nils-herrmann/clean_scirepeval huggingface dataset:
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification

Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings. For high level visual tasks, such low-level image representations are potentially not enough. In this paper, we propose a high-level image representation, called the Object Bank, where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors, blind to the testing dataset or visual task. Leveraging on the Object Bank representation, superior performances on high level visual recognition tasks can be achieved with simple off-the-shelf classifiers such as logistic regression and linear SVM. Sparsity algorithms make our representation more efficient and scalable for large scene datasets, and reveal semantically meaningful feature patterns.
The Uncanny Valley: Effect of Realism on the Impression of Artificial Human Faces

Roboticists believe that people will have an unpleasant impression of a humanoid robot that has an almost, but not perfectly, realistic human appearance. This is called the uncanny valley, and is not limited to robots, but is also applicable to any type of human-like object, such as dolls, masks, facial caricatures, avatars in virtual reality, and characters in computer graphics movies. The present study investigated the uncanny valley by measuring observers' impressions of facial images whose degree of realism was manipulated by morphing between artificial and real human faces. Facial images yielded the most unpleasant impressions when they were highly realistic, supporting the hypothesis of the uncanny valley. However, the uncanny valley was confirmed only when morphed faces had abnormal features such as bizarre eyes. These results suggest that to have an almost perfectly realistic human appearance is a necessary but not a sufficient condition for the uncanny valley. The uncanny valley emerges only when there is also an abnormal feature.
Deep learning in neural networks: An overview

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Multi-style Generative Network for Real-Time Transfer

Despite the rapid progress in style transfer, existing approaches using feed-forward generative network for multistyle or arbitrary-style transfer are usually compromised of image quality and model flexibility. We find it is fundamentally difficult to achieve comprehensive style modeling using 1-dimensional style embedding. Motivated by this, we introduce CoMatch Layer that learns to match the second order feature statistics with the target styles. With the CoMatch Layer, we build a Multi-style Generative Network (MSGNet), which achieves real-time performance. In addition, we employ an specific strategy of upsampled convolution which avoids checkerboard artifacts caused by fractionallystrided convolution. Our method has achieved superior image quality comparing to state-of-the-art approaches. The proposed MSG-Net as a general approach for real-time style transfer is compatible with most existing techniques including content-style interpolation, color-preserving, spatial control and brush stroke size control. MSG-Net is the first to achieve real-time brush-size control in a purely feedforward manner for style transfer. Our implementations and pre-trained models for Torch, PyTorch and MXNet frameworks will be publicly available1.
Video-Based Face Recognition Using Probabilistic Appearance Manifolds

This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the ambient image space. The complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), and the connectivity among them. Each pose manifold is approximated by an affine plane. To construct this representation, exemplars are sampled from videos, and these exemplars are clustered with a K-means algorithm; each cluster is represented as a plane computed through principal component analysis (PCA). The connectivity between the pose manifolds encodes the transition probability between images in each of the pose manifold and is learned from a training video sequences. A maximum a posteriori formulation is presented for face recognition in test video sequences by integrating the likelihoo d that the input image comes from a particular pose manifold and the transition probability to this pose manifold from th e previous frame. To recognize faces with partial occlusion, we introduce a weight mask into the process. Extensive experiments demonstrate that the proposed algorithm outperforms existing frame-based face recognition methods with temporal voting schemes.
Room-temperature near-infrared high-Q perovskite whispering-gallery planar nanolasers.

Near-infrared (NIR) solid-state micro/nanolasers are important building blocks for true integration of optoelectronic circuitry. Although significant progress has been made in III-V nanowire lasers with achieving NIR lasing at room temperature, challenges remain including low quantum efficiencies and high Auger losses. Importantly, the obstacles toward integrating one-dimensional nanowires on the planar ubiquitous Si platform need to be effectively tackled. Here we demonstrate a new family of planar room-temperature NIR nanolasers based on organic-inorganic perovskite CH3NH3PbI(3-) (X = I, Br, Cl) nanoplatelets. Their large exciton binding energies, long diffusion lengths, and naturally formed high-quality planar whispering-gallery mode cavities ensure adequate gain and efficient optical feedback for low-threshold optically pumped in-plane lasing. We show that these remarkable wavelength tunable whispering-gallery nanolasers can be easily integrated onto conductive platforms (Si, Au, indium tin oxide, and so forth). Our findings open up a new class of wavelength tunable planar nanomaterials potentially suitable for on-chip integration.
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the OverFeat network which was trained to perform object classification on ILSVRC13. We use features extracted from the OverFeat network as a generic image representation to tackle the diverse range of recognition tasks of object image classification, scene recognition, fine grained recognition, attribute detection and image retrieval applied to a diverse set of datasets. We selected these tasks and datasets as they gradually move further away from the original task and data the OverFeat network was trained to solve. Astonishingly, we report consistent superior results compared to the highly tuned state-of-the-art systems in all the visual classification tasks on various datasets. For instance retrieval it consistently outperforms low memory footprint methods except for sculptures dataset. The results are achieved using a linear SVM classifier (or L2 distance in case of retrieval) applied to a feature representation of size 4096 extracted from a layer in the net. The representations are further modified using simple augmentation techniques e.g. jittering. The results strongly suggest that features obtained from deep learning with convolutional nets should be the primary candidate in most visual recognition tasks.
Best Reply Search for Multiplayer Games

This paper proposes a new algorithm, called best reply search (BRS), for deterministic multiplayer games with perfect information. In BRS, only the opponent with the strongest counter move is allowed to make a move. More turns of the root player can be searched resulting in long-term planning. We test BRS in the games of Chinese Checkers, Focus, and Rolit™. In all games, BRS is superior to the maxn algorithm. We show that BRS also outperforms paranoid in Chinese Checkers and Focus. In Rolit, BRS is on equal footing with paranoid. We conclude that BRS is a promising search method for deterministic multiplayer games with perfect information.
Validation and verification of computer forensic software tools d Searching Function

The process of using automated software has served law enforcement and the courts very well, and experienced detectives and investigators have been able to use their welldeveloped policing skills, in conjunction with the automated software, so as to provide sound evidence. However, the growth in the computer forensic field has created a demand for new software (or increased functionality to existing software) and a means to verify that this software is truly ‘‘forensic’’ i.e. capable of meeting the requirements of the ‘trier of fact’. In this work, we present a scientific and systemical description of the computer forensic discipline through mapping fundamental functions required in the computer forensic investigation process. Based on the function mapping, we propose a more detailed functionality orientated validation and verification framework of computer forensic tools. We focus this paper on the searching function. We specify the requirements and develop a corresponding reference set to test any tools that possess the searching function. a 2009 Digital Forensic Research Workshop.
2 Conditional Saliency 2 . 1 Lossy Coding

By the guidance of attention, human visual system is able to locate objects of interest in complex scene. In this paper, we propose a novel visual saliency detection method the conditional saliency for both image and video. Inspired by biological vision, the definition of visual saliency follows a strictly local approach. Given the surrounding area, the saliency is defined as the minimum uncertainty of the local region, namely the minimum conditional entropy, when the perceptional distortion is considered. To simplify the problem, we approximate the conditional entropy by the lossy coding length of multivariate Gaussian data. The final saliency map is accumulated by pixels and further segmented to detect the proto-objects. Experiments are conducted on both image and video. And the results indicate a robust and reliable feature invariance saliency.
Speaker identification on the SCOTUS corpus

This paper reports the results of our experiments on speaker identification in the SCOTUS corpus, which includes oral arguments from the Supreme Court of the United States. Our main findings are as follows: 1) a combination of Gaussian mixture models and monophone HMM models attains near-100% textindependent identification accuracy on utterances that are longer than one second; 2) the sampling rate of 11025 Hz achieves the best performance (higher sampling rates are harmful); and a sampling rate as low as 2000 Hz still achieves more than 90% accuracy; 3) a distance score based on likelihood numbers was used to measure the variability of phones among speakers; we found that the most variable phone is the phone UH (as in good), and the velar nasal NG is more variable than the other two nasal sounds M and N; 4.) our models achieved “perfect” forced alignment on very long speech segments (40 minutes). These findings and their significance are discussed.
Object Recognition in X-ray Testing Using Adaptive Sparse Representations

In recent years, X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items such as handguns and explosives. Human operators complete these tasks because automated recognition in baggage inspection is far from perfect. Research and development on X-ray testing is, however, ongoing into new approaches that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition by proposing a new approach called Adaptive Sparse Representation (XASR+). It consists of two stages: learning and testing. In the learning stage, for each object of training dataset, several patches are extracted from its X-ray images in order to construct representative dictionaries. A stop-list is used to remove very common words of the dictionaries. In the testing stage, test patches of the test image are extracted, and for each test patch a dictionary is built concatenating the ‘best’ representative dictionary of each object. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification (SRC) methodology. Finally, the test image is classified by patch voting. Thus, our approach is able to deal with less constrained conditions including some contrast variability, pose, intra-class variability, size of the image and focal distance. We tested the effectiveness of our method for the detection of four different objects. In our experiments, the recognition rate was more than 97% in each class, and more than 94% if the object is occluded less than 15%. Results show that XASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature.
Electrophysiological Studies of Face Perception in Humans

Event-related potentials (ERPs) associated with face perception were recorded with scalp electrodes from normal volunteers. Subjects performed a visual target detection task in which they mentally counted the number of occurrences of pictorial stimuli from a designated category such as butterflies. In separate experiments, target stimuli were embedded within a series of other stimuli including unfamiliar human faces and isolated face components, inverted faces, distorted faces, animal faces, and other nonface stimuli. Human faces evoked a negative potential at 172 msec (N170), which was absent from the ERPs elicited by other animate and inanimate nonface stimuli. N170 was largest over the posterior temporal scalp and was larger over the right than the left hemisphere. N170 was delayed when faces were presented upside-down, but its amplitude did not change. When presented in isolation, eyes elicited an N170 that was significantly larger than that elicited by whole faces, while noses and lips elicited small negative ERPs about 50 msec later than N170. Distorted human faces, in which the locations of inner face components were altered, elicited an N170 similar in amplitude to that elicited by normal faces. However, faces of animals, human hands, cars, and items of furniture did not evoke N170. N170 may reflect the operation of a neural mechanism tuned to detect (as opposed to identify) human faces, similar to the structural encoder suggested by Bruce and Young (1986). A similar function has been proposed for the face-selective N200 ERP recorded from the middle fusiform and posterior inferior temporal gyri using subdural electrodes in humans (Allison, McCarthy, Nobre, Puce, & Belger, 1994c). However, the differential sensitivity of N170 to eyes in isolation suggests that N170 may reflect the activation of an eye-sensitive region of cortex. The voltage distribution of N170 over the scalp is consistent with a neural generator located in the occipitotemporal sulcus lateral to the fusiform/inferior temporal region that generates N200.
Benford's law behavior of Internet traffic

In this paper, we analyze the Internet traffic from a different point of view based on Benford's law, an empirical law describing the distribution of leading digits in a collection of numbers met in naturally occurring phenomena. We claim that Benford's law holds for the inter-arrival times of TCP flows in case of normal traffic. Consequently, any type of anomalies affecting TCP flows, including intentional intrusions or unintended faults and network failures in general, can be detected by investigating the first digit distributions of the inter-arrival times of TCP SYN packets. In this paper we apply our findings to the detection of intentional attacks, whereas other types of anomalies can be studied in future works. We support our claim with the related researches that indicate the TCP flow inter-arrival times can be modeled by Weibull distribution with shape parameter less than one, and show the relation between Weibull distributed data and Benford’s law. Finally, we validate our findings on real traffic and achieve encouraging results.
Crypto-Nets: Neural Networks over Encrypted Data

The problem we address is the following: how can a user employ a predictive model that is held by a third party, without compromising private information. For example, a hospital may wish to use a cloud service to predict the readmission risk of a patient. However, due to regulations, the patient’s medical files cannot be revealed. The goal is to make an inference using the model, without jeopardizing the accuracy of the prediction or the privacy of the data. To achieve high accuracy, we use neural networks, which have been shown to outperform other learning models for many tasks. To achieve the privacy requirements, we use homomorphic encryption in the following protocol: the data owner encrypts the data and sends the ciphertexts to the third party to obtain a prediction from a trained model. The model operates on these ciphertexts and sends back the encrypted prediction. In this protocol, not only the data remains private, even the values predicted are available only to the data owner. Using homomorphic encryption and modifications to the activation functions and training algorithms of neural networks, we present crypto-nets and prove that they can be constructed and may be feasible. This method paves the way to build a secure cloud-based neural network prediction services without invading users’ privacy.
Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets

Social interactions take place in environments that influence people’s behaviours and perceptions. Nowadays, the users of Online Social Network (OSN) generate a massive amount of content based on social interactions. However, OSNs wide popularity and ease of access created a perfect scenario to practice malicious activities, compromising their reliability. To detect automatic information broadcast in OSN, we developed a wavelet-based model that classifies users as being human, legitimate robot, or malicious robot, as a result of spectral patterns obtained from users’ textual content. We create the feature vector from the Discrete Wavelet Transform along with a weighting scheme called Lexicon-based Coefficient Attenuation. In particular, we induce a classification model using the Random Forest algorithm over two real Twitter datasets. The corresponding results show the developed model achieved an average accuracy of 94.47% considering two different scenarios: single theme and miscellaneous one.
FutureMatch: Combining Human Value Judgments and Machine Learning to Match in Dynamic Environments

The preferred treatment for kidney failure is a transplant; however, demand for donor kidneys far outstrips supply. Kidney exchange, an innovation where willing but incompatible patient-donor pairs can exchange organs—via barter cycles and altruist-initiated chains—provides a life-saving alternative. Typically, fielded exchanges act myopically, considering only the current pool of pairs when planning the cycles and chains. Yet kidney exchange is inherently dynamic, with participants arriving and departing. Also, many planned exchange transplants do not go to surgery due to various failures. So, it is important to consider the future when matching. Motivated by our experience running the computational side of a large nationwide kidney exchange, we present FUTUREMATCH, a framework for learning to match in a general dynamic model. FUTUREMATCH takes as input a high-level objective (e.g., “maximize graft survival of transplants over time”) decided on by experts, then automatically (i) learns based on data how to make this objective concrete and (ii) learns the “means” to accomplish this goal—a task, in our experience, that humans handle poorly. It uses data from all live kidney transplants in the US since 1987 to learn the quality of each possible match; it then learns the potentials of elements of the current input graph offline (e.g., potentials of pairs based on features such as donor and patient blood types), translates these to weights, and performs a computationally feasible batch matching that incorporates dynamic, failure-aware considerations through the weights. We validate FUTUREMATCH on real fielded exchange data. It results in higher values of the objective. Furthermore, even under economically inefficient objectives that enforce equity, it yields better solutions for the efficient objective (which does not incorporate equity) than traditional myopic matching that uses the efficiency objective.
NFC Antenna With Nonuniform Meandering Line and Partial Coverage Ferrite Sheet for Metal Cover Smartphone Applications

This paper proposes a near-field communication (NFC) antenna solution for metal-cover smartphone applications. In this NFC antenna solution, a narrow slot is initially loaded into the metal cover, and the position of this slot can be altered (with flexibility) according to the design of the smartphone’s external appearance. Next, an unconventional six-turn coil (with a six-sided irregular hexagonal shape) is designed that has a nonuniform linewidth and a nonuniform line gap between two lines, and it is partially loaded with a rectangular ferrite composite. In this design, an enhanced magnetic line of forces can be realized in certain specific locations, and an excellent inductively coupled near-field receiver is achieved. Notably, this proposed NFC antenna can pass the tests required by NFC forum certification, and its performances are comparable with the traditional NFC antenna that has nonmetallic cover.
An Integer Programming Model for the Sudoku Problem

Sudoku is the recent craze in logic puzzles. Players must fill in an n × n matrix, which contains some given entries, so that each row, column, and m ×m submatrix contains each integer 1 through n exactly once. Two issues associated with these puzzles interest us mathematically: puzzle solution and puzzle creation. A Sudoku puzzle can be solved by creating a feasibility problem where the goal is to find at least one feasible solution to the puzzle. We present a binary integer linear program to solve this feasibility problem. Further, such an approach is extended to variations on the traditional Sudoku puzzle. In addition, we speculate as to how Sudoku puzzles are created, and provide several theorems for generating many new puzzles from one given original puzzle. Exercises and challenges problems that use principles from optimization, combinatorics, linear algebra, and computer science are presented for students.
Clique Graphs and Overlapping Communities

It is shown how to construct a clique graph in which properties of cliques of a fixed order in a given graph are represented by vertices in a weighted graph. Various definitions and motivations for these weights are given. The detection of communities or clusters is used to illustrate how a clique graph may be exploited. In particular a benchmark network is shown where clique graphs find the overlapping communities accurately while vertex partition methods fail. PACS numbers: 89.75.Hc, 89.75.Fb, 89.75.-k