nils-herrmann/clean_scirepeval huggingface dataset:
Auditory-Visual Integration during Multimodal Object Recognition in Humans: A Behavioral and Electrophysiological Study

The aim of this study was (1) to provide behavioral evidence for multimodal feature integration in an object recognition task in humans and (2) to characterize the processing stages and the neural structures where multisensory interactions take place. Event-related potentials (ERPs) were recorded from 30 scalp electrodes while subjects performed a forced-choice reaction-time categorization task: At each trial, the subjects had to indicate which of two objects was presented by pressing one of two keys. The two objects were defined by auditory features alone, visual features alone, or the combination of auditory and visual features. Subjects were more accurate and rapid at identifying multimodal than unimodal objects. Spatiotemporal analysis of ERPs and scalp current densities revealed several auditory-visual interaction components temporally, spatially, and functionally distinct before 200 msec poststimulus. The effects observed were (1) in visual areas, new neural activities (as early as 40 msec poststimulus) and modulation (amplitude decrease) of the N185 wave to unimodal visual stimulus, (2) in the auditory cortex, modulation (amplitude increase) of subcomponents of the unimodal auditory N1 wave around 90 to 110 msec, and (3) new neural activity over the right fronto-temporal area (140 to 165 msec). Furthermore, when the subjects were separated into two groups according to their dominant modality to perform the task in unimodal conditions (shortest reaction time criteria), the integration effects were found to be similar for the two groups over the nonspecific fronto-temporal areas, but they clearly differed in the sensory-specific cortices, affecting predominantly the sensory areas of the nondominant modality. Taken together, the results indicate that multisensory integration is mediated by flexible, highly adaptive physiological processes that can take place very early in the sensory processing chain and operate in both sensory-specific and nonspecific cortical structures in different ways.
Bias in algorithmic filtering and personalization

Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal with the growing amount of information on the social web and the burden it brings on the average user, these gatekeepers recently started to introduce personalization features, algorithms that filter information per individual. In this paper we show that these online services that filter information are not merely algorithms. Humans not only affect the design of the algorithms, but they also can manually influence the filtering process even when the algorithm is operational. We further analyze filtering processes in detail, show how personalization connects to other filtering techniques, and show that both human and technical biases are present in today’s emergent gatekeepers. We use the existing literature on gatekeeping and search engine bias and provide a model of algorithmic gatekeeping.
A time-frequency domain approach of heart rate estimation from photoplethysmographic (PPG) signal

ObjectiveHeart rate monitoring using wrist type Photoplethysmographic (PPG) signals is getting popularity because of construction simplicity and low cost of wearable devices. The task becomes very difficult due to the presence of various motion artifacts. The objective is to develop algorithms to reduce the effect of motion artifacts and thus obtain accurate heart rate estimation. MethodsProposed heart rate estimation scheme utilizes both time and frequency domain analyses. Unlike conventional single stage adaptive filter, multi-stage cascaded adaptive filtering is introduced by using three channel accelerometer data to reduce the effect of motion artifacts. Both recursive least squares (RLS) and least mean squares (LMS) adaptive filters are tested. Moreover, singular spectrum analysis (SSA) is employed to obtain improved spectral peak tracking. The outputs from the filter block and SSA operation are logically combined and used for spectral domain heart rate estimation. Finally, a tracking algorithm is incorporated considering neighbouring estimates. ResultsThe proposed method provides an average absolute error of 1.16 beat per minute (BPM) with a standard deviation of 1.74 BPM while tested on publicly available database consisting of recordings from 12 subjects during physical activities. ConclusionIt is found that the proposed method provides consistently better heart rate estimation performance in comparison to that recently reported by TROIKA, JOSS and SPECTRAP methods.  proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach and thus feasible for implementing in wearable devices to monitor heart rate for fitness and clinical purpose.
Exploring Neural Text Simplification Models

We present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems.
End-to-End Learning of Deep Visual Representations for Image Retrieval

While deep learning has become a key ingredient in the top performing methods for many computer vision tasks, it has failed so far to bring similar improvements to instance-level image retrieval. In this article, we argue that reasons for the underwhelming results of deep methods on image retrieval are threefold: (1) noisy training data, (2) inappropriate deep architecture, and (3) suboptimal training procedure. We address all three issues. First, we leverage a large-scale but noisy landmark dataset and develop an automatic cleaning method that produces a suitable training set for deep retrieval. Second, we build on the recent R-MAC descriptor, show that it can be interpreted as a deep and differentiable architecture, and present improvements to enhance it. Last, we train this network with a siamese architecture that combines three streams with a triplet loss. At the end of the training process, the proposed architecture produces a global image representation in a single forward pass that is well suited for image retrieval. Extensive experiments show that our approach significantly outperforms previous retrieval approaches, including state-of-the-art methods based on costly local descriptor indexing and spatial verification. On Oxford 5k, Paris 6k and Holidays, we respectively report 94.7, 96.6, and 94.8 mean average precision. Our representations can also be heavily compressed using product quantization with little loss in accuracy.
A 4-to-10.5 Gb/s Continuous-Rate Digital Clock and Data Recovery With Automatic Frequency Acquisition

A continuous-rate digital clock and data recovery (CDR) with automatic frequency acquisition is presented. The proposed automatic frequency acquisition scheme implemented using a conventional bang-bang phase detector (BBPD) requires minimum additional hardware, is immune to input data transition density, and is applicable to subrate CDRs. A ring-oscillator-based two-stage fractional-N phase-locked loop (PLL) is used as a digitally controlled oscillator (DCO) to achieve wide frequency range, low noise, and to decouple the tradeoff between jitter transfer (JTRAN) bandwidth and ring oscillator noise suppression in conventional CDRs. The CDR is implemented using a digital D/PLL architecture to decouple JTRAN bandwidth from jitter tolerance (JTOL) corner frequency, eliminate jitter peaking, and remove JTRAN dependence on BBPD gain. Fabricated in a 65 nm CMOS process, the prototype CDR achieves error-free operation (BER <; 10-12) from 4 to 10.5 Gb/s with pseudorandom binary sequence (PRBS) data sequences ranging from PRBS7 to PRBS31. The proposed automatic frequency acquisition scheme always locks the CDR loop within 1000 ppm residual frequency error in worst case. At 10 Gb/s, the CDR consumes 22.5 mW power and achieves a recovered clock long-term jitter of 2.2 psrms/24.0 pspp with PRBS31 input data. The measured JTRAN bandwidth and JTOL corner frequencies are 0.2 and 9 MHz, respectively.
Shape, texture and local movement hand gesture features for Indian Sign Language recognition

This paper proposes an automatic gesture recognition approach for Indian Sign Language (ISL). Indian sign language uses both hands to represent each alphabet. We propose an approach which addresses local-global ambiguity identification, inter-class variability enhancement for each hand gesture. Hand region is segmented and detected by YCbCr skin color model reference. The shape, texture and finger features of each hand are extracted using Principle Curvature Based Region (PCBR) detector, Wavelet Packet Decomposition (WPD-2) and complexity defects algorithms respectively for hand posture recognition process. To classify each hand posture, multi class non linear support vector machines (SVM) is used, for which a recognition rate of 91.3% is achieved. Dynamic gestures are classified using Dynamic Time Warping (DTW) with the trajectory feature vector with 86.3% recognition rate. The performance of the proposed approach is analyzed with well known classifiers like SVM, KNN & DTW. Experimental results are compared with the conventional and existing algorithms to prove the better efficiency of the proposed approach.
Public key cryptography using neural networks and genetic algorithms

By making use of Artificial Intelligence (AI), Human Intelligence can be simulated by a machine, Neural Networks is one such sub field of AI. Artificial Neural Networks (ANN) consists of neurons and weights assigned to inter neuron connections helps in storing the acquired knowledge. This paper makes use of Hebbian learning rule to train the ANN of both sender and receiver machines. In the field of Public Key Cryptography (PKC), Pseudo Random Number Generator (PRNG) are widely used to generate unique keys and random numbers used in ANN which are found to possess many types of possible attacks. It is essential for a key to possess randomness for key strength and security. This paper proposes key generation for PKC by application of ANN using Genetic Algorithm (GA). It was noticed that use of ANN along with GA has not as yet been explored. GA approach is often applied for obtaining optimization and solutions in search problems. GA correlates to the nature to a large extent producing population of numbers where number possessing higher fitness value is replicated more. Thus, making GA a very good contender for PRNGs. Good Fitness function helps in exploring search space of random numbers in more efficient manner. GA PRNGs result samples satisfies frequency test and gap test. Thus the numbers generated after each iteration by GA PRNG are statistically verified to be random and nonrepeating, having no prior relation of next number from the previous ones, acting as an essential initialization parameter for neural algorithm overcomes the problem of acknowledging the random number generated by traditional PRNG. For generating public and private keys, different number of rounds of mixing is used. This ensures that the private key generated cannot be derived from public key. Our algorithm was observed to give fast and improved performance results having practical and feasible implementation.
Deep Visual-Semantic Hashing for Cross-Modal Retrieval

Due to the storage and retrieval efficiency, hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which enables efficient retrieval of images in response to text queries or vice versa, has received increasing attention recently. Most existing work on cross-modal hashing does not capture the spatial dependency of images and temporal dynamics of text sentences for learning powerful feature representations and cross-modal embeddings that mitigate the heterogeneity of different modalities. This paper presents a new Deep Visual-Semantic Hashing (DVSH) model that generates compact hash codes of images and sentences in an end-to-end deep learning architecture, which capture the intrinsic cross-modal correspondences between visual data and natural language. DVSH is a hybrid deep architecture that constitutes a visual-semantic fusion network for learning joint embedding space of images and text sentences, and two modality-specific hashing networks for learning hash functions to generate compact binary codes. Our architecture effectively unifies joint multimodal embedding and cross-modal hashing, which is based on a novel combination of Convolutional Neural Networks over images, Recurrent Neural Networks over sentences, and a structured max-margin objective that integrates all things together to enable learning of similarity-preserving and high-quality hash codes. Extensive empirical evidence shows that our DVSH approach yields state of the art results in cross-modal retrieval experiments on image-sentences datasets, i.e. standard IAPR TC-12 and large-scale Microsoft COCO.
Heptaband Inverted-F Antenna for Metal-Rimmed Mobile Phone Applications

An inverted-F antenna for heptaband WWAN/LTE operations is proposed and applied for the mobile phone surrounded by a metal rim with two slits. The proposed strategy provides a simple and effective solution for designing the multiband antenna for metal-rimmed mobile phone applications. In order to obtain remarkable radiation performance of the antenna, the metal rim with a height of 5 mm is cut into three parts. By taking advantage of this three-part frame, the proposed antenna is capable of covering GSM850/900/DCS/PCS/UMTS 2100/LTE 2300/2500 operating bands. The operation mechanisms of the proposed antenna have been analyzed carefully. To demonstrate the above method, the proposed antenna is fabricated, and the characteristics such as reflection coefficients, radiation efficiency, and radiation patterns have been measured. Reasonable results show that the proposed antenna is a candidate for WWAN/LTE mobile phone applications.
A Geometric Interpretation of v-SVM Classifiers

Christopher J.C. Burges Advanced Technologies, Bell Laboratories, Lucent Technologies Holmdel, New Jersey burges@lucent.com We show that the recently proposed variant of the Support Vector machine (SVM) algorithm, known as v-SVM, can be interpreted as a maximal separation between subsets of the convex hulls of the data, which we call soft convex hulls. The soft convex hulls are controlled by choice of the parameter v. If the intersection of the convex hulls is empty, the hyperplane is positioned halfway between them such that the distance between convex hulls, measured along the normal, is maximized; and if it is not, the hyperplane's normal is similarly determined by the soft convex hulls, but its position (perpendicular distance from the origin) is adjusted to minimize the error sum. The proposed geometric interpretation of v-SVM also leads to necessary and sufficient conditions for the existence of a choice of v for which the v-SVM solution is nontrivial.
A Survey of the Use of Adobe Flash Local Shared Objects to Respawn HTTP Cookies

Website developers can use Adobe’s Flash Player product to store information locally on users’ disks with Local Shared Objects (LSOs). LSOs can be used to store state information and user identifiers, and thus can be used for similar purposes as HTTP cookies. In a paper by Soltani et al, researchers documented at least four instances of “respawning,” where users deleted their HTTP cookies only to have the HTTP cookies recreated based on LSO data. In addition, the Soltani team found half of the 100 most popular websites used Flash technologies to store information about users. Both respawning and using LSOs to store data about users can reduce online privacy. One year later, we visited popular websites plus 500 randomly-selected websites to determine if respawning still occurs. We found no instances at all of respawning in a randomly-selected group of 500 websites. We found two instances of respawning in the most popular 100 websites. While our methods are different from the Soltani team and we cannot compare directly, our results suggest respawning is not increasing, and may be waning. As in the Soltani study, we found LSOs with unique identifiers. In the 100 most popular websites, LSOs were set at 20, and 9 used their LSOs to store unique identifiers. In 500 randomly selected sites, LSOs were set at 41, and 17 used their LSOs to store unique identifiers. Unique identifiers may, or may not, be keys into back-end databases to perform cookie-style tracking. However, unique identifiers could be benign, for example, uniquely identifying a specific animation or music clip. While we can use contextual information like variable names to guess what a given unique identifier is for, using our study methods we cannot conclusively determine how companies use unique identifiers. We cannot quantify how many, if any, sites are using unique identifiers in LSOs for any purpose that might have privacy implications. Even assuming a pessimistic worst case where all websites with unique identifiers in LSOs are using them to track users, the percentage of such sites studied is low — 9% of the top 100, and only 3.4% of the randomly-selected 500 sites we studied. However, over 40% of the LSOs in each data set used unique identifiers, and especially with the top 100 sites, many people could be affected. Because we found sites using LSOs as unique identifiers, we believe further study is needed to determine if these sites are using LSOs to evade users’ choices. However, without visibility into back-end databases, it is difficult to determine how unique identifiers are used. We conclude our paper with policy options and a discussion of implications for industry self-regulation of Internet privacy.
Is Faster R-CNN Doing Well for Pedestrian Detection?

Detecting pedestrian has been arguably addressed as a special topic beyond general object detection. Although recent deep learning object detectors such as Fast/Faster R-CNN [1,2] have shown excellent performance for general object detection, they have limited success for detecting pedestrian, and previous leading pedestrian detectors were in general hybrid methods combining hand-crafted and deep convolutional features. In this paper, we investigate issues involving Faster R-CNN [2] for pedestrian detection. We discover that the Region Proposal Network (RPN) in Faster R-CNN indeed performs well as a stand-alone pedestrian detector, but surprisingly, the downstream classifier degrades the results. We argue that two reasons account for the unsatisfactory accuracy: (i) insufficient resolution of feature maps for handling small instances, and (ii) lack of any bootstrapping strategy for mining hard negative examples. Driven by these observations, we propose a very simple but effective baseline for pedestrian detection, using an RPN followed by boosted forests on shared, high-resolution convolutional feature maps. We comprehensively evaluate this method on several benchmarks (Caltech, INRIA, ETH, and KITTI), presenting competitive accuracy and good speed. Code will be made publicly available.
CB: a humanoid research platform for exploring neuroscience

This paper presents a 50-d.o.f. humanoid robot, Computational Brain (CB). CB is a humanoid robot created for exploring the underlying processing of the human brain while dealing with the real world. We place our investigations within real—world contexts, as humans do. In so doing, we focus on utilizing a system that is closer to humans—in sensing, kinematics configuration and performance. We present the real-time network-based architecture for the control of all 50 d.o.f. The controller provides full position/velocity/force sensing and control at 1 kHz, allowing us the flexibility in deriving various forms of control. A dynamic simulator is also presented; the simulator acts as a realistic testbed for our controllers and acts as a common interface to our humanoid robots. A contact model developed to allow better validation of our controllers prior to final testing on the physical robot is also presented. Three aspects of the system are highlighted in this paper: (i) physical power for walking, (ii) full-body compliant control—physical interactions and (iii) perception and control—visual ocular-motor responses.
Controlled experiments on the web: survey and practical guide

The web provides an unprecedented opportunity to evaluate ideas quickly using controlled experiments, also called randomized experiments, A/B tests (and their generalizations), split tests, Control/Treatment tests, MultiVariable Tests (MVT) and parallel flights. Controlled experiments embody the best scientific design for establishing a causal relationship between changes and their influence on user-observable behavior. We provide a practical guide to conducting online experiments, where end-users can help guide the development of features. Our experience indicates that significant learning and return-on-investment (ROI) are seen when development teams listen to their customers, not to the Highest Paid Person’s Opinion (HiPPO). We provide several examples of controlled experiments with surprising results. We review the important ingredients of running controlled experiments, and discuss their limitations (both technical and organizational). We focus on several areas that are critical to experimentation, including statistical power, sample size, and techniques for variance reduction. We describe common architectures for experimentation systems and analyze their advantages and disadvantages. We evaluate randomization and hashing techniques, which we show are not as simple in practice as is often assumed. Controlled experiments typically generate large amounts of data, which can be analyzed using data mining techniques to gain deeper understanding of the factors influencing the outcome of interest, leading to new hypotheses and creating a virtuous cycle of improvements. Organizations that embrace controlled experiments with clear evaluation criteria can evolve their systems with automated optimizations and real-time analyses. Based on our extensive practical experience with multiple systems and organizations, we share key lessons that will help practitioners in running trustworthy controlled experiments.
Conflict-free replicated data types

A conflict-free replicated data type (CRDT) is an abstract data type, with a well defined interface, designed to be replicated at multiple processes and exhibiting the following properties: (i) any replica can be modified without coordinating with another replicas; (ii) when any two replicas have received the same set of updates, they reach the same state, deterministically, by adopting mathematically sound rules to guarantee state convergence.