nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition

Recurrent neural network language models RNNLMs are becoming increasingly popular for a range of applications including automatic speech recognition. An important issue that limits their possible application areas is the computational cost incurred in training and evaluation. This paper describes a series of new efficiency improving approaches that allows RNNLMs to be more efficiently trained on graphics processing units GPUs and evaluated on CPUs. First, a modified RNNLM architecture with a nonclass-based, full output layer structure F-RNNLM is proposed. This modified architecture facilitates a novel spliced sentence bunch mode parallelization of F-RNNLM training using large quantities of data on a GPU. Second, two efficient RNNLM training criteria based on variance regularization and noise contrastive estimation are explored to specifically reduce the computation associated with the RNNLM output layer softmax normalisation term. Finally, a pipelined training algorithm utilizing multiple GPUs is also used to further improve the training speed. Initially, RNNLMs were trained on a moderate dataset with 20M words from a large vocabulary conversational telephone speech recognition task. The training time of RNNLM is reduced by up to a factor of 53 on a single GPU over the standard CPU-based RNNLM toolkit. A 56 times speed up in test time evaluation on a CPU was obtained over the baseline F-RNNLMs. Consistent improvements in both recognition accuracy and perplexity were also obtained over C-RNNLMs. Experiments on Google's one billion corpus also reveals that the training of RNNLM scales well.
Synthesis and Application of Monolayer Semiconductors (June 2015)

Recently, semiconducting monolayers, such as MoS2 and WSe2, have been highlighted for their spin-valley coupling, diverse band structures, bendability, and excellent optoelectronic performances. With a subnanometer thickness of atomic layers, the transition metal dichalcogenides (TMDc) atomic layers demonstrate a significant photoresponse, considerable absorption to incident sunlight and favorable transport performances, leading to applications in the electronic circuit requiring low stand-by power, diverse optoelectronic devices, and next-generation nanoelectronics. Therefore, the class of monolayer TMDc offers a burgeoning field in materials science, fundamental physics, and optoelectronics. A feasible synthetic process to realize controlled synthesis of large area and high quality of TMDc monolayers is in demands. In this review, we will introduce the progress on synthesis and applications of the TMDc atomic layers.
MAPGEN: Mixed-Initiative Planning and Scheduling for the Mars Exploration Rover Mission

8 1094-7167/04/$20.00  Science is MER’s primary driver, so making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this criticality, the MER project team selected MAPGEN (Mixed Initiative Activity Plan Generator) as an activity-planning tool. MAPGEN combines two existing systems, each with a strong heritage: the APGEN activity-planning tool1 from the Jet Propulsion Laboratory and the Europa planning and scheduling system2 from NASA Ames Research Center. This article discusses the issues arising from combining these tools in this mission’s context.
Use of Virtual Reality Feedback for Patients with Chronic Neck Pain and Kinesiophobia

This study examined how individuals with and without neck pain performed exercises under the influence of altered visual feedback in virtual reality. Chronic neck pain (n = 9) and asymptomatic (n = 10) individuals were recruited for this cross-sectional study. Participants performed head rotations while receiving programmatically manipulated visual feedback from a head-mounted virtual reality display. The main outcome measure was the control-display gain (ratio between actual head rotation angle and visual rotation angle displayed) recorded at the just-noticeable difference. Actual head rotation angles were measured for different gains. Detection of the manipulated visual feedback was affected by gain. The just-noticeable gain for asymptomatic individuals, below and above unity gain, was 0.903 and 1.159, respectively. Head rotation angle decreased or increased 5.45° for every 0.1 increase or decrease in gain, respectively. The just-noticeable gain for chronic pain individuals, below unity gain, was 0.950. The head rotation angle increased 4.29° for every 0.1 decrease in gain. On average, chronic pain individuals reported that neck rotation was feasible for 84% of the unity gain trials, 66% of the individual just-noticeable difference trials, and 50% of the “nudged” just-noticeable difference trials. This research demonstrated that virtual reality may be useful for promoting the desired outcome of increased range of motion in neck rehabilitation exercises by altering visual feedback.
EULAide: Interpretation of End-User License Agreements using Ontology-Based Information Extraction

Ignoring End-User License Agreements (EULAs) for online services due to their length and complexity is a risk undertaken by the majority of online and mobile service users. This paper presents an Ontology-Based Information Extraction (OBIE) method for EULA term and phrase extraction to facilitate a better understanding by humans. An ontology capturing important terms and relationships has been developed and used to guide the OBIE process. Through a feedback cycle we have improved its domain-specific coverage by identifying additional concepts. In the detection and extraction, we focus on three key rights and conditions: permission, prohibition and duty. We present the EULAide system, which comprises a custom information extraction pipeline and a number of custom extraction rules tailored for EULA processing. To evaluate our approach, we created and manually annotated a corpus of 20 well-known licenses. For the gold standard we achieved an Inter-Annotator Agreement (IAA) of 90%, resulting in 193 permissions, 185 prohibitions and 168 duties. An evaluation of the OBIE pipeline against this gold standard resulted in an F-measure of 70-74% which, in the context of the IAA, proves the feasibility of the approach.
In Search of a Dataset for Handwritten Optical Music Recognition: Introducing MUSCIMA++

Optical Music Recognition (OMR) has long been without an adequate dataset and ground truth for evaluating OMR systems, which has been a major problem for establishing a state of the art in the field. Furthermore, machine learning methods require training data. We analyze how the OMR processing pipeline can be expressed in terms of gradually more complex ground truth, and based on this analysis we present the MUSCIMA++ dataset of handwritten music notation that addresses musical symbol recognition and notation reconstruction. The MUSCIMA++ dataset v.0.9 consists of 140 pages of handwritten music, with 91255 manually annotated notation symbols and 82261 explicitly marked relationships between symbol pairs. The dataset allows training and evaluating models for symbol classification, symbol localization, and notation graph assembly, both in isolation and jointly. Open-source tools are provided for manipulating the dataset, visualizing the data and annotating further, and the dataset itself is made available under an open license.
Social Recommendation With Multiple Influence From Direct User Interactions

With the sustained development of social networks, increasing attention has been paid to social recommender systems. Current studies usually focus on indirect factors such as the similarity between users, but multiple direct interactions, such as mentions, reposts, and comments, are seldom considered. This paper addresses direct connections between users in social recommender systems. We analyze direct interactions to investigate the connection strength between users, and then, user preferences and item characteristics can be better described. Based on the analysis of social influence between users and users’ influence over the whole social network, we propose a recommendation method with social influence, which makes full use of information among users in social networks and introduces the mechanisms of macroscopic and microscopic influences. Direct interactions between users are incorporated into a matrix factorization objective function. Real-world microblog data are applied to verify our model, and the results show that the proposed recommendation method outperforms other state-of-the-art recommendation algorithms.
Zero Shot Learning via Multi-scale Manifold Regularization

We address zero-shot learning using a new manifold alignment framework based on a localized multi-scale transform on graphs. Our inference approach includes a smoothness criterion for a function mapping nodes on a graph (visual representation) onto a linear space (semantic representation), which we optimize using multi-scale graph wavelets. The robustness of the ensuing scheme allows us to operate with automatically generated semantic annotations, resulting in an algorithm that is entirely free of manual supervision, and yet improves the state-of-the-art as measured on benchmark datasets.
A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

The Programming by Demonstration (PbD) technique aims at teaching a robot to accomplish a task by learning from a human demonstration. In a manipulation context, recognizing the demonstrator's hand gestures, specifically when and how objects are grasped, plays a significant role. Here, a system is presented that uses both hand shape and contact-point information obtained from a data glove and tactile sensors to recognize continuous human-grasp sequences. The sensor fusion, grasp classification, and task segmentation are made by a hidden Markov model recognizer. Twelve different grasp types from a general, task-independent taxonomy are recognized. An accuracy of up to 95% could be achieved for a multiple-user system.
Chemical structure recognition and prediction: A machine learning technique

A chemical structure recognition system designed to identify and predict hand-drawn chemical diagrams is presented. Although the proposed system is primarily intended to help chemists complete their chemical structure drawings and predict and complete the chemical structure, it is also envisioned to be utilized as a learning aid in chemistry education on a student's touchscreen device or on a teacher's smart board. The proposed system continuously provides the user with real-time feedback about the most relevant combinations to complete the chemical compound. A constantly updated list of prioritized candidate structures is displayed to the user. If the user is an expert, he or she will be allowed to contribute to the learning process of the proposed system. The proposed system deploys two trainable Bayesian networks, one at its input and another one at its output. Between the two networks, the classification of hand- drawn chemical structure is done using morphological image processing, LDA and PCA, incorporating therefore both image- based and feature-based techniques. This incorporation contributes considerably to the increased performance of the proposed system by enabling it to efficiently recognize messy drawings and noisy sketches like those containing over-traced strokes. Performance comparison with existing state of the art techniques proved the system to be outperforming in terms of recognition accuracy.
Minimization of cogging torque in a small axial-flux PMSG with a parallel-teeth stator

A stator with a steel core is well suited for small, low speed PM machines, where core losses are much lower than copper losses. However, a slotted stator introduces cogging torque. A stator with parallel-teeth inherently lowers cogging torque but there is need to further reduce it. This is particularly important in wind generators since it raises the cut-in wind speed, thereby lowering the energy captured yield for a given installed capacity. Pulsating torques also produces noise and mechanical vibrations which accelerates the wear of the machine and its support structure. This paper presents some effective methods of minimizing cogging torque, which have minimal effects on the performance of the machine. Three PM rotor configurations were considered, which include: a pole-arc ratio of 0.80, alternating pole-arcs of 0.61 and 0.80, and skewed PM poles with a skew one slot-pitch. The 3 rotor configurations were simulated numerically using 3D Finite Element Analysis (FEA) and tested experimentally with the same stator. The machine topology with a rotor pole-arc ratio of 0.80 was used as the reference design. From the experimental results, the alternating pole-arcs effectively reduced cogging-torque by 73% whereas the skewed poles reduced it by 48%. The 3D-FEA and experimental cogging torque results are also comparable. The paper also compares cogging torque reduction techniques which are well suited for a parallel-teeth stator.
Trend analysis model: trend consists of temporal words, topics, and timestamps

This paper presents a topic model that identifies interpretable low dimensional components in time-stamped data for capturing the evolution of trends. Unlike other models for time-stamped data, our proposal, the trend analysis model (TAM), focuses on the difference between temporal words and other words in each document to detect topic evolution over time. TAM introduces a latent trend class variable into each document and a latent switch variable into each token for handling these differences. The trend class has a probability distribution over temporal words, topics, and a continuous distribution over time, where each topic is responsible for generating words. The latter class uses a document specific probabilistic distribution to judge which variable each word comes from for generating words in each token. Accordingly, TAM can explain which topic co-occurrence pattern will appear at any given time, and represents documents of similar content and timestamp as sharing the same trend class. Therefore, TAM projects them on a latent space of trend dimensionality and allows us to predict the temporal evolution of words and topics in document collections. Experiments on various data sets show that the proposed model can capture interpretable low dimensionality sets of topics and timestamps, take advantage of previous models, and is useful as a generative model in the analysis of the evolution of trends.
Research on Credit Card Fraud Detection Model Based on Class Weighted Support Vector Machine

To deal with credit card fraud, a detection model based on Class Weighted Support Vector Machine was established. Due to large-scale and high dimensions of data, Principal Component Analysis (PCA) was adopted firstly to screen out the main factors from a great deal of indicative attributes in order to reduce the training dimension of SVM effectively. Then according to the characteristics of credit card transactions data which are imbalance, an improved SVM--Imbalance Class Weighted SVM (ICW-SVM) was adopted. With the application and verification in real dataset from bank, it is demonstrated that this model is more suitable for solving credit card fraud detection problem with higher precision and effective than others.
Clustering Sensors in Wireless Ad Hoc Networks Operating in a Threat Environment

Sensors in a data fusion environment over hostile territory are geographically dispersed and change location with time. In order to collect and process data from these sensors an equally flexible network of fusion beds (i.e., clusterheads) is required. To account for the hostile environment, we allow communication links between sensors and clusterheads to be unreliable. We develop a mixed integer linear programming (MILP) model to determine the clusterhead location strategy that maximizes the expected data covered minus the clusterhead reassignments, over a time horizon. A column generation (CG) heuristic is developed for this problem. Computational results show that CG performs much faster than a standard commercial solver and the typical optimality gap for large problems is less than 5%. Improvements to the basic model in the areas of modeling link failure, consideration of bandwidth capacity, and clusterhead changeover cost estimation are also discussed.
Situation assessment algorithm for a collision prevention assistant

In this paper, we present the concept of a collision prevention assistant, a system that we believe can significantly contribute to road safety. We propose a new situation assessment algorithm which is tailored to the action of braking and that further accounts for the nonlinearities that arise when vehicles cut out or come to a standstill. The effect of sensor uncertainty on the performance of the proposed algorithm is modelled using a Markov chain and analyzed by means of a Monte Carlo simulation on a typical traffic situation.
Artificial Intelligence in the NASA Volcano Sensorweb : Over a Decade in Operations

Volcanic activity can occur with little or no warning. Increasing numbers of space borne assets can enable coordinated measurements of volcanic events to enhance both scientific study and hazard response. We describe the use of space and ground measurements to target further measurements as part of a worldwide volcano monitoring system. We utilize a number of alert systems including the MODVOLC, GOESVOLC, US Air Force Weather Advisory, and Volcanic Ash Advisory Center (VAAC) alert systems. Additionally we use in-situ data from ground instrumentation at a number of volcanic sites, including Iceland. Artificial Intelligence Software plays a key role in the Volcano Sensorweb. First, several in-situ volcano monitoring networks use “intelligent” data interpretation software to trigger alerts that can then be used to allocate network resources, notify human agents, and even task space observations. Second, the Earth Observing One (EO-1) spacecraft uses Artificial Intelligence Software to automatically task the spacecraft to execute observations. Third, EO-1 also interprets thermal data onboard to allow for faster notifications of volcanic activity. Finally some data interpretation steps use intelligent software such as Random Decision Forest Methods used to automatically estimate volcanic plume heights in Worldview-2 Imagery.
Body talk: crowdshaping realistic 3D avatars with words

Realistic, metrically accurate, 3D human avatars are useful for games, shopping, virtual reality, and health applications. Such avatars are not in wide use because solutions for creating them from high-end scanners, low-cost range cameras, and tailoring measurements all have limitations. Here we propose a simple solution and show that it is surprisingly accurate. We use crowdsourcing to generate attribute ratings of 3D body shapes corresponding to standard linguistic descriptions of 3D shape. We then learn a linear function relating these ratings to 3D human shape parameters. Given an image of a new body, we again turn to the crowd for ratings of the body shape. The collection of linguistic ratings of a photograph provides remarkably strong constraints on the metric 3D shape. We call the process crowdshaping and show that our Body Talk system produces shapes that are perceptually indistinguishable from bodies created from high-resolution scans and that the metric accuracy is sufficient for many tasks. This makes body "scanning" practical without a scanner, opening up new applications including database search, visualization, and extracting avatars from books.
A Framework for Fashion Data Gathering, Hierarchical-Annotation and Analysis for Social Media and Online Shop TOOLKIT FOR DETAILED STYLE ANNOTATIONS FOR ENHANCED FASHION RECOMMENDATION

Due to the transformation of different recommendation system from contentbased to hybrid cross-domain-based, there is an urge to prepare a socialnetwork dataset which will provide sufficient data as well as detail-level annotation from a predefined hierarchical clothing category and attribute based vocabulary by considering user interactions. However, existing fashionbased datasets lack either in hierarchical-category based representation or user interactions of social network. The thesis intends to represent two datasetsone from photo-sharing platform Instagram which gathers fashionistas images with all possible user-interactions and another from online-shop Zalando with every cloths detail. We present a design of a customized crawler that enables the user to crawl data based on category or attributes. Moreover, an efficient and collaborative web-solution is designed and implemented to facilitate large-scale hierarchical category-based detaillevel annotation of Instagram data. By considering all user-interactions, the developed solution provides a detail-level annotation facility that reflects the user’s preference. The web-solution is evaluated by the team as well as the Amazon Turk Service. The annotated output from different users proofs the usability of the web-solution in terms of availability and clarity. In addition to data crawling and annotation web-solution development, this project analyzes the Instagram and Zalando data distribution in terms of cloth category, subcategory and pattern to provide meaningful insight over data. Researcher community will benefit by using these datasets if they intend to work on a rich annotated dataset that represents social network and resembles in-detail cloth information.
Sustainable Power Supply Solutions for Off-Grid Base Stations

The telecommunication sector plays a significant role in shaping the global economy and the way people share information and knowledge. At present, the telecommunication sector is liable for its energy consumption and the amount of emissions it emits in the environment. In the context of off-grid telecommunication applications, offgrid base stations (BSs) are commonly used due to their ability to provide radio coverage over a wide geographic area. However, in the past, the off-grid BSs usually relied on emission-intensive power supply solutions such as diesel generators. In this review paper, various types of solutions (including, in particular, the sustainable solutions) for powering BSs are discussed. The key aspects in designing an ideal power supply solution are reviewed, and these mainly include the pre-feasibility study and the thermal management of BSs, which comprise heating and cooling of the BS shelter/cabinets and BS electronic equipment and power supply components. The sizing and optimization approaches used to design the BSs’ power supply systems as well as the operational and control strategies adopted to manage the power supply systems are also reviewed in this paper.
GeeBot: A Robotic Platform for Refugee Integration

The refugee crisis is one of society's leading challenges. After a journey for survival, refugees and host institutions face barriers that hinder the integration process. To design solutions, we interviewed two groups: host institutions and past refugees. We identified critical issues, from legal concerns, like unfamiliarity of their Refugee Status, to grocery shopping. Our envisioned solution is GeeBot, a low-cost egg-shaped robot that institutions would lend to arriving families for eighteen months. GeeBot will be a translator with teaching functions, an information provider, and an active promoter of interaction between native and refugee populations.