nils-herrmann/clean_scirepeval huggingface dataset:
Understanding the roles of signs and norms in organizations - a semiotic approach to information systems design

To apply semiotics to organisational analysis and information systems design, it is essential to unite two basic concepts: the sign and the norm. A sign is anything that stands for something else for some community. A norm is a generalised disposition to the world shared by members of a community. When its condition is met, a norm generates a propositional attitude which may, but not necessarily will, affect the subject's behaviour. Norms reflect regularities in the behaviour of members in an organisation, allowing them to coordinate their actions. Organised behaviour is normgoverned behaviour. Signs trigger the norms leading to more signs being produced. Both signs and norms lend themselves to empirical study. The focus in this paper is on the properties of norms since those for signs are relatively well known. The paper discusses a number of different taxonomies of norms: formal, informal, technical; evaluative, perceptual, behavioural, cognitive; structure, action; substantive, communication and control. A semiotic analysis of information systems is adduced in this paper from the social, pragmatic, semantic, syntactic, empiric and physical perspectives. The paper finally presents a semiotic approach to information systems design, by discussing the method of information modelling and systems architecture. This approach shows advantages over other traditional one in a higher degree of separation of knowledge, and hence system’s consistency, integrity and maintainability.
Should your 8-year-old learn coding?

There has been considerable interest in teaching "coding" to primary school aged students, and many creative "Initial Learning Environments" (ILEs) have been released to encourage this. Announcements and commentaries about such developments can polarise opinions, with some calling for widespread teaching of coding, while others see it as too soon to have students learning industry-specific skills. It is not always clear what is meant by teaching coding (which is often used as a synonym for programming), and what the benefits and costs of this are. Here we explore the meaning and potential impact of learning coding/programming for younger students. We collect the arguments for and against learning coding at a young age, and review the initiatives that have been developed to achieve this (including new languages, school curricula, and teaching resources). This leads to a set of criteria around the value of teaching young people to code, to inform curriculum designers, teachers and parents. The age at which coding should be taught can depend on many factors, including the learning tools used, context, teacher training and confidence, culture, specific skills taught, how engaging an ILE is, how much it lets students explore concepts for themselves, and whether opportunities exist to continue learning after an early introduction.
How Would You Feel versus How Do You Think She Would Feel? A Neuroimaging Study of Perspective-Taking with Social Emotions

Perspective-taking is a complex cognitive process involved in social cognition. This positron emission tomography (PET) study investigated by means of a factorial design the interaction between the emotional and the perspective factors. Participants were asked to adopt either their own (first person) perspective or the (third person) perspective of their mothers in response to situations involving social emotions or to neutral situations. The main effect of third-person versus first-person perspective resulted in hemodynamic increase in the medial part of the superior frontal gyrus, the left superior temporal sulcus, the left temporal pole, the posterior cingulate gyrus, and the right inferior parietal lobe. A cluster in the postcentral gyrus was detected in the reverse comparison. The amygdala was selectively activated when subjects were processing social emotions, both related to self and other. Interaction effects were identified in the left temporal pole and in the right postcentral gyrus. These results support our prediction that the frontopolar, the somatosensory cortex, and the right inferior parietal lobe are crucial in the process of self/ other distinction. In addition, this study provides important building blocks in our understanding of social emotion processing and human empathy.
Fiber optically sensorized multi-fingered robotic hand

We present the design, fabrication, and characterization of a fiber optically sensorized robotic hand for multi purpose manipulation tasks. The robotic hand has three fingers that enable both pinch and power grips. The main bone structure was made of a rigid plastic material and covered by soft skin. Both bone and skin contain embedded fiber optics for force and tactile sensing, respectively. Eight fiber optic strain sensors were used for rigid bone force sensing, and six fiber optic strain sensors were used for soft skin tactile sensing. For characterization, different loads were applied in two orthogonal axes at the fingertip and the sensor signals were measured from the bone structure. The skin was also characterized by applying a light load on different places for contact localization. The actuation of the hand was achieved by a tendon-driven under-actuated system. Gripping motions are implemented using an active tendon located on the volar side of each finger and connected to a motor. Opening motions of the hand were enabled by passive elastic tendons located on the dorsal side of each finger.
ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators

Hardware accelerators are being increasingly deployed to boost the performance and energy efficiency of deep neural network (DNN) inference. In this paper we propose Thundervolt, a new framework that enables aggressive voltage underscaling of high-performance DNN accelerators without compromising classification accuracy even in the presence of high timing error rates. Using post-synthesis timing simulations of a DNN acceleratormodeled on theGoogle TPU,we show that Thundervolt enables between 34%-57% energy savings on stateof-the-art speech and image recognition benchmarks with less than 1% loss in classification accuracy and no performance loss. Further, we show that Thundervolt is synergistic with and can further increase the energy efficiency of commonly used run-timeDNNpruning techniques like Zero-Skip.
Efficient Rendering of Local Subsurface Scattering

A novel approach is presented to efficiently render local subsurface scattering effects. We introduce an importance sampling scheme for a practical subsurface scattering model. It leads to a simple and efficient rendering algorithm, which operates in image-space, and which is even amenable for implementation on graphics hardware. We demonstrate the applicability of our technique to the problem of skin rendering, for which the subsurface transport of light typically remains local. Our implementation shows that plausible images can be rendered interactively using hardware acceleration.
Baymax: QoS Awareness and Increased Utilization for Non-Preemptive Accelerators in Warehouse Scale Computers

Modern warehouse-scale computers (WSCs) are being outfitted with accelerators to provide the significant compute required by emerging intelligent personal assistant (IPA) workloads such as voice recognition, image classification, and natural language processing. It is well known that the diurnal user access pattern of user-facing services provides a strong incentive to co-locate applications for better accelerator utilization and efficiency, and prior work has focused on enabling co-location on multicore processors. However, interference when co-locating applications on non-preemptive accelerators is fundamentally different than contention on multi-core CPUs and introduces a new set of challenges to reduce QoS violation. To address this open problem, we first identify the underlying causes for QoS violation in accelerator-outfitted servers. Our experiments show that queuing delay for the compute resources and PCI-e bandwidth contention for data transfer are the main two factors that contribute to the long tails of user-facing applications. We then present Baymax, a runtime system that orchestrates the execution of compute tasks from different applications and mitigates PCI-e bandwidth contention to deliver the required QoS for user-facing applications and increase the accelerator utilization. Using DjiNN, a deep neural network service, Sirius, an end-to-end IPA workload, and traditional applications on a Nvidia K40 GPU, our evaluation shows that Baymax improves the accelerator utilization by 91.3% while achieving the desired 99%-ile latency target for for user-facing applications. In fact, Baymax reduces the 99%-ile latency of user-facing applications by up to 195x over default execution.
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

The area under the ROC curve, or the equivalent Gini index, is a widely used measure of performance of supervised classification rules. It has the attractive property that it side-steps the need to specify the costs of the different kinds of misclassification. However, the simple form is only applicable to the case of two classes. We extend the definition to the case of more than two classes by averaging pairwise comparisons. This measure reduces to the standard form in the two class case. We compare its properties with the standard measure of proportion correct and an alternative definition of proportion correct based on pairwise comparison of classes for a simple artificial case and illustrate its application on eight data sets. On the data sets we examined, the measures produced similar, but not identical results, reflecting the different aspects of performance that they were measuring. Like the area under the ROC curve, the measure we propose is useful in those many situations where it is impossible to give costs for the different kinds of misclassification.
Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation safety and cost reduction. Nowadays, the catenary support devices are periodically captured by the cameras mounted on the inspection vehicles during the night, but the inspection still mostly relies on human visual interpretation. To reduce the human involvement, this paper proposes a novel vision-based method that applies the deep convolutional neural networks (DCNNs) in the defect detection of the fasteners. Our system cascades three DCNN-based detection stages in a coarse-to-fine manner, including two detectors to sequentially localize the cantilever joints and their fasteners and a classifier to diagnose the fasteners’ defects. Extensive experiments and comparisons of the defect detection of catenary support devices along the Wuhan–Guangzhou high-speed railway line indicate that the system can achieve a high detection rate with good adaptation and robustness in complex environments.
ReSuMe-New Supervised Learning Method for Spiking Neural Networks

In this report I introduce ReSuMe a new supervised learning method for Spiking Neural Networks. The research on ReSuMe has been primarily motivated by the need of inventing an efficient learni ng method for control of movement for the physically disabled. Howeve r, thorough analysis of the ReSuMe method reveals its suitability not on ly to the task of movement control, but also to other real-life applicatio ns including modeling, identification and control of diverse non-statio nary, nonlinear objects. ReSuMe integrates the idea of learning windows, known from t he spikebased Hebbian rules, with a novel concept of remote supervis ion. General overview of the method, the basic definitions, the netwo rk architecture and the details of the learning algorithm are presented . The properties of ReSuMe such as locality, computational simplicity a nd the online processing suitability are discussed. ReSuMe learning abi lities are illustrated in a verification experiment.
Paraphrase identification in short texts using grammar patterns

We can determine whether two texts are paraphrases of each other by finding out the extent to which the texts are similar. The typical lexical matching technique works by matching the sequence of tokens between the texts to recognize paraphrases, and fails when different words are used to convey the same meaning. We can improve this simple method by combining lexical with syntactic or semantic representations of the input texts. The present work makes use of syntactical information in the texts and computes the similarity between them using word similarity measures based on WordNet and lexical databases. The texts are converted into a unified semantic structural model through which the semantic similarity of the texts is obtained. An approach is presented to assess the semantic similarity and the results of applying this approach is evaluated using the Microsoft Research Paraphrase (MSRP)
A Recurrent Neural Network Based Recommendation System

6 Recommendation systems play an extremely important role in e-commerce; 7 by recommending products that suit the taste of the consumers, e-commerce 8 companies can generate large profits. The most commonly used 9 recommender systems typically produce a list of recommendations through 10 collaborative or content-based filtering; neither of those approaches take 11 into account the content of the written reviews, which contain rich 12 information about user’s taste. In this paper, we evaluate the performance of 13 ten different recurrent neural network (RNN) structure on the task of generating 14 recommendations using written reviews. The RNN structures we study include 15 well know implementations such as Multi-stacked bi-directional Gated 16 Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) as well as novel 17 implementation of attention-based RNN structure. The attention-based structures 18 are not only among the best models in terms of prediction accuracy, they also 19 assign an attention weight to each word in the review; by plotting the attention 20 weight of each word we gain additional insight into the underlying mechanisms 21 involved in the prediction process. We develop and test the recommendation 22 systems using the data provided by Yelp Data Challenge.
Attribute selection and Customer Churn Prediction in telecom industry

In this competitive world, business is becoming highly saturated. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore, it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. This paper explores the application of data mining techniques in predicting the likely churners and attribute selection on identifying the churn. It also compares the efficiency of several classifiers and lists their performances for two real telecom datasets.
Going to the MALL: Mobile Assisted Language Learning.

In August 2004, Duke University provided free iPods to its entire freshman class (Belanger, 2005). The next month, a Korean education firm offered free downloadable college entrance exam lectures to students who purchased an iRiver personal multimedia player (Kim, 2004). That October, a financial trading firm in Chicago was reportedly assessing the hand-eye coordination of traders’ using GameBoys (Logan, 2004). Yet while such innovative applications abound, the use of technology in education and training is far from new, a fact as true in language classrooms as it is in medical schools.
The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices

CHiME-3 is a research community challenge organised in 2015 to evaluate speech recognition systems for mobile multi-microphone devices used in noisy daily environments. This paper describes NTT's CHiME-3 system, which integrates advanced speech enhancement and recognition techniques. Newly developed techniques include the use of spectral masks for acoustic beam-steering vector estimation and acoustic modelling with deep convolutional neural networks based on the "network in network" concept. In addition to these improvements, our system has several key differences from the official baseline system. The differences include multi-microphone training, dereverberation, and cross adaptation of neural networks with different architectures. The impacts that these techniques have on recognition performance are investigated. By combining these advanced techniques, our system achieves a 3.45% development error rate and a 5.83% evaluation error rate. Three simpler systems are also developed to perform evaluations with constrained set-ups.
Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space

For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potential. Picbreeder is an online community in which users can evolve and share images, and most importantly, continue evolving others' images. Through this process of branching from other images, and through continually increasing image complexity made possible by the underlying neuroevolution of augmenting topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC system. This paper discusses not only the strengths of the Picbreeder approach, but its challenges and shortcomings as well, in the hope that lessons learned will inform the design of future CIE systems.
Evaluating Bayesian networks' precision for detecting students' learning styles

Students are characterized by different learning styles, focusing on different types of information and processing this information in different ways. One of the desirable characteristics of a Web-based education system is that all the students can learn despite their different learning styles. To achieve this goal we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In this work, we evaluate Bayesian networks at detecting the learning style of a student in a Web-based education system. The Bayesian network models different aspects of a student behavior while he/she works with this system. Then, it infers his/her learning styles according to the modeled behaviors. The proposed Bayesian model was evaluated in the context of an Artificial Intelligence Web-based course. The results obtained are promising as regards the detection of students learning styles. Different levels of precision were found for the different dimensions or aspects of a learning style.
A survey on hardware trojan detection techniques

Hardware Trojans recently emerged as a serious issue for computer systems, especially for those used in critical applications such as medical or military. Trojan proposed so far can affect the reliability of a device in various ways. Proposed effects range from the leakage of secret information to the complete malfunctioning of the device. A crucial point for securing the overall operation of a device is to guarantee the absence of hardware Trojans. In this paper, we survey several techniques for detecting malicious modification of circuit introduced at different phases of the design flow. We also highlight their capabilities limitations in thwarting hardware Trojans.
Using Goal Modeling to Capture Competency Questions in Ontology-based Systems

Most of the available ontology engineering methodologies presuppose the existence of a set of questions which provide the objective and scope of the ontology under development. However, these so-called competency questions are not always clear from start. In this sense, such competency questions are comparable to system requirements, elicited and modeled during the requirements engineering stage of a software development process. Having this in mind, this work proposes the use of the Tropos methodology to help the ontology engineer reason and model competency questions. For that, he should follow the traditional Tropos process, starting with the early requirements activity to analyze the objectives of an organization. This initial stage is followed by the late requirements activity, in which the competency question are captured and linked to the organization’s goals, following the goal modeling process. In this way, we aim at providing a methodology which will consistently drive the ontology engineer in developing an ontology from scratch.
Generic Side-Channel Countermeasures for Reconfigurable Devices

In this work, we propose and evaluate generic hardware countermeasures against DPA attacks for recent FPGA devices. The proposed set of FPGA-specific countermeasures can be combined to resist a large variety of first-order DPA attacks, even with 100 million recorded power traces. This set includes generic and resource-efficient countermeasures for on-chip noise generation, random-data processing delays and S-box scrambling using dual-ported block memories. In particular, it is possible to build many of these countermeasures into a single IP-core or hard macro that then provides basic protection for any cryptographic implementation just by its inclusion in the design process – what is particularly useful for engineers with no or little background on IT security and SCA attacks.
A Bayesian Probabilistic Framework for Rain Detection

Heavy rain deteriorates the video quality of outdoor imaging equipments. In order to improve video clearness, image-based and sensor-based methods are adopted for rain detection. In earlier literature, image-based detection methods fall into spatio-based and temporal-based categories. In this paper, we propose a new image-based method by exploring spatio-temporal united constraints in a Bayesian framework. In our framework, rain temporal motion is assumed to be Pathological Motion (PM), which is more suitable to time-varying character of rain steaks. Temporal displaced frame discontinuity and spatial Gaussian mixture model are utilized in the whole framework. Iterated expectation maximization solving method is taken for Gaussian parameters estimation. Pixels state estimation is finished by an iterated optimization method in Bayesian probability formulation. The experimental results highlight the advantage of our method in rain detection.
Modeling and Control of Legged Robots

The promise of legged robots over standard wheeled robots is to provide improved mobility over rough terrain. This promise builds on the decoupling between the environment and the main body of the robot that the presence of articulated legs allows, with two consequences. First, the motion of the main body of the robot can be made largely independent from the roughness of the terrain, within the kinematic limits of the legs: legs provide an active suspension system. Indeed, one of the most advanced hexapod robots of the 1980s was aptly called the Adaptive Suspension Vehicle . Second, this decoupling allows legs to temporarily leave their contact with the ground: isolated footholds on a discontinuous terrain can be overcome, allowing to visit places absolutely out of reach otherwise. Note that having feet firmly planted on the ground is not mandatory here: skating is an equally interesting option, although rarely approached so far in robotics.