Reviewerly/exHarmony huggingface dataset:
Secure multi-party computation with a quantum manner

Abstract Quantum information processing protocols have great advantages over their classical counterparts, especially on cryptography. Secure multi-party computation is one of the most important issues and has been extensively studied in cryptography. It is of both theoretical and practical significance to develop the quantum information processing protocols for secure multi-party computation. In this paper, we consider the secure multi-party computation for n -variable polynomial functions over the finite field GF( d ). We propose two protocols using quantum resources to compute the function within a one-time execution. One is based on d -level mutually unbiased (orthonormal) bases with cyclic property and the other takes advantage of quantum Fourier transform. Analytical results show that the proposed protocols are secure against a passive adversary with unlimited computing power, including colluding attack mounted by n − 2 parties. We also implement the second protocol of the special case d = 2 on the IBM Q Experience. In principle, our proposals can be experimentally realized in the arbitrary d dimension with the advances in realizations and controls of high-dimensional quantum computation.
Time Complexity Analysis of Optimal Firefly Vector Quantization Algorithms for Image Compression

Vector quantization is a lossy compression method utilized in communication as well as image coding. In scalar quantized, a scalar values are chosen from a restricted list of probable values to signify a model. In vector quantized, vectors are choosing from a limited record of probable vectors to signify an input vector of models. The key function in a vector quantized is the quantized of an arbitrary vector with encoder it as a binary key. VQ became an amazing method in the usage of digital image compression. The usual and extensively utilized manner consisting of the Linde-Buzo-Gray (LBG) technique created local best codebook. In this paper, a comparative analysis is made among the firefly by Tumbling effect (FF-T) and firefly with teaching and learning based optimization (FF-TLBO) techniques to VQ. Experimental outcomes illustrate that the presented FF-LBG techniques are earlier than the alternative 4 methods. Additionally, the recreated images obtain higher quality than that constructed shape the LBG, PSO and QPSO, but it's far no massive superiority to the HBMO set of technique. On the applied set of images, the FF-TLBO-LBG algorithm requires an average CT of 794.213ms whereas the FF-T-LBG model needs a minimum CT of 777.548ms.
Learning sparse graphons and the generalized Kesten–Stigum threshold

The problem of learning graphons has attracted considerable attention across several scientific communities, with significant progress over the recent years in sparser regimes. Yet, the current techniques still require diverging degrees in order to succeed with efficient algorithms in the challenging cases where the local structure of the graph is homogeneous. This paper provides an efficient algorithm to learn graphons in the constant expected degree regime. The algorithm is shown to succeed in estimating the rank-k projection of a graphon in the L2 metric if the top k eigenvalues of the graphon satisfy a generalized Kesten–Stigum condition.
A New Data Association Method Using Kalman Filter Innovation Vector Projections

This paper describes the derivation, analysis and implementation of a new data association method that provides a tight bound on the risk of incorrect association for LiDAR feature-based localization. Data association (DA) is the process of assigning currently-sensed features with ones that were previously observed. Most DA methods use a nearest-neighbor criterion based on the normalized innovation squared (NIS). They require complex algorithms to evaluate the risk of incorrect association because sensor state prediction, prior observations, and current measurements are uncertain. In contrast, in this work, we derive a new DA criterion using projections of the extended Kalman filter's innovation vector. The paper shows that innovation projections (IP) are signed quantities that not only capture the impact of an incorrect association in terms of its magnitude, but also of its direction. The IP-based DA criterion also leverages the fact that incorrect associations are known and well-defined fault modes. Thus, as compared to NIS, IPs provide a much tighter bound on the predicted risk of incorrect association. We analyze and evaluate the new IP method using simulated and experimental data for autonomous inertial-aided LiDAR localization in a structured lab environment.
A robust version of Hegedus’s lemma, with applications

Hegedűs’s lemma is the following combinatorial statement regarding polynomials over finite fields. Over a field F of characteristic p > 0 and for q a power of p, the lemma says that any multilinear polynomial P∈ F[x 1,…,x n ] of degree less than q that vanishes at all points in {0,1} n of Hamming weight k∈ [q,n−q] must also vanish at all points in {0,1} n of weight k + q. This lemma was used by Hegedűs (2009) to give a solution to Galvin’s problem, an extremal problem about set systems; by Alon, Kumar and Volk (2018) to improve the best-known multilinear circuit lower bounds; and by Hrubeš, Ramamoorthy, Rao and Yehudayoff (2019) to prove optimal lower bounds against depth-2 threshold circuits for computing some symmetric functions. In this paper, we formulate a robust version of Hegedűs’s lemma. Informally, this version says that if a polynomial of degree o(q) vanishes at most points of weight k, then it vanishes at many points of weight k+q. We prove this lemma and give the following three different applications.
Post‐quantum protocol for computing set intersection cardinality with linear complexity

Nowadays, the necessity of electronic information increases rapidly. As a consequence, often, that information needs to be shared among mutually distrustful parties. In this area, private set intersection (PSI) and its variants play an important role when the participants wish to do secret operations on their input sets. Unlike the most modern public key cryptosystems relying on number theoretic problems, lattice-based cryptographic constructions provide security in the presence of a quantum computer. Consequently, developing PSI and its variants using lattice based cryptosystem becomes an interesting direction for research. This study presents the first size-hiding post quantum PSI cardinality (PSI-CA) protocol whose complexity is linear in the size of the sets of the participants. The authors use space-efficient probabilistic data structure (Bloom filter) as its building block. Further, they extend the authors’ PSI-CA to its authorised version, i.e. authorised PSI-CA. Security for both of them is achieved in the standard model based on the hardness of the decisional learning with errors problem.
Robotic Helping Hand: A New Mechanism for Helping Disabled People

Many assistive technologies implemented to help the disabled people. The purpose of this research is to design and implement a new mechanism for disabled people which can be used as a helping hand. Generally, disabled people depend on others to live their lives. Our target is to make a robotic system that has different characteristics to help the physically challenged people. The robot will be able to move in any direction. An open-source Android application is used to control the robot via Bluetooth. The robot responds to move commands in the forward, backward, left, and right directions. A disabled person, especially those who cannot walk will be able to send this robot anywhere. The project also implements a robotic arm with pick and place capability. It is able to pick any object and carry it and place it to the required position. The robotic arm is designed such that it can be controlled by a number of different mechanisms, namely a smartphone as the remote control, or human voice command or an RF controller. Disabled people can use any one of these methods according to his or her comfort. The robot also uses an IP camera for video observation as well as video communication with others.
Implementing a Platform to Run Clustering Algorithms Using Distributed Computing

Most of the clustering algorithms are designed to work as a sequential algorithm that requires all data to be present, which limits the actual implementation to run on a single machine and does not support horizontal scalability. This is problematic in today's context when volume of data gets larger each day and the need to process data quickly is essential. Hence, in this paper we propose a platform that allows running clustering algorithms in a distributed manner. This is achieved through splitting the data into smaller and equal partitions, and through redesigning the original clustering algorithms to allow working on a sub-set of the input data without having to interact with the processing of the rest of the input data. At the end the so-called reduce phase aggregates the partial results obtained from processing each partition and it produces the global result.
Part of Speech Tagging for Kayah Language Using Hidden Markov Model

Part of Speech tagging is one of the active research problem in Natural Language Processing involves language understanding, reasoning and the utilization of common sense of knowledge. This paper contributes the pioneer evaluation to the Kayah Language for tagging Part of Speech. Kayah language is one of the low resource languages in Myanmar. Kayah corpus used in this system is translated by Kayah people from Myanmar. The goal is to build the Kayah Language Part of Speech Tagging System based Hidden Markov Model. Sixteen tag sets are defined for this language. Hidden Markov Model is used to learn the Kayah corpus of words annotated with the correct Part of Speech tags and generated the model relating to the Initial, Transition and Emission probabilities for Kayah Language. Then the generated model is used to decode Viterbi algorithm. This Kayah Hidden Markov Model based POS tagging has been experimented and has overall accuracy of 87 % on test data.
An Affinity-Driven Relation Network for Figure Question Answering

Figure question answering (FQA) is a new multimodal task for visual question answering (VQA). Given a scientific-style Figure and a related question, the machine must answer the question through reasoning. The Relation Networks (RN), the earliest proposed approach for FQA, computes the representation of relations between objects within images to infer the answers. However, RN generates numerous relation features, which makes the reasoning process more complicated and restricts the performance. To better solve this problem, we introduce a novel framework, which consists of a deconvolutional network, an LSTM network and an affinitydriven relation network. Specifically, the deconvolutional network enhances the feature fusion by combining low-level and high-level features of images. The affinity-driven relation network efficiently represents the intra-relation within images and the inter-relation between images and questions, and makes the reasoning process more effective. The experimental results show that our approach outperforms most state-of the-art methods in FQA tasks.
A Hybrid Image Codec with Learned Residual Coding

We propose a three-layer image compression system consisting of a base-layer VVC (intra) codec, a learning-based residual layer codec, and a learnable hyperprior. This proposal (Team: NCTU_Commlab) is submitted to the Challenge on Learned Image Compression (CLIC) in March 2020. Our contribution is developing a data fusion attention module and integrating several known components together to form an efficient image codec, which has a higher compression performance than the standard VVC coding scheme. Unlike the conventional residual image coding, both our encoder and decoder take inputs also from the base-layer output. Also, we construct a refinement neural network to merge the residual-layer decoded residual image and the base-layer decoded image together to form the final reconstructed image. We tested two autoencoder structures for the encoder and decoder, namely, CNN with GDN [5], [6], and the generalized octave CNN [4]. Our results show that the transmitted latent representations are very efficient in coding the residuals because the object boundary information can be provided by the proposed spatial attention module. The experiments indicate that the proposed system achieves better performance than the single-layer VVC at both PSNR and subjective quality at around 0.15 bit-per-pixel.
Implementation of single shot detector for object finding in drone platform

Abstract The developing of object detection for many purpose has come to various techniques. Some of those works also implement to solve our daily life problem. Those development which are state-of-the-art are mostly applicable with many pros than others. In this paper we use one of state-of-the-art of object detection to control a quadcopter i.e. Single Shot MultiBox Detector (SSD). SSD is used to detect an object as quadcopter target for approach mission. SSD also use to keep an eye of the target. The target is represented in shape of ROI location. This ROI or a bounding box location is used as feedback of the control system of quadcopter which will guide the quadcopter to approach. The mission is considered as success if the quadcopter is stopped at minimum range 1 meter toward target. This works shows the successful of object detection implementation by serving IMU responses and measured distance to object responses.
Laboratory Environment for Algorithms Testing in Mobile Robotics

The aim of this research is to create fully functional environment for real-life testing of various algorithms in mobile robotic. Main advantage, beside low-cost, is dealing with challenges of real-life implementation. Unlike simulation environments (e.g. MathLab), this setting will allow researchers to test their path planning, collision detection and other algorithms with real challenges, real robots and real static and dynamic obstacles. Environment is particularly adapted for swarm robotics and any other mobile robotics including 2D and 3D scenarios. Enabling researches to prove their algorithms in this environment allows significantly faster path to real implementation in industry, military and science. Concept is based on OpenCV library and low-cost hardware. Solution is based on visual positioning and motion vector detection. System allows controlling mobile robots via radio communication with the range up to 100m which allows practical application of the system. This research paper should be considered as a part of series of research papers published earlier.
Reversible Watermarking Technique for Fingerprint authentication based on DCT

Abstract In this paper, a new reversible and blind fingerprint image watermarking technique based on the differential method and discrete cosine transform (DCT) domains is presented. The focus is to increase the security of the fingerprint image in authentication systems. Two DCT-transformed sub-vectors are employed to embed the bits of the watermark sequence in a differential scheme. The original sub-vectors are acquired by the DCT transform on the host fingerprint image. In the extraction process, a minor variance between the sub-vectors that correspond to the watermarked fingerprint image directly allows access to the embedded watermark sequence; therefore, the extraction process doesn’t require an original fingerprint. The original fingerprint image is then recovered from the watermarked fingerprint image based on the reversible watermarking technique. The similarity between the reversible fingerprint image and the original is considered, and we could extract minutiae points from it without a problem. The proposed technique is evaluated using 80 fingerprint images from 10 persons for each from FVC2002 fingerprint database. Eight fingerprint images have been taken from each person to be used as the template then the process was followed by embedding the watermark into each fingerprint image. The experimental results validate the proposed method able to give promising results in preserving the fingerprint image security.
Learning-Based Quality Enhancement For Scalable Coded Video Over Packet Lossy Networks

The layered feature of scalable video coding (SVC) offers a sufficient adaptation to unreliable transmission. When network condition drops sharply, enhancement layers will be abandoned, and only base layers are delivered. However, this will cause noticeable visual artifacts due to quality differences between different layers. To alleviate this problem, we novelly introduce a deep learning-based method into video reconstruction phase of scalable bitstreams. A super-resolution motivated recurrent network is proposed to extract and fuse features from both previous high-resolution frames and the current low-resolution frame. To the best of our knowledge, this is the first attempt to improve the performance of scalable bitstreams reconstruction by a specifically designed super-resolution network. By seamlessly integrating the accessible features, significant video quality improvements in terms of PSNR, SSIM, and VMAF are achieved. At the same time, the improvement of overall visual quality stability is apparent under packet lossy networks, indicating both efficiency and robustness of our approach.
LowFER: Low-rank Bilinear Pooling for Link Prediction

Knowledge graphs are incomplete by nature, with only a limited number of observed facts from the world knowledge being represented as structured relations between entities. To partly address this issue, an important task in statistical relational learning is that of link prediction or knowledge graph completion. Both linear and non-linear models have been proposed to solve the problem. Bilinear models, while expressive, are prone to overfitting and lead to quadratic growth of parameters in number of relations. Simpler models have become more standard, with certain constraints on bilinear map as relation parameters. In this work, we propose a factorized bilinear pooling model, commonly used in multi-modal learning, for better fusion of entities and relations, leading to an efficient and constraint-free model. We prove that our model is fully expressive, providing bounds on the embedding dimensionality and factorization rank. Our model naturally generalizes Tucker decomposition based TuckER model, which has been shown to generalize other models, as efficient low-rank approximation without substantially compromising the performance. Due to low-rank approximation, the model complexity can be controlled by the factorization rank, avoiding the possible cubic growth of TuckER. Empirically, we evaluate on real-world datasets, reaching on par or state-of-the-art performance. At extreme low-ranks, model preserves the performance while staying parameter efficient.
BubbleMap: Privilege Mapping for Behavior-Based Implicit Authentication Systems

Leveraging users' behavioral data sampled by various sensors during the identification process, implicit authentication (IA) relieves users from explicit actions such as remembering and entering passwords.Various IA schemes have been proposed based on different behavioral and contextual features such as gait, touch, and GPS.However, existing IA schemes suffer from false positives, i.e., falsely accepting an adversary, and false negatives, i.e., falsely rejecting the legitimate user due to users' behavior change and noise.To deal with this problem, we propose BubbleMap (BMap), a framework that can be seamlessly incorporated into any existing IA system to balance between security (reducing false positives) and usability (reducing false negatives) as well as reducing the equal error rate (EER).To evaluate the proposed framework, we implemented BMap on five state-of-the-art IA systems.We also conducted an experiment in a real-world environment from 2016 to 2020.Most of the experimental results show that BMap can greatly enhance the IA schemes' performances in terms of the EER, security, and usability, with a small amount of penalty on energy consumption.
Inaccurate Supervised Saliency Detection Based on Iterative Feedback Framework

Machine learning based bottom-up saliency detection (MLBU) methods are very popular recently.These MLBU methods firstly use prior knowledge to select some regions from the given image as training samples and label them.Based on training set, a saliency classifier is learned to classify salient object and background by applying machine learning algorithms in the given image.Nevertheless, training labels obtained by prior knowledge are not always accurate in some complex scenes, inaccurate training set is hard to make subsequent learning process succeed.To solve this problem, we propose an inaccurate supervised learning (ISL) based saliency detection framework, which assumes that training labels obtained by prior knowledge might be inaccurate and constructs three checking rules to remove mislabeled samples for more accurate training set construction.The refined training set is used to learn a saliency classifier which can better predict each image region.To obtain more accurate saliency inference, the proposed ISL process is introduced into a novel iterative feedback (IF) framework to generate better saliency result.Finally, we use smoothness operator to further smooth saliency result for performance improvement.Experimental results on three benchmark datasets demonstrate adequately the superiority of the proposed method.
Predicting mid-air gestural interaction with public displays based on audience behaviour

Knowledge about the expected interaction duration and expected distance from which users will interact with public displays can be useful in many ways. For example, knowing upfront that a certain setup will lead to shorter interactions can nudge space owners to alter the setup. If a system can predict that incoming users will interact at a long distance for a short amount of time, it can accordingly show shorter versions of content (e.g., videos/advertisements) and employ at-a-distance interaction modalities (e.g., mid-air gestures). In this work, we propose a method to build models for predicting users’ interaction duration and distance in public display environments, focusing on mid-air gestural interactive displays. First, we report our findings from a field study showing that multiple variables, such as audience size and behaviour, significantly influence interaction duration and distance. We then train predictor models using contextual data, based on the same variables. By applying our method to a mid-air gestural interactive public display deployment, we build a model that predicts interaction duration with an average error of about 8 s, and interaction distance with an average error of about 35 cm. We discuss how researchers and practitioners can use our work to build their own predictor models, and how they can use them to optimise their deployment.
A DSL-MCDA Model for Energy Consumption-Aware in Cloud Computing

Cloud Computing environment provides an infinite number of resources to Cloud users with different utilities such as computing power, storage space, servers and applications. There are various issues in the cloud resource allocation area that still need to be solved. To address some of these issues, we propose in this paper the DSL-MCDA (Deep Supervised Learning - Multi Criteria Decision Analysis) Model for Energy Consumption-Aware in Cloud Computing as a proactive model of energy consumption-aware. The main goal of the proposed model is to reduce the energy consumption since virtual machines (VM) are dynamically consolidated to lesser number of physical machines (PMs) by selecting an optimal resource that satisfies the demands of cloud users, and this based on classification in deep supervised learning and multi-criteria decision analysis (MCDA) methods.