nils-herrmann/clean_scirepeval huggingface dataset:
A Serverless Real-Time Data Analytics Platform for Edge Computing

Contemporary solutions for cloud-supported, edge-data analytics mostly apply analytics techniques in a rigid bottom-up approach, regardless of the data's origin. Typically, data are generated at the edge of the infrastructure and transmitted to the cloud, where traditional data analytics techniques are applied. Currently, developers are forced to resort to ad hoc solutions specifically tailored for the available infrastructure (for example, edge devices) when designing, developing, and operating the data analytics applications. Here, a novel approach implements cloud-supported, real-time data analytics in edge-computing applications. The authors introduce their serverless edge-data analytics platform and application model and discuss their main design requirements and challenges, based on real-life healthcare use case scenarios.
Autonomous car driving by a humanoid robot

Enabling a humanoid robot to drive a car, requires the development of a set of basic primitive actions. These include: walking to the vehicle, manually controlling its commands (e.g., ignition, gas pedal and steering), and moving with the whole-body, to ingress/egress the car. In this paper, we present a sensorbased reactive framework for realizing the central part of the complete task, consisting in driving the car along unknown roads. The proposed framework provides three driving strategies by which a human supervisor can teleoperate the car, ask for assistive driving, or give the robot full control of the car. A visual servoing scheme uses features of the road image to provide the reference angle for the steering wheel to drive the car at the center of the road. Simultaneously, a Kalman filter merges optical flow and accelerometer measurements, to estimate the car linear velocity and correspondingly compute the gas pedal command for driving at a desired speed. The steering wheel and gas pedal reference are sent to the robot control to achieve the driving task with the humanoid. We present results from a driving experience with a real car and the humanoid robot HRP-2Kai. Part of the framework has been used to perform the driving task at the DARPA Robotics Challenge.
Feature selection for breast cancer detection from ultrasound images

Breast cancer is the most lethal form of cancer in women after lung cancer. Early detection of cancer is likely to improve the patient's ability to deal with the disease and live further. Ultrasound imaging technique is one of the available tools for cancer diagnosis. Over the years, several high precision features were suggested by researchers to distinguish between malignant and benign lesions. This work employs more than fifty of these features which may serve as a reference feature pool to the researchers. Eventually, we seek to select an optimized subset of this feature set by using three different feature selection methods. In this work, we have successfully employed Multi-Cluster Feature Selection, a recently developed feature selection method, to find a feature set that best describes breast cancer. Thus, we propose a Computer Aided Diagnosis tool with an optimum combination of 25 different features to differentiate between malignant and benign tumors. These features were fed into Sparse Representation Classifier to classify tumors. The proposed technique was examined on ultrasound scans of 504 pathologically diagnosed breast tumors including 454 benign and 50 malignant tumors. The resulting Area Under the Receiver Operating Characteristic Curve was found to be 93.31%.
Routing Protocols in Wireless Sensor Networks - A Survey

Advances in wireless sensor network (WSN) technology has provided the availability of small and low-cost sensor nodes with capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. Variety of sensing capabilities results in profusion of application areas. However, the characteristics of wireless sensor networks require more effective methods for data forwarding and processing. In WSN, the sensor nodes have a limited transmission range, and their processing and storage capabilities as well as their energy resources are also limited. Routing protocols for wireless sensor networks are responsible for maintaining the routes in the network and have to ensure reliable multi-hop communication under these conditions. In this paper, we give a survey of routing protocols for Wireless Sensor Network and compare their strengths and limitations.
High-Order Attention Models for Visual Question Answering

The quest for algorithms that enable cognitive abilities is an important part of machine learning. A common trait in many recently investigated cognitive-like tasks is that they take into account different data modalities, such as visual and textual input. In this paper we propose a novel and generally applicable form of attention mechanism that learns high-order correlations between various data modalities. We show that high-order correlations effectively direct the appropriate attention to the relevant elements in the different data modalities that are required to solve the joint task. We demonstrate the effectiveness of our high-order attention mechanism on the task of visual question answering (VQA), where we achieve state-of-the-art performance on the standard VQA dataset.
ELLA: An Efficient Lifelong Learning Algorithm

The problem of learning multiple consecutive tasks, known as lifelong learning, is of great importance to the creation of intelligent, general-purpose, and flexible machines. In this paper, we develop a method for online multi-task learning in the lifelong learning setting. The proposed Efficient Lifelong Learning Algorithm (ELLA) maintains a sparsely shared basis for all task models, transfers knowledge from the basis to learn each new task, and refines the basis over time to maximize performance across all tasks. We show that ELLA has strong connections to both online dictionary learning for sparse coding and state-of-the-art batch multi-task learning methods, and provide robust theoretical performance guarantees. We show empirically that ELLA yields nearly identical performance to batch multi-task learning while learning tasks sequentially in three orders of magnitude (over 1,000x) less time.
Protective Effect of Calendula officinalis L. Flowers Against Monosodium Glutamate Induced Oxidative Stress and Excitotoxic Brain Damage in Rats

Monosodium glutamate (MSG) is a popular flavour enhancer used in food industries; however, excess MSG is neurotoxic. Oxidative stress is well documented in MSG induced neurotoxicity. The compounds having antioxidant and anti-inflammatory properties reportedly possess beneficial effects against various neurotoxic insults. Calendula officinalis Linn. flower extract (COE) is known for its potent antioxidant and anti-inflammatory activities. Hence, this present study has been designed to evaluate the neuroprotective effect of COE on MSG-induced neurotoxicity in rats. Adult Wistar rats were administered systemically for 7 days with MSG and after one h of MSG injection, rats were treated with COE (100 and 200 mg/kg) orally. At the end the treatment period, animals were assessed for locomotor activity and were sacrificed; brains were isolated for estimation of LPO, GSH, CAT, TT, GST, Nitrite and histopathological studies. MSG caused a significant alteration in animal behavior, oxidative defense (raised levels of LPO, nitrite concentration, depletion of antioxidant levels) and hippocampal neuronal histology. Treatment with COE significantly attenuated behavioral alterations, oxidative stress, and hippocampal damage in MSG-treated animals. Hence, this study demonstrates that COE protects against MSG-induced neurotoxicity in rats. The antioxidant and anti-inflammatory properties of COE may be responsible for its observed neuroprotective action.
Sharing behind the Scenes: Understanding User Bypassing Behavior in Sharing Economy

Sharing economy platforms facilitate people’s sharing of underutilized resources by adding value to their users, such as reducing transaction costs and building trust. However, it is discovered by practitioners that users may actually bypass, or “disintermediate”, the platforms to strike direct deals on their own. This phenomenon motivates this research-in-progress to understand sharing economy user bypassing behaviour. Specifically, we investigate their motivations of bypassing and behavioral strategies of overcoming trust barriers. Drawing insights from disintermediation literature, we conduct a single case study on Airbnb, a renowned accommodation sharing platform. Our preliminary findings show that Airbnb hosts have non-economic motivation to bypass the platform, and they are able to overcome trust barriers through leveraging the unbundling of intermediary functions. Upon completion of the research, the study is expected to make three theoretical contributions: uncovering the loopholes in sharing economy business models, augmenting predominant economic view of disintermediation, and proposing a “spillover effect” of embedded relationship on economic action.
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay

Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment. A significant issue with transferring this success to the robotics domain is that interaction with the real world is costly, but training on limited experience is prone to overfitting. We present a method for learning to navigate, to a fixed goal and in a known environment, on a mobile robot. The robot leverages an interactive world model built from a single traversal of the environment, a pre-trained visual feature encoder, and stochastic environmental augmentation, to demonstrate successful zero-shot transfer under real-world environmental variations without fine-tuning.
RBox-CNN: rotated bounding box based CNN for ship detection in remote sensing image

In this paper, we propose a rotated bounding box based convolutional neural network (RBox-CNN) for arbitrary-oriented ship detection. RBox-CNN is an end-to-end model based on Faster R-CNN. The region proposal network generates proposals as the rotated bounding box, and then the rotation region-of-interest (RRoI) pooling layer is applied to extract region features corresponding the proposals. In addition, the diagonal region-of-interest (DRoI) pooling layer is applied simultaneously to extract context features and alleviate the problem of misalignment in RRoI pooling layer. To stably predict locations with the angle, we apply the regression of distance's projection in width/height. Experiments on HRSC2016 show that our model achieves state-of-the-art detection accuracy on ship detection. Furthermore, RBox-CNN achieves a significant improvement on DOTA for oriented general object detection in remote sensing images.
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets

This paper proposes an approach for applying GANs to NMT. We build a conditional sequence generative adversarial net which comprises of two adversarial sub models, a generator and a discriminator. The generator aims to generate sentences which are hard to be discriminated from human-translated sentences ( i.e., the golden target sentences); And the discriminator makes efforts to discriminate the machine-generated sentences from humantranslated ones. The two sub models play a mini-max game and achieve the win-win situation when they reach a Nash Equilibrium. Additionally, the static sentence-level BLEU is utilized as the reinforced objective for the generator, which biases the generation towards high BLEU points. During training, both the dynamic discriminator and the static BLEU objective are employed to evaluate the generated sentences and feedback the evaluations to guide the learning of the generator. Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-ofthe-art Transformer on English-German and Chinese-English translation tasks.
Effectiveness of Marketing Strategies and Corporate Image on Brand Equity as a Sustainable Competitive Advantage

Having favorite corporate image and powerful brand equity build a strategic position in market for corporations. This position plays vital role of sustainable advantage. Therefore, we focus on the impacts of marketing strategies such as channel performance, value-oriented price, promotion, and after-sales service on brand equity directly and by corporate image indirectly. The explored results of Chi-square test analysis show that all the marketing-mix efforts positively affect the overall value of brand equity, which is a proxy of market performance, via the three dimensions of brand equity. Corporate image mediates the effect of the marketing-mix efforts on the three dimensions of brand equity.
Fingerprint image enhancement and recognition algorithms: a survey

Fingerprint systems have received a great deal of research and attracted many researchers’ effort since they provide a powerful tool for access control and security and for practical applications. A literature review of the techniques used to extract the features of fingerprint as well as recognition techniques is given in this paper. Some of the reviewed research articles have used traditional methods such as recognition techniques, whereas the other articles have used neural networks methods. In addition, fingerprint techniques of enhancement are introduced.
Riemannian Optimization for Skip-Gram Negative Sampling

Step 2. Search for a good low-rank representation X = WC> in terms of linguistic metrics, where W is a matrix of word embeddings and C is a matrix of context embeddings. 5.  Three different methods: RO-SGNS , SVD-SPPMI  and SGD-SGNS (original “word2vec”). • Three popular benchmarks for semantic similarity evaluation (“wordsim-353”, “simlex”, “men”) • Each dataset contains word pairs together with assessor-assigned similarity scores for each pair • Original “wordsim-353” is a mixture of the word pairs for both word similarity and word relatedness tasks which we also use in our experiments (“ws-sim” and “ws-rel”)
Becoming literate in different languages: similar problems, different solutions.

The teaching of reading in different languages should be informed by an effective evidence base. Although most children will eventually become competent, indeed skilled, readers of their languages, the pre-reading (e.g. phonological awareness) and language skills that they bring to school may differ in systematic ways for different language environments. A thorough understanding of potential differences is required if literacy teaching is to be optimized in different languages. Here we propose a theoretical framework based on a psycholinguistic grain size approach to guide the collection of evidence in different countries. We argue that the development of reading depends on children's phonological awareness in all languages studied to date. However, we propose that because languages vary in the consistency with which phonology is represented in orthography, there are developmental differences in the grain size of lexical representations, and accompanying differences in developmental reading strategies across orthographies.
Extended Tower Number Field Sieve with Application to Finite Fields of Arbitrary Composite Extension Degree

We propose a generalization of exTNFS algorithm recently introduced by Kim and Barbulescu (CRYPTO 2016). The algorithm, exTNFS, is a state-of-the-art algorithm for discrete logarithm in Fpn in the medium prime case, but it only applies when n = ηκ is a composite with nontrivial factors η and κ such that gcd(η, κ) = 1. Our generalization, however, shows that exTNFS algorithm can be also adapted to the setting with an arbitrary composite n maintaining its best asymptotic complexity. We show that one can solve discrete logarithm in medium case in the running time of Lpn(1/3, 3 √ 48/9) (resp. Lpn(1/3, 1.71) if multiple number fields are used), where n is an arbitrary composite. This should be compared with a recent variant by Sarkar and Singh (Asiacrypt 2016) that has the fastest running time of Lpn(1/3, 3 √ 64/9) (resp. Lpn(1/3, 1.88)) when n is a power of prime 2. When p is of special form, the complexity is further reduced to Lpn(1/3, 3 √ 32/9). On the practical side, we emphasize that the keysize of pairing-based cryptosystems should be updated following to our algorithm if the embedding degree n remains composite.
Bayesian clinical trials in action.

Although the frequentist paradigm has been the predominant approach to clinical trial design since the 1940s, it has several notable limitations. Advancements in computational algorithms and computer hardware have greatly enhanced the alternative Bayesian paradigm. Compared with its frequentist counterpart, the Bayesian framework has several unique advantages, and its incorporation into clinical trial design is occurring more frequently. Using an extensive literature review to assess how Bayesian methods are used in clinical trials, we find them most commonly used for dose finding, efficacy monitoring, toxicity monitoring, diagnosis/decision making, and studying pharmacokinetics/pharmacodynamics. The additional infrastructure required for implementing Bayesian methods in clinical trials may include specialized software programs to run the study design, simulation and analysis, and web-based applications, all of which are particularly useful for timely data entry and analysis. Trial success requires not only the development of proper tools but also timely and accurate execution of data entry, quality control, adaptive randomization, and Bayesian computation. The relative merit of the Bayesian and frequentist approaches continues to be the subject of debate in statistics. However, more evidence can be found showing the convergence of the two camps, at least at the practical level. Ultimately, better clinical trial methods lead to more efficient designs, lower sample sizes, more accurate conclusions, and better outcomes for patients enrolled in the trials. Bayesian methods offer attractive alternatives for better trials. More Bayesian trials should be designed and conducted to refine the approach and demonstrate their real benefit in action.
Multi-atlas segmentation of biomedical images: A survey

Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.
Deficient Emotional Self-Regulation in Adults With Attention-Deficit / Hyperactivity Disorder ( ADHD ) : The Relative Contributions of Emotional Impulsiveness and ADHD Symptoms to Adaptive Impairments in Major Life Activities

Recent reviews have argued that emotional impulsiveness (EI) and deficient emotional selfregulation are central components in attention-deficit/hyperactivity disorder (ADHD), not merely associated features or the consequence of comorbidity.  Our study has 2 aims: (1) to determine the frequency/severity of EI in adults with ADHD relative to these control groups; and (2) to evaluate the degree to which EI contributed to impairment in various domains of major life activities beyond that made by severity of the traditional 2 dimensions of ADHD (inattention, hyperactivity-impulsivity).  We examined the frequency and severity of problems with EI in 3 groups: adults with ADHD (n = 146), clinical-control adults not diagnosed with ADHD (n = 97), and a community-control group (n = 109). Selfand other ratings of EI were utilized.  Results indicated that adults with ADHD had significantly more EI than either clinical or community controls, whether by selfor other reports, and whether symptoms were studied individually or in total. We also evaluated the extent to which EI contributed to the prediction of global ratings of selfand other rated impairments in 10 different domains beyond the contribution made by the traditional 2 dimensions of ADHD symptoms. EI uniquely contributed to 6 of 10 domains and overall impairment. We then evaluated this issue using more detailed measures of occupational impairment, educational history, criminal history, adverse driving outcomes, marital satisfaction, parenting stress, and offspring severity of ADHD, oppositional defiant disorder, and conduct disorder. Severity of EI independently contributed to most measures of impairment beyond severity of the 2 ADHD symptom dimensions and, in many cases, was the only predictor of some impairments.  Our results indicate that EI is as central a component of ADHD as are its 2 traditional symptom dimensions. EI severity is not merely redundant with the other ADHD symptom dimensions, but adds additional explanatory and predictive power to understanding various forms of adult impairment.