nils-herrmann/clean_scirepeval huggingface dataset:
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning

Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy. The rule-based policy is often reliable within predefined scope but not self-adaptable, whereas RL is evolvable with data but often suffers from a bad initial performance. We employ a companion learning framework to integrate the two approaches for on-line dialogue policy learning, in which a predefined rule-based policy acts as a teacher and guides a data-driven RL system by giving example actions as well as additional rewards. A novel agent-aware dropout Deep Q-Network (AAD-DQN) is proposed to address the problem of when to consult the teacher and how to learn from the teacher’s experiences. AADDQN, as a data-driven student policy, provides (1) two separate experience memories for student and teacher, (2) an uncertainty estimated by dropout to control the timing of consultation and learning. Simulation experiments showed that the proposed approach can significantly improve both safety and efficiency of on-line policy optimization compared to other companion learning approaches as well as supervised pre-training using static dialogue corpus.
Sums of Products of Generalized Ramanujan Sums Soichi Ikeda

We consider weighted averages for the products t (1) k1 (j) · · · t kn (j) of generalized Ramanujan sums t (i) ki (j) = ∑ d| gcd(ki,j) fi(d)gi(ki/d)hi(j/d) with any arithmetical functions fi, gi and hi (i = 1, . . . , n), and derive formulas for several weighted averages with weights concerning completely multiplicative functions, completely additive functions, and others.
Simulated Annealing Modeling and Analog MPPT Simulation for Standalone Photovoltaic Arrays

This paper proposes a method formodelling and simulation of photovoltaic arrays. The method is used to obtain the parameters of the array model using its datasheet information. To reduce computational time, the input parameters are reduced to four and the values of shunt resistance Rp and series resistance Rs are estimated by simulated annealing optimization method. Then we draw I-V and P-V curves at different irradiance levels. Lowcomplexityanalogue MPPT circuit can be developedby usingtwo voltage approximation lines (VALs) that approximate the maximum power point (MPP) locus.In this paper, a fast and low cost analog MPPT method for low power PVsystems is proposed.The Simulation results coincide with experimental results at different PV systems to validate the powerful of the proposed method.
Context-aware Distributed Complex Event Processing Method for Event Cloud in Internet of Things

With the rapid development of Internet of Things (IoT), enormous events are produced by various kinds of devices at high speed. Complex Event Processing (CEP) is the key part of IoT middleware which can help the user to get semantic meanings of primitive events. Context-awareness is an important feature of CEP engine. In this paper a high performance distributed context-aware CEP architecture and method is proposed for internet of things. Context is modeled as fuzzy ontology to support uncertainty and linguistic variables in event queries. Based on fuzzy ontology query and similarity based distributed reasoning, complex event query plans are generated and context-aware queries are rewritten into context independent sub-queries. Data window is partitioned according to different event patterns and context. The sub-queries are optimized and executed parallel based on data partition. The experiments show that this method can support fuzzy context in CEP and have acceptable performance and scalability.
Rapid estimation of camera motion from compressed video with application to video annotation

As digital video becomes more pervasive, e cient ways of searching and annotating video according to content will be increasingly important. Such tasks arise, for example, in the management of digital video libraries for content-based retrieval and browsing. In this paper, we develop tools based on camera motion for analyzing and annotating a class of structured video using the low-level information available directly from MPEG compressed video. In particular, we show that in certain structured settings it is possible to obtain reliable estimates of camera motion by directly processing data easily obtained from the MPEG format. Working directly with the compressed video greatly reduces the processing time and enhances storage e ciency. As an illustration of this idea, we have developed a simple basketball annotation system which combines the low-level information extracted from an MPEG stream with the prior knowledge of basketball structure to provide high level content analysis, annotation and browsing for events such as wide-angle and close-up views, fast breaks, probable shots at the basket, etc. The methods used in this example should also be useful in the analysis of high-level content of structured video in other domains.
Microglia development and function.

Proper development and function of the mammalian central nervous system (CNS) depend critically on the activity of parenchymal sentinels referred to as microglia. Although microglia were first described as ramified brain-resident phagocytes, research conducted over the past century has expanded considerably upon this narrow view and ascribed many functions to these dynamic CNS inhabitants. Microglia are now considered among the most versatile cells in the body, possessing the capacity to morphologically and functionally adapt to their ever-changing surroundings. Even in a resting state, the processes of microglia are highly dynamic and perpetually scan the CNS. Microglia are in fact vital participants in CNS homeostasis, and dysregulation of these sentinels can give rise to neurological disease. In this review, we discuss the exciting developments in our understanding of microglial biology, from their developmental origin to their participation in CNS homeostasis and pathophysiological states such as neuropsychiatric disorders, neurodegeneration, sterile injury responses, and infectious diseases. We also delve into the world of microglial dynamics recently uncovered using real-time imaging techniques.
Resolve: Generation of High-Performance Sorting Architectures from High-Level Synthesis

Field Programmable Gate Array (FPGA) implementations of sorting algorithms have proven to be efficient, but existing implementations lack portability and maintainability because they are written in low-level hardware description languages that require substantial domain expertise to develop and maintain. To address this problem, we develop a framework that generates sorting architectures for different requirements (speed, area, power, etc.). Our framework provides ten highly optimized basic sorting architectures, easily composes basic architectures to generate hybrid sorting architectures, enables non-hardware experts to quickly design efficient hardware sorters, and facilitates the development of customized heterogeneous FPGA/CPU sorting systems. Experimental results show that our framework generates architectures that perform at least as well as existing RTL implementations for arrays smaller than 16K elements, and are comparable to RTL implementations for sorting larger arrays. We demonstrate a prototype of an end-to-end system using our sorting architectures for large arrays (16K-130K) on a heterogeneous FPGA/CPU system.
Simple, fast and accurate hyper-parameter tuning in Gaussian-kernel SVM

We consider the parameter tuning problem for Gaussian-kernel support vector machines, i.e., how to set its two hyperparameters — σ (bandwidth) and C (tradeoff). Among the many methods in the literature, the majority handle this task by maximizing the cross validation accuracy over the first quadrant of the (σ, C) plane. However, they are all computationally expensive because the objective function has no explicit formula so that one has to resort to numerical methods (which require training and testing the classifier many times). Additionally, these methods ignore the intrinsic geometry of training data and always operate in a large set, thus being computationally inefficient. In this paper we propose a two-step procedure for efficient parameter selection: First, we use a nearest neighbor method to directly set the value of σ based on the data geometry; afterwards, for the tradeoff parameter C we employ an elbow method that finds the smallest C leading to “nearly” the highest validation accuracy. By slightly sacrificing the validation accuracy our method gains additional attractive properties such as (1) faster training (i.e., much less candidate points to be examined) and (2) better generalizability (due to larger class margins). We conduct extensive experiments to show that such a combination of simple techniques achieves excellent performance — the classification accuracy of our method is comparable to its competitors in most cases, but it is much faster.
Effects of increasing amounts of hempseed cake in the diet of dairy cows on the production and composition of milk.

This study explored the potential for using seed cake from hemp (Cannabis sativa L.) as a protein feed for dairy cows. The aim was to evaluate the effects of increasing the proportion of hempseed cake (HC) in the diet on milk production and milk composition. Forty Swedish Red dairy cows were involved in a 5-week dose-response feeding trial. The cows were allocated randomly to one of four experimental diets containing on average 494 g/kg of grass silage and 506 g/kg of concentrate on a dry matter (DM) basis. Diets containing 0 g (HC0), 143 g (HC14), 233 g (HC23) or 318 g (HC32) HC/kg DM were achieved by replacing an increasing proportion of compound pellets with cold-pressed HC. Increasing the proportion of HC resulted in dietary crude protein (CP) concentrations ranging from 126 for HC0 to 195 g CP/kg DM for HC32. Further effects on the composition of the diet with increasing proportions of HC were higher fat and NDF and lower starch concentrations. There were no linear or quadratic effects on DM intake, but increasing the proportion of HC in the diet resulted in linear increases in fat and NDF intake, as well as CP intake (P < 0.001), and a linear decrease in starch intake (P < 0.001). The proportion of HC had significant quadratic effects on the yields of milk, energy-corrected milk (ECM) and milk protein, fat and lactose. The curvilinear response of all yield parameters indicated maximum production from cows fed diet HC14. Increasing the proportion of HC resulted in linear decreases in both milk protein and milk fat concentration (P = 0.005 and P = 0.017, respectively), a linear increase in milk urea (P < 0.001), and a linear decrease in CP efficiency (milk protein/CP intake; P < 0.001). In conclusion, the HC14 diet, corresponding to a dietary CP concentration of 157 g/kg DM, resulted in the maximum yields of milk and ECM by dairy cows in this study.
Chinese Handwriting Imitation with Hierarchical Generative Adversarial Network

Automatic character generation, expected to save much time and labor, is an appealing solution for new typeface design. Inspired by the recent advancement in Generative Adversarial Networks (GANs), this paper proposes a Hierarchical Generative Adversarial Network (HGAN) for typeface transformation. The proposed HGAN consists of two sub-networks: (1) a transfer network mapping characters from one typeface to another preserving the corresponding structural information, which includes a content encoder and a hierarchical generator. (2) a hierarchical adversarial discriminator which distinguishes samples generated by the transfer network from real samples. Considering the unique properties of characters, different from original GANs, a hierarchical structure is proposed, which output the transferred characters in different phase of generator and at the same time, making the True/False judgment not only based on the final extracting features but also intermediate features in discriminator. Experimenting with Chinese typeface transformation, we show that HGAN is an effective framework for font style transfer, from standard printed typeface to personal handwriting styles.
Activity logging using lightweight classification techniques in mobile devices

Automated activity recognition enables a wide variety of applications related to child and elderly care, disease diagnosis and treatment, personal health or sports training, for which it is key to seamlessly determine and log the user’s motion. This work focuses on exploring the use of smartphones to perform activity recognition without interfering in the user’s lifestyle. Thus, we study how to build an activity recognition system to be continuously executed in a mobile device in background mode. The system relies on device’s sensing, processing and storing capabilities to estimate significant movements/postures (walking at different paces—slow, normal, rush, running, sitting, standing). In order to evaluate the combinations of sensors, features and algorithms, an activity dataset of 16 individuals has been gathered. The performance of a set of lightweight classifiers (Naïve Bayes, Decision Table and Decision Tree) working on different sensor data has been fully evaluated and optimized in terms of accuracy, computational cost and memory fingerprint. Results have pointed out that a priori information on the relative position of the mobile device with respect to the user’s body enhances the estimation accuracy. Results show that computational low-cost Decision Tables using the best set of features among mean and variance and considering all the sensors (acceleration, gravity, linear acceleration, magnetometer, gyroscope) may be enough to get an activity estimation accuracy of around 88 % (78 % is the accuracy of the Naïve Bayes algorithm with the same characteristics used as a baseline). To demonstrate its applicability, the activity recognition system has been used to enable a mobile application to promote active lifestyles.
Homocysteine and cardiovascular disease: interactions between nutrition, genetics and lifestyle.

Homocysteine is a sulfur-containing amino acid that arises during methionine metabolism. Although its concentration in plasma is only about 10 micromolar, even moderate hyperhomocysteinemia is associated with increased incidence of cardiovascular disease and Alzheimer's disease. Elevations in plasma homocysteine are commonly found as a result of vitamin deficiencies, polymorphisms of enzymes of methionine metabolism, and renal disease. Pyridoxal, folic acid, riboflavin, and Vitamin B(12) are all required for methionine metabolism, and deficiency of each of these vitamins result in elevated plasma homocysteine. A polymorphism of methylenetetrahydrofolate reductase (C677T), which is quite common in most populations with a homozygosity rate of 10-15 %, is associated with moderate hyperhomocysteinemia, especially in the context of marginal folate intake. Plasma homocysteine is inversely related to plasma creatinine in patients with renal disease. This is due to an impairment in homocysteine removal in renal disease. The role of these factors, and of modifiable lifestyle factors, in affecting methionone metabolism and in determining plasma homocysteine levels is discussed.
A component-based programming model for autonomic applications

The emergence of pervasive wide-area distributed computing environments, such as pervasive information systems and computational grids, has enabled new generations of applications that are based on seamless access, aggregation and interaction. However, the inherent complexity, heterogeneity and dynamism of these systems require a change in how the applications are developed and managed. In this paper we present a component-based programming framework to support the development of autonomic self-managed applications. The framework enables the development of autonomic components and the formulation of autonomic applications as the dynamic composition and management of autonomic components. The operation of the proposed framework is illustrated using a forest fire application.
The Key Concepts of Ethics of Artificial Intelligence

The growing influence and decision-making capacities of Autonomous systems and Artificial Intelligence in our lives force us to consider the values embedded in these systems. But how ethics should be implemented into these systems? In this study, the solution is seen on philosophical conceptualization as a framework to form practical implementation model for ethics of AI. To take the first steps on conceptualization main concepts used on the field needs to be identified. A keyword based Systematic Mapping Study (SMS) on the keywords used in AI and ethics was conducted to help in identifying, defying and comparing main concepts used in current AI ethics discourse. Out of 1062 papers retrieved SMS discovered 37 re-occurring keywords in 83 academic papers. We suggest that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.
Transformation of pasta wheat (Triticum turgidum L. var. durum) with high-molecular-weight glutenin subunit genes and modification of dough functionality

Particle bombardment has been used to transform three cultivars (L35, Ofanto, Svevo) and one breeding line (Latino × Lira) of durum wheat (Triticum turgidum L. var. durum). These varieties were co-transformed with plasmids containing selectable and scorable marker genes (bar and uidA) and plasmids containing one of two high-molecular-weight (HMW) glutenin subunit genes (encoding subunits 1Ax1 or 1Dx5). Ten independent transgenic lines were recovered from 1683 bombarded scutella (transformation efficiency thus 0.6%). Five lines expressed either subunit 1Dx5 or 1Ax1 at levels similar to those of endogenous subunits encoded on chromosome 1B. To identify the effects of the transgenes on the functional properties of grain, three lines showing segregation for transgene expression were used to isolate sibling T2 plants which were null or positive for the transgene product. Analysis of these plants using a small-scale mixograph showed that expression of the additional subunits resulted in increased dough strength and stability, demonstrating that transformation can be used to modify the quality of durum wheat for bread and pasta making.
Tangible User Interfaces

There is a long history of Tangible User Interfaces (TUI) in the community of humancomputer interaction, but surprisingly few of these interfaces have made it beyond lab and gallery spaces. This thesis explores how the research community may begin to remedy the disconnect between modern TUIs and the everyday computing experience via the creation and dissemination of Trackmate, an accessible (both ubiquitous and enabling) tabletop tangible user interface that scales to a large number of users with minimal hardware and configuration overhead. Trackmate is entirely open source and designed: to be community-centric; to leverage common objects and infrastructure; to provide a low floor, high ceiling, and wide walls for development; to allow user modifications and improvisation; to be shared easily via the web; and to work alongside a broad range of existing applications and new research interface prototypes.  Hiroshi Ishii Title: Muriel R. Cooper Professor of Media Arts and Sciences
Autoencoder Trees

We discuss an autoencoder model in which the encoding and decoding functions are implemented by decision trees. We use the soft decision tree where internal nodes realize soft multivariate splits given by a gating function and the overall output is the average of all leaves weighted by the gating values on their path. The encoder tree takes the input and generates a lower dimensional representation in the leaves and the decoder tree takes this and reconstructs the original input. Exploiting the continuity of the trees, autoencoder trees are trained with stochastic gradient descent. On handwritten digit and news data, we see that the autoencoder trees yield good reconstruction error compared to traditional autoencoder perceptrons. We also see that the autoencoder tree captures hierarchical representations at different granularities of the data on its different levels and the leaves capture the localities in the input space.
Raw meat based diet influences faecal microbiome and end products of fermentation in healthy dogs

Dietary intervention studies are required to deeper understand the variability of gut microbial ecosystem in healthy dogs under different feeding conditions and to improve diet formulations. The aim of the study was to investigate in dogs the influence of a raw based diet supplemented with vegetable foods on faecal microbiome in comparison with extruded food.



Eight healthy adult Boxer dogs were recruited and randomly divided in two experimental blocks of 4 individuals. Dogs were regularly fed a commercial extruded diet (RD) and starting from the beginning of the trial, one group received the raw based diet (MD) and the other group continued to be fed with the RD diet (CD) for a fortnight. After 14 days, the two groups were inverted, the CD group shifted to the MD and the MD shifted to the CD, for the next 14 days. Faeces were collected at the beginning of the study (T0), after 14 days (T14) before the change of diet and at the end of experimental period (T28) for DNA extraction and analysis of metagenome by sequencing 16SrRNA V3 and V4 regions, short chain fatty acids (SCFA), lactate and faecal score.



A decreased proportion of Lactobacillus, Paralactobacillus (P < 0.01) and Prevotella (P < 0.05) genera was observed in the MD group while Shannon biodiversity Index significantly increased (3.31 ± 0.15) in comparison to the RD group (2.92 ± 0.31; P < 0.05). The MD diet significantly (P < 0.05) decreased the Faecal Score and increased the lactic acid concentration in the feces in comparison to the RD treatment (P < 0.01). Faecal acetate was negatively correlated with Escherichia/Shigella and Megamonas (P < 0.01), whilst butyrate was positively correlated with Blautia and Peptococcus (P < 0.05). Positive correlations were found between lactate and Megamonas (P < 0.05), Escherichia/Shigella (P < 0.01) and Lactococcus (P < 0.01).



These results suggest that the diet composition modifies faecal microbial composition and end products of fermentation. The administration of MD diet promoted a more balanced growth of bacterial communities and a positive change in the readouts of healthy gut functions in comparison to RD diet.
A NEW ONE ROUND IMAGE ENCRYPTION ALGORITHM BASED ON MULTIPLE CHAOTIC SYSTEMS

This paper presents a bit level pixel by pixel chaotic image encryption scheme with a permutation–diffusion structure. In this scheme, a Neural Network Like Structure (NNLS) is proposed to do the diffusion efficiently. The plain image, split into 16 blocks is permuted by the keys from the key generator. Each key will produce different permutation order (PO) generated using a chaotic map. After permutation, the pixels of the permuted image is converted into bits and given to the proposed NNLS, where the diffusion takes place. The bit by bit diffusion performed by NNLS on the pixels will give more randomness to the cipher image. The performance of the NNLS is contingent upon sensitivity of the keys from the key generator. The test results and analyses performed using several security standards shows that the proposed scheme is more secure, reliable and can be used for real time image encryption.