nils-herrmann/clean_scirepeval huggingface dataset:
A Dataset of Scratch Programs: Scraped, Shaped and Scored

Scratch is increasingly popular, both as an introductory programming language and as a research target in the computing education research field. In this paper, we present a dataset of 250K recent Scratch projects from 100K different authors scraped from the Scratch project repository. We processed the projects' source code and metadata to encode them into a database that facilitates querying and further analysis. We further evaluated the projects in terms of programming skills and mastery, and included the project scoring results. The dataset enables the analysis of the source code of Scratch projects, of their quality characteristics, and of the programming skills that their authors exhibit. The dataset can be used for empirical research in software engineering and computing education.
Reusing Hardware Performance Counters to Detect and Identify Kernel Control-Flow Modifying Rootkits

Kernel rootkits are formidable threats to computer systems. They are stealthy and can have unrestricted access to system resources. This paper presents NumChecker, a new virtual machine (VM) monitor based framework to detect and identify control-flow modifying kernel rootkits in a guest VM. NumChecker detects and identifies malicious modifications to a system call in the guest VM by measuring the number of certain hardware events that occur during the system call's execution. To automatically count these events, NumChecker leverages the hardware performance counters (HPCs), which exist in modern processors. By using HPCs, the checking cost is significantly reduced and the tamper-resistance is enhanced. We implement a prototype of NumChecker on Linux with the kernel-based VM. An HPC-based two-phase kernel rootkit detection and identification technique is presented and evaluated on a number of real-world kernel rootkits. The results demonstrate its practicality and effectiveness.
Observers for Takagi-Sugeno fuzzy systems

We focus on the analysis and design of two different sliding mode observers for dynamic Takagi-Sugeno (TS) fuzzy systems. A nonlinear system of this class is composed of multiple affine local linear models that are smoothly interpolated by weighting functions resulting from a fuzzy partitioning of the state space of a given nonlinear system subject to observation. The Takagi-Sugeno fuzzy system is then an accurate approximation of the original nonlinear system. Our approach to the analysis and design of observers for Takagi-Sugeno fuzzy systems is based on extending sliding mode observer schemes to the case of interpolated multiple local affine linear models. Thus, our main contribution is nonlinear observer analysis and design methods that can effectively deal with model/plant mismatches. Furthermore, we consider the difficult case when the weighting functions in the Takagi-Sugeno fuzzy system depend on the estimated state.
Design and Analysis of Low-Power 11- Transistor Full Adder

Full adders are exigent components in applications such as digital signal processors (DSP) architectures and microprocessors. In this paper, we propose a technique to build a new 11-transistor FA .We have done HSPICE simulation runs the new design 11-T full adders .In CMOS integrated circuit design there is a tradeoff between static power consumption and technology scaling. Static power dissipation is a challenge for the circuit designer. So we reduce the static power dissipation. In order to achieve lower static power consumption, one has to scarifies design area and circuit performance. In this paper we propose a new circuit of 11-Transistor full adder in CMOS VLSI circuit.
Lightweight eye capture using a parametric model

Facial scanning has become ubiquitous in digital media, but so far most efforts have focused on reconstructing the skin. Eye reconstruction, on the other hand, has received only little attention, and the current state-of-the-art method is cumbersome for the actor, time-consuming, and requires carefully setup and calibrated hardware. These constraints currently make eye capture impractical for general use. We present the first approach for high-quality lightweight eye capture, which leverages a database of pre-captured eyes to guide the reconstruction of new eyes from much less constrained inputs, such as traditional single-shot face scanners or even a single photo from the internet. This is accomplished with a new parametric model of the eye built from the database, and a novel image-based model fitting algorithm. Our method provides both automatic reconstructions of real eyes, as well as artistic control over the parameters to generate user-specific eyes.
Folding and unfolding origami tessellation by reusing folding path

Recent advances in robotics engineering have enabled the realization of self-folding machines. Rigid origami is usually used as the underlying model for the self-folding machines whose surface remains rigid during folding except at joints. A key issue in designing rigid origami is foldability that concerns about finding folding steps from a flat sheet of crease pattern to a desired folded state. Although recent computational methods allow rapid simulation of folding process of certain rigid origamis, these methods can fail even when the input crease pattern is extremely simple. In this paper, we take on the challenge of planning folding and unfolding motion of origami tessellations, which are composed of repetitive crease patterns. The number of crease lines of a tessellation is usually large, thus searching in such a high dimensional configuration space with the requirement of maintaining rigidity is nontrivial. We propose a motion planner that takes symmetry into consideration and reuses folding path found on the essential crease pattern. Both of these strategies enable us to fold large origami tessellation much more efficiently than existing methods. Our experimental results show that the proposed method successfully folds several types of rigid origami tessellations that existing methods fail to fold.
Interactive bookshelf surface for in situ book searching and storing support

We propose an interactive bookshelf surface to augment a human ability for in situ book searching and storing. In book searching support, when a user touches the edge of the bookshelf, the cover image of a stored book located above the touched position is projected directly onto the book spine. As a result, the user can search for a desired book by sliding his (or her) finger across the shelf edge. In book storing support, when a user brings a book close to the bookshelf, the place where the book should be stored is visually highlighted by a projection light. This paper also presents sensing technologies to achieve the above mentioned interactive techniques. In addition, by considering the properties of the human visual system, we propose a simple visual effect to reduce the legibility degradation of the projected image contents by the complex textures and geometric irregularities of the spines. We confirmed the feasibility of the system and the effectiveness of the proposed interaction techniques through user studies.
Evaluation of Cost Estimation Metrics: Towards a Unified Terminology

Cost overrun of software projects is major cause of their failures. In order to facilitate accurate software cost estimation, there are several metrics, tools and datasets. In this paper, we evaluate and compare different metrics and datasets in terms of similarities and differences of involved software attributes. These metrics forecast project cost estimations based on different software attributes. Some of these metrics are public and standard while others are only employed in a particular metric tool/dataset.
Gradient Descent Provably Optimizes Over-parameterized Neural Networks

One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For anm hidden node shallow neural network with ReLU activation and n training data, we show as long asm is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function. Our analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods.
Idle sense: an optimal access method for high throughput and fairness in rate diverse wireless LANs

We consider wireless LANs such as IEEE 802.11 operating in the unlicensed radio spectrum. While their nominal bit rates have increased considerably, the MAC layer remains practically unchanged despite much research effort spent on improving its performance. We observe that most proposals for tuning the access method focus on a single aspect and disregard others. Our objective is to define an access method optimized for throughput and fairness, able to dynamically adapt to physical channel conditions, to operate near optimum for a wide range of error rates, and to provide equal time shares when hosts use different bit rates.We propose a novel access method derived from 802.11 DCF  (Distributed Coordination Function) in which all hosts use similar values of the contention window CW to benefit from good short-term access fairness. We call our method Idle Sense, because each host observes the mean number of idle slots between transmission attempts to dynamically control its contention window. Unlike other proposals, Idle Sense enables each host to estimate its frame error rate, which can be used for switching to the right bit rate. We present simulations showing how the method leads to high throughput, low collision overhead, and low delay. The method also features fast reactivity and time-fair channel allocation.
An investigation of the impacts of some external contextual factors on ERP systems success assessment: a case of firms in Baltic-Nordic region

Enterprise Resource Planning (ERP) systems are among the largest information systems (IS) investments made by firms, and the use of such systems is spreading globally. Many researchers have discussed their adoption and implementation, few have investigated the impact of external contextual factors on the success of such technologies in adopting firms. This study aims to fill this gap in research by examining the effects of three external contextual factors, i.e., industry type, industry climate, and national economic climate on ERP success assessment. We obtained data from Estonia and Finland and our analysis shows that industry and national economic climates have significant relationships with ERP success.
Enforcing dynamic spectrum access with spectrum permits

Dynamic spectrum access is a maturing technology that allows next generation wireless devices to make highly efficient use of wireless spectrum. Spectrum can be allocated on an on-demand basis for a given geographic location, time duration and frequency range. However, a major obstacle to adoption remains. There are no effective solutions to protect licensed users from spectrum misuse, where users transmit without properly licensing spectrum, and in doing so, interfere and disrupt legitimate flows to whom the spectrum is assigned. Given the flexibility of today's cognitive radios, an application can easily transmit on frequencies outside of its allocated range, either accidentally due to misconfiguration, or intentionally to avoid spectrum licensing costs. In this paper, we propose a system to secure dynamic spectrum transmissions, where authorized users embed secure spectrum permits into data transmissions, thus enabling patrolling trusted devices to detect devices transmitting without authorization. We focus our attention on the development of spectrum permits, and describe Gelato, a spectrum misuse detection system that minimizes both hardware costs and performance overhead on legitimate data transmissions.
FastFlow: High-level and Efficient Streaming on Multi-core

Computer hardware manufacturers have moved decisively to multi-core and are currently experimenting with increasingly advanced many-core architectures. In the long term, writing efficient, portable and correct parallel programs targeting multiand many-core architectures must become no more challenging than writing the same programs for sequential computers. To date, however, most applications running on multicore machines do not exploit fully the potential of these architectures. This situation is in part due to the scarcity of good high-level programming tools suitable for multi/manycore architectures, and in part to the fact that multi-core programming is still viewed as a kind of exotic branch of high-performance computing (HPC) rather than being perceived as the de facto standard programming practice for the masses. Some efforts have been made to provide programmers with tools suitable for mapping data parallel computations onto both multi-cores and GPUs–the most popular many-core currently available. Tools have also been developed to support stream parallel computations [34, 31] as stream parallelism de facto represents a pattern characteristic of a large class of (potentially) parallel applications. Two major issues with these programming environments and tools relate to programmability and efficiency. Programmability is often impaired by the modest level of abstraction provided to the programmer. Efficiency more generally suffers from the peculiarities related to effective exploitation of the memory hierarchy. As a consequence, two distinct but synergistic needs exist: on the one hand, increasingly efficient mechanisms supporting correct concurrent access to shared memory data structures are needed; on the other hand there is a need for higher level programming environments capable of hiding the difficulties related to the correct and efficient use of shared memory objects by raising the level of abstraction provided to application programmers. To address these needs we introduce and discuss FastFlow, a programming framework specifically targeting cache-coherent shared-memory multi-cores. FastFlow is implemented as a stack of C++ template libraries. The lowest layer of FastFlow provides very efficient lock-free (and memory fence free) synchronization base mechanisms. The middle layer provides distinctive communication mechanisms supporting both single producer-multiple consumer and multiple producer-single consumer communications.
Health at hand: A systematic review of smart watch uses for health and wellness

Smart watches have the potential to support health in everyday living by: enabling self-monitoring of personal activity; obtaining feedback based on activity measures; allowing for in-situ surveys to identify patterns of behavior; and supporting bi-directional communication with health care providers and family members. However, smart watches are an emerging technology and research with these devices is at a nascent stage.



We conducted a systematic review of smart watch studies that engaged people in their use by searching PubMed, Embase, IEEE XPlore and ACM Digital libraries. Participant demographics, device features, watch applications and methods, and technical challenges were abstracted from included studies.



Seventy-three studies were returned in the search. Seventeen studies published were included. Included studies were published from 2014 to 2016, with the exception of one published in 2011. Most studies employed the use of consumer-grade smart watches (14/17, 82%). Patient-related studies focused on activity monitoring, heart rate monitoring, speech therapy adherence, diabetes self-management, and detection of seizures, tremors, scratching, eating, and medication-taking behaviors. Most patient-related studies enrolled participants with few exclusion criteria to validate smart watch function (10/17, 58%). Only studies that focused on Parkinson's disease, epilepsy, and diabetes management enrolled persons living with targeted conditions. One study focused on nursing work in the ICU and one focused on CPR training for laypeople.



Consumer-grade smart watches have penetrated the health research space rapidly since 2014. Smart watch technical function, acceptability, and effectiveness in supporting health must be validated in larger field studies that enroll actual participants living with the conditions these devices target.
Weakly-supervised convolutional neural networks for multimodal image registration

One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.
Exploiting Software: How to Break Code

To be useful, software must respond to events in a predictable manner. The results can then be used as a window to the interior workings of the code, revealing some of the mechanisms of operations, which may be used to find ways to make it fail in a dangerous way. To some, the window is as clear as a six inch thick pane of lead, but to those with a high level of understanding it can be clear, or at the very least serve as a keyhole. This is an allusion to the old detective stories where someone looks through the keyhole to see what is behind the door. For these reasons, no software that interacts with humans can ever be considered completely secure, and human error in the development of the software can leave the equivalent of keyholes throughout the code.
Enhancing the Effectiveness of Work Groups and Teams.

Teams of people working together for a common purpose have been a centerpiece of human social organization ever since our ancient ancestors first banded together to hunt game, raise families, and defend their communities. Human history is largely a story of people working together in groups to explore, achieve, and conquer. Yet, the modern concept of work in large organizations that developed in the late 19th and early 20th centuries is largely a tale of work as a collection of individual jobs. A variety of global forces unfolding over the last two decades, however, has pushed organizations worldwide to restructure work around teams, to enable more rapid, flexible, and adaptive responses to the unexpected. This shift in the structure of work has made team effectiveness a salient organizational concern. Teams touch our lives everyday and their effectiveness is important to well-being across a wide range of societal functions. There is over 50 years of psychological research-literally thousands of studies-focused on understanding and influencing the processes that underlie team effectiveness. Our goal in this monograph is to sift through this voluminous literature to identify what we know, what we think we know, and what we need to know to improve the effectiveness of work groups and teams. We begin by defining team effectiveness and establishing the conceptual underpinnings of our approach to understanding it. We then turn to our review, which concentrates primarily on topics that have well-developed theoretical and empirical foundations, to ensure that our conclusions and recommendations are on firm footing. Our review begins by focusing on cognitive, motivational/affective, and behavioral team processes-processes that enable team members to combine their resources to resolve task demands and, in so doing, be effective. We then turn our attention to identifying interventions, or "levers," that can shape or align team processes and thereby provide tools and applications that can improve team effectiveness. Topic-specific conclusions and recommendations are given throughout the review. There is a solid foundation for concluding that there is an emerging science of team effectiveness and that findings from this research foundation provide several means to improve team effectiveness. In the concluding section, we summarize our primary findings to highlight specific research, application, and policy recommendations for enhancing the effectiveness of work groups and teams.
Growth and morphological development of laboratory-reared larval and juvenile giant gourami Osphronemus goramy (Perciformes: Osphronemidae)

Morphological development in laboratory-reared larval and juvenile Osphronemus goramy is described. Body lengths were 4.8 ± 0.1 (mean ± SD) mm just after hatching, and reached 9.8 ± 0.8 mm by day 16 and 15.3 ± 0.6 mm by day 40. Aggregate fin ray numbers attained full complements by 11.9 mm BL on day 22. All larvae began feeding by day 5 (ca. 6.7 mm BL). Conical jaw teeth appeared on day 7 (ca. 7.0 mm BL). Notochord flexion began on day 3 (ca. 6.5 mm BL), the yolk being absorbed during days 14–15 (ca. 8.0–10.0 mm BL). Pale melanophores, initially visible on the upper yolksac surface during days 0 and 1, became more intense and numerous with growth, and covered almost the entire body in juveniles. Seven or eight vertical bars appeared on the body on day 13 (ca. 9.0 mm BL), thereafter becoming more discernible. Pre-anal length, body depth, and eye diameter proportions became relatively constant after yolk absorption, with those of the head/snout/upper jaw lengths subsequently increasing. The labyrinth organ began to develop in juveniles larger than ca. 14.5 mm BL (day 30), and air-breathing behavior was first observed on days 35–40.
Analysis of Statistical Question Classification for Fact-Based Questions

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.