nils-herrmann/clean_scirepeval huggingface dataset:
Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification

Person re-identification (re-ID) requires rapid, flexible yet discriminant representations to quickly generalize to unseen observations on-the-fly and recognize the same identity across disjoint camera views. Recent effective methods are developed in a pair-wise similarity learning system to detect a fixed set of features from distinct regions which are mapped to their vector embeddings for the distance measuring. However, the most relevant and crucial parts of each image are detected independently without referring to the dependency conditioned on one and another. Also, these region based methods rely on spatial manipulation to position the local features in comparable similarity measuring. To combat these limitations, in this paper we introduce the Deep Co-attention based Comparators (DCCs) that fuse the co-dependent representations of the paired images so as to focus on the relevant parts of both images and produce their relative representations. Given a pair of pedestrian images to be compared, the proposed model mimics the foveation of human eyes to detect distinct regions concurrent on both images, namely co-dependent features, and alternatively attend to relevant regions to fuse them into the similarity learning. Our comparator is capable of producing dynamic representations relative to a particular sample every time, and thus well-suited to the case of re-identifying pedestrians on-the-fly. We perform extensive experiments to provide the insights and demonstrate the effectiveness of the proposed DCCs in person re-ID. Moreover, our approach has achieved the state-of-the-art performance on three benchmark data sets: DukeMTMC-reID [1], CUHK03 , and Market-1501 .
Learning to Compose Domain-Specific Transformations for Data Augmentation

Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it is often easy for domain experts to specify individual transformations, constructing and tuning the more sophisticated compositions typically needed to achieve state-of-the-art results is a time-consuming manual task in practice. We propose a method for automating this process by learning a generative sequence model over user-specified transformation functions using a generative adversarial approach. Our method can make use of arbitrary, non-deterministic transformation functions, is robust to misspecified user input, and is trained on unlabeled data. The learned transformation model can then be used to perform data augmentation for any end discriminative model. In our experiments, we show the efficacy of our approach on both image and text datasets, achieving improvements of 4.0 accuracy points on CIFAR-10, 1.4 F1 points on the ACE relation extraction task, and 3.4 accuracy points when using domain-specific transformation operations on a medical imaging dataset as compared to standard heuristic augmentation approaches.
Fulminant course in a patient with anti-N-methyl-d-aspartate receptor encephalitis with bilateral ovarian teratomas

Anti-N-methyl-D-aspartate (NMDA) receptor encephalitis is an autoimmune disorder that can be controlled and reversed by immunotherapy. The presentation of NMDA receptor encephalitis varies, but NMDA receptor encephalitis is seldom reported in patients with both bilateral teratomas and preexisting brain injury.



A 28-year-old female with a history of traumatic intracranial hemorrhage presented acute psychosis, seizure, involuntary movement, and conscious disturbance with a fulminant course. Anti-NMDA receptor antibody was identified in both serum and cerebrospinal fluid, confirming the diagnosis of anti-NMDA receptor encephalitis. Bilateral teratomas were also identified during tumor survey.  anti-N-methyl-D-aspartate receptor encephalitis.



Tumor resection and immunotherapy were performed early during the course.



The patient responded well to tumor resection and immunotherapy. Compared with other reports in the literature, her symptoms rapidly improved without further relapse.



This case report demonstrates that bilateral teratomas may be related to high anybody titers and that the preexisting head injury may be responsible for lowering the threshold of neurological deficits. Early diagnosis and therapy are crucial for a good prognosis in such patients.
EARNING AN E MBEDDING S PACE FOR T RANSFERABLE R OBOT S KILLS

We present a method for reinforcement learning of closely related skills that are parameterized via a skill embedding space. We learn such skills by taking advantage of latent variables and exploiting a connection between reinforcement learning and variational inference. The main contribution of our work is an entropyregularized policy gradient formulation for hierarchical policies, and an associated, data-efficient and robust off-policy gradient algorithm based on stochastic value gradients. We demonstrate the effectiveness of our method on several simulated robotic manipulation tasks. We find that our method allows for discovery of multiple solutions and is capable of learning the minimum number of distinct skills that are necessary to solve a given set of tasks. In addition, our results indicate that the hereby proposed technique can interpolate and/or sequence previously learned skills in order to accomplish more complex tasks, even in the presence of sparse rewards.
Electrospinning of polymeric nanofibers for tissue engineering applications: a review.

Interest in electrospinning has recently escalated due to the ability to produce materials with nanoscale properties. Electrospun fibers have been investigated as promising tissue engineering scaffolds since they mimic the nanoscale properties of native extracellular matrix. In this review, we examine electrospinning by providing a brief description of the theory behind the process, examining the effect of changing the process parameters on fiber morphology, and discussing the potential applications and impacts of electrospinning on the field of tissue engineering.
City of things: An integrated and multi-technology testbed for IoT smart city experiments

While smart cities have the potential to monitor and control the city in real-time through sensors and actuators, there is still an important road ahead to evolve from isolated smart city experiments to real large-scale deployments. Important research questions remain on how and which wireless technologies should be setup for connecting the city, how the data should be analysed and how the acceptance by users of applications can be assessed. In this paper we present the City of Things testbed, which is a smart city testbed located in the city of Antwerp, Belgium to address these questions. It allows the setup and validation of new smart city experiments both at a technology and user level. City of Things consists of a multi-wireless technology network infrastructure, the capacity to easily perform data experiments on top and a living lab approach to validate the experiments. In comparison to other smart city testbeds, City of Things consists of an integrated approach, allowing experimentation on three different layers: networks, data and living lab while supporting a wide range of wireless technologies. We give an overview of the City of Things architecture, explain how researchers can perform smart city experiments and illustrate this by a case study on air quality.
From Classical to Generalized Zero-Shot Learning: A Simple Adaptation Process

Zero-shot learning (ZSL) is concerned with the recognition of previously unseen classes. It relies on additional semantic knowledge for which a mapping can be learned with training examples of seen classes. While classical ZSL considers the recognition performance on unseen classes only, generalized zero-shot learning (GZSL) aims at maximizing performance on both seen and unseen classes. In this paper, we propose a new process for training and evaluation in the GZSL setting; this process addresses the gap in performance between samples from unseen and seen classes by penalizing the latter, and enables to select hyper-parameters well-suited to the GZSL task. It can be applied to any existing ZSL approach and leads to a significant performance boost: the experimental evaluation shows that GZSL performance, averaged over eight state-of-the-art methods, is improved from 28.5 to 42.2 on CUB and from 28.2 to 57.1 on AwA2.
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations

We present a feature vector formation technique for documents Sparse Composite Document Vector (SCDV) which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation. In SCDV, word embeddings are clustered to capture multiple semantic contexts in which words occur. They are then chained together to form document topic-vectors that can express complex, multi-topic documents. Through extensive experiments on multi-class and multi-label classification tasks, we outperform the previous state-of-the-art method, NTSG (Liu et al., 2015a). We also show that SCDV embeddings perform well on heterogeneous tasks like Topic Coherence, context-sensitive Learning and Information Retrieval. Moreover, we achieve significant reduction in training and prediction times compared to other representation methods. SCDV achieves best of both worlds better performance with lower time and space complexity.
Tunable interacting composite fermion phases in a half-filled bilayer-graphene Landau level

Non-Abelian anyons are a type of quasiparticle with the potential to encode quantum information in topological qubits protected from decoherence. Experimental systems that are predicted to harbour non-Abelian anyons include p-wave superfluids, superconducting systems with strong spin–orbit coupling, and paired states of interacting composite fermions that emerge at even denominators in the fractional quantum Hall (FQH) regime. Although even-denominator FQH states have been observed in several two-dimensional systems, small energy gaps and limited tunability have stymied definitive experimental probes of their non-Abelian nature. Here we report the observation of robust even-denominator FQH phases at half-integer Landau-level filling in van der Waals heterostructures consisting of dual-gated, hexagonal-boron-nitride-encapsulated bilayer graphene. The measured energy gap is three times larger than observed previously. We compare these FQH phases with numerical and theoretical models while simultaneously controlling the carrier density, layer polarization and magnetic field, and find evidence for the paired Pfaffian phase that is predicted to host non-Abelian anyons. Electric-field-controlled level crossings between states with different Landau-level indices reveal a cascade of FQH phase transitions, including a continuous phase transition between the even-denominator FQH state and a compressible composite fermion liquid. Our results establish graphene as a pristine and tunable experimental platform for studying the interplay between topology and quantum criticality, and for detecting non-Abelian qubits.
Pedestrian detection with LeNet-like convolutional networks

We present a detection method that is able to detect a learned target and is valid for both static and moving cameras. As an application, we detect pedestrians, but could be anything if there is a large set of images of it. The data set is fed into a number of deep convolutional networks, and then, two of these models are set in cascade in order to filter the cutouts of a multi-resolution window that scans the frames in a video sequence. We demonstrate that the excellent performance of deep convolutional networks is very difficult to match when dealing with real problems, and yet we obtain competitive results.
Data Quality : A Statistical Perspective

We present the old-but–new problem of data quality from a statistical perspective, in part with the goal of attracting more statisticians, especially academics, to become engaged in research on a rich set of exciting challenges. The data quality landscape is described, and its research foundations in computer science, total quality management and statistics are reviewed. Two case studies based on an EDA approach to data quality are used to motivate a set of research challenges for statistics that span theory, methodology and software tools.
Acoustic Controlled Robotic Vehicle

This paper presents a robotic vehicle that can be operated by the voice commands given from the user. Here, we use the speech recognition system for giving &processing voice commands. The speech recognition system use an I.C called HM2007, which can store and recognize up to 20 voice commands. The R.F transmitter and receiver are used here, for the wireless transmission purpose. The micro controller used is AT89S52, to give the instructions to the robot for its operation. This robotic car can be able to avoid vehicle collision , obstacle collision and it is very secure and more accurate. Physically disabled persons can use these robotic cars and they can be used in many industries and for many applications Keywords—SpeechRecognitionSystem,AT89S52 micro controller, R. F. Transmitter and Receiver.
SMS spam filtering: Methods and data

Mobile or SMS spam is a real and growing problem primarily due to the availability of very cheap bulk pre-pay SMS packages and the fact that SMS engenders higher response rates as it is a trusted and personal service. SMS spam filtering is a relatively new task which inherits many issues and solutions from email spam filtering. However it poses its own specific challenges. This paper motivates work on filtering SMS spam and reviews recent developments in SMS spam filtering. The paper also discusses the issues with data collection and availability for furthering research in this area, analyses a large corpus of SMS spam, and provides some initial benchmark results.
Stripes: Bit-Serial Deep Neural Network Computing

The numerical representation precision required by the computations performed by Deep Neural Networks (DNNs) varies across networks and between layers of a same network. This observation motivates a precision-based approach to acceleration which takes into account both the computational structure and the required numerical precision representation. This work presents <italic>Stripes</italic> (<italic>STR</italic>), a hardware accelerator that uses bit-serial computations to improve energy efficiency and performance. Experimental measurements over a set of state-of-the-art DNNs for image classification show that <italic>STR</italic> improves performance over a state-of-the-art accelerator from 1.35<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:href="judd-ieq1-2597140.gif"/></alternatives></inline-formula> to 5.33<inline-formula> <tex-math notation="LaTeX">$\times$</tex-math><alternatives><inline-graphic xlink:href="judd-ieq2-2597140.gif"/> </alternatives></inline-formula> and by 2.24<inline-formula><tex-math notation="LaTeX">$\times$</tex-math> <alternatives><inline-graphic xlink:href="judd-ieq3-2597140.gif"/></alternatives></inline-formula> on average. <italic>STR</italic>’s area and power overhead are estimated at 5 percent and 12 percent respectively. <italic> STR</italic> is 2.00<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:href="judd-ieq4-2597140.gif"/></alternatives></inline-formula> more energy efficient than the baseline.
Thermochemical Biomass Gasification : A Review of the Current Status of the Technology

A review was conducted on the use of thermochemical biomass gasification for producing biofuels, biopower and chemicals. The upstream processes for gasification are similar to other biomass processing methods. However, challenges remain in the gasification and downstream processing for viable commercial applications. The challenges with gasification are to understand the effects of operating conditions on gasification reactions for reliably predicting and optimizing the product compositions, and for obtaining maximal efficiencies. Product gases can be converted to biofuels and chemicals such as Fischer-Tropsch fuels, green gasoline, hydrogen, dimethyl ether, ethanol, methanol, and higher alcohols. Processes and challenges for these conversions are also summarized.
Impact of the Sakoe-Chiba Band on the DTW Time Series Distance Measure for kNN Classification

For classification of time series, the simple 1-nearest neighbor (1NN) classifier in combination with an elastic distance measure such as Dynamic Time Warping (DTW) distance is considered superior in terms of classification accuracy to many other more elaborate methods, including k-nearest neighbor (kNN) with neighborhood size k > 1. In this paper we revisit this apparently peculiar relationship and investigate the differences between 1NN and kNN classifiers in the context of time-series data and constrained DTW distance. By varying neighborhood size k, constraint width r, and evaluating 1NN and kNN with and without distance-based weighting in different schemes of cross-validation, we show that the first nearest neighbor indeed has special significance in labeled time-series data, but also that weighting can drastically improve the accuracy of kNN. This improvement is manifested by better accuracy of weighted kNN than 1NN for small values of k (3–4), better accuracy of weighted kNN than unweighted kNN in general, and reduced need to use large values of constraint r with weighted kNN.
Security Issues for Cloud Computing

In this paper we discuss security issues for cloud computing including storage security, data security, and network security and secure virtualization. Then we select some topics and describe them in more detail. In particular, we discuss a scheme for secure third party publications of documents in a cloud. Next we discuss secure federated query processing with map Reduce and Hadoop. Next we discuss the use of secure coprocessors for cloud computing. Third we discuss XACML implementation for Hadoop. We believe that building trusted applications from untrusted components will be a major aspect of secure cloud computing.
Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies

Educational data mining and learning analytics promise better understanding of student behavior and knowledge, as well as new information on the tacit factors that contribute to student actions. This knowledge can be used to inform decisions related to course and tool design and pedagogy, and to further engage students and guide those at risk of failure. This working group report provides an overview of the body of knowledge regarding the use of educational data mining and learning analytics focused on the teaching and learning of programming. In a literature survey on mining students' programming processes for 2005-2015, we observe a significant increase in work related to the field. However, the majority of the studies focus on simplistic metric analysis and are conducted within a single institution and a single course. This indicates the existence of further avenues of research and a critical need for validation and replication to better understand the various contributing factors and the reasons why certain results occur. We introduce a novel taxonomy to analyse replicating studies and discuss the importance of replicating and reproducing previous work. We describe what is the state of the art in collecting and sharing programming data. To better understand the challenges involved in replicating or reproducing existing studies, we report our experiences from three case studies using programming data. Finally, we present a discussion of future directions for the education and research community.
Development of Fingerprint Biometric Attendance System for Non-Academic Staff in a Tertiary Institution

Institutions, companies and organisations where security and net productivity is vital, access to certain areas must be controlled and monitored through an automated system of attendance. Managing people is a difficult task for most of the organizations and maintaining the attendance record is an important factor in people management. When considering the academic institute, taking the attendance of non-academic staff on daily basis and maintaining the records is a major task. Manually taking attendance and maintaining it for a long time adds to the difficulty of this task as well as wastes a lot of time. For this reason, an efficient system is proposed in this paper to solve the problem of manual attendance. This system takes attendance electronically with the help of a fingerprint recognition system, and all the records are saved for subsequent operations. Staff biometric attendance system employs an automated system to calculate attendance of staff in an organization and do further calculations of monthly attendance summary in order to reduce human errors during calculations. In essence, the proposed system can be employed in curbing the problems of lateness, buddy punching and truancy in any institution, organization or establishment. The proposed system will also improve the productivity of any organization if properly implemented.
Prediction of winners in MOBA games

Multiplayer Online Battle Arena (MOBA) games are very popular in the current eSport scenario, being highlighted in several competitions around the world. However, the domain of knowledge contained in these games is large, which makes it difficult to discover and predict the course of a match. The present work proposes the application of classification algorithms to determine the team with more chances to win a match. Two classifications procedures were used, one based on the composition of heroes in each team and another considering the duration of the match. The experiments were performed on data collected from 123,326 matches of Dota 2, showing that it was possible to achieve approximately 77% accuracy. The results demonstrate the effectiveness of the application when using techniques assisted by computers, and when using the methodology described in championships or other similar games that require the definition of strategies.
Logistic regression models.

In the health sciences it is quite common to carry out studies designed to determine the influence of one or more variables upon a given response variable. When this response variable is numerical, simple or multiple regression techniques are used, depending on the case. If the response variable is a qualitative variable (dichotomic or polychotomic), as for example the presence or absence of a disease, linear regression methodology is not applicable, and simple or multinomial logistic regression is used, as applicable.