nils-herrmann/clean_scirepeval huggingface dataset:
Classifiers and machine learning techniques for image processing and computer vision

We propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are interested in: two and multiclass image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multi-classification. Machine Learning Techniques; Digital Forensics; Steganalysis; Feature Fusion; Classifier Fusion; Multi-class Classification; Image Categorization.
Circle planarity of level graphs

In this thesis we generalise the notion of level planar graphs in two directions: track planarity and radial planarity. Our main results are linear time algorithms both for the planarity test and for the computation of an embedding, and thus a drawing. Our algorithms use and generalise PQ-trees, which are a data structure for efficient planarity tests. A graph is a level graph, if it has a partition of the vertices in levels such that the vertices of each level can be placed on a horizontal line and the edges are strictly downwards. It is level planar if there are no edge crossings. Level planarity can be tested efficiently in linear time by sophisticated and complex algorithms. Level graphs exclude horizontal edges between vertices on the same level. Such edges are allowed by our track graphs. In radial level graphs the vertices of each level are placed on concentric circles and the edges are outwards. We characterise essential differences between level and radial level planar graphs, which are expressed by level non-planar biconnected components called rings. The presence of rings introduces the particular problem of the nesting of non-connected components. Further, we study forbidden subgraphs which destroy radial level planarity. The track and circle extensions are combined to form circle graphs, which allow edges along the concentric circles. Level planar graphs arise as a specialisation of directed acyclic graphs that are usually drawn by the Sugiyama algorithm, which avoids edge crossings. Applications of level or track planar drawings include for example biochemical pathways, entity relationship and UML class diagrams, or flow charts which occur for example in project management. Typical applications of radial drawings are social networks.
MoFAP: A Multi-level Representation for Action Recognition

This paper proposes a multi-level video representation by stacking the activations of motion features, atoms, and phrases (MoFAP). Motion features refer to those low-level local descriptors, while motion atoms and phrases can be viewed as mid-level “temporal parts”. Motion atom is defined as an atomic part of action, and captures the motion information of video in a short temporal scale. Motion phrase is a temporal composite of multiple motion atoms defined with an AND/OR structure. It further enhances the discriminative capacity of motion atoms by incorporating temporal structure in a longer temporal scale. Specifically, we first design a discriminative clustering method to automatically discover a set of representative motion atoms. Then, we mine effective motion phrases with high discriminative and representative capacity in a bottom-up manner. Based on these basic units of motion features, atoms, and phrases, we construct a MoFAP network by stacking them layer by layer. This MoFAP network enables us to extract the effective representation of video data from different levels and scales. The separate representations from motion features, motion atoms, and motion phrases are concatenated as a whole one, called Activation of MoFAP. The effectiveness of this representation is demonstrated on four challenging datasets: Olympic Sports, UCF50, HMDB51, and UCF101. Experimental results show that our representation achieves the state-of-the-art performance on these datasets.
Robust stereo matching with surface normal prediction

Traditional stereo matching approaches generally have problems in handling textureless regions, strong occlusions and reflective regions that do not satisfy a Lambertian surface assumption. In this paper, we propose to combine the predicted surface normal by deep learning to overcome these inherent difficulties in stereo matching. With the selected reliable disparities from stereo matching method and effective edge fusion strategy, we can faithfully convert the predicted surface normal map to a disparity map by solving a least squares system which maintains discontinuity on object boundaries and continuity on other regions. Then we refine the disparity map iteratively by bilateral filtering-based completion and edge feature refinement. Experimental results on the Middlebury dataset and our own captured stereo sequences demonstrate the effectiveness of the proposed approach.
Methods for Sentence Compression

Sentence compression is the task of producing a summary of a single sentence. The compressed sentence should be shorter, contain the important content from the original, and itself be grammatical. The three papers discussed here take different approaches to identifying important content, determining which sentences are grammatical, and jointly optimizing these objectives. One family of approaches we will discuss is those that are tree-based, which create a compressed sentence by making edits to the syntactic tree of the original sentence. A second type of approach is sentence-based, which generates strings directly. Orthogonal to either of these two approaches is whether sentences are treated in isolation or if the surrounding discourse affects compressions. We compare a tree-based, a sentence-based, and a discourse-based approach and conclude with ideas for future work in this area. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-10-20. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/929 Methods for Sentence
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods

In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping. Model-free deep reinforcement learning (RL) has been successfully applied to a range of challenging environments, but the proliferation of algorithms makes it difficult to discern which particular approach would be best suited for a rich, diverse task like grasping. To answer this question, we propose a simulated benchmark for robotic grasping that emphasizes off-policy learning and generalization to unseen objects. Off-policy learning enables utilization of grasping data over a wide variety of objects, and diversity is important to enable the method to generalize to new objects that were not seen during training. We evaluate the benchmark tasks against a variety of Q-function estimation methods, a method previously proposed for robotic grasping with deep neural network models, and a novel approach based on a combination of Monte Carlo return estimation and an off-policy correction. Our results indicate that several simple methods provide a surprisingly strong competitor to popular algorithms such as double Q-learning, and our analysis of stability sheds light on the relative tradeoffs between the algorithms 11Accompanying video: https://goo.gl/pyMd6p.
RelEx - Relation extraction using dependency parse trees

The discovery of regulatory pathways, signal cascades, metabolic processes or disease models requires knowledge on individual relations like e.g. physical or regulatory interactions between genes and proteins. Most interactions mentioned in the free text of biomedical publications are not yet contained in structured databases.



We developed RelEx, an approach for relation extraction from free text. It is based on natural language preprocessing producing dependency parse trees and applying a small number of simple rules to these trees. We applied RelEx on a comprehensive set of one million MEDLINE abstracts dealing with gene and protein relations and extracted approximately 150,000 relations with an estimated performance of both 80% precision and 80% recall.


AVAILABILITY
The used natural language preprocessing tools are free for use for academic research. Test sets and relation term lists are available from our website (http://www.bio.ifi.lmu.de/publications/RelEx/).
Search Engine Optimization Research for Website Promotion

In order to make the search engine transfer information efficiently and accurately and do this optimization to improve the web search ranking, beginning with understanding the principle of search engine, this paper exports the specific explanation of search engine optimization. And then it introduces the new website building concepts and design concepts for the purpose of the construction of search engine optimization. Through an empirical research from the fields of the internal coding method, the website content realizable form and website overall architecture, the paper expounds search engine optimization tools, strategies and methods, and analysis the new thought that the enterprise and e-commerce sites with the search engine do the effective website promotion. And when the user through the search engine to search, the website can get a good rankings position in the search results, so as to improve the site traffic and finally enhance the website sales ability or advocacy capacity.
Essential Oils: Sources of Antimicrobials and Food Preservatives

Aromatic and medicinal plants produce essential oils in the form of secondary metabolites. These essential oils can be used in diverse applications in food, perfume, and cosmetic industries. The use of essential oils as antimicrobials and food preservative agents is of concern because of several reported side effects of synthetic oils. Essential oils have the potential to be used as a food preservative for cereals, grains, pulses, fruits, and vegetables. In this review, we briefly describe the results in relevant literature and summarize the uses of essential oils with special emphasis on their antibacterial, bactericidal, antifungal, fungicidal, and food preservative properties. Essential oils have pronounced antimicrobial and food preservative properties because they consist of a variety of active constituents (e.g., terpenes, terpenoids, carotenoids, coumarins, curcumins) that have great significance in the food industry. Thus, the various properties of essential oils offer the possibility of using natural, safe, eco-friendly, cost-effective, renewable, and easily biodegradable antimicrobials for food commodity preservation in the near future.
Opinion spam and analysis

Evaluative texts on the Web have become a valuable source of opinions on products, services, events, individuals, etc. Recently, many researchers have studied such opinion sources as product reviews, forum posts, and blogs. However, existing research has been focused on classification and summarization of opinions using natural language processing and data mining techniques. An important issue that has been neglected so far is opinion spam or trustworthiness of online opinions. In this paper, we study this issue in the context of product reviews, which are opinion rich and are widely used by consumers and product manufacturers. In the past two years, several startup companies also appeared which aggregate opinions from product reviews. It is thus high time to study spam in reviews. To the best of our knowledge, there is still no published study on this topic, although Web spam and email spam have been investigated extensively. We will see that opinion spam is quite different from Web spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that opinion spam in reviews is widespread. This paper analyzes such spam activities and presents some novel techniques to detect them
Biopsy interpretation of colonic biopsies when inflammatory bowel disease is excluded

The interpretation of colonic biopsies related to inflammatory conditions can be challenging because the colorectal mucosa has a limited repertoire of morphologic responses to various injurious agents. Only few processes have specific diagnostic features, and many of the various histological patterns reflect severity and duration of the disease. Importantly the correlation with endoscopic and clinical information is often cardinal to arrive at a specific diagnosis in many cases.
Improving Security Level through Obfuscation Technique for Source Code Protection using AES Algorithm

PHP is a server-side scripting programming language that is widely used to develop website services. However, web-based PHP applications are distributed in source code so that the security is vulnerable and weak because the lines of source code can be easily copied, modified, or used in other applications. These research aims to implement obfuscation techniques design in PHP extension code using AES algorithm. The AES algorithm recommended by NIST (National Institute of Standards and Technology) to protect the US government's national information security system. Through obfuscation technique using encryption, it is expected that programmers have an option to protect the PHP source code so that the copyright or intellectual property of the program can be protected
Accelerated learning method using edmodo to increase students' mathematical connection and self-regulated learning

This study aims to analyze implementation of accelerated e-learning method using edmodo media to increase mathematical connection ability and self-regulated learning of students. The research method employed was mixed method research with embedded design. Subjects of the study were eleventh grade students of a vocational secondary school in Tasikmalaya, Indonesia. Research instruments used were test for mathematical connection ability, questionnaire of students' self-regulated learning, observation and interview guidelines. Statistical analysis were t-test, mann-whitney test, and two-way anova. results of this study showed that (1) mathematical connection abillity of high and low clasification students who studied used accelerated learning with edmodo was better than that of students who studied with conventional learning; (2) no differences were found in self-regulated learning of high and low classification students who studied used accelerated learning with edmodo was better than that of students who studied with conventional learning; (3) a positive correlation was found between mathematical connection ability and self-regulated learning students.
Imputation of Missing Data in Industrial Databases

A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records. But every cell in a database is not an independent datum. Statistical relationships will constrain and, often determine, missing values. Data imputation, the filling in of missing values for partially missing data, can thus be an invaluable first step in many IDA projects. New imputation methods that can handle the large-scale problems and large-scale sparsity of industrial databases are needed. To illustrate the incomplete database problem, we analyze one database with instrumentation maintenance and test records for an industrial process. Despite regulatory requirements for process data collection, this database is less than 50% complete. Next, we discuss possible solutions to the missing data problem. Several approaches to imputation are noted and classified into two categories: data-driven and model-based. We then describe two machine-learning-based approaches that we have worked with. These build upon well-known algorithms: AutoClass and C4.5. Several experiments are designed, all using the maintenance database as a common test-bed but with various data splits and algorithmic variations. Results are generally positive with up to 80% accuracies of imputation. We conclude the paper by outlining some considerations in selecting imputation methods, and by discussing applications of data imputation for intelligent data analysis.
Learning computer science concepts with scratch

Scratch is a visual programming environment that is widely used by young people. We investigated if Scratch can be used to teach concepts of computer science. We developed new learning materials for middle-school students that were designed according to the constructionist philosophy of Scratch and evaluated them in two schools. The classes were normal classes, not extracurricular activities whose participants are self-selected. Questionnaires and a test were constructed based upon a novel combination of the Revised Bloom Taxonomy and the SOLO taxonomy. These quantitative instruments were augmented with a qualitative analysis of observations within the classes. The results showed that in general students could successfully learn important concepts of computer science, although there were some problems with initialization, variables and concurrency; these problems can be overcome by modifications to the teaching process.
A Tutorial on Network Embeddings

Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. In this survey, we give an overview of network embeddings by summarizing and categorizing recent advancements in this research field. We first discuss the desirable properties of network embeddings and briefly introduce the history of network embedding algorithms. Then, we discuss network embedding methods under different scenarios, such as supervised versus unsupervised learning, learning embeddings for homogeneous networks versus for heterogeneous networks, etc. We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.
Capital Structure Effect on Firms Performance : Focusing on Consumers and Industrials Sectors on Malaysian Firms

This study seeks to investigate the impact of capital structure on firm performance by analyzing the relationship between operating performance of Malaysian firms, measured by return on asset (ROA) and return on equity (ROE) with short-term debt (STD), long-term debt (LTD) and total debt (TD). Four variables found by most literature to have an influence on firm operating performance, namely, size, asset grow, sales grow and efficiency, are used as control variables. This study covers two major sectors in Malaysian equity market which are the consumers and industrials sectors. 58 firms were identified as the sample firms and financial data from the year 2005 through 2010 are used as observations for this study, resulting in a total numbers of observations of 358. A series of regression analysis were executed for each model. Lag values for the proxies were also used to replace the non lag values in order to ensure that any extended effect of capital structure on firm performance is also examined. The study finds that only STD and TD have significant relationship with ROA while ROE has significant on each of debt level. However, the analysis with lagged values shows that non of lagged values for STD, TD and LTD has significant relationship with performance.
The blocker tag: selective blocking of RFID tags for consumer privacy

We propose the use of "selective blocking" by "blocker tags" as a way of protecting consumers from unwanted scanning of RFID tags attached to items they may be carrying or wearing.While an ordinary RFID tag is a simple, cheap (e.g. five-cent) passive device intended as an "electronic bar-code" for use in supply-chain management, a blocker tag is a cheap passive RFID device that can simulate many ordinary RFID tags simultaneously. When carried by a consumer, a blocker tag thus "blocks" RFID readers. It can do so universally by simulating all possible RFID tags. Or a blocker tag can block selectively by simulating only selected subsets of ID codes, such as those by a particular manufacturer, or those in a designated "privacy zone.We believe that this approach, when used with appropriate care, provides a very attractive alternative for addressing privacy concerns raised by the potential (and likely) widespread use of RFID tags in consumer products.We also discuss possible abuses arising from blocker tags, and means for detecting and dealing with them.
How low does ethical leadership flow ? Test of a trickle-down model

0749-5978/$ see front matter 2008 Elsevier Inc. A doi:10.1016/j.obhdp.2008.04.002 * Corresponding author.  E-mail address: dmayer@bus.ucf.edu (D.M. Mayer) This research examines the relationships between top management and supervisory ethical leadership and group-level outcomes (e.g., deviance, OCB) and suggests that ethical leadership flows from one organizational level to the next. Drawing on social learning theory [Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.; Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall.] and social exchange theory [Blau, p. (1964). Exchange and power in social life. ], the results support our theoretical model using a sample of 904 employees and 195 managers in 195 departments. We find a direct negative relationship between both top management and supervisory ethical leadership and group-level deviance, and a positive relationship with group-level OCB. Finally, consistent with the proposed trickle-down model, the effects of top management ethical leadership on group-level deviance and OCB are mediated by supervisory ethical leadership.
Contributions of memory circuits to language: the declarative/procedural model

The structure of the brain and the nature of evolution suggest that, despite its uniqueness, language likely depends on brain systems that also subserve other functions. The declarative/procedural (DP) model claims that the mental lexicon of memorized word-specific knowledge depends on the largely temporal-lobe substrates of declarative memory, which underlies the storage and use of knowledge of facts and events. The mental grammar, which subserves the rule-governed combination of lexical items into complex representations, depends on a distinct neural system. This system, which is composed of a network of specific frontal, basal-ganglia, parietal and cerebellar structures, underlies procedural memory, which supports the learning and execution of motor and cognitive skills, especially those involving sequences. The functions of the two brain systems, together with their anatomical, physiological and biochemical substrates, lead to specific claims and predictions regarding their roles in language. These predictions are compared with those of other neurocognitive models of language. Empirical evidence is presented from neuroimaging studies of normal language processing, and from developmental and adult-onset disorders. It is argued that this evidence supports the DP model. It is additionally proposed that "language" disorders, such as specific language impairment and non-fluent and fluent aphasia, may be profitably viewed as impairments primarily affecting one or the other brain system. Overall, the data suggest a new neurocognitive framework for the study of lexicon and grammar.