nils-herrmann/clean_scirepeval huggingface dataset:
On the difficulty of benchmarking inductive program synthesis methods

A variety of inductive program synthesis (IPS) techniques have recently been developed, emerging from different areas of computer science. However, these techniques have not been adequately compared on general program synthesis problems. In this paper we compare several methods on problems requiring solution programs to handle various data types, control structures, and numbers of outputs. The problem set also spans levels of abstraction; some would ordinarily be approached using machine code or assembly language, while others would ordinarily be approached using high-level languages. The presented comparisons are focused on the possibility of success; that is, on whether the system can produce a program that passes all tests, for all training and unseen testing inputs. The compared systems are Flash Fill, MagicHaskeller, TerpreT, and two forms of genetic programming. The two genetic programming methods chosen were PushGP and Grammar Guided Genetic Programming. The results suggest that PushGP and, to an extent, TerpreT and Grammar Guided Genetic Programming are more capable of finding solutions than the others, albeit at a higher computational cost. A more salient observation is the difficulty of comparing these methods due to drastically different intended applications, despite the common goal of program synthesis.
Schema Independent Relational Learning

Learning novel relations from relational databases is an important problem with many applications. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same database may be represented under different schemas for various reasons, such as data quality, efficiency and usability. The output of current relational learning algorithms tends to vary quite substantially over the choice of schema. This variation complicates their off-the-shelf application. We introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We show that current algorithms are not schema independent. We propose Castor, a relational learning algorithm that achieves schema independence by leveraging data dependencies.
Object-Oriented Software Engineering - a Use Case Driven Approach

Excellent book is always being the best friend for spending little time in your office, night time, bus, and everywhere. It will be a good way to just look, open, and read the book while in that time. As known, experience and skill don't always come with the much money to acquire them. Reading this book with the PDF object oriented software engineering a use case driven approach will let you know more things.
Privacy preservation for V2G networks in smart grid: A survey

Vehicle to grid (V2G) network is a crucial part of smart grid. An electric vehicle (EV) in a V2G network uses electricity instead of gasoline, and this benefits the environment and helps mitigate the energy crisis. By using its battery capacity, the vehicle can serve temporarily as a distributed energy storage system to mitigate peak load of the power grid. However, the two-way communication and power flows not only facilitate the functionality of V2G network, but they also facilitate attackers as well. Privacy is now a big obstacle in the way of the development of V2G networks. The privacy preservation problem in V2G networks could be more severe than in other parts of Smart Grid due to its e-mobility. In this paper, we will analyze and summarize privacy preservation approaches which achieve various privacy preservation goals. We will survey research works, based on existing privacy preservation techniques, which address various privacy preservation problems in V2G networks, including anonymous authentication, location privacy, identification privacy, concealed data aggregation, privacy-preserving billing and payment, and privacy-preserving data publication. These techniques include homomorphic encryption, blind signature, group signature, ring signature, third party anonymity, and anonymity networks. We will summarize solved problems and issues of these techniques, and introduce possible solutions for unsolved problems.
When a city tells a story: urban topic analysis

This paper explores the use of textual and event-based citizen-generated data from services such as Twitter and Foursquare to study urban dynamics. It applies a probabilistic topic model to obtain a decomposition of the stream of digital traces into a set of urban topics related to various activities of the citizens in the course of a week. Due to the combined use of implicit textual and movement data, we obtain semantically rich modalities of the urban dynamics and overcome the drawbacks of several previous attempts. Other important advantages of our method include its flexibility and robustness with respect to the varying quality and volume of the incoming data. We describe an implementation architecture of the system, the main outputs of the analysis, and the derived exploratory visualisations. Finally, we discuss the implications of our methodology for enriching location-based services with real-time context.
On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models

We consider two graph models of semantic change. The first is a time-series model that relates embedding vectors from one time period to embedding vectors of previous time periods. In the second, we construct one graph for each word: nodes in this graph correspond to time points and edge weights to the similarity of the word’s meaning across two time points. We apply our two models to corpora across three different languages. We find that semantic change is linear in two senses. Firstly, today’s embedding vectors (= meaning) of words can be derived as linear combinations of embedding vectors of their neighbors in previous time periods. Secondly, self-similarity of words decays linearly in time. We consider both findings as new laws/hypotheses of semantic change.
Designing Gabor filters for optimal texture separability

The discrimination of textures is a critical aspect of identi"cation in digital imagery. Texture features generated by Gabor "lters have been increasingly considered and applied to image analysis. Here, a comprehensive classi"cation and segmentation comparison of di!erent techniques used to produce texture features using Gabor "lters is presented. These techniques are based on existing implementations as well as new, innovative methods. The functional characterization of the "lters as well as feature extraction based on the raw "lter outputs are both considered. Overall, using the Gabor "lter magnitude response given a frequency bandwidth and spacing of one octave and orientation bandwidth and spacing of 303 augmented by a measure of the texture complexity generated preferred results. ( 2000 Pattern Recognition Society.
Speech and gesture share the same communication system

Humans speak and produce symbolic gestures. Do these two forms of communication interact, and how? First, we tested whether the two communication signals influenced each other when emitted simultaneously. Participants either pronounced words, or executed symbolic gestures, or emitted the two communication signals simultaneously. Relative to the unimodal conditions, multimodal voice spectra were enhanced by gestures, whereas multimodal gesture parameters were reduced by words. In other words, gesture reinforced word, whereas word inhibited gesture. In contrast, aimless arm movements and pseudo-words had no comparable effects. Next, we tested whether observing word pronunciation during gesture execution affected verbal responses in the same way as emitting the two signals. Participants responded verbally to either spoken words, or to gestures, or to the simultaneous presentation of the two signals. We observed the same reinforcement in the voice spectra as during simultaneous emission. These results suggest that spoken word and symbolic gesture are coded as single signal by a unique communication system. This signal represents the intention to engage a closer interaction with a hypothetical interlocutor and it may have a meaning different from when word and gesture are encoded singly.
A Geometric Perspective on Optimal Representations for Reinforcement Learning

This paper proposes a new approach to representation learning based on geometric properties of the space of value functions. We study a two-part approximation of the value function: a nonlinear map from states to vectors, or representation, followed by a linear map from vectors to values. Our formulation considers adapting the representation to minimize the (linear) approximation of the value function of all stationary policies for a given environment. We show that this optimization reduces to making accurate predictions regarding a special class of value functions which we call adversarial value functions (AVFs). We argue that these AVFs make excellent auxiliary tasks, and use them to construct a loss which can be efficiently minimized to find a near-optimal representation for reinforcement learning. We highlight characteristics of the method in a series of experiments on the four-room domain.
Zero-Voltage- and Zero-Current-Switching Full-Bridge Converter With Secondary Resonance

A zero-voltage- and zero-current-switching full-bridge (FB) converter with secondary resonance is presented and analyzed. The primary side of the converter is composed of FB insulated-gate bipolar transistors, which are driven by phase-shift control. The secondary side is composed of a resonant tank and a half-wave rectifier. Without an auxiliary circuit, zero-voltage switching (for leading-leg switches) and zero-current switching (for lagging-leg switches) are achieved in the entire operating range. To implement the converter without an additional inductor, the leakage inductance of the transformer is utilized as the resonant inductor. Due to its many advantages, including high efficiency, minimum number of devices, and low cost, this converter is attractive for high-voltage and high-power applications. The analysis and design considerations of the converter are presented. A prototype was implemented for an application requiring a 5-kW output power, an input-voltage range varying from 250 to 350 V, and a 350-V output voltage. The experimental results obtained from a prototype verify the analysis. The prototype's efficiency at full load is over 95.5%.
Preschool children learn about causal structure from conditional interventions.

The conditional intervention principle is a formal principle that relates patterns of interventions and outcomes to causal structure. It is a central assumption of experimental design and the causal Bayes net formalism. Two studies suggest that preschoolers can use the conditional intervention principle to distinguish causal chains, common cause and interactive causal structures even in the absence of differential spatiotemporal cues and specific mechanism knowledge. Children were also able to use knowledge of causal structure to predict the patterns of evidence that would result from interventions. A third study suggests that children's spontaneous play can generate evidence that would support such accurate causal learning.
Suspicious URL Filtering Based on Logistic Regression with Multi-view Analysis

The current malicious URLs detecting techniques based on whole URL information are hard to detect the obfuscated malicious URLs. The most precise way to identify a malicious URL is verifying the corresponding web page contents. However, it costs very much in time, traffic and computing resource. Therefore, a filtering process that detecting more suspicious URLs which should be further verified is required in practice. In this work, we propose a suspicious URL filtering approach based on multi-view analysis in order to reduce the impact from URL obfuscation techniques. URLs are composed of several portions, each portion has a specific use. The proposed method intends to learn the characteristics from multiple portions (multi-view) of URLs for giving the suspicion level of each portion. Adjusting the suspicion threshold of each portion, the proposed system would select the most suspicious URLs. This work uses the real dataset from T. Co. to evaluate the proposed system. The requests from T. Co. are (1) detection rate should be less than 25%, (2) missing rate should be lower than 25%, and (3) the process with one hour data should be end in an hour. The experiment results show that our approach is effective, is capable to reserve more malicious URLs in the selected suspicious ones and satisfy the requests given by practical environment, such as T. Co. daily works.
Plant Disease Detection using Image Processing- A Review

This paper holds a survey on plant leaf diseases classification using image processing. Digital image processing has three basic steps: image processing, analysis and understanding. Image processing contains the preprocessing of the plant leaf as segmentation, color extraction, diseases specific data extraction and filtration of images. Image analysis generally deals with the classification of diseases. Plant leaf can be classified based on their morphological features with the help of various classification techniques such as PCA, SVM, and Neural Network. These classifications can be defined various properties of the plant leaf such as color, intensity, dimensions. Back propagation is most commonly used neural network. It has many learning, training, transfer functions which is used to construct various BP networks. Characteristics features are the performance parameter for image recognition. BP networks shows very good results in classification of the grapes leaf diseases. This paper provides an overview on different image processing techniques along with BP Networks used in leaf disease classification.
Optimizing Segmentation Strategies for Simultaneous Speech Translation

In this paper, we propose new algorithms for learning segmentation strategies for simultaneous speech translation. In contrast to previously proposed heuristic methods, our method finds a segmentation that directly maximizes the performance of the machine translation system. We describe two methods based on greedy search and dynamic programming that search for the optimal segmentation strategy. An experimental evaluation finds that our algorithm is able to segment the input two to three times more frequently than conventional methods in terms of number of words, while maintaining the same score of automatic
Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement

The back propagation (BP) neural network algorithm is a multi-layer feedforward network trained according to error back propagation algorithm and is one of the most widely applied neural network models. BP network can be used to learn and store a great deal of mapping relations of input-output model, and no need to disclose in advance the mathematical equation that describes these mapping relations. Its learning rule is to adopt the steepest descent method in which the back propagation is used to regulate the weight value and threshold value of the network to achieve the minimum error sum of square. This paper focuses on the analysis of the characteristics and mathematical theory of BP neural network and also points out the shortcomings of BP algorithm as well as several methods for improvement.
Tweet Recommendation with Graph Co-Ranking

As one of the most popular micro-blogging services, Twitter attracts millions of users, producing millions of tweets daily. Shared information through this service spreads faster than would have been possible with traditional sources, however the proliferation of user-generation content poses challenges to browsing and finding valuable information. In this paper we propose a graph-theoretic model for tweet recommendation that presents users with items they may have an interest in. Our model ranks tweets and their authors simultaneously using several networks: the social network connecting the users, the network connecting the tweets, and a third network that ties the two together. Tweet and author entities are ranked following a co-ranking algorithm based on the intuition that that there is a mutually reinforcing relationship between tweets and their authors that could be reflected in the rankings. We show that this framework can be parametrized to take into account user preferences, the popularity of tweets and their authors, and diversity. Experimental evaluation on a large dataset shows that our model outperforms competitive approaches by a large margin.
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance

0950-7051/$ see front matter 2011  A doi:10.1016/j.knosys.2011.06.013 ⇑ Corresponding author. E-mail addresses: jimenezv@uji.es (V. García), s mollined@uji.es (R.A. Mollineda). The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluating how learning is affected when different resampling algorithms transform the originally imbalanced data into artificially balanced class distributions. Experiments over 17 real data sets using eight different classifiers, four resampling algorithms and four performance evaluation measures show that over-sampling the minority class consistently outperforms under-sampling the majority class when data sets are strongly imbalanced, whereas there are not significant differences for databases with a low imbalance. Results also indicate that the classifier has a very poor influence on the effectiveness of the resampling strategies.
A Position Paper on Data Sovereignty: The Importance of Geolocating Data in the Cloud

In this paper we define the problem and scope of data sovereignty – the coupling of stored data authenticity and geographical location in the cloud. Establishing sovereignty is an especially important concern amid legal and policy constraints when data and resources are virtualized and widely distributed. We identify the key challenges that need to be solved to achieve an effective and un-cheatable solution as well as propose an initial technique for data sovereignty.
Identifying Key Users in Online Social Networks: A PageRank Based Approach

Online social networks evolved into a global mainstream medium that generates an increasing social and economic impact. However, many online social networks face the question how to leverage on their fast growing popularity to achieve sustainable revenues. In that context, particularly more effective advertising strategies and sophisticated customer loyalty programs to foster users’ retention are needed. Thereby, key users in terms of users’ connectivity and communication activity play a decisive role. However, quantitative approaches for the identification of key users in online social networks merging concepts and findings from research on users’ connectivity and communication activity are missing. Based on the design science research paradigm, we therefore propose a novel PageRank based approach bringing together both research streams. To demonstrate its practical applicability, we use a publicly available dataset of Facebook.com. Finally, we evaluate our novel PageRank based approach in comparison to existing approaches, which could alternatively be used.
Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks

The class average accuracies of different methods on the NYU V2: The Proposed Network Structure The model has a convolutional network and deconvolutional network for each modality, as well as a feature transformation network. In this structure, 1. The RGB and depth convolutional network have the same structure; 2. The deconvolutional networks are the mirrored version of the convolutional networks; 3. The feature transformation network extracts common features and modality specific features; 4. One modality can borrow the common features learned from the other modality.
Middle class and marginal? Socioeconomic status, stigma, and self-regulation at an elite university.

In four studies, the authors investigated the proposal that in the context of an elite university, individuals from relatively lower socioeconomic status (SES) backgrounds possess a stigmatized identity and, as such, experience (a) concerns regarding their academic fit and (b) self-regulatory depletion as a result of managing these concerns. Study 1, a correlational study, revealed the predicted associations between SES, concerns about academic fit, and self-regulatory strength. Results from Studies 2 and 3 suggested that self-presentation involving the academic domain is depleting for lower (but not higher) SES students: After a self-presentation task about academic achievement, lower SES students consumed more candy (Study 2) and exhibited poorer Stroop performance (Study 3) relative to their higher SES peers; in contrast, the groups did not differ after discussing a nonacademic topic (Study 3). Study 4 revealed the potential for eliminating the SES group difference in depletion via a social comparison manipulation. Taken together, these studies support the hypothesis that managing concerns about marginality can have deleterious consequences for self-regulatory resources.
Digital audio forensics: a first practical evaluation on microphone and environment classification

In this paper a first approach for digital media forensics is presented to determine the used microphones and the environments of recorded digital audio samples by using known audio steganalysis features. Our first evaluation is based on a limited exemplary test set of 10 different audio reference signals recorded as mono audio data by four microphones in 10 different rooms with 44.1 kHz sampling rate and 16 bit quantisation. Note that, of course, a generalisation of the results cannot be achieved. Motivated by the syntactical and semantical analysis of information and in particular by known audio steganalysis approaches, a first set of specific features are selected for classification to evaluate, whether this first feature set can support correct classifications. The idea was mainly driven by the existing steganalysis features and the question of applicability within a first and limited test set. In the tests presented in this paper, an inter-device analysis with different device characteristics is performed while intra-device evaluations (identical microphone models of the same manufacturer) are not considered. For classification the data mining tool WEKA with K-means as a clustering and Naive Bayes as a classification technique are applied with the goal to evaluate their classification in regard to the classification accuracy on known audio steganalysis features. Our results show, that for our test set, the used classification techniques and selected steganalysis features, microphones can be better classified than environments. These first tests show promising results but of course are based on a limited test and training set as well a specific test set generation. Therefore additional and enhanced features with different test set generation strategies are necessary to generalise the findings.