain specific challenges such as limited access to in-domain tools and data sets.A bi-directional Long Short-Term Memory network is applied to clinical notes in Spanish and Swedish for the task of medical named entity recognition. Several types of embeddings, both generated from in-domain and out-of-domain text corpora, and a number of generation and combination strategies for embeddings have been evaluated in order to investigate different input representations and the influence of domain on the final results.For Spanish, a micro averaged F1-score of 75.25 was obtained and for Swedish, the corresponding score was 76.04. The best results for both languages were achieved using embeddings generated from in-domain corpora extracted from electronic health records, but embeddings generated from related domains were also found to be beneficial.A recurrent neural network with in-domain embeddings improved the medical named entity recognition compared to shallow learning methods, showing this combination to be suitable for entity recognition in clinical text for both languages.
LeSSA: A Unified Framework based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment Classification

Sentiment Analysis (SA) is an active research area. SA aims to classify the online unstructured user-generated contents (UUGC) into positive and negative classes. A reliable training data is vital to learn a sentiment classifier for textual sentiment classification, but due to domain heterogeneity, manually construction of reliable labeled sentiment corpora is a laborious and time-consuming task. In the absence of enough labeled data, the alternative usage of sentiment lexicons and semi-supervised learning approaches for sentiment classification have substantially attracted the attention of the research community. However, state-of-the-art techniques for semi-supervised sentiment classification present research challenges expressed in questions like the following. How to effectively utilize the concealed significant information in the unstructured data? How to learn the model while considering the most effective sentiment features? How to remove the noise and redundant features? How to refine the initial training data for initial model learning as the random selection may lead to performance degradation? Besides, mainly existing lexicons have trouble with word coverage, which may ignore key domain-specific sentiment words. Further research is required to improve the sentiment lexicons for textual sentiment classification. In order to address such research issues, in this paper, we propose a novel unified sentiment analysis framework for textual sentiment classification called LeSSA. Our main contributions are threefold. (a) lexicon construction, generating quality and wide coverage sentiment lexicon. (b) training classification models based on a high-quality training dataset generated by using k-mean clustering, active learning, self-learning, and co-training algorithms. (c) classification fusion, whereby the predictions from numerous learners are confluences to determine final sentiment polarity based on majority voting, and (d) practicality, that is, we validate our claim while applying our model on benchmark datasets. The empirical evaluation of multiple domain benchmark datasets demonstrates that the proposed framework outperforms existing semi-supervised learning techniques in terms of classification accuracy.
On using AI-Based Human Identification in Improving Surveillance System Efficiency

Surveillance camera systems are one of the most popular tech tools keeping homes and business safe. Many of these systems record continuously while some others start recording when a movement is detected. These recording methods consume a huge storage, and they need a dramatic time to search in the recorded files. Here, an AI-based desktop application which is designed and developed in order to start recording only if a person or a human-face is identified. This technique will improve system's efficiency in terms of reducing the storage needed for saving recordings, and reducing the processing and searching time in the recordings. This proposed system uses deep-learning algorithms, OpenCV libraries and built on Linux OS. It has two detection modes; human body identification and human-face identification. This will trigger the suitable detection algorithms based on mode selected. The system has been tested and evaluated by different evaluators and organizations that use surveillance systems, and questionnaire and interviews were conducted. Based on the evaluation results, it can be deduced how much this system is an important application to the target group.
Information And Communication Technology Adoption And Innovation For Sustainable Entrepreneurship

Abstract ICT adoption has experienced an increasing trend in the past two decades and simultaneously, achieving sustainability in entrepreneurship is the major goal of every entrepreneur; this study seeks to empirically investigate ICT adoption’s contribution to sustainability in entrepreneurship. The objective of this study is to examine the relationship between ICT adoption and sustainable entrepreneurial development in Western Africa. Panel data on ECOWAS countries were collected and estimated using econometric tools for the purpose of the study. The findings show that a positive statistically significant positive relationship exists between ICT adoption and sustainability in entrepreneurship. The outcome of this study is expected to have microeconomic and macroeconomic implications for sustainability in entrepreneurship within and outside ECOWAS.
A 4-Stage Pipelined Architecture for Point Multiplication of Binary Huff Curves

This work has proposed a 4-stage pipelined architecture to achieve an optimized throughput over area ratio for point multiplication (PM) computation in binary huff curves (BHC) cryptography. The original mathematical formulation of BHC is revisited with an objective to reduce the required area. Consequently, a simplified formulation of BHC is obtained with 43% reduction in the hardware resources. As far as the throughput is concerned, it is improved first by reducing the critical path and second by minimizing the number of clock cycles (CCs) required to compute one PM. The critical path is reduced through the placement of pipeline registers, whereas the number of required CCs are minimized through an efficient scheduling of computations. These two factors i.e., the area reduction and throughput optimizations, have resulted in maximizing the throughput over area ratio. The proposed pipelined architecture is implemented over [Formula: see text] field, using standard NIST curve parameters. The architecture is modeled in Verilog and synthesized using Xilinx (ISE 14.7) design tool on Virtex 7 FPGA. The implementation results show that 17% improvement in clock frequency, 13% reduction in the time required to compute one PM and 2.6% improvement in throughput/area are achieved when compared with the most recent state of the art solutions.