nils-herrmann/clean_scirepeval huggingface dataset:
A neuromorphic VLSI design for spike timing and rate based synaptic plasticity

Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety of biological experiments, while the PSTDP rule fails to reproduce them. Additionally, it has been shown that the behaviour inherent to the spike rate-based Bienenstock-Cooper-Munro (BCM) synaptic plasticity rule can also emerge from the TSTDP rule. This paper proposes an analogue implementation of the TSTDP rule. The proposed VLSI circuit has been designed using the AMS 0.35 μm CMOS process and has been simulated using design kits for Synopsys and Cadence tools. Simulation results demonstrate how well the proposed circuit can alter synaptic weights according to the timing difference amongst a set of different patterns of spikes. Furthermore, the circuit is shown to give rise to a BCM-like learning rule, which is a rate-based rule. To mimic an implementation environment, a 1000 run Monte Carlo (MC) analysis was conducted on the proposed circuit. The presented MC simulation analysis and the simulation result from fine-tuned circuits show that it is possible to mitigate the effect of process variations in the proof of concept circuit; however, a practical variation aware design technique is required to promise a high circuit performance in a large scale neural network. We believe that the proposed design can play a significant role in future VLSI implementations of both spike timing and rate based neuromorphic learning systems.
Scaling to Very Very Large Corpora for Natural Language Disambiguation

The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be optimized, tested and compared after training on corpora consisting of only one million words or less. In this paper, we evaluate the performance of different learning methods on a prototypical natural language disambiguation task, confusion set disambiguation, when trained on orders of magnitude more labeled data than has previously been used. We are fortunate that for this particular application, correctly labeled training data is free. Since this will often not be the case, we examine methods for effectively exploiting very large corpora when labeled data comes at a cost.
Internal validity , performance and dimensionality of the Body Shape Questionnaire among female students in the Kurdistan Iraq Region

The objective of this study to modify and adapt the “Body Shape Questionnaire” for use in the Kurdistan Iraq society; to study the internal validity and the dimensionality of the measure when used with normal sample in the Iraqi society. Across-survey research was complete comprising (160) female students selected in the Salahaddin University, and (120) female students selected in the AL-Hyate University and Chihan University in Kurdistan Iraq. The thirty four items of “Body shape Questionnaire” original version and a questionnaire to evaluate demographic data were used. The result of “internal consistency” of BSQ-34 measured by “Cronbach Alpha” was (0.98) for Kurdish version and (0.97) for Arabic version, indicated that the items of the measure converge to the same concept. The principle component analysis “PCA” of the measure resulted in four-dimension solution accounting by160 Kurdish female student, however the “PCA” for the measure resulted three-dimensions accounting by 120 Arab female students. The old age was (18-20) 53.1% and (21-25) 46.9% for Kurdish sample, while Arabs student old age was (18-20) 55.0% and (21-25) 45.0%. Regarding the final results of BSQ, the mean score of BSQ Arab students were 89.1 3 with SD (33.83). The mean score of BSQ Kurdish female students were 103.03 with SD (43.40). The study found statistically significant race variances in the mean of “BSQ” scores. Kurdish female students showed higher dissatisfaction than Arab female students as to their body shape concern as measured by “BSQ” scale. The adapted versions of the scale seem to maintain the properties of the original one.
A self-boost charge pump topology for a gate drive high-side power supply

A self-boost charge pump topology is presented for a floating high-side gate drive power supply that features high voltage and current capabilities for use in integrated power electronic modules (IPEMs). The transformerless topology uses a small capacitor to transfer energy to the high-side switch from a single power supply referred to the negative rail. Unlike conventional bootstrap power supplies, no switching of the main phase-leg switches is required to provide power continuously to the high-side gate drive, even if the high-side switch is permanently on. Additional advantages include low parts-count and simple control requirements. A piecewise linear model of the self-boost charge pump is derived and the circuit's operating characteristics are analyzed. Simulation and experimental results are provided to verify the desired operation of the new charge pump circuit. Guidelines are provided to assist with circuit component selection in new applications.
MagNet: A Two-Pronged Defense against Adversarial Examples

Deep learning has shown impressive performance on hard perceptual problems. However, researchers found deep learning systems to be vulnerable to small, specially crafted perturbations that are imperceptible to humans. Such perturbations cause deep learning systems to mis-classify adversarial examples, with potentially disastrous consequences where safety or security is crucial. Prior defenses against adversarial examples either targeted specific attacks or were shown to be ineffective.
 We propose MagNet, a framework for defending neural network classifiers against adversarial examples. MagNet neither modifies the protected classifier nor requires knowledge of the process for generating adversarial examples. MagNet includes one or more separate detector networks and a reformer network. The detector networks learn to differentiate between normal and adversarial examples by approximating the manifold of normal examples. Since they assume no specific process for generating adversarial examples, they generalize well. The reformer network moves adversarial examples towards the manifold of normal examples, which is effective for correctly classifying adversarial examples with small perturbation. We discuss the intrinsic difficulties in defending against whitebox attack and propose a mechanism to defend against graybox attack. Inspired by the use of randomness in cryptography, we use diversity to strengthen MagNet. We show empirically that MagNet is effective against the most advanced state-of-the-art attacks in blackbox and graybox scenarios without sacrificing false positive rate on normal examples.
Frontal cortex function as derived from hierarchical predictive coding

The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal-directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single-units, and neuropsychological studies of both lateral and medial prefrontal cortex. By reconceptualizing lateral prefrontal activity as anticipating prediction errors, the HER model provides a novel unifying account of prefrontal cortex function with broad implications for understanding the frontal cortex across multiple levels of description, from the level of single neurons to behavior.
Wheelchair navigation assisted by Human-Machine shared-control and a P300-based Brain Computer Interface

This paper presents a new shared-control approach for assistive mobile robots, using Brain Computer Interface (BCI) as the Human-Machine Interface (HMI). A P300-based paradigm that allows the selection of brain-actuated commands to steer a Robotic Wheelchair (RW), is proposed. At least one specific motor skill, such as the control of arms, legs, head or voice, is required to operate a conventional HMI. Due to this reason, they are not suited for people suffering from severe motor disorders. BCI may open a new communication channel to these users, since it does not require any muscular activity. The number of decoded symbols per minute (SPM) in a BCI is still very low, which means that users can only provide sparse, and discrete commands. The RW must rely on the navigation system to validate user commands effectively. A two-layer shared-control approach is proposed. The first, a virtual-constraint layer, is responsible for enabling/disabling the user commands, based on certain context restrictions. The second layer is an user-intent matching responsible for determining the suitable steering command, that better fits the user command, taking the user competence on steering the wheelchair into account. Experimental results using Robchair, the RW platform developed at ISR-UC ,  are presented, showing the effectiveness of the proposed methodologies.
Adaptive loading algorithms for IMDD optical OFDM PON systems using directly modulated lasers

A detailed study on the effectiveness of three proposed optical orthogonal frequency-division multiplexing (OOFDM) adaptive loading algorithms (ALAs) is conducted, addressing, for the first time, a number of crucial issues related to OOFDM directly modulated laser-based intensity-modulated and direct-detected passive optical networks (PONs). The ALAs include the power loading (PL), bit loading, and bit-and-power loading (BPL) algorithms. It is shown that in comparison with the most sophisticated BPL algorithm, the simplest PL algorithm is effective in escalating a single-channel OOFDM PON performance to its maximum potential. On the other hand, for multi-channel OOFDM PON and when employing a large number of subcarriers and high digital-to-analog/analog-to-digital converter sampling speed, the BPL algorithm should be adopted. The utilization of the three ALAs can also extend the optimum clipping ratio range up to at least 3 dB compared to a conventional identical modulation scheme.
The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature

The recent appearance of low cost virtual reality (VR) technologies - like the Oculus Rift, the HTC Vive and the Sony PlayStation VR - and Mixed Reality Interfaces (MRITF) - like the Hololens - is attracting the attention of users and researchers suggesting it may be the next largest stepping stone in technological innovation. However, the history of VR technology is longer than it may seem: the concept of VR was formulated in the 1960s and the first commercial VR tools appeared in the late 1980s. For this reason, during the last 20 years, 100s of researchers explored the processes, effects, and applications of this technology producing 1000s of scientific papers. What is the outcome of this significant research work? This paper wants to provide an answer to this question by exploring, using advanced scientometric techniques, the existing research corpus in the field. We collected all the existent articles about VR in the Web of Science Core Collection scientific database, and the resultant dataset contained 21,667 records for VR and 9,944 for augmented reality (AR). The bibliographic record contained various fields, such as author, title, abstract, country, and all the references (needed for the citation analysis). The network and cluster analysis of the literature showed a composite panorama characterized by changes and evolutions over the time. Indeed, whether until 5 years ago, the main publication media on VR concerned both conference proceeding and journals, more recently journals constitute the main medium of communication. Similarly, if at first computer science was the leading research field, nowadays clinical areas have increased, as well as the number of countries involved in VR research. The present work discusses the evolution and changes over the time of the use of VR in the main areas of application with an emphasis on the future expected VR's capacities, increases and challenges. We conclude considering the disruptive contribution that VR/AR/MRITF will be able to get in scientific fields, as well in human communication and interaction, as already happened with the advent of mobile phones by increasing the use and the development of scientific applications (e.g., in clinical areas) and by modifying the social communication and interaction among people.
STRIP: A Defence Against Trojan Attacks on Deep Neural Networks

Recent trojan attacks on deep neural network (DNN) models are one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker’s chosen trojan trigger input. Trojan attacks are easy to craft; survive even in adverse conditions such as different viewpoints, and lighting conditions on images; and threaten real world applications such as autonomous vehicles and robotics. The trojan trigger is a secret guarded and exploited by the attacker. Therefore, detecting such trojaned inputs is a challenge, especially at run-time when models are in active operation. We focus on vision systems and build the STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of the predicted classes for the perturbed inputs for a given model—malicious or benign—under deployment. A low entropy in the predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input—a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on two popular and contrasting datasets: MNIST and CIFAR10. We achieve an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for four different tested trojan trigger types—three triggers are identified in previous attack works and one dedicated trigger is crafted by us to demonstrate the trigger-size insensitivity of the STRIP detection approach. In particular, on the dataset of natural images in CIFAR10, we have empirically achieved the desired result of 0% for both FRR and FAR.
Wireless Geophone Network for remote monitoring and detection of landslides

Recent years have shown an alarmous increase in rain fall induced landslides. This has facilitated the need for having a monitoring system to predict the landslides which could eventually reduce the loss of human life. We have developed and deployed a Wireless Sensor Network to monitor rainfall induced landslide, in Munnar, South India. A successful landslide warning was issued in June 2009 using this system. The system is being enhanced by incorporating a Wireless Geophone Network to locate the initiation of landslide. The paper discusses an algorithm that was developed to analyze the geophone data and automatically detect the landslide signal. A novel method to localize the landslide initiation point is detailed. The algorithm is based on the time delay inherent in the transmission of waves through the surface of the earth. The approach detailed here does not require additional energy since the geophones are self excitatory. The error rate of the approach is much less when compared to the other localization methods like RSSI. The proposed algorithm is being tested and validated, in the landslide laboratory set up at our university.
Food Image Recognition with Convolutional Neural Networks

In this paper, we propose a food image recognition system with convolutional neural networks(CNN), which has been applied to image recognition successfully in the literature. A CNN which consists of five layers has been built and two group of controlled trials have been processed on it. Two datasets are prepared: one is UEC-FOOD100 dataset which is an open 100-class food image dataset including about 15000 images and the other is a fruit dataset that established by ourselves including over 40000 images. We have achieved the best accuracy of 80.8% on the fruit dataset and 60.9% on the multi-food dataset. In addition, we validate the method on two groups of controlled trials and discover the effect of color under various conditions that the color feature is not always helpful for improving the accuracy by comparing the results of two group of controlled trials. As future work, we will combine image segmentation with image recognition to get a better performance.
The contribution of electrophysiology to functional connectivity mapping

A powerful way to probe brain function is to assess the relationship between simultaneous changes in activity across different parts of the brain. In recent years, the temporal activity correlation between brain areas has frequently been taken as a measure of their functional connections. Evaluating 'functional connectivity' in this way is particularly popular in the fMRI community, but has also drawn interest among electrophysiologists. Like hemodynamic fluctuations observed with fMRI, electrophysiological signals display significant temporal fluctuations, even in the absence of a stimulus. These neural fluctuations exhibit a correlational structure over a wide range of spatial and temporal scales. Initial evidence suggests that certain aspects of this correlational structure bear a high correspondence to so-called functional networks defined using fMRI. The growing family of methods to study activity covariation, combined with the diverse neural mechanisms that contribute to the spontaneous fluctuations, has somewhat blurred the operational concept of functional connectivity. What is clear is that spontaneous activity is a conspicuous, energy-consuming feature of the brain. Given its prominence and its practical applications for the functional connectivity mapping of brain networks, it is of increasing importance that we understand its neural origins as well as its contribution to normal brain function.
Data-Driven Agent-based Modeling of Innovation Diffusion

We present a novel data-driven agent-based modeling framework to study innovation diffusion. Our first step is to learn a model of individual agent behavior from individual adoption characteristics. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. Finally, we exemplify the proposed method can be used to explore and analyze a broad class of policies aimed at spurring innovation adoption.
Group Linguistic Bias Aware Neural Response Generation

For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users’ preference on language styles, topics, etc. To address this issue, this paper proposes to incorporate linguistic biases, which implicitly involved in the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoderdecoder based response generator. By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented. The experimental results on the dataset from the Chinese SNS show that the proposed architecture outperforms the current response generating models by producing both meaningful and vivid responses with customized styles.
Path Planning with Phased Array SLAM and Voronoi Tessellation

Autonomous vehicles must often navigate environments that are at least partially unknown. They are faced with the tasks of creating a coordinate system to localize themselves on, identify the positions of obstacles, and chart safe paths through the environment. This process is known as Simultaneous Localization and Mapping, or SLAM. SLAM has traditionally been executed using measurements of distance to features in the environment. We propose an angle based methodology using a single phased array antenna and DSP.aimed to reduce this requirement to a single path for each data type. Additionally, our method makes use of rudimentary echo-location to discover reflective obstacles. Finally, our method uses Voronoi Tessellation for path planning.
Iron Loss Reduction in an Interior PM Automotive Alternator

This paper examines the iron loss characteristics of a high-flux interior permanent-magnet machine. The machine was designed as a concept demonstrator for a 6-kW automotive alternator and has a wide field-weakening range. Initial experimental results revealed a high iron loss during field-weakening operation. Finite-element analysis was used to investigate the cause of the high iron losses and to predict their magnitude as a function of speed. The effects of changes in the machine design were examined in order to reduce iron losses and hence improve the machine performance
Dynamic load balancing on single- and multi-GPU systems

The computational power provided by many-core graphics processing units (GPUs) has been exploited in many applications. The programming techniques currently employed on these GPUs are not sufficient to address problems exhibiting irregular, and unbalanced workload. The problem is exacerbated when trying to effectively exploit multiple GPUs concurrently, which are commonly available in many modern systems. In this paper, we propose a task-based dynamic load-balancing solution for single-and multi-GPU systems. The solution allows load balancing at a finer granularity than what is supported in current GPU programming APIs, such as NVIDIA's CUDA. We evaluate our approach using both micro-benchmarks and a molecular dynamics application that exhibits significant load imbalance. Experimental results with a single-GPU configuration show that our fine-grained task solution can utilize the hardware more efficiently than the CUDA scheduler for unbalanced workload. On multi-GPU systems, our solution achieves near-linear speedup, load balance, and significant performance improvement over techniques based on standard CUDA APIs.
Research on Software Security Testing

Software security testing is an important means to ensure software security and trustiness. This paper first mainly discusses the definition and classification of software security testing, and investigates methods and tools of software security testing widely. Then it analyzes and concludes the advantages and disadvantages of various methods and the scope of application, presents a taxonomy of security testing tools. Finally, the paper points out future focus and development directions of software security testing technology. security testing, security functional testing, security vulnerability testing, testing method, testing tool