Reviewerly/exHarmony huggingface dataset:
Difference Between Young and Old Adults’ Performance on the Psychology Experiment Building Language (PEBL) Test Battery: What Is the Role of Familiarity With Technology in Cognitive Performance?

Objective: The Psychology Experiment Building Language (PEBL) Test Battery is a recent computerized software for the assessment of cognitive functioning in clinical and healthy populations. However, its applicability for the assessment of old adults and the role of level of familiarity with technology on performance have not been tested. Method: To verify if PEBL Test Battery is sensitive to aging cognitive decline, we assessed young and older adults’ performance on the neuropsychological tests relative to the following domains: verbal and visuospatial short-term memory, selective attention, inhibition to interference, planning and problem-solving, set-shifting, and cognitive flexibility. The difference between the two groups was studied taking into account the self-reported level of familiarity with technology, measured through the Attitudes Toward Computers Questionnaire. Results: Old participants showed a lower level of cognitive performance compared with the younger group, in line with the traditional literature about cognitive function decline in aging. However, this difference was not related to the level of familiarity with technology. Conclusion: Our descriptive study seemed to support the applicability of PEBL Test Battery for testing old adults’ cognitive functioning. However, some criticisms and future improvements about this computerized battery were discussed for its applicability in clinical, rehabilitative and research contexts.
PRISE: A process to support iStar extensions

iStar is a goal-based requirement modelling language, being used both in industrial and academic projects of different domains. Often the language is extended to incorporate new constructs related to an application domain or to adjust it to practical situations during requirements modelling. These iStar extensions have been proposed in an ad hoc way resulting in many problems of incompleteness, inconsistency and conflicts. Recently, the language was standardised, but it continues being extended. Thus, we consider that this is an adequate moment to study how to support the proposals of the next iStar extensions. In this paper, we define PRISE, a process to support the creation of iStar extensions which is driven by model-based development concepts, reuse of existing iStar extensions and guidelines of experts. This process can be customised. We illustrate the usage of PRISE by recreating five existing iStar extensions. Finally, we evaluated PRISE with interviews and a survey with experts; and, we performed an interview to analyse the opinion about the usage of the PRISE to create a new iStar extension by a novice. The evaluation and validation indicate good results to avoid problems and increase the quality of the proposals and well receptivity by the experts and novice.
A Novel Data Collection Framework for Telemetry and Anomaly Detection in Industrial IoT Systems

The advent of IoTs has catalyzed the development of a variety of cyber-physical systems in which hundreds of sensor-actuator enabled devices (including industrial IoTs) cooperatively interact with the physical and human worlds. However, due to the large volume and heterogeneity of data generated by such systems and the stringent time requirements of industrial applications, the design of efficient frameworks to store, monitor and analyze the IoT data is quite challenging. This paper proposes an industrial IoT architectural framework that allows data offloading between the cloud and the edge. Specifically, we use this framework for telemetry of a set of heterogeneous sensors attached to a scale replica of an industrial assembly plant. We also design an anomaly detection algorithm that exploits deep learning techniques to assess the working conditions of the plant. Experimental results show that the proposed anomaly detector is able to detect 99% of the anomalies occurred in the industrial system demonstrating the feasibility of our approach.
Use of Mobile Technologies in Personal Learning Environments of Intercultural Contexts: Individual and Group Tasks

This paper presents the results of the analysis of the personal learning environments (PLE) used individually and in groups by fifth grade primary education students. The main objective was to determine if the use of mobile technologies in the students’ PLEs encouraged their school integration and learning in intercultural communities. For this, a content analysis of the students’ responses to an ad hoc interview was carried out, with a content validity index of 0.89. The students represented their answers using 41 concept maps in the individual tasks and 5 in the group tasks, which were analyzed with the Nvivo software in its latest version. The results show the categorization of the students’ responses in three dimensions: read, make/reflection and relationship. Among the main conclusions, it was obtained that, in both types of tasks, the strategies and tools that fostered intercultural relationships, intercultural education and communication between the students, and therefore school integration, are mostly linked to the use of mobile technologies applications, such as Wikipedia, the internet, Word, PowerPoint, social networks and YouTube, although it is essential to develop more studies to have more data to understand the phenomenon in depth.
Deep-Learning based Global and Semantic Feature Fusion for Indoor Scene Classification

This paper focuses on the task of RGB indoor scene classification. A single scene may contain various configurations and points of view, but there are a small number of objects that can characterize the scene. In this paper we propose a deep-learning based Global and Semantic Feature Fusion Approach (GSF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> App) with two branches. In the first branch (top branch), a CNN model is trained to extract global features from RGB images, taking leverage from the ImageNet pre-trained model to initialize our CNN's weights. In the second branch (bottom branch), we develop a semantic feature vector that represents the objects in the image, which are detected and classified through the COCO dataset pre-trained YOLOv3 model. Then, both global and semantic features are combined in an intermediate feature fusion stage. The proposed approach was evaluated on the SUN RGB-D Dataset and NYU Depth Dataset V2 achieving state-of-the-art results on both datasets.
Learning to Detect Brain Lesions from Noisy Annotations

Supervised training of deep neural networks in medical imaging applications relies heavily on expert-provided annotations. These annotations, however, are often imperfect, as voxel-by-voxel labeling of structures on 3D images is difficult and laborious. In this paper, we focus on one common type of label imperfection, namely, false negatives. Focusing on brain lesion detection, we propose a method to train a convolutional neural network (CNN) to segment lesions while simultaneously improving the quality of the training labels by identifying false negatives and adding them to the training labels. To identify lesions missed by annotators in the training data, our method makes use of the 1) CNN predictions, 2) prediction uncertainty estimated during training, and 3) prior knowledge about lesion size and features. On a dataset of 165 scans of children with tuberous sclerosis complex from five centers, our method achieved better lesion detection and segmentation accuracy than the baseline CNN trained on the noisy labels, and than several alternative techniques.
Locally Recoverable Codes From Automorphism Group of Function Fields of Genus<i>g</i>≥ 1

A Locally Recoverable Code is a code such that the value of any single coordinate of a codeword can be recovered from the values of a small subset of other coordinates. When we have δ non-overlapping subsets of cardinality ri that can be used to recover the missing coordinate we say that a linear code C with length n, dimension k, minimum distance d has (r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , . . . , r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</sub> )locality and denote by [n, k, d; r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , . . . , r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</sub> ]. In this paper we provide a new upper bound for the minimum distance of these codes. Working with a finite number of subgroups of cardinality r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> + 1 of the automorphism group of a function field F|F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> of genus g ≥ 1 we propose a construction of [n, k, d; r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , . . . , r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</sub> ]-codes and apply the results to some well known families of function fields.
Provably secure identity-based two-party authenticated key agreement protocol based on CBi-ISIS and Bi-ISIS problems on lattices

The two-party authenticated key agreement (2PAKA) protocol establishes a secure channel over the Internet between two users. This secure channel helps to transfer messages between them in the presence of an adversary. The invention of Shor's algorithms makes current 2PAKA protocols vulnerable to quantum attacks. To mitigate quantum attacks, we designed an identity-based two-party authenticated key agreement (LB-ID-2PAKA) protocol on a lattice L(B) of dimension n. Besides, to avoid the public key infrastructure (PKI), which is required for the authentication of the public keys of users, we use the identity-based cryptography (IBC) in our protocol. We analyzed that our proposed LB-ID-2PAKA protocol is provably secure with the hardness assumptions of the computational bilateral inhomogeneous small integer solution (CBi-ISIS) and bilateral small integer solution (Bi-SIS) problems. We also analyzed the computation and communication overheads of our LB-ID-2PAKA protocol and compared its performance with a related protocol. The comparison results demonstrate that our LB-ID-2PAKA protocol yields better performance and can be used in post-quantum environments.
Understanding and Supporting Knowledge Decomposition for Machine Teaching

Machine teaching (MT) is an emerging field that studies non-machine learning (ML) experts incrementally building semantic ML models in efficient ways. While MT focuses on the types of knowledge a human teacher provides a machine learner, not much is known about how people perform or can be supported in this essential task of identifying and expressing useful knowledge. We refer to this process as knowledge decomposition. To address the challenges of this type of Human-AI collaboration, we seek to build foundational frameworks for understanding and supporting knowledge decomposition. We present results of a study investigating what types of knowledge people teach, what cognitive processes they use, and what challenges they encounter when teaching a learner to classify text documents. From our observations, we introduce design opportunities for new tools to support knowledge decomposition. Our findings carry implications for applying the benefits of knowledge decomposition to MT and ML.
“We are a mutual fund:” how Ponzi scheme operators in Nigeria apply indexical markers to shield deception and fraud on their websites

ABSTRACTIn this study, we examine how Ponzi schemes with international outlooks situate themselves in the Nigerian web space. We study five Ponzi schemes that operated in Nigeria between 2016 and 2019 within the frameworks of multimodal-discourse analysis and critical discourse analysis (CDA). The websites, some of which were developed for only Nigerian users adopt discourses that match the social and cultural contexts in order to appeal to the Nigerian social and cultural ideologies. Applying CDA, the study conceptualizes how indexical discourses in the web target individual participation and attempts to legitimize fraud – how Ponzi operators represent themselves and others, and how they tactfully shield their deception agenda through the use of indexical that point to cultural and social practices.
X-ray of Tire Defects Detection via Modified Faster R-CNN

With the rapid development of deep learning model in computer vision area, it has outperformed most of traditional machine learning algorithms. Since tire factories pay much attention to defects detection of tires based on x-ray image, lots of tire x-ray image based defects detection methods are brought up. However, there are still challenges in detection accuracy. This paper put forward a novel deep learning model and modified Faster R-CNN to conduct x-ray defects detection. Some proper processing is done on x-ray image before extracting the features and detecting the defects and then adjusting the feature extractor, proposal generator and box classifier of Faster R-CNN respectively. Comprehensive experiments are carried out to testify that our proposed model is capable of achieving higher detection accuracy compared with other methods.
De-identification of Clinical Text via Bi-LSTM-CRF with Neural Language Models.

De-identification of clinical text, the prerequisite of electronic clinical data reuse, is a typical named entity recogni tion (NER) problem. A number of state-of-the-art deep learning methods for NER, such as Bi-LSTM-CRF (bidirec tional long-short-term-memory conditional random fields), have been applied for de-identification. Neural language models used for language representation bring great improvement in lots of NLP tasks when they are integrated with other deep learning methods. In this paper, we introduce Bi-LSTM-CRF with neural language models for de- identification of clinical text, and evaluate it on the de-identification datasets of the i2b2 2014 and the CEGS N- GRID 2016 challenges. Four neural language models of three types individually integrated with Bi-LSTM-CRF are compared in this study. Bi-LSTM-CRF with neural language models achieves the highest "strict" micro-averaged F1-score of 95.50% on the i2b2 2014 dataset and 91.82% on the CEGS N-GRID 2016 dataset, becoming new benchmark results on these two datasets respectively Keywords: De-identification, Named entity recognition, Bidirectional long-short-term-memory, Conditional ran dom fields, Neural language models.
Response score of deep learning for out-of-distribution sample detection of medical images

Deep learning Convolutional Neural Networks have achieved remarkable performance in a variety of classification tasks. The data-driven nature of deep learning indicates that a model behaves in response to the data used to train the model, and the quality of datasets may lead to substantial influence on the model's performance, especially when dealing with complicated clinical images. In this paper, we propose a simple and novel method to investigate and quantify a deep learning model's response with respect to a given sample, allowing us to detect out-of-distribution samples based on a newly proposed metric, Response Score. The key idea is that samples belonging to different classes may have different degrees of influence on a model. We quantify the resulting consequence of a single sample to a trained-model and relate the quantitative measure of the consequence (by the Response Score) to detect the out-of-distribution samples. The proposed method can find multiple applications such as (1) recognizing abnormal samples, (2) detecting mixed-domain data, and (3) identifying mislabeled data. We present extensive experiments on the three different applications using four biomedical imaging datasets. Experimental results show that our method exhibits remarkable performance and outperforms the compared methods.
Mobile Robot Localization Using Extended Kalman Filter

Localizing the mobile robot in an indoor environment is one of the problems encountered repeatedly. Achieving the target precisely in any environment is not an easy task since there are noises and obstacles in the surrounding environment. Therefore, filtering the signals to reduce noises is essential for more accurate and precise motion. In this paper, we selected the extended Kalman filter, which is used for non-linear models' signals to predict the coordinates of a wheeled mobile robot. We tested the efficiency of this filter under three noise cases: no noise, Gaussian noise and non-Gaussian noise using MATLAB software.
Job-work fit as a determinant of the acceptance of large-scale agile methodology

The declaration of the Agile Manifesto in 2001 was hailed as a paradigmatic shift in the development of software systems. Initially, agile development was typically deployed in projects with a single team with fewer than ten members. Recently, a new kind of agile development has emerged for larger projects that may be referred to as large-scale agile methodology (LSAM). The taxonomy of LSAM is more enhanced and subsumes the Agile Manifesto. Based on the software development literature, the study investigates five important antecedents of methodology acceptance: perceived usefulness, compatibility, subjective norm, mandatoriness, and external support. As a result of the initial PLS analysis, the study introduces the notion of Job-Work Fit as a second-order construct composed of perceived usefulness and compatibility. Based on a survey of 123 respondents, the study finds that the construct job-work fit significantly explains the LSAM acceptance for large projects. Job-work fit also mediates the relationships between subjective norm and external support with LSAM acceptance. This research suggests that as the stakes become higher because of project size and agility, job-work fit emerges as the central determinant of LSAM acceptance.
On local active contour model for automatic detection of tumor in MRI and mammogram images

Tumor segmentation in medical images is an important step to determine and predict the stage, size and progression of tumors in realistic geometries and may be used for diagnosis and treatment follow-up in MRI and mammogram images. Automating this challenging task helps radiologists to reduce the high manual workload of brain or breast cancer analysis. Detecting tumor in medical images means to quantify the structure contents of the tumor. In this paper, we propose a model for quantifying the structure contents of the tumor in MRI and mammogram images. The proposed model is based on optimized Laplacian of Gaussian, which is useful in smoothing the homogeneous region and highlight the boundaries of the tumor structure. The proposed model uses local intensity information in later stage of segmentation as image data fitting. For this purpose, local Gaussian distribution is used for fitting image data and is combined with level set function. Due to usage of local intensities and level set, the proposed model is able to deal with intensity inhomogeneity and capture different topologies. To stop the leakage of contour at the boundary of tumor, we take some geometrical points near the tumor's boundary and introduce a distance constraint in model. The gradient flow equation and other optimal values are obtained through minimization of the energy functional. The gradient flow equation is solved by using additive operator splitting method. The experimental results of the proposed model are validated by comparing it with existing state of the art models both qualitatively and quantitatively. The proposed model achieves average values of 99.96%, 99.88%, 99.91%, 99.91%, 99.95% for Jaccard similarity, dice coefficient, accuracy, sensitivity and specificity respectively, which are better than the existing models in comparison.
A Multi-Task Self-Supervised Learning Framework for Scopy Images

The training of deep learning models requires large amount of training data. However, as the annotations of medical data are difficult to acquire, the quantity of annotated medical images is often not enough to well train the deep learning networks. In this paper, we propose a novel multi-task self-supervised learning framework, namely ColorMe, for the scopy images, which deeply exploits the rich information embedded in raw data and looses the demand of training data. The approach pre-trains neural networks on multiple proxy tasks, i.e., green to red/blue colorization and color distribution estimation, which are defined in terms of the prior-knowledge of scopy images. Compared to the train-from-scratch strategy, fine-tuning from these pre-trained networks leads to a better accuracy on various tasks - cervix type classification and skin lesion segmentation.
Modal analysis of machine tools using visual vibrometry and output-only methods

This paper presents visual vibrometry as a new video camera-based vibration measuring technique for machine tools. Pixels within images from recordings of the vibrating machine are treated as motion sensors to detect and track vibrating edges. Modal parameters are extracted from the measured pixel-displacement time series data using output-only mass-change methods. Methods are experimentally demonstrated on a small milling machine, on a slender boring bar, and on a regular end mill. Modal parameters extracted using visual vibrometry agree with those extracted using experimental modal analysis procedures. Moreover, visual vibrometry aids shape visualization and maintains advantages of other non-contact measurement techniques.
FastChain: Lightweight Blockchain with Sharding for Internet of Battlefield-Things in NS-3

FastChain is a simulator built in NS-3 which simulates the networked battlefield scenario with military applications, connecting tankers, soldiers and drones to form Internet-of-Battlefield-Things (IoBT). Computing, storage and communication resources in IoBT are limited during certain situations in IoBT. Under these circumstances, these resources should be carefully combined to handle the task to accomplish the mission. FastChain simulator uses Sharding approach to provide an efficient solution to combine resources of IoBT devices by identifying the correct and the best set of IoBT devices for a given scenario. Then, the set of IoBT devices for a given scenario collaborate together for sharding enabled Blockchain technology. Interested researchers, policy makers and developers can download and use the FastChain simulator to design, develop and evaluate blockchain enabled IoBT scenarios that helps make robust and trustworthy informed decisions in mission-critical IoBT environment.