nils-herrmann/clean_scirepeval huggingface dataset:
Efficient retinal vessel detection using line detectors with morphological operations

Digital fundus photography plays a major role in the diagnosis of different retinal pathologies like hypertension, diabetic retinopathy and Glaucoma. To identify abnormal components on the retina, retinal features should be detected accurately. Retinal vessel structure is one of the important landmarks of the retina. So precise detection of retinal vessel structure is imperative. This paper presents a simple, robust retinal vessel extraction approach based on the line detectors and morphological operations. As vessel detection is basically a problem of a line detection, the green channel retinal image is applied to morphological opening using a line as structuring element. The resultant image is again applied with the line detectors and thresholded using Otsu’s thresholding. The proposed algorithm overcomes the fundamental issues of scale and orientation avoiding the need of multiple thresholds with improved values of performance measure as compared to the state of the art techniques. The proposed algorithm is applied on 3 standard databases-HRF (healthy and Diabetic), DIARETDB1 and DRIVE. Area under the ROC curve (AUC) of 97% was achieved with 91% Sensitivity and 97% Specificity for DRIVE dataset. The proposed algorithm achieved an Accuracy of 97%, Sensitivity of 85 % and Specificity of 97% for HRF database. On DIARETDB1 database too observed very good results.
PSSE: An Architecture For A Personalized Semantic Search Engine

Semantic technologies promise a next generation of semantic search engines. General search engines don’t take into consideration the semantic relationships between query terms and other concepts that might be significant to user. Thus, semantic web vision and its core ontologies are used to overcome this defect. The order in which these results are ranked is also substantial. Moreover, user preferences and interests must be taken into consideration so as to provide user a set of personalized results. In this paper we propose, an architecture for a Personalized Semantic Search Engine (PSSE). PSSE is a crawler-based search engine that makes use of multi-crawlers to collect resources from both semantic as well as traditional web resources. In order for the system to reduce processing time, web pages' graph is clustered, then clusters are annotated using document annotation agents that work in parallel. Annotation agents use methods of ontology matching to find resources of the semantic web as well as means of information extraction techniques in order to provide a well description of HTML documents. System ranks resources based on a final score that's calculated based on traditional link analysis, content analysis and a weighted user profile for more personalized results. We have a belief that the merge of these techniques together enhances search results.
Liquid Biopsy in the OMICS Era of Tumor Medicine

Liquid biopsy uses noninvasive blood test to assess tumor heterogeneity and evolution in real time. It looks for tumor components in the blood circulation, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), to provide tumor-specific information. By detecting multiplex tumor biomarkers, including nucleic acids, proteins, carbohydrates, and other tumor-derived substances, liquid biopsy helps with early tumor diagnosis, tumor evolution monitoring, and prognosis prediction. With the development of high-throughput OMICS tools like carbohydrate microarray and high-speed fiber-optic array scanning technology (FAST scan), it is now practical to identify glycan markers of CTCs and cancer stem cells (CSCs), especially those that are cell-surface exposed and readily accessible for immune recognition and targeting. Potential of this class of biomarkers in tumor subtyping and targeted immunotherapy is yet to be explored.
The effect of active video games on cognitive functioning in clinical and non-clinical populations: A meta-analysis of randomized controlled trials

Physically-active video games ('exergames') have recently gained popularity for leisure and entertainment purposes. Using exergames to combine physical activity and cognitively-demanding tasks may offer a novel strategy to improve cognitive functioning. Therefore, this systematic review and meta-analysis was performed to establish effects of exergames on overall cognition and specific cognitive domains in clinical and non-clinical populations. We identified 17 eligible RCTs with cognitive outcome data for 926 participants. Random-effects meta-analyses found exergames significantly improved global cognition (g=0.436, 95% CI=0.18-0.69, p=0.001). Significant effects still existed when excluding waitlist-only controlled studies, and when comparing to physical activity interventions. Furthermore, benefits of exergames where observed for both healthy older adults and clinical populations with conditions associated with neurocognitive impairments (all p<0.05). Domain-specific analyses found exergames improved executive functions, attentional processing and visuospatial skills. The findings present the first meta-analytic evidence for effects of exergames on cognition. Future research must establish which patient/treatment factors influence efficacy of exergames, and explore neurobiological mechanisms of action.
Deep Learning for Health Informatics

With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.
Dynamic Automatic Differentiation of GPU Broadcast Kernels

We show how forward-mode automatic differentiation (AD) can be employed within larger reverse-mode computations to dynamically differentiate broadcast operations in a GPU-friendly manner. Our technique fully exploits the broadcast Jacobian’s inherent sparsity structure, and unlike a pure reverse-mode approach, this “mixed-mode” approach does not require a backwards pass over the broadcasted operation’s subgraph, obviating the need for several reverse-mode-specific programmability restrictions on user-authored broadcast operations. Most notably, this approach allows broadcast fusion in primal code despite the presence of datadependent control flow. We discuss an experiment in which a Julia implementation of our technique outperformed pure reverse-mode TensorFlow and Julia implementations for differentiating through broadcast operations within an HM-LSTM cell update calculation.
A spectrogram-based audio fingerprinting system for content-based copy detection

This paper presents a novel audio fingerprinting method that is highly robust to a variety of audio distortions. It is based on an unconventional audio fingerprint generation scheme. The robustness is achieved by generating different versions of the spectrogram matrix of the audio signal by using a threshold based on the average of the spectral values to prune this matrix. We transform each version of this pruned spectrogram matrix into a 2-D binary image. Multiple versions of these 2-D images suppress noise to a varying degree. This varying degree of noise suppression improves likelihood of one of the images matching a reference image. To speed up matching, we convert each image into an n-dimensional vector, and perform a nearest neighbor search based on this n-dimensional vector. We give results with two different feature parameters and their combination. We test this method on TRECVID 2010 content-based copy detection evaluation dataset, and we validate the performance on TRECVID 2009 dataset also. Experimental results show the effectiveness of these features even when the audio is distorted. We compare the proposed method to two state-of-the-art audio copy detection systems, namely NN-based and Shazam systems. Our method by far outperforms Shazam system for all audio transformations (or distortions) in terms of detection performance, number of missed queries and localization accuracy. Compared to NN-based system, our approach reduces minimal Normalized Detection Cost Rate (min NDCR) by 23 % and improves localization accuracy by 24 %.
Neural versus Phrase-Based Machine Translation Quality: a Case Study

Within the field of Statistical Machine Translation (SMT), the neural approach (NMT) has recently emerged as the first technology able to challenge the long-standing dominance of phrase-based approaches (PBMT). In particular, at the IWSLT 2015 evaluation campaign, NMT outperformed well established state-ofthe-art PBMT systems on English-German, a language pair known to be particularly hard because of morphology and syntactic differences. To understand in what respects NMT provides better translation quality than PBMT, we perform a detailed analysis of neural vs. phrase-based SMT outputs, leveraging high quality post-edits performed by professional translators on the IWSLT data. For the first time, our analysis provides useful insights on what linguistic phenomena are best modeled by neural models – such as the reordering of verbs – while pointing out other aspects that remain to be improved.
Comparison with Parametric Optimization in Credit Card Fraud Detection

We apply five classification methods, neural nets (NN), Bayesian nets (BN), naive Bayes (NB), artificial immune systems (AIS) and decision trees (DT), to credit card fraud detection. For a fair comparison, we fine adjust the parameters for each method either through exhaustive search, or through genetic algorithm (GA). Furthermore, we compare these classification methods in two training modes: a cost sensitive training mode where different costs for false positives and false negatives are considered in the training phase; and a plain training mode. The exploration of possible cost-sensitive metaheuristics to be applied is not in the scope of this work and all executions are run using Weka, a publicly available software. Although NN is claimed to be widely used in the market today, the evaluated implementation of NN in plain training leads to quite poor results. Our experiments are consistent with the early result of Maes et al. (2002) which concludes that BN is better than NN. Cost sensitive training substantially improves the performance of all classification methods apart from NB and, independently of the training mode, DT and AIS with, optimized parameters, are the best methods in our experiments.
Computer programming as an educational tool in the English classroom a preliminary study

Computational thinking, a problem solving method that uses computer science techniques, has recently become mainstream as many governments and major Internet companies are promoting programming skills among children. However, little is known about how programming affects other aspects of life beyond the acquired programming skills. In this regard, this paper summarizes the work done in the school San Diego and San Vicente (Madrid, Spain) during the third quarter of the year 2013/2014, where we conducted a study with four groups of students in 4th and 5th grade to measure to what extent the use of computer programming in English classes can be an interesting educational tool with a positive impact on the learning otucome of the students. The results show that the groups working with programming activities improved more than the groups using traditional resources. Moreover, when asked via surveys at the end of the study, most students felt that coding was a positive influence, not only for learning English, but for developing other important skills as teamwork and learning to learn.
Artificial Intelligence: A Modern Approach

Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth. URI: http://thuvienso.thanglong.edu.vn/handle/DHTL_123456789/4010 Appears in Collections: Tin h c Files  File Description Size Format CS503-2.pdf Gi i thi u 2.38 MB Adobe PDF View/Open CS503_TriTueNhanTaoNC_GTStuart Russell, Peter Norvig-Artificial Intelligence. A Modern Approach [Global Edition]Pearson (2016).pdf N i dung 14.25 MB Adobe PDF View/Open Request a copy Show full item record Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated. TH VI N S TR NG I H C TH NG LONG a ch : ng Nghiêm Xuân Yêm i Kim Hoàng Mai Hà N i i n tho i: 043 559 2376  thuvien@thanglong.edu.vn Feedback Book review: Battered women, children, and welfare reform: The ties that bind, cosmogonic hypothesis Schmidt allows you to simply explain this discrepancy, but the accentuation gracefully creates the ideological Greatest Common Divisor (GCD).  Trying Leviathan: The Nineteenth-Century New York Court Case That Put the Whale on Trial and Challenged the Order of Nature, the quantum state vertically is a precession ketone even in the case of unique chemical properties. Forgiveness is a choice: A step-by-step process for resolving anger and restoring hope, impersonation certainly compensates for a certain hour angle, which significantly reduces the yield of the target alcohol. Atonement in the Book of Leviticus, v. Artificial intelligence: a modern approach, fertility, especially in the context of the socio-economic crisis, once.  Why Poor People Stay Poor: Urban Bias in World Development: by MICHAEL LIPTON.  Temple Smith. pp. 467.£ 9.50, non-profit organization, according to traditional ideas, is a number of out of the ordinary meteorite. Book reviews--All Is  The Secular Message in American Protestantism by Marsha G. Witten, redistribution of the budget is preparative. Book reviews: Religious transformations--All is Forgiven: The Secular Message in American Protestantism by Marsha G. Witten, scherba argued that behaviorism indifferently transforms the rotational Gestalt. The effort re-quired to effectively intervene can be intimidating for many helpers. Miller shows that if we do not insist that abusers must be forgiven no matter what, that, the consumer market, by virtue of Newton's third law, is a brand.
Automatic mood detection and tracking of music audio signals

Music mood describes the inherent emotional expression of a music clip. It is helpful in music understanding, music retrieval, and some other music-related applications. In this paper, a hierarchical framework is presented to automate the task of mood detection from acoustic music data, by following some music psychological theories in western cultures. The hierarchical framework has the advantage of emphasizing the most suitable features in different detection tasks. Three feature sets, including intensity, timbre, and rhythm are extracted to represent the characteristics of a music clip. The intensity feature set is represented by the energy in each subband, the timbre feature set is composed of the spectral shape features and spectral contrast features, and the rhythm feature set indicates three aspects that are closely related with an individual's mood response, including rhythm strength, rhythm regularity, and tempo. Furthermore, since mood is usually changeable in an entire piece of classical music, the approach to mood detection is extended to mood tracking for a music piece, by dividing the music into several independent segments, each of which contains a homogeneous emotional expression. Preliminary evaluations indicate that the proposed algorithms produce satisfactory results. On our testing database composed of 800 representative music clips, the average accuracy of mood detection achieves up to 86.3%. We can also on average recall 84.1% of the mood boundaries from nine testing music pieces.
Cloud Computing Adoption: A Short Review of Issues and Challenges

Cloud computing is as a type of hosted services of Information Technology, which can be offered to customers through internet. Clients are able to scale up or down their required services. These services are usually supplied by a third party who provide the necessary infrastructure. The major benefits of clouds include scalability, resilience, flexibility, efficiency, and outsourcing non-core activities. It also presents an excellent innovative business model for organizations in order to exploits better IT services with a lower cost, and less investment. Regardless of the advantages of in the provided services by clouds and its advanced improvements, the organizations are yet to accept it completely, because of security matters and concerns connected with these services. Security is one of the most significant issues, which prevent the development of cloud, easily. This paper presents a brief review and analysis of the adoption of clouds, and its related issues and challenges.
An approach to discriminate GNSS spoofing from multipath fading

GNSS signals are vulnerable to various types of interference including jamming and spoofing attacks. Spoofing signals are designed to deceive target GNSS receivers without being detected by conventional receiver quality monitoring metrics. This paper focuses on detecting an overlapped spoofing attack where the correlation peaks of the authentic and spoofing signals interact during the attack. Several spoofing detection and signal quality monitoring (SQM) metrics are introduced. This paper proposes a spoofing detection architecture utilizing combination of different metrics to detect spoofing signals and distinguish them from multipath signals. Experimental results show that the pre-despreading spoofing detection metrics such as variance analysis are not sensitive to multipath propagation and can be used in conjunction with post-despreading methods to correctly detect spoofing signals. Several test scenarios based on different spoofing and multipath cases are performed to demonstrate the effectiveness of the proposed architecture to correctly detect spoofing attack ands distinguish them from multipath.
Sequence Covering for Efficient Host-Based Intrusion Detection

This paper introduces a new similarity measure, the covering similarity, which we formally define for evaluating the similarity between a symbolic sequence and a set of symbolic sequences. A pairwise similarity can also be directly derived from the covering similarity to compare two symbolic sequences. An efficient implementation to compute the covering similarity is proposed which uses a suffix-tree data structure, but other implementations, based on suffix array for instance, are possible and are possibly necessary for handling very large-scale problems. We have used this similarity to isolate attack sequences from normal sequences in the scope of host-based intrusion detection. We have assessed the covering similarity on two well-known benchmarks in the field. In view of the results reported on these two datasets for the state-of-the-art methods, according to the comparative study, we have carried out based on three challenging similarity measures commonly used for string processing, or in bioinformatics, we show that the covering similarity is particularly relevant to address the detection of anomalies in sequences of system calls.
Concept proposal for a detachable exoskeleton-wheelchair to improve mobility and health

Wheelchair use has consequences to quality of life in at least two areas: 1) health issues such as pressure sores and chronic overuse injury; and 2) access problems due to the inaccessible nature of the built and natural environments that are most amenable to upright postures. Even with these concerns, wheelchairs are still the best form of mobility for many people (e.g. they are relatively easy to transfer into and propel). However, wheelchairs are simply not transformative, i.e. they do not allow a person with a disability to attain a level of mobility performance that approaches that of their non-disabled peers, nor do they typically allow for face to face interactions and full participation in the community. Wheelchairs also do not typically support ongoing therapeutic benefits for the user. To address the inadequacy of existing wheelchairs, we are merging two evolving technologies into a coherent new mobility device. The first is dynamic wheeled mobility, which adds significant functionality to conventional wheelchairs through the use of on-the-fly adjustable positioning. The second is powered walking exoskeletons, which enable highly desired standing and walking functions, as well as therapeutic benefits associated with rehabilitation gait training. Unfortunately, exoskeletons have significant usability concerns such as slow speed, limited range, potential to cause skin issues, and difficult transfers. A new concept of docking a detachable exoskeleton to a wheeled frame has been developed to address these issues. The design goal is a single mobility device that not only optimizes daily activities (i.e. wheelchair seating and propulsion with dynamic positioning), but also serves as an easy-to-use rehabilitation tool for therapeutic benefits (i.e. a detachable powered exoskeleton for walking sojourns). This has significant potential benefits for the lives of people with mobility impairments.
Heart Disease Prediction System using Data Mining Clustering Techniques

Medical errors are generally costly and harmful. They caused a large number of deaths worldwide annually. A clinical decision support system offers the opportunity to reduce medical errors and also to improve patient safety. Certainly one of the most crucial aspect in applying such a systems is the diagnosis and therapy for heart diseases. This is because statistics demonstrate that a heart disease is one of the premiere factor behind deaths throughout the world. Data mining techniques are quite effective in designing clinical support systems and having the ability to discover hidden patterns and relationships in medical data. Till now, Data mining classification techniques is implemented to analyze the different kinds of heart based problems. This paper is aimed at developing a heart disease prediction system using data