nils-herrmann/clean_scirepeval huggingface dataset:
Direct charge measurement in Floating Gate transistors of Flash EEPROM using Scanning Electron Microscopy

We present a characterization methodology for fast direct measurement of the charge accumulated on Floating Gate (FG) transistors of Flash EEPROM cells. Using a Scanning Electron Microscope (SEM) in Passive Voltage Contrast (PVC) mode we were able to distinguish between '0' and '1' bit values stored in each memory cell. Moreover, it was possible to characterize the remaining charge on the FG; thus making this technique valuable for Failure Analysis applications for data retention measurements in Flash EEPROM. The technique is at least two orders of magnitude faster than state-of-the-art Scanning Probe Microscopy (SPM) methods. Only a relatively simple backside sample preparation is necessary for accessing the FG of memory transistors. The technique presented was successfully implemented on a 0.35 μm technology node microcontroller and a 0.21 μm smart card integrated circuit. We also show the ease of such technique to cover all cells of a memory (using intrinsic features of SEM) and to automate memory cells characterization using standard image processing technique.
Preventive and Regenerative Foam Rolling are Equally Effective in Reducing Fatigue-Related Impairments of Muscle Function following Exercise.

Objectives of the study were to compare the effects of a single bout of preventive or regenerative foam rolling (FR) on exercise-induced neuromuscular exhaustion. Single-centre randomised-controlled study was designed. Forty-five healthy adults (22 female; 25±2 yrs) were allocated to three groups: 1) FR of the lower limb muscles prior to induction of fatigue, 2) FR after induction of fatigue, 3) no-treatment control. Neuromuscular exhaustion was provoked using a standardized and validated functional agility short-term fatigue protocol. Main outcome measure was the maximal isometric voluntary force of the knee extensors (MIVF). Secondary outcomes included pain and reactive strength (RSI). Preventive (-16%) and regenerative FR (-12%) resulted in a decreased loss in MIVF compared to control (-21%; p < 0.001) five minutes after exhaustion. Post-hoc tests indicated a large-magnitude, non-significant trend towards regenerative foam rolling to best restore strength (Cohen's d > 0.8, p < 0.1). Differences over time (p < 0.001) between groups regarding pain and RSI did not turn out to be clinically meaningful. A single bout of foam rolling reduces neuromuscular exhaustion with reference to maximal force production. Regenerative rather than preventive foam rolling seems sufficient to prevent further fatigue.
Malware in the future? Forecasting of analyst detection of cyber events

Cyber attacks endanger physical, economic, social, and political security. There have been extensive efforts in government, academia, and industry to anticipate, forecast, and mitigate such cyber attacks. A common approach is time-series forecasting of cyber attacks based on data from network telescopes, honeypots, and automated intrusion detection/prevention systems. This research has uncovered key insights such as systematicity in cyber attacks. Here, we propose an alternate perspective of this problem by performing forecasting of attacks that are analyst-detected and -verified occurrences of malware. We call these instances of malware cyber event data. Specifically, our dataset was analyst-detected incidents from a large operational Computer Security Service Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on automated systems. Our data set consists of weekly counts of cyber events over approximately seven years. This curated dataset has characteristics that distinguish it from most datasets used in prior research on cyber attacks. Since all cyber events were validated by analysts, our dataset is unlikely to have false positives which are often endemic in other sources of data. Further, the higher-quality data could be used for a number of important tasks for CSSPs such as resource allocation, estimation of security resources, and the development of effective risk-management strategies. To quantify bursts, we used a Markov model of state transitions. For forecasting, we used a Bayesian State Space Model and found that events one week ahead could be predicted with reasonable accuracy, with the exception of bursts. Our findings of systematicity in analyst-detected cyber attacks are consistent with previous work using cyber attack data from other sources. The advanced information provided by a forecast may help with threat awareness by providing a probable value and range for future cyber events one week ahead, similar to a weather forecast. Other potential applications for cyber event forecasting include proactive allocation of resources and capabilities for cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs. Enhanced threat awareness may improve cybersecurity by helping to optimize human and technical capabilities for cyber defense.
A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

Artificial Bee Colony ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony CABC , which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization PSO , and its cooperative version CPSO are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.
Platform for Enterprise Privacy Practices: Privacy-Enabled Management of Customer Data

Enterprises collect a large amount of personal data about their customers. Even though enterprises promise privacy to their customers using privacy statements or P3P, there is no methodology to enforce these promises throughout and across multiple enterprises. This article describes the Platform for Enterprise Privacy Practices (E-P3P), which defines technology for privacy-enabled management and exchange of customer data. Its comprehensive privacy-specific access control language expresses restrictions on the access to personal data, possibly shared between multiple enterprises. E-P3P separates the enterprise-specific deployment policy from the privacy policy that covers the complete life cycle of collected data. E-P3P introduces a viable separation of duty between the three “administrators” of a privacy system: The privacy officer designs and deploys privacy policies, the security officer designs access control policies, and the customers can give consent while selecting opt-in and opt-out choices.  2002.
Mining Association Rules between Sets of Items in Large Databases

In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. To overcome this drawback, several methods were proposed in the literature such as item set concise representations, redundancy reduction, and post processing. However, being generally based on statistical information, most of these methods do not guarantee that the extracted rules are interesting for the user. Thus, it is crucial to help the decision-maker with an efficient post processing step in order to reduce the number of rules. This paper proposes a new interactive approach to prune and filter discovered rules. First, we propose to use ontologies in order to improve the integration of user knowledge in the post processing task. Second, we propose the Rule Schema formalism extending the specification language proposed by Liu et al. for user expectations. Furthermore, an interactive framework is designed to assist the user throughout the analyzing task. Applying our new approach over voluminous sets of rules, we were able, by integrating domain expert knowledge in the post processing step, to reduce the number of rules to several dozens or less. Moreover, the quality of the filtered rules was validated by the domain expert at various points in the interactive process. KeywordsClustering, classification, and association rules, interactive data exploration and discovery, knowledge management applications.
BUYING BEHAVIOUR OF CONSUMERS TOWARDS INSTANT FOOD PRODUCTS

The present study made an attempt to analyze the existing buying behaviour of Instant Food Products by individual households and to predict the demand for Instant Food Products of Hyderabad city in Andra Padesh .All the respondents were aware of pickles and Sambar masala but only 56.67 per cent of respondents were aware of Dosa/Idli mix. About 96.11 per cent consumers of Dosa/Idli mix and more than half of consumers of pickles and Sambar masala prepared their own. Low cost of home preparation and differences in tastes were the major reasons for non consumption, whereas ready availability and save time of preparation were the reasons for consuming Instant Food Products. Retail shops are the major source of information and source of purchase of Instant Food Products. The average monthly expenditure on Instant Food Products was found to be highest in higher income groups. The average per capita purchase and per capita expenditure on Instant food Products had a positive relationship with income of households.High price and poor taste were the reasons for not purchasing particular brand whereas best quality, retailers influence and ready availability were considered for preferring particular brand of products by the consumers.
Inverting Convolutional Networks with Convolutional Networks

Deep representations, in particular ones implemented by convolutional neural networks, have led to good progress on many learning problems. However, the learned representations are hard to analyze and interpret, even when they are extracted from visual data. We propose a new approach to study deep image representations by inverting them with an up-convolutional neural network. Application of this method to a deep network trained on ImageNet provides numerous insights into the properties of the feature representation. Most strikingly, the colors and the rough contours of an input image can be reconstructed from activations in higher network layers and even from the predicted class probabilities.
Training Structured Efficient Convolutional Layers

Typical recent neural network designs are primarily convolutional layers, but 1 the tricks enabling structured efficient linear layers (SELLs) have not yet been 2 adapted to the convolutional setting. We present a method to express the weight 3 tensor in a convolutional layer using diagonal matrices, discrete cosine transforms 4 (DCTs) and permutations that can be optimised using standard stochastic gradient 5 methods. A network composed of such structured efficient convolutional layers 6 (SECL) outperforms existing low-rank networks and demonstrates competitive 7 computational efficiency.
Commonsense Knowledge in Machine Intelligence

There is growing conviction that the future of computing depends on our ability to exploit big data on theWeb to enhance intelligent systems. This includes encyclopedic knowledge for factual details, common sense for human-like reasoning and natural language generation for smarter communication. With recent chatbots conceivably at the verge of passing the Turing Test, there are calls for more common sense oriented alternatives, e.g., the Winograd Schema Challenge. The Aristo QA system demonstrates the lack of common sense in current systems in answering fourth-grade science exam questions. On the language generation front, despite the progress in deep learning, current models are easily confused by subtle distinctions that may require linguistic common sense, e.g.quick food vs. fast food. These issues bear on tasks such as machine translation and should be addressed using common sense acquired from text. Mining common sense from massive amounts of data and applying it in intelligent systems, in several respects, appears to be the next frontier in computing. Our brief overview of the state of Commonsense Knowledge (CSK) in Machine Intelligence provides insights into CSK acquisition, CSK in natural language, applications of CSK and discussion of open issues. This paper provides a report of a tutorial at a recent conference with a brief survey of topics.
Generalization Error in Deep Learning

Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this article, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results.
Word Embeddings via Tensor Factorization

Most popular word embedding techniques involve implicit or explicit factorization of a word co-occurrence based matrix into low rank factors. In this paper, we aim to generalize this trend by using numerical methods to factor higher-order word co-occurrence based arrays, or tensors. We present four word embeddings using tensor factorization and analyze their advantages and disadvantages. One of our main contributions is a novel joint symmetric tensor factorization technique related to the idea of coupled tensor factorization. We show that embeddings based on tensor factorization can be used to discern the various meanings of polysemous words without being explicitly trained to do so, and motivate the intuition behind why this works in a way that doesn’t with existing methods. We also modify an existing word embedding evaluation metric known as Outlier Detection [Camacho-Collados and Navigli, 2016] to evaluate the quality of the order-N relations that a word embedding captures, and show that tensor-based methods outperform existing matrix-based methods at this task. Experimentally, we show that all of our word embeddings either outperform or are competitive with state-of-the-art baselines commonly used today on a variety of recent datasets. Suggested applications of tensor factorization-based word embeddings are given, and all source code and pre-trained vectors are publicly available online.
Learning Entity and Relation Embeddings for Knowledge Graph Completion

Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation embeddings by regarding a relation as translation from head entity to tail entity. We note that these models simply put both entities and relations within the same semantic space. In fact, an entity may have multiple aspects and various relations may focus on different aspects of entities, which makes a common space insufficient for modeling. In this paper, we propose TransR to build entity and relation embeddings in separate entity space and relation spaces. Afterwards, we learn embeddings by first projecting entities from entity space to corresponding relation space and then building translations between projected entities. In experiments, we evaluate our models on three tasks including link prediction, triple classification and relational fact extraction. Experimental results show significant and consistent improvements compared to stateof-the-art baselines including TransE and TransH. The source code of this paper can be obtained from https: //github.com/mrlyk423/relation extraction.
Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation

Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis. Specifically, as a small, soft, and flexible abdominal organ, the pancreas demonstrates very high inter-patient anatomical variability in both its shape and volume. This inhibits traditional automated segmentation methods from achieving high accuracies, especially compared to the performance obtained for other organs, such as the liver, heart or kidneys. To fill this gap, we present an automated system from 3D computed tomography (CT) volumes that is based on a two-stage cascaded approach-pancreas localization and pancreas segmentation. For the first step, we localize the pancreas from the entire 3D CT scan, providing a reliable bounding box for the more refined segmentation step. We introduce a fully deep-learning approach, based on an efficient application of holistically-nested convolutional networks (HNNs) on the three orthogonal axial, sagittal, and coronal views. The resulting HNN per-pixel probability maps are then fused using pooling to reliably produce a 3D bounding box of the pancreas that maximizes the recall. We show that our introduced localizer compares favorably to both a conventional non-deep-learning method and a recent hybrid approach based on spatial aggregation of superpixels using random forest classification. The second, segmentation, phase operates within the computed bounding box and integrates semantic mid-level cues of deeply-learned organ interior and boundary maps, obtained by two additional and separate realizations of HNNs. By integrating these two mid-level cues, our method is capable of generating boundary-preserving pixel-wise class label maps that result in the final pancreas segmentation. Quantitative evaluation is performed on a publicly available dataset of 82 patient CT scans using 4-fold cross-validation (CV). We achieve a (mean  ±  std. dev.) Dice similarity coefficient (DSC) of 81.27 ± 6.27% in validation, which significantly outperforms both a previous state-of-the art method and a preliminary version of this work that report DSCs of 71.80 ± 10.70% and 78.01 ± 8.20%, respectively, using the same dataset.
New Horizons in the Study of Language and Mind: New horizons in the study of language

This book is an outstanding contribution to the philosophical study of language and mind, by one of the most influential thinkers of our time. In a series of penetrating essays, Noam Chomsky cuts through the confusion and prejudice which has infected the study of language and mind, bringing new solutions to traditional philosophical puzzles and fresh perspectives on issues of general interest, ranging from the mind–body problem to the unification of science. Using a range of imaginative and deceptively simple linguistic analyses, Chomsky argues that there is no coherent notion of “language” external to the human mind, and that the study of language should take as its focus the mental construct which constitutes our knowledge of language. Human language is therefore a psychological, ultimately a “biological object,” and should be analysed using the methodology of the natural sciences. His examples and analyses come together in this book to give a unique and compelling perspective on language and the mind.
Guided HTM: Hierarchical Topic Model with Dirichlet Forest Priors

Despite the proliferation of topic models, the organization of topics from the probabilistic models needs improvement in two ways: the better structured presentation of topics and the incorporation of domain knowledge on the corpus. The structured presentation, i.e., the hierarchical topic model, helps in categorizing similar topics; the incorporation of domain knowledge enables the concentrated sampling of predefined keywords in the mixture parameter learning. This paper presents a hierarchical topic models with incorporated domain knowledge, called Guided Hierarchical Topic Model (GHTM). Specifically, we allocated the prior information from the knowledge to the Dirichlet Forest prior. From the prior adjustment, we obtained the topic tree guided by the domain knowledge. This paper also contributes in enumerating four different knowledge extraction methods and applying the extracted knowledge to GHTM. We evaluated the performance of GHTM in terms of the hierarchical clustering accuracy, and we found a significant improvement of hierarchical clustering measured by F-measures. This improvement is also verified by the perplexity analyses. Additionally, we measured topic quality with KL-divergence and visualization, and these confirm the ability to better separate topic distributions. Finally, we tested the hierarchical topic quality through human experiments, and this also revealed significant improvements originating from the guidance.
Dynamic Task Prioritization for Multitask Learning

We propose dynamic task prioritization for multitask learning. This allows a model to dynamically prioritize difficult tasks during training, where difficulty is inversely proportional to performance, and where difficulty changes over time. In contrast to curriculum learning, where easy tasks are prioritized above difficult tasks, we present several studies showing the importance of prioritizing difficult tasks first. We observe that imbalances in task difficulty can lead to unnecessary emphasis on easier tasks, thus neglecting and slowing progress on difficult tasks. Motivated by this finding, we introduce a notion of dynamic task prioritization to automatically prioritize more difficult tasks by adaptively adjusting the mixing weight of each task’s loss objective. Additional ablation studies show the impact of the task hierarchy, or the task ordering, when explicitly encoded in the network architecture. Our method outperforms existing multitask methods and demonstrates competitive results with modern single-task models on the COCO and MPII datasets.
Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model

We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and causes of fault behavior in the plant. Having a self-consistent data set with labeled faults, we used an LSTM architecture with a forecasting error threshold to obtain precision and recall quality metrics. The dependency of the quality metric on the threshold level is considered. An appropriate mechanism such as “one handle” was introduced for filtering faults that are outside of the plant operator field of interest.
A new approach to automatic signature complexity assessment

Understanding signature complexity has been shown to be a crucial facet for both forensic and biometric appbcations. The signature complexity can be defined as the difficulty that forgers have when imitating the dynamics (constructional aspects) of other users signatures. Knowledge of complexity along with others facets such stability and signature length can lead to more robust and secure automatic signature verification systems. The work presented in this paper investigates the creation of a novel mathematical model for the automatic assessment of the signature complexity, analysing a wider set of dynamic signature features and also incorporating a new layer of detail, investigating the complexity of individual signature strokes. To demonstrate the effectiveness of the model this work will attempt to reproduce the signature complexity assessment made by experienced FDEs on a dataset of 150 signature samples.