Reviewerly/exHarmony huggingface dataset:
Subcortical Brain Segmentation Based on Atlas Registration and Linearized Kernel Sparse Representative Classifier

Automatic subcortical brain segmentation in a magnetic resonance image (MRI) is crucial to disease diagnosis and various clinical applications. In this paper, we proposed an efficient segmentation method based on atlas registration (AR) and linearized kernel sparse representative classifier (SRC) to segment thalamus, putamen, caudate, pallidum, hippocampus, and amygdala. Many multi-atlas-based segmentation methods (MAS) have been proposed with success. However, the optimization problems in MAS for each voxel cause heavy computational burden. Besides, brain structures in the MR image have the characteristics of small volume, large morphological difference, and blur edge, which make the precise segmentation more difficult. To address these challenges, in the first step of our proposed method, we used AR to estimate subcortical locations in MRI. We constructed a probabilistic atlas for deep structures by AR and then obtained coarse results with initial shapes of structures. Candidate boundary regions were obtained in this stage. Then, in the second step, the kernel SRC was linearized and was applied to refine results around boundaries. The labels of voxels in candidate boundary regions were predicted in this stage, and this refinement was utilized to smooth the boundary. Finally, combining initial shapes and refined boundaries, we obtained a final segmentation of the subcortical brain. The experiments were conducted on IBSR, LPBA40, and SATA MICCAI 2013 challenge datasets. The results showed that our method outperformed other methods including AR, linearized SRC, and deep learning models with mean DSC of 0.843, 0.83, and 0.827, respectively. The time-consumption was relatively lower than the comparison methods. The proposed method has great potential for other segmentation tasks.
The Witten index for 1D supersymmetric quantum walks with anisotropic coins

Chiral symmetric discrete-time quantum walks possess supersymmetry, and their associated Witten indices can be naturally defined. The Witten index is known to give a lower bound for the number of topologically protected bound states. The purpose of this paper is to give a complete classification of the Witten index for a one-dimensional split-step quantum walk. It turns out that the Witten index of this model exhibits a striking similarity to that of a Dirac particle model in supersymmetric quantum mechanics.
Enhancing Video QoE Over High-Speed Train Using Segment-Based Prefetching and Caching

The big picture of 5G will bring a range of new unique service capabilities, where ensuring Quality of Experience (QoE) continuity in challenging situations such as high mobility, e.g., on-board user equipments (UEs) in high-speed train (HST) are one of the sharp killer applications. In this paper, we propose a mobile edge computing (MEC) driven solution to improve the QoE for UEs in the HST with perceived dynamic adaptive streaming over HTTP video demands. Considering the challenging wireless communication conditioning (e.g., path loss and Doppler Effect due to high mobility) between HST and base station along the railway for enabling progress and seamless video consuming, the case study shows the benefit of MEC functions mainly from content prefetching and complementarily from content caching, over benchmark solution where UEs solely download video segments through challenging wireless channel.
Adaptive Recommender System for an Intelligent Classroom Teaching Model

The development of information technology has facilitated the use of the intelligent classroom model supported by information technology to improve the college students’ comprehensive quality and ability. However, the existing models are too sophisticated to be applied to the actual teaching process, and ignore the individualized teaching characteristics of students. Therefore, an intelligent classroom model with adaptive learning resource recommendation was proposed. First, the entire teaching process was divided into three stages which were used to combine teachers’ teaching and students’ learning. Then the key problems of the learning resources recommendation system was studied and a learning resource recommendation based on TR-LDA (Teaching Resources-Latent Dirichlet Allocation) was proposed and how to be achieved. Finally, the proposed intelligent classroom model was verified in practical teaching. Results show that the intelligent classroom model with adaptive learning resources recommendation can help to improve students’ learning efficiency. The relevant conclusions can be used as a reference for exploring the use of information technology to improve the quality of undergraduate professional course teaching.
Beyond context: Exploring semantic similarity for small object detection in crowded scenes

Small object detection in crowded scene aims to find those tiny targets with very limited resolution from crowded scenes. Due to very little information available on tiny objects, it is often not suitable to detect them merely based on the information presented inside their bounding boxes, resulting low accuracy. In this paper, we propose to exploit the semantic similarity among all predicted objects’ candidates to boost the performance of detectors when handling tiny objects. For this purpose, we construct a pairwise constraint to depict such semantic similarity and propose a new framework based on Discriminative Learning and Graph-Cut techniques. Experiments conducted on three widely used benchmark datasets demonstrate the improvement over the state-of-the-art approaches gained by applying this idea.
Internet of Entities (IoE): A Blockchain-based Distributed Paradigm for Data Exchange between Wireless-based Devices

The exponential growth of wireless-based solutions, such as those related to the mobile smart devices (e.g., smart-phones and tablets) and Internet of Things (IoT) devices, has lead to countless advantages in every area of our society.Such a scenario has transformed the world a few decades back, dominated by latency, into a new world based on an efficient real-time interaction paradigm.Recently, cryptocurrency has contributed to this technological revolution, whose fulcrum is a decentralization model and a certification function offered by the so-called blockchain infrastructure, which makes it possible to certify the financial transactions, anonymously.This paper aims to indicate a possible approach able to exploit this challenging scenario synergistically by introducing a novel blockchain-based distributed paradigm for data exchange between wireless-based devices defined Internet of Entities (IoE).It is based on two core elements with interchangeable roles, entities and trackers, which can be implemented by using existing infrastructures and devices, such as those related to smart-phones, tablets, and IoT systems.The employment of the blockchain-based distributed paradigm allows our approach ensuring the anonymization and immutability of the involved data, which is key in many scenarios and domains (e.g.financial applications, health and legal applications dealing with personal and sensitive data), requirements more and more searched in recent innovations.The possibility to exchange data among a huge number of devices gives rise to a novel and widely exploitable data environment, whose applications are possible in different domains, such as, in Security, eHealth, and Smart Cities.
Toward Keyword Extraction in Constrained Information Retrieval in Vehicle Social Network

With the emergence of various vehicular apps for automotive navigation, driving safety, and in-car entertainment system, vehicular social data is starting to explode. Such an explosion has posed a great challenge for a centralized pattern of computing, storing, and managing information, calling for a more decentralized (cloud-based) style of processing data for vehicular social network. Decentralized information processing requires encryption of sensitive information prior to out-sourcing for the purpose of protecting data from unsolicited access and thus a searchable keyword index needs to be extracted from the encrypted document for solicited access. However, most existing searchable encryption schemes, based on the words in the subject or content, suffers from poor retrieval performance. In this study, a novel keyword extraction metric based on spatial distribution of a particular text is proposed with the view to improving the retrieval performance. In the proposed solution, constrained information retrieval in vehicle social network is conceptualized as retrieval of data with extremely strict security and privacy preserving policies and a cloud-based vehicle social information retrieval framework is developed. In line with the framework, experiments have been conducted on benchmark data sets and diverse evaluation metrics. The results suggest that the solution proposed in this paper is capable of a wider application and a better performance.
EVMFuzz: Differential fuzz testing of Ethereum virtual machine

Abstract The vulnerabilities in Ethereum virtual machine (EVM) may lead to serious problems for the Ethereum ecosystem. With lots of techniques being developed for the validation of smart contracts, the testing of EVM has not been well‐studied. In this paper, we propose EVMFuzz, the first that uses the differential fuzzing technique to detect vulnerabilities in EVM. The core idea of EVMFuzz is to continuously generate seed contracts for different EVMs' execution, so as to find as many inconsistencies among execution results as possible, and eventually discover vulnerabilities with output cross‐referencing. First, we present the evaluation metric for the internal inconsistency indicator. Then, we construct seed contracts via predefined mutators and employ a dynamic priority scheduling algorithm to guide seed contract selection and maximize the inconsistency. Finally, we leverage different EVMs as cross‐referencing oracles avoiding manual checking. For evaluation, we selected four widely used EVMs for the test, conducted large‐scale mutation on 36,295 real‐world smart contracts, and generated 253,153 smart contracts as initial seeds. Accompanied by manual root cause analysis, we found five previously unknown security bugs and all had been included in the common vulnerabilities and exposures (CVE) database.
Measuring Software Modularity Based on Software Networks

Modularity has been regarded as one of the most important properties of a successful software design. It has significant impact on many external quality attributes such as reusability, maintainability, and understandability. Thus, proposing metrics to measure the software modularity can be very useful. Although several metrics have been proposed to characterize some modularity-related attributes, they fail to characterize software modularity as a whole. A complex network uses network models to abstract the internal structure of complex systems, providing a general way to analyze complex systems as a whole. In this paper, we introduce the complex network theory into software engineering and employ modularity, a metric widely used in the field of community detection in complex network research, to measure software modularity as a whole. First, a specific piece of software is represented by a software network, feature coupling network (FCN), where methods and attributes are nodes, couplings between methods and attributes are edges, and the weight on the edges denotes the coupling strength. Then, modularity is applied to the FCN to measure software modularity. We apply the Weyuker’s criteria which is widely used in the field of software metrics, to validate the modularity as a software metric theoretically, and also perform an empirical evaluation using open-source Java software systems to show its effectiveness as a software metric to measure software modularity.
A Low Cost and Computationally Efficient Approach for Occlusion Handling in Video Surveillance Systems

In the development of intelligent video surveillance systems for tracking a vehicle, occlusions are one of the major challenges.It becomes difficult to retain features during occlusion especially in case of complete occlusion.In this paper, a target vehicle tracking algorithm for Smart Video Surveillance (SVS) is proposed to track an unidentified target vehicle even in case of occlusions.This paper proposes a computationally efficient approach for handling occlusions named as Kalman Filter Assisted Occlusion Handling (KFAOH) technique.The algorithm works through two periods namely tracking period when no occlusion is seen and detection period when occlusion occurs, thus depicting its hybrid nature.Kanade-Lucas-Tomasi (KLT) feature tracker governs the operation of algorithm during the tracking period, whereas, a Cascaded Object Detector (COD) of weak classifiers, specially trained on a large database of cars governs the operation during detection period or occlusion with the assistance of Kalman Filter (KF).The algorithm's tracking efficiency has been tested on six different tracking scenarios with increasing complexity in real-time.Performance evaluation under different noise variances and illumination levels shows that the tracking algorithm has good robustness against high noise and low illumination.All tests have been conducted on the MATLAB platform.The validity and practicality of the algorithm are also verified by success plots and precision plots for the test cases.
Springs discharge variations induced by strong earthquakes: the Mw 6.5 Norcia event (Italy, October 30th 2016)

Variations of springs discharge due to seismic events are rather common. On 2016 October 30th a Mw 6.5 earthquake occurred 5 km NNE of Norcia Town (Central Italy), at a depth of around 9 km b.g.l. The Torbidone spring, dry since 1979, was re-activated after the Norcia earthquake, and its discharge rose up to 1.6 m3/s in thefollowing weeks. The Torbidone, with other minor springs, feeds the baseflow of the Sordo River (Nera River basin), the discharge of which increased up to 2m3/s after the earthquake. The seismic events did not affect only the Torbidone spring but also the entire groundwater circulation of the Nera River hydrogeological basin.We analyzed the Torbidone spring area and its geological framework using hydrogeological, structural and geochemical methods, to evaluate the causes of the perturbations and the possible system evolution.The main factors thought to be responsible for the perturbations are the changes of permeability and the variations of hydraulic head. The mechanisms of these hydrogeological changes are currently being studied in order to define a conceptual model consistent with the observed response of Torbidone spring to the seismic event.
Intelligent Fitness Trainer System Based on Human Pose Estimation

With the popularization of health concept, the demand of fitness trainer system has increased. However, the existent trainer systems only provide motion demonstration but lack users’ motion feedback. This paper designs and implements intelligent fitness trainer system based on human pose estimation, which not only shows fitness training courses but also provides motion correction. The system obtains users’ motion data by optical camera, and then applies human pose estimation, finally providing motion correction advice. In this paper, we present the system design on hardware and software, and introduce the applied human pose estimation algorithm in detail. The field trail results show that the system exerts a good influence on fitness training.
New Methods for Collaborative Experiential Learning to Provide Personalised Formative Assessment

Abstract—¬Supporting diverse and rapidly changing learning styles of new digital age generations is one of the major hurdles to higher education in the age of massification of education markets. Higher education institutions must now utilize unprecedented network speed and mobile technology to create stimulating learning environments for new digital age generations. This paper presents a new learning and teaching model that combines dynamic learning space (DLS) and mobile collaborative experimental learning (MCEL) for supporting diverse learning styles of students. DLS assists students with state-of-art modern wireless network technologies in order to support fast-paced, multi-tasking, data and content intensive collaborative learning in class. The model further extends student learning activities beyond classroom by allowing students to continue their learning anywhere and anytime conveniently using their mobile devices. MCEL provides automated continuous personalized formative-feedback 24/7. The main objectives of the model are to improve student engagement and to provide ownership of their learning journey, experiential learning, contextualized learning, and formative assessment at low cost. The model employs three factors that influence collaborative experiential learning and formative assessment. The three factors are: (1) the use of learning space within the classroom, (2) wireless learning technology, and (3) mobile learning system (m-Learning). Pilot studies of the model are conducted and evaluated on two groups of postgraduate students. Their participation is observed, and a survey is conducted. The results show that (1) DLS encourages high-level learning and diverse learning styles to move away from passive low-level knowledge intensive learning activities; (2) MCEL supports Bigg's constructive alignment in curriculum design, contextualized experimental learning, and personalized formative learning.
Breast Cancer Image Classification on WSI with Spatial Correlations

As common cancer, breast cancer kills thousands of women every year. It’s significant to provide doctors computer-aided diagnosis (CAD) to ease their workload as well as improve detection quality. Patch-level CNNs are usually used to classify the breast tissue slice, and the CNNs classify each patch independently ignoring the spatial correlations, resulting in wrong isolated label map. However, the probability distribution of cancer type is related to their adjacent patches. In this paper, we propose a framework integrating CNN and filter algorithm aimed at extracting spatial information and improving the performance of the classification. The network was trained on a breast cancer dataset provided by ICIAR18. For 4-class classification, compared to CNN methods without using spatial correlations, the proposed method achieved about 10% improvement on accuracy over the validation dataset and get smoother probability maps. Our experiments also show that larger kernel size gets better performance. The code is available at https://github.com/dong100136/Breast-Cancer-Image-Classification-On-WSI-With-Spatial-Correlations.
A fine-grained Chinese word segmentation and part-of-speech tagging corpus for clinical text

Chinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental tasks of Chinese text processing. They are usually preliminary steps for lots of Chinese natural language processing (NLP) tasks. There have been a large number of studies on CWS and POS tagging in various domains, however, few studies have been proposed for CWS and POS tagging in the clinical domain as it is not easy to determine granularity of words. In this paper, we investigated CWS and POS tagging for Chinese clinical text at a fine-granularity level, and manually annotated a corpus. On the corpus, we compared two state-of-the-art methods, i.e., conditional random fields (CRF) and bidirectional long short-term memory (BiLSTM) with a CRF layer. In order to validate the plausibility of the fine-grained annotation, we further investigated the effect of CWS and POS tagging on Chinese clinical named entity recognition (NER) on another independent corpus. When only CWS was considered, CRF achieved higher precision, recall and F-measure than BiLSTM-CRF. When both CWS and POS tagging were considered, CRF also gained an advantage over BiLSTM. CRF outperformed BiLSTM-CRF by 0.14% in F-measure on CWS and by 0.34% in F-measure on POS tagging. The CWS information brought a greatest improvement of 0.34% in F-measure, while the CWS&POS information brought a greatest improvement of 0.74% in F-measure. Our proposed fine-grained CWS and POS tagging corpus is reliable and meaningful as the output of the CWS and POS tagging systems developed on this corpus improved the performance of a Chinese clinical NER system on another independent corpus.
Improvement of Chatbots Semantics Using Wit.ai and Word Sequence Kernel: Education Chatbot as a Case Study

Designing interactivequestion-response systems has become an important challenge in Artificial Intelligence which aims to build a computer program, referred to as Chatbot, able to manage an online humancomputer conversation with natural language.The number of chatbots continues to increase these recent years using different languages, tools and platforms and has been used in several domains, such as marketing, education and medicine.One of the most important issues in designing chatbots is its degree of interoperability with human natural language.In this context, we propose in this work a new messenger chatbot design approach based on Wit.ai and Word Sequence kernel in order to improve semantics.Wit.ai is used to detect contexts and concepts while the Word Sequence Kernel is used as a similarity measure between textual conversations taking into account the order of appearance of words in the conversation.A testing educate chatbot has been build which aims to provide FAQBot system for university students and acts as undergraduate advisor in student information desk.The performance of the proposed chatbot was compared to conventional messenger chatbots and showed better results.
Interactive Virtual Reality Reduces Quadriceps Pain during High-Intensity Cycling

ABSTRACT Purpose Brief, high-intensity cycling is popular because physiological benefits accrue with a short workout time, but burning pain in the quadriceps is a potential barrier to engaging in this type of exercise. Virtual reality (VR) can temporarily decrease pain, but its effect on muscle pain during high-intensity exercise is unknown. The primary purpose of this experiment was to test whether adding interactive VR (I-VR) to high-intensity cycling could reduce quadriceps pain or improve performance. Methods Ninety-four adults who were physically active in their leisure time and age 18 to 29 yr completed three 30-s sprint interval cycling trials at a high resistance (0.085- and 0.075-kg resistance to the flywheel per kilogram body weight for men and women, respectively). In this randomized between-subject experiment, participants cycled while wearing a head-mounted display and viewing either (i) a dynamically changing cityscape perceived as interactively cycling through a virtual city (I-VR group) or (ii) a static picture of the cityscape with instructions to mentally imagine cycling through that city (static VR/motor imagery control group). Results Sphericity-adjusted 2 × 3 (group–time) ANOVA revealed a significant group–time interaction ( F = 4.568; df = 1.499, 133.301; η p 2 = 0.047, P = 0.021) for pain intensity. With I-VR, pain intensities were 13.3% (mean, 4.60 vs 5.31; d = 0.28) and 11.8% (mean, 5.68 vs 6.44; d = 0.27) lower at sprint trials 2 and 3, respectively. The group–time interaction ( P = 0.412) was not significant for total work. Conclusion Compared with a static VR/motor imagery control condition, I-VR during brief, high-intensity, fatigue-inducing leg cycling attenuates quadriceps pain intensity without reducing performance.