nils-herrmann/clean_scirepeval huggingface dataset:
Pyro: Deep Universal Probabilistic Programming

Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large datasets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.
Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations.

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.
Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network

Electric load forecasting plays a vital role in smart grids. Short term electric load forecasting forecasts the load that is several hours to several weeks ahead. Due to the nonlinear, non-stationary and nonseasonal nature of the short term electric load time series in small scale power systems, accurate forecasting is challenging. This paper explores Long-Short-Term-Memory (LSTM) based Recurrent Neural Network (RNN) to deal with this challenge. LSTM-based RNN is able to exploit the long term dependencies in the electric load time series for more accurate forecasting. Experiments are conducted to demonstrate that LSTM-based RNN is capable of forecasting accurately the complex electric load time series with a long forecasting horizon. Its performance compares favorably to many other forecasting methods.
Development of Marathi part of speech tagger using statistical approach

Part-of-speech (POS) tagging is a process of assigning the words in a text corresponding to a particular part of speech. A fundamental version of POS tagging is the identification of words as nouns, verbs, adjectives etc. For processing natural languages, Part of Speech tagging is a prominent tool. It is one of the simplest as well as most constant and statistical model for many NLP applications. POS Tagging is an initial stage of linguistics, text analysis like information retrieval, machine translator, text to speech synthesis, information extraction etc. In POS Tagging we assign a Part of Speech tag to each word in a sentence and literature. Various approaches have been proposed to implement POS taggers. In this paper we present a Marathi part of speech tagger. It is morphologically rich language. Marathi is spoken by the native people of Maharashtra. The general approach used for development of tagger is statistical using Unigram, Bigram, Trigram and HMM  It presents a clear idea about all the algorithms with suitable examples. It also introduces a tag set for Marathi which can be used for tagging Marathi text. In this paper we have shown the development of the tagger as well as compared to check the accuracy of taggers output. The three Marathi POS taggers viz. Unigram, Bigram, Trigram and HMM gives the accuracy of 77.38%, 90.30%, 91.46% and 93.82% respectively.
SPIRAL: Code Generation for DSP Transforms

Fast changing, increasingly complex, and diverse computing platforms pose central problems in scientific computing: How to achieve, with reasonable effort, portable optimal performance? We present SPIRAL, which considers this problem for the performance-critical domain of linear digital signal processing (DSP) transforms. For a specified transform, SPIRAL automatically generates high-performance code that is tuned to the given platform. SPIRAL formulates the tuning as an optimization problem and exploits the domain-specific mathematical structure of transform algorithms to implement a feedback-driven optimizer. Similar to a human expert, for a specified transform, SPIRAL "intelligently" generates and explores algorithmic and implementation choices to find the best match to the computer's microarchitecture. The "intelligence" is provided by search and learning techniques that exploit the structure of the algorithm and implementation space to guide the exploration and optimization. SPIRAL generates high-performance code for a broad set of DSP transforms, including the discrete Fourier transform, other trigonometric transforms, filter transforms, and discrete wavelet transforms. Experimental results show that the code generated by SPIRAL competes with, and sometimes outperforms, the best available human tuned transform library code.
Dopamine, learning and motivation

The hypothesis that dopamine is important for reward has been proposed in a number of forms, each of which has been challenged. Normally, rewarding stimuli such as food, water, lateral hypothalamic brain stimulation and several drugs of abuse become ineffective as rewards in animals given performance-sparing doses of dopamine antagonists. Dopamine release in the nucleus accumbens has been linked to the efficacy of these unconditioned rewards, but dopamine release in a broader range of structures is implicated in the 'stamping-in' of memory that attaches motivational importance to otherwise neutral environmental stimuli.
Learning deep neural network policies with continuous memory states

Policy learning for partially observed control tasks requires policies that can remember salient information from past observations. In this paper, we present a method for learning policies with internal memory for high-dimensional, continuous systems, such as robotic manipulators. Our approach consists of augmenting the state and action space of the system with continuous-valued memory states that the policy can read from and write to. Learning general-purpose policies with this type of memory representation directly is difficult, because the policy must automatically figure out the most salient information to memorize at each time step. We show that, by decomposing this policy search problem into a trajectory optimization phase and a supervised learning phase through a method called guided policy search, we can acquire policies with effective memorization and recall strategies. Intuitively, the trajectory optimization phase chooses the values of the memory states that will make it easier for the policy to produce the right action in future states, while the supervised learning phase encourages the policy to use memorization actions to produce those memory states. We evaluate our method on tasks involving continuous control in manipulation and navigation settings, and show that our method can learn complex policies that successfully complete a range of tasks that require memory.
An improved calibration framework for electromagnetic tracking devices

Electromagnetic trackers have many favorable characteristics but are notorious for their sensitivity to magnetic field distortions resulting from metal and electronic equipment in the environment. We categorize existing tracker calibration methods and present an improved technique for reducing the static position and orientation errors that are inherent to these devices. A quaternion-based formulation provides a simple and fast computational framework for representing orientation errors. Our experimental apparatus consists of a 6-DOF mobile platform and an optical position measurement system, allowing the collection of full-pose data at nearly arbitrary orientations of the receiver. A polynomial correction technique is applied and evaluated using a Polhemus Fastrak resulting in a substantial improvement of tracking accuracy. Finally, we apply advanced visualization algorithms to give new insight into the nature of the magnetic distortion field.
Stealthy Domain Generation Algorithms

Botnets are groups of compromised computers that botmasters (botherders) use to launch attacks over the Internet. To avoid detection, botnets use DNS fast flux to change the mapping between IP addresses and domain names periodically. Domain generation algorithms (DGAs) are employed to generate a large number of domain names. Detection techniques have been proposed to identify malicious domain names generated by DGAs. Three metrics, Kullback–Leibler (KL) distance, Edit distance (ED), and Jaccard index (JI), are used to detect botnet domains with up to 100% detection rate and 2.5% false-positive rate. In this paper, we propose two DGAs that use hidden Markov models (HMMs) and probabilistic context-free grammars (PCFGs), respectively. Experiment results show that DGA detection metrics (KL, JI, and ED) and detection systems (BotDigger and Pleiades) have difficulty detecting domain names generated using the proposed approaches. Game theory is used to optimize strategies for both botmasters and security personnel. Results show that, to optimize DGA detection, security personnel should use the ED detection technique with probability 0.78 and JI detection with probability 0.22, and botmasters should choose the HMM-based DGA with probability 0.67 and PCFG-based DGA with probability 0.33.
Storing and querying ordered XML using a relational database system

XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using relational database systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a relational database system. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.
Clustering by pattern similarity in large data sets

Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, similar objects are required to have close values on at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collaborative filtering, can also benefit from the new model, which captures not only the closeness of values of certain leading indicators but also the closeness of (purchasing, browsing, etc.) patterns exhibited by the customers. Our paper introduces an effective algorithm to detect such clusters, and we perform tests on several real and synthetic data sets to show its effectiveness.
Depression Estimation Using Audiovisual Features and Fisher Vector Encoding

We investigate the use of two visual descriptors: Local Binary Patterns-Three Orthogonal Planes(LBP-TOP) and Dense Trajectories for depression assessment on the AVEC 2014 challenge dataset. We encode the visual information generated by the two descriptors using Fisher Vector encoding which has been shown to be one of the best performing methods to encode visual data for image classification. We also incorporate audio features in the final system to introduce multiple input modalities. The results produced using Linear Support Vector regression outperform the baseline method.
Ankle-knee prosthesis with active ankle and energy transfer: Development of the CYBERLEGs Alpha-Prosthesis

This paper presents the development of the CYBERLEGs Alpha-Prototype prosthesis, a new transfemoral prosthesis incorporating a new variable stiffness ankle actuator based on the MACCEPA architecture, a passive knee with two locking mechanisms, and an energy transfer mechanism that harvests negative work from the knee and delivers it to the ankle to assist pushoff. The CYBERLEGs Alpha-Prosthesis is part of the CYBERLEGs FP7-ICT project, which combines a prosthesis system to replace a lost limb in parallel with an exoskeleton to assist the sound leg, and sensory array to control both systems. The prosthesis attempts to produce a natural level ground walking gait that approximates the joint torques and kinematics of a non-amputee while maintaining compliant joints, which has the potential to decrease impulsive losses, and ultimately reduce the end user energy consumption. This first prototype consists of a passive knee and an active ankle which are energetically coupled to reduce the total power consumption of the device. Here we present simulations of the actuation system of the ankle and the passive behavior of the knee module with and without the energy transfer effects, the mechanical design of the prosthesis, and empirical results from testing of the A preliminary version of this paper was presented at the Wearable Robotics Workshop, Neurotechnix 2013. ∗Corresponding author Email addresses: lflynn@vub.ac.be (Louis Flynn), jgeeroms@vub.ac.be (Joost Geeroms), rjimenez@vub.ac.be (Rene Jimenez-Fabian), bram.vanderborght@vub.ac.be (Bram Vanderborght), n.vitiello@sssup.it (Nicola Vitiello), dlefeber@vub.ac.be ( 2014 physical device with amputee subjects.
Know Your Phish: Novel Techniques for Detecting Phishing Sites and Their Targets

Phishing is a major problem on the Web. Despite the significant attention it has received over the years, there has been no definitive solution. While the state-of-the-art solutions have reasonably good performance, they require a large amount of training data and are not adept at detecting phishing attacks against new targets. In this paper, we begin with two core observations: (a) although phishers try to make a phishing webpage look similar to its target, they do not have unlimited freedom in structuring the phishing webpage, and (b) a webpage can be characterized by a small set of key terms, how these key terms are used in different parts of a webpage is different in the case of legitimate and phishing webpages. Based on these observations, we develop a phishing detection system with several notable properties: it requires very little training data, scales well to much larger test data, is language-independent, fast, resilient to adaptive attacks and implemented entirely on client-side. In addition, we developed a target identification component that can identify the target website that a phishing webpage is attempting to mimic. The target detection component is faster than previously reported systems and can help minimize false positives in our phishing detection system.
CMOS on-chip stacked Marchand balun for millimeter-wave applications

A millimeter-wave CMOS on-chip stacked Marchand balun is presented in this paper. The balun is fabricated using a top pad metal layer as the single-ended port and is stacked above two metal conductors at the next highest metal layer in order to achieve sufficient coupling to function as the differential ports. Strip metal shields are placed underneath the structure to reduce substrate losses. An amplitude imbalance of 0.5 dB is measured with attenuations below 6.5 dB at the differential output ports at 30 GHz. The corresponding phase imbalance is below 5 degrees. The area occupied is 229μm × 229μm.
JavaML: a markup language for Java source code

The classical plain-text representation of source code is c onvenient for programmers but requires parsing to uncover t he deep structure of the program. While sophisticated software too ls parse source code to gain access to the program’s structur e, many lightweight programming aids such as grep rely instead on only the lexical structure of source code. I d escribe a new XML application that provides an alternative representation o f Java source code. This XML-based representation, called J avaML, is more natural for tools and permits easy specification of nume rous software-engineering analyses by leveraging the abun dance of XML tools and techniques. A robust converter built with th e Jikes Java compiler framework translates from the classic l Java source code representation to JavaML, and an XSLT style sheet converts from JavaML back into the classical textual f orm.
E-Commerce : A Study on Online Shopping in Malaysia

Despite the high potential of online shopping in Malaysia, there is still a lack of understanding concerning the subject matter and its potential impact on consumer marketing. This study aims to establish a preliminary assessment, evaluation and understanding of the characteristics of online shopping in Malaysia. The Chisquire test was used to determine the relationship between current online shoppers and demographic variables. Samples t-test was then used to compare the mean differences between current online shoppers and non-online shoppers. Then, correlation coefficient matrix was employed to determine the degree of correlation between future online shopping and issues concerning online shopping. The study revealed that online shopping in Malaysia is significantly associated with the Chinese ethnic group, university degree holders and credit cards or chequebooks owners. The results also indicated a significant mean difference between online shoppers and non-online shoppers in gathering product information and comparing price of goods and services before a purchase decision. Results also indicate that this group of non-online shoppers constitutes more than 60% of the total number of online consumers in Malaysia, implying a huge potential of online shopping to online retailers. In conclusion, online consumers in Malaysia still lack confidence and trust in utilizing the Internet as a shopping channel. They are mainly concerned about issues related to privacy and trust when dealing with online retailers. These issues, if left unchecked, will have a detrimental effect on the future growth of online shopping in Malaysia.
Effect of Non-linear Deep Architecture in Sequence Labeling

If we compare the widely used Conditional Random Fields (CRF) with newly proposed “deep architecture” sequence models (Collobert et al., 2011), there are two things changing: from linear architecture to non-linear, and from discrete feature representation to distributional. It is unclear, however, what utility nonlinearity offers in conventional featurebased models. In this study, we show the close connection between CRF and “sequence model” neural nets, and present an empirical investigation to compare their performance on two sequence labeling tasks – Named Entity Recognition and Syntactic Chunking. Our results suggest that non-linear models are highly effective in low-dimensional distributional spaces. Somewhat surprisingly, we find that a nonlinear architecture offers no benefits in a high-dimensional discrete feature space.