nils-herrmann/clean_scirepeval huggingface dataset:
An analysis of convolutional neural networks for speech recognition

Despite the fact that several sites have reported the effectiveness of convolutional neural networks (CNNs) on some tasks, there is no deep analysis regarding why CNNs perform well and in which case we should see CNNs' advantage. In the light of this, this paper aims to provide some detailed analysis of CNNs. By visualizing the localized filters learned in the convolutional layer, we show that edge detectors in varying directions can be automatically learned. We then identify four domains we think CNNs can consistently provide advantages over fully-connected deep neural networks (DNNs): channel-mismatched training-test conditions, noise robustness, distant speech recognition, and low-footprint models. For distant speech recognition, a CNN trained on 1000 hours of Kinect distant speech data obtains relative 4% word error rate reduction (WERR) over a DNN of a similar size. To our knowledge, this is the largest corpus so far reported in the literature for CNNs to show its effectiveness. Lastly, we establish that the CNN structure combined with maxout units is the most effective model under small-sizing constraints for the purpose of deploying small-footprint models to devices. This setup gives relative 9.3% WERR from DNNs with sigmoid units.
Lidar guided stereo simultaneous localization and mapping (SLAM) for UAV outdoor 3-D scene reconstruction

Lidars can be extremely useful tools for measuring outdoor geometry. However while lidar measurements are championed for their high accuracy their point clouds are individually rather sparse and lack colour information. In this work the sparse nature of lidar point clouds is addressed by merging multiple lidar scans into a single large point cloud. This is done by restricting the lidar motion to a single axis of translation and then using interpolation and iterative refinement to acquire a denser model by combining co-registered sets of point clouds. This newly constructed model is then used to guide a basic stereo SLAM (simultaneous localization and mapping) algorithm in order to produce a final dense coloured point cloud that preserves the accuracy of the original lidar measurements. Our experiments were performed at various locations using a 16 channel “Puck” Velodyne lidar and a stereo acquisition system consisting of a DJI Phantom quadcopter and a synchronized pair of GoPro HERO 3+ black edition cameras. Results of these experiments demonstrate that the produced reconstructions are both ascetically sound and quantitatively consistent with a set of individual measurements taken around the scene.
On The Architecture Scheduling Problem Of Industry 4.0

The recently emerged Fourth Industrial Revolution (Industry 4.0) is characterised by the introduction of the new Cyber-Physical System (CPS) concepts and the Internet of Things (IoT) paradigm. These new collaborating computational entities offer a broad range of opportunity to consider with a different perspective. One of the perennial problems of the manufacturing operation is the scheduling problem of typical job-shop manufacturing systems. Starting from a comparison with the typical architecture of an operating systems scheduler module, we introduce a new manufacturing scheduling architecture. Overcoming the typical Full-Hierarchical configuration defined in the ANSI/ISA 95 in favour of a SemiHeterarchical one, the introduced scheduling architecture leads to a mixture of proactive and reactive approach to the Job-shop Scheduling Problem (JSP), taking advantage from both the common decentralised and the centralised methodology.  Industry 4.0; Cyber-Physical System (CPS);  Manufacturing System; System of
3D head pose estimation with convolutional neural network trained on synthetic images

In this paper, we propose a method to estimate head pose with convolutional neural network, which is trained on synthetic head images. We formulate head pose estimation as a regression problem. A convolutional neural network is trained to learn head features and solve the regression problem. To provide annotated head poses in the training process, we generate a realistic head pose dataset by rendering techniques, in which we consider the variation of gender, age, race and expression. Our dataset includes 74000 head poses rendered from 37 head models. For each head pose, RGB image and annotated pose parameters are given. We evaluate our method on both synthetic and real data. The experiments show that our method improves the accuracy of head pose estimation.
A 1.2 V Low-Noise-Amplifier with Double Feedback for High Gain and Low Noise Figure

Faculdade de Ciências e Tecnologia Departamento de Engenharia Eletrotécnica e de Computadores Mestrado Integrado em Engenharia Eletrotécnica e de Computadores por David Jorge Tiago Amoêdo In this thesis we present a balun low noise amplifier (LNA) in which the gain is boosted using a double feedback structure. The circuit is based in a Balun LNA with noise and distortion cancellation. The LNA is based in two basic stages: common-gate (CG) and common-source (CS). We propose to replace the resistors by active loads, which have two inputs that will be used to provide the feedback (in the CG and CS stages). This proposed methodology will boost the gain and reduce the NF (Noise Figure). Simulation results, with a 130 nm CMOS technology, show that the gain is 19.65 dB and the NF is less than 2.17 dB. The total power dissipation is only 5 mW (since no extra blocks are required), leading to an FOM (Figure of Merit) of 3.13 mW from a nominal 1.2 supply.
A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining

Most opinion mining methods in English rely successfully on sentiment lexicons, such as English SentiWordnet (ESWN). While there have been efforts towards building Arabic sentiment lexicons, they suffer from many deficiencies: limited size, unclear usability plan given Arabic’s rich morphology, or nonavailability publicly. In this paper, we address all of these issues and produce the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) using a combination of existing resources: ESWN, Arabic WordNet, and the Standard Arabic Morphological Analyzer (SAMA). We compare and combine two methods of constructing this lexicon with an eye on insights for Arabic dialects and other low resource languages. We also present an extrinsic evaluation in terms of subjectivity and sentiment analysis.
Calcium hydroxylapatite filler: an overview of safety and tolerability.

Soft tissue fillers are becoming increasingly important as nonsurgical treatment options for facial rejuvenation. Calcium hydroxylapatite (CaHA) is an injectable dermal filler that contains uniform CaHA microspheres suspended in an aqueous carboxymethylcellulose gel carrier. It is considered a long-lasting, but non-permanent filler, and is highly biocompatible with human tissue. No osteogenesis has been reported in extensive literature describing the use of CaHA in a variety of soft tissue applications. Injection of CaHA into the oral mucosa and the lips is an unapproved indication and may result in nodule formation. This occurs soon after injection and is a result of accumulated particles and not a granulomatous reaction. As with all biodegradable dermal fillers, CaHA can be associated with rare incidences of foreign body reactions, but only a handful of case reports have been documented in 10 years of clinical use. CaHA can be associated with local, short-term, injection-related adverse events, which are generally mild and resolve within a few days. Clinical trials that have followed patients for up to 3 years post-injection report no long-term or delayed-onset adverse events. CaHA is an effective and safe treatment option for a variety of aesthetic indications. This paper focuses on common safety concerns of patients and aesthetic physicians, including unfounded fears of osteogenesis and foreign body granulomas, providing an up-to-date overview of the tolerability and long-term safety of CaHA for aesthetic indications.
CELIA: A Device and Architecture Co-Design Framework for STT-MRAM-Based Deep Learning Acceleration

A large variety of applications rely on deep learning to process big data, learn sophisticated features, and perform complicated tasks. Utilizing emerging non-volatile memory (NVM)'s unique characteristics, including the crossbar array structure and gray-scale cell resistances, to perform neural network (NN) computation is a well-studied approach in accelerating deep learning tasks. Compared to other NVM technologies, STT-MRAM has its unique advantages in performing NN computation. However, the state-of-the-art research have not utilized STT-MRAM for deep learning acceleration due to its device- and architecture-level challenges. Consequently, this paper enables STT-MRAM, for the firs time, as an effective and practical deep learning accelerator. In particular, it proposes a full-stack solution across multiple design layers, including device-level fabrication, circuit-level enhancement, architecture-level data quantization, and system-level accelerator design. The proposed framework significantly mitigates the model accuracy loss due to reduced data precision in a cohesive manner, constructing a comprehensive STT-MRAM accelerator system for fast NN computation with high energy efficiency and low cost.
Sex for fun: a synthesis of human and animal neurobiology

Sex is a fundamental pleasure, and crucial to the survival of our species. Though not many people would disagree with the proposition that sexual behaviour depends on the brain, the neuroscientific study of human sex is still relatively taboo and much remains to be discovered. On the contrary, excellent experimental animal models (mostly rat) are available that have uncovered major behavioural, neurochemical, and neuroanatomical characteristics of sexual behaviour. Restructuring sexual behaviour into broader terms reflecting behavioural states (wanting, liking, and inhibition) facilitates species comparison, revealing many similarities between animal and human sexual pleasure cycles, some of which can serve as potential avenues of new human sex research. In particular, behavioural and brain evidence clearly shows that motivational and consummatory phases are fundamentally distinct, and that genitally-induced sexual reward is a major factor in sexual learning mechanisms.
The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries

A predictive spatio-temporal query retrieves the set of moving objects that will intersect a query window during a future time interval. Currently, the only access method for processing such queries in practice is the TPR-tree. In this paper we first perform an analysis to determine the factors that affect the performance of predictive queries and show that several of these factors are not considered by the TPR-tree, which uses the insertion/deletion algorithms of the R*-tree designed for static data. Motivated by this, we propose a new index structure called the TPR*tree, which takes into account the unique features of dynamic objects through a set of improved construction algorithms. In addition, we provide cost models that determine the optimal performance achievable by any data-partition spatio-temporal access method. Using experimental comparison, we illustrate that the TPR*-tree is nearly-optimal and significantly outperforms the TPR-tree under all conditions.
Intrinsic Evaluations of Word Embeddings: What Can We Do Better?

This paper presents an analysis of existing methods for the intrinsic evaluation of word embeddings. We show that the main methodological premise of such evaluations is “interpretability” of word embeddings: a “good” embedding produces results that make sense in terms of traditional linguistic categories. This approach is not only of limited practical use, but also fails to do justice to the strengths of distributional meaning representations. We argue for a shift from abstract ratings of word embedding “quality” to exploration of their strengths and weaknesses.
Recent advances in image processing techniques for automated harvesting purposes: A review

Image processing has been proved to be an effective tool for analysis in various human activity areas, namely, agricultural applications. Interpreting a digital color image of fruit orchard captured in field environment is extremely challenging due to adverse weather conditions, luminance variability and the presence of dust, insects and other unavoidable image noises. The purpose of this survey is to categorize and briefly review the literature on computer analysis of fruit images in agricultural operations, which comprises more than 60 papers published in the last 10 years. With the aim to perform applied research in agricultural imaging, this paper intends to focus on advanced image processing and analysis techniques used in applications for detection and classifications of fruits, developed in the last decade. For the reviewed techniques, some performance evaluation metrics achieved in various experiments are emphasized to help the researchers when making choices and develop new computer vision applications in fruit images.
If Only my Leader Would just Do Something! Passive Leadership Undermines Employee Well-being Through Role Stressors and Psychological Resource Depletion.

The goal of this study was to develop and test a sequential mediational model explaining the negative relationship of passive leadership to employee well-being. Based on role stress theory, we posit that passive leadership will predict higher levels of role ambiguity, role conflict and role overload. Invoking Conservation of Resources theory, we further hypothesize that these role stressors will indirectly and negatively influence two aspects of employee well-being, namely overall mental health and overall work attitude, through psychological work fatigue. Using a probability sample of 2467 US workers, structural equation modelling supported the model by showing that role stressors and psychological work fatigue partially mediated the negative relationship between passive leadership and both aspects of employee well-being. The hypothesized, sequential indirect relationships explained 47.9% of the overall relationship between passive leadership and mental health and 26.6% of the overall relationship between passive leadership and overall work attitude.
Minimally-Constrained Multilingual Embeddings via Artificial Code-Switching

We present a method that consumes a large corpus of multilingual text and produces a single, unified word embedding in which the word vectors generalize across languages. Our method is agnostic about the languages with which the documents in the corpus are expressed, and does not rely on parallel corpora to constrain the spaces. Instead we utilize a small set of human provided word translations to artificially induce code switching; thus, allowing words in multiple languages to appear in contexts together and share distributional information. We evaluate the embeddings on a new multilingual word analogy dataset. We also find that our embeddings allow an NLP model trained in one language to generalize to another, achieving up to 80% of the accuracy of an in-language model.
Improving Estimation Accuracy using Better Similarity Distance in Analogy-based Software Cost Estimation

Software cost estimation nowadays plays a more and more important role in practical projects since modern software projects become more and more complex as well as diverse. To help estimate software development cost accurately, this research does a systematic analysis of the similarity distances in analogy-based software cost estimation and based on this, a new non-orthogonal space distance (NoSD) is proposed as a measure of the similarities between real software projects. Different from currently adopted measures like the Euclidean distance and so on, this non-orthogonal space distance not only considers the different features to have different importance for cost estimation, but also assumes project features to have a non-orthogonal dependent relationship which is considered independent to each other in Euclidean distance. Based on such assumptions, NoSD method describes the non-orthogonal angles between feature axes using feature redundancy and it represents the feature weights using feature relevance, where both redundancy and relevance are defined in terms of mutual information. It can better reveal the real dependency relationships between real life software projects based on this non-orthogonal space distance. Also experiments show that it brings a greatest of 13.1% decrease of MMRE and a 12.5% increase of PRED(0.25) on ISBSG R8 dataset, and 7.5% and 20.5% respectively on the Desharnais dataset. Furthermore, to make it better fit the complex data distribution of real life software projects data, this research leverages the particle swarm optimization algorithm for an optimization of the proposed non-orthogonal space distance and proposes a PSO optimized non-orthogonal space distance (PsoNoSD). It brings further improvement in the estimation accuracy. As shown in experiments, compared with the normally used Euclidean distance, PsoNoSD improves the estimation accuracy by 38.73% and 11.59% in terms of MMRE and PRED(0.25) on ISBSG R8 dataset. On the Desharnais dataset, the improvements are 23.38% and 24.94% respectively. In summary, the new methods proposed in this research, which are based on theoretical study as well as systematic experiments, have solved some problems of currently used techniques and they show a great ability of notably improving the software cost estimation accuracy.
Charging Li-ion Batteries for Maximum Run Times An understanding of battery-charging fundamentals and system requirements enable designers to choose a suitable linear or switch-mode charging topology and optimize battery performance in the application

An understanding of battery-charging fundamentals and system requirements enable designers to choose a suitable linear or switch-mode charging topology and optimize battery performance in the application. F ar too often, the battery-charging system is given low priority, especially in cost-sensitive applications. However, the quality of the charging system plays a key role in the life and reliability of the battery. To develop an optimized charging system for lithium-ion (Li-ion) batteries, designers must be familiar with the fundamental requirements for charging these batteries. Designers also should be aware of the tradeoffs of linear versus switch-mode charging solutions. The rate of charge or discharge often is expressed in relation to the capacity of the battery. This rate is known as the Crate and equates to a charge or discharge current and is defined as: I=M C n ⋅ where I is the charge or discharge current, expressed in amperes (A); M is a multiple or fraction of C; C is a numerical value of rated capacity expressed in ampere-hour (Ah); and n is the time in hours at which C is declared. A battery discharging at a Crate of 1 will deliver its nominal-rated capacity in 1 hr. For example, if the rated capacity is 1000 mAh, a discharge rate of 1 C corresponds to a discharge current of 1000 mA. A rate of C/10 corresponds to a discharge current of 100 mA. Typically, manufacturers specify the capacity of a battery at a 5-hr rate, where n = 5. For example, the battery mentioned previously would provide 5 hr of operating time when discharged at a constant current of 200 mA. In theory, the battery would provide 1 hr of operating time when discharged at a constant current of 1000 mA. In practice, however, the operating time will be less than 1 hr because of inefficiencies in the discharge cycle. The preferred charge algorithm for Li-ion battery chem-istries is a constant current-constant voltage (CC-CV) algorithm. The charge cycle can be broken up into four stages: trickle charge, constant current charge, constant voltage charge and charge termination . In stage one, a trickle charge is employed to restore charge to deeply depleted cells. These are cells in which the cell voltage is below approximately 3 V. During this stage, the cell is charged with a constant current of 0.1 C maximum. After the cell voltage has risen above the trickle charge threshold, …
Child and adolescent mental health worldwide: evidence for action

Mental health problems affect 10-20% of children and adolescents worldwide. Despite their relevance as a leading cause of health-related disability in this age group and their longlasting effects throughout life, the mental health needs of children and adolescents are neglected, especially in low-income and middle-income countries. In this report we review the evidence and the gaps in the published work in terms of prevalence, risk and protective factors, and interventions to prevent and treat childhood and adolescent mental health problems. We also discuss barriers to, and approaches for, the implementation of such strategies in low-resource settings. Action is imperative to reduce the burden of mental health problems in future generations and to allow for the full development of vulnerable children and adolescents worldwide.
Certain Topics in Telegraph Transmission Theory

The most obvious method for determining the distortion of telegraph signals is to calculate the transients of the telegraph system. This method has been treated by various writers, and solutions are available for telegraph lines with simple terminal conditions. It is well known that the extension of the same methods to more complicated terminal conditions, which represent the usual terminal apparatus, leads to great difficulties. The present paper attacks the same problem from the alternative standpoint of the steady-state characteristics of the system. This method has the advantage over the method of transients that the complication of the circuit which results from the use of terminal apparatus does not complicate the calculations materially. This method of treatment necessitates expressing the criteria of distortionless transmission in terms of the steady-state characteristics. Accordingly, a considerable portion of the paper describes and illustrates a method for making this translation. A discussion is given of the minimum frequency range required for transmission at a given speed of signaling. In the case of carrier telegraphy, this discussion includes a comparison of single-sideband and double-sideband transmission. A number of incidental topics is also discussed.