nils-herrmann/clean_scirepeval huggingface dataset:
Social coding in GitHub: transparency and collaboration in an open software repository

Social applications on the web let users track and follow the activities of a large number of others regardless of location or affiliation. There is a potential for this transparency to radically improve collaboration and learning in complex knowledge-based activities. Based on a series of in-depth interviews with central and peripheral GitHub users, we examined the value of transparency for large-scale distributed collaborations and communities of practice. We find that people make a surprisingly rich set of social inferences from the networked activity information in GitHub, such as inferring someone else's technical goals and vision when they edit code, or guessing which of several similar projects has the best chance of thriving in the long term. Users combine these inferences into effective strategies for coordinating work, advancing technical skills and managing their reputation.
Estimating mutual information.

We present two classes of improved estimators for mutual information M(X,Y), from samples of random points distributed according to some joint probability density mu(x,y). In contrast to conventional estimators based on binnings, they are based on entropy estimates from k -nearest neighbor distances. This means that they are data efficient (with k=1 we resolve structures down to the smallest possible scales), adaptive (the resolution is higher where data are more numerous), and have minimal bias. Indeed, the bias of the underlying entropy estimates is mainly due to nonuniformity of the density at the smallest resolved scale, giving typically systematic errors which scale as functions of k/N for N points. Numerically, we find that both families become exact for independent distributions, i.e. the estimator M(X,Y) vanishes (up to statistical fluctuations) if mu(x,y)=mu(x)mu(y). This holds for all tested marginal distributions and for all dimensions of x and y. In addition, we give estimators for redundancies between more than two random variables. We compare our algorithms in detail with existing algorithms. Finally, we demonstrate the usefulness of our estimators for assessing the actual independence of components obtained from independent component analysis (ICA), for improving ICA, and for estimating the reliability of blind source separation.
Multilingual OCR (MOCR): An Approach to Classify Words to Languages

There are immense efforts to design a complete OCR for most of the world’s leading languages, however, multilingual documents either of handwritten or of printed form. As a united attempt, Unicode based OCRs were studied mostly with some positive outcomes, despite the fact that a large character set slows down the recognition significantly. In this paper, we come out with a method to classify words to a language as the word segmentation is complete. For the purpose, we identified the characteristics of writings of several languages and utilized projecting method combined with some other feature extraction methods. In addition, this paper intends a modified statistical approach to correct the skewness before processing a segmented document. The proposed procedure, evaluated for a collection of both handwritten and printed documents, came with excellent outcomes in assigning words to languages. General Terms Pattern Recognition, Document Processing, Optical Character Recognition.
Mechanics and energetics of human locomotion on sand.

Moving about in nature often involves walking or running on a soft yielding substratum such as sand, which has a profound effect on the mechanics and energetics of locomotion. Force platform and cinematographic analyses were used to determine the mechanical work performed by human subjects during walking and running on sand and on a hard surface. Oxygen consumption was used to determine the energetic cost of walking and running under the same conditions. Walking on sand requires 1.6-2.5 times more mechanical work than does walking on a hard surface at the same speed. In contrast, running on sand requires only 1.15 times more mechanical work than does running on a hard surface at the same speed. Walking on sand requires 2.1-2.7 times more energy expenditure than does walking on a hard surface at the same speed; while running on sand requires 1.6 times more energy expenditure than does running on a hard surface. The increase in energy cost is due primarily to two effects: the mechanical work done on the sand, and a decrease in the efficiency of positive work done by the muscles and tendons.
Humans use directed and random exploration to solve the explore-exploit dilemma.

All adaptive organisms face the fundamental tradeoff between pursuing a known reward (exploitation) and sampling lesser-known options in search of something better (exploration). Theory suggests at least two strategies for solving this dilemma: a directed strategy in which choices are explicitly biased toward information seeking, and a random strategy in which decision noise leads to exploration by chance. In this work we investigated the extent to which humans use these two strategies. In our "Horizon task," participants made explore-exploit decisions in two contexts that differed in the number of choices that they would make in the future (the time horizon). Participants were allowed to make either a single choice in each game (horizon 1), or 6 sequential choices (horizon 6), giving them more opportunity to explore. By modeling the behavior in these two conditions, we were able to measure exploration-related changes in decision making and quantify the contributions of the two strategies to behavior. We found that participants were more information seeking and had higher decision noise with the longer horizon, suggesting that humans use both strategies to solve the exploration-exploitation dilemma. We thus conclude that both information seeking and choice variability can be controlled and put to use in the service of exploration.
Watch and learn: Semi-supervised learning of object detectors from videos

We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic drift. Our approach does not assume exhaustive labeling of each object instance in any single frame, or any explicit annotation of negative data. Working in such a generic setting allow us to tackle multiple object instances in video, many of which are static. In contrast, existing approaches either do not consider multiple object instances per video, or rely heavily on the motion of the objects present. The experiments demonstrate the effectiveness of our approach by evaluating the automatically labeled data on a variety of metrics like quality, coverage (recall), diversity, and relevance to training an object detector.
Prediction of Student Academic Performance by an Application of K-Means Clustering Algorithm

Clustering is used to extract meaningful information and to develop significant relationships among variables stored in large data set/data warehouse. In this paper data clustering technique named k-means clustering is applied to analyze student’s learning behavior. The student’s evaluation factor like class quizzes, mid and final exam assignment are studied. It is recommended that all these correlated information should be conveyed to the class advisor before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students.
Forensic Analysis and Anonymisation of Printed Documents

Contrary to popular belief, the paperless office has not yet established itself. Printer forensics is therefore still an important field today to protect the reliability of printed documents or to track criminals. An important task of this is to identify the source device of a printed document. There are many forensic approaches that try to determine the source device automatically and with commercially available recording devices. However, it is difficult to find intrinsic signatures that are robust against a variety of influences of the printing process and at the same time can identify the specific source device. In most cases, the identification rate only reaches up to the printer model. For this reason we reviewed document colour tracking dots, an extrinsic signature embedded in nearly all modern colour laser printers. We developed a refined and generic extraction algorithm, found a new tracking dot pattern and decoded pattern information. Through out we propose to reuse document colour tracking dots, in combination with passive printer forensic methods. From privacy perspective we additional investigated anonymization approaches to defeat arbitrary tracking. Finally we propose our toolkitdeda which implements the entire workflow of extracting, analysing and anonymisation of a tracking dot pattern.
Two-Sided Markets and Electronic Intermediaries ∗

The object of this paper is to discuss intermediation on-line from the perspective of two-sided markets. It builds a simple model of the intermediation activity when trading partners are involved into a commercial relationship and uses it to illustrate some of the results that emerge in the two-sided market literature, as well as to discuss some new aspects. The first part concentrates on a monopoly intermediation service and discusses both efficient pricing and monopoly pricing. The second part builds on Jullien and Caillaud (2003) to discuss the nature of competition between intermediaries.
ING FOR TEXT CLASSIFICATION

Zero-shot Learners are models capable of predicting unseen classes. In this work, we propose a Zero-shot Learning approach for text categorization. Our method involves training model on a large corpus of sentences to learn the relationship between a sentence and embedding of sentence’s tags. Learning such relationship makes the model generalize to unseen sentences, tags, and even new datasets provided they can be put into same embedding space. The model learns to predict whether a given sentence is related to a tag or not; unlike other classifiers that learn to classify the sentence as one of the possible classes. We propose three different neural networks for the task and report their accuracy on the test set of the dataset used for training them as well as two other standard datasets for which no retraining was done. We show that our models generalize well across new unseen classes in both cases. Although the models do not achieve the accuracy level of the state of the art supervised models, yet it evidently is a step forward towards general intelligence in natural language processing.
Mining User Mobility Features for Next Place Prediction in Location-Based Services

Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.
Decision Support based on Bio-PEPA Modeling and Decision Tree Induction: A New Approach, Applied to a Tuberculosis Case Study

The problem of selecting determinant features generating appropriate model structure is a challenge in epidemiological modelling. Disease spread is highly complex, and experts develop their understanding of its dynamic over years. There is an increasing variety and volume of epidemiological data which adds to the potential confusion. The authors propose here to make use of that data to better understand disease systems. Decision tree techniques have been extensively used to extract pertinent information and improve decision making. In this paper, the authors propose an innovative structured approach combining decision tree induction with Bio-PEPA computational modelling, and illustrate the approach through application to tuberculosis. By using decision tree induction, the enhanced Bio-PEPA model shows considerable improvement over the initial model with regard to the simulated results matching observed data. The key finding is that the developer expresses a realistic predictive model using relevant features, thus considering this approach as decision support, empowers the epidemiologist in his policy decision making.  Modelling, Data Mining, Decision Support, Decision Tree Induction, Epidemiology, Modelling and Simulation, Optimisation, Refinement, Tuberculosis
Predicting defect-prone software modules using support vector machines

Effective prediction of defect-prone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Support vector machines (SVM) have been successfully applied for solving both classification and regression problems in many applications. This paper evaluates the capability of SVM in predicting defect-prone software modules and compares its prediction performance against eight statistical and machine learning models in the context of four NASA datasets. The results indicate that the prediction performance of SVM is generally better than, or at least, is competitive against the compared models.
Event log imperfection patterns for process mining : Towards a systematic approach to cleaning event logs

Process-oriented data mining (process mining) uses algorithms and data (in the form of event logs) to construct models that aim to provide insights into organisational processes. The quality of the data (both form and content) presented to the modeling algorithms is critical to the success of the process mining exercise. Cleaning event logs to address quality issues prior to conducting a process mining analysis is a necessary, but generally tedious and ad hoc task. In this paper we describe a set of data quality issues, distilled from our experiences in conducting process mining analyses, commonly found in process mining event logs or encountered while preparing event logs from raw data sources. We show that patterns are used in a variety of domains as a means for describing commonly encountered problems and solutions. The main contributions of this article are in showing that a patterns-based approach is applicable to documenting commonly encountered event log quality issues, the formulation of a set of components for describing event log quality issues as patterns, and the description of a collection of 11 event log imperfection patterns distilled from our experiences in preparing event logs. We postulate that a systematic approach to using such a pattern repository to identify and repair event log quality issues benefits both the process of preparing an event log and the quality of the resulting event log. The relevance of the pattern-based approach is illustrated via application of the patterns in a case study and through an evaluation by researchers and practitioners in the field.
Design and Optimization of Nonvolatile Multibit 1 T 1 R Resistive RAM

Memristor-based random access memory (RAM) is being explored as a potential replacement for flash memory to sustain the historic trends in the improvement of density, access time, and energy consumption of nonvolatile memory. In this paper, we present the detailed functionality of multibit one-transistor one-memristor (1T1R) cell-based memory arrays, and propose circuit-level performance and energy models for an individual memory cell and the memory array as a whole. We consider titanium dioxide (TiO2)and hafnium oxide (HfOx )based memristors, and for these technologies, there is a sub-10% difference between energy and performance computed using our models and HSPICE simulations. Using a performance-driven design approach, the energy-optimized TiO2-based resistive RAM (RRAM) array consumes the least write (4.06 pJ/b) and read energy (188 fJ/b) when storing 3 b/cell for 100-ns write and 1-ns read access times. Similarly, HfOx -based RRAM array consumes the least write (365 fJ/b) and read energy (173 fJ/b) when storing 3 b/cell for 1-ns write and 200-ns read access times. We also present a detailed analysis of the implications of process, voltage, and temperature variations on the performance and energy consumption of a multibit RRAM cell.
Automatic Generation of Data-Oriented Exploits

As defense solutions against control-flow hijacking attacks gain wide deployment, control-oriented exploits from memory errors become difficult. As an alternative, attacks targeting non-control data do not require diverting the application’s control flow during an attack. Although it is known that such data-oriented attacks can mount significant damage, no systematic methods to automatically construct them from memory errors have been developed. In this work, we develop a new technique called data-flow stitching, which systematically finds ways to join data flows in the program to generate data-oriented exploits. We build a prototype embodying our technique in a tool called FLOWSTITCH that works directly on Windows and Linux binaries. In our experiments, we find that FLOWSTITCH automatically constructs 16 previously unknown and three known data-oriented attacks from eight real-world vulnerable programs. All the automatically-crafted exploits respect fine-grained CFI and DEP constraints, and 10 out of the 19 exploits work with standard ASLR defenses enabled. The constructed exploits can cause significant damage, such as disclosure of sensitive information (e.g., passwords and encryption keys) and escalation of privilege.
CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

Parallel computing using accelerators has gained widespre ad research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power, and other metrics. Howev er, accelerator based computing has significantly relegated the role of CPUs in computation. As CPUs evo lv and also offer matching computational resources, it is important to also include CPUs in the comput ation. We call this thehybrid computing model. Indeed, most computer systems of the present age offe r a d gree of heterogeneity and therefore such a model is quite natural. We reevaluate the claim of a recent paper by Lee et al.(ISCA 20 10). We argue that the right question arising out of Lee et al. (ISCA 2010) should be how to use a CPU+ GPU platform efficiently, instead of whether one should use a CPU or a GPU exclusively. To this end, we experiment with a set of 13 diverse workloads ranging from databases, image processing, spars e matrix kernels, and graphs. We experiment with two different hybrid platforms: one consisting of a 6-c ore Intel i7-980X CPU and an NVidia Tesla T10 GPU, and another consisting of an Intel E7400 dual core CP U with an NVidia GT520 GPU. On both these platforms, we show that hybrid solutions offer good ad vantage over CPU or GPU alone solutions. On both these platforms, we also show that our solutions are 9 0% resource efficient on average. Our work therefore suggests that hybrid computing can offer tremendous advantages at not only research-scale platforms but also the more realistic scale yst ms with significant performance gains and resource efficiency to the large scale user community.
Multi-view gait recognition using 3D convolutional neural networks

In this work we present a deep convolutional neural network using 3D convolutions for Gait Recognition in multiple views capturing spatio-temporal features. A special input format, consisting of the gray-scale image and optical flow enhance color invariance. The approach is evaluated on three different datasets, including variances in clothing, walking speeds and the view angle. In contrast to most state-of-the-art Gait Recognition systems the used neural network is able to generalize gait features across multiple large view angle changes. The results show a comparable to better performance in comparison with previous approaches, especially for large view differences.
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning

Ranking is a fundamental and widely studied problem in scenarios such as search, advertising, and recommendation. However, joint optimization for multi-scenario ranking, which aims to improve the overall performance of several ranking strategies in different scenarios, is rather untouched. Separately optimizing each individual strategy has two limitations. The first one is lack of collaboration between scenariosmeaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a suboptimal overall performance. The second limitation is the inability of modeling the correlation between scenarios meaning that independent optimization in one scenario only uses its own user data but ignores the context in other scenarios. In this paper, we formulate multi-scenario ranking as a fully cooperative, partially observable, multi-agent sequential decision problem. We propose a novel model named Multi-Agent Recurrent Deterministic Policy Gradient (MA-RDPG) which has a communication component for passing messages, several private actors (agents) for making actions for ranking, and a centralized critic for evaluating the overall performance of the co-working actors. Each scenario is treated as an agent (actor). Agents collaborate with each other by sharing a global action-value function (the critic) and passing messages that encodes historical information across scenarios. The model is evaluated with online settings on a large E-commerce platform. Results show that the proposed model exhibits significant improvements against baselines in terms of the overall performance.