nils-herrmann/clean_scirepeval huggingface dataset:
Cyber Physical Systems: Design Challenges

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. The economic and societal potential of such systems is vastly greater than what has been realized, and major investments are being made worldwide to develop the technology. There are considerable challenges, particularly because the physical components of such systems introduce safety and reliability requirements qualitatively different from those in general- purpose computing. Moreover, physical components are qualitatively different from object-oriented software components. Standard abstractions based on method calls and threads do not work. This paper examines the challenges in designing such systems, and in particular raises the question of whether today's computing and networking technologies provide an adequate foundation for CPS. It concludes that it will not be sufficient to improve design processes, raise the level of abstraction, or verify (formally or otherwise) designs that are built on today's abstractions. To realize the full potential of CPS, we will have to rebuild computing and networking abstractions. These abstractions will have to embrace physical dynamics and computation in a unified way.
The Java memory model

This paper describes the new Java memory model, which has been revised as part of Java 5.0. The model specifies the legal behaviors for a multithreaded program; it defines the semantics of multithreaded Java programs and partially determines legal implementations of Java virtual machines and compilers.The new Java model provides a simple interface for correctly synchronized programs -- it guarantees sequential consistency to data-race-free programs. Its novel contribution is requiring that the behavior of incorrectly synchronized programs be bounded by a well defined notion of causality. The causality requirement is strong enough to respect the safety and security properties of Java and weak enough to allow standard compiler and hardware optimizations. To our knowledge, other models are either too weak because they do not provide for sufficient safety/security, or are too strong because they rely on a strong notion of data and control dependences that precludes some standard compiler transformations.Although the majority of what is currently done in compilers is legal, the new model introduces significant differences, and clearly defines the boundaries of legal transformations. For example, the commonly accepted definition for control dependence is incorrect for Java, and transformations based on it may be invalid.In addition to providing the official memory model for Java, we believe the model described here could prove to be a useful basis for other programming languages that currently lack well-defined models, such as C++ and C#.
Time, Clocks, and the Ordering of Events in a Distributed System

The concept of one event happening before another in a distributed system is examined, and is shown to define a partial ordering of the events. A distributed algorithm is given for synchronizing a system of logical clocks which can be used to totally order the events. The use of the total ordering is illustrated with a method for solving synchronization problems. The algorithm is then specialized for synchronizing physical clocks, and a bound is derived on how far out of synchrony the clocks can become.
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes

It is widely observed that deep learning models with learned parameters generalize well, even with much more model parameters than the number of training samples. We systematically investigate the underlying reasons why deep neural networks often generalize well, and reveal the difference between the minima (with the same training error) that generalize well and those they don’t. We show that it is the characteristics the landscape of the loss function that explains the good generalization capability. For the landscape of loss function for deep networks, the volume of basin of attraction of good minima dominates over that of poor minima, which guarantees optimization methods with random initialization to converge to good minima. We theoretically justify our findings through analyzing 2-layer neural networks; and show that the low-complexity solutions have a small norm of Hessian matrix with respect to model parameters. For deeper networks, extensive numerical evidence helps to support our arguments.
Towards Massively Multi-user Augmented Reality on Handheld Devices

Augmented Reality (AR) can naturally complement mobile computing on wearable devices by providing an intuitive interface to a three-dimensional information space embedded within physical reality. Unfortunately, current wearable AR systems are relatively complex, expensive and heavyweight, rendering them unfit for large-scale deployment to untrained users outside a constrained laboratory environment. Consequently, collaborative multi-user experiments have been prevented from exceeding just a hand full of participants. In this paper, we present a software architecture for interactive, infrastructure-independent multiuser AR applications on off-the-shelf handheld devices. We implemented a four-user interactive game installation as an evaluation scenario that would encourage participants to playfully engage in a cooperative task. Over the course of four weeks, more than five thousand visitors from a wide range of professional and socio-demographic backgrounds interacted with our system at a total of four different locations and events. The findings from an informal summative assessment of user performance were generally positive, and will hopefully advance our long-term effort to deploy massively multi-user AR applications to the general public.
On the Individuality of Fingerprints

Fingerprint identification is based on two basic premises: (i) persistence: the basic characteristics of fingerprints do not change with time; and (ii) individuality: the fingerprint is unique to an individual. The validity of the first premise has been established by the anatomy and morphogenesis of friction ridge skin. While the second premise has been generally accepted to be true based on empirical results, the underlying scientific basis of fingerprint individuality has not been formally established. As a result, the validity of fingerprint evidence is now being challenged in several court cases. A scientific basis for establishing fingerprint individuality will not only result in the admissibility of fingerprint identification in the courts of law but will also establish an upper bound on the performance of an automatic fingerprint verification system. We address the problem of fingerprint individuality by quantifying the amount of information available in minutiae features to establish a correspondence between two fingerprint images. We derive an expression which estimates the probability of a false correspondence between minutiae-based representations from two arbitrary fingerprints belonging to different fingers. For example, the probability that a fingerprint with 36 minutiae points will share 12 minutiae points with another arbitrarily chosen fingerprint with 36 minutiae ∗An earlier version of this paper appeared in the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 805-812, Hawaii, December 11-13, 2001.
Facial Expression Recognition Using a Dynamic Model and Motion Energy

Previous efforts at facial expression recognition have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static facial “mugshots.” In this paper we develop new, more accurate representations for facial expression by building a video database of facial expressions and then probabilistically characterizing the facial muscle activation associated with each expression using a detailed physical model of the skin and muscles. This produces a muscle-based representation of facial motion, which is then used to recognize facial expressions in two different ways. The first method uses the physics-based model directly, by recognizing expressions through comparison of estimated muscle activations. The second method uses the physics-based model to generate spatio-temporal motion-energy templates of the whole face for each different expression. These simple, biologically-plausible motion energy “templates” are then used for recognition. Both methods show substantially greater accuracy at expression recognition than has been previously achieved.
Recognizing emotion from facial expressions: psychological and neurological mechanisms.

Recognizing emotion from facial expressions draws on diverse psychological processes implemented in a large array of neural structures. Studies using evoked potentials, lesions, and functional imaging have begun to elucidate some of the mechanisms. Early perceptual processing of faces draws on cortices in occipital and temporal lobes that construct detailed representations from the configuration of facial features. Subsequent recognition requires a set of structures, including amygdala and orbitofrontal cortex, that links perceptual representations of the face to the generation of knowledge about the emotion signaled, a complex set of mechanisms using multiple strategies. Although recent studies have provided a wealth of detail regarding these mechanisms in the adult human brain, investigations are also being extended to nonhuman primates, to infants, and to patients with psychiatric disorders.
Is There a Cost to Privacy Breaches? An Event Study

While the literature on information security economics has begun to investigate the stock market impact of security breaches and vulnerability announcements, little more than anecdotal evidence exists on the effects of privacy breaches. In this paper we present the first comprehensive analysis of the impact of a company’s privacy incidents on its market value. We compile a broad data set of instances of exposure of personal information due to failures of some security mechanism (hacking, stolen or lost equipment, poor data handling processes, and others) and we present the results of various empirical analyses, including event study analysis. We show that there exists a negative and statistically significant impact of data breaches on a company’s market value on the announcement day for the breach. The cumulative effect increases in magnitudes over the day following the breach announcement, but then decreases and loses statistical significance. We also present regression analyses that aim at disentangling the eff ects of a number of factors on abnormal stock returns due to reported breaches. Finally, we comment on the differences between the impact of the security breaches already described in the literature, and the privacy breaches described here.
A systematic review and meta-analysis of clinical studies on major depression and BDNF levels: implications for the role of neuroplasticity in depression.

Several clinical studies on major depressive disorder (MDD) have shown that blood brain-derived neurotrophic factor (BDNF) - a factor used to index neuroplasticity - is associated with depression response; however, the results are mixed. The purpose of our study was to evaluate whether BDNF levels are correlated with improvement of depression. We performed a systematic review and meta-analysis of the literature, searching Medline, Cochrane Central, SciELO databases and reference lists from retrieved articles for clinical studies comparing mean BDNF blood levels in depressed patients pre- and post-antidepressant treatments or comparing depressed patients with healthy controls. Two reviewers independently searched for eligible studies and extracted outcome data using a structured form previously elaborated. Twenty articles, including 1504 subjects, met our inclusion criteria. The results showed that BDNF levels increased significantly after antidepressant treatment (effect size 0.62, 95% CI 0.36-0.88, random effects model). In addition, there was a significant correlation between changes in BDNF level and depression scores changes (p=0.02). Moreover, the results were robust according to the sensitivity analysis and Begg's funnel plot results did not suggest publication bias. Finally, there was a difference between pre-treatment patients and healthy controls (effect size 0.91, 95% CI 0.70-1.11) and a small but significant difference between treated patients and healthy controls (effect size 0.34, 95% CI 0.02-0.66). Our results show that BDNF levels are associated with clinical changes in depression; supporting the notion that depression improvement is associated with neuroplastic changes.
A typology for the classification , description and valuation of ecosystem functions , goods and services

An increasing amount of information is being collected on the ecological and socio-economic value of goods and services provided by natural and semi-natural ecosystems. However, much of this information appears scattered throughout a disciplinary academic literature, unpublished government agency reports, and across the World Wide Web. In addition, data on ecosystem goods and services often appears at incompatible scales of analysis and is classified differently by different authors. In order to make comparative ecological economic analysis possible, a standardized framework for the comprehensive assessment of ecosystem functions, goods and services is needed. In response to this challenge, this paper presents a conceptual framework and typology for describing, classifying and valuing ecosystem functions, goods and services in a clear and consistent manner. In the following analysis, a classification is given for the fullest possible range of 23 ecosystem functions that provide a much larger number of goods and services. In the second part of the paper, a checklist and matrix is provided, linking these ecosystem functions to the main ecological, socio–cultural and economic valuation methods.
Flood forecasting using Internet of things and artificial neural networks

Floods are the most common natural disasters, and cause significant damage to life, agriculture and economy. Research has moved on from mathematical modeling or physical parameter based flood forecasting schemes, to methodologies focused around algorithmic approaches. The Internet of Things (IoT) is a field of applied electronics and computer science where a system of devices collects data in real time and transfers it through a Wireless Sensor Network (WSN) to the computing device for analysis. IoT generally combines embedded system hardware techniques along with data science or machine learning models. In this work, an IoT and machine learning based embedded system is proposed to predict the probability of floods in a river basin. The model uses a modified mesh network connection over ZigBee for the WSN to collect data, and a GPRS module to send the data over the internet. The data sets are evaluated using an artificial neural network model. The results of the analysis which are also appended show a considerable improvement over the currently existing methods.
On Leaf Disease Detection Using Image Processing Techniques

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract This paper holds a survey on leaf disease detection using various image processing technique. Digital image processing is fast, reliable and accurate technique for detection of diseases also various algorithms can be used for identification and classification of leaf diseases in plant. This paper presents techniques used by different author to identify disease such as clustering method, color base image analysis method, classifier and artificial neural network for classification of diseases. The main focus of our work is on the analysis of different leaf disease detection techniques and also provides an overview of different image processing techniques.
Data Mining in Education

Data mining techniques are used to extract useful knowledge from raw data. The extracted knowledge is valuable and significantly affects the decision maker. Educational data mining (EDM) is a method for extracting useful information that could potentially affect an organization. The increase of technology use in educational systems has led to the storage of large amounts of student data, which makes it important to use EDM to improve teaching and learning processes. EDM is useful in many different areas including identifying at-risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, and optimizing subject curriculum renewal. This paper surveys the relevant studies in the EDM field and includes the data and methodologies used in those studies.
Automated Concurrency-Bug Fixing

Concurrency bugs are widespread in multithreaded programs. Fixing them is time-consuming and error-prone. We present CFix, a system that automates the repair of concurrency bugs. CFix works with a wide variety of concurrency-bug detectors. For each failure-inducing interleaving reported by a bug detector, CFix first determines a combination of mutual-exclusion and order relationships that, once enforced, can prevent the buggy interleaving. CFix then uses static analysis and testing to determine where to insert what synchronization operations to force the desired mutual-exclusion and order relationships, with a best effort to avoid deadlocks and excessive performance losses. CFix also simplifies its own patches by merging fixes for related bugs. Evaluation using four different types of bug detectors and thirteen real-world concurrency-bug cases shows that CFix can successfully patch these cases without causing deadlocks or excessive performance degradation. Patches automatically generated by CFix are of similar quality to those manually written by developers.
Key Factors for Determining Student Satisfaction in Online Courses

H istorically, retention of distance learners has been problematic with dropout rates disproportionably high compared to traditional course settings (Richards & Ridley, 1997; Wetzel, Radtke, & Stern, 1994). Dropout rates of 30 to 50% have been common (Moore & Kearsley, 1996). Students may experience feelings of isolation in distance courses compared to prior faceto-face educational experiences (Shaw & Polovina, 1999). If the distance courses feature limited contact with instructors and fellow students, the result of this isolation can be unfinished courses or degrees (Keegan, 1990). Student satisfaction in traditional learning environments has been overlooked in the past (Astin, 1993; DeBourgh, 1999; Navarro & Shoemaker, 2000). Student satisfaction has also not been given the proper attention in distance learning environments (Biner, Dean, & Mellinger, 1994). Richards and Ridley (1997) suggested further research is necessary to study factors affecting student enrollment and satisfaction. Prior studies in classroom-based courses have shown there is a high correlation between student satisfaction and retention (Astin, 1993;  1982). This high correlation has also been found in studies in which distance learners were the target population (Bailey, Bauman, & Lata, 1998). The purpose of this study was to identify factors influencing student satisfaction in online courses, and to create and validate an instrument to measure student satisfaction in online courses.
Attention Models with Random Features for Multi-layered Graph Embeddings

Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent all interactions, and hence multi-layered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multi-layered case. In this paper, we consider the problem of semi-supervised learning with multi-layered graphs. Though deep network embeddings, e.g. DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning, and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the inter-layer dependencies for building multi-layered graph embeddings. Using empirical studies on several benchmark datasets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison to state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.
Two-agent formation control of magnetic microrobots

This paper presents a new method to control multiple micro-scale magnetic agents operating in close proximity to each other for applications in microrobotics. Controlling multiple magnetic microrobots close to each other is difficult due to magnetic interactions between the agents, and here we seek to control those interactions for the creation of desired multi-agent formations. We use the fact that all magnetic agents orient to the global input magnetic field to modulate the local attraction-repulsion forces between nearby agents. Here we study these controlled interaction magnetic forces for agents at a water-air interface and devise two controllers to regulate the inter-agent spacing and heading of the set, for motion in two dimensions. Simulation and experimental demonstrations show the feasibility of the idea and its potential for the completion of complex tasks using teams of microrobots. Average tracking error of less than 73 μm and 14° is accomplished for the regulation of the inter-agent space and the pair heading angle, respectively, for identical disk-shape agents with nominal radius of 500 μm and thickness of 80 μm operating within several body-lengths of each other.
Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

This study focuses on personality prediction of protagonists in novels based on the Five-Factor Model of personality. We present and publish a novel collaboratively built dataset of fictional character personality and design our task as a text classification problem. We incorporate a range of semantic features, including WordNet and VerbNet sense-level information and word vector representations. We evaluate three machine learning models based on the speech, actions and predicatives of the main characters, and show that especially the lexical-semantic features significantly outperform the baselines. The most predictive features correspond to reported findings in personality psychology.