nils-herrmann/clean_scirepeval huggingface dataset:
Vibration Energy Harvesting: Machinery Vibration, Human Movement and Flow Induced Vibration

With the development of low power electronics and energy harvesting technology, selfpowered systems have become a research hotspot over the last decade. The main advantage of self-powered systems is that they require minimum maintenance which makes them to be deployed in large scale or previously inaccessible locations. Therefore, the target of energy harvesting is to power autonomous ‘fit and forget’ electronic systems over their lifetime. Some possible alternative energy sources include photonic energy (Norman, 2007), thermal energy (Huesgen et al., 2008) and mechanical energy (Beeby et al., 2006). Among these sources, photonic energy has already been widely used in power supplies. Solar cells provide excellent power density. However, energy harvesting using light sources restricts the working environment of electronic systems. Such systems cannot work normally in low light or dirty conditions. Thermal energy can be converted to electrical energy by the Seebeck effect while working environment for thermo-powered systems is also limited. Mechanical energy can be found in instances where thermal or photonic energy is not suitable, which makes extracting energy from mechanical energy an attractive approach for powering electronic systems. The source of mechanical energy can be a vibrating structure, a moving human body or air/water flow induced vibration. The frequency of the mechanical excitation depends on the source: less than 10Hz for human movements and typically over 30Hz for machinery vibrations (Roundy et al., 2003). In this chapter, energy harvesting from various vibration sources will be reviewed. In section 2, energy harvesting from machinery vibration will be introduced. A general model of vibration energy harvester is presented first followed by introduction of three main transduction mechanisms, i.e. electromagnetic, piezoelectric and electrostatic transducers. In addition, vibration energy harvesters with frequency tunability and wide bandwidth will be discussed. In section 3, energy harvesting from human movement will be introduced. In section 4, energy harvesting from flow induced vibration (FIV) will be discussed. Three types of such generators will be introduced, i.e. energy harvesting from vortex-induced vibration (VIV), fluttering energy harvesters and Helmholtz resonator. Conclusions will be given in section 5.
Watch this: Scalable cost-function learning for path planning in urban environments

In this work, we present an approach to learn cost maps for driving in complex urban environments from a large number of demonstrations of human driving behaviour. The learned cost maps are constructed directly from raw sensor measurements, bypassing the effort of manually designing cost maps as well as features. When deploying the cost maps, the trajectories generated not only replicate human-like driving behaviour but are also demonstrably robust against systematic errors in putative robot configuration. To achieve this we deploy a Maximum Entropy based, non-linear IRL framework which uses Fully Convolutional Neural Networks (FCNs) to represent the cost model underlying expert driving behaviour. Using a deep, parametric approach enables us to scale efficiently to large datasets and complex behaviours while being run-time independent of dataset extent during deployment. We demonstrate scalability and performance on an ambitious dataset collected over the course of one year including more than 25k demonstration trajectories extracted from over 120km of driving and 13 different drivers. We evaluate against a carefully designed cost map and, in addition, demonstrate robustness to systematic errors by learning precise cost-maps even in the presence of system calibration perturbations.
MINING FOR FORECASTING SALES

The growing popularity of online product review forums invites people to express opinions and sentiments toward the products .It gives the knowledge about the product as well as sentiment of people towards the product. These online reviews are very important for forecasting the sales performance of product. In this paper, we discuss the online review mining techniques in movie domain. Sentiment PLSA which is responsible for finding hidden sentiment factors in the reviews and ARSA model used to predict sales performance. An Autoregressive Sentiment and Quality Aware model (ARSQA) also in consideration for to build the quality for predicting sales performance. We propose clustering and classification based algorithm for sentiment analysis.
Cloud, fog and edge: Cooperation for the future?

Traditional cloud-based infrastructures are not enough for the current demands of Internet of Things (IoT) applications. Two major issues are the limitations in terms of latency and network bandwidth. In recent years, the concepts of fog computing and edge computing were proposed to alleviate these limitations by moving data processing capabilities closer to the network edge. Considering IoT growth and development forecasts, we believe the full potential of IoT can, in many cases, only be unlocked by combining cloud, fog and edge computing. This paper discusses four possible approaches for distributing workload among these levels. We also highlight developments and possibilities as well as consider challenges for implementation in the areas of hardware, machine learning, security, privacy and communication.
The INTERSPEECH 2009 emotion challenge

The last decade has seen a substantial body of literature on the recognition of emotion from speech. However, in comparison to related speech processing tasks such as Automatic Speech and Speaker Recognition, practically no standardised corpora and test-conditions exist to compare performances under exactly the same conditions. Instead a multiplicity of evaluation strategies employed – such as cross-validation or percentage splits without proper instance definition – prevents exact reproducibility. Further, in order to face more realistic scenarios, the community is in desperate need of more spontaneous and less prototypical data.  The FAU Aibo Emotion Corpus  serves as basis with clearly defined test and training partitions incorporating speaker independence and different room acoustics as needed in most reallife settings. This paper introduces the challenge, the corpus, the features, and benchmark results of two popular approaches towards emotion recognition from speech.
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

Count-based exploration algorithms are known to perform near-optimally when used in conjunction with tabular reinforcement learning (RL) methods for solving small discrete Markov decision processes (MDPs). It is generally thought that count-based methods cannot be applied in high-dimensional state spaces, since most states will only occur once. Recent deep RL exploration strategies are able to deal with high-dimensional continuous state spaces through complex heuristics, often relying on optimism in the face of uncertainty or intrinsic motivation. In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various highdimensional and/or continuous deep RL benchmarks. States are mapped to hash codes, which allows to count their occurrences with a hash table. These counts are then used to compute a reward bonus according to the classic count-based exploration theory. We find that simple hash functions can achieve surprisingly good results on many challenging tasks. Furthermore, we show that a domaindependent learned hash code may further improve these results. Detailed analysis reveals important aspects of a good hash function: 1) having appropriate granularity and 2) encoding information relevant to solving the MDP. This exploration strategy achieves near state-of-the-art performance on both continuous control tasks and Atari 2600 games, hence providing a simple yet powerful baseline for solving MDPs that require considerable exploration.
First-Person Action-Object Detection with EgoNet

Unlike traditional third-person cameras mounted on robots, a first-person camera, captures a person’s visual sensorimotor object interactions from up close. In this paper, we study the tight interplay between our momentary visual attention and motor action with objects from a first-person camera. We propose a concept of action-objects—the objects that capture person’s conscious visual (watching a TV) or tactile (taking a cup) interactions. Action-objects may be task-dependent but since many tasks share common person-object spatial configurations, action-objects exhibit a characteristic 3D spatial distance and orientation with respect to the person. We design a predictive model that detects action-objects using EgoNet, a joint two-stream network that holistically integrates visual appearance (RGB) and 3D spatial layout (depth and height) cues to predict per-pixel likelihood of action-objects. Our network also incorporates a first-person coordinate embedding, which is designed to learn a spatial distribution of the actionobjects in the first-person data. We demonstrate EgoNet’s predictive power, by showing that it consistently outperforms previous baseline approaches. Furthermore, EgoNet also exhibits a strong generalization ability, i.e., it predicts semantically meaningful objects in novel first-person datasets. Our method’s ability to effectively detect action-objects could be used to improve robots’ understanding of human-object interactions.
Bat Bot (B2), a biologically inspired flying machine

It is challenging to analyze the aerial locomotion of bats because of the complicated and intricate relationship between their morphology and flight capabilities. Developing a biologically inspired bat robot would yield insight into how bats control their body attitude and position through the complex interaction of nonlinear forces (e.g., aerodynamic) and their intricate musculoskeletal mechanism. The current work introduces a biologically inspired soft robot called Bat Bot (B2). The overall system is a flapping machine with 5 Degrees of Actuation (DoA). This work reports on some of the preliminary untethered flights of B2. B2 has a nontrivial morphology and it has been designed after examining several biological bats.  DoAs, which contribute significantly to bat flight, are picked and incorporated in B2's flight mechanism design. These DoAs are: 1) forelimb flapping motion, 2) forelimb mediolateral motion (folding and unfolding) and 3) hindlimb dorsoventral motion (upward and downward movement).
Unsupervised Learning via Meta-Learning

A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data. Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disentanglement, prediction, and other metrics. Instead, we develop an unsupervised meta-learning method that explicitly optimizes for the ability to learn a variety of tasks from small amounts of data. To do so, we construct tasks from unlabeled data in an automatic way and run meta-learning over the constructed tasks. Surprisingly, we find that, when integrated with meta-learning, relatively simple task construction mechanisms, such as clustering embeddings, lead to good performance on a variety of downstream, human-specified tasks. Our experiments across four image datasets indicate that our unsupervised meta-learning approach acquires a learning algorithm without any labeled data that is applicable to a wide range of downstream classification tasks, improving upon the embedding learned by four prior unsupervised learning methods.
HyperQA : Hyperbolic Embeddings for Fast and E icient Ranking of estion Answer Pairs

Deep learning has become a fundamental component in question answering (QA) research. It is a well established fact that the top QA systems are mostly comprised of neural ranking architectures. Model aer model, we see architectural innovations ranging from word interaction layers to creative new methods for learning aentions. It also seems like these complex interaction mechanisms are mandatory for good performance. Unfortunately, many of these mechanisms incur a prohibitive computation and memory cost making them unfavorable for practical applications. In lieu of that, this paper tackles the question of whether it is possible to achieve competitive performance with very simple neural networks. As such, we propose a simple but novel deep learning architecture for fast and ecient QA. Specically, we present HyperQA, a parameter ecient neural network model that outperforms several parameter heavy models such as Aentive Pooling BiLSTMs and Multi Perspective CNN on multiple standard benchmark datasets such as TrecQA, WikiQA and YahooQA. e key innovation to our model is a pairwise ranking objective that performs QA matching in Hyperbolic space instead of Euclidean space. is empowers our model with a self-organizing ability and enables automatic discovery of latent hierarchies while learning embeddings of questions and answers. Our model requires no feature engineering, no similarity matrix matching, no complicated aention mechanisms nor over-parameterized layers and yet outperforms many models that have these functionalities on multiple benchmarks.
Learning deep structured semantic models for web search using clickthrough data

Latent semantic models, such as LSA, intend to map a query to its relevant documents at the semantic level where keyword-based matching often fails. In this study we strive to develop a series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them. The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data. To make our models applicable to large-scale Web search applications, we also use a technique called word hashing, which is shown to effectively scale up our semantic models to handle large vocabularies which are common in such tasks. The new models are evaluated on a Web document ranking task using a real-world data set. Results show that our best model significantly outperforms other latent semantic models, which were considered state-of-the-art in the performance prior to the work presented in this paper.
Multifocal melanoma of the glans penis.

Malignant melanomas of the penis and urethra are rare.1–7 These lesions typically appear in an older age group.1–3,5–8 Presentation is usually late,1,6 with thick primary lesions9 and a high incidence of regional metastatic disease.1,5,8 Little is known about risk factors for or the pathogenesis of this disease. Furthermore, there is lack of consensus as to the extent of treatment that is indicated. The authors present a case of multifocal melanoma of the glans penis that has not been previously described. A review of the literature with emphasis on the pathogenesis and treatment of melanoma of the penis and urethra is discussed. Further efforts are needed to identify those at increased risk so that earlier diagnosis, surgical intervention, and an improved prognosis for those afflicted with this disease will be possible in the future.
Characterizing the Semantic Web on the Web

Semantic Web languages are being used to represent, encode and exchangesemanticdata in many contexts beyond the Web – in databases, multiagent systems, mobile computing, and ad hoc networking environments. The core paradigm, however, remains what we call the W b aspect of the Semantic Web – its use by independent and distributed agents who publish and consume data on the World Wide Web. To better understand this central use case, we have harvested and analyzed a collection of Semantic Web documents from an estimated ten million available on the Web. Using a corpus of more than 1.7 million documents comprising over 300 million RDF triples, we describe a number of global metrics, properties and usage patterns. Most of the metrics, such as the size of Semantic Web documents and the use frequency of Semantic Web terms, were found to follow a power law distribution.
Molecular basis of long-term plasticity underlying addiction

Studies of human addicts and behavioural studies in rodent models of addiction indicate that key behavioural abnormalities associated with addiction are extremely long lived. So, chronic drug exposure causes stable changes in the brain at the molecular and cellular levels that underlie these behavioural abnormalities. There has been considerable progress in identifying the mechanisms that contribute to long-lived neural and behavioural plasticity related to addiction, including drug-induced changes in gene transcription, in RNA and protein processing, and in synaptic structure. Although the specific changes identified so far are not sufficiently long lasting to account for the nearly permanent changes in behaviour associated with addiction, recent work has pointed to the types of mechanism that could be involved.
Power Approaches for Implantable Medical Devices

Implantable medical devices have been implemented to provide treatment and to assess in vivo physiological information in humans as well as animal models for medical diagnosis and prognosis, therapeutic applications and biological science studies. The advances of micro/nanotechnology dovetailed with novel biomaterials have further enhanced biocompatibility, sensitivity, longevity and reliability in newly-emerged low-cost and compact devices. Close-loop systems with both sensing and treatment functions have also been developed to provide point-of-care and personalized medicine. Nevertheless, one of the remaining challenges is whether power can be supplied sufficiently and continuously for the operation of the entire system. This issue is becoming more and more critical to the increasing need of power for wireless communication in implanted devices towards the future healthcare infrastructure, namely mobile health (m-Health). In this review paper, methodologies to transfer and harvest energy in implantable medical devices are introduced and discussed to highlight the uses and significances of various potential power sources.
High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.
Security for Automated , Distributed Configuration Management

Installation, configuration, and administration of desktop software is a non-trivial process. Even a simple application can have numerous dependencies on hardware, device drivers, operating system versions, dynamically linked libraries, and even on other applications. These dependencies can cause surprising failures during the normal process of installations, updates and re-configurations. Diagnosing and resolving such failures involves detailed knowledge of the hardware and software installed in the machine, configuration manifests of particular applications, version incompatibilities, etc. This is usually too hard for end-users, and even for technical support personnel, specially in small businesses. It may be necessary to involve software vendors and outside consultants or laboratories. Employees working on sensitive, proprietary projects may even have to resort to calling the help line of an application vendor and discussing details of their desktop configuration. In order establish valid licensing, the user may be forced to disclose additional details such as the user’s identity, machine identification, software serial number, etc. This type of disclosure may reveal proprietary information or (worse) security vulnerabilities, and increase the risk of attack by hackers or cyber-criminals. An adequate solution to the distributed configuration management problem needs to address the security concerns of users, administrators, software vendors and outside consultants: keeping details of installations private, authenticating licensed users and software vendors, protecting the integrity of software, secure delegation across administrative boundaries, and protecting proprietary information. Existing commercial and research systems [12, 8] provide distributed configuration management by distributing configuration information and software over local and wide-area networks. They provide flexible, automated, distributed configuration management. However, many or most of the security issues listed above remain to be addressed. These issues are the central focus of our research. ∗Address for the first two authors: Dept. of Computer Science, Room 2063, Engineering Unit II, Davis, CA 95616.
Suicidality, Self-Harm, and Body Dissatisfaction in Transgender Adolescents and Emerging Adults with Gender Dysphoria.

Prevalence of suicide attempts, self-injurious behaviors, and associated psychosocial factors were examined in a clinical sample of transgender (TG) adolescents and emerging adults (n = 96). Twenty-seven (30.3%) TG youth reported a history of at least one suicide attempt and 40 (41.8%) reported a history of self-injurious behaviors. There was a higher frequency of suicide attempts in TG youth with a desire for weight change, and more female-to-male youth reported a history of suicide attempts and self-harm behaviors than male-to-female youth. Findings indicate that this population is at a high risk for psychiatric comorbidities and life-threatening behaviors.