nils-herrmann/clean_scirepeval huggingface dataset:
Practical Signal-Dependent Noise Parameter Estimation From a Single Noisy Image

The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.
A Novel Yagi-Uda Dipole Array Fed by A Microstrip-To-CPS Transition

We report the design and experiment of a novel Yagi-Uda dipole array fed by a MS-to-CPS transition. The Yagi-Uda array having one director on the same plane of the dielectric substrate as the driver element shows higher gain (9.1dB) than microstrip patch antennas and a much wider bandwidth (10.3%). The measured front-to-back ratio is about 15dB. This novel design should become an important alternative to tapered-slot type antennas for millimeter wave imaging array application.
The head direction signal: origins and sensory-motor integration.

Navigation first requires accurate perception of one's spatial orientation within the environment, which consists of knowledge about location and directional heading. Cells within several limbic system areas of the mammalian brain discharge allocentrically as a function of the animal's directional heading, independent of the animal's location and ongoing behavior. These cells are referred to as head direction (HD) cells and are believed to encode the animal's perceived directional heading with respect to its environment. Although HD cells are found in several areas, the principal circuit for generating this signal originates in the dorsal tegmental nucleus and projects serially, with some reciprocal connections, to the lateral mammillary nucleus --> anterodorsal thalamus --> PoS, and terminates in the entorhinal cortex. HD cells receive multimodal information about landmarks and self-generated movements. Vestibular information appears critical for generating the directional signal, but motor/proprioceptive and landmark information are important for updating it.
Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines

Image processing pipelines combine the challenges of stencil computations and stream programs. They are composed of large graphs of different stencil stages, as well as complex reductions, and stages with global or data-dependent access patterns. Because of their complex structure, the performance difference between a naive implementation of a pipeline and an optimized one is often an order of magnitude. Efficient implementations require optimization of both parallelism and locality, but due to the nature of stencils, there is a fundamental tension between parallelism, locality, and introducing redundant recomputation of shared values.
 We present a systematic model of the tradeoff space fundamental to stencil pipelines, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule. Combining this compiler with stochastic search over the space of schedules enables terse, composable programs to achieve state-of-the-art performance on a wide range of real image processing pipelines, and across different hardware architectures, including multicores with SIMD, and heterogeneous CPU+GPU execution. From simple Halide programs written in a few hours, we demonstrate performance up to 5x faster than hand-tuned C, intrinsics, and CUDA implementations optimized by experts over weeks or months, for image processing applications beyond the reach of past automatic compilers.
Sensorless Brushless DC Motor Drive Based on the Zero-Crossing Detection of Back Electromotive Force (EMF) From the Line Voltage Difference

This paper describes a position sensorless operation of permanent magnet brushless direct current (BLDC) motor. The position sensorless BLDC drive proposed, in this paper, is based on detection of back electromotive force (back EMF) zero crossing from the terminal voltages. The proposed method relies on a difference of line voltages measured at the terminals of the motor. It is shown, in the paper, that this difference of line voltages provides an amplified version of an appropriate back EMF at its zero crossings. The commutation signals are obtained without the motor neutral voltage. The effectiveness of the proposed method is demonstrated through simulation and experimental results.
Machine Comprehension using BiDAF

Bi-directional Attention Flow(BiDAF) network is a recent deep learning model which has been quite successful in the Machine Comprehension problem domain. This report details the study I did using a re-implementation of the complete BiDAF network as laid out in Bi-Directional Attention Flow for Machine Comprehension (Minjoon Seo et al). My BiDAF implementation got an F1 score 72.6 and EM 62.8 on the Test leaderboard. The effect of changing word vector embedding size was investigated and the experiments showed that while Glove embedding size impacted learning time of BiDAF model, the final performance metrics of the models were quite close to one another. The observation was interpret qualitatively using examples. Finally an ensemble of the different models with varying word vector dimensions was created which improved upon the performance of individual models as anticipated.
An ultra low power system architecture for sensor network applications

Recent years have seen a burgeoning interest in embedded wireless sensor networks with applications ranging from habitat monitoring to medical applications. Wireless sensor networks have several important attributes that require special attention to device design. These include the need for inexpensive, long-lasting, highly reliable devices coupled with very low performance requirements. Ultimately, the "holy grail" of this design space is a truly untethered device that operates off of energy scavenged from the ambient environment. In this paper, we describe an application-driven approach to the architectural design and implementation of a wireless sensor device that recognizes the event-driven nature of many sensor-network workloads. We have developed a full-system simulator for our sensor node design to verify and explore our architecture. Our simulation results suggest one to two orders of magnitude reduction in power dissipation over existing commodity-based systems for an important class of sensor network applications. We are currently in the implementation stage of design, and plan to tape out the first version of our system within the next year.
Network-Based Intrusion Detection Using Unsupervised Adaptive Resonance Theory ( ART )

This paper introduces the Unsupervised Neural Net based Intrusion Detector (UNNID) system, which detects network-based intrusions and attacks using unsupervised neural networks. The system has facilities for training, testing, and tunning of unsupervised nets to be used in intrusion detection. Using the system, we tested two types of unsupervised Adaptive Resonance Theory (ART) nets (ART-1 and ART-2). Based on the results, such nets can efficiently classify network traffic into normal and intrusive. The system uses a hybrid of misuse and anomaly detection approaches, so is capable of detecting known attack types as well as new attack types as anomalies.
Smart irrigation with embedded system

This paper proposes intelligent and smart Irrigation system which can be used for controlling the watering or irrigation of flowering plants. It controls the irrigation of plants automatically where the need of human intervention can be reduced. This mainly focused on wastage of water, which is a major concern of modern era. It also aids time saving, cost effectiveness, environmental protection, low maintenance and operating cost and efficient irrigation service. Raspberry Pi (open source) is used in the design of the prototype model in making the system compact and sustainable. The system has sensor which measures the moisture of the soil and switches relay which controls solenoid valve according to the requirement. The model demonstrated gave expected results at the different moisture levels.
ReSeg: A Recurrent Neural Network for Object Segmentation

We propose a structured prediction architecture for images centered around deep recurrent neural networks. The proposed network, called ReSeg, is based on the recently introduced ReNet model for object classification. We modify and extend it to perform object segmentation, noting that the avoidance of pooling can greatly simplify pixel-wise tasks for images. Each layer of the network is composed of four recurrent neural networks that sweep the image horizontally and vertically in both directions encoding patches, along with a final layer that resizes the prediction to be of the same size of the original image. This architecture is quite flexible and makes it suitable for a variety of structured prediction tasks. We evaluate ReSeg on the specific task of object segmentation with three widely-used image segmentation datasets, namely Weizmann Horse, Fashionista and Oxford Flower. The results suggest that ReSeg can perform successfully on the object segmentation task, and may have further applications in structured prediction at large.
Determination of spinal curvature from scoliosis X-ray images using K-means and curve fitting for early detection of scoliosis disease

One of the disease that require X-ray diagnosis is scoliosis. Early detection of scoliosis is important to do for anyone. From the early detection information, the doctor may take the firts step to further treatment quickly. Determination of spinal curvature is a first step method that used to measure how severe the degree of scoliosis. The severity degree of scoliosis can be assess by using Cobb angle. Therefore, by approximate the spinal curvature, we can approximate the cobb angle too. From previous work that interobserver measurement value may reach 11.8° and intraobserver measurement error is 6°. So, as far as the cobb angle measuring, the subjectivity aspect is the natural thing and can be tolerated until now. This research propose an algorithm how to define spinal curvature with the aid of a computer in digital X-ray image quickly but has a standard error that can be tolerated. The preprocessing has been done by canny edge detection. The k-means clustering algorithm can detect the centroid point after segmentation preprocessing of the spinal segment and polynomial curve fitting will be used in the process for determining the spinal curve. From the spinal curvature information, the scoliosis curve can be classified into 4 condition, normal, mild, moderate, and severe scoliosis.
On the coverage of LPWANs: range evaluation and channel attenuation model for LoRa technology

In addition to long battery life and low cost, coverage is one of the most critical performance metrics for the low power wide area networks (LPWAN). In this work we study the coverage of the recently developed LoRa LPWAN technology via real-life measurements. The experiments were conducted in the city of Oulu, Finland, using the commercially available equipment. The measurements were executed for cases when a node located on ground (attached on the roof rack of a car) or on water (attached to the radio mast of a boat) reporting their data to a base station. For a node operating in the 868 MHz ISM band using 14 dBm transmit power and the maximum spreading factor, we have observed the maximum communication range of over 15 km on ground and close to 30 km on water. Besides the actual measurements, in the paper we also present a channel attenuation model derived from the measurement data. The model can be used to estimate the path loss in 868 MHz ISM band in an area similar to Oulu, Finland.
Cross-Sentence N-ary Relation Extraction with Graph LSTMs

Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, such as sequential, syntactic, and discourse relations. A robust contextual representation is learned for the entities, which serves as input to the relation classifier. This simplifies handling of relations with arbitrary arity, and enables multi-task learning with related relations. We evaluate this framework in two important precision medicine settings, demonstrating its effectiveness with both conventional supervised learning and distant supervision. Cross-sentence extraction produced larger knowledge bases. and multi-task learning significantly improved extraction accuracy. A thorough analysis of various LSTM approaches yielded useful insight the impact of linguistic analysis on extraction accuracy.
Technologies for Trust in Electronic Commerce

Lack of consumer trust in e-commerce merchants, e-commerce technology, and the social, financial and legal infrastructures of the e-commerce environment, poses a major challenge to the large-scale uptake of business to consumer e-commerce. Most traditional cues for assessing trust in the physical world are not available online. This paper gives an overview of some of the work being done to devise alternative methods for assessing, communicating and establishing trust in this environment. Examples are drawn from a wide range of disciplines including human-computer interaction, usability, marketing, information technology, mathematics, linguistics and law. Industry, self-regulatory and government initiatives aimed at building consumer trust and confidence in e-commerce are also discussed.
THE FOURFOLD PATTERN OF RISK ATTITUDES IN CHOICE AND PRICING TASKS

Using simple gambles with real payoffs we examine the robustness of the fourfold pattern of risk attitudes under two different elicitation procedures. That is, we determine if on average individuals are (1) risk-seeking over lowprobability gains, (2) risk-averse over high-probability gains, (3) risk-averse over low-probability losses, and (4) risk-seeking over high-probability losses. We find that participants’ risk attitudes are consistent with the fourfold pattern when using the Becker-DeGroot-Marschak procedure to elicit prices for the gambles. However, when instead relying on a simple choice-based elicitation where participants choose between the gamble and its expected value, individual decisions are not distinguishable from random choice. This sensitivity to the elicitation procedure holds both betweenand within-participants, and it remains even when participants review their price and choice decisions simultaneously and are allowed to change them. Given the greater complexity of the price elicitation procedure this finding may be further evidence that an increase in cognitive load exacerbates behavioral anomalies.
Experiences of System-Level Model-Based GUI Testing of an Android Application

This paper presents experiences in model-based graphical user interface testing of Android applications. We present how model-based testing and test automation was implemented with Android, including how applications were modeled, how tests were designed and executed, and what kind of problems were found in the tested application during the whole process. The main focus is on a case study that was performed with an Android application, the BBC News Widget. Our goal is to present actual data on the experiences and to discuss if advantages can be gained using model-based testing when compared with traditional graphical user interface testing. Another contribution of this paper is a description of a keyword-based test automation tool that was implemented for the Android emulator during the case study. All the models and the tools created or used in this case study are available as open source.
A first step toward understanding inter-domain routing dynamics

BGP updates are triggered by a variety of events such as link failures, resets, routers crashing, configuration changes, and so on. Making sense of these updates and identifying the underlying events is key to debugging and troubleshooting BGP routing problems. In this paper, as a first step toward the much harder problem of root cause analysis of BGP updates, we discuss if, and how, updates triggered by distinct underlying events can be separated. Specifically, we explore using PCA (Principal Components Analysis), a well known statistical multi-variate technique, to achieve this goal.We propose a method based on PCA to obtain a set of clusters from a BGP update stream; each of these is a set of entities (either prefixes or ASes) which are affected by the same underlying event. Then we demonstrate our approach using BGP data obtained by simulations and show that the method is quite effective. In addition, we perform a high level analysis of BGP data containing well known, large scale events.
Gated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech

Despite the enormous interest in emotion classification from speech, the impact of noise on emotion classification is not well understood. This is important because, due to the tremendous advancement of the smartphone technology, it can be a powerful medium for speech emotion recognition in the outside laboratory natural environment, which is likely to incorporate background noise in the speech. We capitalize on the current breakthrough of Recurrent Neural Network (RNN) and seek to investigate its performance for emotion classification from noisy speech. We particularly focus on the recently proposed Gated Recurrent Unit (GRU), which is yet to be explored for emotion recognition from speech. Experiments conducted with speech compounded with eight different types of noises reveal that GRU incurs an 18.16% smaller run-time while performing quite comparably to the Long Short-Term Memory (LSTM), which is the most popular Recurrent Neural Network proposed to date. This result is promising for any embedded platform in general and will initiate further studies to utilize GRU to its full potential for emotion recognition on smartphones.
Mining and analyzing the enterprise knowledge graph

Today's enterprises hold ever-growing amounts of public data, stemming from different organizational systems, such as development environments, CRM systems, business intelligence systems, and enterprise social media. This data unlocks rich and diverse information about entities, people, terms, and the relationships among them. A lot of insight can be gained through analyzing this knowledge graph, both by individual employees and by the organization as a whole. In this talk, I will review recent work done by the Social Technologies & Analytics group at IBM Research-Haifa to mine these relationships, represent them in a generalized model, and use the model for different aims within the enterprise, including social search [5], expertise location , social recommendation [2, 3], and network analysis .
Ultrasound measurements of axillary recess capsule thickness in unilateral frozen shoulder: study of correlation with MRI measurements

The aims of this study were to compare the ultrasound thickness of the affected axillary recess capsule (ARC) with that of the unaffected ARC in patients with frozen shoulder (FS), to analyze whether the ultrasound measurements of the ARC thickness correlate with those obtained using MRI, and to assess whether the ultrasound thickness of the ARC correlates with the patterns of range of motion limitation. Forty-four patients with clinically diagnosed unilateral FS and MRI evaluation performed ultrasound measurement of ARC. The ultrasound measurement of the ARC thickness was performed with the patients in a supine position with their shoulder abducted by 40°. The ARC thickness was also measured by MRI on oblique coronal images by another physician blinded to the ultrasound measurements. With both ultrasound and MRI, ARC thickness was determined at the widest portion of the capsule. The ultrasound thickness of ARC was significantly higher in the affected shoulder (4.4 ± 1.1 mm) than in the unaffected one (2.2 ± 0.5 mm) (p < 0.001). The ultrasound thickness of the ARC in the affected shoulder correlated with that measured by MRI (8.9 ± 1.9 mm) (p < 0.001, r = 0.83). The ARC thickness, whether measured by ultrasound or MRI, was not significantly related to the limitation of movement in specific directions. Ultrasound can demonstrate the difference in ARC thickness between affected and unaffected shoulders in patients with unilateral FS. The ARC thickness measured by ultrasound correlates with that measured by MRI.
Robust speech recognition using long short-term memory recurrent neural networks for hybrid acoustic modelling

One method to achieve robust speech recognition in adverse conditions including noise and reverberation is to employ acoustic modelling techniques involving neural networks. Using long short-term memory (LSTM) recurrent neural networks proved to be efficient for this task in a setup for phoneme prediction in a multi-stream GMM-HMM framework. These networks exploit a self-learnt amount of temporal context, which makes them especially suited for a noisy speech recognition task. One shortcoming of this approach is the necessity of a GMM acoustic model in the multi-stream framework. Furthermore, potential modelling power of the network is lost when predicting phonemes, compared to the classical hybrid setup where the network predicts HMM states. In this work, we propose to use LSTM networks in a hybrid HMM setup, in order to overcome these drawbacks. Experiments are performed using the medium-vocabulary recognition track of the 2nd CHiME challenge, containing speech utterances in a reverberant and noisy environment. A comparison of different network topologies for phoneme or state prediction used either in the hybrid or double-stream setup shows that state prediction networks perform better than networks predicting phonemes, leading to stateof-the-art results for this database.