nils-herrmann/clean_scirepeval huggingface dataset:
Toward an Integration of Deep Learning and Neuroscience

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.
Performance Evaluation of Object Detection and Tracking Systems

This paper presents a set of metrics and algorithms for performance evaluation of object tracking systems. Our emphasis is on wide-ranging, robust metrics which can be used for evaluation purposes without inducing any bias towards the evaluation results. The goal is to report a set of unbiased metrics and to leave the final evaluation of the evaluation process to the research community analyzing the results, keeping the human in the loop. We propose metrics from statistical detection and estimation theory tailored to object detection and tracking tasks using frame-based as well as object-based evaluation paradigms. Object correspondences between multiple ground truth objects to multiple tracker result objects are established from a correspondence matrix. The correspondence matrix is built using three different methods of distance computation between trajectories. Results on PETS 2001 data set are presented in terms of 1 and 2 order statistical descriptors of these metrics.
Minority report in fraud detection: classification of skewed data

This paper proposes an innovative fraud detection method, built upon existing fraud detection research and Minority Report, to deal with the data mining problem of skewed data distributions. This method uses backpropagation (BP), together with naive Bayesian (NB) and C4.5 algorithms, on data partitions derived from minority oversampling with replacement. Its originality lies in the use of a single meta-classifier (stacking) to choose the best base classifiers, and then combine these base classifiers' predictions (bagging) to improve cost savings (stacking-bagging). Results from a publicly available automobile insurance fraud detection data set demonstrate that stacking-bagging performs slightly better than the best performing bagged algorithm, C4.5, and its best classifier, C4.5 (2), in terms of cost savings. Stacking-bagging also outperforms the common technique used in industry (BP without both sampling and partitioning). Subsequently, this paper compares the new fraud detection method (meta-learning approach) against C4.5 trained using undersampling, oversampling, and SMOTEing without partitioning (sampling approach). Results show that, given a fixed decision threshold and cost matrix, the partitioning and multiple algorithms approach achieves marginally higher cost savings than varying the entire training data set with different class distributions. The most interesting find is confirming that the combination of classifiers to produce the best cost savings has its contributions from all three algorithms.
Bregman Divergence-Based Regularization for Transfer Subspace Learning

The regularization principals  lead approximation schemes to deal with various learning problems, e.g., the regularization of the norm in a reproducing kernel Hilbert space for the ill-posed problem. In this paper, we present a family of subspace learning algorithms based on a new form of regularization, which transfers the knowledge gained in training samples to testing samples. In particular, the new regularization minimizes the Bregman divergence between the distribution of training samples and that of testing samples in the selected subspace, so it boosts the performance when training and testing samples are not independent and identically distributed. To test the effectiveness of the proposed regularization, we introduce it to popular subspace learning algorithms, e.g., principal components analysis (PCA) for cross-domain face modeling; and Fisher's linear discriminant analysis (FLDA), locality preserving projections (LPP), marginal Fisher's analysis (MFA), and discriminative locality alignment (DLA) for cross-domain face recognition and text categorization. Finally, we present experimental evidence on both face image data sets and text data sets, suggesting that the proposed Bregman divergence-based regularization is effective to deal with cross-domain learning problems.
Design of miniaturized 3-way Bagley polygon power divider using non-uniform transmission lines

In this paper, a design of miniaturized 3-way Bagley polygon power divider (BPD) is presented. The design is based on using non-uniform transmission lines (NTLs) in each arm of the divider instead of the conventional uniform ones. For verification purposes, a 3-way BPD is designed, simulated, fabricated, and measured. Besides suppressing the fundamental frequency's odd harmonics, a size reduction of almost 30% is achieved.
Acoustic/prosodic and lexical correlates of charismatic speech

Charisma, the ability to command authority on the basis of personal qualities, is more difficult to define than to identify. How do charismatic leaders such as Fidel Castro or Pope John Paul II attract and retain their followers? We present results of an analysis of subjective ratings of charisma from a corpus of American political speech. We identify the associations between charisma ratings and ratings of other personal attributes. We also examine acoustic/prosodic and lexical features of this speech and correlate these with charisma ratings.
Double Inverted Pendulum Control Based on Three-loop PID and Improved BP Neural Network

To deal with the defects of BP neural networks used in balance control of inverted pendulum, such as longer train time and converging in partial minimum, this article reaLizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks(ANN), builds up a training model of test simulation and the BP network is 6-10-1 structure. Tansig function is used in hidden layer and PureLin function is used in output layer, LM is used in training algorithm. The training data is acquried by three-loop PID algorithm. The model is learned and trained with Matlab calculating software, and the simuLink simulation experiment results prove that improved BP algorithm for inverted pendulum control has higher precision, better astringency and lower calculation. This algorithm has wide appLication on nonLinear control and robust control field in particular.
Translating Names and Technical Terms in Arabic Text

It is challenging to translate names and technical terms from English into Arabic. Translation is usually done phonetically: different alphabets and sound inventories force various compromises. For example, Peter Streams may come out as hr..~ ~ bytr szrymz. This process is called transliteration. We address here the reverse problem: given a foreign name or loanword in Arabic text, we want to recover the original in Roman script. For example, an input like .~..A~ bytr strymz should yield an output like Peter Streams. Arabic presents special challenges due to unwritten vowels and phonetic-context effects. We present results and examples of use in an Arabic-to-English machine translator.  It is not trivial to write an algorithm for turning Translators must deal with many problems, and one of the most frequent is translating proper names and technical terms. For language pairs like Spanish/English, this presents no great challenge: a phrase like Antonio Gil usually gets translated as Antonio Gil. However, the situation is more complicated for language pairs that employ very different alphabets and sound systems, such as Japanese/English and Arabic/English. Phonetic translation across these pairs is called transliteration. (Knight and Graehl, 1997) present a computational treatment of Japanese/English transliteration, which we adapt here to the case in Arabic. Arabic text, like Japanese, frequently contains foreign names and technical terms that are translated phonetically. Here are some examples from newspaper text: a
Convolutional Neural Networks for patient-specific ECG classification

We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).
Terrain data compression using wavelet-tiled pyramids for online 3D terrain visualization

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A BLE-based data collection system for IoT

The data collection is an important phase in the internet of things (IoT). The efficient way to collect data from the IoT environment is one of the challenges for the future of IoT. In this paper, we first design a data collection system for IoT. This system is based Bluetooth Low Energy (BLE) technology to forward the collected data from the data collector to the data gateways and proposes the use of smart phones as data collectors. We also propose a first prototype of this system. This prototype was developed with the help of two well-known open technologies, Arduino (Bluno) and Android. Finally, we evaluate the performances of the proposed system through performing a set of experiments on the developed prototype. The experiments show the feasibility and the applicability of the system especially where distance between the data collector and the data gateway is blow 6 meters. They also demonstrate the energy efficiency of the system.
Depth hole filling using the depth distribution of neighboring regions of depth holes in the Kinect sensor

This paper proposes a depth hole filling method for RGBD images obtained from the Microsoft Kinect sensor. First, the proposed method labels depth holes based on 8-connectivity. For each labeled depth hole, the proposed method fills depth hole using the depth distribution of neighboring pixels of the depth hole. Then, we refine the hole filling result with cross-bilateral filtering. In experiments, by simply using the depth distribution of neighboring pixels, the proposed method improves the acquired depth map and reduces false filling caused by incorrect depth-color fusion.
Robust real-time pupil tracking in highly off-axis images

Robust, accurate, real-time pupil tracking is a key component for online gaze estimation. On head-mounted eye trackers, existing algorithms that rely on circular pupils or contiguous pupil regions fail to detect or accurately track the pupil. This is because the pupil ellipse is often highly eccentric and partially occluded by eyelashes. We present a novel, real-time dark-pupil tracking algorithm that is robust under such conditions. Our approach uses a Haar-like feature detector to roughly estimate the pupil location, performs a k-means segmentation on the surrounding region to refine the pupil centre, and fits an ellipse to the pupil using a novel image-aware Random Sample Concensus (RANSAC) ellipse fitting. We compare our approach against existing real-time pupil tracking implementations, using a set of manually labelled infra-red dark-pupil eye images. We show that our technique has a higher pupil detection rate and greater pupil tracking accuracy.
WDAqua-core0: A Question Answering Component for the Research Community

We describe and present a new Question Answering (QA) component that can be easily used by the QA research community. It can be used to answer questions over DBpedia and Wikidata. The language support over DBpedia is restricted to English, while it can be used to answer questions in 4 different languages over Wikidata namely English, French, German and Italian. Moreover it supports both full natural language queries as well as keyword queries. We describe the interfaces to access and reuse it and the services it can be combined with. Moreover we show the evaluation results we achieved on the QALD-7 benchmark.
Opening Pandora's Box: Effective Techniques for Reverse Engineering IoT Devices

With the growth of the Internet of Things, many insecure embedded devices are entering into our homes and businesses. Some of these web-connected devices lack even basic security protections such as secure password authentication. As a result, thousands of IoT devices have already been infected with malware and enlisted into malicious botnets and many more are left vulnerable to exploitation. In this paper we analyze the practical security level of 16 popular IoT devices from high-end and low-end manufacturers. We present several low-cost black-box techniques for reverse engineering these devices, including software and fault injection based techniques for bypassing password protection. We use these techniques to recover device rmware and passwords. We also discover several common design aws which lead to previously unknown vulnerabilities. We demonstrate the e ectiveness of our approach by modifying a laboratory version of the Mirai botnet to automatically include these devices. We also discuss how to improve the security of IoT devices without signi cantly increasing their cost.
Low-frequency neuronal oscillations as instruments of sensory selection

Neuroelectric oscillations reflect rhythmic shifting of neuronal ensembles between high and low excitability states. In natural settings, important stimuli often occur in rhythmic streams, and when oscillations entrain to an input rhythm their high excitability phases coincide with events in the stream, effectively amplifying neuronal input responses. When operating in a 'rhythmic mode', attention can use these differential excitability states as a mechanism of selection by simply enforcing oscillatory entrainment to a task-relevant input stream. When there is no low-frequency rhythm that oscillations can entrain to, attention operates in a 'continuous mode', characterized by extended increase in gamma synchrony. We review the evidence for early sensory selection by oscillatory phase-amplitude modulations, its mechanisms and its perceptual and behavioral consequences.
Botnet: Survey and Case Study

Botnet is a hybrid of previous threats integrated with a command and control system and hundreds of millions of computers are infected. Although botnets are widespread development, the research and solutions for botnets are not mature. In this paper, we present an overview of research on botnets. We discuss in detail the botnet and related research including infection mechanism, botnet malicious behavior, command and control models, communication protocols, botnet detection, and botnet defense. We also present a simple case study of IRC-based SpyBot.
Burstiness and memory in complex systems

The dynamics of a wide range of real systems, from email patterns to earthquakes, display a bursty, intermittent nature, characterized by short timeframes of intense activity followed by long times of no or reduced activity. The understanding of the origin of such bursty patterns is hindered by the lack of tools to compare different systems using a common framework. Here we propose to characterize the bursty nature of real signals using orthogonal measures quantifying two distinct mechanisms leading to burstiness: the interevent time distribution and the memory. We find that while the burstiness of natural phenomena is rooted in both the interevent time distribution and memory, for human dynamics memory is weak, and the bursty character is due to the changes in the interevent time distribution. Finally, we show that current models lack in their ability to reproduce the activity pattern observed in real systems, opening up avenues for future work.  While it is difficult to offer a definite answer to this question, a common feature across many systems is increasingly documented: the burstiness of the system’s activity patterns. Bursts, vaguely corresponding to significantly enhanced activity levels over short periods of time followed by long periods of inactivity, have been observed in a wide range of systems, from email patterns [6] to earthquakes [11,12] and gene expression . Yet, often a burstiness is more of a metaphor than a quantitative feature, and opinions about its origin diverge. In human dynamics, burstiness has been reduced to the fat-tailed nature of the response time distribution [6,7], in contrast with earthquakes and weather patterns, where memory effects appear to play a key role as well [13,14]. Once present, burstiness can affect the spreading of viruses  or resource allocation [16,17]. Also, deviations towards a regular, “anti-bursty” behavior in heartbeat indicate disease progression [18]. Given the diversity of systems in which it emerges, there is a need to place burstiness on a firmer quantitative basis. Our goal in this letter is to make a step in this direction, by developing a diagnosis tool that can help quantify the magnitude and potential origin of the bursty patterns seen in different real systems. Such a tool may also lend insights into the analysis of fractal and self-similar bursty signals [19]. Let us consider a system whose components have a measurable activity pattern that can be mapped into a discrete signal, recording the moments when some events take place, like an email being sent, or a protein being translated. The activity pattern is random (Poisson process) if the probability of an event is time-independent. In this case the interevent time, τ , between two consecutive events follows an exponential distribution, PP(τ)∼ exp(−τ/τ0) (fig. 1a). An apparently bursty For systems with continuous signal, we may adopt a threshold method to transform it into a discrete one, and in many systems the statistical properties of the obtained signal are known to be threshold-independent [12,13].
Changes in use and perception of facebook

As social computing systems persist over time, the user experiences and interactions they support may change. One type of social computing system, Social Network Sites (SNSs), are becoming more popular across broad segments of Internet users. Facebook, in particular, has very broad participation amongst college attendees, and has been growing in other populations as well. This paper looks at how use of Facebook has changed over time, as indicated by three consecutive years of survey data and interviews with a subset of survey respondents. Reported uses of the site remain relatively constant over time, but the perceived audience for user profiles and attitudes about the site show differences over the study period.
IR and visible light face recognition

This paper presents the results of several large-scale studies of face recognition employing visible light and infra-red (IR) imagery in the context of principal component analysis. We find that in a scenario involving time lapse between gallery and probe, and relatively controlled lighting, (1) PCA-based recognition using visible light images outperforms PCA-based recognition using infra-red images, (2) the combination of PCA-based recognition using visible light and infra-red imagery substantially outperforms either one individually. In a same session scenario (i.e. nearsimultaneous acquisition of gallery and probe images) neither modality is significantly better than the other. These experimental results reinforce prior research that employed a smaller data set, presenting a convincing argument that, even across a broad experimental spectrum, the behaviors enumerated above are valid and consistent.
Adolescents' Exposure to Sexually Explicit Material on the Internet

Drawing on a survey of 745 Dutch adolescents ages 13 to 18, the authors investigated (a) the occurrence and frequency of adolescents’ exposure to sexually explicit material on the Internet and (b) the correlates of this exposure. Seventy-one percent of the male adolescents and 40% of the female adolescents had been exposed to some kind of online sexually explicit material in the 6 months prior to the interview. Adolescents were more likely to be exposed to sexually explicit material online if they were male, were high sensation seekers, were less satisfied with their lives, were more sexually interested, used sexual content in other media more often, had a fast Internet connection, and had friends that were predominantly younger. Among male adolescents, a more advanced pubertal status was also associated with more frequent exposure to online sexually explicit material. Among female adolescents, greater sexual experience decreased exposure to online sexually explicit material.