Reviewerly/exHarmony huggingface dataset:
Applications of Semantic Web in integrating open data and bibliographic records: a development example of an infomediary of Taiwanese indigenous people

Purpose This study aims to develop a Web-based application system called Infomediary of Taiwanese Indigenous Peoples (ITIP) that can help individuals comprehend the society and culture of indigenous people. The ITIP is based on the use of Semantic Web technologies to integrate a number of data sources, particularly including the bibliographic records of a museum. Moreover, an ontology model was developed to help users search cultural collections by topic concepts. Design/methodology/approach Two issues were identified that needed to be addressed: the integration of heterogeneous data sources and semantic-based information retrieval. Two corresponding methods were proposed: SPARQL federated queries were designed for data integration across the Web and ontology-driven queries were designed to semantically search by knowledge inference. Furthermore, to help users perform searches easily, three searching interfaces, namely, ethnicity, region and topic, were developed to take full advantage of the content available on the Web. Findings Most open government data provides structured but non-resource description framework data, Semantic Web consumers, therefore, require additional data conversion before the data can be used. On the other hand, although the library, archive and museum (LAM) community has produced some emerging linked data, very few data sets are released to the general public as open data. The Semantic Web’s vision of “web of data” remains challenging. Originality/value This study developed data integration from various institutions, including those of the LAM community. The development was conducted based on the mode of non-institution members (i.e. institutional outsiders). The challenges encountered included uncertain data quality and the absence of institutional participation.
SGTBN: Generating Dense Depth Maps From Single-Line LiDAR

Depth completion aims to generate a dense depth map from the sparse depth map and aligned RGB image. However, current depth completion methods use extremely expensive 64-line LiDAR(about $100,000) to obtain sparse depth maps, which will limit their application scenarios. Compared with the 64-line LiDAR, the single-line LiDAR is much less expensive and much more robust. Therefore, we propose a method to tackle the problem of single-line depth completion, in which we aim to generate a dense depth map from the single-line LiDAR info and the aligned RGB image. A single-line depth completion dataset is proposed based on the existing 64-line depth completion dataset(KITTI). A network called Semantic Guided Two-Branch Network(SGTBN) which contains global and local branches to extract and fuse global and local info is proposed for this task. A Semantic guided depth upsampling module is used in our network to make full use of the semantic info in RGB images. Except for the usual MSE loss, we add the virtual normal loss to increase the constraint of high-order 3D geometry in our network. Our network outperforms the state-of-the-art in the single-line depth completion task. Besides, compared with the monocular depth estimation, our method also has significant advantages in precision and model size.
A High Speed NTT Accelerator for Lattice-Based Cryptography

As the most computing-intensive operation in lattice-based cryptography, polynomial multiplication is the bottleneck for high speed and low-latency implementation of these schemes. In this paper, a vector number theoretic transform (NTT) accelerator is proposed to speed up this process. First, an efficient addressing scheme for the vectorized NTT/INTT is proposed to achieve a conflict free situation and make full use of hardware resources. Then, a flexible architecture is presented with a series of timing and low-power optimizations. Finally, the accelerator is implemented using TSMC 28nm technology. Approximately, 274k equivalent gates and 10kB SRAM is consumed by the proposed accelerator and 47.5ns is only needed to complete a 256-point NTT. Experimental results show that the energy-efficiency and processing speed achieves more than 17.6× and 3× improvements with 50% area saving than the state-of-the-art design.
Teaching Method for Software Measurement Process Based on Gamification or Serious Games: A Systematic Review of the Literature

Software process improvement programs are partly founded on software measurement. However, despite their importance, it has been pointed out in the literature that many students are leaving the academic world without the necessary skills to conduct this kind of process. This can be understood by people’s attitudes to this process which is regarded as time-consuming and difficult to understand—factors that explain the lack of interest in it during a student’s academic life. In light of this, the application of serious games or gamification can show useful alternative ways of meeting this need, because the strategies they involve are well accepted by students and have a motivational and engaging effect on them. The objective of this work is to discover different approaches to the teaching of software measurement and software process improvement through gamification projects and serious games. This involves carrying out a systematic review of the literature, which is aimed at characterizing the state-of-the-art on the use of methods related to gamification and serious games in the abovementioned subjects. We conducted a systematic review of the literature to identify primary studies that address the use, planning, or evaluation of gamification, serious games, their features, and game mechanics in software engineering. We located 137 primary studies, published between 2000 and 2019. Although the use of serious games and gamification in software engineering is not recent, there still remains a large area to be explored, especially in software process improvement and software measurement. The study expands and advances the research on how serious games and gamification proposals can be used for teaching software measurement in the context of software process improvement programs by conducting a systematic review of the literature.
PROV4ITDaTa: Transparent and direct transferof personal data to personal stores

Data is scattered across service providers, heterogeneously structured in various formats. By lack of interoperability, data portability is hindered, and thus user control is inhibited. An interoperable data portability solution for transferring personal data is needed. We demo PROV4ITDaTa: a Web application, that allows users to transfer personal data into an interoperable format to their personal data store. PROV4ITDaTa leverages the open-source solutions RML.io, Comunica, and Solid: (i) the RML.io toolset to describe how to access data from service providers and generate interoperable datasets; (ii) Comunica to query these and more flexibly generate enriched datasets; and (iii) Solid Pods to store the generated data as Linked Data in personal data stores. As opposed to other (hard-coded) solutions, PROV4ITDaTa is fully transparent, where each component of the pipeline is fully configurable and automatically generates detailed provenance trails. Furthermore, transforming the personal data into RDF allows for an interopable solution. By maximizing the use of open-source tools and open standards, PROV4ITDaTa facilitates the shift towards a data ecosystem wherein users have control of their data, and providers can focus on their service instead of trying to adhere to interoperability requirements.
Spaced repetition for authentic mobile-assisted word learning: nature, learner perceptions, and factors leading to positive perceptions

Spaced repetition has been widely implemented and examined in mobile-assisted word learning as an important learning strategy. However, the nature of spaced repetition by commercial word-learning apps and the factors leading to the favoured mobile-assisted spaced repetition have yet to be investigated in authentic contexts. In this study, we coded the spaced repetition patterns and methods of nine apps and interviewed 72 Chinese English learners about their perceptions of spaced repetition for word learning. The results showed three major repetition patterns at three knowledge levels (i.e. the word is unknown, familiar-but-unsure, and known to the learner). The three most common repetition methods were using text-plus-audio for multimedia learning, conducting retrieval practice through flashcards and multiple-choice questions, and integrating game elements such as Goals/Rules and Rewards/Points into learning. Concerning learner preferences, they preferred to have (a) six or seven learning sessions for 'unknown' words, three or four sessions for 'familiar-but-unsure' words, and two or three sessions for 'known' words over ten- to fourteen- day periods, (b) gradually longer intervals between learning sessions, (c) text-plus-audio-plus-image as multimedia, (d) two or three innovative formats of retrieval practice, and (e) integration of Goals/Rules, Rewards/Points, and Time Limits. The results indicate that teachers, researchers, and app designers ought to consider both learning effectiveness and learner perceptions when applying, designing, and developing spaced repetition patterns and methods for mobile-assisted word learning.
A Method for Extracting Unstructured Threat Intelligence Based on Dictionary Template and Reinforcement Learning

In recent years, individuals, organizations and countries are all threatened by cyber threats to some degree. The proposal of threat intelligence sharing scheme has greatly helped the protection of cyber security. Traditional threat intelligence sharing scheme mainly collects and analyzes information manually, which include but not limited to Indicators of Compromise (IOC) and forms a machine readable report for Security Operations Center (SOC) to take corresponding action. Therefore, it is challenging and significant to easily and automatically share and exchange cyber threat intelligence (CTI). Aiming at extracting the information of CTI efficiently, we construct a model of automatic information extraction process of the entity recognition and relationship extraction, which are used to extract effective entities and relationships in threat intelligence reports and improve the efficiency of threat intelligence sharing. The specific content and research results include two aspects: (1) Research on threat intelligence entity recognition model. We use the BERT model as a corpus pre-training model based on the classic neural network BiLSTM-CRF, and proposes a model DT-BERT-BiLSTM-CRF based on the dictionary template. The BERT pre-training model makes full use of the contextual semantic information of the corpus and alleviates the problem of ambiguity in the process of threat intelligence entity recognition. By constructing a dictionary template of threat intelligence entities, the accuracy of entity recognition in the threat intelligence field is further improved. (2) Research on the extraction of ITC relations. We constructed the relation extraction data set with distant supervision methods. For alleviating the noise annotation data, we introduce the attention mechanism and reinforcement learning into traditional neural networks, proposing a model NR-RL-PCNN-ATT. Through a new reward mechanism, our model improves the sentence selection quality and the efficiency of relationship extraction.
Are Your Friends Also Haters? Identification of Hater Networks on Social Media

Hate speech on social media platforms has become a severe issue in recent years. To cope with it, researchers have developed machine learning-based classification models. Due to the complexity of the problem, the models are far from perfect. A promising approach to improve them is to integrate social network data as additional features in the classification. Unfortunately, there is a lack of datasets containing text and social network data to investigate this phenomenon. Therefore, we develop an approach to identify and collect hater networks on Twitter that uses a pre-trained classification model to focus on hateful content. The contributions of this article are (1) an approach to identify hater networks and (2) an anonymized German offensive language dataset that comprises social network data. The dataset consists of 4,647,200 labeled tweets and a social graph with 49,353 users and 122,053 edges.
Predictive analytics for step-up therapy: Supervised or semi-supervised learning?

• Step-up therapy prediction can support resource planning and chronic care management. • Machine learning methods for step-up therapy prediction were evaluated. • Supervised learning had inadequate performance in previous studies. • Semi-supervised learning outperformed supervised learning methods. • One-class support vector machine achieved the highest performance. Step-up therapy is a patient management approach that aims to balance the efficacy, costs and risks posed by different lines of medications. While the initiation of first line medications is a straightforward decision, stepping-up a patient to the next treatment line is often more challenging and difficult to predict. By identifying patients who are likely to move to the next line of therapy, prediction models could be used to help healthcare organizations with resource planning and chronic disease management. To compared supervised learning versus semi-supervised learning to predict which rheumatoid arthritis patients will move from the first line of therapy (i.e., conventional synthetic disease-modifying antirheumatic drugs ) to the next line of therapy (i.e., disease-modifying antirheumatic drugs or targeted synthetic disease-modifying antirheumatic drugs ) within one year. Five groups of features were extracted from an administrative claims database: demographics, medications, diagnoses, provider characteristics, and procedures. Then, a variety of supervised and semi-supervised learning methods were implemented to identify the most optimal method of each approach and assess the contribution of each feature group. Finally, error analysis was conducted to understand the behavior of misclassified patients. XGBoost yielded the highest F-measure (42%) among the supervised approaches and one-class support vector machine achieved the highest F-measure (65%) among the semi-supervised approaches. The semi-supervised approach had significantly higher F-measure (65% vs. 42%; p < 0.01), precision (51% vs. 33%; p < 0.01), and recall (89% vs. 59%; p < 0.01) than the supervised approach. Excluding demographic, drug, diagnosis, provider, and procedure features reduced the F-measure from 65% to 61%, 57%, 54%, 51% and 49% respectively (p < 0.01). The error analysis showed that a substantial portion of false positive patients will change their line of therapy shortly after the prediction period. This study showed that supervised learning approaches are not an optimal option for a difficult clinical decision regarding step-up therapy. More specifically, negative class labels in step-up therapy data are not a robust ground truth, because the costs and risks associated with higher line of therapy impact objective decision making of patients and providers. The proposed semi-supervised learning approach can be applied to other step-up therapy applications.
Optimizing genetic programming by exploiting semantic impact of sub trees

Now-a-days researchers have diverted their attentions towards making stochastic algorithms deterministic. This is to reduce the fruitless exploration during the search process and to give direction to the search process. Lack of locality in the algorithms is the biggest hindrance in achieving this goal. Locality in GP is described as the correlation between the change in genotype and the semantics of its phenotype (solution). In strong locality, neighboring genotype and phenotype correspond to each other in a search space. It is believed that search algorithms exhibiting strong locality perform better than the algorithms with weak locality. Genetic Programming is among the best performing stochastic algorithms for solving challenging problems and is cursed with the same problem. This means, a small change in GP tree may result in a huge change in the behavior of the solution and vice versa. Unfortunately, this stochastic behavior stops GP from achieving its true potential. 30 years of research since GP's inception has not solved this problem and even today it is among the biggest challenges faced by the GP community. In this paper we propose a partial derivative based technique for calculating impact of a sub tree on the output of a GP tree. This information is then used to define an impact aware crossover operator. This operator reduces semantic error of a GP tree by intelligently picking crossover points in the tree. Performance of the GP augmented with the new proposed crossover operator is compared with the state of the art techniques. The proposed technique is found efficient, reliable and outperforms the state of the art algorithms on all the tested problems.
Language Support for Secure Software Development with Enclaves

Confidential computing is a promising technology for securing code and data-in-use on untrusted host machines, e.g., the cloud. Many hardware vendors offer different implementations of Trusted Execution Environments (TEEs). A TEE is a hardware protected execution environment that allows performing confidential computations over sensitive data on untrusted hosts. Despite the appeal of achieving strong security guarantees against low-level attackers, two challenges hinder the adoption of TEEs. First, developing software in high-level managed languages, e.g., Java or Scala, taking advantage of existing TEEs is complex and error-prone. Second, partitioning an application into components that run inside and outside a TEE may break application-level security policies, resulting in an insecure application when facing a realistic attacker. In this work, we study both these challenges. We present JE, a programming model that seamlessly integrates a TEE, abstracting away low-level programming details such as initialization and loading of data into the TEE. JE only requires developers to add annotations to their programs to enable the execution within the TEE. Drawing on information flow control, we develop a security type system that checks confidentiality and integrity policies against realistic attackers with full control over the code running outside the TEE. We formalize the security type system for the JE core and prove it sound for a semantic characterization of security. We implement JE and the security type system, enable Java programs to run on Intel SGX with strong security guarantees. We evaluate our approach on use cases from the literature, including a battleship game, a secure event processing system, and a popular processing framework for big data, showing that we correctly handle complex cases of partitioning, information flow, declassification, and trust.
Critical literacies in a digital age: current and future issues

Resurgent social, political, cultural, and economic tensions, in part facilitated by emerging information and communication technologies, underscore the need to cultivate new forms of critical literacy in our digital age. These critical digital literacy (CDL) practices share a specific focus on navigating, interrogating, critiquing, and shaping textual meaning across digital and face-to-face contexts. In this introductory article, the guest editors overview several examples of pedagogical scholarship concerned with these practices, collectively referred to as CDL. Specifically, the guest editors frame this collection of articles in this issue around the following question: In an educational context increasingly marked by volatility and uncertainty, but also connection and creative potential, in what ways might a focus on CDL inform pedagogical theory and practice? Drawing from a variety of disciplinary perspectives and contexts, this article discusses how educators and researchers are currently addressing the nature and role of CDL in contemporary educational settings within and beyond institutions: in K-12 and higher-education classroom pedagogy, pre- and in-service teacher education, affinity spaces, the role of design and imagination, critical computation, and digital civic participation.
Markov solution processes: Modeling human problem solving with procedural knowledge space theory

Stefanutti (2019) recently developed procedures and a related theory for deriving learning spaces from problem spaces. The approach provides a deterministic model for partially ordering individuals, on the basis of their performances in problem-solving tasks. This deterministic model accounts for both the accuracy of the responses and, especially, the sequence of "moves" (observable solution process) made by the problem solver. A Markov model of the solution process of a problem-solving task is proposed, that provides a stochastic framework for the empirical test of the deterministic model and the related problem-space-derived learning space. This type of model allows for making predictions with respect to both the observable solution process, and the unobservable knowledge state on which the solution process is assumed to be based. The Tower of London test has been chosen as the problem-solving task for the empirical validation of the model. The results of a simulation study and of two different empirical studies are presented and discussed.
Deep Learning Techniques for Spanish Sign Language Interpretation

Around 5% of the world population suffers from hearing impairment. One of its main barriers is communication with others since it could lead to their social exclusion and frustration. To overcome this issue, this paper presents a system to interpret the Spanish sign language alphabet which makes the communication possible in those cases, where it is necessary to sign proper nouns such as names, streets, or trademarks. For this, firstly, we have generated an image dataset of the signed 30 letters composing the Spanish alphabet. Then, given that there are static and in-motion letters, two different kinds of neural networks have been tested and compared: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). A comparative analysis of the experimental results highlights the importance of the spatial dimension with respect to the temporal dimension in sign interpretation. So, CNNs obtain a much better accuracy, with 96.42% being the maximum value.
PD-Net: Quantitative Motor Function Evaluation for Parkinson's Disease via Automated Hand Gesture Analysis

Parkinson's Disease (PD) is a commonly diagnosed movement disorder with more than 10 million patients worldwide. Its clinical evaluation relies on a rating system called MDS-UPDRS, which includes subjective and error-prone motor examinations. This paper proposes an objective and interpretable visual system (PD-Net ) to quantitatively evaluate motor function of PD patients using video footage. The PD-Net consists of three modules: 1) a pose detector to infer 21 hand keypoints directly from RGB videos, 2) a movement analysis module to study temporal patterns of hand keypoints and discover motor symptoms, and 3) a scoring module to predict MDS-UPDRS ratings with retrieved symptoms. Trained with an in-house clinical dataset, PD-Net can effectively handle the unique challenges of PD examination videos, such as clinically-defined gestures, distinct self-occlusion/foreshortening effect and contextual background. And it detects hand keypoints of PD patients with an average accuracy of 84.1%, a 32.9% improvement over OpenPose. When compared to the ratings of experienced clinicians, PD-Net achieves an overall MDS-UPDRS rating score accuracy of 87.6% and Cohen's kappa of 0.82 on a testing dataset of 509 examination videos at a level exceeding human raters. This study demonstrates a clinically applicable automated video analysis system for PD clinical evaluation, which can facilitate early detection, routine monitoring, and treatment assessment.