nils-herrmann/clean_scirepeval huggingface dataset:
Reconstruction of Personalized 3D Face Rigs from Monocular Video

We present a novel approach for the automatic creation of a personalized high-quality 3D face rig of an actor from just monocular video data (e.g., vintage movies). Our rig is based on three distinct layers that allow us to model the actor’s facial shape as well as capture his person-specific expression characteristics at high fidelity, ranging from coarse-scale geometry to fine-scale static and transient detail on the scale of folds and wrinkles. At the heart of our approach is a parametric shape prior that encodes the plausible subspace of facial identity and expression variations. Based on this prior, a coarse-scale reconstruction is obtained by means of a novel variational fitting approach. We represent person-specific idiosyncrasies, which cannot be represented in the restricted shape and expression space, by learning a set of medium-scale corrective shapes. Fine-scale skin detail, such as wrinkles, are captured from video via shading-based refinement, and a generative detail formation model is learned. Both the medium- and fine-scale detail layers are coupled with the parametric prior by means of a novel sparse linear regression formulation. Once reconstructed, all layers of the face rig can be conveniently controlled by a low number of blendshape expression parameters, as widely used by animation artists. We show captured face rigs and their motions for several actors filmed in different monocular video formats, including legacy footage from YouTube, and demonstrate how they can be used for 3D animation and 2D video editing. Finally, we evaluate our approach qualitatively and quantitatively and compare to related state-of-the-art methods.
Towards Evaluating the Robustness of Neural Networks

Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is similar to x but classified as t. This makes it difficult to apply neural networks in security-critical areas. Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from 95% to 0.5%.In this paper, we demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with 100% probability. Our attacks are tailored to three distance metrics used previously in the literature, and when compared to previous adversarial example generation algorithms, our attacks are often much more effective (and never worse). Furthermore, we propose using high-confidence adversarial examples in a simple transferability test we show can also be used to break defensive distillation. We hope our attacks will be used as a benchmark in future defense attempts to create neural networks that resist adversarial examples.
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification

There are many clustering tasks which are closely related in the real world, e.g. clustering the web pages of different universities. However, existing clustering approaches neglect the underlying relation and treat these clustering tasks either individually or simply together. In this paper, we will study a novel clustering paradigm, namely multi-task clustering, which performs multiple related clustering tasks together and utilizes the relation of these tasks to enhance the clustering performance. We aim to learn a subspace shared by all the tasks, through which the knowledge of the tasks can be transferred to each other. The objective of our approach consists of two parts: (1) Within-task clustering: clustering the data of each task in its input space individually; and (2) Cross-task clustering: simultaneous learning the shared subspace and clustering the data of all the tasks together. We will show that it can be solved by alternating minimization, and its convergence is theoretically guaranteed. Furthermore, we will show that given the labels of one task, our multi-task clustering method can be extended to transductive transfer classification (a.k.a. cross-domain classification, domain adaption). Experiments on several cross-domain text data sets demonstrate that the proposed multi-task clustering outperforms traditional single-task clustering methods greatly. And the transductive transfer classification method is comparable to or even better than several existing transductive transfer classification approaches.
Learning spatio-temporal patterns in the presence of input noise using phase-change memristors

Neuromorphic systems increasingly attract research interest owing to their ability to provide biologically inspired methods of computing, alternative to the classic von Neumann architecture. In these systems, computing relies on spike-based communication between neurons, and memory is represented by evolving states of the synaptic interconnections. In this work, we first demonstrate how spike-timing-dependent plasticity (STDP) based synapses can be realized using the crystal-growth dynamics of phase-change memristors. Then, we present a novel learning architecture comprising an integrate-and-fire neuron and an array of phase-change synapses that is capable of detecting temporal correlations in parallel input streams. We demonstrate a continuous re-learning operation on a sequence of binary 20×20 pixel images in the presence of significant background noise. Experimental results using an array of phase-change cells as synaptic elements confirm the functionality and performance of the proposed learning architecture.
Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation

Optic nerve head (ONH) segmentation problem has been of interest for automated glaucoma assessment. Although various segmentation methods have been proposed in the recent past, it is difficult to evaluate and compare the performance of individual methods due to a lack of a benchmark dataset. The problem of segmentation involves segmentation of optic disk and cup region within ONH region. Available datasets do not incorporate challenges present in this problem. In this data paper, we present a comprehensive dataset of retinal images which include both normal and glaucomatous eyes and manual segmentations from multiple human experts. Both area and boundary-based evaluation measures are presented to evaluate a method on various aspects relevant to the problem of glaucoma assessment.
Detection of Computer-Generated Papers in Scientific Literature

Meaningless computer generated scientific texts can be used in several ways. For example, they have allowed Ike Antkare to become one of the most highly cited scientists of the modern world. Such fake publications are also appearing in real scientific conferences and, as a result, in the bibliographic services (Scopus, ISI-Web of Knowledge, Google Scholar,...). Recently, more than 120 papers have been withdrawn from subscription databases of two high-profile publishers, IEEE and Springer, because they were computer generated thanks to the SCIgen software. This software, based on a Probabilistic Context Free Grammar (PCFG), was designed to randomly generate computer science research papers. Together with PCFG, Markov Chains (MC) are the mains ways to generated Meaning-less texts. This paper presents the mains characteristic of texts generated by PCFG and MC. For the time being, PCFG generators are quite easy to spot by an automatic way, using intertextual distance combined with automatic clustering, because these generators are behaving like authors with specifics features such as a very low vocabulary richness and unusual sentence structures. This shows that quantitative tools are effective to characterize originality (or banality) of authors’ language. Cyril Labbé Univ. Grenoble Alpes, LIG, F-38000 Grenoble, France, e-mail: first.last@imag.fr CNRS, LIG, F-38000 Grenoble, France Dominique Labbé Univ. Grenoble Alpes, PACTE, F-38000 Grenoble, France,  first.last@pacte.fr CNRS, PACTE, F-38000 Grenoble, France François Portet Univ. Grenoble Alpes, LIG, F-38000 Grenoble, France, e-mail: first.last@imag.fr CNRS, LIG, F-38000 Grenoble, France
Using smart-phones and floor plans for indoor location tracking

We implement pedestrian dead reckoning (PDR) for indoor localization. With a waist-mounted PDR based system on a smart-phone, we estimate the user's step length that utilizes the height change of the waist based on the Pythagorean Theorem. We propose a zero velocity update (ZUPT) method to address sensor drift error: Simple harmonic motion and a low-pass filtering mechanism combined with the analysis of gait characteristics. This method does not require training to develop the step length model. Exploiting the geometric similarity between the user trajectory and the floor map, our map matching algorithm includes three different filters to calibrate the direction errors from the gyro using building floor plans. A sliding-window-based algorithm detects corners. The system achieved 98% accuracy in estimating user walking distance with a waist-mounted phone and 97% accuracy when the phone is in the user's pocket. ZUPT improves sensor drift error (the accuracy drops from 98% to 84% without ZUPT) using 8 Hz as the cut-off frequency to filter out sensor noise. Corner length impacted the corner detection algorithm. In our experiments, the overall location error is about 0.48 meter.
The Value of Information Sharing in a Two-Level Supply Chain

Many companies have embarked on initiatives that enable more demand information sharing between retailers and their upstream suppliers. While the literature on such initiatives in the business press is proliferating, it is not clear how one can quantify the benefits of these initiatives and how one can identify the drivers of the magnitudes of these benefits. Using analytical models, this paper aims at addressing these questions for a simple two-level supply chain with nonstationary end demands. Our analysis suggests that the value of demand information sharing can be quite high, especially when demands are significantly correlated over time. (Supply Chain Management; Mathematical Models; Production Planning and Inventory Control; Approximate Analysis; Electronic Data Interchange; Quick Response; Information Sharing)
TRANSITION FLIGHT OF QUAD TILT WING VTOL UAV

The QTW VTOL UAV, which features tandem tilt wings with propellers mounted at the mid-span of each wing, is one of the most promising UAV configurations, having both VTOL capability and high cruise performance. A six-degree-of-freedom dynamic simulation model covering the full range of the QTW flight envelope was developed and a flight control system including a transition schedule and a stability and control augmentation system (SCAS) was designed. The flight control system was installed in a small prototype QTW and a full transition flight test including vertical takeoff, accelerating transition, cruise, decelerating transition and hover landing was successfully accomplished.
Dual wide-band four-unit MIMO antenna system for 4G/LTE and WLAN mobile phone applications

A compact planar multiband four-unit multiple-input multiple-output (MIMO) antenna system with high isolation is developed. At VSWR ≤ 2.75, the proposed MIMO antenna operates in the frequency range of LTE Band-1, 2, 3, 7, 40 and WLAN 2.4 GHz band. A T-strip and dumbbell shaped slots are studied to mitigate mutual coupling effects. The measured worst case isolation is better that 15.3 dB and envelope correlation coefficient is less than 0.01. The received signals satisfy the equal power gain condition and radiation patterns confirm the pattern diversity to combat multipath fading effects. At 29 dB SNR, the achieved MIMO channel capacity is about 22.2 b/s/Hz. These results infer that the proposed MIMO antenna is an attractive candidate for 4G-LTE mobile phone applications.
A Fast Algorithm for Multilevel Thresholding

Otsu reference proposed a criterion for maximizing the between-class variance of pixel intensity to perform picture thresholding. However, Otsu’s method for image segmentation is very time-consuming because of the inefficient formulation of the between-class variance. In this paper, a faster version of Otsu’s method is proposed for improving the efficiency of computation for the optimal thresholds of an image. First, a criterion for maximizing a modified between-class variance that is equivalent to the criterion of maximizing the usual between-class variance is proposed for image segmentation. Next, in accordance with the new criterion, a recursive algorithm is designed to efficiently find the optimal threshold. This procedure yields the same set of thresholds as the original method. In addition, the modified between-class variance can be pre-computed and stored in a look-up table. Our analysis of the new criterion clearly shows that it takes less computation to compute both the cumulative probability (zeroth order moment) and the mean (first order moment) of a class, and that determining the modified between-class variance by accessing a look-up table is quicker than that by performing mathematical arithmetic operations. For example, the experimental results of a five-level threshold selection show that our proposed method can reduce down the processing time from more than one hour by the conventional Otsu’s method to less than 107 seconds.
MEC 2016: The Multimodal Emotion Recognition Challenge of CCPR 2016

Emotion recognition is a significant research filed of pattern recog‐ nition and artificial intelligence. The Multimodal Emotion Recognition Challenge (MEC) is a part of the 2016 Chinese Conference on Pattern Recognition (CCPR). The goal of this competition is to compare multimedia processing and machine learning methods for multimodal emotion recognition. The challenge also aims to provide a common benchmark data set, to bring together the audio and video emotion recognition communities, and to promote the research in multimodal emotion recognition. The data used in this challenge is the Chinese Natural AudioVisual Emotion Database (CHEAVD), which is selected from Chinese movies and TV programs. The discrete emotion labels are annotated by four experienced assistants. Three sub-challenges are defined: audio, video and multimodal emotion recognition. This paper introduces the baseline audio, visual features, and the recognition results by Random Forests.
Knowledge-based clustering of ship trajectories using density-based approach

Maritime traffic monitoring is an important aspect of safety and security, particularly in close to port operations. While there is a large amount of data with variable quality, decision makers need reliable information about possible situations or threats. To address this requirement, we propose extraction of normal ship trajectory patterns that builds clusters using, besides ship tracing data, the publicly available International Maritime Organization (IMO) rules. The main result of clustering is a set of generated lanes that can be mapped to those defined in the IMO directives. Since the model also takes non-spatial attributes (speed and direction) into account, the results allow decision makers to detect abnormal patterns - vessels that do not obey the normal lanes or sail with higher or lower speeds.
Fall Detection Systems for Elderly Care: A Survey

With the introduction of new technology in our daily life, it is essential that this technology is used for the aid of the elderly. Falls cause a very high risk to the elderly's life. Accordingly, this paper's focus is on technology that would aid the elderly. These technologies include: Wearable- based, audio- based, and video-based fall detection systems. This paper surveys the literature regarding fall detection algorithms using those three branches and the various sensors they employ. Looking at wearable technology, the technology is cheap and accurate but inconvenient. Audio-based technology on the other hand is more convenient and is cheaper than video-based technology. However audio-based technology is hard to set up compared to video and wearable-based technologies. Video- based technology is accurate and easy to set up. At the moment, video-based technology is the most expensive compared to the other two, and it is also prone to occlusion. However as homes become smarter and prices for cameras continue to drop, it is expected that this technology will be the best of the three due to its versatility.
Visualizing the Intellectual Structure with Paper-Reference Matrices

Visualizing the intellectual structure of scientific domains using co-cited units such as references or authors has become a routine for domain analysis. In previous studies, paper-reference matrices are usually transformed into reference-reference matrices to obtain co-citation relationships, which are then visualized in different representations, typically as node-link networks, to represent the intellectual structures of scientific domains. Such network visualizations sometimes contain tightly knit components, which make visual analysis of the intellectual structure a challenging task. In this study, we propose a new approach to reveal co-citation relationships. Instead of using a reference-reference matrix, we directly use the original paper-reference matrix as the information source, and transform the paper-reference matrix into an FP-tree and visualize it in a Java-based prototype system. We demonstrate the usefulness of our approach through visual analyses of the intellectual structure of two domains: information visualization and Sloan Digital Sky Survey (SDSS). The results show that our visualization not only retains the major information of co-citation relationships, but also reveals more detailed sub-structures of tightly knit clusters than a conventional node-link network visualization.
A Multidimensional Walking Aid for Visually Impaired Using Ultrasonic Sensors Network with Voice Guidance

The application of engineering practices in medicine has immensely contributed to the recent findings in biomedical research areas. One of the products of this application is the development of sophisticated aids for physically challenged people. In this paper, visually impaired walking aid is designed and implemented using a network of ultrasonic sensors, thereby capable of detecting the direction and position of obstacle(s). The performance and functionality are also improved by the addition of alert light, and voice guidance signal which is relayed to a miniature headset. The recorded voice alerts the user of the presence and direction of the obstacle(s). The prototype of the multidimensional walking aid was able to detect obstacles within the range of 0m to 1m at the left, right and front of the stick with an appropriate voice alert. The test results of the prototype showed that the stick can efficiently
A Static Malware Detection System Using Data Mining Methods

A serious threat today is malicious executables. It is designed to damage computer system and some of them spread over network without the knowledge of the owner using the system. Two approaches have been derived for it i.e. Signature Based Detection and Heuristic Based Detection. These approaches performed well against known malicious programs but cannot catch the new malicious programs. Different researchers have proposed methods using data mining and machine learning for detecting new malicious programs. The method based on data mining and machine learning has shown good results compared to other approaches. This work presents a static malware detection system using data mining techniques such as Information Gain, Principal component analysis, and three classifiers: SVM, J48, and Naïve Bayes. For overcoming the lack of usual anti-virus products, we use methods of static analysis to extract valuable features of Windows PE file. We extract raw features of Windows executables which are PE header information, DLLs, and API functions inside each DLL of Windows PE file. Thereafter, Information Gain, calling frequencies of the raw features are calculated to select valuable subset features, and then Principal Component Analysis is used for dimensionality reduction of the selected features. By adopting the concepts of machine learning and data-mining, we construct a static malware detection system which has a detection rate of 99.6%.
AUTOMATIC ROUTING OF FORKLIFT ROBOTS IN WAREHOUSE APPLICATIONS

Forklift robots are frequently applied in automated logistics systems to optimize the transportation tasks and, consequently, to reduce costs. Nowadays, in a scenario of extremely fast technological development and constant search for costs minimization, the automation of logistic process is essential to improve the productivity and reduce costs. In order to decrease costs of logistics and distribution of goods, it is quite common to find in developed countries mechatronic systems performing several tasks in harbor, warehouses, storages and products distribution center. Therefore, research in this topic is considered strategic to ensure a greater insertion of the individual countries in the international trade scenario. In this application, the vehicle routing decision is one of the main issues to be solved. It is important to emphasize that its productivity is highly dependent on the adopted routing scheme. Consequently, it is essential to use efficient routes schemes. This paper proposes an algorithm that produces optimal routes for AGVs (Automated Guided Vehicles) working inside warehouse as forklift robots. The algorithm was conceived to deal with different real situations, such as the need of conflict-free paths and the presence of obstacles. In the routing algorithm each AGV executes the task starting in an initial position and orientation and moving to a pre-established position and orientation, generating a minimum path. This path is a continuous sequence of positions and orientations of the AGVs. The algorithm is based on Dijkstra's shortest-path method and was implemented in C++. Computer simulation tests are used to validate the algorithm efficiency in different working conditions.
NESMO: Network slicing management and orchestration framework

This paper presents a novel network slicing management and orchestration architectural framework. A brief description of business scenarios and potential customers of network slicing is provided, illustrating the need for ordering network services with very different requirements. Based on specific customer goals (of ordering and building an end-to-end network slice instance) and other requirements gathered from industry and standardization associations, a solution is proposed enabling the automation of end-to-end network slice management and orchestration in multiple resource domains. This architecture distinguishes between two main design time and runtime components: Network Slice Design and Multi-Domain Orchestrator, belonging to different competence service areas with different players in these domains, and proposes the required interfaces and data structures between these components.