Reviewerly/exHarmony huggingface dataset:
A cross-cutting approach for tracking architectural distortion locii on digital breast tomosynthesis slices

Background and objective: Full-field digital mammography (FFDM) is the predominant breast cancer screening exam used. However, with the emergence of digital breast tomosynthesis (DBT) the radiologists could improve early recognition of breast cancer signs. In this scenario, the detection of architectural distortion (AD) is still a challenging task. ADs are very subtle contraction of the breast parenchyma that could represent the earliest manifestation of cancer, assessing at present 50% of missed cases. Methods: This paper proposes a new paradigm to detect AD in DBT exams by a cross-cutting approach exploiting the 3-dimensionality of the imaging modality. After locating AD candidates in each DBT slice, the suspicious spots are tracked in cross-slice direction and then characterized in terms of neighboring texture. In this approach, which mimics radiologist's scrolling down over zoomed slices, we reduce the amount of uninformative signs collected in DBT exams by preserving the large variability of AD appearance. Results: Using 37 sets of DBT slices containing at least one AD locus indicated by a radiologist, the proposed methodology reaches an AUC of 0.84, with only one false negative exam at sensitivity of 0.9. Conclusions: The results show that the proposed algorithm can be a promising tool for the automatic detection of AD locii. Future work will address the extension of the dataset of DBT slices as well the improvement of algorithm performance toward the application in the clinical practice.
A Latent-Dirichlet-Allocation Based Extension for Domain Ontology of Enterprise’s Technological Innovation

This paper proposed a method for building enterprise's technological innovation domain ontology automatically from plain text corpus based on Latent Dirichlet Allocation (LDA). The proposed method consisted of four modules: 1) introducing the seed ontology for domain of enterprise's technological innovation, 2) using Natural Language Processing (NLP) technique to preprocess the collected textual data, 3) mining domain specific terms from document collections based on LDA, 4) obtaining the relationship between the terms through the defined relevant rules. The experiments have been carried out to demonstrate the effectiveness of this method and the results indicated that many terms in domain of enterprise's technological innovation and the semantic relations between terms are discovered. The proposed method is a process of continuously cycles and iterations, that is the obtained objective ontology can be re-iterated as initial seed ontology. The constant knowledge acquisition in the domain of enterprise's technological innovation to update and perfect the initial seed ontology.
Multiple-correlation similarity for block-matching based fast CT to ultrasound registration in liver interventions

In this work we present a fast approach to perform registration of computed tomography to ultrasound volumes for image guided intervention applications. The method is based on a combination of block-matching and outlier rejection. The block-matching uses a correlation based multimodal similarity metric, where the intensity and the gradient of the computed tomography images along with the ultrasound volumes are the input images to find correspondences between blocks in the computed tomography and the ultrasound volumes. A variance and octree based feature point-set selection method is used for selecting distinct and evenly spread point locations for block-matching. Geometric consistency and smoothness criteria are imposed in an outlier rejection step to refine the block-matching results. The block-matching results after outlier rejection are used to determine the affine transformation between the computed tomography and the ultrasound volumes. Various experiments are carried out to assess the optimal performance and the influence of parameters on accuracy and computational time of the registration. A leave-one-patient-out cross-validation registration error of 3.6 mm is achieved over 29 datasets, acquired from 17 patients.
An Inquiry into the Underlying Reasons for the Impact of Technology Enhanced Problem-Based Learning Activities on Students’ Attitudes and Achievement

Purpose: In the constantly changing and evolving world of today, there is a need to raise individuals who are able to renew themselves, learn autonomously, and have higher order thinking skills such as critical thinking and deducing.The purpose of the present study is to investigate the effects of Technology Enhanced Problem-Based Learning Activities (TEPLA), developed with regard to the aforementioned need, on learners' attitudes towards mathematics and academic achievement in mathematics through qualitative data in order to designate the underlying causes of these effects.Method: In the research, conducted as a case study, the analysis of the written and oral interviews were held
No free lunch in linguistics or machine learning: Response to Pater

Pater’s (2019) target article proposes that neural networks will provide theories of learning that generative grammar lacks. We argue that his enthusiasm is premature since the biases of neural networks are largely unknown, and he disregards decades of work on machine learning and learnability. Learning biases form a two-way street: all learners have biases, and those biases constrain the space of learnable grammars in mathematically measurable ways. Analytical methods from the related fields of computational learning theory and grammatical inference allow one to study language learning, neural networks, and linguistics at an appropriate level of abstraction. The only way to satisfy our hunger and to make progress on the science of language learning is to confront these core issues directly.
Chat Analysis Triage Tool: Differentiating contact-driven vs. fantasy-driven child sex offenders

Investigating crimes against children, specifically sexual solicitations, are complicated because not all offenders are contact-driven, meaning they want to meet the minor for sex in the physical world; instead, some offenders are fantasy-driven, in that they are more interested in cybersex and role-play. In addition, the sheer volume of cases involving the online sexual solicitation of minors makes it difficult for law enforcement to determine whether an offender is contact-driven vs. fantasy-driven. However, research shows that there are language-based differences between minors and contact-driven offenders vs. fantasy driven-offenders. Thus, we developed the Chat Analysis Triage Tool (CATT), a forensically sound investigative tool that, based on natural language processing methods, analyzes and compares chats between minors and contact-driven vs. non-contract driven offenders. Using an SVM classifier, we were successful in differentiating the classes based on character trigrams. In a matter of seconds, the existing algorithms provide an identification of an offender’s risk level based on the likelihood of contact offending as inferred from the model, which assists law enforcement in their ability to triage and prioritize cases involving the sexual solicitation of minors.
Statistics and Samples in Distributional Reinforcement Learning.

We present a unifying framework for designing and analysing distributional reinforcement learning (DRL) algorithms in terms of recursively estimating statistics of the return distribution. Our key insight is that DRL algorithms can be decomposed as the combination of some statistical estimator and a method for imputing a return distribution consistent with that set of statistics. With this new understanding, we are able to provide improved analyses of existing DRL algorithms as well as construct a new algorithm (EDRL) based upon estimation of the expectiles of the return distribution. We compare EDRL with existing methods on a variety of MDPs to illustrate concrete aspects of our analysis, and develop a deep RL variant of the algorithm, ER-DQN, which we evaluate on the Atari-57 suite of games.
Matching Tumour Candidate Points in Multiple Mammographic Views for Breast Cancer Detection

Matching candidate points from multiple mammographic views corresponding to the same patient may lead to an improvement in the accuracy of Computer Aided Diagnosis systems and it can help the radiologists to detect breast cancer in early stages, leading to a reduction of the percentage of mortality. In this paper, we propose a matching approach in order to detect correspondences between some candidate points from multiple mammographic views. Initially, a Scale Invariant Feature Transform detector is used to determine some candidate points in the mammographic views, then a combination between texture features is proposed to check the abnormality of the local region that surrounds each candidate point. The candidate points can be matched by integrating the information given by the texture analysis, the distance from the nipple and the location of the candidate points relative to the nipple. Some experiments are presented to show the effectiveness of the proposed approach.
Remote and Autonomous Controlled Robotic Car based on Arduino with Real Time Obstacle Detection and Avoidance

In robotic car, real time obstacle detection and obstacle avoidance are significant issues.In this study, design and implementation of a robotic car have been presented with regards to hardware, software and communication environments with real time obstacle detection and obstacle avoidance.Arduino platform, android application and Bluetooth technology have been used to implementation of the system.In this paper, robotic car design and application with using sensor programming on a platform has been presented.This robotic device has been developed with the interaction of Android-based device.Arduino Uno has been used as the robot's brain.The robot has many hardware components such as Bluetooth module, PIR sensor, ultrasonic sensor, and buzzers.It also consists of the software component that uses a mobile application.The desired direction or mode by mobile application can be selected by the user of the robotic car to control the movement of the car.The user can control the robot movements from his/her own intelligent device or take the robot in automatic mode and let the car drive its own way.Thus, the robot can flee from the obstacle and also detect live objects.The purpose of this article is to alert the civilian and military personnel to potential terrorist attacks especially in military areas with live detectable sensors.
Reading Circular Analogue Gauges using Digital Image Processing

This paper presents an image processing based pipeline for automated recognition and translation of pointer movement in analogue circular gauges.The proposed method processes an input video frame-wise in a module based manner.Noise is minimized in each image using a bilateral filter before a Gaussian mean adaptive threshold is applied to segment objects.Subsequently, the objects are described by a set of proposed features and classified using probability distributions estimated using Expectation Maximization.The pointer is classified by the Mahalanobis distance and the angle of the pointer is determined using PCA.The output is a low pass filtered digital time series based on the temporal estimations of the pointer angle.Seven test videos have been processed by the algorithm showing promising results.Both source code and video data are publicly available.
A Fast Sampling Algorithm for Maximum Inner Product Search

Maximum Inner Product Search (MIPS) has been recognized as an important operation for the inference phase of many machine learning algorithms, including matrix factorization, multi-class/multi-label prediction and neural networks. In this paper, we propose Sampling-MIPS, which is the first sampling based algorithm that can be applied to the MIPS problem on a set of general vectors with both positive and negative values. Our Sampling-MIPS algorithm is efficient in terms of both time and sample complexity. In particular, by designing a two-step sampling with alias table, Sampling-MIPS only requires constant time to draw a candidate. In addition, we show that the probability of candidate generation in our algorithm is consistent with the true ranking induced by the value of the corresponding inner products, and derive the sample complexity of Sampling-MIPS to obtain the true candidate. Furthermore, the algorithm can be easily extended to large problems with sparse candidate vectors. Experimental results on real and synthetic datasets show that Sampling-MIPS is consistently better than other previous approaches such as LSH-MIPS, PCA-MIPS and Diamond sampling approach.
DIGITAL LITERACY: A MUST FOR OPEN DISTANCE AND E-LEARNING (ODEL) STUDENTS

Digital literacy is generally defined as the ability to utilise information and communication technologies in learning. Open distance and e-learning students have their learning separated by space and time. The utilisation of information and communication technologies comes in handy to enhance learning for open and distance learners. However, open and distance learners should exhibit high levels of digital literacy in order to make use of available information and communication technologies in learning. This paper explores the concept digital literacy and related theories informing it. The paper further discusses the importance of digital literacy for the enhancement of learning for open and distance learners in as well as interrogating the unique nature of open and distance learners and information access. Opportunities and challenges in the utilisation on ICTs by open and distance learners in the developing world shall also be explored. Article visualizations:
A Dataset of Vietnamese Junior High School Students’ Reading Preferences and Habits

Books are the invaluable, colossal storage of mankind’s immense scholarship and are still commonly perceived as a more reliable source of knowledge even in this age of digitized information. Extensive reading is often promoted as being vital to cognitive development, especially for students in primary and secondary education. While it could now be considered common knowledge that reading is highly beneficial, reading habits vary among individuals in the same culture and receiving the same public education. This could be due to demographic variations and differences in socioeconomic status, or other factors such as family background and education. Despite the ample literature on reading habits, there still exists a lack of holistic approach with empirical results concerning the reciprocal interactions of reading and its relevant affecting factors. This data article presents a dataset of 1676 responses to the survey “Studying reading habits and preferences” of junior high school students in Vietnam. Result analysis facilitates evaluation of reading habits and their affecting factors, thus holding implications on education measures and policy. The dataset is available with the paper.
Jaccard Coefficient-Based Bi-clustering and Fusion Recommender System for Solving Data Sparsity

Recommender systems have been very common and useful nowadays, which recommend suitable items to users by predicting ratings for items. The most used collaborative filtering recommender system suffers from the sparsity issue due to insufficient data. To cope with this issue, we propose a Jaccard Coefficient-based Bi-clustering and Fusion (JC-BiFu) method for Recommender system. JC-BiFu uses density peak clustering for both users and items, and then makes estimations for missing values in the user-item rating matrix when finding the similar users. Finally, we utilize both users and items to generate the final predictions. Experimental analysis shows that our approach can improve the performance of user recommendations at the extreme levels of sparsity in user-item rating matrix.
A fuzzy data envelopment analysis based on credibility theory for estimating road safety

Road accidents as a global challenge, imposing irreparable financial and human life losses in almost all countries, especially in developing countries, annually.According to world health organization (WHO), if this trend continues, road accidents will become the 7th cause of human death by 2030.Thus, road safety policy makers have been trying to use safety promotion and preventative actions.In this paper, the road safety performance of Iranian provinces is studied.To evaluate road safety efficiency scores, data envelopment analysis based on road safety (DEA-RS) method in two deterministic and non-deterministic situations is used.To consider the uncertainty in input and output data, this paper develops credibility DEA-RS (CreDEA-RS) model.In fact, the constraints of DEA-RS model are considered as credibility constraints and a counterpart credibility DEA-RS (CreDEA-RS) model is proposed for evaluating road safety of provinces of Iran.According to the results, provinces located in mountainous and forest areas such as Gilan had a much weaker performance than provinces in desert areas such as Yazd..
A clustering based variable sub-window approach using particle swarm optimisation for biomedical sensor data monitoring

Advances in information technologies enable data to be ubiquitously generated from sensors, especially in the industrial healthcare research and application fields. The aim is to develop an adaptive windowing pre-processing approach using clustering-based metaheuristics search for biomedical data stream classification, which uses a sliding window to scan the multivariate data stream segment to segment. Our new model is put under test with other temporal data stream pre-processing methods on those biomedical sensor datasets. The experiments give higher accuracy and less time cost especially in dynamically adjusting the window size according to clustering outcomes that are optimised by metaheuristics.
Making long-wave infrared face recognition robust against image quality degradations

Face identification systems that operate on long wave infrared (LWIR) images are able to overcome some of the limitations of approaches based on visible images, such as dealing effectively with illumination variations. Nonetheless, distortions of perceptual image quality can impair the performance of thermal face recognition systems. Although the interaction between perceptual image quality and tasks such as face detection has been studied on visual images, the development of similar models has not been applied to the LWIR-based face recognition problem. Here, we analyze the impact of four common infrared image distortions (gaussian noise, blur, non-uniformity, and JPEG compression) on two thermal face recognition systems. We propose an LWIR image face recognition framework, based on thermal signature templates, and enhanced by natural scene statistics image quality descriptors, which achieves system robustness against image quality distortions. Furthermore, we develop a novel infrared face recognition system that is based on the complex wavelet structural similarity (CW-SSIM) index, which exhibits resistance to image distortions, within a relatively simple implementation. Our results validate the applicability of image quality assessment models to biometric tasks on LWIR images. To facilitate our study, we created two new LWIR facial image databases, with different poses, expressions and illumination conditions.
Systematic Literature Review (SLR) of Resource Scheduling and Security in Cloud Computing

Resource scheduling in cloud computing is a complex task due to the number and variety of resources available and the volatility of usage-patterns of resources considering that the resource setting is on the service provider.This compounded further when security issues are also factored in.This paper provide a Systematic Literature Review (SLR) that will help to identify as much prior relevant research that has been done in the area of research topic.Also, all papers that are found from the search will be classified into groups to stand on the current situation and to identify possible existing gaps.
Online ADMM-Based Extreme Learning Machine for Sparse Supervised Learning

Sparse learning is an efficient technique for feature selection and avoiding overfitting in machine learning research areas. Considering sparse learning for real-world problems with online learning demands in neural networks, an online sparse supervised learning of extreme learning machine (ELM) algorithm is proposed based on alternative direction method of multipliers (ADMM), termed OAL1-ELM. In OAL1-ELM, an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularization penalty is added in loss function for generating a sparse solution to enhance the generalization ability. This convex combinatorial loss function is solved by using ADMM in a distributed way. Furthermore, an improved ADMM is used to reduce computational complexity and to achieve online learning. The proposed algorithm can learn data one-by-one or batch-by-batch. The convergence analysis for the fixed point of the solution is given to show the efficiency and optimality of the proposed method. The experimental results show that the proposed method can obtain a sparse solution and have strong generalization performance in a wide range of regression tasks, multiclass classification tasks, and a real-world industrial project.
PRAC: private and rateless adaptive coded computation at the edge

Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the tactical edge in applications such as Internet of Battlefield Things (IoBT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm by taking into account (i) the privacy requirements of IoBT applications and devices, and (ii) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations.
Video Super Resolution via Deep Global-Aware Network

Video super-resolution aims to increase the resolution of videos by exploiting the intra-frame and inter-frame dependencies of the low-resolution video sequences.There are usually two dependent steps in the video super-resolution: the motion compensation and the super-resolution reconstruction.In this paper, we propose a new deep learning framework without the explicit motion estimation by utilizing the self-attention model to exploit the full receptive field of the input video frames.In other words, the proposed deep neural network extracts the local features at all spatial-temporal locations for combining into global features using the self-attention networks in order to reconstruct the high-resolution video frame.The proposed global-aware network outperforms the state-of-the-art deep learning-based image and video super-resolution algorithms in terms of subjective and objective quality with less computational operations, as verified by extensive experiments on public image and video datasets, including Set5, Set14, B100, Urban100, and Vid4.