nils-herrmann/clean_scirepeval huggingface dataset:
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data

Paucity of large curated hand-labeled training data forms a major bottleneck in the deployment of machine learning models in computer vision and other fields. Recent work (Data Programming) has shown how distant supervision signals in the form of labeling functions can be used to obtain labels for given data in near-constant time. In this work, we present Adversarial Data Programming (ADP), which presents an adversarial methodology to generate data as well as a curated aggregated label, given a set of weak labeling functions. We validated our method on the MNIST, Fashion MNIST, CIFAR 10 and SVHN datasets, and it outperformed many state-of-the-art models. We conducted extensive experiments to study its usefulness, as well as showed how the proposed ADP framework can be used for transfer learning as well as multi-task learning, where data from two domains are generated simultaneously using the framework along with the label information. Our future work will involve understanding the theoretical implications of this new framework from a game-theoretic perspective, as well as explore the performance of the method on more complex datasets.
A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News

Due to the Web expansion, the prediction of online news popularity is becoming a trendy research topic. In this paper, we propose a novel and proactive Intelligent Decision Support System (IDSS) that analyzes articles prior to their publication. Using a broad set of extracted features (e.g., keywords, digital media content, earlier popularity of news referenced in the article) the IDSS first predicts if an article will become popular. Then, it optimizes a subset of the articles features that can more easily be changed by authors, searching for an enhancement of the predicted popularity probability. Using a large and recently collected dataset, with 39,000 articles from the Mashable website, we performed a robust rolling windows evaluation of five state of the art models. The best result was provided by a Random Forest with a discrimination power of 73%. Moreover, several stochastic hill climbing local searches were explored. When optimizing 1000 articles, the best optimization method obtained a mean gain improvement of 15 percentage points in terms of the estimated popularity probability. These results attest the proposed IDSS as a valuable tool for online news authors.
Battery Management Systems in Electric and Hybrid Vehicles

The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery’s performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well.
Evidence that emotional intelligence is related to job performance and affect and attitudes at work.

The relation between emotional intelligence, assessed with a performance measure, and positive workplace outcomes was examined in 44 analysts and clerical employees from the finance department of a Fortune 400 insurance company. Emotionally intelligent individuals received greater merit increases and held higher company rank than their counterparts. They also received better peer and/or supervisor ratings of interpersonal facilitation and stress tolerance than their counterparts. With few exceptions, these associations remained statistically significant after controlling for other predictors, one at a time, including age, gender, education, verbal ability, the Big Five personality traits, and trait affect.
Review : friction stir welding tools

Friction stir welding (FSW) is a widely used solid state joining process for soft materials such as aluminium alloys because it avoids many of the common problems of fusion welding. Commercial feasibility of the FSW process for harder alloys such as steels and titanium alloys awaits the development of cost effective and durable tools which lead to structurally sound welds consistently. Material selection and design profoundly affect the performance of tools, weld quality and cost. Here we review and critically examine several important aspects of FSW tools such as tool material selection, geometry and load bearing ability, mechanisms of tool degradation and process economics.
Analyzing Language Learned by an Active Question Answering Agent

We analyze the language learned by an agent trained with reinforcement learning as a component of the ActiveQA system [Buck et al., 2017]. In ActiveQA, question answering is framed as a reinforcement learning task in which an agent sits between the user and a black box question-answering system. The agent learns to reformulate the user’s questions to elicit the optimal answers. It probes the system with many versions of a question that are generated via a sequence-to-sequence question reformulation model, then aggregates the returned evidence to find the best answer. This process is an instance of machine-machine communication. The question reformulation model must adapt its language to increase the quality of the answers returned, matching the language of the question answering system. We find that the agent does not learn transformations that align with semantic intuitions but discovers through learning classical information retrieval techniques such as tf-idf re-weighting and stemming.
A Wavenet for Speech Denoising

Most speech processing techniques use magnitude spectrograms as front-end and are therefore by default discarding part of the signal: the phase. In order to overcome this limitation’ we propose an end-to-end learning method for speech denoising based on Wavenet. The proposed model adaptation retains Wavenet's powerful acoustic modeling capabilities, while significantly reducing its time-complexity by eliminating its autoregressive nature. Specifically, the model makes use of non-causal, dilated convolutions and predicts target fields instead of a single target sample. The discriminative adaptation of the model we propose, learns in a supervised fashion via minimizing a regression loss. These modifications make the model highly parallelizable during both training and inference. Both quantitative and qualitative evaluations indicate that the proposed method is preferred over Wiener filtering, a common method based on processing the magnitude spectrogram.
Game theory based mitigation of Interest flooding in Named Data Network

The Distributed Denial of Service (DDoS) attacks are a serious threat in today's Internet where packets from large number of compromised hosts block the path to the victim nodes and overload the victim servers. In the newly proposed future Internet Architecture, Named Data Networking (NDN), the architecture itself has prevention measures to reduce the overload to the servers. This on the other hand increases the work and security threats to the intermediate routers. Our project aims at identifying the DDoS attack in NDN which is known as Interest flooding attack, mitigate the consequence of it and provide service to the legitimate users. We have developed a game model for the DDoS attacks and provide possible countermeasures to stop the flooding of interests. Through this game theory model, we either forward or redirect or drop the incoming interest packets thereby reducing the PIT table consumption. This helps in identifying the nodes that send malicious interest packets and eradicate their actions of sending malicious interests further. The main highlight of this work is that we have implemented the Game Theory model in the NDN architecture. It was primarily imposed for the IP internet architecture.
Automated discovery, interaction and composition of Semantic Web services

In this paper, we introduce a vision for Semantic Web services which combines the growing Web services architecture and the Semantic Web and we will propose DAML-S as a prototypical example of an ontology for describing Semantic Web services. Furthermore, we show that DAML-S is not just an abstract description, but it can be efficiently implemented to support capability matching and to manage interaction between Web services. Specifically, we will describe the implementation of the DAML-S/UDDI Matchmaker that expands on UDDI by providing semantic capability matching, and we will present the DAML-S Virtual Machine that uses the DAML-S Process Model to manage the interaction with Web service. We will also show that the use of DAML-S does not produce a performance penalty during the normal operation of Web services.
AUGMENTATION POLICIES USING AUGMENTED RANDOM SEARCH

Previous attempts for data augmentation are designed manually, and the augmentation policies are dataset-specific. Recently, an automatic data augmentation approach, named AutoAugment, is proposed using reinforcement learning. AutoAugment searches for the augmentation polices in the discrete search space, which may lead to a sub-optimal solution. In this paper, we employ the Augmented Random Search method (ARS) to improve the performance of AutoAugment. Our key contribution is to change the discrete search space to continuous space, which will improve the searching performance and maintain the diversities between sub-policies. With the proposed method, state-of-the-art accuracies are achieved on CIFAR-10, CIFAR-100, and ImageNet (without additional data). Our code is available at https://github.com/gmy2013/ARS-Aug.
Understand Functionality and Dimensionality of Vector Embeddings : the Distributional Hypothesis , the Pairwise Inner Product Loss and Its Bias-Variance Trade

Vector embedding is a foundational building block of many deep learning models, especially in natural language processing. In this paper, we present a theoretical framework for understanding the effect of dimensionality on vector embeddings. We observe that the distributional hypothesis, a governing principle of statistical semantics, requires a natural unitary-invariance for vector embeddings. Motivated by the unitary-invariance observation, we propose the Pairwise Inner Product (PIP) loss, a unitary-invariant metric on the similarity between two embeddings. We demonstrate that the PIP loss captures the difference in functionality between embeddings, and that the PIP loss is tightly connect with two basic properties of vector embeddings, namely similarity and compositionality. By formulating the embedding training process as matrix factorization with noise, we reveal a fundamental bias-variance trade-off between the signal spectrum and noise power in the dimensionality selection process. This bias-variance trade-off sheds light on many empirical observations which have not been thoroughly explained, for example the existence of an optimal dimensionality. Moreover, we discover two new results about vector embeddings, namely their robustness against over-parametrization and their forward stability. The bias-variance trade-off of the PIP loss explicitly answers the fundamental open problem of dimensionality selection for vector embeddings.
LIMBO: Scalable Clustering of Categorical Data

Clustering is a problem of great practical importance in numerous applications. The problem of clustering becomes more challenging when the data is categorical, that is, when there is no inherent distance measure between data values. We introduce LIMBO, a scalable hierarchical categorical clustering algorithm that builds on the Information Bottleneck (IB) framework for quantifying the relevant information preserved when clustering. As a hierarchical algorithm, LIMBO has the advantage that it can produce clusterings of different sizes in a single execution. We use the IB framework to define a distance measure for categorical tuples and we also present a novel distance measure for categorical attribute values. We show how the LIMBO algorithm can be used to cluster both tuples and values. LIMBO handles large data sets by producing a memory bounded summary model for the data. We present an experimental evaluation of LIMBO, and we study how clustering quality compares to other categorical clustering algorithms. LIMBO supports a trade-off between efficiency (in terms of space and time) and quality. We quantify this trade-off and demonstrate that LIMBO allows for substantial improvements in efficiency with negligible decrease in quality.
DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks

Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating  $1024\times 1024$  network size, using 128 input cases per batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA) (Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both are implemented in an FPGA device (XC2VP70).
Sacral dome resection and single-stage posterior reduction in the treatment of high-grade high dysplastic spondylolisthesis in adolescents and young adults

The description of the operation technique and retrospective review of 15 consecutive patients who were treated by posterior sacral dome resection and single-stage reduction with pedicle screw fixation for high-grade, high-dysplastic spondylolisthesis. All the patients had high-grade, high-dysplatic spondylolisthesis L5 and were treated by posterior sacral dome resection and posterior single-stage reduction from L4–S1. The average age at the time of surgery was 17.3 (11–28) years. The average follow-up time is 5.5 (2–11.6) years. Clinical and radiologica data were retrospectively reviewed. Spondylolisthesis was reduced from average 99% preoperative to 29% at the last follow-up. L5 incidence improved from 74° to 56°, the lumbosacral angle improved from 15° kyphosis to 6° lordosis, lumbar lordosis decreased from 69° to 53° from preoperative to the last follow-up. While pelvic incidence of 77° remained unchanged, sacral slope decreased from 51° to 46° and pelvic tilt increased from 25° to 30°. Clinical outcome was subjectively rated to be much better than before surgery by 14 out of 15 patients. Four out of 15 patients had temporary sensory impairment of the L5 nerve root which resolved completely within 12 weeks. There were no permanent neurological complications or no pseudarthrosis. The sacral dome resection is a shortening osteotomy of the lumbosacral spine which allows a single-stage reduction of L5 without lengthening of lumbosacral region in high-grade spondylolisthesis, which helps to avoid neurological complications. This is a safe surgical technique resulting in a good multidimensional deformity correction and restoration of spino-pelvic alignment towards normal values with a satisfactory clinical outcome.
An introduction to occupational science, a foundation for occupational therapy in the 21st century.

Occupational science is an emerging basic science which supports the practice of occupational therapy. Its roots in the rich traditions of occupational therapy are explored and its current configuration is introduced. Specifications which the science needs to meet as it is further developed and refined are presented. Compatible disciplines and research approaches are identified. example's of basic science research questions and their potential contributions to occupational therapy practice are suggested.
Swarm-Bot: A New Distributed Robotic Concept

The swarm intelligence paradigm has proven to have very interesting properties such as robustness, flexibility and ability to solve complex problems exploiting parallelism and self-organization. Several robotics implementations of this paradigm confirm that these properties can be exploited for the control of a population of physically independent mobile robots. The work presented here introduces a new robotic concept called swarm-bot in which the collective interaction exploited by the swarm intelligence mechanism goes beyond the control layer and is extended to the physical level. This implies the addition of new mechanical functionalities on the single robot, together with new electronics and software to manage it. These new functionalities, even if not directly related to mobility and navigation, allow to address complex mobile robotics problems, such as extreme all-terrain exploration. The work shows also how this new concept is investigated using a simulation tool (swarmbot3d) specifically developed for quickly designing and evaluating new control algorithms. Experimental work shows how the simulated detailed representation of one s-bot has been calibrated to match the behaviour of the real robot.
Understanding Emoji Ambiguity in Context: The Role of Text in Emoji-Related Miscommunication

Recent studies have found that people interpret emoji characters inconsistently, creating significant potential for miscommunication. However, this research examined emoji in isolation, without consideration of any surrounding text. Prior work has hypothesized that examining emoji in their natural textual contexts would substantially reduce the observed potential for miscommunication. To investigate this hypothesis, we carried out a controlled study with 2,482 participants who interpreted emoji both in isolation and in multiple textual contexts. After comparing the variability of emoji interpretation in each condition, we found that our results do not support the hypothesis in prior work: when emoji are interpreted in textual contexts, the potential for miscommunication appears to be roughly the same. We also identify directions for future research to better understand the interplay between emoji and textual context.
Curcumin Ameliorates Cisplatin-Induced Nephrotoxicity and Potentiates Its Anticancer Activity in SD Rats: Potential Role of Curcumin in Breast Cancer Chemotherapy

Breast malignant neoplastic disease is one of the most complex diseases, as it is a multifactorial disease in which virtually all the targets are instantly or indirectly inter-reliant on each other. Cisplatin (CIS), an inorganic antineoplastic agent is widely utilized in the treatment of various solid tumors including breast cancer. Despite everything, its clinical use is limited, due to ototoxicity, peripheral neuropathy, and nephrotoxicity. The present work was directed to assess the combined result of curcumin (CUR) and CIS in 7, 12-dimethyl benz[a]anthracene (DMBA) induced breast cancer in rats and the prevention of nephrotoxicity induced by the latter. CIS-induced nephrotoxicity was assessed by change in body weight, kidney weight, altered levels of BUN, creatinine, TNF-α, IL-6, IL-8, IL-10, and histopathology of the kidney. Anticancer activity was assessed by measurement of tumor weight, tumor volume, % tumor inhibition, levels of PPAR-γ, and BDNF in mammary tumors and histopathology of mammary tumors. CUR pre-treatment mitigated nephrotoxicity by reducing the inflammatory markers (TNF-α, IL-6, and IL-8; p < 0.001). Further, it reduced mammary cancer via increasing the expression of PPAR-γ (p < 0.001) and decreasing the expression of BDNF (p < 0.001) in mammary tumors. It also reduced tumor volume, further postulating that CUR might adjunct the anticancer activity of the CIS. To the best of our knowledge, this is the first report, which showed that CUR ameliorated CIS-induced nephrotoxicity and improved its anticancer activity in DMBA induced breast cancer in female Sprague-Dawley rats.
Bifurcation and chaos in the double well Duffing-van der Pol oscillator : Numerical and analytical studies

The behaviour of a driven double well Duffing-van der Pol (DVP) oscillator for a specific parametric choice (| α |= β) is studied. The existence of different attractors in the system parameters (f − ω) domain is examined and a detailed account of various steady states for fixed damping is presented. Transition from quasiperiodic to periodic motion through chaotic oscillations is reported. The intervening chaotic regime is further shown to possess islands of phase-locked states and periodic windows (including period doubling regions), boundary crisis, all the three classes of intermittencies, and transient chaos. We also observe the existence of local-global bifurcation of intermittent catastrophe type and global bifurcation of blue-sky catastrophe type during transition from quasiperiodic to periodic solutions. Using a perturbative periodic solution, an investigation of the various forms of instablities allows one to predict Neimark instablity in the (f − ω) plane and eventually results in the approximate predictive criteria for the chaotic region.
Graph 2 Seq : Graph to Sequence Learning with Attention-based Neural Networks

Celebrated Sequence to Sequence learning (Seq2Seq) and its fruitful variants are powerful models to achieve excellent performance on the tasks that map sequences to sequences. However, these are many machine learning tasks with inputs naturally represented in a form of graphs, which imposes significant challenges to existing Seq2Seq models for lossless conversion from its graph form to the sequence. In this work, we present a general end-to-end approach to map the input graph to a sequence of vectors, and then another attention-based LSTM to decode the target sequence from these vectors. Specifically, to address inevitable information loss for data conversion, we introduce a novel graph-to-sequence neural network model that follows the encoder-decoder architecture. Our method first uses an improved graph-based neural network to generate the node and graph embeddings by a novel aggregation strategy to incorporate the edge direction information into the node embeddings. We also propose an attention based mechanism that aligns node embeddings and decoding sequence to better cope with large graphs. Experimental results on bAbI task, Shortest Path Task and Natural Language Generation Task demonstrate that our model achieves the state-of-the-art performance and significantly outperforms other baselines. We also show that with the proposed aggregation strategy, our proposed model is able to quickly converge to good performance.