nils-herrmann/clean_scirepeval huggingface dataset:
Organic Computing – Technical Systems for Survival in the Real World

ion is the selection and (possibly) coarsening (i.e. quantisation) of certain system characteristics (attributes, performance indicators, parameters) from the total set of system characteristics. The abstraction process comprises: • A simplification (Example: The colour Red is an abstraction, which neglects the different possible shades of Red, the wavelengths, the intensity etc.), • an aggregation (Example: ‘Temperature’ condenses the myriad of individual molecule movements in a gas volume into a single number.), • and consequently: loss of information. The opposite of abstraction is concretisation. It comprises: • gain of information, • detailing, • refinement, • disaggregation, • in engineering: the design process. 3.2.2 System Boundary Systems are defined by their system boundary, which makes a distinction between inside and outside possible (i.e. between self and non-self, see Sect. 4.1). When 3.2 What is a System? 87 Fig. 3.3 Trade-off between initial development cost and later adaptation cost systems are designed, we must choose where to place the boundary. This involves a consideration of cost (Fig. 3.3): A narrow boundary will reduce the cost for planning and development in the first place but bears the risk of a high effort in case a subsequent adaptation and extension of the system should be necessary. A wide system boundary reverses the cost curves. Apparently, the total cost minimum lies somewhere in the middle. 3.2.3 Some System Types and Properties Most systems are transient, i.e. they change over time. They are developed, assembled, modified, destroyed. Reactive systems react on inputs (events, signals, sensor data) by applying outputs (events, control signals, commands) to the environment. All open systems are reactive! In a more focused definition: Only systems reacting in real time (within a predefined time) are reactive systems. Such real-time systems are characterised by (1) time restrictions (deadlines), and (2) time determinism (i.e. guarantees).  Unplanned systems: During the development process of technical systems, only predictable events are taken into account in the designed core. But there will always be an unpredictable rest. Therefore, the ‘spontaneous closure’  has to cover these events. In technical systems, the exception handler or the diagnosis system play the role of a simple spontaneous closure. Living systems are characterised by a powerful spontaneous closure. Recurring reactions of the spontaneous closure become part of the designed core. This amounts to learning.
Design Optimization of Transverse Flux Linear Motor for Weight Reduction and Performance Improvement Using Response Surface Methodology and Genetic Algorithms

Permanent magnet (PM) type transverse flux linear motors (TFLMs) are electromagnetic devices, which can develop directly powerful linear motion. These motors have been developed to apply to high power system, such as railway traction, electrodynamics vibrator, free-piston generator, etc. This paper presents an optimum design of a PM-type TFLM to reduce the weight of motor with constraints of thrust and detent force using response surface methodology (RSM) and genetic algorithms (GAs). RSM is well adapted to make analytical model of motor weight with constraints of thrust and detent forces, and enable objective function to be easily created and a great computational time to be saved. Finite element computations have been used for numerical experiments on geometrical design variables in order to determine the coefficients of a second-order analytical model for the RSM. GAs are used as a searching tool for design optimization of TFLM to reduce the weight of motor and improve the motor performance.
A Novel, Simple Interpretation of Nesterov's Accelerated Method as a Combination of Gradient and Mirror Descent

First-order methods play a central role in large-scale convex optimization. Even though many variations exist, each suited to a particular problem form, almost all such methods fundamentally rely on two types of algorithmic steps and two corresponding types of analysis: gradient-descent steps, which yield primal progress, and mirror-descent steps, which yield dual progress. In this paper, we observe that the performances of these two types of step are complementary, so that faster algorithms can be designed by coupling the two steps and combining their analyses. In particular, we show how to obtain a conceptually simple interpretation of Nesterov’s accelerated gradient method [Nes83, Nes04, Nes05], a cornerstone algorithm in convex optimization. Nesterov’s method is the optimal first-order method for the class of smooth convex optimization problems. However, to the best of our knowledge, the proof of the fast convergence of Nesterov’s method has not found a clear interpretation and is still regarded by many as crucially relying on an “algebraic trick”[Jud13]. We apply our novel insights to express Nesterov’s algorithm as a natural coupling of gradient descent and mirror descent and to write its proof of convergence as a simple combination of the convergence analyses of the two underlying steps. We believe that the complementary view of gradient descent and mirror descent proposed in this paper will prove very useful in the design of first-order methods as it allows us to design fast algorithms in a conceptually easier way. For instance, our view greatly facilitates the adaptation of non-trivial variants of Nesterov’s method to specific scenarios, such as packing and covering problems [AO14b, AO14a]. ar X iv :1 40 7. 15 37 v2 [ cs .D S] 9 A ug 2 01 4
Training and operation of an integrated neuromorphic network based on metal-oxide memristors

Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 1014 synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks based on circuits combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one or several crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits, including first demonstrations of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks. Very recently, such experiments have been extended to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors, whose nonlinear current–voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.
Unobtrusive and Wearable Systems for Automatic Dietary Monitoring

The threat of obesity, diabetes, anorexia, and bulimia in our society today has motivated extensive research on dietary monitoring. Standard self-report methods such as 24-h recall and food frequency questionnaires are expensive, burdensome, and unreliable to handle the growing health crisis. Long-term activity monitoring in daily living is a promising approach to provide individuals with quantitative feedback that can encourage healthier habits. Although several studies have attempted automating dietary monitoring using wearable, handheld, smart-object, and environmental systems, it remains an open research problem. This paper aims to provide a comprehensive review of wearable and hand-held approaches from 2004 to 2016. Emphasis is placed on sensor types used, signal analysis and machine learning methods, as well as a benchmark of state-of-the art work in this field. Key issues, challenges, and gaps are highlighted to motivate future work toward development of effective, reliable, and robust dietary monitoring systems.
Efficient Multi-task Feature and Relationship Learning

We consider a multitask learning problem, in which several predictors are learned jointly. Prior research has shown that learning the relations between tasks, and between the input features, together with the predictor, can lead to better generalization and interpretability, which proved to be useful for applications in many domains. In this paper, we consider a formulation of multitask learning that learns the relationships both between tasks and between features, represented through a task covariance and a feature covariance matrix, respectively. First, we demonstrate that existing methods proposed for this problem present an issue that may lead to ill-posed optimization. We then propose an alternative formulation, as well as an efficient algorithm to optimize it. Using ideas from optimization and graph theory, we propose an efficient coordinate-wise minimization algorithm that has a closed form solution for each block subproblem. Our experiments show that the proposed optimization method is orders of magnitude faster than its competitors. We also provide a nonlinear extension that is able to achieve better generalization than existing methods.
Spectral collaborative filtering

Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the spectral domain, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the spectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the cold-start problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the spectral domains of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly out-performs state-of-the-art models. Code and data are available at https://github.com/lzheng21/SpectralCF.
Design and Evaluation of Visual Interpenetration Cues in Virtual Grasping

We present design and impact studies of visual feedback for virtual grasping. The studies suggest new or updated guidelines for feedback. Recent grasping techniques incorporate visual cues to help resolve undesirable visual or performance artifacts encountered after real fingers enter a virtual object. Prior guidelines about such visuals are based largely on other interaction types and provide inconsistent and potentially-misleading information when applied to grasping. We address this with a two-stage study. In the first stage, users adjusted parameters of various feedback types, including some novel aspects, to identify promising settings and to give insight into preferences regarding the parameters. In the next stage, the tuned feedback techniques were evaluated in terms of objective performance (finger penetration, release time, and precision) and subjective rankings (visual quality, perceived behavior impact, and overall preference). Additionally, subjects commented on the techniques while reviewing them in a final session. Performance wise, the most promising techniques directly reveal penetrating hand configuration in some way. Subjectively, subjects appreciated visual cues about interpenetration or grasp force, and color changes are most promising. The results enable selection of the best cues based on understanding the relevant tradeoffs and reasonable parameter values. The results also provide a needed basis for more focused studies of specific visual cues and for choosing conditions in comparisons to other feedback modes, such as haptic, audio, or multimodal. Considering results, we propose that 3D interaction guidelines must be updated to capture the importance of interpenetration cues, possible performance benefits of direct representations, and tradeoffs involved in cue selection.
An Android Malware Detection Approach Using Weight-Adjusted Deep Learning

Smartphones recently become an indispensable part of our daily lives. Among various smartphones, Android-based smartphone has become a most popular choice because of the large amount of Android applications (apps) which are developed to make user experience more convenient with rich features. To cope with malicious applications (malwares) which severely threaten the security of Android smartphones, we propose an Android malware characterization and identification approach that uses deep learning algorithm to address the urgent need for malware detection. Extensive experimental results show that our approach achieves over 90% accuracy with only 237 features.
Anti-counterfeiting with hardware intrinsic security

Counterfeiting of goods and electronic devices is a growing problem that has a huge economic impact on the electronics industry. Sometimes the consequences are even more dramatic, when critical systems start failing due to the use of counterfeit lower quality components. Hardware Intrinsic security (i.e. security systems built on the unique electronic fingerprint of a device) offers the potential to reduce the counterfeiting problem drastically. In this paper we will show how Hardware Intrinsic Security (HIS) can be used to prevent various forms of counterfeiting and over-production. HIS technology can also be used to bind software or user data to specific hardware devices, which provides additional security to both soft- and hardware vendors as well as consumers using HIS-enabled products. Besides showing the benefits of HIS, we will also provide an extensive overview of the results (both scientific and industrial) that Intrinsic-ID has achieved studying and implementing HIS.
Inferring Employee Engagement from Social Media

Employees increasingly are expressing ideas and feelings through enterprise social media. Recent work in CHI and CSCW has applied linguistic analysis towards understanding employee experiences. In this paper, we apply dictionary based linguistic analysis to measure 'Employee Engagement'. Employee engagement is a measure of employee willingness to apply discretionary effort towards organizational goals, and plays an important role in organizational outcomes such as financial or operational results. Organizations typically use surveys to measure engagement. This paper describes an approach to model employee engagement based on word choice in social media. This method can potentially complement surveys, thus providing more real-time insights into engagement and allowing organizations to address engagement issues faster. Our results predicting engagement scores on a survey by combining demographics with social media text demonstrate that social media text has significant predictive power compared to demographic data alone. We also find that engagement may be a state than a stable trait since social media posts closer to the administration of the survey had the most predictive power. We further identify the minimum number of social media posts required per employee for the best prediction.
Analyzing hidden populations online: topic, emotion, and social network of HIV-related users in the largest Chinese online community

Traditional survey methods are limited in the study of hidden populations due to the hard to access properties, including lack of a sampling frame, sensitivity issue, reporting error, small sample size, etc. The rapid increase of online communities, of which members interact with others via the Internet, have generated large amounts of data, offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. In this study, we try to understand the multidimensional characteristics of a hidden population by analyzing the massive data generated in the online community.



By elaborately designing crawlers, we retrieved a complete dataset from the "HIV bar," the largest bar related to HIV on the Baidu Tieba platform, for all records from January 2005 to August 2016. Through natural language processing and social network analysis, we explored the psychology, behavior and demand of online HIV population and examined the network community structure.



In HIV communities, the average topic similarity among members is positively correlated to network efficiency (r = 0.70, p < 0.001), indicating that the closer the social distance between members of the community, the more similar their topics. The proportion of negative users in each community is around 60%, weakly correlated with community size (r = 0.25, p = 0.002). It is found that users suspecting initial HIV infection or first in contact with high-risk behaviors tend to seek help and advice on the social networking platform, rather than immediately going to a hospital for blood tests.



Online communities have generated copious amounts of data offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. It is recommended that support through online services for HIV/AIDS consultation and diagnosis be improved to avoid privacy concerns and social discrimination in China.
Reduction of Fuzzy Rules and Membership Functions and Its Application to Fuzzy PI and PD Type Controllers

Fuzzy controller’s design depends mainly on the rule base and membership functions over the controller’s input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechanism to obtain the reduced rule set covering the whole input/output space as well as membership functions for each input variable. But it is found that some membership functions projected from different clusters have high degree of similarity. The number of membership functions of each input variable is then reduced using a similarity measure. In this paper, the fuzzy subtractive clustering approach is shown to reduce 49 rules to 8 rules and number of membership functions to 4 and 6 for input variables (error and change in error) maintaining almost the same level of performance. Simulation on a wide range of linear and nonlinear processes is carried out and results are compared with fuzzy PI and PD type controllers without clustering in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error (IAE) and integral-of-time multiplied absolute error (ITAE) and in each case the proposed schemes shows an identical performance.
Wayfinding Choremes

How can we represent spatial information in maps in a cognitively adequate way? The present article outlines a cognitive conceptual approach that proposes primitive conceptual elements from which maps can be constructed. Based on work in geography that starts with abstract models of geographic phenomena, namely modelisation chorematique by R. Brunet (1980, 1987), we coin primitive conceptual elements of route directions wayfinding choremes. Sketch map drawings were analyzed as they obey the same medial constraints as maps but are constructed in a way that provides insights into human conceptions. A distinction between structural and functional aspects of wayfinding presents a useful method to gain further knowledge about human conceptualizations and leads to a practicable cognitive conceptual approach to map construction.
The implementation of a full EMV smartcard for a point-of-sale transaction

This paper examines the changes in the payment card environment as they relate to EMV (named after Europay, MasterCard and Visa). This research shows that if the combined dynamic data authentication (CDA) card variant of the EMV card is deployed in a full EMV environment, given the relevant known vulnerabilities and attacks against the EMV technology, the consequences of unauthorized disclosure of the cardholder data is of significantly reduced value to a criminal.
Interpretable and effective opinion spam detection via temporal patterns mining across websites

Millions of ratings and reviews on online review websites are influential over business revenues and customer experiences. However, spammers are posting fake reviews in order to gain financial benefits, at the cost of harming honest businesses and customers. Such fake reviews can be illegal and it is important to detect spamming attacks to eliminate unjust ratings and reviews. However, most of the current approaches can be incompetent as they can only utilize data from individual websites independently, or fail to detect more subtle attacks even they can fuse data from multiple sources. Further, the revealed evidence fails to explain the more complicated real world spamming attacks, hindering the detection processes that usually have human experts in the loop. We close this gap by introducing a novel framework that can jointly detect and explain the potential attacks. The framework mines both macroscopic level temporal sentimental patterns and microscopic level features from multiple review websites. We construct multiple sentimental time series to detect atomic dynamics, based on which we mine various cross-site sentimental temporal patterns that can explain various attacking scenarios. To further identify individual spams within the attacks with more evidence, we study and identify effective microscopic textual and behavioral features that are indicative of spams. We demonstrate via human annotations, that the simple and effective framework can spot a sizable collection of spams that have bypassed one of the current commercial anti-spam systems.
Advanced universal remote controller for home automation and security

There has been inconvenience in controlling each digital home appliance which requires its own remote controller. In this paper, we present an advanced universal remote controller (URC) with the total solution for home automation and security. All kinds of home appliances can be controlled with the URC, which can be also connected to a PC dealing with Internet as well. To use the URC, we need several receivers with wired or wireless communication methods to be connected to all appliances. The receivers have many channels and IDs to control many appliances at the same time and to support multi-zone services. In addition, we propose a PC-based interface for end-users to use the URC conveniently. With the proposed URC, we can easily construct a ubiquitous home automation and security environment with the total solution. Furthermore, this solution can be applied to the automated control of all kinds of appliances installed within buildings for companies, schools, hospitals, and so on.