Reviewerly/exHarmony huggingface dataset:
TedEval: A Fair Evaluation Metric for Scene Text Detectors

Despite the recent success of scene text detection methods, common evaluation metrics fail to provide a fair and reliable comparison among detectors. They have obvious drawbacks in reflecting the inherent characteristic of text detection tasks, unable to address issues such as granularity, multiline, and character incompleteness. In this paper, we propose a novel evaluation protocol called TedEval (Text detector Evaluation), which evaluates text detections by an instance-level matching and a character-level scoring. Based on a firm standard rewarding behaviors that result in successful recognition, TedEval can act as a reliable standard for comparing and quantizing the detection quality throughout all difficulty levels. In this regard, we believe that TedEval can play a key role in developing state-of-the-art scene text detectors. The code is publicly available at https://github.com/clovaai/TedEval.
Energy-Optimal Motion Trajectory of an Omni-Directional Mecanum-Wheeled Robot via Polynomial Functions

SUMMARY The Mecanum wheel is one of the practical omni-directional wheel designs in industry, especially for heavy-duty tasks in a confined floor. An issue with Mecanum-wheeled robots is inefficient use of energy. In this study, the robotic motion trajectories are optimized to minimize the energy consumption, where a robotic path is expressed in polynomial functions passing through a given set of via points, and a genetic algorithm is used to find the polynomial’s coefficients being decision variables. To attempt a further reduction in the energy consumption, the via points are also taken as decision variables for the optimization. Both simulations and experiments are conducted, and the results show that the optimized trajectories result in a significant reduction in energy consumption, which can be further lowered when the via points become decision variables. It is also found that the higher the order of the polynomials the larger the reduction in the energy consumption.
Unification-based Pointer Analysis without Oversharing

Pointer analysis is indispensable for effectively verifying heap-manipulating programs. Even though it has been studied extensively, there are no publicly available pointer analyses that are moderately precise while scalable to large real-world programs. In this paper, we show that existing context-sensitive unification-based pointer analyses suffer from the problem of oversharing - propagating too many abstract objects across the analysis of different procedures, which prevents them from scaling to large programs. We present a new pointer analysis for LLVM, called TEADSA, without such an oversharing. We show how to further improve precision and speed of TEADSA with extra contextual information, such as flow-sensitivity at call- and return-sites, and type information about memory accesses. We evaluate TEADSA on the verification problem of detecting unsafe memory accesses and compare it against two state-of-the-art pointer analyses: SVF and SEADSA. We show that TEADSA is one order of magnitude faster than either SVF or SEADSA, strictly more precise than SEADSA, and, surprisingly, sometimes more precise than SVF.
Post-Quantum Variants of ISO/IEC Standards

ISO/IEC standardizes several chosen ciphertext-secure key encapsulation mechanism (KEM) schemes in ISO/IEC 18033-2. However, all ISO/IEC KEM schemes are not quantum resilient. In this paper, we introduce new isogeny-based KEM schemes (i.e., CSIDH-ECIES-KEM and CSIDH-PSEC-KEM) by modifying Diffie-Hellman-based KEM schemes in ISO/IEC standards. The main advantage of our schemes are compactness. The key size and the ciphertext overhead of our schemes are about five times smaller than these of SIKE-KEM which is submitted to NIST's post-quantum cryptosystems standardization.
An Intelligent Recommendation Engine for Selecting the University for Graduate Courses in KSA: SARS Student Admission Recommender System

Completing the school degree and selecting an appropriate university/college for graduation could be a stressful and a confusing venture. Searching for an appropriate college online in a specialized field is a challenging task. This research focusses on building a recommender engine which can classify KSA universities according to the preferences of students and voting from others. The proposed system is a recommender system that depends on differentiating between the preferences/voting of the college and a student profile. The preference/voting of each college is represented as a corpus (dataset) of features (embedded in raw files), specifically the words appearing in the file/document. The student profile is depicted using the same terminology and is constructed by performing the analysis of the content that have been done by the user. In order to propose the model for the Recommendation engine research is conducted on the used algorithms and comparison has also been made of various algorithms in this paper, so as to choose ours, which is random forest with regression.
Tracking Multiple Marine Ships via Multiple Sensors with Unknown Backgrounds

In multitarget tracking, knowledge of the backgrounds plays a crucial role in the accuracy of the tracker. Clutter and detection probability are the two essential background parameters which are usually assumed to be known constants although they are, in fact, unknown and time varying. Incorrect values of these parameters lead to a degraded or biased performance of the tracking algorithms. This paper proposes a method for online tracking multiple targets using multiple sensors which jointly adapts to the unknown clutter rate and the probability of detection. An effective filter is developed from parallel estimation of these parameters and then feeding them into the state-of-the-art generalized labeled multi-Bernoulli filter. Provided that the fluctuation of these unknown backgrounds is slowly-varying in comparison to the rate of measurement-update data, the validity of the proposed method is demonstrated via numerical study using multistatic Doppler data.
Accurate Localization of Defective Circular PCB Mark Based on Sub-Pixel Edge Detection and Least Square Fitting

High-precision positioning of PCB Mark plays an important role in the production of PCB. This paper proposes a high-accuracy method to recognize and locate the defective circular PCB Mark. Firstly, the template matching is used to extract the interested Mark region. And then Canny operator which has good noise resistance is used to detect the edges of the Mark. On the basis of angle features analysis of each small edge, the regular and similar edges are retained and the deformed or noisy edges are removed, which is the key process to reduce the influence of the defective edges. After that, the retained edges are used to fit an ellipse by least square method. Finally, the sub-pixel edge points near the ellipse are used to fit an ellipse again more accurately. The experimental results indicate that the positioning error of our method is small. And when processing the deformed or noisy Mark images, our method is robust and can achieve better results than Hough transform and the least square fitting based on sub-pixel edge points.
A Metric for Video Blending Quality Assessment

We propose an objective approach to assess the quality of video blending. Blending is a fundamental operation in video editing, which can smooth the intensity changes of relevant regions. However blending also generates artefacts such as bleeding and ghosting. To assess the quality of the blended videos, our approach considers the illuminance consistency as a positive aspect while regard the artefacts as a negative aspect. Temporal coherence between frames is also considered. We evaluate our metric on a video blending dataset where the results of subjective evaluation are available. Experimental results validate the effectiveness of our proposed metric, and shows that this metric gives superior performance over existing video quality metrics.
Structure-Preserving Signatures on Equivalence Classes from Standard Assumptions

Structure-preserving signatures on equivalence classes (SPS-EQ) introduced at ASIACRYPT 2014 are a variant of SPS where a message is considered as a projective equivalence class, and a new representative of the same class can be obtained by multiplying a vector by a scalar. Given a message and corresponding signature, anyone can produce an updated and randomized signature on an arbitrary representative from the same equivalence class. SPS-EQ have proven to be a very versatile building block for many cryptographic applications. In this paper, we present the first EUF-CMA secure SPS-EQ scheme under standard assumptions. So far only constructions in the generic group model are known. One recent candidate under standard assumptions are the weakly secure equivalence class signatures by Fuchsbauer and Gay (PKC'18), a variant of SPS-EQ satisfying only a weaker unforgeability and adaption notion. Fuchsbauer and Gay show that this weaker unforgeability notion is sufficient for many known applications of SPS-EQ. Unfortunately, the weaker adaption notion is only proper for a semi-honest (passive) model and as we show in this paper, makes their scheme unusable in the current models for almost all of their advertised applications of SPS-EQ from the literature. We then present a new EUF-CMA secure SPS-EQ scheme with a tight security reduction under the SXDH assumption providing the notion of perfect adaption (under malicious keys). To achieve the strongest notion of perfect adaption under malicious keys, we require a common reference string (CRS), which seems inherent for constructions under standard assumptions. However, for most known applications of SPS-EQ we do not require a trusted CRS (as the CRS can be generated by the signer during key generation). Technically, our construction is inspired by a recent work of Gay et al. (EUROCRYPT'18), who construct a tightly secure message authentication code and translate it to an SPS scheme adapting techniques due to Bellare and Goldwasser (CRYPTO'89).
On Kilian’s Randomization of Multilinear Map Encodings

Indistinguishability obfuscation constructions based on matrix branching programs generally proceed in two steps: first apply Kilian's randomization of the matrix product computation, and then encode the matrices using a multilinear map scheme. In this paper we observe that by applying Kilian's randomization after encoding, the complexity of the best attacks is significantly increased for CLT13 multilinear maps. This implies that much smaller parameters can be used, which improves the efficiency of the constructions by several orders of magnitude. As an application, we describe the first concrete implementation of multiparty non-interactive Diffie-Hellman key exchange secure against existing attacks. Key exchange was originally the most straightforward application of multilinear maps; however it was quickly broken for the three known families of multilinear maps (GGH13, CLT13 and GGH15). Here we describe the first implementation of key exchange that is resistant against known attacks, based on CLT13 multilinear maps. For $$N=4$$ users and a medium level of security, our implementation requires 18 GB of public parameters, and a few minutes for the derivation of a shared key.
Optimization of a CNN-based Object Detector for Fisheye Cameras

Fisheye cameras are widely used in the automobile industry, due to their wide field of view for the environment. There are a lot of algorithms that researchers use for object detection, among them Convolutional Neural Networks (CNN) detectors have been widespread during the course of the last decade, however, they are mostly trained and applied on conventional pinhole camera images. In this paper, an attempt to optimize a CNN-based detector for fisheye cameras was made, taking into consideration the barrel distortion, which complicates the object detection. Several experiments were carried out to optimize one state-of-the-art detector, using synthetic fisheye effect on training dataset. The obtained result proves that fisheye augmentation can considerably advance a CNN-based detector’s performance on fisheye images in spite of the distortion. Along the way, a new object detection framework based on the Tensorflow Estomator API was constructed to perform the experiments as convenient as possible.
Private and public aspects of smart jewellery

In recent years, design and form factors of wearables have converged towards traditional jewellery, such as rings, necklaces and bracelets. Even though the aesthetics of smart jewellery have been gradually improved, many current commercial pieces are still mainly functionality and technology-driven. In this paper, we take steps to better understand the design and wearing aspects of smart jewellery. We investigate how jewellery wearers associate functionalities with body locations in a design exploration session with industrial design students using the terms "private" and "public" as keys. Results suggest that with privacy in mind, smart jewellery designs move towards the wearer's hands and fingers and selected data sources originating from the wearer's body were preferred, while designs for "public" information presentation tended to be moved to higher locations on the body, i.e. head, neck and chest, and to be fed from external data sources.
Graph simulation on large scale temporal graphs

Graph simulation is one of the most important queries in graph pattern matching, and it is being increasingly used in various applications, e.g., protein interaction networks, software plagiarism detection. Most previous studies mainly focused on the simulation problem on static graphs, which neglected the temporal factors in daily life. In this paper, we investigate a novel problem, namely, temporal bounded simulation on temporal graphs. Specifically, given a pattern query Q with bound constraint and a temporal graph G, we aim to find out the matches Q(G) of Q on G, such that Q(G) satisfies temporal reachability and bound constraint of Q. To tackle this problem efficiently, we propose a simulation matching framework, which consists of three phases, namely pattern segmentation, temporal bounded simulation of pattern segments, and result integration. We first divide the Q into simple small subgraphs (segments). We construct a temporal reachable tree of G. Based on the tree, we propose a basic matching algorithm for the pattern segments. Furthermore, we propose two optimization algorithms by leveraging multi-layer partition strategy to accelerate query processing. After that, we integrate the simulation results obtained from the pattern segments according to the constraints of temporal reachability and the bound of query pattern. Finally, we use real temporal and synthetic datasets to empirically verify the efficiency and effectiveness of our solutions for temporal bounded simulation matching.
Accelerating Number Theoretic Transform in GPU Platform for qTESLA Scheme

Post-quantum cryptography had attracted a lot of attentions in recent years, due to the potential threat emerged from quantum computer against traditional public key cryptography. Among all post-quantum candidates, lattice-based cryptography is considered the most promising and well studied one. The most time consuming operation in lattice-based cryptography schemes is polynomial multiplication. Through careful selection of the lattice parameters, the polynomial multiplication can be accelerated by Number Theoretic Transform (NTT) and massively parallel architecture like Graphics Processing Units (GPU). However, existing NTT implementation in GPU only focuses on parallelizing one of the three for loop, which eventually causes slow performance and warp divergence. In this paper, we proposed a strategy to mitigate this problem and avoid the warp divergence. To verify the effectiveness of the proposed strategy, the NTT was implemented following the lattice parameters in qTESLA, which is one of the round 2 candidates in NIST Post-Quantum Standardization competition. To the best of our knowledge, this is the first implementation of NTT in GPU with parameters from qTESLA. The proposed implementation can be used to accelerate qTESLA signature generation and verification in batch, which is very useful under server environment. On top of that, the proposed GPU implementation can also be generalized to other lattice-based schemes.
Social image refinement and annotation via weakly-supervised variational auto-encoder

The ever-increasing size of social images and their corresponding imperfect labels have made social image refinement and annotation a crucial problem in supervised learning. However, previous models based on nearest neighbors or matrix completion are limited when the social image set is huge and labels are highly sparse. Deep generative models utilize inference and generative networks to infer latent variables by introducing an observed data variable; they can handle imperfect data, capture noisy data, and fill in missing data variables. In this paper, we propose a new social image refinement and annotation model based on the weakly-supervised variational auto-encoder generative model. First, we formulate the social image refinement and annotation problem as a joint distribution of social images and labels in a probabilistic generative model. Secondly, we derive a new evidence lower bound object to handle imperfect labels. Thirdly, we design a new multi-layer neural network including inference and generative networks to optimize the new evidence lower bound efficiently. Finally, we perform a comparison of our model with other representative models on several real-world social image datasets. Experimental results on social image refinement and annotation tasks show that the proposed model is competitive or even better than existing state-of-the-arts.
Electrical Energy Consumption Model of Internal Components in Data Centers

In the context of modern information technology (IT) industry, cloud computing is gaining popularity for big data handling. Therefore, IT service providers like Google, Facebook and Amazon are expanding their technical resources by building data centers to improve the data processing and data storage facilities under cloud service pattern. However, data centers consume a large amount of electrical energy. In recent years, a lot of research has been done to reduce the electrical energy consumption of data centers by high performance computing. However, very few researchers have focused on the electrical energy consumption by the electrical components inside the data center. In this paper, a component based electrical energy consumption modelling approach is presented to identify the losses of different components as well as their interactions to the total electrical energy consumption of the data center. The electrical energy consumption models of servers and other components are presented as a function of server utilization.
Attack on AES Encryption Microcontroller Devices With Correlation Power Analysis

Encryption is a technology that works by scrambling data so that it cannot be read by unwanted parties. The Advanced Encryption Standard (AES), also known as Rijndael, is a specification for electronic data encryption The tool used in the implementation of AES is ATmega328p. ATmega328P is an 8-bit AVR CMOS based microcontroller with RISC architecture. Correlation Power Analysis (CPA) is an attack that allows us to find the secret encryption key stored on a hardware device. Notduino is a device that is used as a target for the ChipWisperer Capture system. With this notduino, the writer can do attack activities to get the AES key used on the microcontroller. The researcher used the CW1173 board to retrieve the trace when the AES program worked. With CPA analysis with the Hamming distance S-box output method the researcher managed to find all the subkey keys used in the AES encryption algorithm. With the results obtained, it shows that higher security is needed for AES encryption so that it is not easy to attack.
Entropic nonclassicality and quantum non-Gaussianity tests via beam splitting

Abstract We propose entropic nonclassicality criteria for quantum states of light that can be readily tested using homodyne detection with beam splitting operation. Our method draws on the fact that the entropy of quadrature distributions for a classical state is non-increasing under an arbitrary loss channel. We show that our test is strictly stronger than the variance-based squeezing condition and that it can also be extended to detect quantum non-Gaussianity in conjunction with phase randomization. Furthermore, we address how our criteria can be used to identify single-mode resource states to generate two-mode states demonstrating EPR paradox, i.e., quantum steering, via beam-splitter setting.
Online Handwritten Diagram Recognition with Graph Attention Networks

Handwritten text recognition has been extensively researched over decades and achieved extraordinary success in recent years. However, handwritten diagram recognition is still a challenging task because of the complex 2D structure and writing style variation. This paper presents a general framework for online handwritten diagram recognition based on graph attention networks (GAT). We model each diagram as a graph in which nodes represent strokes and edges represent the relationships between strokes. Then, we learn GAT models to classify graph nodes taking both stroke features and the relationships between strokes into consideration. To better exploit the spatial and temporal relationships, we enhance the original GAT model with a novel attention mechanism. Experiments on two online handwritten flowchart datasets and a finite automata dataset show that our method consistently outperforms previous methods and achieves the state-of-the-art performance.
3D Facial Expression Recognition Using Deep Feature Fusion CNN

As an important way of human communication, facial expression not only reflects our mental activities but also provides useful information for human behavior research. Recently, 3D technology becomes promising method to achieve robust facial expression analysis. 3D face scans are robust to lighting and pose variations. In this paper, a novel deep feature fusion convolution neural network (CNN) is designed for 3D facial expression recognition (FER). Each 3D face scan is firstly represented as 2D facial attribute maps (including depth, normal, and shape index values). Then, we combine different of facial attribute maps to learn facial representations by fine-tuning a pre-trained deep feature fusion CNN subnet trained from a large-scale image dataset for universal visual tasks. Moreover, Global Average Pooling is utilized to reduce the number of parameters to decrease the effect of overfitting. The experiments are conducted on the Bosphorus database and the results demonstrate the effectiveness of the proposed method.
Point Cloud-Based Target-Oriented 3D Path Planning for UAVs

This paper explores 3D path planning for unmanned aerial vehicles (UAVs) in 3D point cloud environments. Derivative maps such as dense point clouds, mesh maps, octomaps, etc. are frequently used for path planning purposes. A target-oriented 3D path planning algorithm, directly using point clouds to compute optimized trajectories for a UAV, is presented in this article. This approach searches for obstacle-free, low computational cost, smooth, and dynamically feasible paths by analyzing a point cloud of the target environment, using a modified connect RRT-based path planning algorithm, with a k-d tree-based obstacle avoidance strategy and three-step optimization. This presented approach bypasses the common 3D map discretization, directly leveraging point cloud data. Following trajectory generation, the algorithm creates way-point based, closed-loop quadrotor controls for pitch, roll, and yaw attitude angle as well as dynamics commands for the UAV. Simulations of UAV 3D path planning based on different target points in the point cloud map are presented, showing the effectiveness and feasibility of this approach.