nils-herrmann/clean_scirepeval huggingface dataset:
A Low-Power Speech Recognizer and Voice Activity Detector Using Deep Neural Networks

This paper describes digital circuit architectures for automatic speech recognition (ASR) and voice activity detection (VAD) with improved accuracy, programmability, and scalability. Our ASR architecture is designed to minimize off-chip memory bandwidth, which is the main driver of system power consumption. A SIMD processor with 32 parallel execution units efficiently evaluates feed-forward deep neural networks (NNs) for ASR, limiting memory usage with a sparse quantized weight matrix format. We argue that VADs should prioritize accuracy over area and power, and introduce a VAD circuit that uses an NN to classify modulation frequency features with 22.3-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power consumption. The 65-nm test chip is shown to perform a variety of ASR tasks in real time, with vocabularies ranging from 11 words to 145 000 words and full-chip power consumption ranging from 172 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> to 7.78 mW.
The grand challenges of Science Robotics

One of the ambitions of Science Robotics is to deeply root robotics research in science while developing novel robotic platforms that will enable new scientific discoveries. Of our 10 grand challenges, the first 7 represent underpinning technologies that have a wider impact on all application areas of robotics. For the next two challenges, we have included social robotics and medical robotics as application-specific areas of development to highlight the substantial societal and health impacts that they will bring. Finally, the last challenge is related to responsible innovation and how ethics and security should be carefully considered as we develop the technology further.
Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming

Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness.
Comparing LDA and LSA Topic Models for Content-Based Movie Recommendation Systems

We propose a plot-based recommendation system, which is based upon an evaluation of similarity between the plot of a video that was watched by a user and a large amount of plots stored in a movie database. Our system is independent from the number of user ratings, thus it is able to propose famous and beloved movies as well as old or unheard movies/programs that are still strongly related to the content of the video the user has watched. The system implements and compares the two Topic Models, Latent Semantic Allocation (LSA) and Latent Dirichlet Allocation (LDA), on a movie database of two hundred thousand plots that has been constructed by integrating different movie databases in a local NoSQL (MongoDB) DBMS. The topic models behaviour has been examined on the basis of standard metrics and user evaluations, performance assessments with 30 users to compare our tool with a commercial system have been conducted.
Problematic mobile phone use in adolescence: a cross-sectional study

In recent years, mobile phone use has become increasingly common among Italian youth, while a growing scientific literature has been identifying the occurrence of a problematic mobile phone use which seems to share some features of other conditions often referred to as behavioural addictions. The study aimed to assess the prevalence of problematic mobile phone use in a population of Italian adolescents and its association with other behavioural addictions. The Mobile Addiction Test (MAT) was administered to 2,790 high school students from Barletta, an Italian town, together with the South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA), the Compulsive Buying Scale (CBS), the Internet Addiction Test (IAT), the Exercise Addiction Inventory (EAI), the Work Addiction Risk Test (WART). MAT scores fitted a Gaussian distribution model. Scores ≥ 17 was found as a cut-off value over which identifying problematic mobile phone users. Overall prevalence of problematic mobile phone use was 6.3%; this condition was associated with other behavioural addictions like compulsive buying. Problematic mobile phone use in adolescence should become a public health issue, and it could be a cause of health problems and social costs.
Adaptive Dynamic Walking of a Quadruped Robot on Irregular Terrain Based on Biological Concepts

We have been trying to induce a quadruped robot to walk with medium walking speed on irregular terrain based on biological concepts. We propose the necessary conditions for stable dynamic walking on irregular terrain in general, and we design the mechanical system and the neural system by comparing biological concepts with those necessary conditions described in physical terms. A PD controller at the joints can construct the virtual spring–damper system as the visco-elasticity model of a muscle. The neural system model consists of a central pattern generator (CPG) and reflexes. A CPG receives sensory input and changes the period of its own active phase. The desired angle and P-gain of each joint in the virtual spring– damper system is switched based on the phase signal of the CPG. CPGs, the motion of the virtual spring–damper system of each leg and the rolling motion of the body are mutually entrained through the rolling motion feedback to CPGs, and can generate adaptive walking. We report on our experimental results of dynamic walking on terrains of medium degrees of irregularity in order to verify the effectiveness of the designed neuro-mechanical system. We point out the trade-off problem between the stability and the energy consumption in determining the cyclic period of walking on irregular terrain, and we show one example to solve this problem. MPEG footage of these experiments can be seen at http://www.kimura.is.uec.ac.jp. quadruped, central pattern generator (CPG), walking on irregular terrain The International  No. 3–4, March–April 2003,
The brain's conversation with itself: neural substrates of dialogic inner speech.

Inner speech has been implicated in important aspects of normal and atypical cognition, including the development of auditory hallucinations. Studies to date have focused on covert speech elicited by simple word or sentence repetition, while ignoring richer and arguably more psychologically significant varieties of inner speech. This study compared neural activation for inner speech involving conversations ('dialogic inner speech') with single-speaker scenarios ('monologic inner speech'). Inner speech-related activation differences were then compared with activations relating to Theory-of-Mind (ToM) reasoning and visual perspective-taking in a conjunction design. Generation of dialogic (compared with monologic) scenarios was associated with a widespread bilateral network including left and right superior temporal gyri, precuneus, posterior cingulate and left inferior and medial frontal gyri. Activation associated with dialogic scenarios and ToM reasoning overlapped in areas of right posterior temporal cortex previously linked to mental state representation. Implications for understanding verbal cognition in typical and atypical populations are discussed.
A Simple Geometrical Approach for Deinterleaving Radar Pulse Trains

Some periodic and quasi-periodic pulse trains are emitted by different sources in the environment and a number of sensors receive them through a single channel simultaneously. We are often interested in separating these pulse trains for source identification at sensors. This identification process is termed as deinterleaving pulse trains. Deinterleaving pulse trains has wide applications in communications, radar systems, neural systems, biomedical engineering, and so on. This paper studies the deinterleaving problem with the assumption that both sources and sensors are fixed. In this study, the problem of deinterleaving pulse trains is modeled as a blind source separation (BSS) problem. To solve the BSS problem, we propose a novel geometry-based producer that has not been discussed in the literature yet. The proposed method has superiority over the previous ones in a number of aspects. First, it is a computationally simple method. Second, the proposed algorithm is capable of deinterleaving similar pulse trains. Third, it is able to separate pulse trains with complex pulse repetition interval (PRI) modulations. Finally, the algorithm's performance is not influenced by missing and spurious pulses. Some numerical simulations are provided in order to illustrate the effectiveness of the proposed method.
Integrated 105 dB SNR, 0.0031% THD+N Class-D Audio Amplifier With Global Feedback and Digital Control in 55 nm CMOS

It is traditionally challenging to implement higher-order PWM closed-loop Class-D audio amplifiers using analog intensive techniques in deep-submicron, low voltage process technologies. This is primarily attributed to reduced power supply, degraded analog transistor characteristics, including short-channel effects, increased flicker noise, random telegraph noise, transistor reliability concerns and passive component performance. In this paper, we introduce a global closed-loop mixed-signal architecture incorporating digital control and integrate a fourth-order amplifier prototype in 55 nm CMOS. A systematic approach to analyze, design and compensate the feedback loop in the digital domain is also presented. The versatility of implementing the loop gain poles and zeros digitally attains high gain throughout the audio band and attenuates residual high frequency ripples around the loop, simultaneously accomplishing improvements in THD+N and PSRR. The overall architecture is inherently amenable to implementation in deep-submicron and is therefore compatible with scaled CMOS. The measured prototype achieves a high 105 dBA SNR, 0.0031% THD+N, 92 dB PSRR and 85% efficiency when supplying 1 W into emulated 8 Ω speaker load. This performance is competitive with conventional designs using large feature size precision CMOS or specialized BCD technologies and reports the highest output power (1.5 W) for deep-submicron designs.
The infrared camera-based system to evaluate the human sleepiness

The eye's blinking is a significant indicator of the sleepiness. The existing systems of blink detection and sleepiness analysis require usually to fix camera on spectacle frame or a special helmet that is not convenient and can affect the obtained result. In this paper, the infrared camera-based contact-less system is proposed to evaluate the human's sleepiness. The infrared light switching is used to detect the pupil in each frame and, as a result, the blink event. The active pan-tilt unit is used to make possible free head movements. The algorithm is pointed out to process the camera frames in order to distinguish the involuntary blinks from voluntary ones. Preliminary experimental tests are shown with the intent to validate the proposed hardware and software system pointed out.
Psychometric Properties of Physical Activity and Leisure Motivation Scale in Farsi: an International Collaborative Project on Motivation for Physical Activity and Leisure.

Given the importance of regular physical activity, it is crucial to evaluate the factors favoring participation in physical activity. We aimed to report the psychometric analysis of the Farsi version of the Physical Activity and Leisure Motivation Scale (PALMS).



The Farsi version of PALMS was completed by 406 healthy adult individuals to test its factor structure and concurrent validity and reliability.



Conducting the exploratory factor analysis revealed nine factors that accounted for 64.6% of the variances. The PALMS reliability was supported with a high internal consistency of 0.91 and a high test-retest reliability of 0.97 (95% CI: 0.97-0.98). The association between the PALMS and its previous version Recreational Exercise Motivation Measure scores was strongly significant (r= 0.86, P < 0.001).



We have shown that the Farsi version of the PALMS appears to be a valuable instrument to measure motivation for physical activity and leisure.
Machine Learning for Real Time Poses Classification Using Kinect Skeleton Data

Poses recognition is an important research topic because some situations require silent communication (sign language, surgeon poses to the nurse for assistance etc.). Traditionally, poses recognition requires high quality expensive cameras and complicated computer vision algorithms. This is not the case thanks to the Microsoft Kinect sensor which provides an inexpensive and easy way for real time user interaction. In this paper, we proposed a real time human poses classification technique, by using skeleton data provided by the Kinect sensor. Different users performed a set of tasks from a vocabulary of eighteen poses. From skeleton data of each pose, twenty features are extracted so that they are invariant with respect to the user's size and its position in the scene. We then compared the generalization performances of four machine learning algorithms, support vectors machines (SVM), artificial neural networks (ANN), k-nearest neighbors (KNN) and Bayes classifier (BC). The method used in this work shows that SVM outperforms the other algorithms.
Ontology-guided feature engineering for clinical text classification

In this study we present novel feature engineering techniques that leverage the biomedical domain knowledge encoded in the Unified Medical Language System (UMLS) to improve machine-learning based clinical text classification. Critical steps in clinical text classification include identification of features and passages relevant to the classification task, and representation of clinical text to enable discrimination between documents of different classes. We developed novel information-theoretic techniques that utilize the taxonomical structure of the Unified Medical Language System (UMLS) to improve feature ranking, and we developed a semantic similarity measure that projects clinical text into a feature space that improves classification. We evaluated these methods on the 2008 Integrating Informatics with Biology and the Bedside (I2B2) obesity challenge. The methods we developed improve upon the results of this challenge's top machine-learning based system, and may improve the performance of other machine-learning based clinical text classification systems. We have released all tools developed as part of this study as open source, available at http://code.google.com/p/ytex.
ConcaveCubes: Supporting Cluster-based Geographical Visualization in Large Data Scale

In this paper we study the problem of supporting effective and scalable visualization for the rapidly increasing volumes of urban data. From an extensive literature study, we find that the existing solutions suffer from at least one of the drawbacks below: (i) loss of interesting structures/outliers due to sampling; (ii) supporting heatmaps only, which provides limited information; and (iii) no notion of real-world geography semantics (e.g., country, state, city) is captured in the visualization result as well as the underlying index. Therefore, we propose ConcaveCubes, a cluster-based data cube to support interactive visualization of large-scale multidimensional urban data. Specifically, we devise an appropriate visualization abstraction and visualization design based on clusters. We propose a novel concave hull construction method to support boundary based cluster map visualization, where real-world geographical semantics are preserved without any information loss. Instead of calculating the clusters on demand, ConcaveCubes (re)utilizes existing calculation and visualization results to efficiently support different kinds of user interactions. We conduct extensive experiments using real-world datasets and show the efficiency and effectiveness of ConcaveCubes by comparing with the state-of-the-art cube-based solutions.
Food image analysis: Segmentation, identification and weight estimation

We are developing a dietary assessment system that records daily food intake through the use of food images taken at a meal. The food images are then analyzed to extract the nutrient content in the food. In this paper, we describe the image analysis tools to determine the regions where a particular food is located (image segmentation), identify the food type (feature classification) and estimate the weight of the food item (weight estimation). An image segmentation and classification system is proposed to improve the food segmentation and identification accuracy. We then estimate the weight of food to extract the nutrient content from a single image using a shape template for foods with regular shapes and area-based weight estimation for foods with irregular shapes.
Disease and pest prediction IoT system in orchard: A preliminary study

Each species of diseases and pests is considered to be harmful to plants and has a great adverse effect on agriculture. In addition to decreasing crop productivity and quality, pesticides or fungicides which cause environmental pollution are needed to kill diseases and pests. Therefore, the IoT system is designed to reduce the frequent use of insecticides and fungicides and to predict when the pests appear in order to lower the appearance of pests. Therefore, weather stations near the orchard have been installed and used to analyze the correlation between pests and weather data that had a large effect on pests. The prediction models have been created utilizing correlation information. In other words, we proposed a system that provides disease and pest prediction information so that farmers can quickly control them.
Learning model-based strategies in simple environments with hierarchical q-networks

Recent advances in deep learning have allowed artificial agents to rival human-level performance on a wide range of complex tasks; however, the ability of these networks to learn generalizable strategies remains a pressing challenge. This critical limitation is due in part to two factors: the opaque information representation in deep neural networks and the complexity of the task environments in which they are typically deployed. Here we propose a novel Hierarchical Q-Network (HQN), motivated by theories of the hierarchical organization of the human prefrontal cortex, that attempts to identify lower dimensional patterns in the value landscape that can be exploited to construct an internal model of rules in simple environments. We draw on combinatorial games, where there exists a single optimal strategy for winning that generalizes across other features of the game, to probe the strategy generalization of the HQN and other reinforcement learning (RL) agents using variations of Wythoff’s game. Traditional RL approaches failed to reach satisfactory performance on variants of Wythoff’s Game; however, the HQN learned heuristic-like strategies that generalized across changes in board configuration. More importantly, the HQN allowed for transparent inspection of the agent’s internal model of the game following training. Our results show how a biologically inspired hierarchical learner can facilitate learning abstract rules to promote robust and flexible action policies in simplified training environments with clearly delineated optimal strategies. 1 ar X iv :1 80 1. 06 68 9v 1 [ cs .A I] 2 0 Ja n 20 18
A 486k S/s CMOS time-domain smart temperature sensor with −0.85°C/0.78°C voltage-calibrated error

This paper presents the first voltage-calibrated CMOS time-domain smart temperature sensor to reduce the cost of mass production. A digitally adjustable relaxation oscillator designed as the temperature sensor vibrates between CMOS-based CTAT, PTAT voltage references with mutual curvature compensation to generate linear temperature-dependent output pulses. Voltage instead of temperature calibration is adopted to alleviate the impact of process variation and TDC is used for output coding. Fabricated in a TSMC 0.18-μm standard CMOS process, the proposed sensor is able to operate at a high speed of 486k samples/sec for SoC thermal monitoring. The active area is merely 0.122 mm2 and the inaccuracy is measured to be less than ±1°C for 15 test chips in a wide temperature range of -40°C to 120°C. The performance is even superior to some chips with one- or two-point temperature calibrations. A milestone is established for time-domain smart temperature sensor to get rid of the heavy burden of fixed-temperature calibration with reason error budget.
Optimal kinematic design of 2-DOF parallel manipulators with well-shaped workspace bounded by a specified conditioning index

This paper presents a hybrid method for the optimum kinematic design of two-degree-of-freedom (2-DOF) parallel manipulators with mirror symmetrical geometry. By taking advantage of both local and global approaches, the proposed method can be implemented in two steps. In the first step, the optimal architecture, in terms of isotropy and the behavior of the direct Jacobian matrix, is achieved, resulting in a set of closed-form parametric relationships that enable the number of design variables to be reduced. In the second step, the workspace bounded by the specified conditioning index is generated, which allows only one design parameter to be determined by optimizing a comprehensive index in a rectangular workspace. The kinematic optimization of a revolute-jointed 2-DOF parallel robot has been taken as an example to illustrate the effectiveness of this approach.
Personalized recommendation of social software items based on social relations

We study personalized recommendation of social software items, including bookmarked web-pages, blog entries, and communities. We focus on recommendations that are derived from the user's social network. Social network information is collected and aggregated across different data sources within our organization. At the core of our research is a comparison between recommendations that are based on the user's familiarity network and his/her similarity network. We also examine the effect of adding explanations to each recommended item that show related people and their relationship to the user and to the item. Evaluation, based on an extensive user survey with 290 participants and a field study including 90 users, indicates superiority of the familiarity network as a basis for recommendations. In addition, an important instant effect of explanations is found - interest rate in recommended items increases when explanations are provided.