nils-herrmann/clean_scirepeval huggingface dataset:
On Combining Side Information and Unlabeled Data for Heterogeneous Multi-Task Metric Learning

Distance metric learning (DML) is critical for a wide variety of machine learning algorithms and pattern recognition applications. Transfer metric learning (TML) leverages the side information (e.g., similar/dissimilar constraints over pairs of samples) from related domains to help the target metric learning (with limited information). Current TML tools usually assume that different domains exploit the same feature representation, and thus are not applicable to tasks where data are drawn from heterogeneous domains. Heterogeneous transfer learning approaches handle heterogeneous domains by usually learning feature transformations across different domains. The learned transformation can be used to derive a metric, but these approaches are mostly limited by their capability of only handling two domains. This motivates the proposed heterogeneous multi-task metric learning (HMTML) framework for handling multiple domains by combining side information and unlabeled data. Specifically, HMTML learns the metrics for all different domains simultaneously by maximizing their high-order correlation (parameterized by feature covariance of unlabeled data) in a common subspace, which is induced by the transformations derived from the metrics. Extensive experiments on both multi-language text categorization and multi-view social image annotation demonstrate the effectiveness of the proposed method.
Complications of Lateral Epicondylar Release.

Reported complication rates are low for lateral epicondylitis management, but the anatomic complexity of the elbow allows for possible catastrophic complication. This review documents complications associated with lateral epicondylar release: 67 studies reporting outcomes of lateral epicondylar release with open, percutaneous, or arthroscopic methods were reviewed and 6 case reports on specific complications associated with the procedure are included. Overall complication rate was 3.3%. For open procedures it was 4.3%, percutaneous procedures 1.9%, and arthroscopic procedures 1.1%. In higher-level studies directly comparing modalities, the complication rates were 1.3%, 0%, and 1.2%, respectively.
Low Level Laser Therapy Versus Ultrasonic Cavitation in Abdominal Adiposity After Gastric Bypass in Female Saly

The purpose of this study was to investigate the effect of the ultrasonic cavitation versus low level laser therapy in the treatment of abdominal adiposity in female post gastric bypass.  Sixty female suffering from localized fat deposits at the abdomen area after gastric bypass were divided randomly and equally into three equal groups Group (1): were received low level laser therapy plus bicycle exercises and abdominal exercises for 3 months, Group (2): were received ultrasonic cavitation therapy plus bicycle exercises and abdominal exercises for 3 months, and Group (3): were received bicycle exercises and abdominal exercises for 3 months.  data were obtained for each patient from waist circumferences, skin fold and ultrasonography measurements were done after six weeks postoperative (preexercise) and at three months postoperative. The physical therapy program began, six weeks postoperative for experimental group. Including aerobic exercises performed on the stationary bicycle, for 30 min, 3 sessions per week for three months Results: showed a statistically significant decrease in waist circumferences, skin fold and ultrasonography measurements in the three groups, with a higher rate of reduction in Group (1) and Group (2) .Also there was a non-significant difference between Group (1) and Group (2).  these results suggested that bothlow level laser therapy and ultrasonic cavitation had a significant effect on abdominal adiposity after gastric bypass in female.
An improved apriori algorithm

In this study, it proposes a new optimization algorithm called APRIORI-IMPROVE based on the insufficient of Apriori. APRIORI-IMPROVE algorithm presents optimizations on 2-items generation, transactions compression and so on. APRIORI-IMPROVE uses hash structure to generate L2, uses an efficient horizontal data representation and optimized strategy of storage to save time and space. The performance study shows that APRIORI-IMPROVE is much faster than Apriori.
Big Data Analysis and Visualization for the Smart Grid

Smart grids have been proposed as a mechanism to modernize energy grids. In a smart grid, sensors, computers and communication networks are integrated into the power network. As the number of sensors and the ability to collect large amounts of data increases, big data analysis techniques are required to support data analysis and decision making. This paper discusses big data challenges and techniques in the context of smart grids and illustrates a practical scenario visualizing diverse data sources.
The first archaic Homo from Taiwan

Recent studies of an increasing number of hominin fossils highlight regional and chronological diversities of archaic Homo in the Pleistocene of eastern Asia. However, such a realization is still based on limited geographical occurrences mainly from Indonesia, China and Russian Altai. Here we describe a newly discovered archaic Homo mandible from Taiwan (Penghu 1), which further increases the diversity of Pleistocene Asian hominins. Penghu 1 revealed an unexpectedly late survival (younger than 450 but most likely 190-10 thousand years ago) of robust, apparently primitive dentognathic morphology in the periphery of the continent, which is unknown among the penecontemporaneous fossil records from other regions of Asia except for the mid-Middle Pleistocene Homo from Hexian, Eastern China. Such patterns of geographic trait distribution cannot be simply explained by clinal geographic variation of Homo erectus between northern China and Java, and suggests survival of multiple evolutionary lineages among archaic hominins before the arrival of modern humans in the region.
How to initialize the CNN for small datasets: Extracting discriminative filters from pre-trained model

In this paper, we study how to initialize the convolutional neural network (CNN) model for training on a small dataset. Specially, we try to extract discriminative filters from the pre-trained model for a target task. On the basis of relative entropy and linear reconstruction, two methods, Minimum Entropy Loss (MEL) and Minimum Reconstruction Error (MRE), are proposed. The CNN models initialized by the proposed MEL and MRE methods are able to converge fast and achieve better accuracy. We evaluate MEL and MRE on the CIFAR10, CIFAR100, SVHN, and STL-10 public datasets. The consistent performances demonstrate the advantages of the proposed methods.
Marginal Replay vs Conditional Replay for Continual Learning

We present a new replay-based method of continual classification learning that we term "conditional replay" which generates samples and labels together by sampling from a distribution conditioned on the class. We compare conditional replay to another replay-based continual learning paradigm (which we term "marginal replay") that generates samples independently of their class and assigns labels in a separate step. The main improvement in conditional replay is that labels for generated samples need not be inferred, which reduces the margin for error in complex continual classification learning tasks. We demonstrate the effectiveness of this approach using novel and standard benchmarks constructed from MNIST and Fashion MNIST data, and compare to the regularization-based EWC method (Kirkpatrick et al., 2016; Shin et al., 2017).
Closure of defects of the malar region.

Reconstruction of the skin defects of malar region poses some challenging problems including obvious scar formation, dog-ear formation, trapdoor deformity and displacement of surrounding anatomic landmarks such as the lower eyelid, oral commissure, ala nasi, and sideburn.



Here, a new local flap procedure, namely the reading man procedure, for reconstruction of large malar skin defects is described.



In this technique, 2 flaps designed in an unequal Z-plasty manner are used. The first flap is transposed to the defect area, whereas the second flap is used for closure of the first flap's donor site. In the last 5 years, this technique has been used for closure of the large malar defects in 18 patients (11 men and 7 women) aged 21 to 95 years. The defect size was ranging between 3 and 8.5 cm in diameter.



A tension-free defect closure was obtained in all patients. There was no patient with dog-ear formation, ectropion, or distortion of the surrounding anatomic structures. No tumor recurrence was observed. A mean follow-up of 26 months (range, 5 mo to 3.5 y) revealed a cosmetically acceptable scar formation in all patients.



The reading man procedure was found to be a useful and easygoing technique for the closure of malar defects, which allows defect closure without any additional excision of surrounding healthy tissue. It provides a tension-free closure of considerably large malar defects without creating distortions of the mobile anatomic structures.
IEC 61850 GOOSE Message over WAN

—The publication of IEC 61850-90-1 " Use of IEC 61850 for the communication between substations " and the draft of IEC 61850-90-5 " Use of IEC 61850 to transmit synchrophasor information " opened the possibility to study IEC 61850 GOOSE Message over WAN not only in the layer 2 (link layer) but also in the layer 3 (network layer) in the OSI model. In this paper we examine different possibilities to make feasible teleprotection in the network layer over WAN sharing the communication channel with automation, management and maintenance convergence services among electrical energy substations.
Research of Detection Algorithm for Time Series Abnormal Subsequence

The recent advancements in sensor technology have made it possible to collect enormous amounts of data in real time. How to find out unusual pattern from time series data plays a very important role in data mining. In this paper, we focus on the abnormal subsequence detection. The original definition of discord subsequences is defective for some kind of time series, in this paper we give a more robust definition which is based on the k nearest neighbors. We also donate a novel method for time series representation, it has better performance than traditional methods (like PAA/SAX) to represent the characteristic of some special time series. To speed up the process of abnormal subsequence detection, we used the clustering method to optimize the outer loop ordering and early abandon subsequence which is impossible to be abnormal. The experiment results validate that the algorithm is correct and has a high efficiency.
Remote Control Laboratory Based On LabVIEW

This paper studied a remote control system based on the LabVIEW and ZLG PCI-5110 CAN card, in which students could perform experiments by remote control laboratory via the Internet. Due to the fact that the internet becomes more integrated into our daily lives, several possibilities have arisen to use this cost-effective worldwide standard for distributing data. National Instruments LabVIEW is available to publish data from the development environment to the Web. The student can access the remote laboratory and perform experiments without any limitation of time and location. They can also observe the signals by changing the parameters of the experiment and evaluating the results. During the session, the teacher can watch and communicate with students who perform their experiment. The usefulness of remote laboratory in teaching environments is already known: it saves equipment, personnel for the institution and it saves time and money for the remote students. It also allows the same equipment to be used in research purposes by many teams, through Internet. The experiments proved the feasibility of technical solutions, as well as the correctness of implementation in this paper.
Foot-and-mouth disease vaccines.

Foot-and-mouth disease (FMD) is a highly contagious disease of cloven-hoofed animals. The disease affects many areas of the world, often causing extensive epizootics in livestock, mostly farmed cattle and swine, although sheep, goats and many wild species are also susceptible. In countries where food and farm animals are essential for subsistence agriculture, outbreaks of FMD seriously impact food security and development. In highly industrialized developed nations, FMD endemics cause economic and social devastation mainly due to observance of health measures adopted from the World Organization for Animal Health (OIE). High morbidity, complex host-range and broad genetic diversity make FMD prevention and control exceptionally challenging. In this article we review multiple vaccine approaches developed over the years ultimately aimed to successfully control and eradicate this feared disease.
An overlay network approach to find objects in the future internet

The discovery of network objects (content, services, devices, persons, etc.) is an important challenge to face when researching towards the Future Internet. In this paper we give an overview of the challenge regarding network object discovery for the Future Internet, proposing a new architecture that provides mechanisms to achieve network object discovery from partial object descriptions, building an overlay network to organize object descriptions and using strong statistical mechanisms to find them from partial descriptions. With a prototype implementation of the architecture we demonstrate experimentally its feasibility and general behavior in different scenarios.
A New Localization System for Indoor Service Robots in Low Luminance and Slippery Indoor Environment Using Afocal Optical Flow Sensor Based Sensor Fusion

In this paper, a new localization system utilizing afocal optical flow sensor (AOFS) based sensor fusion for indoor service robots in low luminance and slippery environment is proposed, where conventional localization systems do not perform well. To accurately estimate the moving distance of a robot in a slippery environment, the robot was equipped with an AOFS along with two conventional wheel encoders. To estimate the orientation of the robot, we adopted a forward-viewing mono-camera and a gyroscope. In a very low luminance environment, it is hard to conduct conventional feature extraction and matching for localization. Instead, the interior space structure from an image and robot orientation was assessed. To enhance the appearance of image boundary, rolling guidance filter was applied after the histogram equalization. The proposed system was developed to be operable on a low-cost processor and implemented on a consumer robot. Experiments were conducted in low illumination condition of 0.1 lx and carpeted environment. The robot moved for 20 times in a 1.5 × 2.0 m square trajectory. When only wheel encoders and a gyroscope were used for robot localization, the maximum position error was 10.3 m and the maximum orientation error was 15.4°. Using the proposed system, the maximum position error and orientation error were found as 0.8 m and within 1.0°, respectively.
Orchestrating Caching, Transcoding and Request Routing for Adaptive Video Streaming Over ICN

Information-centric networking (ICN) has been touted as a revolutionary solution for the future of the Internet, which will be dominated by video traffic. This work investigates the challenge of distributing video content of adaptive bitrate (ABR) over ICN. In particular, we use the in-network caching capability of ICN routers to serve users; in addition, with the help of named function, we enable ICN routers to transcode videos to lower-bitrate versions to improve the cache hit ratio. Mathematically, we formulate this design challenge into a constrained optimization problem, which aims to maximize the cache hit ratio for service providers and minimize the service delay for endusers. We design a two-step iterative algorithm to find the optimum. First, given a content management scheme, we minimize the service delay via optimally configuring the routing scheme. Second, we maximize the cache hits for a given routing policy. Finally, we rigorously prove its convergence. Through extensive simulations, we verify the convergence and the performance gains over other algorithms. We also find that more resources should be allocated to ICN routers with a heavier request rate, and the routing scheme favors the shortest path to schedule more traffic.
Family stressors as predictors of codependency.

Codependency has been defined as an extreme focus on relationships, caused by a stressful family background (J. L. Fischer, L. Spann, & D. W. Crawford, 1991). In this study the authors assessed the relationship of the Spann-Fischer Codependency Scale (J. L. Fischer et al., 1991) and the Potter-Efron Codependency Assessment (L. A. Potter-Efron & P. S. Potter-Efron, 1989) with self-reported chronic family stress and family background. Students (N = 257) completed 2 existing self-report codependency measures and provided family background information. Results indicated that women had higher codependency scores than men on the Spann-Fischer scale. Students with a history of chronic family stress (with an alcoholic, mentally ill, or physically ill parent) had significantly higher codependency scores on both scales. The findings suggest that other types of family stressors, not solely alcoholism, may be predictors of codependency.
Distribution Aware Active Learning

Discriminative learning machines often need a large set of labeled samples for training. Active learning (AL) settings assume that the learner has the freedom to ask an oracle to label its desired samples. Traditional AL algorithms heuristically choose query samples about which the current learner is uncertain. This strategy does not make good use of the structure of the dataset at hand and is prone to be misguided by outliers. To alleviate this problem, we propose to distill the structural information into a probabilistic generative model which acts as a teacher in our model. The active learner uses this information effectively at each cycle of active learning. The proposed method is generic and does not depend on the type of learner and teacher. We then suggest a query criterion for active learning that is aware of distribution of data and is more robust against outliers. Our method can be combined readily with several other query criteria for active learning. We provide the formulation and empirically show our idea via toy and real examples.
A High-Frequency Three-Level Buck Converter With Real-Time Calibration and Wide Output Range for Fast-DVS

This paper presents a 50-MHz 5-V-input 3-W-output three-level buck converter. A real-time flying capacitor (<inline-formula> <tex-math notation="LaTeX">$C_{F}$ </tex-math></inline-formula>) calibration is proposed to ensure a constant voltage of <inline-formula> <tex-math notation="LaTeX">$V_{g}$ </tex-math></inline-formula>/2 across <inline-formula> <tex-math notation="LaTeX">$C_{F}$ </tex-math></inline-formula>, which is highly dependent on various practical conditions, such as parasitic capacitance, time mismatches, or any loading circuits from <inline-formula> <tex-math notation="LaTeX">$C_{F}$ </tex-math></inline-formula>. The calibration is essential to ensure the reliability and minimize the inductor current and output voltage ripple, thus maintaining the advantages of the three-level operation and further extending the system bandwidth without encountering sub-harmonic oscillation. The converter is fabricated in a UMC 65-nm process using standard 2.5-V I/O devices, and is able to handle a 5-V input voltage and provide a 0.6–4.2-V-wide output range. In the measurement, the voltage across <inline-formula> <tex-math notation="LaTeX">$C_{F}$ </tex-math></inline-formula> is always calibrated to <inline-formula> <tex-math notation="LaTeX">$V_{g}$ </tex-math></inline-formula>/2 under various conditions to release the voltage stress on the high- and low-side power transistors and <inline-formula> <tex-math notation="LaTeX">$C_{F}$ </tex-math></inline-formula>, and to ensure reliability with up to 69% output voltage ripple reduction. A 90% peak efficiency and a 23–29-ns/V reference-tracking response are also observed.
ColorSketch: A Drawing Assistant for Generating Color Sketches from Photos

A color sketch creates a vivid depiction of a scene using sparse pencil strokes and casual colored brush strokes. The interactive drawing system ColorSketch can help novice users generate color sketches from photos. To preserve artistic freedom and expressiveness, the proposed system gives users full control over pencil strokes, while automatically augmenting pencil sketches using color mapping, brush stroke rendering, and blank area creation. Experimental and user study results demonstrate that users, especially novices, can create better color sketches with our system than when using traditional manual tools.
Application of multiple change point detection methods to large urban telecommunication networks

An integral enabler of the smart city vision is the ability to effectively model collective population behaviour. The realisation of sustainable smart mobility is underpinned by the effective modelling of the spatial movements of the population. Furthermore, it is also crucial to identify significant deviations in collective behaviour over time. For example, a change in urban mobility patterns would subsequently impact traffic management systems. This paper focuses on the issue of modelling the collective behaviour of a population by utilizing mobile phone data and investigates the ability to identify significant deviations in behaviour over time. Mobile phone data facilitates the inference of real social networks from their call data records (CDR). We use this data to model collective behaviour and apply change-point detection algorithms, a category of anomaly detection, in order to identify statistically significant changes in collective behaviour over time. The result off the empirical analysis demonstrate that modern change point detection can accurately identify change points with an R2 value of 0.9633.
An Investigation on Organisation Cyber Resilience

Cyber exercises used to assess the preparedness of a community against cyber crises, technology failures and Critical Information Infrastructure (CII) incidents. The cyber exercises also called cyber crisis exercise or cyber drill, involved partnerships or collaboration of public and private agencies from several sectors. This study investigates Organisation Cyber Resilience (OCR) of participation sectors in cyber exercise called X Maya in Malaysia. This study used a principal based cyber resilience survey called CSuite Executive checklist developed by World Economic Forum in 2012. To ensure suitability of the survey to investigate the OCR, the reliability test was conducted on C-Suite Executive checklist items. The research further investigates the differences of OCR in ten Critical National Infrastructure Information (CNII) sectors participated in the cyber exercise. The One Way ANOVA test result showed a statistically significant difference of OCR among ten CNII sectors participated in the cyber exercise.