nils-herrmann/clean_scirepeval huggingface dataset:
An Effective RSU Allocation Strategy for Maximizing Vehicular Network Connectivity

Roadside Unit (RSU) in Vehicular Ad-hoc Network (VANET) is an essential unit usually placed at intersections to collect and analyze traffic data given from smart vehicles. Due to the costs of RSU setup, effective RSU allocation is one of important issues in VANET. In this paper, we provide an optimal RSU allocation algorithm with a concept of intersectionconnectivity between intersections. We initially find optimal RSU candidate intersections which can cover the entire intersections, in the meantime minimizing the overlapped transmission coverage of RSUs. Then we repeatedly eliminate RSU candidates by the value of intersection-connectivity between RSU candidate intersections. The intersection-connectivity between two intersections is basically measured by the number of vehicles with the same moving pattern such that pass through both intersections. Thus, a high value of intersectionconnectivity between two intersections stands for that the traffic information obtained at one intersection can be carried-and-forwarded to the other intersection with a high probability by those vehicles. With this feature, we repeatedly remove RSUs having high intersectionconnectivity with its neighbors. Finally, we provide simulated analyses of our algorithms using real urban roadmaps of JungGu and Seochogu in Seoul. We analyze how our algorithms work in different types of roadways with real traffic data, and find the optimal number and positions of RSUs in these areas.
Speech Recognition: Keyword Spotting Through Image Recognition

The problem of identifying voice commands has always been a challenge due to the presence of noise and variability in speed, pitch, etc. We will compare the efficacies of several neural network architectures for the speech recognition problem. In particular, we will build a model to determine whether a one second audio clip contains a particular word (out of a set of 10), an unknown word, or silence. The models to be implemented are a CNN recommended by the Tensorflow Speech Recognition tutorial, a low-latency CNN, and an adversarially trained CNN. The result is a demonstration of how to convert a problem in audio recognition to the better-studied domain of image classification, where the powerful techniques of convolutional neural networks are fully developed. Additionally, we demonstrate the applicability of the technique of Virtual Adversarial Training (VAT) to this problem domain, functioning as a powerful regularizer with promising potential future applications.
On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators

This paper shows, by means of an operator called a splitting operator, that the Douglas-Rachford splitting method for finding a zero of the sum of two monotone operators is a special case of the proximal point algorithm, Therefore, applications of Douglas -Rachford splitting, such as the alternating direction method of multipliers for convex programming decomposit ion, are also special cases of the proximal point algorithm. This observation allows the unification and generalization of a variety of convex programming algorithms. By introducing a modified version of the proximal point algorithm, we derive a new, generalized alternating direction method of multipliers for convex programming. Advances of this sort illustrate the power and generality gained by adopting monotone operator theory as a conceptual framework.
The cinema-cognition dialogue: a match made in brain

That human evolution amalgamates biological and cultural change is taken as a given, and that the interaction of brain, body, and culture is more reciprocal then initially thought becomes apparent as the science of evolution evolves (Jablonka and Lamb, 2005). The contribution of science and technology to this evolutionary process is probably the first to come to mind. The biology of Homo sapiens permits and promotes the development of technologies and artefacts that enable us to sense and reach physical niches previously inaccessible. This extends our biological capabilities, but is also expected to create selective pressures on these capabilities. The jury is yet out on the pace at which critical biological changes take place in evolution. There is no question, however, that the kinetics of technological and cultural change is much faster, rendering the latter particularly important in the biography of the individual and the species alike. The capacity of art to enrich human capabilities is recurrently discussed by philosophers and critics (e.g., Arsitotle/Poetics, Richards, 1925; Smith and Parks, 1951; Gibbs, 1994). Yet less attention is commonly allotted to the role of the arts in the aforementioned ongoing evolutional tango. My position is that the art of cinema is particularly suited to explore the intriguing dialogue between art and the brain. Further, in the following set of brief notes, intended mainly to trigger further thinking on the subject, I posit that cinema provides an unparalleled and highly rewarding experimentation space for the mind of the individual consumer of that art. In parallel, it also provides a useful and promising device for investigating brain and cognition.
Adding Semantic Role Annotation to a Corpus of Written Dutch

We present an approach to automatic semantic role labeling (SRL) carried out in the context of the Dutch Language Corpus Initiative (D-Coi) project. Adapting earlier research which has mainly focused on English to the Dutch situation poses an interesting challenge especially because there is no semantically annotated Dutch corpus available that can be used as training data. Our automatic SRL approach consists of three steps: bootstrapping from a syntactically annotated corpus by means of a rulebased tagger developed for this purpose, manual correction on the basis of the PropBank guidelines which have been adapted to Dutch and training a machine learning system on the manually corrected data.
Freebase: A Shared Database of Structured General Human Knowledge

Freebase is a practical, scalable, graph-shaped database of structured general human knowledge, inspired by Semantic Web research and collaborative data communities such as the Wikipedia. Freebase allows public read and write access through an HTTP-based graph-query API for research, the creation and maintenance of structured data, and application building. Access is free and all data in Freebase has a very open (e.g. Creative Commons, GFDL) license.
Understanding the effective receptive field in semantic image segmentation

Deep convolutional neural networks trained with strong pixel-level supervision have recently significantly boosted the performance in semantic image segmentation. The receptive field is a crucial issue in such visual tasks, as the output must capture enough information about large objects to make a better decision. In DCNNs, the theoretical receptive field size could be very large, but the effective receptive field may be quite small. The latter is an really important factor in performance. In this work, we defined a method of measuring effective receptive field. We observed that stacking layers with large receptive field can increase the size of receptive field and increase the density of receptive field. Based on the observation, we designed a Dense Global Context Module, which makes the effective receptive field coverage larger and density higher. With the Dense Global Context Module, segmentation model reduces a large number of parameters while the performance has been substantially improved. Massive experiments proved that our Dense Global Context Module exhibits very excellent performance on the PASCAL VOC2012 and PASCAL CONTEXT data set.
The survey of GSM wireless communication system

In the past decade, wireless communications experienced an explosive growth period and became an integral part of modern society. The convenience and flexibility offered by mobile communications have made it one of the fastest growing areas of telecommunications. Mobile communication systems have experienced rapid growth in the number of users as well as the range of services provided during the last two decades. The Global System for Mobile communications, GSM, is a pan-European Mobile communication system in the 900 MHz band which was first introduced in the early years of this decade. An increasing demand for data transmission services over the GSM system has been driven by the wide use of the internet applications. In this thesis, we focus on the survey of GSM for wireless communication, which is one of the most widely deployed second generation wireless cellular systems in the world. While voice was the primary service provided early communication systems, current systems offer other transmission services. One of the most commonly used cellular systems is based on the Global System for Mobile communication, GSM, standard. We briefly introduce the GSM System configuration and major properties. It included five parts. It is about service and features, architecture of GSM system, channel and frame structure of GSM, GSM security features, data in the GSM System.
Predicting Facial Attributes in Video Using Temporal Coherence and Motion-Attention

Recent research progress in facial attribute recognition has been dominated by small improvements on the only large-scale publicly available benchmark dataset, CelebA . We propose to extend attribute prediction research to unconstrained videos. Applying attribute models trained on CelebA – a still image dataset – to video data highlights several major problems with current models, including the lack of consideration for both time and motion. Many facial attributes (e.g. gender, hair color) should be consistent throughout a video, however, current models do not produce consistent results. We introduce two methods to increase the consistency and accuracy of attribute responses in videos: a temporal coherence constraint, and a motionattention mechanism. Both methods work on weakly labeled data, requiring attribute labels for only one frame in a sequence, which we call the anchor frame. The temporal coherence constraint moves the network responses of non-anchor frames toward the responses of anchor frames for each sequence, resulting in more stable and accurate attribute predictions. We use the motion between anchor and non-anchor video frames as an attention mechanism, discarding the information from parts of the non-anchor frame where no motion occurred. This motion-attention focuses the network on the moving parts of the non-anchor frames (i.e. the face). Since there is no large-scale video dataset labeled with attributes, it is essential for attribute models to be able to learn from weakly labeled data. We demonstrate the effectiveness of the proposed methods by evaluating them on the challenging YouTube Faces video dataset . The proposed motion-attention and temporal coherence methods outperform attribute models trained on CelebA, as well as those fine-tuned on video data. To the best of our knowledge, this paper is the first to address the problem of facial attribute prediction in video.
An improved algorithm for feature selection using fractal dimension

Dimensionality reduction is an important issue in data mining and machine learning. Traina[1] proposed a feature selection algorithm to select the most important attributes for a given set of n-dimensional vectors based on correlation fractal dimension. The author used a kind of multi-dimensional “quad-tree” structure to compute the fractal dimension. Inspired by his work, we propose a new and simpler algorithm to compute the fractal dimension, and design a novel and faster feature selection algorithm using correlation fractal dimension, whose time complexity is lower than that of Traina’s. The main idea is when computing the fractal dimension of (d-1)-dimensional data, the intermediate generated results of the extended d-dimensional data is reused. It inherits the desirable properties described as in . Also, Our algorithm does not require the grid sizes decrease by half as the original “quad-tree” algorithm. Experiments show our feature selection algorithm has a good efficiency over the test dataset.
Sentiment analysis techniques in recent works

Sentiment Analysis (SA) task is to label people's opinions as different categories such as positive and negative from a given piece of text. Another task is to decide whether a given text is subjective, expressing the writer's opinions, or objective, expressing. These tasks were performed at different levels of analysis ranging from the document level, to the sentence and phrase level. Another task is aspect extraction which originated from aspect-based sentiment analysis in phrase level. All these tasks are under the umbrella of SA. In recent years a large number of methods, techniques and enhancements have been proposed for the problem of SA in different tasks at different levels. This survey aims to categorize SA techniques in general, without focusing on specific level or task. And also to review the main research problems in recent articles presented in this field. We found that machine learning-based techniques including supervised learning, unsupervised learning and semi-supervised learning techniques, Lexicon-based techniques and hybrid techniques are the most frequent techniques used. The open problems are that recent techniques are still unable to work well in different domain; sentiment classification based on insufficient labeled data is still a challenging problem; there is lack of SA research in languages other than English; and existing techniques are still unable to deal with complex sentences that requires more than sentiment words and simple parsing.
A log buffer-based flash translation layer using fully-associative sector translation

Flash memory is being rapidly deployed as data storage for mobile devices such as PDAs, MP3 players, mobile phones, and digital cameras, mainly because of its low electronic power, nonvolatile storage, high performance, physical stability, and portability. One disadvantage of flash memory is that prewritten data cannot be dynamically overwritten. Before overwriting prewritten data, a time-consuming erase operation on the used blocks must precede, which significantly degrades the overall write performance of flash memory. In order to solve this “erase-before-write” problem, the flash memory controller can be integrated with a software module, called “flash translation layer (FTL).” Among many FTL schemes available, the log block buffer scheme is considered to be optimum. With this scheme, a small number of log blocks, a kind of write buffer, can improve the performance of write operations by reducing the number of erase operations. However, this scheme can suffer from low space utilization of log blocks. In this paper, we show that there is much room for performance improvement in the log buffer block scheme, and propose an enhanced log block buffer scheme, called FAST (full associative sector translation). Our FAST scheme improves the space utilization of log blocks using fully-associative sector translations for the log block sectors. We also show empirically that our FAST scheme outperforms the pure log block buffer scheme.
Identification of High Yielding Investment Programs in Bitcoin via Transactions Pattern Analysis

Although Bitcoin is one of the most successful decentralized cryptocurrency, recent research has revealed that it can be used as fraudulent activities such as HYIP (High Yield Investment Program). To identify such undesired activities, it is important to obtain Bitcoin addresses related with fraud. So far, the identification of such activities is based upon relating Bitcoin addresses with graph mining procedures. In this paper, we follow a different approach for identifying Bitcoin addresses related with HYIP by analyzing transactions patterns. In particular, based on the individual inspection of HYIP activity in Bitcoin, we propose a number of features that can be extracted from transactions. In particular, a signed integer called pattern is assigned to each transaction and the frequency of each pattern is calculated as key features. By evaluating the classification performance with more than 1,500 labeled Bitcoin addresses, it is shown that about 83% of HYIP addresses are correctly classified while maintaining false positive rate less than 4.4%.
A review and outlook for a 'Building Information Model' (BIM): A multi-standpoint framework for technological development

1474-0346/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.aei.2010.06.003 * Tel.: +386 1 4768 521; fax: +386 1 4250 681. E-mail address: tomo.cerovsek@fgg.uni-lj.si This study provides a review of important issues for ‘Building Information Modelling’ (BIM) tools and standards and comprehensive recommendations for their advancement and development that may improve BIM technologies and provide a basis for inter-operability, integration, model-based communication, and collaboration in building projects. Based on a critical review of Building Product Modelling, including the development of standards for exchange and the features of over 150 AEC/O (Architecture, Engineering, Construction, and Operation) tools and digital models, a methodological framework is proposed for improvements to both BIM tools and schemata. The features relevant to the framework were studied using a conceptual process model and a ‘BIM System-of-Systems’ (BIM-SoS) model. The development, implementation, and use of the BIM Schema are analysed from the standpoint of standardisation. The results embrace the requirements for a BIM research methodology, with an example of methods and procedures, an R&D review with critique, and a multi-standpoint framework for developments with concrete recommendations, supported by BIM metrics, upon which the progress of tools, models, and standards may be measured, evaluated, streamlined, and judged. It is also proposed that any BIM Schema will never be ‘completed’ but should be developed as evolutionary ontology by ‘segmented standpoint models’ to better account for evolving tools and AEC/O practices.
A 12-b, 1-GS/s 6.1 mW current-steering DAC in 14 nm FinFET with 80 dB SFDR for 2G/3G/4G cellular application

A 14nm FinFET CMOS 12-b current-steering digital-to-analog (DAC) for 2G/3G/4G cellular applications is presented. A bit segmentation of 6-bit thermometer and 6-bit binary is adopted, and it utilizes the dynamic element matching (DEM) technique to suppress the spurious tones caused by the current source mismatches in 3-D FinFETs. In addition, to keep the voltage drop across each transistor within long-term reliability limit, output switches are designed with shielding transistors while achieving make-before-break operation with the proposed low crossing point level shifter. The active area of a single DAC is 0.036 mm2, and its power consumption is 6.1 mW with SFDR of 80 dBc.
Hybrid context dependent CD-DNN-HMM Keyword Spotting (KWS) in speech conversations

We present detailed analysis of phoneme recognition performance of a context dependent tied-state triphone Gaussian Mixture Model Hidden Markov Model (CD-GMM-HMM) acoustic model (state-of-the-art large acoustic model (AM)) and a four hidden layer context dependent Deep Neural Network (CD-DNN-HMM) AM on the WSJ speech corpus. Using a bigram phoneme language model, phoneme recognition experiments are performed on a two hour independent test set using the Viterbi decoding which show a relative 33.3% improvement by our CD-DNN acoustic model. We then present a filler based Hybrid DNN-HMM Keyword Spotting KWS system which to our knowledge is the first KWS architecture using context dependent DNN and HMM. In our experiments, a strong baseline of CD-GMM-HMM KWS provide 79.0% correct detection accuracy at a false alarm (FA) rate of 5.0 FA/Hr. Whereas, the proposed hybrid CD-DNN-HMM KWS results in 88.5% correct detection accuracy at 5.0 FA/Hr - a relative improvement of 43.3%. We provide further analysis and conclude that Hybrid CD-DNN-HMM KWS provides an attractive alternate solution for near real-time KWS applications with high detection accuracy and low FA.
A Combination of Amino Acids and Caffeine Enhances Sprint Running Capacity in a Hot, Hypoxic Environment.

Heat and hypoxia exacerbate central nervous system (CNS) fatigue. We therefore investigated whether essential amino acid (EAA) and caffeine ingestion attenuates CNS fatigue in a simulated team sport-specific running protocol in a hot, hypoxic environment. Subelite male team sport athletes (n = 8) performed a repeat sprint running protocol on a nonmotorized treadmill in an extreme environment on 4 separate occasions. Participants ingested one of four supplements: a double placebo, 3 mg.kg-1 body mass of caffeine + placebo, 2 x 7 g EAA (Musashi Create)+placebo, or caffeine + EAA before each exercise session using a randomized, double-blind crossover design. Electromyography (EMG) activity and quadriceps evoked responses to magnetic stimulation were assessed from the dominant leg at preexercise, halftime, and postexercise. Central activation ratio (CAR) was used to quantify completeness of quadriceps activation. Oxygenation of the prefrontal cortex was measured via near-infrared spectroscopy. Mean sprint work was higher (M = 174 J, 95% CI [23, 324], p < .05, d = 0.30; effect size, likely beneficial) in the caffeine + EAA condition versus EAAs alone. The decline in EMG activity was less (M = 13%, 95% CI [0, 26]; p < .01, d = 0.58, likely beneficial) in caffeine + EAA versus EAA alone. Similarly, the pre- to postexercise decrement in CAR was significantly less (M = -2.7%, 95% CI [0.4, 5.4]; p < .05, d = 0.50, likely beneficial) when caffeine + EAA were ingested compared with placebo. Cerebral oxygenation was lower (M = -5.6%, 95% CI [1.0, 10.1]; p < .01, d = 0.60, very likely beneficial) in the caffeine + EAA condition compared with LNAA alone. Co-ingestion of caffeine and EAA appears to maintain muscle activation and central drive, with a small improvement in running performance.
3D CNNs on Distance Matrices for Human Action Recognition

In this paper we are interested in recognizing human actions from sequences of 3D skeleton data. For this purpose we combine a 3D Convolutional Neural Network with body representations based on Euclidean Distance Matrices (EDMs), which have been recently shown to be very effective to capture the geometric structure of the human pose. One inherent limitation of the EDMs, however, is that they are defined up to a permutation of the skeleton joints, i.e., randomly shuffling the ordering of the joints yields many different representations. In oder to address this issue we introduce a novel architecture that simultaneously, and in an end-to-end manner, learns an optimal transformation of the joints, while optimizing the rest of parameters of the convolutional network. The proposed approach achieves state-of-the-art results on 3 benchmarks, including the recent NTU RGB-D dataset, for which we improve on previous LSTM-based methods by more than 10 percentage points, also surpassing other CNN-based methods while using almost 1000 times fewer parameters.