nils-herrmann/clean_scirepeval huggingface dataset:
Automated speech-unit delimitation in spoken learner English

In order to apply computational linguistic analyses and pass information to downstream applications, transcriptions of speech obtained via automatic speech recognition (ASR) need to be divided into smaller meaningful units, in a task we refer to as ‘speech-unit (SU) delimitation’. We closely recreate the automatic delimitation system described by Lee and Glass (2012), ‘Sentence detection using multiple annotations’, Proceedings of INTERSPEECH, which combines a prosodic model, language model and speech-unit length model in log-linear fashion. Since stateof-the-art natural language processing (NLP) tools have been developed to deal with written text and its characteristic sentence-like units, SU delimitation helps bridge the gap between ASR and NLP, by normalising spoken data into a more canonical format. Previous work has focused on native speaker recordings; we test the system of Lee and Glass (2012) on non-native speaker (or ‘learner’) data, achieving performance above the state-of-the-art. We also consider alternative evaluation metrics which move away from the idea of a single ‘truth’ in SU delimitation, and frame this work in the context of downstream NLP applications.
Assisted Lead Sheet Composition Using FlowComposer

We present FlowComposer, a web application that helps users compose musical lead sheets, i.e. melodies with chord labels. FlowComposer integrates a constrained-based lead sheet generation tool in which the user retains full control over the generation process. Users specify the style of the lead sheet by selecting a corpus of existing lead sheets. The system then produces a complete lead sheet in that style, either from scratch, or from a partial lead sheet entered by the user. The generation algorithm is based on a graphical model that combines two Markov chains enriched by Regular constraints, representing the melody and its related chord sequence. The model is sampled using our recent result in efficient sampling of the Regular constraint. The paper reports on the design and deployment of FlowComposer as a web-service, part of an ecosystem of online tools for the creation of lead sheets. FlowComposer is currently used in professional musical productions, from which we collect and show a number of representative examples.
Illustrate It! An Arabic Multimedia Text-to-Picture m-Learning System

Multimedia learning is the process of building mental representation from words associated with images. Due to the intuitiveness and vividness of visual illustration, many texts to picture systems have been proposed. However, we observe some common limitations in the existing systems, such as the retrieved pictures may not be suitable for educational purposes. Also, finding pedagogic illustrations still requires manual work, which is difficult and time-consuming. The commonly used systems based on the best keyword selection and the best sentence selection may suffer from loss of information. In this paper, we present an Arabic multimedia text-to-picture mobile learning system that is based on conceptual graph matching. Using a knowledge base, a conceptual graph is built from the text accompanied with the pictures in the multimedia repository as well as for the text entered by the user. Based on the matching scores of both conceptual graphs, matched pictures are assigned relative rankings. The proposed system demonstrated its effectiveness in the domain of Arabic stories, however, it can be easily shifted to any educational domain to yield pedagogical illustrations for organizational or institutional needs. Comparisons with the current state-of-the-art systems, based on the best keyword selection and the best sentence selection techniques, have demonstrated significant improvements in the performance. In addition, to facilitate educational needs, conceptual graph visualization and visual illustrative assessment modules are also developed. The conceptual graph visualization enables learners to discover relationships between words, and the visual illustrative assessment allows the system to automatically assess the performance of a learner. The profound user studies demonstrated the efficiency of the proposed multimedia learning system.
Association between Facebook addiction, self-esteem and life satisfaction: A cross-sectional study

In recent years, many research efforts have been focused on investigation of potential connection between social networking and mental health issues. Particularly important and controversial remains the association between Facebook use, self-esteem and life satisfaction. In our cross-sectional study, on a sample of 381 Facebook users, we tested the existence and strength of this relationship using Bergen Facebook Addiction Scale (BFAS), Facebook Intensity Scale (FBI), Rosenberg's Self-Esteem Scale (SES), and Satisfaction With Life Scale (SWLS). With k-means cluster analysis, we divided the sample into 3 groups: ordinary, intensive, and addicted Facebook users. The results of our study show that ordinary Facebook users differ statistically in self-esteem and life satisfaction from both addicted and intensive users. Facebook addiction was in relation with lower self-esteem. Facebook addiction was also negatively related to life satisfaction. These results are in accordance with the previously published findings of other authors in the fields of social networking psychology and psychiatry.
A new stepwise adiabatic charging circuit with a smaller capacitance in a regenerator than a load capacitance

A stepwise-voltage-generation circuit was devised that is based on a capacitor bank and that dissipates no energy when a stepwise voltage is generated. The stepwise voltage is generated spontaneously, and depends neither on the initial voltages to the capacitors nor on the switching order. A new adiabatic-charging circuit based on this circuit was also devised that increases the voltage in a stepwise fashion. The total capacitance of the capacitors in the regenerator is much smaller than a load capacitance, which enables construction of a very small adiabatic regenerator. This regenerator cannot be made with a conventional circuit, which uses a tank capacitor that is much larger than a load capacitor for adiabatic charging.
Applying Argumentation Schemes for Essay Scoring

Under the framework of the argumentation scheme theory (Walton, 1996), we developed annotation protocols for an argumentative writing task to support identification and classification of the arguments being made in essays. Each annotation protocol defined argumentation schemes (i.e., reasoning patterns) in a given writing prompt and listed questions to help evaluate an argument based on these schemes, to make the argument structure in a text explicit and classifiable. We report findings based on an annotation of 600 essays. Most annotation categories were applied reliably by human annotators, and some categories significantly contributed to essay score. An NLP system to identify sentences containing scheme-relevant critical questions was developed based on the human annotations.
Taxonomy-based job recommender systems on Facebook and LinkedIn profiles

This paper presents taxonomy-based recommender systems that propose relevant jobs to Facebook and LinkedIn users; they are being developed by Work4, a San Francisco-based software company and the Global Leader in Social and Mobile Recruiting that offers Facebook recruitment solutions; to use its applications, Facebook or LinkedIn users explicitly grant access to some parts of their data, and they are presented with the jobs whose descriptions are matching their profiles the most. In this paper, we use the O*NET-SOC taxonomy, a taxonomy that defines the set of occupations across the world of work, to develop a new taxonomy-based vector model for social network users and job descriptions suited to the task of job recommendation; we propose two similarity functions based on the AND and OR fuzzy logic's operators, suited to the proposed vector model. We compare the performance of our proposed vector model to the TF-IDF model using our proposed similarity functions and the classic heuristic measures; the results show that the taxonomy-based vector model outperforms the TF-IDF model. We then use SVMs (Support Vector Machines) with a mechanism to handle unbalanced datasets, to learn similarity functions from our data; the learnt models yield better results than heuristic similarity measures. The comparison of our methods to two methods of the literature (a matrix factorization method and the Collaborative Topic Regression) shows that our best method yields better results than those two methods in terms of AUC. The proposed taxonomy-based vector model leads to an efficient dimensionality reduction method in the task of job recommendation.
Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering

Inferring the emotional content of words is important for text-based sentiment analysis, dialogue systems and psycholinguistics, but word ratings are expensive to collect at scale and across languages or domains. We develop a method that automatically extends word-level ratings to unrated words using signed clustering of vector space word representations along with affect ratings. We use our method to determine a word’s valence and arousal, which determine its position on the circumplex model of affect, the most popular dimensional model of emotion. Our method achieves superior out-of-sample word rating prediction on both affective dimensions across three different languages when compared to state-of-theart word similarity based methods. Our method can assist building word ratings for new languages and improve downstream tasks such as sentiment analysis and emotion detection.
Memory consolidation, retrograde amnesia and the hippocampal complex

Results from recent studies of retrograde amnesia following damage to the hippocampal complex of human and non-human subjects have shown that retrograde amnesia is extensive and can encompass much of a subject's lifetime; the degree of loss may depend upon the type of memory assessed. These and other findings suggest that the hippocampal formation and related structures are involved in certain forms of memory (e.g. autobiographical episodic and spatial memory) for as long as they exist and contribute to the transformation and stabilization of other forms of memory stored elsewhere in the brain.
SilkRoad: Making Stateful Layer-4 Load Balancing Fast and Cheap Using Switching ASICs

In this paper, we show that up to hundreds of software load balancer (SLB) servers can be replaced by a single modern switching ASIC, potentially reducing the cost of load balancing by over two orders of magnitude. Today, large data centers typically employ hundreds or thousands of servers to load-balance incoming traffic over application servers. These software load balancers (SLBs) map packets destined to a service (with a virtual IP address, or VIP), to a pool of servers tasked with providing the service (with multiple direct IP addresses, or DIPs). An SLB is stateful, it must always map a connection to the same server, even if the pool of servers changes and/or if the load is spread differently across the pool. This property is called per-connection consistency or PCC. The challenge is that the load balancer must keep track of millions of connections simultaneously.
 Until recently, it was not possible to implement a load balancer with PCC in a merchant switching ASIC, because high-performance switching ASICs typically can not maintain per-connection states with PCC. Newer switching ASICs provide resources and primitives to enable PCC at a large scale. In this paper, we explore how to use switching ASICs to build much faster load balancers than have been built before. Our system, called SilkRoad, is defined in a 400 line P4 program and when compiled to a state-of-the-art switching ASIC, we show it can load-balance ten million connections simultaneously at line rate.
Design of FPGA-based Traffic Light Controller System

This paper proposed a design of a modern FPGA-based Traffic Light Control (TLC) System to manage the road traffic. The approach is by controlling the access to areas shared among multiple intersections and allocating effective time between various users; during peak and off-peak hours. The implementation is based on real location in a city in Malaysia where the existing traffic light controller is a basic fixed-time method. This method is inefficient and almost always leads to traffic congestion during peak hours while drivers are given unnecessary waiting time during off-peak hours. The proposed design is a more universal and intelligent approach to the situation and has been implemented using FPGA. The system is implemented on ALTERA FLEX10K chip and simulation results are proven to be successful. Theoretically the waiting time for drivers during off-peak hours has been reduced further, therefore making the system better than the one being used at the moment. Future improvements include addition of other functions to the proposed design to suit various traffic conditions at different locations.
A 1mV voltage ripple 0.97mm2 fully integrated low-power hybrid buck converter

The output voltage ripple is one of the most significant system parameters in switch-mode power supplies. This ripple degrades the performance of application specific integrated circuits (ASICs). The most common way to reduce it is to use additional integrated low drop-out regulators (LDO) on the ASIC. This technique usually suffers from high system efficiency as it is required for portable electronic systems. It also increases the design challenges of on-chip power management circuits and area required for the LDOs. This work presents a low-power fully integrated 0.97mm2 DC-DC Buck converter with a tuned series LDO with 1mV voltage ripple in a 0.25μm BiCMOS process. The converter prodives a power supply rejection ratio of more than 60 dB from 1 to 6MHz and a load current range of 0...400 mA. A peak efficiency of 93.7% has been measured. For high light load efficiency, automatic mode operation is implemented. To decrease the form factor and costs, the external components count has been reduced to a single inductor of 1 μH and two external capacitors of 2 μF each.
Reflectance and Illumination Recovery in the Wild

The appearance of an object in an image encodes invaluable information about that object and the surrounding scene. Inferring object reflectance and scene illumination from an image would help us decode this information: reflectance can reveal important properties about the materials composing an object; the illumination can tell us, for instance, whether the scene is indoors or outdoors. Recovering reflectance and illumination from a single image in the real world, however, is a difficult task. Real scenes illuminate objects from every visible direction and real objects vary greatly in reflectance behavior. In addition, the image formation process introduces ambiguities, like color constancy, that make reversing the process ill-posed. To address this problem, we propose a Bayesian framework for joint reflectance and illumination inference in the real world. We develop a reflectance model and priors that precisely capture the space of real-world object reflectance and a flexible illumination model that can represent real-world illumination with priors that combat the deleterious effects of image formation. We analyze the performance of our approach on a set of synthetic data and demonstrate results on real-world scenes. These contributions enable reliable reflectance and illumination inference in the real world.
An electronically guided walking stick for the blind

Mobility for the blind is always a great problem. Just like a sighted, blind also needs to travel around inside a closed premises like house, factory, office, school etc. They may also like to go for shopping, visiting friends and other places of their interest. Presently available electronic travelling aids like sonic path finder, sonic torch etc. are not suitable for using inside a closed premises such as school, factory, office etc. In this paper an electronically guided walking stick that can be used conveniently inside a closed premises has been discussed.
Untreated depression and hippocampal volume loss.

The purpose of this study was to investigate the effect of antidepressant treatment on hippocampal volumes in patients with major depression.



For 38 female outpatients, the total time each had been in a depressive episode was divided into days during which the patient was receiving antidepressant medication and days during which no antidepressant treatment was received. Hippocampal gray matter volumes were determined by high resolution magnetic resonance imaging and unbiased stereological measurement.



Longer durations during which depressive episodes went untreated with antidepressant medication were associated with reductions in hippocampal volume. There was no significant relationship between hippocampal volume loss and time depressed while taking antidepressant medication or with lifetime exposure to antidepressants.



Antidepressants may have a neuroprotective effect during depression.
Moving towards efficient decision tree construction

Motivated by the desire to construct compact (in terms of expected length to be traversed to reach a decision) decision trees, we propose a new node splitting measure for decision tree construction. We show that the proposed measure is convex and cumulative and utilize this in the construction of decision trees for classification. Results obtained from several datasets from the UCI repository show that the proposed measure results in decision trees that are more compact with classification accuracy that is comparable to that obtained using popular node splitting measures such as Gain Ratio and the Gini Index.
SpiroCall: Measuring Lung Function over a Phone Call

Cost and accessibility have impeded the adoption of spirometers (devices that measure lung function) outside clinical settings, especially in low-resource environments. Prior work, called SpiroSmart, used a smartphone's built-in microphone as a spirometer. However, individuals in low- or middle-income countries do not typically have access to the latest smartphones. In this paper, we investigate how spirometry can be performed from any phone-using the standard telephony voice channel to transmit the sound of the spirometry effort. We also investigate how using a 3D printed vortex whistle can affect the accuracy of common spirometry measures and mitigate usability challenges. Our system, coined SpiroCall, was evaluated with 50 participants against two gold standard medical spirometers. We conclude that SpiroCall has an acceptable mean error with or without a whistle for performing spirometry, and advantages of each are discussed.
Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment

Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Glaucoma is one of the most common causes of blindness. The manual examination of optic disk (OD) is a standard procedure used for detecting glaucoma. In this paper, we present an automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images. A novel OD segmentation method is proposed which integrates the local image information around each point of interest in multidimensional feature space to provide robustness against variations found in and around the OD region. We also propose a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is employed to derive a reliable subset of vessel bends called r-bends followed by a local spline fitting to derive the desired cup boundary. The method has been evaluated on 138 images comprising 33 normal and 105 glaucomatous images against three glaucoma experts. The obtained segmentation results show consistency in handling various geometric and photometric variations found across the dataset. The estimation error of the method for vertical cup-to-disk diameter ratio is 0.09/0.08 (mean/standard deviation) while for cup-to-disk area ratio it is 0.12/0.10. Overall, the obtained qualitative and quantitative results show effectiveness in both segmentation and subsequent OD parameterization for glaucoma assessment.
Self-determination theory and work motivation

Cognitive evaluation theory, which explains the effects of extrinsic motivators on intrinsic motivation, received some initial attention in the organizational literature. However, the simple dichotomy between intrinsic and extrinsic motivation made the theory difficult to apply to work settings. Differentiating extrinsic motivation into types that differ in their degree of autonomy led to self-determination theory, which has received widespread attention in the education, health care, and sport domains. This article describes self-determination theory as a theory of work motivation and shows its relevance to theories of organizational behavior.
High-Resistivity Silicon Dielectric Ribbon Waveguide for Single-Mode Low-Loss Propagation at F/G-Bands

In this paper, dielectric ribbon waveguides (DRWs) are designed and characterized to work in the F (90-140 GHz) and G (140-220 GHz) frequency bands. High resistivity silicon (HR-silicon) due to its high transparency in these bands, and its low-cost fabrication process, were selected for the fabrication of these waveguides. The guided modes of DRW are studied using Marcatili and multi-line methods. A design procedure for optimal single-mode propagation was introduced. The wave attenuation inside the waveguide is discussed. Our experiments show that HR-silicon DRW has less than 0.087 dB/cm (0.01 cm-1) of loss over the guiding bandwidth. The designed DRWs were realized using microfabrication techniques. The measurements are in very good agreement with theoretical predictions.
Investigation of 1.2 kV SiC MOSFETs for hard- and soft-switching converters

In this paper, current silicon carbide (SiC) MOSFETs from two different manufacturers are evaluated including static and dynamic characteristics for different gate resistances, different load currents and at various temperatures. These power semiconductors are operated continuously at a high switching frequency of 1MHz comparing a hard- and a soft-switching converter. A calorimetric power loss measurement method is realized in order to achieve a good measurement accuracy, and the results are compared to the electrical measurements.
Bit Division Multiplexing for Broadcasting

This paper aims to improve the throughput of a broadcasting system that supports the transmission of multiple services with differentiated minimum signal-to-noise ratios (SNRs) required for successful receptions simultaneously. We propose a novel multiplexing method called bit division multiplexing (BDM), which outperforms the conventional time division multiplexing (TDM) counterpart by extending the multiplexing from symbol level to bit level. Benefiting from multiple error protection levels of bits within each high-order constellation symbol, BDM can provide so-called nonlinear allocation of the channel resources. Both average mutual information (AMI) analysis and simulation results demonstrate that, compared with TDM, BDM can significantly improve the overall transmission rate of multiple services subject to the differentiated minimum SNRs required for successful receptions, or decrease the minimum SNRs required for successful receptions subject to the transmission rate requirements of multiple services.