nils-herrmann/clean_scirepeval huggingface dataset:
Handbook on Implementing Gender Recognition: Using Speech Processing Techniques in LabVIEW

Traditionally the interest in voice-gender conversion was of a more theoretical nature rather than founded in real–life applications. However, with the increase in biometric security applications, mobile and automated telephonic communication and the resulting limitation in transmission bandwidth, practical applications of gender recognition have increased many folds. In this paper, using various speech processing techniques and algorithms, two models were made, one for generating Formant values of the voice sample and the other for generating pitch value of the voice sample. These two models were to be used for extracting gender biased features, i.e. Formant 1 and Pitch Value of a speaker. A preprocessing model was prepared in LabView for filtering out the noise components and also to enhance the high frequency formants in the voice sample. To calculate the mean of formants and pitch of all the samples of a speaker, a model containing loop and counters were implemented which generated a mean of Formant 1 and Pitch value of the speaker. Using nearest neighbor method, calculating Euclidean distance from the Mean value of Males and Females of the generated mean values of Formant 1 and Pitch, the speaker was classified between Male and Female. The algorithm was implemented in real time using NI LabVIEW.
Impact of IEEE 1609.4 channel switching on the IEEE 802.11p beaconing performance

The IEEE 802.11p Wireless Access in Vehicular Environment (WAVE) protocol can only transmit packets on one channel. To support multi-channel operations, channel switching procedures have been proposed in IEEE 1609.4. This paper provides an analysis of the beaconing performance of IEEE 802.11p when these channel switching procedures are used. To analyse the performance of beaconing, simulation experiments are conducted using the simulation tool OMNeT++. An overview is given of existing solutions which can be applied to minimise the impact of channel switching on IEEE 802.11p performance. An interesting observation is that present solutions focus on optimising for use of the Service Channels, and some even deteriorate the performance of the Control Channel.
Guiding Deep Learning System Testing using Surprise Adequacy

Deep Learning (DL) systems are rapidly being adopted in safety and security critical domains, urgently calling for ways to test their correctness and robustness. Testing of DL systems has traditionally relied on manual collection and labelling of data. Recently, a number of coverage criteria based on neuron activation values have been proposed. These criteria essentially count the number of neurons whose activation during the execution of a DL system satisfied certain properties, such as being above predefined thresholds. However, existing coverage criteria are not sufficiently fine grained to capture subtle behaviours exhibited by DL systems. Moreover, evaluations have focused on showing correlation between adversarial examples and proposed criteria rather than evaluating and guiding their use for actual testing of DL systems. We propose a novel test adequacy criterion for testing of DL systems, called Surprise Adequacy for Deep Learning Systems (SADL), which is based on the behaviour of DL systems with respect to their training data. We measure the surprise of an input as the difference in DL system’s behaviour between the input and the training data (i.e., what was learnt during training), and subsequently develop this as an adequacy criterion: a good test input should be sufficiently but not overtly surprising compared to training data. Empirical evaluation using a range of DL systems from simple image classifiers to autonomous driving car platforms shows that systematic sampling of inputs based on their surprise can improve classification accuracy of DL systems against adversarial examples by up to 77.5% via retraining.
Image guided depth enhancement via deep fusion and local linear regularizaron

Depth maps captured by RGB-D cameras are often noisy and incomplete at edge regions. Most existing methods assume that there is a co-occurrence of edges in depth map and its corresponding color image, and improve the quality of depth map guided by the color image. However, when the color image is noisy or richly detailed, the high frequency artifacts will be introduced into depth map. In this paper, we propose a deep residual network based on deep fusion and local linear regularization for guided depth enhancement. The presented scheme can effectively extract the correlation between depth map and color image in the deep feature space. To reduce the difficulty of training, a specific layer of network which introduces a local linear regularization constraint on the output depth is designed. Experiments on various applications, including depth denoising, super-resolution and inpainting, demonstrate the effectiveness and reliability of our proposed approach.
Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

In this paper, we investigate two new strategies to detect objects accurately and efficiently using deep convolutional neural network: 1) scale-dependent pooling and 2) layerwise cascaded rejection classifiers. The scale-dependent pooling (SDP) improves detection accuracy by exploiting appropriate convolutional features depending on the scale of candidate object proposals. The cascaded rejection classifiers (CRC) effectively utilize convolutional features and eliminate negative object proposals in a cascaded manner, which greatly speeds up the detection while maintaining high accuracy. In combination of the two, our method achieves significantly better accuracy compared to other state-of-the-arts in three challenging datasets, PASCAL object detection challenge, KITTI object detection benchmark and newly collected Inner-city dataset, while being more efficient.
A Novel Fast Framework for Topic Labeling Based on Similarity-preserved Hashing

Recently, topic modeling has been widely applied in data mining due to its powerful ability. A common, major challenge in applying such topic models to other tasks is to accurately interpret the meaning of each topic. Topic labeling, as a major interpreting method, has attracted significant attention recently. However, most of previous works only focus on the effectiveness of topic labeling, and less attention has been paid to quickly creating good topic descriptors; Meanwhile, it’s hard to assign labels for new emerging topics by using most of existing methods. To solve the problems above, in this paper, we propose a novel fast topic labeling framework that casts the labeling problem as a k-nearest neighbor (KNN) search problem in probability distributions. Our experimental results show that the proposed sequential interleaving method based on locality sensitive hashing (LSH) technology is efficient in boosting the comparison speed among probability distributions, and the proposed framework can generate meaningful labels to interpret topics, including new emerging topics.
Cross-Entropy Method for Reinforcement Learning

Reinforcement Learning methods have been succesfully applied to various optimalization problems. Scaling this up to real world sized problems has however been more of a problem. In this research we apply Reinforcement Learning to the game of Tetris which has a very large state space. We not only try to learn policies for Standard Tetris but try to learn parameterized policies for Generalized Tetris, which varies from game to game in field size and the chance of a single block occuring. In comparison to a non-parameterized policy we find that a parameterized policy is able to outperform the non-parameterized policy. The increased complexity of learning such a policy using the Cross-Entropy method however reaches a limit at which the policy is too complex to learn and performance drops below the non-parameterized policy.
Is multiagent deep reinforcement learning the answer or the question? A brief survey

Deep reinforcement learning (DRL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. In this context, first, this article provides a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Second, it provides guidelines to complement this emerging area by (i) showcasing examples on how methods and algorithms from DRL and multiagent learning (MAL) have helped solve problems in MDRL and (ii) providing general lessons learned from these works. We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists in both areas (DRL and MAL) in a joint effort to promote fruitful research in the multiagent community.
Brain-brain integration in 2035: metaphysical and ethical implications

The purpose of this study is to think ahead into the year 2035 and reflect on the ethical implications of brain-to-brain linking.  Philosophical argument.  It is quite likely that the direction of technological research today is heading toward a closer integration of mind and machine in 2035. What is interesting is that the integration also makes mind-mind or brain-brain integration possible too. There is nothing in principle that would prevent hooking up more than one brain to a machine, or connecting two or more brains together to harness their processing power to tackle a very complicated task. If that happens, the whole notion of what it is to be an individual and a self will have to be rethought. I have offered a way in which that can be done: Instead of viewing the self as being contained in a closed space traditionally defined by the skin, the self can expand outside of the skin and merge temporarily with other selves too. This also has profound implications on the notion of privacy, especially on how it is conceptualized and justified.  This research is limited to theoretical argumentation only. It relies on the current empirical and scientific investigations that are going on at the moment and provide ethical reflections on them.  We need to anticipate technological innovations to be more proactive in deliberating and formulating policy and ethical guidelines; otherwise, ethicists will just muse after the fact, implying that there is nothing further to be done. Social implications – Brain-to-brain linking has tremendous social implications, so is the ethical reflection on the issue.  Argument purporting to show the specific content in ethical guidelines on brain-to-brain interlinking based on the metaphysics of the self that is directly implicated by the technology has not been done before, according to the author’s best knowledge.
Learning Context-Dependent Mappings from Sentences to Logical Form

We consider the problem of learning context-dependent mappings from sentences to logical form. The training examples are sequences of sentences annotated with lambda-calculus meaning representations. We develop an algorithm that maintains explicit, lambda-calculus representations of salient discourse entities and uses a context-dependent analysis pipeline to recover logical forms. The method uses a hidden-variable variant of the perception algorithm to learn a linear model used to select the best analysis. Experiments on context-dependent utterances from the ATIS corpus show that the method recovers fully correct logical forms with 83.7% accuracy.
Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization

We consider the problem of modeling the content structureof texts within a specific domain, in terms of the topics the texts address and the order in which these topics appear. We first present an effective knowledge-lean method for learning content models from unannotated documents, utilizing a novel adaptation of algorithms for Hidden Markov Models. We then apply our method to two complementary tasks: information ordering and extractive summarization. Our experiments show that incorporating content models in these applications yields substantial improvement over previously-proposed methods. Publication info: HLT-NAACL 2004:
eSOLHotel: Generación de un lexicón de opinión en español adaptado al dominio turístico

Since Web 2.0 is the largest container for subjective expressions about different topics or issues expressed in all languages, the study of Sentiment Analysis has grown exponentially. In this work, we focus on Spanish polarity classification of hotel reviews and a new domain-dependent lexical resource (eSOLHotel) is presented. This new lexicon has been compiled following a corpus-based approach. We have carried out several experiments using an unsupervised approach for the polarity classification over the category of hotels from corpus SFU. The results obtained with the new lexicon eSOLHotel outperform the results with other general purpose lexicon.
Large-Scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex

Dual decomposition methods are the current state-of-the-art for training multiclass formulations of Support Vector Machines (SVMs). At every iteration, dual decomposition methods update a small subset of dual variables by solving a restricted optimization problem. In this paper, we propose an exact and efficient method for solving the restricted problem. In our method, the restricted problem is reduced to the well-known problem of Euclidean projection onto the positive simplex, which we can solve exactly in expected O(k) time, where k is the number of classes. We demonstrate that our method empirically achieves state-of-the-art convergence on several large-scale high-dimensional datasets.
Recent advances in LVCSR : A benchmark comparison of performances

Large Vocabulary Continuous Speech Recognition (LVCSR), which is characterized by a high variability of the speech, is the most challenging task in automatic speech recognition (ASR). Believing that the evaluation of ASR systems on relevant and common speech corpora is one of the key factors that help accelerating research, we present, in this paper, a benchmark comparison of the performances of the current state-of-the-art LVCSR systems over different speech recognition tasks. Furthermore, we put objectively into evidence the best performing technologies and the best accuracy achieved so far in each task. The benchmarks have shown that the Deep Neural Networks and Convolutional Neural Networks have proven their efficiency on several LVCSR tasks by outperforming the traditional Hidden Markov Models and Guaussian Mixture Models. They have also shown that despite the satisfying performances in some LVCSR tasks, the problem of large-vocabulary speech recognition is far from being solved in some others, where more research efforts are still needed.
High-Frame-Rate Synthetic Aperture Ultrasound Imaging Using Mismatched Coded Excitation Waveform Engineering: A Feasibility Study

Mismatched coded excitation (CE) can be employed to increase the frame rate of synthetic aperture ultrasound imaging. The high autocorrelation and low cross correlation (CC) of transmitted signals enables the identification and separation of signal sources at the receiver. Thus, the method provides B-mode imaging with simultaneous transmission from several elements and capability of spatial decoding of the transmitted signals, which makes the imaging process equivalent to consecutive transmissions. Each transmission generates its own image and the combination of all the images results in an image with a high lateral resolution. In this paper, we introduce two different methods for generating multiple mismatched CEs with an identical frequency bandwidth and code length. Therefore, the proposed families of mismatched CEs are able to generate similar resolutions and signal-to-noise ratios. The application of these methods is demonstrated experimentally. Furthermore, several techniques are suggested that can be used to reduce the CC between the mismatched codes.
Designing for Exploratory Search on Touch Devices

Exploratory search confront users with challenges in expressing search intents as the current search interfaces require investigating result listings to identify search directions, iterative typing, and reformulating queries. We present the design of Exploration Wall, a touch-based search user interface that allows incremental exploration and sense-making of large information spaces by combining entity search, flexible use of result entities as query parameters, and spatial configuration of search streams that are visualized for interaction. Entities can be flexibly reused to modify and create new search streams, and manipulated to inspect their relationships with other entities. Data comprising of task-based experiments comparing Exploration Wall with conventional search user interface indicate that Exploration Wall achieves significantly improved recall for exploratory search tasks while preserving precision. Subjective feedback supports our design choices and indicates improved user satisfaction and engagement. Our findings can help to design user interfaces that can effectively support exploratory search on touch devices.
Cross-Level Sensor Network Simulation with COOJA

Simulators for wireless sensor networks are a valuable tool for system development. However, current simulators can only simulate a single level of a system at once. This makes system development and evolution difficult since developers cannot use the same simulator for both high-level algorithm development and low-level development such as device-driver implementations. We propose cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels. We present an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system. COOJA allows for simultaneous simulation at the network level, the operating system level, and the machine code instruction set level. With COOJA, we show the feasibility of the cross-level simulation approach
Online Tourism Communities on the Path to Web 2.0: An Evaluation

In recent years a technological and sociological paradigm shift has taken place in the Internet that is often referred to as Web 2.0. Companies and individuals have started to adapt existing Web sites to the new standards and principles and created new types of Web services and communities. The tourism domain is no exception to this trend new tourism communities emerged and long-established ones integrated new features to keep up with this trend. In this paper we are evaluating eight tourism communities with respect to Web 2.0. Each community is evaluated based on a criteria catalogue that draws ideas from online community studies. The findings are discussed in the context of the tourist life cycle that is structured in a pre-trip, on-site and after-trip phase. The value for the traveller is highlighted for each phase and potential problems are discussed.
Amino acid substitution matrices from an information theoretic perspective.

Protein sequence alignments have become an important tool for molecular biologists. Local alignments are frequently constructed with the aid of a "substitution score matrix" that specifies a score for aligning each pair of amino acid residues. Over the years, many different substitution matrices have been proposed, based on a wide variety of rationales. Statistical results, however, demonstrate that any such matrix is implicitly a "log-odds" matrix, with a specific target distribution for aligned pairs of amino acid residues. In the light of information theory, it is possible to express the scores of a substitution matrix in bits and to see that different matrices are better adapted to different purposes. The most widely used matrix for protein sequence comparison has been the PAM-250 matrix. It is argued that for database searches the PAM-120 matrix generally is more appropriate, while for comparing two specific proteins with suspected homology the PAM-200 matrix is indicated. Examples discussed include the lipocalins, human alpha 1 B-glycoprotein, the cystic fibrosis transmembrane conductance regulator and the globins.
Consumer-driven definition of traditional food products and innovation in traditional foods. A qualitative cross-cultural study

Traditional food products (TFP) are an important part of European culture, identity, and heritage. In order to maintain and expand the market share of TFP, further improvement in safety, health, or convenience is needed by means of different innovations. The aim of this study was to obtain a consumer-driven definition for the concept of TFP and innovation and to compare these across six European countries (Belgium, France, Italy, Norway, Poland and Spain) by means of semantic and textual statistical analyses. Twelve focus groups were performed, two per country, under similar conditions. The transcriptions obtained were submitted to an ordinary semantic analysis and to a textual statistical analysis using the software ALCESTE. Four main dimensions were identified for the concept of TFP: habit-natural, origin-locality, processing-elaboration and sensory properties. Five dimensions emerged around the concept of innovation: novelty-change, variety, processing-technology, origin-ethnicity and convenience. TFP were similarly perceived in the countries analysed, while some differences were detected for the concept of innovation. Semantic and statistical analyses of the focus groups led to similar results for both concepts. In some cases and according to the consumers' point of view the application of innovations may damage the traditional character of TFP.
Efficient Mitigation Strategies for Epidemics in Rural Regions

Containing an epidemic at its origin is the most desirable mitigation. Epidemics have often originated in rural areas, with rural communities among the first affected. Disease dynamics in rural regions have received limited attention, and results of general studies cannot be directly applied since population densities and human mobility factors are very different in rural regions from those in cities. We create a network model of a rural community in Kansas, USA, by collecting data on the contact patterns and computing rates of contact among a sampled population. We model the impact of different mitigation strategies detecting closely connected groups of people and frequently visited locations. Within those groups and locations, we compare the effectiveness of random and targeted vaccinations using a Susceptible-Exposed-Infected-Recovered compartmental model on the contact network. Our simulations show that the targeted vaccinations of only 10% of the sampled population reduced the size of the epidemic by 34.5%. Additionally, if 10% of the population visiting one of the most popular locations is randomly vaccinated, the epidemic size is reduced by 19%. Our results suggest a new implementation of a highly effective strategy for targeted vaccinations through the use of popular locations in rural communities.