nils-herrmann/clean_scirepeval huggingface dataset:
Iterated learning and the evolution of language

Iterated learning describes the process whereby an individual learns their behaviour by exposure to another individual's behaviour, who themselves learnt it in the same way. It can be seen as a key mechanism of cultural evolution. We review various methods for understanding how behaviour is shaped by the iterated learning process: computational agent-based simulations; mathematical modelling; and laboratory experiments in humans and non-human animals. We show how this framework has been used to explain the origins of structure in language, and argue that cultural evolution must be considered alongside biological evolution in explanations of language origins.
Transparent and Efficient CFI Enforcement with Intel Processor Trace

Current control flow integrity (CFI) enforcement approaches either require instrumenting application executables and even shared libraries, or are unable to defend against sophisticated attacks due to relaxed security policies, or both, many of them also incur high runtime overhead. This paper observes that the main obstacle of providing transparent and strong defense against sophisticated adversaries is the lack of sufficient runtime control flow information. To this end, this paper describes FlowGuard, a lightweight, transparent CFI enforcement approach by a novel reuse of Intel Processor Trace (IPT), a recent hardware feature that efficiently captures the entire runtime control flow. The main challenge is that IPT is designed for offline performance analysis and software debugging such that decoding collected control flow traces is prohibitively slow on the fly. FlowGuard addresses this challenge by reconstructing applications' conservative control flow graphs (CFG) to be compatible with the compressed encoding format of IPT, and labeling the CFG edges with credits in the help of fuzzing-like dynamic training. At runtime, FlowGuard separates fast and slow paths such that the fast path compares the labeled CFGs with the IPT traces for fast filtering, while the slow path decodes necessary IPT traces for strong security. We have implemented and evaluated FlowGuard on a commodity Intel Skylake machine with IPT support. Evaluation results show that FlowGuard is effective in enforcing CFI for several applications, while introducing only small performance overhead. We also show that, with minor hardware extensions, the performance overhead can be further reduced.
Throughput Analysis of Synchronous Data Flow Graphs

Synchronous data flow graphs (SDFGs) are a useful tool for modeling and analyzing embedded data flow applications, both in a single processor and a multiprocessing context or for application mapping on platforms. Throughput analysis of these SDFGs is an important step for verifying throughput requirements of concurrent real-time applications, for instance within design-space exploration activities. Analysis of SDFGs can be hard, since the worst-case complexity of analysis algorithms is often high. This is also true for throughput analysis. In particular, many algorithms involve a conversion to another kind of data flow graph, the size of which can be exponentially larger than the size of the original graph. In this paper, we present a method for throughput analysis of SDFGs, based on explicit state-space exploration and we show that the method, despite its worst-case complexity, works well in practice, while existing methods often fail. We demonstrate this by comparing the method with state-of-the-art cycle mean computation algorithms. Moreover, since the state-space exploration method is essentially the same as simulation of the graph, the results of this paper can be easily obtained as a byproduct in existing simulation tools
A Deep Reinforcement Learning Based Multimodal Coaching Model (DCM) for Slot Filling in Spoken Language Understanding(SLU)

In this paper, a deep reinforcement learning(DRL) based multimodal coaching model (DCM) for slot filling task in SLU is proposed. The DCM takes advantage of a DRL based model as a coach of the system to learn the wrong labeled slots with/without user’s feedback, hence may further improve the performance of an SLU system. This DCM model is an improved model of the deep reinforcement learning based augmented tagging model as introduced in , by using a better DRL model with different rewards and adding in a user’s feedback modal to achieve one-shot learning.The performance of DCM is evaluated on two datasets: one is the benchmark ATIS corpus dataset, another is our in-house dataset with three different domains. It shows that the new system gives a better performance than the current state-of-the-art results on ATIS by using DCM. Furthermore, we build a demo application to further explain how user’s input can also be used as a real-time coach to improve model’s performance even more.
Bag of words representation and SVM classifier for timber knots detection on color images

Knots as well as their density have a huge impact on the mechanical properties of wood boards. This paper addresses the issue of their automatic detection. An image processing pipeline which associates low level processing (contrast enhancement, thresholding, mathematical morphology) with bag-of-words approach is developed. We propose a SVM classification based on features obtained by SURF descriptors on RGB images, followed by a dictionary created using the bag-of-words approach. Our method was tested on color images from two different datasets with a total number of 640 knots. The mean recall (true positive) rate achieved was (92%) and (97%) for a single dictionary (built only on samples from the first dataset), for the two datasets respectively, illustrating the robustness of our method.
Sustainable Food Supply Chains : The Role of Collaboration and Sustainable Relationships

The life cycle approach is widely used in the analysis of sustainability. Its application to supply chains is necessary since the product flows, from processing of raw materials to the final customer, are considered. The role of the organizational aspects, expressed in terms of relationships between the supply chain agents, is little considered in the life cycle analysis approach. The aim of this paper is to extend the scope of the food chain life cycle analysis by adding the organizational dimension to the environmental, economic and social ones. Within this context, Collaboration and Sustainable Relationships concepts have been explored based on a literature survey. A theoretical framework, describing their role in assessing the organizational dimension in the life cycle analysis of the food supply chains, is defined. A hypothesis on their joint influence on the supply chains performances is formulated.
A 240 GHz ultra-wideband FMCW radar system with on-chip antennas for high resolution radar imaging

Nowadays emerging industrial radar applications demand for high-resolution, high-precision and at the same time low-cost radar sensors. Recent advances in semiconductor technology allow highly integrated radar sensors at frequencies up to several hundred GHz in mass-production suitable and cost-effective SiGe bipolar technologies. In this contribution, a SiGe MMIC-based 240 GHz radar sensor with more than 60GHz bandwidth is presented. It consists of a MMIC chip including the high-frequency components and a digital control module with the PLL stabilisation, ramp generation, and data acquisition. The antenna is realized by on-chip patch antennas, which are focused by using an additional dielectric lens. The radar allows fast and highly linear frequency sweeps from 204 GHz to 265 GHz with an maximum output power of ≈ -1dBm EIRP (patch only). A phase noise of <;-65 dBc/Hz (>1 kHz offset) is achieved over the complete tuning range. Additionally range profile, jitter and imaging measurements are presented to demonstrate the achieved system performance.
Mindsets That Promote Resilience : When Students Believe That Personal Characteristics Can Be Developed

Mindsets That Promote Resilience: When Students Believe That Personal Characteristics Can Be Developed David Scott Yeager & Carol S. Dweck To cite this article: David Scott Yeager & Carol S. Dweck (2012) Mindsets That Promote Resilience: When Students Believe That Personal Characteristics Can Be Developed, Educational Psychologist, 47:4, 302-314,  To link to this article: https://doi.org/10.1080/00461520.2012.722805
Active learning for imbalance problem using L-GEM of RBFNN

In lots of important applications, such as malignant cell detection, network intrusion detection, error signal detection in power system, the data distributions of positive and negative classes are usually imbalance. Many classifiers could not perform well in data imbalance cases. The major problem is that classifiers tend to ignore samples and accuracy of the minority class without regarding the higher cost of misclassification in this minor class. Therefore, pattern classification for imbalance data becomes a hot challenge to both academy and industry. In this paper, we propose an active learning method for imbalance data using a stochastic sensitivity measure (ST-SM) of Radial Basis Function Neural Network (RBFNN). A large ST-SM indicates the RBFNN is uncertain and yields a large output fluctuation around a particular sample. These samples yielding large ST-SM values are selected for adding to the training set in each turn. Empirically, samples with large output perturbation (i.e. large ST-SM) should be located near the classification boundary and is of great significance for the training of classifier. As for the imbalance characteristic of the data set, the ST-SM should be able to reduce the number of redundant samples being selected in the majority class, rebalance the sample distribution of the training set, and finally improve the performance of the classifier.
Empiricism without magic: transformational abstraction in deep convolutional neural networks

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain.
Distance metric between 3D models and 2D images for recognition and classification

Similarity measurements between 3D objects and 2D images are useful for the tasks of object recognition and classi cation. We distinguish between two types of similarity metrics: metrics computed in image-space (image metrics) and metrics computed in transformationspace (transformation metrics). Existing methods typically use image metrics; namely, metrics that measure the di erence in the image between the observed image and the nearest view of the object. Example for such a measure is the Euclidean distance between feature points in the image and their corresponding points in the nearest view. (Computing this measure is equivalent to solving the exterior orientation calibration problem.) In this paper we introduce a di erent type of metrics: transformation metrics. These metrics penalize for the deformations applied to the object to produce the observed image. We present a transformation metric that optimally penalizes for \a ne deformations" under weak-perspective. A closed-form solution, together with the nearest view according to this metric, are derived. The metric is shown to be equivalent to the Euclidean image metric, in the sense that they bound each other from both above and below. For the Euclidean image metric we o er a sub-optimal closed-form solution and an iterative scheme to compute the exact solution. c Massachusetts Institute of Technology (1992) This report describes research done at the Massachusetts Institute of Technology within the Arti cial Intelligence Laboratory and the McDonnell-Pew Center for Cognitive Neuroscience. Support for the laboratory's arti cial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under O ce of Naval Research contract N00014-91-J-4038. Ronen Basri is supported by the McDonnell-Pew and the Rothchild postdoctoral fellowships. Daphna Weinshall is at IBM T.J. Watson Research Center, Hawthorne, NY.
A Procedure for Measuring Microplastics using Pressurized Fluid Extraction.

A method based on pressurized fluid extraction (PFE) was developed for measuring microplastics in environmental samples. This method can address some limitations of the current microplastic methods and provide laboratories with a simple analytical method for quantifying common microplastics in a range of environmental samples. The method was initially developed by recovering 101% to 111% of spiked plastics on glass beads and was then applied to a composted municipal waste sample with spike recoveries ranging from 85% to 94%. The results from municipal waste samples and soil samples collected from an industrial area demonstrated that the method is a promising alternative for determining the concentration and identity of microplastics in environmental samples.
A large-stroke flexure fast tool servo with new displacement amplifier

As the rapid progress of science and technology, the free-form surface optical component has played an important role in spaceflight, aviation, national defense, and other areas of the technology. While the technology of fast tool servo (FTS) is the most promising method for the machining of free-form surface optical component. However, the shortcomings of short-stroke of fast tool servo device have constrained the development of free-form surface optical component. To address this problem, a new large-stroke flexible FTS device is proposed in this paper. A series of mechanism modeling and optimal designs are carried out via compliance matrix theory, pseudo-rigid body theory, and Particle Swarm Optimization (PSO) algorithm, respectively. The mechanism performance of the large-stroke FTS device is verified by the Finite Element Analysis (FEA) method. For this study, a piezoelectric (PZT) actuator P-840.60 that can travel to 90 µm under open-loop control is employed, the results of experiment indicate that the maximum of output displacement can achieve 258.3µm, and the bandwidth can achieve around 316.84 Hz. Both theoretical analysis and the test results of prototype uniformly verify that the presented FTS device can meet the demand of the actual microstructure processing.
The Origins of Fears of Compassion: Shame and Lack of Safeness Memories, Fears of Compassion and Psychopathology.

Empirical and clinical research suggests that some individuals find self-generating compassion and receiving compassion from others difficult and aversive. However, it is unclear how these fears of compassion are linked to early experiences and to psychopathological symptoms. This study explores the relationship between shame traumatic memories, centrality of shame memories, early memories of warmth and safeness, fears of compassion, and depression, anxiety and paranoid symptoms. Participants were 302 individuals from the general community population, who completed self-report measures of fears of compassion, shame memories, early affiliative memories, and psychopathology. Shame traumatic and central memories were positively associated with fears of compassion for self, for others and from others, whereas early memories of warmth and safeness were negatively related to such fears. Path analyses revealed that fears of compassion for self and of receiving compassion from others mediated the effects of shame traumatic memory, centrality of shame memory and early memories of warmth and safeness on depressive, anxious and paranoid symptoms. These findings have implications for therapeutic interventions as these fears, as well as the negative shame-based emotional memories fuelling them, may need to be addressed in therapy to assist patients in self-generating and receiving compassion.
SMART LOW VOLTAGE AC SOLID STATE CIRCUIT BREAKERS FOR SMART GRIDS

The solid state circuit breaker (SSCB) is a device used in the power system in order to provide protection when a short circuit or fault current occurs. The objective of this paper is to study and implement a smart prototype of SSCB for smart grids. The presented SSCB is controlled through the current/time characteristics of that used in the conventional mechanical circuit breakers in addition to limit the high fault current levels (fault current limiter) especially with the proliferation of the distributed generation and the associated fault current level increase. In this paper, the principle of operation of the mechanical circuit breakers (MCB) and their classifications are introduced, andswitches used in the design of SSCBs are presented. Simulation of SSCB is carried out to study its feasibility and performance in the case of various operating conditions. Then, ahardware prototype of SSCB using IGBTs devices is constructed and tested to elucidate the proposed
State of the Art of Finite Control Set Model Predictive Control in Power Electronics

This paper addresses to some of the latest contributions on the application of Finite Control Set Model Predictive Control (FCS-MPC) in Power Electronics. In FCS-MPC , the switching states are directly applied to the power converter, without the need of an additional modulation stage. The paper shows how the use of FCS-MPC provides a simple and efficient computational realization for different control objectives in Power Electronics. Some applications of this technology in drives, active filters, power conditioning, distributed generation and renewable energy are covered. Finally, attention is paid to the discussion of new trends in this technology and to the identification of open questions and future research topics.
AMMON : A Speech Analysis Library for Analyzing Affect , Stress , and Mental Health on Mobile Phones

The human voice encodes a wealth of information about emotion, mood and mental state. With mobile phones this information is potentially available to a host of applications. In this paper we describe the AMMON (Affective and Mentalhealth MONitor) library, a low footprint C library designed for widely available phones. The library incorporates both core features for emotion recognition (from the Interspeech 2009 emotion recognition challenge), and the most important features for mental health analysis (glottal timing features). To comfortably run the library on feature phones (the most widely-used class of phones today), we implemented most of the routines in fixed-point arithmetic, and minimized computational and memory footprint. While there are still floating-point routines to be revised in fixed-point, on identical test data, emotion and mental stress classification accuracy was indistinguishable from a state-of-the-art reference system running on a PC.
3D Sampling Textures for Creative Design and Manufacturing

1 Texture synthesized from 3D scans of snake-skin and mushroom laminae. ABSTRACT 3D sampling is a robust new strategy for exploring and creating designs. 3D textures are sampled and mixed almost like music to create new multifunctional surfaces and material microstructures . This paper presents several design cases performed using 3D sampling techniques, demonstrating how they can be used to explore and enhance product ideas, variations, and functions in subtle and responsive ways. In each case, design variations are generated and their mechanical behavior is evaluated against performance criteria using computational simulation or empirical testing. This work aims to promote creativity and discovery by introducing irregular geometric structures within trial-and-error feedback loops. Sayjel Vijay Patel SUTD DManD Centre
COMPACT METAL MOUNTABLE UHF RFID TAG ON A BARIUM TITANATE BASED SUBSTRATE

The development of a compact metal mountable RadioFrequency IDentification (RFID) tag antenna on a ceramic substrate based on Barium Titanate is presented. The performance limitations and design trade-offs of metal mountable RFID tag antennas are reviewed and the favorable features of a high-permittivity antenna substrate for the development of antennas for metal mountable RFID tags are discussed. The simulation-based tag antenna design process is outlined and the measured read range of the developed metal mountable tag on conductive platforms of various sizes is presented.
Continuous Integration and Continuous Delivery Pipeline Automation for Agile Software Project Management

Agile practices with Continuous Integration and Continuous Delivery (CICD) pipeline approach has increased the efficiency of projects. In agile, new features are introduced to the system in each sprint delivery, and although it may be well developed, the delivery failures are possible due to performance issues. By considering delivery timeline, moving for system scaling is common solution in such situations. But, how much system should be scaled? System scale requires current system benchmark status and expected system status. Benchmarking the production is a critical task, as it interrupts the live system. The new version should go through a load test to measure expected system status. The traditional load test methods are unable to identify production performance behavior due to simulated traffic patterns are highly deviated from production. To overcome those issues, this approach has extended CICD pipeline to have three automation phases named benchmark, load test and scaling. It minimizes the system interruption by using test bench approach when system benchmarking and it uses the production traffic for load testing which gives more accurate results. Once benchmark and load test phases are completed, system scaling can be evaluated. Initially, the pipeline was developed using Jenkins CI server, Git repository and Nexus repository with Ansible automation. Then GoReplay is used for traffic duplication from production to test bench environment. Nagios monitoring is used to analyze the system behavior in each phase and the result of test bench has proven that scaling is capable to handle the same load while changing the application software, but it doesn't optimize response time of application at significant level and it helps to reduce the risk of application deployment by integrating this three phase approach as CICD automation extended feature. Thereby the research provides effective way to manage Agile based CICD projects.