nils-herrmann/clean_scirepeval huggingface dataset:
A genetic programming approach to automated software repair

Genetic programming is combined with program analysis methods to repair bugs in off-the-shelf legacy C programs. Fitness is defined using negative test cases that exercise the bug to be repaired and positive test cases that encode program requirements. Once a successful repair is discovered, structural differencing algorithms and delta debugging methods are used to minimize its size. Several modifications to the GP technique contribute to its success: (1) genetic operations are localized to the nodes along the execution path of the negative test case; (2) high-level statements are represented as single nodes in the program tree; (3) genetic operators use existing code in other parts of the program, so new code does not need to be invented. The paper describes the method, reviews earlier experiments that repaired 11 bugs in over 60,000 lines of code, reports results on new bug repairs, and describes experiments that analyze the performance and efficacy of the evolutionary components of the algorithm.
An Area-Efficient Fully R-DAC-Based TFT-LCD Column Driver

This paper proposes an area-efficient fully resistor-string digital-to-analog-converter (R-DAC)-based thin-film transistor liquid crystal display (TFT-LCD) column driver in which DACs supply only negative-polarity voltages, while polarity inverters generate positive-polarity voltages from negative-polarity voltages. An offset cancellation technique is employed in negative-polarity buffers and polarity inverters. An experimental prototype 8-bit column driver was implemented using 0.35-m CMOS technology to verify the proposed driving scheme. The settling time is within 5 s, and the maximum deviation is 0.68 LSB. The average area per channel is 0.04 mm2. Compared with a conventional fully R-DAC-based TFT-LCD column driver, the proposed driving scheme has a DAC area saving of 41%.
Boltzmann Chains and Hidden Markov Models

We propose a statistical mechanical framework for the modeling of discrete time series. Maximum likelihood estimation is done via Boltzmann learning in one-dimensional networks with tied weights. We call these networks Boltzmann chains and show that they contain hidden Markov models (HMMs) as a special case. Our framework also motivates new architectures that address particular shortcomings of HMMs. We look at two such architectures: parallel chains that model feature sets with disparate time scales, and looped networks that model long-term dependencies between hidden states. For these networks, we show how to implement the Boltzmann learning rule exactly, in polynomial time, without resort to simulated or mean-field annealing. The necessary computations are done by exact decimation procedures from statistical mechanics.
A Decision Tree Approach for Predicting Students Academic Performance

This research is on the use of a decision tree approach for predicting students‟ academic performance. Education is the platform on which a society improves the quality of its citizens. To improve on the quality of education, there is a need to be able to predict academic performance of the students. The IBM Statistical Package for Social Studies (SPSS) is used to apply the Chi-Square Automatic Interaction Detection (CHAID) in producing the decision tree structure. Factors such as the financial status of the students, motivation to learn, gender were discovered to affect the performance of the students. 66.8% of the students were predicted to have passed while 33.2% were predicted to fail. It is observed that much larger percentage of the students were likely to pass and there is also a higher likely of male students passing than female students.
Development of a Mobile Phone Addiction Craving Scale and Its Validation in a Spanish Adult Population

In some people, problematic cell phone use can lead to situations in which they lose control, similar to those observed in other cases of addiction. Although different scales have been developed to assess its severity, we lack an instrument that is able to determine the desire or craving associated with it. Thus, with the objective of evaluating craving for cell phone use, in this study, we develop and present the Mobile Phone Addiction Craving Scale (MPACS). It consists of eight Likert-style items, with 10 response options, referring to possible situations in which the interviewee is asked to evaluate the degree of restlessness that he or she feels if the cell phone is unavailable at the moment. It can be self-administered or integrated in an interview when abuse or problems are suspected. With the existence of a single dimension, reflected in the exploratory factor analysis (EFA), the scale presents adequate reliability and internal consistency (α = 0.919). Simultaneously, we are able to show significantly increased correlations (r = 0.785, p = 0.000) with the Mobile Phone Problematic Use Scale (MPPUS) and state anxiety (r = 0.330, p = 0.000). We are also able to find associations with impulsivity, measured using the urgency, premeditation, perseverance, and sensation seeking scale, particularly in the dimensions of negative urgency (r = 0.303, p = 0.000) and positive urgency (r = 0.290, p = 0.000), which confirms its construct validity. The analysis of these results conveys important discriminant validity among the MPPUS user categories that are obtained using the criteria by Chow et al. (1). The MPACS demonstrates higher levels of craving in persons up to 35 years of age, reversing with age. In contrast, we do not find significant differences among the sexes. Finally, a receiver operating characteristic (ROC) analysis allows us to establish the scores from which we are able to determine the different levels of craving, from the absence of craving to that referred to as addiction. Based on these results, we can conclude that this scale is a reliable tool that complements ongoing studies on problematic cell phone use.
Tumour invasion and metastasis initiated by microRNA-10b in breast cancer

MicroRNAs have been implicated in regulating diverse cellular pathways. Although there is emerging evidence that some microRNAs can function as oncogenes or tumour suppressors, the role of microRNAs in mediating cancer metastasis remains unexplored. Here we show, using a combination of mouse and human cells, that microRNA-10b (miR-10b) is highly expressed in metastatic breast cancer cells and positively regulates cell migration and invasion. Overexpression of miR-10b in otherwise non-metastatic breast tumours initiates robust invasion and metastasis. Expression of miR-10b is induced by the transcription factor Twist, which binds directly to the putative promoter of mir-10b (MIRN10B). The miR-10b induced by Twist proceeds to inhibit translation of the messenger RNA encoding homeobox D10, resulting in increased expression of a well-characterized pro-metastatic gene, RHOC. Significantly, the level of miR-10b expression in primary breast carcinomas correlates with clinical progression. These findings suggest the workings of an undescribed regulatory pathway, in which a pleiotropic transcription factor induces expression of a specific microRNA, which suppresses its direct target and in turn activates another pro-metastatic gene, leading to tumour cell invasion and metastasis.
Design Method and Experimental Verification of a Novel Technique for Torque Ripple Reduction in Stator Claw-Pole PM Machines

Although relatively small in size and power output, automotive accessory motors play a vital role in improving such critical vehicle characteristics as drivability, comfort, and, most importantly, fuel economy. This paper describes a design method and experimental verification of a novel technique for torque ripple reduction in stator claw-pole permanent-magnet (PM) machines, which are a promising technology prospect for automotive accessory motors.
Fast GPU-based locality sensitive hashing for k-nearest neighbor computation

We present an efficient GPU-based parallel LSH algorithm to perform approximate k-nearest neighbor computation in high-dimensional spaces. We use the Bi-level LSH algorithm, which can compute k-nearest neighbors with higher accuracy and is amenable to parallelization. During the first level, we use the parallel RP-tree algorithm to partition datasets into several groups so that items similar to each other are clustered together. The second level involves computing the Bi-Level LSH code for each item and constructing a hierarchical hash table. The hash table is based on parallel cuckoo hashing and Morton curves. In the query step, we use GPU-based work queues to accelerate short-list search, which is one of the main bottlenecks in LSH-based algorithms. We demonstrate the results on large image datasets with 200,000 images which are represented as 512 dimensional vectors. In practice, our GPU implementation can obtain more than 40X acceleration over a single-core CPU-based LSH implementation.
A novel hybrid classification model of artificial neural networks and multiple linear regression models

The classification problem of assigning several observations into different disjoint groups plays an important role in business decision making and many other areas. Developing more accurate and widely applicable classification models has significant implications in these areas. It is the reason that despite of the numerous classification models available, the research for improving the effectiveness of these models has never stopped. Combining several models or using hybrid models has become a common practice in order to overcome the deficiencies of single models and can be an effective way of improving upon their predictive performance, especially when the models in combination are quite different. In this paper, a novel hybridization of artificial neural networks (ANNs) is proposed using multiple linear regression models in order to yield more general and more accurate model than traditional artificial neural networks for solving classification problems. Empirical results indicate that the proposed hybrid model exhibits effectively improved classification accuracy in comparison with traditional artificial neural networks and also some other classification models such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), K-nearest neighbor (KNN), and support vector machines (SVMs) using benchmark and real-world application data sets. These data sets vary in the number of classes (two versus multiple) and the source of the data (synthetic versus real-world). Therefore, it can be applied as an appropriate alternate approach for solving classification problems, specifically when higher forecasting
The Museum Wearable : real-time sensor-driven understanding of visitors ’ interests for personalized visually-augmented museum experiences

This paper describes the museum wearable: a wearable computer which orchestrates an audiovisual narration as a function of the visitor’s interests gathered from his/her physical path in the museum and length of stops. The wearable is made by a lightweight and small computer that people carry inside a shoulder pack. It offers an audiovisual augmentation of the surrounding environment using a small, lightweight eye-piece display (often called private-eye) attached to conventional headphones. Using custom built infrared location sensors distributed in the museum space, and statistical mathematical modeling, the museum wearable builds a progressively refined user model and uses it to deliver a personalized audiovisual narration to the visitor. This device will enrich and personalize the museum visit as a visual and auditory storyteller that is able to adapt its story to the audience’s interests and guide the public through the path of the exhibit.
Inferring Air Quality for Station Location Recommendation Based on Urban Big Data

This paper tries to answer two questions. First, how to infer real-time air quality of any arbitrary location given environmental data and historical air quality data from very sparse monitoring locations. Second, if one needs to establish few new monitoring stations to improve the inference quality, how to determine the best locations for such purpose? The problems are challenging since for most of the locations (>99%) in a city we do not have any air quality data to train a model from. We design a semi-supervised inference model utilizing existing monitoring data together with heterogeneous city dynamics, including meteorology, human mobility, structure of road networks, and point of interests (POIs). We also propose an entropy-minimization model to suggest the best locations to establish new monitoring stations. We evaluate the proposed approach using Beijing air quality data, resulting in clear advantages over a series of state-of-the-art and commonly used methods.
Action Recognition from Depth Sequences Using Depth Motion Maps-Based Local Binary Patterns

This paper presents a computationally efficient method for action recognition from depth video sequences. It employs the so called depth motion maps (DMMs) from three projection views (front, side and top) to capture motion cues and uses local binary patterns (LBPs) to gain a compact feature representation. Two types of fusion consisting of feature-level fusion and decision-level fusion are considered. In the feature-level fusion, LBP features from three DMMs are merged before classification while in the decision-level fusion, a soft decision-fusion rule is used to combine the classification outcomes. The introduced method is evaluated on two standard datasets and is also compared with the existing methods. The results indicate that it outperforms the existing methods and is able to process depth video sequences in real-time.
Heuristic Evaluation Functions for General Game Playing

This dissertation explores the problem of constructing an effective general game-playing program, with an emphasis on techniques for automatically constructing effective heuristic evaluation functions from game descriptions. A technique based on abstract models of games is presented. The abstract model treats mobility, payoff and termination as the most salient elements of a game. Each of these aspects are quantified in terms of stable features. Evidence is presented that the technique produces heuristic evaluation functions that are both comprehensible and effective. Empirical work includes a series of general game-playing programs that placed first or second for the three consecutive years of the AAAI General Game-Playing Competition. The full dissertation can be downloaded at https://sites.google.com/site/jimcluneresearch/ .
BitShred: feature hashing malware for scalable triage and semantic analysis

The sheer volume of new malware found each day is growing at an exponential pace. This growth has created a need for automatic malware triage techniques that determine what malware is similar, what malware is unique, and why. In this paper, we present BitShred, a system for large-scale malware similarity analysis and clustering, and for automatically uncovering semantic inter- and intra-family relationships within clusters. The key idea behind BitShred is using feature hashing to dramatically reduce the high-dimensional feature spaces that are common in malware analysis. Feature hashing also allows us to mine correlated features between malware families and samples using co-clustering techniques. Our evaluation shows that BitShred speeds up typical malware triage tasks by up to 2,365x and uses up to 82x less memory on a single CPU, all with comparable accuracy to previous approaches. We also develop a parallelized version of BitShred, and demonstrate scalability within the Hadoop framework.
Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images

Semantic image segmentation has recently witnessed considerable progress by training deep convolutional neural networks (CNNs). The core issue of this technique is the limited capacity of CNNs to depict visual objects. Existing approaches tend to utilize approximate inference in a discrete domain or additional aides and do not have a global optimum guarantee. We propose the use of the multi-label manifold ranking (MR) method in solving the linear objective energy function in a continuous domain to delineate visual objects and solve these problems. We present a novel embedded single stream optimization method based on the MR model to avoid approximations without sacrificing expressive power. In addition, we propose a novel network, which we refer to as dual multi-scale manifold ranking (DMSMR) network, that combines the dilated, multi-scale strategies with the single stream MR optimization method in the deep learning architecture to further improve the performance. Experiments on high resolution images, including close-range and remote sensing datasets, demonstrate that the proposed approach can achieve competitive accuracy without additional aides in an end-to-end manner.
Language identification using Gaussian mixture model tokenization

Phone tokenization followed by n-gram language modeling has consistently provided good results for the task of language identification. In this paper, this technique is generalized by using Gaussian mixture models as the basis for tokenizing. Performance results are presented for a system employing a GMM tokenizer in conjunction with multiple language processing and score combination techniques. On the 1996 CallFriend LID evaluation set, a 12-way closed set error rate of 17% was obtained.
Stably maintained dendritic spines are associated with lifelong memories

Changes in synaptic connections are considered essential for learning and memory formation. However, it is unknown how neural circuits undergo continuous synaptic changes during learning while maintaining lifelong memories. Here we show, by following postsynaptic dendritic spines over time in the mouse cortex, that learning and novel sensory experience lead to spine formation and elimination by a protracted process. The extent of spine remodelling correlates with behavioural improvement after learning, suggesting a crucial role of synaptic structural plasticity in memory formation. Importantly, a small fraction of new spines induced by novel experience, together with most spines formed early during development and surviving experience-dependent elimination, are preserved and provide a structural basis for memory retention throughout the entire life of an animal. These studies indicate that learning and daily sensory experience leave minute but permanent marks on cortical connections and suggest that lifelong memories are stored in largely stably connected synaptic networks.
Consistent Regions: Guaranteed Tuple Processing in IBM Streams

Guaranteed tuple processing has become critically important for many streaming applications. This paper describes how we enabled IBM Streams, an enterprise-grade stream processing system, to provide data processing guarantees. Our solution goes from language-level abstractions to a runtime protocol. As a result, with a couple of simple annotations at the source code level, IBM Streams developers can define consistent regions, allowing any subgraph of their streaming application to achieve guaranteed tuple processing. At runtime, a consistent region periodically executes a variation of the Chandy-Lamport snapshot algorithm to establish a consistent global state for that region. The coupling of consistent states with data replay enables guaranteed tuple processing.
RaBit EscAPE: a board game for computational thinking

Computational thinking (CT) is increasingly seen as a core literacy skill for the modern world on par with the longestablished skills of reading, writing, and arithmetic. To promote the learning of CT at a young age we capitalized on children's interest in play. We designed RabBit EscApe, a board game that challenges children, ages 610, to orient tangible, magnetized manipulatives to complete or create paths. We also ran an informal study to investigate the effectiveness of the game in fostering children's problemsolving capacity during collaborative game play. We used the results to inform our instructional interaction design that we think will better support the learning activities and help children hone the involved CT skills. Overall, we believe in the power of such games to challenge children to grow their understanding of CT in a focused and engaging activity.
A General Formal Framework for Pathfinding Problems with Multiple Agents

Pathfinding for a single agent is the problem of planning a route from an initial location to a goal location in an environment, going around obstacles. Pathfinding for multiple agents also aims to plan such routes for each agent, subject to different constraints, such as restrictions on the length of each path or on the total length of paths, no self-intersecting paths, no intersection of paths/plans, no crossing/meeting each other. It also has variations for finding optimal solutions, e.g., with respect to the maximum path length, or the sum of plan lengths. These problems are important for many real-life applications, such as motion planning, vehicle routing, environmental monitoring, patrolling, computer games. Motivated by such applications, we introduce a formal framework that is general enough to address all these problems: we use the expressive high-level representation formalism and efficient solvers of the declarative programming paradigm Answer Set Programming. We also introduce heuristics to improve the computational efficiency and/or solution quality. We show the applicability and usefulness of our framework by experiments, with randomly generated problem instances on a grid, on a real-world road network, and on a real computer game terrain.
Semi-supervised Learning by Sparse Representation

In this paper, we present a novel semi-supervised learning framework based on `1 graph. The `1 graph is motivated by that each datum can be reconstructed by the sparse linear superposition of the training data. The sparse reconstruction coefficients, used to deduce the weights of the directed `1 graph, are derived by solving an `1 optimization problem on sparse representation. Different from conventional graph construction processes which are generally divided into two independent steps, i.e., adjacency searching and weight selection, the graph adjacency structure as well as the graph weights of the `1 graph is derived simultaneously and in a parameter-free manner. Illuminated by the validated discriminating power of sparse representation in , we propose a semi-supervised learning framework based on `1 graph to utilize both labeled and unlabeled data for inference on a graph. Extensive experiments on semi-supervised face recognition and image classification demonstrate the superiority of our proposed semi-supervised learning framework based on `1 graph over the counterparts based on traditional graphs.
Feedback Linearization vs . Adaptive Sliding Mode Control for a Quadrotor Helicopter

This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects.