nils-herrmann/clean_scirepeval huggingface dataset:
Options for Control of Reactive Power by Distributed Photovoltaic Generators

High-penetration levels of distributed photovoltaic (PV) generation on an electrical distribution circuit present several challenges and opportunities for distribution utilities. Rapidly varying irradiance conditions may cause voltage sags and swells that cannot be compensated by slowly responding utility equipment resulting in a degradation of power quality. Although not permitted under current standards for interconnection of distributed generation, fast-reacting, VAR-capable PV inverters may provide the necessary reactive power injection or consumption to maintain voltage regulation under difficult transient conditions. As side benefit, the control of reactive power injection at each PV inverter provides an opportunity and a new tool for distribution utilities to optimize the performance of distribution circuits, e.g., by minimizing thermal losses. We discuss and compare via simulation various design options for control systems to manage the reactive power generated by these inverters. An important design decision that weighs on the speed and quality of communication required is whether the control should be centralized or distributed (i.e., local). In general, we find that local control schemes are able to maintain voltage within acceptable bounds. We consider the benefits of choosing different local variables on which to control and how the control system can be continuously tuned between robust voltage control, suitable for daytime operation when circuit conditions can change rapidly, and loss minimization better suited for nighttime operation.
Perceptual distortions in the adaptation of English consonant clusters: syllable structure or consonantal contact constraints?

We present the results from an experiment that tests the perception of English consonantal sequences by Korean speakers and we confirm that perceptual epenthesis in a second languge (L2) arises from syllable structure restrictions of the first language (L1), rather than linear co-occurence restrictions. Our study replicates and extends Dupoux, Kakehi, Hirose, Pallier, & Mehler's (1999) results that suggested that listeners perceive epenthetic vowels within consonantal sequences that violate the phonotactics of their L1. Korean employs at least two kinds of phonotactic restrictions: (i) syllable structure restrictions that prohibit the occurence of certain consonants in coda position STRUCTURE (e.g., *[c.], *[g.]), while allowing others (e.g., [k.], [l.]), and (ii) consonantal contact restrictions that ban the co-occurrence of certain heterosyllabic consonants (e.g., *[k.m]; *[l.n]) due to various phonological processes that repair such sequences on the surface (i.e., /k.m/ --> [n.m]; /ll.n/ --> [l.l]). The results suggest that Korean syllable structure restrictions, rather than consonantal contact restrictions, result in the perception of epenthetic vowels. Furthermore, the frequency of co-occurrence fails to explain the epenthesis effects in the percept of consonant clusters employed in the present study. We address questions regarding the interaction between speech perception and phonology and test the validity of Steriade's (2001 a,b) Perceptual-Mapping (P-Map) hypothesis for the Korean sonorant assimilation processes. Our results indicate that Steriade's hypothesis makes incorrect predictions about Korean phonology and that speech perception is not isomorphic to speech production.
[POSTER] Realtime Shape-from-Template: System and Applications

An important yet unsolved problem in computer vision and Augmented Reality (AR) is to compute the 3D shape of nonrigid objects from live 2D videos. When the object's shape is provided in a rest pose, this is the Shape-from-Template (SfT) problem. Previous realtime SfT methods require simple, smooth templates, such as flat sheets of paper that are densely textured, and which deform in simple, smooth ways. We present a realtime SfT framework that handles generic template meshes, complex deformations and most of the difficulties present in real imaging conditions. Achieving this has required new, fast solutions to the two core sub-problems: robust registration and 3D shape inference. Registration is achieved with what we call Deformable Render-based Block Matching (DRBM): a highly-parallel solution which densely matches a time-varying render of the object to each video frame. We then combine matches from DRBM with physical deformation priors and perform shape inference, which is done by quickly solving a sparse linear system with a Geometric Multi-Grid (GMG)-based method. On a standard PC we achieve up to 21fps depending on the object. Source code will be released.
Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching

Automatic face recognition capability in surveillance systems is important for security applications. However, few studies have addressed the problem of outdoor face recognition at a long distance (over 100 meters) in both daytime and nighttime environments. In this paper, we first report on a system that we have designed to collect face image database at a long distance, called the Long Distance Heterogeneous Face Database (LDHF-DB) to advance research on this topic. The LDHF-DB contains face images collected in an outdoor environment at distances of 60 meters, 100 meters, and 150 meters, with both visible light (VIS) face images captured in daytime and near infrared (NIR) face images captured in nighttime. Given this database, we have conducted two types of cross-distance face matching (matching long-distance probe to 1-meter gallery) experiments: (i) intra-spectral (VIS to VIS) face matching, and (ii) cross-spectral (NIR to VIS) face matching. The proposed face recognition algorithm consists of following three major steps: (i) Gaussian filtering to remove high frequency noise, (ii) Scale Invariant Feature Transform (SIFT) in local image regions for feature representation, and (iii) a random subspace method to build discriminant subspaces for face recognition. Experimental results show that the proposed face recognition algorithm outperforms two commercial state-of-the-art face recognition SDKs (FaceVACS and PittPatt) for long distance face recognition in both daytime and nighttime operations. These results highlight the need for better data capture setup and robust face matching algorithms for cross spectral matching at distances greater than 100 meters.
Diagnostically relevant facial gestalt information from ordinary photos

Craniofacial characteristics are highly informative for clinical geneticists when diagnosing genetic diseases. As a first step towards the high-throughput diagnosis of ultra-rare developmental diseases we introduce an automatic approach that implements recent developments in computer vision. This algorithm extracts phenotypic information from ordinary non-clinical photographs and, using machine learning, models human facial dysmorphisms in a multidimensional 'Clinical Face Phenotype Space'. The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses. Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders. Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification. We demonstrate that this approach provides a novel method for inferring causative genetic variants from clinical sequencing data through functional genetic pathway comparisons. http://dx.doi.org/10.7554/eLife.02020.001.
The Race Between Man and Machine : Implications of Technology for Growth , Factor Shares and Employment

The advent of automation and the simultaneous decline in the labor share and employment among advanced economies raises concerns that labor will be marginalized and made redundant by new technologies. This paper examines this proposition in a task-based framework wherein not only are tasks previously performed by labor automated, but also more complex versions of existing tasks, in which labor has a comparative advantage, can be created. We fully characterize the structure of equilibrium in this model, establishing how the allocation of factors to tasks and factor prices are determined by the available technology and the endogenous choices of firms between capital and labor. We then demonstrate that although automation tends to reduce employment and the share of labor in national income, the creation of more complex tasks has the opposite effect. Our full model endogenizes the direction of research and development towards automation and the creation of new complex tasks. We show that, under reasonable conditions, there exists a stable balanced growth path in which the two types of innovations go hand-in-hand. Consequently, an increase in automation reduces the wage to rental rate ratio, and thus discourages further automation, encourages greater creation of more labor-intensive tasks, and restores the share of labor in national income and the employment to population ratio back towards their initial values. Though the economy is self-correcting, the equilibrium allocation of research effort is not optimal: to the extent that wages reflect quasi-rents for workers, firms will engage in too much automation. Finally, we extend the model to include workers of different skills. We find that inequality increases during transitions, but the self-correcting forces also serve to limit the increase in inequality over longer periods.
Word Sense Disambiguation Using Skip-Gram and LSTM Models

In this paper, we explore supervised word sense disambiguation (WSD) through two main techniques. Central to both techniques is a learned word vector embedding that has separate word vectors for each sense of a word. When predicting on an unlabeled word, we can consider a window of word that contains it, and iterate over all possible combinations of word senses within this window. Out of all these possibilities, we choose the sense labeled window with the highest probability of occurring. Another way to compute senses would be to do sense-label initial window (either through exhaustive search or some other greedy method like assigning most likely sense) and compute the following senses with an RNN. The RNN will output a probability distribution over the next possible words. We choose the sense of the next word with the maximum probability when predicting with the RNN. The results of our models are promising and should improve as more hand-labeled sense-tagged data becomes available.
The effect of glycolic acid on the treatment of acne in Asian skin.

Glycolic acid has become important and popular for treating acne.



To evaluate the efficacy and safety of serial glycolic acid peels with glycolic acid home care products on facial acne lesions and other associated skin problems.



We collected 40 Asian candidates with moderate to moderately severe acne. They were divided into two groups according to the degree of greasiness of their facial skin. The two groups' members were treated with four series of 35% and 50% glycolic acid peels, respectively. They also used 15% glycolic acid home care products during this study period. The improvement of acne as well as other associated problems were assessed by both the physicians and the patient themselves.



Significant resolution of comedones, papules, and pustules was found. The skin texture of each candidate was dramatically rejuvenated. Consistent and repetitive treatment with glycolic acid was needed for the apparent improvement of acne scars and cystic lesions. The follicular pores also became comparatively smaller. Furthermore, most of the candidates had much brighter and lighter looking skin. Only small percentage of patients (5.6%) developed side effects.



Glycolic acid has considerable therapeutic value for acne with minimal side effects even in Asian skin. It may be an ideal adjunctive treatment of acne.
Quasi-Cyclic LDPC Codes: Influence of Proto- and Tanner-Graph Structure on Minimum Hamming Distance Upper Bounds

Quasi-cyclic (QC) low-density parity-check (LDPC) codes are an important instance of proto-graph-based LDPC codes. In this paper we present upper bounds on the minimum Hamming distance of QC LDPC codes and study how these upper bounds depend on graph structure parameters (like variable degrees, check node degrees, girth) of the Tanner graph and of the underlying proto-graph. Moreover, for several classes of proto-graphs we present explicit QC LDPC code constructions that achieve (or come close to) the respective minimum Hamming distance upper bounds. Because of the tight algebraic connection between QC codes and convolutional codes, we can state similar results for the free Hamming distance of convolutional codes. In fact, some QC code statements are established by first proving the corresponding convolutional code statements and then using a result by Tanner that says that the minimum Hamming distance of a QC code is upper bounded by the free Hamming distance of the convolutional code that is obtained by “unwrapping” the QC code.
Fast time series classification using numerosity reduction

Many algorithms have been proposed for the problem of time series classification. However, it is clear that one-nearest-neighbor with Dynamic Time Warping (DTW) distance is exceptionally difficult to beat. This approach has one weakness, however; it is computationally too demanding for many realtime applications. One way to mitigate this problem is to speed up the DTW calculations. Nonetheless, there is a limit to how much this can help. In this work, we propose an additional technique, numerosity reduction, to speed up one-nearest-neighbor DTW. While the idea of numerosity reduction for nearest-neighbor classifiers has a long history, we show here that we can leverage off an original observation about the relationship between dataset size and DTW constraints to produce an extremely compact dataset with little or no loss in accuracy. We test our ideas with a comprehensive set of experiments, and show that it can efficiently produce extremely fast accurate classifiers.
Computing arbitrary functions of encrypted data

Suppose that you want to delegate the ability to process your data, without giving away access to it. We show that this separation is possible: we describe a "fully homomorphic" encryption scheme that keeps data private, but that allows a worker that does not have the secret decryption key to compute any (still encrypted) result of the data, even when the function of the data is very complex. In short, a third party can perform complicated processing of data without being able to see it. Among other things, this helps make cloud computing compatible with privacy.
A Survey on Mobile Banking Applications and the Adopted Models

Many banks around the world are starting to offer banking services through mobile phones. Therefore, many studies have been proposed to help bankers to increase the number of customers. Despite the effort that has been founded in the state of the art, the adoption rate of mobile banking application has not reached the expected amount yet. Therefore, the aim of this study is to analyse the most well-known and accepted models to provide a comprehensive understanding of their impacts toward the adoption of mobile banking applications. Furthermore, this study aims also at exploring the most effected factors that have been used to influence the adoption behaviour intention of mobile banking applications. As a result of this survey study, some critical recommendations were stated
2 A Physically Unclonable Function with BER < 10-8 for Robust Chip Authentication Using Oscillator Collapse in 40 nm CMOS

Security is a key concern in today’s mobile devices and a number of hardware implementations of security primitives have been proposed, including true random number generators, differential power attack avoidance, and chip-ID generators [1-4]. Recently, physically unclonable functions (PUFs) were proposed as a secure method for chip authentication in unsecure environments [5-7]. A PUF is a function that maps an input code (“challenge”) to an output code (“response”) in a manner that is unique for every chip. PUFs are increasingly used for IC authentication to offer protection against identity theft, cloning, and counterfeit components [2-4].
ECG-Cryptography and Authentication in Body Area Networks

Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data commnications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.
Electromyographic analysis of specific exercises for scapular control in early phases of shoulder rehabilitation.

Restoration of control of dynamic scapular motion by specific activation of the serratus anterior and lower trapezius muscles is an important part of functional rehabilitation. This study evaluated activation of those muscles in specific exercises.



Specific exercises will activate key scapular-stabilizing muscles in clinically significant amplitudes and patterns.



Controlled laboratory study.



Muscle activation amplitudes and patterns were evaluated in the serratus anterior, upper trapezius, lower trapezius, anterior deltoid, and posterior deltoid muscles with electromyography in symptomatic (n = 18) and asymptomatic (n = 21) subjects as they executed the low row, inferior glide, lawnmower, and robbery exercises.



There were no significant differences in muscle activation amplitude between groups. Muscle activation was moderate across all of the exercises and varied slightly with the specific exercise. The serratus anterior and lower trapezius were activated between 15% and 30% in all exercises. Upper trapezius activation was high (21%-36%) in the dynamic exercises (lawnmower and robbery). Serratus anterior was activated first in the low row and last in the lawnmower and robbery. The upper trapezius and lower trapezius were activated first in the lawnmower and robbery.



These specific exercises activate key scapular-stabilizing muscles at amplitudes that are known to increase muscle strength.



These exercises can be used as part of a comprehensive rehabilitation program for restoration of shoulder function. They activate the serratus anterior and lower trapezius-key muscles in dynamic shoulder control-while variably activating the upper trapezius. Activation patterns depended on scapular position resulting in variability of amplitude and activation sequencing between exercises. Inferior glide and low row can be performed early in rehabilitation because of their limited range of motion, while lawnmower and robbery, which require larger movements, can be instituted later in the sequence.
Fully Connected Deep Structured Networks

Convolutional neural networks with many layers have recently been shown to achieve excellent results on many high-level tasks such as image classification, object detection and more recently also semantic segmentation. Particularly for semantic segmentation, a two-stage procedure is often employed. Hereby, convolutional networks are trained to provide good local pixel-wise features for the second step being traditionally a more global graphical model. In this work we unify this two-stage process into a single joint training algorithm. We demonstrate our method on the semantic image segmentation task and show encouraging results on the challenging PASCAL VOC 2012 dataset.
API recommendation system for software development

Nowadays, software developers often utilize existing third party libraries and make use of Application Programming Interface (API) to develop a software. However, it is not always obvious which library to use or whether the chosen library will play well with other libraries in the system. Furthermore, developers need to spend some time to understand the API to the point that they can freely use the API methods and putting the right parameters inside them. In this work, I plan to automatically recommend relevant APIs to developers. This API recommendation can be divided into multiple stages. First, we can recommend relevant libraries provided a given task to complete. Second, we can recommend relevant API methods that developer can use to program the required task. Third, we can recommend correct parameters for a given method according to its context. Last but not least, we can recommend how different API methods can be combined to achieve a given task. 
 In effort to realize this API recommendation system, I have published two related papers. The first one deals with recommending additional relevant API libraries given known useful API libraries for the target program. This system can achieve recall rate@5 of 0.852 and recall rate@10 of 0.894 in recommending additional relevant libraries. The second one deals with recommending relevant API methods a given target API and a textual description of the task. This system can achieve recall-rate@5 of 0.690 and recallrate@10 of 0.779. The results for both system indicate that the systems are useful and capable in recommending the right API/library reasonably well. Currently, I am working on another system which can recommend web APIs (i.e., libraries) given a description of the task. I am also working on a system that recommends correct parameters given an API method. In the future, I also plan to realize API composition recommendation for the given task
Hardware resource estimation for heterogeneous FPGA-based SoCs

The increasing complexity of recent System-on-Chip (SoC) designs introduces new challenges for design space exploration tools. In addition to the time-to-market challenge, designers need to estimate rapidly and accurately both performance and area occupation of complex and diverse applications. High-Level Synthesis (HLS) has been emerged as an attractive solution for designers to address these challenges in order to explore a large number of SoC configurations. In this paper, we target hybrid CPU-FPGA based SoCs. We propose a high-level area estimation tool based on an analytic model without requiring register-transfer level (RTL) implementations. This technique allows to estimate the required FPGA resources at the source code level to map an application to a hybrid CPU-FPGA system. The proposed model also enables a fast design exploration with different trade-offs through HLS optimization pragmas. Experimental results show that the proposed area analytic model provides an accurate estimation with a negligible error (less than 5+) compared to RTL implementations.