nils-herrmann/clean_scirepeval huggingface dataset:
Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation

Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.
Computing Neck-Shaft Angle of Femur for X-Ray Fracture Detection

Worldwide, 30% – 40% of women and 13% of men suffer from osteoporotic fractures of the bone, particularly the older people. Doctors in the hospitals need to manually inspect a large number of x-ray images to identify the fracture cases. Automated detection of fractures in x-ray images can help to lower the workload of doctors by screening out the easy cases, leaving a small number of difficult cases and the second confirmation to the doctors to examine more closely. To our best knowledge, such a system does not exist as yet. This paper describes a method of measuring the neck-shaft angle of the femur, which is one of the main diagnostic rules that doctors use to determine whether a fracture is present at the femur. Experimental tests performed on test images confirm that the method is accurate in measuring neck-shaft angle and detecting certain types of femur fractures.
The Effect of Edmodo on EFL Learners ’ Writing Performance

Writing as a productive skill in EFL classes is outstandingly significant. In writing classes there needs to be an efficient relationship between the teacher and students. The teacher as the only audience in many writing classes responds to students’ writing. In the early part of the 21 century the range of technologies available for use in classes has become very diverse and the ways they are being used in classrooms all over the world might affect the outcome we expect from our classes. As the present generations of students are using new technologies, the application of these recent technologies in classes might be useful. Using technology in writing classes provides opportunities for students to hand their written work to the teacher without the need for any face-to-face interaction. This present study investigates the effect of Edmodo on EFL learners’ writing performance. A quasi-experimental design was used in this study. The participants were 40 female advanced-level students attending advanced writing classes at Irana English Institute, Razan Hamedan. The focus was on the composition writing ability. The students were randomly assigned to two groups, experimental and control. Edmodo was used in the experimental group. Mann-Whitney U test was used for data analysis; the results indicated that the use of Edmodo in writing was more effective on EFL learners’ writing performance participating in this study.
A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data

Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis, by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost function, these rules are nonlocal and hence biologically implausible. At the same time, biologically plausible local rules have been postulated rather than derived from a principled cost function. Here, to bridge this gap, we derive a biologically plausible network for subspace learning on streaming data by minimizing a principled cost function. In a departure from previous work, where cost was quantified by the representation, or reconstruction, error, we adopt a multidimensional scaling cost function for streaming data. The resulting algorithm relies only on biologically plausible Hebbian and anti-Hebbian local learning rules. In a stochastic setting, synaptic weights converge to a stationary state, which projects the input data onto the principal subspace. If the data are generated by a nonstationary distribution, the network can track the principal subspace. Thus, our result makes a step toward an algorithmic theory of neural computation.
Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors

One of the challenges in 3D shape matching arises from the fact that in many applications, models should be considered to be the same if they differ by a rotation. Consequently, when comparing two models, a similarity metric implicitly provides the measure of similarity at the optimal alignment. Explicitly solving for the optimal alignment is usually impractical. So, two general methods have been proposed for addressing this issue: (1) Every model is represented using rotation invariant descriptors. (2) Every model is described by a rotation dependent descriptor that is aligned into a canonical coordinate system defined by the model. In this paper, we describe the limitations of canonical alignment and discuss an alternate method, based on spherical harmonics, for obtaining rotation invariant representations. We describe the properties of this tool and show how it can be applied to a number of existing, orientation dependent descriptors to improve their matching performance. The advantages of this tool are two-fold: First, it improves the matching performance of many descriptors. Second, it reduces the dimensionality of the descriptor, providing a more compact representation, which in turn makes comparing two models more efficient.
Shoulder kinematics and kinetics during two speeds of wheelchair propulsion.

The primary objective of this study was to examine the kinematics and kinetics of the shoulder during wheelchair propulsion at a slow and moderate speed. Twenty-seven individuals with paraplegia propelled their wheelchairs at speeds of 0.9 m/s and 1.8 m/s while a motion analysis system captured movements of their upper limbs and SMART(Wheel)s simultaneously recorded their pushrim kinetics. Intraclass R correlation and Cronbach's coefficient alpha statistics revealed that all shoulder parameters were stable and consistent between strokes and speeds. The shoulder exhibited a greater range of motion, and forces and moments at the shoulder were 1.2 to 2.0 times greater (p < 0.05) during the 1.8 m/s speed trial. Peak posterior forces occurred near the end of the propulsion phase, and at the same time, the shoulder was maximally flexed and minimally abducted (p > 0.1). Shoulder positioning and the associated peak shoulder loads during propulsion may be important indicators for identifying manual wheelchair users at risk for developing shoulder pain and injury.
The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
UNIQ: Uniform Noise Injection for the Quantization of Neural Networks

Previous approaches  Using low-precision weights for forward pass and shadow fullprecision weights for backward pass  Applying soft thresholding during training and hard thresholding at inference  Usually using uniform quantization, not optimal for the empirically normally distributed weights:  Injecting noise to weights at training time emulating quantization noise at inference  The model becomes (quantization) noise invariant  k-quantile quantization – equiprobable bin (same number of samples in each bin):  For applying k-quantile quantization “uniformization trick” is used  Uniform quantization on FX x , FX is the CDF  The quantization noise is modeled as an additive uniform noise applied to FX x  “Plug and play” in each model, minimal deviation from standard training scheme  Work both for training from scratch and fine-tuning
English class on the air: mobile language learning with cell phones

Busy, active adults often do not have the time or ability to study in traditional, face-to-face classroom settings. In addition, adult learners may need access to content or expertise as they go about their daily activities, which may take them far from their fixed computers. Yet, these adults often have compelling needs to learn new knowledge and skills. Mobile learning with cell phones offers a unique response to this need. Mobile phones can support many kinds of learning, including language learning. This article explores the emerging technologies available for mobile language learning, and provides a model for integrating research-based pedagogy with available and emerging technologies to develop learning objects for effective, engaging mobile language learning.
Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification

Dermoscopy image as a non-invasive diagnosis technique plays an important role for early diagnosis of malignant melanoma. Even for experienced dermatologists, however, diagnosis by human vision can be subjective, inaccurate and non-reproducible. This is attributed to the challenging image characteristics including varying lesion sizes and their shapes, fuzzy lesion boundaries, different skin color types and presence of hair. To aid in the image interpretation, automatic classification of dermoscopy images have been shown to be a valuable aid in the clinical decision making. Existing methods however have problems in representing and differentiating skin lesions due to high degree of similarities between melanoma and non-melanoma images and large variations inherited from skin lesion images. To overcome these limitations, this study proposes a new automatic melanoma detection method for dermoscopy images via multi-scale lesion-biased representation (MLR) and joint reverse classification (JRC). Our proposed MLR representation enable us to represent skin lesions using multiple closely related histograms derived from different rotations and scales while traditional methods can only represent skin lesion using a single-scale histogram. The MLR representation was then used with JRC for melanoma detection. The proposed JRC model allows us to use a set of closely related histograms to derive additional information for melanoma detection, where existing methods mainly rely on histogram itself. Our method was evaluated on a public dataset of dermoscopy images, and we demonstrate superior classification performance compared to the current state-of-the-art methods.
A survey of multimodal sentiment analysis

Sentiment analysis aims to automatically uncover the underlying attitude that we hold towards an entity. The aggregation of these sentiment over a population represents opinion polling and has numerous applications. Current text-based sentiment analysis rely on the construction of dictionaries and machine learning models that learn sentiment from large text corpora. Sentiment analysis from text is currently widely used for customer satisfaction assessment and brand perception analysis, among others. With the proliferation of social media, multimodal sentiment analysis is set to bring new opportunities with the arrival of complementary data streams for improving and going beyond text-based sentiment analysis. Since sentiment can be detected through affective traces it leaves, such as facial and vocal displays, multimodal sentiment analysis offers promising avenues for analyzing facial and vocal expressions in addition to the transcript or textual content. These approaches leverage emotion recognition and context inference to determine the underlying polarity and scope of an individual’s sentiment. In this survey, we define sentiment and the problem of multimodal sentiment analysis and review recent developments in multimodal sentiment analysis in different domains, including spoken reviews, images, video blogs, human-machine and human-human interaction. Challenges and opportunities of this emerging field are also discussed leading to our thesis that multimodal sentiment analysis holds a significant untapped potential.
Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities

Standard word embeddings lack the possibility to distinguish senses of a word by projecting them to exactly one vector. This has a negative effect particularly when computing similarity scores between words using standard vector-based similarity measures such as cosine similarity. We argue that minor senses play an important role in word similarity computations, hence we use an unsupervised sense inventory resource to retrofit monolingual word embeddings, producing sense-aware embeddings. Using retrofitted sense-aware embeddings, we show improved word similarity and relatedness results on multiple word embeddings and multiple established word similarity tasks, sometimes up to an impressive margin of +0.15 Spearman correlation score.
Cascaded H-bridge multilevel converter multistring topology for large scale photovoltaic systems

Large scale grid connected photovoltaic (PV) energy conversion systems have reached the megawatt level. This imposes new challenges on existing grid interface converter topologies and opens new opportunities to be explored. In this paper a new medium voltage multilevel-multistring configuration is introduced based on a three-phase cascaded H-bridge (CHB) converter and multiple string dc-dc converters. The proposed configuration enables a large increase of the total capacity of the PV system, while improving power quality and efficiency. The converter structure is very flexible and modular since it decouples the grid converter from the PV string converter, which allows to accomplish independent control goals. The main challenge of the proposed configuration is to handle the inherent power imbalances that occur not only between the different cells of one phase of the converter but also between the three phases. The control strategy to deal with these imbalances is also introduced in this paper. Simulation results of a 7-level CHB for a multistring PV system are presented to validate the proposed topology and control method.
ECO: Efficient Convolutional Network for Online Video Understanding

The state of the art in video understanding suffers from two problems: (1) The major part of reasoning is performed locally in the video, therefore, it misses important relationships within actions that span several seconds. (2) While there are local methods with fast perframe processing, the processing of the whole video is not efficient and hampers fast video retrieval or online classification of long-term activities. In this paper, we introduce a network architecture that takes longterm content into account and enables fast per-video processing at the same time. The architecture is based on merging long-term content already in the network rather than in a post-hoc fusion. Together with a sampling strategy, which exploits that neighboring frames are largely redundant, this yields high-quality action classification and video captioning at up to 230 videos per second, where each video can consist of a few hundred frames. The approach achieves competitive performance across all datasets while being 10x to 80x faster than state-of-the-art methods.
Ontologies in Ubiquitous Computing

Ontologies are an explicit specification of a conceptualization, that is understood to be an abstract and simplified version of the world to be represented. In recent years, ontologies have been used in Ubiquitous Computing, especially for the development of context-aware applications. In this paper, we offer a taxonomy for classifying ontologies used in Ubiquitous Computing, in which two main categories are distinguished: Domain ontologies, created to represent and communicate agreed knowledge within some sub-domain of Ubiquitous Computing; and Ontologies as software artifacts, when ontologies play the role of an additional type of artifact in ubiquitous computing applications. The latter category is subdivided according with the moment in that ontologies are used: at development time or at run time. Also, we analyze and classify (based on this taxonomy) some recently published works.
Crocodile tears: facial, verbal and body language behaviours associated with genuine and fabricated remorse.

Emotional deception is a common behaviour that can have major consequences if undetected. For example, the sincerity of an offender's expressed remorse is an important factor in sentencing and parole hearings. The present study was the first to investigate the nature of true and false remorse. We examined facial, verbal and body language behaviours associated with emotional deception in videotaped accounts of true personal transgressions accompanied by either genuine or falsified remorse. Analyses of nearly 300,000 frames indicated that descriptions of falsified remorse were associated with a greater range of emotional expressions. Further, sequential analyses revealed that negative emotions were more commonly followed by other emotions-rather than a return to neutral emotion-in falsified versus sincere remorse. Participants also exhibited more speech hesitations while expressing deceptive relative to genuine remorse. In general, the results suggest that falsified remorse may be conceived as an emotionally turbulent display of deliberate, falsified expressions and involuntary, genuine, emotional leakage. These findings are relevant to judges and parole board members who consider genuine remorse to be an important factor in sentencing and release decisions.
Scalable System Scheduling for HPC and Big Data

In the rapidly expanding field of parallel processing, job schedulers are the “operating systems” of modern big data architectures and supercomputing systems. Job schedulers allocate computing resources and control the execution of processes on those resources. Historically, job schedulers were the domain of supercomputers, and job schedulers were designed to run massive, long-running computations over days and weeks. More recently, big data workloads have created a need for a new class of computations consisting of many short computations taking seconds or minutes that process enormous quantities of data. For both supercomputers and big data systems, the efficiency of the job scheduler represents a fundamental limit on the efficiency of the system. Detailed measurement and modeling of the performance of schedulers are critical for maximizing the performance of a large-scale computing system. This paper presents a detailed feature analysis of 15 supercomputing and big data schedulers. For big data workloads, the scheduler latency is the most important performance characteristic of the scheduler. A theoretical model of the latency of these schedulers is developed and used to design experiments targeted at measuring scheduler latency. Detailed benchmarking of four of the most popular schedulers (Slurm, Son of Grid Engine, Mesos, and Hadoop YARN) are conducted. The theoretical model is compared with data and demonstrates that scheduler performance can be characterized by two key parameters: the marginal latency of the scheduler ts and a nonlinear exponent αs. For all four schedulers, the utilization of the computing system decreases to <10% for computations lasting only a few seconds. Multi-level schedulers (such as LLMapReduce) that transparently aggregate short computations can improve utilization for these short computations to >90% for all four of the schedulers that were tested.
Learning Distributed Representations of Symbolic Structure Using Binding and Unbinding Operations

Widely used recurrent units, including Longshort Term Memory (LSTM) and Gated Recurrent Unit (GRU), perform well on natural language tasks, but their ability to learn structured representations is still questionable. Exploiting Tensor Product Representations (TPRs) — distributed representations of symbolic structure in which vector-embedded symbols are bound to vector-embedded structural positions — we propose the TPRU, a recurrent unit that, at each time step, explicitly executes structural-role binding and unbinding operations to incorporate structural information into learning. Experiments are conducted on the Logical Entailment, Multigenre Natural Language Inference (MNLI), and language modelling tasks, and our TPR-derived recurrent unit provides strong performance compared to LSTM and GRU baselines. Furthermore, our TPRU demonstrates expected performance gain when certain symbol-related hyperparameters are varied.
A Novel Approach for Logo Recognition System Using Machine Learning Algorithm SVM

In different applications like Complex document image processing, Advertisement and Intelligent transportation logo recognition is an important issue. Logo Recognition is an essential sub process although there are many approaches to study logos in these fields. In this paper a robust method for recognition of a logo is proposed, which involves K-nearest neighbors distance classifier and Support Vector Machine classifier to evaluate the similarity between images under test and trained images. For test images eight set of logo image with a rotation angle of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315° are considered. A Dual Tree Complex Wavelet Transform features were used for determining features. Final result is obtained by measuring the similarity obtained from the feature vectors of the trained image and image under test. Total of 31 classes of logo images of different organizations are considered for experimental results. An accuracy of 87.49% is obtained using KNN classifier and 92.33% from SVM classifier.
Infrared detectors : an overview

The paper presents progress in infrared (IR) detector technologies during 200 history of their development. Classification of two types of IR detectors (photon detectors and thermal detectors) is done on the basis of their principle of operation. The overview of IR systems and detectors is presented. Also recent progress in different IR technologies is described. Discussion is focused mainly on current and the most rapidly developing detectors: HgCdTe heterostructure photodiodes, quantum well AlGaAs/GaAs photoresistors, and thermal detectors. The outlook for near-future trends in IR technologies is also presented.
On Minimum Discrepancy Estimation for Deep Domain Adaptation

In the presence of large sets of labeled data, Deep Learning (DL) has accomplished extraordinary triumphs in the avenue of computer vision, particularly in object classification and recognition tasks. However, DL cannot always perform well when the training and testing images come from different distributions or in the presence of domain shift between training and testing images. They also suffer in the absence of labeled input data. Domain adaptation (DA) methods have been proposed to make up the poor performance due to domain shift. In this paper, we present a new unsupervised deep domain adaptation method based on the alignment of second order statistics (covariances) as well as maximum mean discrepancy of the source and target data with a two stream Convolutional Neural Network (CNN). We demonstrate the ability of the proposed approach to achieve state-of-the-art performance for image classification on three benchmark domain adaptation datasets: Office-31 [27], Office-Home [37] and Office-Caltech .