nils-herrmann/clean_scirepeval huggingface dataset:
An Energy-Efficient Micropower Neural Recording Amplifier

This paper describes an ultralow-power neural recording amplifier. The amplifier appears to be the lowest power and most energy-efficient neural recording amplifier reported to date. We describe low-noise design techniques that help the neural amplifier achieve input-referred noise that is near the theoretical limit of any amplifier using a differential pair as an input stage. Since neural amplifiers must include differential input pairs in practice to allow robust rejection of common-mode and power supply noise, our design appears to be near the optimum allowed by theory. The bandwidth of the amplifier can be adjusted for recording either neural spikes or local field potentials (LFPs). When configured for recording neural spikes, the amplifier yielded a midband gain of 40.8 dB and a -3-dB bandwidth from 45 Hz to 5.32 kHz; the amplifier's input-referred noise was measured to be 3.06 muVrms while consuming 7.56 muW of power from a 2.8-V supply corresponding to a noise efficiency factor (NEF) of 2.67 with the theoretical limit being 2.02. When configured for recording LFPs, the amplifier achieved a midband gain of 40.9 dB and a -3-dB bandwidth from 392 mHz to 295 Hz; the input-referred noise was 1.66 muVrms while consuming 2.08 muW from a 2.8-V supply corresponding to an NEF of 3.21. The amplifier was fabricated in AMI's 0.5-mum CMOS process and occupies 0.16 mm2 of chip area. We obtained successful recordings of action potentials from the robust nucleus of the arcopallium (RA) of an anesthesized zebra finch brain with the amplifier. Our experimental measurements of the amplifier's performance including its noise were in good accord with theory and circuit simulations.
Scalable Similarity Matching in Streaming Time Series

Nowadays online monitoring of data streams is essential in many real life applications, like sensor network monitoring, manufacturing process control, and video surveillance. One major problem in this area is the online identification of streaming sequences similar to a predefined set of pattern-sequences. In this paper, we present a novel solution that extends the state of the art both in terms of effectiveness and efficiency. We propose the first online similarity matching algorithm based on Longest Common SubSequence that is specifically designed to operate in a streaming context, and that can effectively handle time scaling, as well as noisy data. In order to deal with high stream rates and multiple streams, we extend the algorithm to operate on multilevel approximations of the streaming data, therefore quickly pruning the search space. Finally, we incorporate in our approach error estimation mechanisms in order to reduce the number of false negatives. We perform an extensive experimental evaluation using forty real datasets, diverse in nature and characteristics, and we also compare our approach to previous techniques. The experiments demonstrate the validity of our approach.
Experimental Analysis of a Wideband Pattern Diversity Antenna With Compact Reconfigurable CPW-to-Slotline Transition Feed

A wideband antenna with a reconfigurable coplanar waveguide (CPW)-to-slotline transition feed is proposed for pattern diversity applications. The feed provides three modes -CPW feed, left slotline (LS) feed and right slotline (RS) feed- without extra matching structures. Changes between modes are controlled by only two p-I-n diodes. Features of the proposed switchable feed include compact size and simple bias circuit. The equivalent transmission line model is used in the analysis of the proposed design. A prototype of the proposed antenna is fabricated, tested, and the obtained results including reflection coefficient, radiation patterns and gains, are present. A measurement of channel capacity is carried out to prove the benefit of pattern diversity when using the proposed antenna in both line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios.
Pricing in Ride-Sharing Platforms: A Queueing-Theoretic Approach

We study optimal pricing strategies for ride-sharing platforms, using a queueing-theoretic economic model. Analysis of pricing in such settings is complex: On one hand these platforms are two-sided - this requires economic models that capture the incentives of both drivers and passengers. On the other hand, these platforms support very high temporal-resolution for data collection and pricing - this requires stochastic models that capture the dynamics of drivers and passengers in the system.
 We focus our attention on the value of dynamic pricing: where prices can react to instantaneous imbalances between available supply and incoming demand. We find two main results: We first show that profit under any dynamic pricing strategy cannot exceed profit under the optimal static pricing policy (i.e., one which is agnostic of stochastic fluctuations in the system load). This result belies the prevalence of dynamic pricing in practice. Our second result explains the apparent paradox: we show that dynamic pricing is much more robust to fluctuations in system parameters compared to static pricing. Moreover, these results hold even if the monopolist maximizes welfare or throughput. Thus dynamic pricing does not necessarily yield higher performance than static pricing - however, it lets platforms realize the benefits of optimal static pricing, even with imperfect knowledge of system parameters.
Motion-based grasp selection: Improving traditional control strategies of myoelectric hand prosthesis

This paper introduces a novel prosthetic hand control architecture using inertial information for grasp prediction in order to reduce the cognitive burden of amputees. A pair of inertial measurement sensors (IMUs) are fitted on the wrist and bicep to record arm trajectory when reaching to grasp an object. Each object class can be associated with different methods for grasping and manipulation. An observation experiment was conducted to find the most common grasping methods for generic object classes: Very Small (VS), Small (S), and Medium (M). A Cup (CP) class was also examined to find differences in grasping habits for pick and place, and drinking applications. The resulting grasps were used to test the discriminatory ability of inertial motion features in the upper limb for VS, S and CP object classes. Subject experiments demonstrated an average classification success rate of 90.8%, 69.2% and 88.1% for VS, S and CP classes respectively when using a k-nearest neighbors algorithm with a Euclidean distance metric. The results suggest that inertial motion features have great potential to predict the grasp pattern during reach, and to the authors' knowledge, is the first IMU-based control strategy to utilize natural motion that is aimed at hand prosthesis control.
Deep Neural Networks for i-Vector Language Identification of Short Utterances in Cars

This paper is focused on the application of the Language Identification (LID) technology for intelligent vehicles. We cope with short sentences or words spoken in moving cars in four languages: English, Spanish, German, and Finnish. As the response time of the LID system is crucial for user acceptance in this particular task, speech signals of different durations with total average of 3.8s are analyzed. In this paper, the authors propose the use of Deep Neural Networks (DNN) to model effectively the i-vector space of languages. Both raw i-vectors and session variability compensated i-vectors are evaluated as input vectors to DNNs. The performance of the proposed DNN architecture is compared with both conventional GMM-UBM and i-vector/LDA systems considering the effect of durations of signals. It is shown that the signals with durations between 2 and 3s meet the requirements of this application, i.e., high accuracy and fast decision, in which the proposed DNN architecture outperforms GMM-UBM and i-vector/LDA systems by 37% and 28%, respectively.
Gains from Others ’ Losses : Technology Trajectories and the Global Division of Firms

Gains from Others’ Losses: Technology Trajectories and the Global Division of Firms Chia-Hsuan Yang, Rebecca Nugent, Erica R.H. Fuchs Carnegie Mellon University This Draft: April 2, 2012  After the burst of the telecommunications bubble in March 2000, the majority of U.S. optoelectronic component firms moved manufacturing offshore. This research explores (1) whether due to different offshore production economics, firms who move manufacturing offshore stop or slow U.S.-based R&D activities in the emerging technology necessary to access larger markets and (2) whether the inventors originally within these offshoring firms, leave, and continue to innovate in the emerging technology at different institutions. We focus on the 28 leading smallor medium-sized U.S. firms that manufacture optoelectronic components for telecommunications (18 offshore, 10 not) and the inventors who patent at these firms. We triangulate hand-classified USPTO patents, firm SEC filings, inventor CVs, and structured interview data we collect from the firms. Our results show that there is a relationship between firms’ type and extent of offshoring facilities (fabrication, assembly) and their innovation directions. In particular, we find that while offshoring is associated with a statistically significant decrease in innovation in the emerging technology, it can be associated with an increase in all other types of patenting. While an important minority of inventors of the emerging technology who worked at offshoring firms depart to a single onshore firm in the same industry (which subsequently dominates this space), the majority of inventors depart to firms outside our study scope and stop work in the emerging technology.
Salt and Pepper Noise Detection and removal by Modified Decision based Unsymmetrical Trimmed Median Filter for Image Restoration

In this paper, six different image filtering algorithms are compared based on their ability to reconstruct noise affected images. The purpose of these algorithms is to remove noise from a signal that might occur through the transmission of an image. A new algorithm, the Spatial Median Filter, is introduced and compared with current image smoothing techniques. Experimental results demonstrate that the proposed algorithm is comparable to these techniques.. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).
Inducing Embeddings for Rare and Unseen Words by Leveraging Lexical Resources

We put forward an approach that exploits the knowledge encoded in lexical resources in order to induce representations for words that were not encountered frequently during training. Our approach provides an advantage over the past work in that it enables vocabulary expansion not only for morphological variations, but also for infrequent domain specific terms. We performed evaluations in different settings, showing that the technique can provide consistent improvements on multiple benchmarks across domains.
T-SKIRT: Online Estimation of Student Proficiency in an Adaptive Learning System

We develop T-SKIRT: a temporal, structuredknowledge, IRT-based method for predicting student responses online. By explicitly accounting for student learning and employing a structured, multidimensional representation of student proficiencies, the model outperforms standard IRTbased methods on an online response prediction task when applied to real responses collected from students interacting with diverse pools of educational content.
The use of Likert scales with children.

We investigated elementary school children's ability to use a variety of Likert response formats to respond to concrete and abstract items.



111 children, aged 6-13 years, responded to 2 physical tasks that required them to make objectively verifiable judgments, using a 5-point response format. Then, using 25 items, we ascertained the consistency between responses using a "gold standard" yes/no format and responses using 5-point Likert formats including numeric values, as well as word-based frequencies, similarities to self, and agreeability.



All groups responded similarly to the physical tasks. For the 25 items, the use of numbers to signify agreement yielded low concordance with the yes/no answer format across age-groups. Formats based on words provided higher, but not perfect, concordance for all groups.



Researchers and clinicians need to be aware of the limited understanding that children have of Likert response formats.
A General Purpose Approach for Global and Local Path Planning Combination

Path planning is a key and complex element for every unmanned ground vehicle development. Once the 3D reconstruction of the environment is completed and the objective configuration (desired position and pose) is defined, there has to be a careful path planning algorithm. That path is subject to many restrictions: it has to be time optimal, we have limited degrees of freedom to work with since the vehicle is a non-holonomic robot, we have limited computational power and real-time constraints regarding on-board equipments, and finally the vehicle's mechanical limitations, like the maximum curvature. In this paper we present a new methodology for the path planning calculation. It was meant to be a one for all methodology, useful for different scenarios (automotive, industrial applications, mining, etc.) and different platforms (car-like vehicles, forklift trucks, etc.). This paper splits the problem in two stages. The first one faces the problem of reaching the goal with an a priori knowledge of the position affected by noise. The second approach develops a system capable of reaching the goal, enhancing the precision using a detection system, mainly based on computer vision. Particular focus is given to the interaction between the two methods proposed.
Advertising Keyword Recommendation based on Supervised Link Prediction in Multi-Relational Network

In the sponsored search system, advertisers bid on keywords that are related to their products or services to participate in repeated auctions and display their ads on the search result pages. Since there is a huge inventory of possible terms, instructive keyword recommendation is an important component to help advertisers optimize their campaigns and improve ad monetization. In this paper, by constructing a heterogeneous network which contains four types of links between advertisers and keywords based on different data resources and mining complex representations of network structure and task-guided attributes of nodes, we propose an approach to keyword recommendation based on supervised link prediction in multi-relational network. This method can retrieve ample candidates and provide informative ranking scores for recommended list of keywords, experimental results with real sponsored search data validate the effectiveness of the proposed algorithm in producing valuable keywords for advertisers.
Contactless vital signs measurement for self-service healthcare kiosk in intelligent building

Contactless healthcare system is becoming a new trend to solve the existing healthcare system limitations. High flexibility and accessibility are the main advantages of contactless measurements. In this paper, we proposed a contactless healthcare kiosk for intelligent building application. The Remote Imaging Photoplethysmography (rIPPG) based framework to measure multiple human's vital signs, including pulse rate (PR), respiratory rate (RR), and systolic blood pressure (SBP) was developed to fulfill the performance requirement with a personal computer. Our experimental results show the Mean Absolute Error (MAE) of each measured parameters are 1.7 BPM, 0.41 BPM and 8.15 mmHg for PR, RR, and SBP respectively compared with clinical apparatus. These statistical numbers show the feasibility of using the contactless based system to measure numerous vital signs and also meet the requirements of standard healthcare device.
PREVALENCE OF ACADEMIC PROCRASTINATION BEHAVIOR AMONG PRE-SERVICE TEACHERS , AND ITS RELATIONSHIP WITH DEMOGRAPHICS AND INDIVIDUAL PREFERENCES

The aim of this study is to investigate the prevalence of academic procrastination behavior among preservice teachers, and its relationship with demographics and individual preferences. The participants were 580 students who were studying in different major fields at the Faculty of Education in Pamukkale University. The age range varied from 19 to 28. In this study, Aitken’s Procrastination Inventory and Personal Information Sheet were used to gather data. The analysis showed that 23 % of pre-service teachers exhibited procrastination behavior at a high level. Other findings indicated that procrastination behavior significantly differed by gender, time preferences for studying courses and exams, and was negatively related to academic achievement. Implications of the findings are discussed and some suggestions are made for the educator and counselors.
Affine and Regional Dynamic Time Warping

Pointwise matches between two time series are of great importance in time series analysis, and dynamic time warping (DTW) is known to provide generally reasonable matches. There are situations where time series alignment should be invariant to scaling and offset in amplitude or where local regions of the considered time series should be strongly reflected in pointwise matches. Two different variants of DTW, affine DTW (ADTW) and regional DTW (RDTW), are proposed to handle scaling and offset in amplitude and provide regional emphasis respectively. Furthermore, ADTW and RDTW can be combined in two different ways to generate alignments that incorporate advantages from both methods, where the affine model can be applied either globally to the entire time series or locally to each region. The proposed alignment methods outperform DTW on specific simulated datasets, and one-nearest-neighbor classifiers using their associated difference measures are competitive with the difference measures associated with state-of-the-art alignment methods on real datasets.
Aggregate Profile Clustering for Streaming Analytics

Many analytic applications require analyzing user interaction data. In particular, such data can be aggregated over a window to build user activity profiles. Clustering such aggregate profiles is useful for grouping together users with similar behaviors, so that common models could be built for them. In this paper, we present an approach for clustering profiles that are incrementally maintained over a stream of updates. Owing to the potentially large number of users and high rate of interactions, maintaining profile clusters can have high processing and memory resource requirements. To tackle this problem, we apply distributed stream processing. However, in the presence of distributed state, it is a major challenge to partition the profiles over nodes such that memory and computation balance is maintained, while keeping the clustering accuracy high. Furthermore, in order to adapt to potentially changing user interaction patterns, the partitioning of profiles to nodes should be continuously revised, yet one should minimize the migration of profiles so as not to disturb the online processing of updates. We develop a re-partitioning technique that achieves all these goals. To achieve this, we keep micro-cluster summaries at each node and periodically collect these summaries at a central node to perform re-partitioning. We use a greedy algorithm with novel affinity heuristics to revise the partitioning and update the routing tables without introducing a lengthy pause. We showcase the effectiveness of our approach using an application that clusters customers of a telecommunications company based on their aggregate calling profiles.
Named Entity Recognition using Support Vector Machine: A Language Independent Approach

Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is now-a-days considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Indian languages (ILs) is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named (NE) classes, such as Person name, Location name, Organization name and Miscellaneous name. We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes , defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL) . In addition, we have manually annotated 150K wordforms of the Bengali news corpus, developed from the web-archive of a leading Bengali newspaper. We have also developed an unsupervised algorithm in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus. Lexical patterns have been used as the features of SVM in order to improve the system performance. The NER system has been tested with the gold standard test sets of 35K, and 60K tokens for Bengali, and Hindi, respectively. Evaluation results have demonstrated the recall, precision, and f-score values of 88.61%, 80.12%, and 84.15%, respectively, for Bengali and 80.23%, 74.34%, and 77.17%, respectively, for Hindi. Results show the improvement in the f-score by 5.13% with the use of context patterns. Statistical analysis, ANOVA is also performed to compare the performance of the proposed NER system with that of the existing HMM based system for both the languages. Named Entity (NE); Named Entity Recognition (NER); Support Vector Machine (SVM); Bengali;
Anti-Theft Vehicle Tracking with Automatic Police Notifying using Haversine Formula

Security, especially theft security of vehicle in common parking places has become a matter of concern. An efficient automotive security system is implemented for anti-theft using an embedded system integrated with Global Positioning System (GPS) and Global System for Mobile Communication (GSM). This proposed work is an attempt to design and develop a smart anti-theft system that uses GPS and GSM system to prevent theft and to determine the exact location of the vehicle. In case of an accident this system automatically sends the message for help to one's relatives. The preventive measures like engine ignition cutoff, fuel supply cutoff, electric shock system is also available. The owner can lock or unlock his/her vehicle with the help of SMS. This complete system is designed taking into consideration the low range vehicles to provide them extreme security. Tracking framework or system is getting to be progressively vital in expansive urban areas and it is more secured than different frameworks. The proposed work is cost-effective, reliable and has the function of preventing theft and providing accurate tracking system. A smart anti-theft system is one of the essential systems that homogenize both GPS and GSM systems. It is fundamental because of the huge numbers of uses of both GSM and GPS frameworks and the wide use of them by a great many individuals all through the world.