nils-herrmann/clean_scirepeval huggingface dataset:
Texture analysis and classification of diffuse thyroid diseases based on ultrasound images

This paper describes a method of classification of diffuse thyroid disease based on ultrasound images. Our method proposes a new Co-occurrence Matrix named as Wavelet Multi-sub-bands Co-occurrence Matrix (WMCM), based on which a series of new texture features can be calculated. A new feature of fibrous variant texture is also proposed. The mRMR method is used to select an effective feature set from the feature space composed by those feature we proposed and other common texture feature. 180 samples of thyroid ultrasound images have been classified based on the selected feature set. Classification accuracies of Normal, Graves disease and Hashimoto's disease with SVM classifier are 87.83%, 83.67%, and 92.17%, respectively. It can be seen from contrast experiments that the features proposed in this paper can improve the classification accuracy by 5% or more comparing to other features.
High-risk sex offenders may not be high risk forever.

This study examined the extent to which sexual offenders present an enduring risk for sexual recidivism over a 20-year follow-up period. Using an aggregated sample of 7,740 sexual offenders from 21 samples, the yearly recidivism rates were calculated using survival analysis. Overall, the risk of sexual recidivism was highest during the first few years after release, and decreased substantially the longer individuals remained sex offense-free in the community. This pattern was particularly strong for the high-risk sexual offenders (defined by Static-99R scores). Whereas the 5-year sexual recidivism rate for high-risk sex offenders was 22% from the time of release, this rate decreased to 4.2% for the offenders in the same static risk category who remained offense-free in the community for 10 years. The recidivism rates of the low-risk offenders were consistently low (1%-5%) for all time periods. The results suggest that offense history is a valid, but time-dependent, indicator of the propensity to sexually reoffend. Further research is needed to explain the substantial rate of desistance by high-risk sexual offenders.
Digital Transformation of Primarily Physical Industries - Exploring the Impact of Digital Trends on Business Models of Automobile Manufacturers

The phenomenon of digital transformation received some attention in previous literature concerning industries such as media, entertainment and publishing. However, there is a lack of understanding about digital transformation of primarily physical industries, whose products cannot be completely digitized, e.g., automotive industry. We conducted a rigorous content analysis of substantial secondary data from industry magazines aiming to generate insights to this phenomenon in the automotive industry. We examined the impact of major digital trends on dominant business models. Our findings indicate that trends related to social media, mobile, big data and cloud computing are driving automobile manufactures to extend, revise, terminate, and create business models. By doing so, they contribute to the constitution of a digital layer upon the physical mobility infrastructure. Despite its strong foundation in the physical world, the industry is undergoing important structural changes due to the ongoing digitalization of consumer lives and business.
Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction

The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors just discovering is healthcare. The healthcare environment is still „information rich‟ but „knowledge poor‟. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today‟s medical research particularly in Heart Disease Prediction. Number of experiment has been conducted to compare the performance of predictive data mining technique on the same dataset and the outcome reveals that Decision Tree outperforms and some time Bayesian classification is having similar accuracy as of decision tree but other predictive methods like KNN, Neural Networks, Classification based on clustering are not performing well. The second conclusion is that the accuracy of the Decision Tree and Bayesian Classification further improves after applying genetic algorithm to reduce the actual data size to get the optimal subset of attribute sufficient for heart disease prediction.
Electronic system with memristive synapses for pattern recognition

Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system. The system learns, and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.
Style Transfer Through Back-Translation

Style transfer is the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context. This paper introduces a new method for automatic style transfer. We first learn a latent representation of the input sentence which is grounded in a language translation model in order to better preserve the meaning of the sentence while reducing stylistic properties. Then adversarial generation techniques are used to make the output match the desired style. We evaluate this technique on three different style transformations: sentiment, gender and political slant. Compared to two state-of-the-art style transfer modeling techniques we show improvements both in automatic evaluation of style transfer and in manual evaluation of meaning preservation and fluency.
Easy contextual intent prediction and slot detection

Spoken language understanding (SLU) is one of the main tasks of a dialog system, aiming to identify semantic components in user utterances. In this paper, we investigate the incorporation of context into the SLU tasks of intent prediction and slot detection. Using a corpus that contains session-level information, including the start and end of a session and the sequence of utterances within it, we experiment with the incorporation of information from previous intra-session utterances into the SLU tasks on a given utterance. For slot detection, we find that including features indicating the slots appearing in the previous utterances gives no significant increase in performance. In contrast, for intent prediction we find that a similar approach that incorporates the intent of the previous utterance as a feature yields relative error rate reductions of 6.7% on transcribed data and 8.7% on automatically-recognized data. We also find similar gains when treating intent prediction of utterance sequences as a sequential tagging problem via SVM-HMMs.
Bayesian Filtering and Smoothing

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.
Improving Question Answering by Commonsense-Based Pre-Training

Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge. We believe the main reason is the lack of commonsense connections between concepts. To remedy this, we provide a simple and effective method that leverages external commonsense knowledge base such as ConceptNet. We pre-train direct and indirect relational functions between concepts, and show that these pre-trained functions could be easily added to existing neural network models. Results show that incorporating commonsense-based function improves the state-ofthe-art on two question answering tasks that require commonsense reasoning. Further analysis shows that our system discovers and leverages useful evidences from an external commonsense knowledge base, which is missing in existing neural network models and help derive the correct answer.
High-Level Rent Seeking and Corruption in African Regimes : Theory and Cases

2  An intense debate has raged for years over Africa's economic difficulties. Aside from the obvious problems of warfare, drought, and disease, the usual suspect is economic policy. However, the record of over a decade of structural adjustment efforts is difficult to read. A recent analysis by the World Bank provides significant evidence that improved policies lead to improved prospects for growth, and that the continuing economic problems in Africa are the result of a failure to carry liberalization far enough (World Bank 1993a). According to that analysis, no African governments were rated as having " good " economic policies and only one, Ghana, was deemed " adequate; " with an annual growth rate of 1.3 percent per capita (1987-1991). Opponents of World Bank/IMF policy have criticized the Bank's analysis on numerous grounds, but even World Bank economists mutter that rates of private investment and economic growth are higher in Viet Nam and China (whose economic policies still bear a strong socialist imprint) than almost anywhere in Africa. Something more than standard macroeconomic policy failures must be at work. This paper focuses on one of the " usual suspects "-rent seeking by officials at the highest government levels. Based on both theory and concrete African examples, it demonstrates how such rent seeking can harm an economy and stifle investment and growth. " Rent seeking " is often used interchangeably with " corruption, " and there is a large area of overlap. While corruption involves the misuse of public power for private gain, rent seeking derives from the economic concept of " rent "-earnings in excess of all relevant costs (including a market rate of return on invested assets). Rent is equivalent to what most non-economists think of as monopoly 3 profits. Rent seeking is then the effort to acquire access to or control over opportunities for earning rents. These efforts are not necessarily illegal, or even immoral. They include much lobbying and some forms of advertising. Some can be efficient, such as an auction of scare and valuable assets. However, economists and public sector management specialists are concerned with what Jagdish Bhagwati termed " directly unproductive " rent seeking activities, because they waste resources and can contribute to economic inefficiency (Bhagwati 1974, see also Krueger 1974). Corruption and other forms of rent seeking have been well-documented in every society on earth, from the banks of the Congo River …
ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism

ConvNets and Imagenet have driven the recent success of deep learning for image classification. However, the marked slowdown in performance improvement, the recent studies on the lack of robustness of neural networks to adversarial examples and their tendency to exhibit undesirable biases questioned the reliability and sustained development of these methods. This work investigates these questions from the perspective of the end-user by using human subject studies and explanations. We experimentally demonstrate that the accuracy and robustness of ConvNets measured on Imagenet are underestimated. We show that explanations can mitigate the impact of misclassified adversarial examples from the perspective of the end-user and we introduce a novel tool for uncovering the undesirable biases learned by a model. These contributions also show that explanations are a promising tool for improving our understanding of ConvNets’ predictions and for designing more reliable models1.
Discourse Mode Identification in Essays

Discourse modes play an important role in writing composition and evaluation. This paper presents a study on the manual and automatic identification of narration, exposition, description, argumentandemotion expressingsentences in narrative essays. We annotate a corpus to study the characteristics of discourse modes and describe a neural sequence labeling model for identification. Evaluation results show that discourse modes can be identified automatically with an average F1-score of 0.7. We further demonstrate that discourse modes can be used as features that improve automatic essay scoring (AES). The impacts of discourse modes for AES are also discussed.
Simulated annealing algorithms: an overview

A brief introduction is given to the actual mechanics of simulated annealing, and a simple example from an IC layout is used to illustrate how these ideas can be applied. The complexities and tradeoffs involved in attacking a realistically complex design problem are illustrated by dissecting two very different annealing algorithms for VLSI chip floorplanning. Several current research problems aimed at determining more precisely how and why annealing algorithms work are examined. Some philosophical issues raised by the introduction of annealing are discussed.<<ETX>>
MD-HBase: A Scalable Multi-dimensional Data Infrastructure for Location Aware Services

The ubiquity of location enabled devices has resulted in a wide proliferation of location based applications and services. To handle the growing scale, database management systems driving such location based services (LBS) must cope with high insert rates for location updates of millions of devices, while supporting efficient real-time analysis on latest location. Traditional DBMSs, equipped with multi-dimensional index structures, can efficiently handle spatio-temporal data. However, popular open source relational database systems are overwhelmed by the high insertion rates, real-time querying requirements, and terabytes of data that these systems must handle. On the other hand, Key-value stores can effectively support large scale operation, but do not natively support multi-attribute accesses needed to support the rich querying functionality essential for the LBSs. We present MD-HBase, a scalable data management system for LBSs that bridges this gap between scale and functionality. Our approach leverages a multi-dimensional index structure layered over a Key-value store. The underlying Key-value store allows the system to sustain high insert throughput and large data volumes, while ensuring fault-tolerance, and high availability. On the other hand, the index layer allows efficient multi-dimensional query processing. We present the design of MD-HBase that builds two standard index structuresâ€"the K-d tree and the Quad treeâ€"over a range partitioned Key-value store. Our prototype implementation using HBase, a standard open-source Key-value store, can handle hundreds of thousands of inserts per second using a modest 16 node cluster, while efficiently processing multidimensional range queries and nearest neighbor queries in real-time with response times as low as hundreds of milliseconds.
CAPTCHA Recognition Based on Faster R-CNN

In this paper, Faster R-CNN was employed to recognize the CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart). Unlike traditional method, the proposed method is based on deep learning object detection framework. By inputting the database into the network and training the Faster R-CNN, the feature map can be obtained through the convolutional layers. The proposed method can recognize the character and it is location. Experiments show that Faster R-CNN can be used in CAPTCHA recog‐ nition with promising speed and accuracy. The experimental results also show that the mAP (mean average precision) value will improve with the depth of the network increasing.
Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels.

Proteins from silver-stained gels can be digested enzymatically and the resulting peptide analyzed and sequenced by mass spectrometry. Standard proteins yield the same peptide maps when extracted from Coomassie- and silver-stained gels, as judged by electrospray and MALDI mass spectrometry. The low nanogram range can be reached by the protocols described here, and the method is robust. A silver-stained one-dimensional gel of a fraction from yeast proteins was analyzed by nano-electrospray tandem mass spectrometry. In the sequencing, more than 1000 amino acids were covered, resulting in no evidence of chemical modifications due to the silver staining procedure. Silver staining allows a substantial shortening of sample preparation time and may, therefore, be preferable over Coomassie staining. This work removes a major obstacle to the low-level sequence analysis of proteins separated on polyacrylamide gels.
Percepts to recollections: insights from single neuron recordings in the human brain

Transformation of experience into memories that can guide future behavior is a common ability across species. However, only humans can declare their perceptions and memories of experienced events (episodes). The medial temporal lobe (MTL) is central to episodic memory, yet the neuronal code underlying the translation from sensory information to memory remains unclear. Recordings from neurons within the brain in patients who have electrodes implanted for clinical reasons provide an opportunity to bridge physiology with cognitive theories. Recent evidence illustrates several striking response properties of MTL neurons. Responses are selective yet invariant, associated with conscious perception, can be internally generated and modulated, and spontaneously retrieved. Representation of information by these neurons is highly explicit, suggesting abstraction of information for future conscious recall.
Secure Smartphone Unlock : Robust Face Spoof Detection on Mobile

With the wide deployment of face recognition systems in applications from de-duplication to mobile device unlocking, security against face spoofing attacks requires increased attention; such attacks can be easily launched via printed photos, video replays and 3D masks of a face. We address the problem of facial spoof detection against print (photo) and replay (photo or video) attacks based on the analysis of image aliasing (e.g., surface reflection, moiré pattern, color distortion, and shape deformation) in spoof face images (or video frames). The application domain of interest is mobile phone unlock, given that growing number of phones have face unlock and mobile payment capabilities. We build a mobile spoof face database (MSU MSF) containing more than 1, 000 subjects, which is, to our knowledge, the largest spoof face database in terms of the number of subjects. Both print and replay attacks are captured using the front and rear cameras of a Nexus 5 phone. We analyze the aliasing of print and replay attacks using (i) different intensity channels (R, G, B and grayscale), (ii) different image regions (entire image, detected face, and facial component between the nose and chin), and (iii) different feature descriptors. We develop an efficient face spoof detection system on an Android smartphone. Experimental results on three public-domain face spoof databases (Idiap Print-Attack and Replay-Attack, and CASIA), and the MSU MSF show that the proposed approach is effective in face spoof detection for both cross-database and intra-database testing scenarios. User studies of our Android face spoof detection system involving 20 participants’ show that the proposed approach works very well in real application scenarios.
A practical approach to dialogue response generation in closed domains

We describe a prototype dialogue response generation model for the customer service domain at Amazon. The model, which is trained in a weakly supervised fashion, measures the similarity between customer questions and agent answers using a dual encoder network, a Siamese-like neural network architecture. Answer templates are extracted from embeddings derived from past agent answers, without turn-by-turn annotations. Responses to customer inquiries are generated by selecting the best template from the final set of templates. We show that, in a closed domain like customer service, the selected templates cover >70% of past customer inquiries. Furthermore, the relevance of the model-selected templates is significantly higher than templates selected by a standard tf-idf baseline.
Digitized Locksmith Forensics: Design and Implementation of a Computer-Aided Forensic Analysis

At the moment, there is a change of paradigm in the field of criminalistic forensics. While the classic approach to forensic analysis, especially in the fields of dactyloscopy and toolmark analysis, heavily and solely relied on experience and training of human experts, the modern forensics more and more advances into a digitized domain. Experts are supported by high resolution sensors, machine learning algorithms and computer visualizations. The goal thereby is not the replacement of human experts but the efficient support to allow for objective, deterministic and reproducible analysis results with less effort, high performance, and known and well documented error rates. For firearm forensics and dactyloscopy there are generally accepted systems existent which allow for a computer-supported, (semi-) automated analysis. Their success motivates the introduction of similar systems to other fields of classic forensics. In this paper, we propose the introduction of such a system to the highly specialized field of locksmith forensics. We present a general design of such a system with an exemplary overall analysis goal, discuss challenges, and describe possibilities for an effective implementation. The design covers all necessary steps from acquisition, pre-processing and analysis of a chosen lock component (’key pin’). Our implementation is evaluated with the help of a preliminary test set consisting of a selection of significant samples.