nils-herrmann/clean_scirepeval huggingface dataset:
The Jigsaw continuous sensing engine for mobile phone applications

Supporting continuous sensing applications on mobile phones is challenging because of the resource demands of long-term sensing, inference and communication algorithms. We present the design, implementation and evaluation of the Jigsaw continuous sensing engine, which balances the performance needs of the application and the resource demands of continuous sensing on the phone. Jigsaw comprises a set of sensing pipelines for the accelerometer, microphone and GPS sensors, which are built in a plug and play manner to support: i) resilient accelerometer data processing, which allows inferences to be robust to different phone hardware, orientation and body positions; ii) smart admission control and on-demand processing for the microphone and accelerometer data, which adaptively throttles the depth and sophistication of sensing pipelines when the input data is low quality or uninformative; and iii) adaptive pipeline processing, which judiciously triggers power hungry pipeline stages (e.g., sampling the GPS) taking into account the mobility and behavioral patterns of the user to drive down energy costs. We implement and evaluate Jigsaw on the Nokia N95 and the Apple iPhone, two popular smartphone platforms, to demonstrate its capability to recognize user activities and perform long term GPS tracking in an energy-efficient manner.
Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization

We give novel algorithms for stochastic strongly-convex optimization in the gradient oracle model which return a O( 1 T )-approximate solution after T iterations. The first algorithm is deterministic, and achieves this rate via gradient updates and historical averaging. The second algorithm is randomized, and is based on pure gradient steps with a random step size. This rate of convergence is optimal in the gradient oracle model. This improves upon the previously known best rate of O( log(T ) T ), which was obtained by applying an online stronglyconvex optimization algorithm with regret O(log(T )) to the batch setting. We complement this result by proving that any algorithm has expected regret of Ω(log(T )) in the online stochastic strongly-convex optimization setting. This shows that any online-to-batch conversion is inherently suboptimal for stochastic strongly-convex optimization. This is the first formal evidence that online convex optimization is strictly more difficult than batch stochastic convex optimization.
Research on Fast Charge Method for Lead-Acid Electric Vehicle Batteries

Electric vehicle (EV) is environment friendly and high efficient. But the shortages of traction battery limited the rapid development of EV. Battery as a key part of EV has aroused lots of engineers to explore the management method and fast charge method is a key technology of battery management for electric vehicle. Constant current-constant voltage (CC-CV) and multistage constant current-constant voltage (MCC-CV) are two traditional charging ways. According to the dynamic circuit model of Lead-acid battery and fast charge theory, on the basic of CC-CV and MCC-CV method, explored the fast charge method for Lead-acid battery of electric vehicle. Compare experiment result of the fast charge method and traditional method. The two major parameters like temperature rise and the capacity-time ratio are considered in order to compare the result. Lots of experiment result support that the negative pulse could eliminate the polarization effective. The MCC-CV with negative pulse method proves working efficient and practical. Fast charge; Electric Vehicle; Lead-acid battery;
Training cellular automata for image processing

Experiments were carried out to investigate the possibility of training cellular automata (CA) to perform several image processing tasks. Even if only binary images are considered, the space of all possible rule sets is still very large, and so the training process is the main bottleneck of such an approach. In this paper, the sequential floating forward search method for feature selection was used to select good rule sets for a range of tasks, namely noise filtering (also applied to grayscale images using threshold decomposition), thinning, and convex hulls. Various objective functions for driving the search were considered. Several modifications to the standard CA formulation were made (the B-rule and two-cycle CAs), which were found, in some cases, to improve performance.
Understanding integer overflow in C/C++

Integer overflow bugs in C and C++ programs are difficult to track down and may lead to fatal errors or exploitable vulnerabilities. Although a number of tools for finding these bugs exist, the situation is complicated because not all overflows are bugs. Better tools need to be constructed, but a thorough understanding of the issues behind these errors does not yet exist. We developed IOC, a dynamic checking tool for integer overflows, and used it to conduct the first detailed empirical study of the prevalence and patterns of occurrence of integer overflows in C and C++ code. Our results show that intentional uses of wraparound behaviors are more common than is widely believed; for example, there are over 200 distinct locations in the SPEC CINT2000 benchmarks where overflow occurs. Although many overflows are intentional, a large number of accidental overflows also occur. Orthogonal to programmers' intent, overflows are found in both well-defined and undefined flavors. Applications executing undefined operations can be, and have been, broken by improvements in compiler optimizations. Looking beyond SPEC, we found and reported undefined integer overflows in SQLite, PostgreSQL,  GNU MPC and GMP, Firefox, LLVM, Python, BIND, and OpenSSL; many of these have since been fixed.
Motion rings for interactive gait synthesis

This paper presents a practical system for synthesizing gait animation in game environments. As well as improving the reality of animation, we should improve the efficiency and the maneuverability of the character, both of which are essential for interactive games. Our system supplies these practical demands by integrating a motion interpolation technique and a sampling-based control mechanism. We introduce a parameterized looped motion data structure, called a motion ring, for synthesizing a variety of cyclic motions. A continuous gait motion is synthesized by circulating through the motion ring while the interpolation parameter is adaptively controlled according to the terrain condition. The gait controller uses a sampling-based precomputation technique which efficiently searches natural foot contact on terrain of an arbitrary surface shape. The interpolation parameter is also controlled to obey the user control within the duration of quarter gait cycle. Although our system slightly sacrifices the physical correctness of the synthesized motion in order to quickly respond to user input, critical visual artifacts such as foot-skating and jerky movement are prevented. We demonstrate the efficiency and versatility of our integrated system by interactively navigating the character on complex, uneven terrain.
Metalearning: a survey of trends and technologies

Metalearning attracted considerable interest in the machine learning community in the last years. Yet, some disagreement remains on what does or what does not constitute a metalearning problem and in which contexts the term is used in. This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.
The feeling of effort during mental activity

The feeling of effort is familiar to most, if not all, humans. Prior research shows that the feeling of effort shapes judgments (e.g., of agency) and decisions (e.g., to quit the current task) in various ways, but the proximal causes of the feeling of effort are not well understood. In this research, I address these proximal causes. In particular, I conducted two preregistered experiments in which participants performed a difficult vs. easy cognitive task, while I measured effort-related phenomenology (feeling of effort) and physiology (pupil dilation) on a moment-to-moment basis. In both experiments, difficult tasks increased the feeling of effort; however, this effect could not be explained by concurrent increases in physiological effort. To explain these findings, I suggest that the feeling of effort during mental activity stems from the decision to exert physiological effort, rather than from physiological effort itself.
Principal components analysis based control of a multi-dof underactuated prosthetic hand

Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG). Driving a multi degrees of freedom (DoF) hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user. A Principal Components Analysis (PCA) based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand) with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs). Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control. Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture) may be achieved. This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.
Gilbreth: A Conveyor-Belt Based Pick-and-Sort Industrial Robotics Application

This paper describes an industrial robotics application, named Gilbreth, for picking up objects of different types from a moving conveyor belt and sorting the objects into bins according to type. The environment, which consists of a moving conveyor belt, a break beam sensor, a 3D camera Kinect sensor, a UR10 industrial robot arm with a vacuum gripper, and different object types such as gears, pulleys, piston rods, was inspired by the NIST ARIAC competition. A first version of the Gilbreth application was implemented leveraging many ROS and ROS-I packages. Gazebo was used to simulate the environment, and six external ROS nodes were implemented to execute the required functions. Experimental measurements of CPU usage and processing times of ROS nodes were obtained. Object recognition required the highest processing times that were on par with the time required for the robot arm to execute its movement between four poses: pick approach, pick, pick retreat and place. A need for enhancing the performance of object recognition and Gazebo simulation was identified.
Upscaledb: Efficient Integer-Key Compression in a Key-Value Store using SIMD Instructions

Compression can sometimes improve performance by making more of the data available to the processors faster. We consider the compression of integer keys in a B+-tree index. For this purpose, systems such as IBM DB2 use variable-byte compression over differentially coded keys. We revisit this problem with various compression alternatives such as Google’s VarIntGB, Binary Packing and Frame-of-Reference. In all cases, we describe algorithms that can operate directly on compressed data. Many of our alternatives exploit the single-instruction-multiple-data (SIMD) instructions supported by modern CPUs. We evaluate our techniques in a database environment provided by Upscaledb, a production-quality key-value database. Our best techniques are SIMD accelerated: they simultaneously reduce memory usage while improving single-threaded speeds. In particular, a differentially coded SIMD binarypacking techniques (BP128) can offer a superior query speed (e.g., 40 % better than an uncompressed database) while providing the best compression (e.g., by a factor of ten). For analytic workloads, our fast compression techniques offer compelling benefits. Our software is available as open source.
Earworms (stuck song syndrome): towards a natural history of intrusive thoughts.

Two studies examine the experience of 'earworms', unwanted catchy tunes that repeat. Survey data show that the experience is widespread but earworms are not generally considered problematic, although those who consider music to be important to them report earworms as longer, and harder to control, than those who consider music as less important. The tunes which produce these experiences vary considerably between individuals but are always familiar to those who experience them. A diary study confirms these findings and also indicates that, although earworm recurrence is relatively uncommon and unlikely to persist for longer than 24 h, the length of both the earworm and the earworm experience frequently exceed standard estimates of auditory memory capacity. Active attempts to block or eliminate the earworm are less successful than passive acceptance, consistent with Wegner's theory of ironic mental control.
Survey on Cloud Computing Security

Cloud is a modern age computing paradigm which has potential to bring great benefits to information and Communication Technology (ICT) and ICT enabled business. The term Cloud comes from the graphic that was often used to show the heterogeneous networks and complex infrastructure. This graphic was adopted to describe the many aspects of Cloud Computing. In this paper, we aim to identify the security issues in cloud computing. Here we present an analysis of security issues in a cloud environment. Solutions exist to a certain extent for various issues. Cloud computing, security challenges, threats
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge

We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and nally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.
Pivoted Document Length Normalization

Automatic information retrieval systems have to deal with documents of varying lengths in a text collection. Document length normalization is used to fairly retrieve documents of all lengths. In this study, we ohserve that a normalization scheme that retrieves documents of all lengths with similar chances as their likelihood of relevance will outperform another scheme which retrieves documents with chances very different from their likelihood of relevance. We show that the retrievaf probabilities for a particular normalization method deviate systematically from the relevance probabilities across different collections. We present pivoted normalization, a technique that can be used to modify any normalization function thereby reducing the gap between the relevance and the retrieval probabilities. Training pivoted normalization on one collection, we can successfully use it on other (new) text collections, yielding a robust, collectzorz independent normalization technique. We use the idea of pivoting with the well known cosine normalization function. We point out some shortcomings of the cosine function andpresent two new normalization functions--pivoted unique normalization and piuotert byte size normalization.
Controllability of complex networks

The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.
Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization

The cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. This paper proposes a cloud task scheduling policy based on Load Balancing Ant Colony Optimization (LBACO) algorithm. The main contribution of our work is to balance the entire system load while trying to minimizing the make span of a given tasks set. The new scheduling strategy was simulated using the CloudSim toolkit package. Experiments results showed the proposed LBACO algorithm outperformed FCFS (First Come First Serve) and the basic ACO (Ant Colony Optimization).
Ecology of the Family as a Context for Human Development : Research Perspectives

This review collates and examines critically a theoretically convergent but widely dispersed body of research on the influence of external environments on the functioning of families as contexts of human development. Investigations falling within this expanding domain include studies of the interaction of genetics and environment in family processes; transitions and linkages between the family and other major settings influencing development, such as hospitals, day care, peer groups, school, social networks, the world of work (both for parents and children), and neighborhoods and communities; and public policies affecting families and children. A second major focus is on the patterning of environmental events and transitions over the life course as these affect and are affected by intrafamilial processes. Special emphasis is given to critical research gaps in knowledge and priorities for future investigation.
Nonparametric Bayesian data analysis

We review the current state of nonparametric Bayesian inference. The discussion follows a list of important statistical inference problems, including density estimation, regression, survival analysis, hierarchical models and model validation. For each inference problem we review relevant nonparametric Bayesian models and approaches including Dirichlet process (DP) models and variations, Polya trees, wavelet based models, neural network models, spline regression, CART, dependent DP models, and model validation with DP and Polya tree extensions of parametric models.
Guidelines for medical care of children evaluated for suspected sexual abuse: an update for 2008.

Child sexual abuse is a common problem in our society and medical professionals who provide evaluations of children who may have been abused need to be updated as to recent research findings and recommendations for conducting examinations and interpreting results.



Research studies have provided important new information regarding the qualifications of examiners, the recovery of forensic evidence in children, the frequency of abnormal findings in children and adolescents, the healing of genital injuries, and the interpretation of medical findings and sexually transmissible infections with respect to abuse.



The recommendations for the timing and type of examinations for prepubertal children, in contrast to adolescent sexual assault victims, may need to be changed. Studies showing that partial tears of the hymen, as well as abrasions and contusions, may heal to leave very little or no sign of previous injury emphasize the importance of urgent evaluations. There is a need for standardization of the training of medical professionals who perform child sexual abuse evaluations to ensure continuing competence.
Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives

We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. In a single framework, we integrated semantic data mapping and neural embedding technique to produce a text processing method that extracts relevant information from heterogeneous types of clinical notes in an unsupervised manner, and we designed a recurrent neural network to model the temporal dependency of the patient visits. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset (1818 patients). Our method achieved an area under the ROC curve (AUC) of 0.89. To provide explain-ability, we developed an interactive graphical tool that may improve physician understanding of the basis for the model’s predictions. The high accuracy and explain-ability of the PPES-Met model may enable our model to be used as a decision support tool to personalize metastatic cancer treatment and provide valuable assistance to the physicians.