Reviewerly/exHarmony huggingface dataset:
Design of Online Learning Platform with Vietnamese Virtual Assistant

This paper proposes an online learning platform with a Vietnamese Virtual Assistant (VVA) that is utilized to help instructors in presenting lessons as well as testing and assessing learners. Currently, in online learning platforms, the lesson content is recorded in videos. In the proposed approach, the lesson content is delivered through slides (PDF format) combined with the instructor's voice and face that are synthesized from the text (TTS: Text-to-Speech and SDF: Speech-driven-Face). In addition, our approach ensures the highest quality in displaying the lesson content and allows us to edit the lesson content directly from text without recording video again as the current way.
Advanced Genetic Algorithm for the Embedded FPGA Logic Diagnostic

FPGAs' logic is diagnosed to detect failures, finding a place of its occurrence, and determining its type. Diagnostic sequences are often used for diagnostics. They include several input sets. It is determined which failure occurred, according to the model of failures and the results of the function on the input sets. IEEE standards require systems on a chip (SoC) and FPGAs to contain built-in diagnostics. It is advisable to use genetic algorithms for this purpose. However, there is a problem of limited memory of the built-in diagnostic microcontroller that implements GA. This article describes the construction of a diagnostic sequence using a genetic algorithm with intermediate coding of individuals. Estimates of the efficiency of the algorithm are given, as well as estimates of the required memory for its execution. The simulation of the GA execution on an embedded microcontroller with limited memory has been carried out.
Mondrian conformal anomaly detection for fault sequence identification in heterogeneous fleets

We considered the case of monitoring a large fleet where heterogeneity in the operational behavior among its constituent units (i.e., systems or machines) is non-negligible, and no labeled data is available. Each unit in the fleet, referred to as a target, is tracked by its sub-fleet. A conformal sub-fleet (CSF) is a set of units that act as a proxy for the normal operational behavior of a target unit by relying on the Mondrian conformal anomaly detection framework. Two approaches, the k-nearest neighbors and conformal clustering, were investigated for constructing such a sub-fleet by formulating a stability criterion. Moreover, it is important to discover the sub-sequence of events that describes an anomalous behavior in a target unit. Hence, we proposed to extract such sub-sequences for further investigation without pre-specifying their length. We refer to it as a conformal anomaly sequence (CAS). Furthermore, different nonconformity measures were evaluated for their efficiency, i.e., their ability to detect anomalous behavior in a target unit, based on the length of the observed CAS and the S-criterion value. The CSF approach was evaluated in the context of monitoring district heating substations. Anomalous behavior sub-sequences were corroborated with the domain expert leading to the conclusion that the proposed approach has the potential to be useful for both diagnostic and knowledge extraction purposes, especially in domains where labeled data is not available or hard to obtain.
Parallel Divide-and-Conquer Algorithms for Bubble Sort, Selection Sort and Insertion Sort

Abstract We present efficient parallel recursive divide-and-conquer algorithms for bubble sort, selection sort, and insertion sort. Our algorithms have excellent data locality and are highly parallel. The computational complexity of our insertion sort is ${{\mathcal{O}}}\left ({n^{\log _2 3}}\right )$ in contrast to ${{\mathcal{O}}}\left ({n^2}\right )$ of standard insertion sort.
LIMDD: A Decision Diagram for Simulation of Quantum Computing Including Stabilizer States

Efficient methods for the representation and simulation of quantum states and quantum operations are crucial for the optimization of quantum circuits. Decision diagrams (DDs), a well-studied data structure originally used to represent Boolean functions, have proven capable of capturing relevant aspects of quantum systems, but their limits are not well understood. In this work, we investigate and bridge the gap between existing DD-based structures and the stabilizer formalism, an important tool for simulating quantum circuits in the tractable regime. We first show that although DDs were suggested to succinctly represent important quantum states, they actually require exponential space for certain stabilizer states. To remedy this, we introduce a more powerful decision diagram variant, called Local Invertible Map-DD (LIMDD). We prove that the set of quantum states represented by poly-sized LIMDDs strictly contains the union of stabilizer states and other decision diagram variants. Finally, there exist circuits which LIMDDs can efficiently simulate, while their output states cannot be succinctly represented by two state-of-the-art simulation paradigms: the stabilizer decomposition techniques for Clifford + <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> circuits and Matrix-Product States. By uniting two successful approaches, LIMDDs thus pave the way for fundamentally more powerful solutions for simulation and analysis of quantum computing.
Building a Secure Video Conference System with Customized Cryptographic USB Keys

The video conference has been widely adopted for telecommuting due to the convenience, and various schemes are proposed to provide the confidentiality and integrity of the stream data. However, due to the limitation of the video terminals, the adversary can still obtain the cryptographic key by exploiting the vulnerabilities of OS or other binaries in the video terminals, or launching various attacks such as cold-boot attacks, DMA attacks and side channel attacks. Once the cryptographic key is leaked, the confidentiality and integrity of the stream data will be broken. In this paper, we propose a secure video conference system by customizing the cryptographic USB key (called CCUK) for efficient cryptographic computation and secure key storage. In our scheme, the cryptographic key is stored and used (i.e., key agreement and data encryption/decryption) in CCUK, a dedicated hardware security module (HSM), which avoids the leakage of cryptographic key even when the video terminal is compromised. Moreover, CCUK only has one requirement on the video terminal, i.e., having the USB interface, and therefore can be deployed in almost all types of video terminals. CCUK is cost-effective compared to other types of HSMs, and its efficiency avoids the stream data encryption/decryption to be the performance bottleneck. We have implemented the proposed scheme, and deployed it to provide the confidentiality and integrity for a practical video conference system. The evaluation demonstrates that CCUK can support 1080P HD video conference.
An Advanced Server System Design with 3-Dimensional Vaper Chamber Heat Sink Solution

As data explosion dramatically accelerating from IoT (Internet of Thing) edge devices, high performance server has been dominating deployment in the recent CSPs (Cloud Service Providers) data center for conquering the challenge of huge amount of data processing on cloud computing. Microprocessor architecture advance continues to involve with more core count, higher frequency, as well as larger on-die memory cache size, pursuing for higher computing performance while trading off with more power consumption. Nowadays, CSPs have been experiencing the power constraint challenge of high-performance server deployed for TCO (total cost ownership) efficiency in their traditional typical 4KW co-lo or 8KW self-built air-cooling data center where the infrastructures restructured for liquid cooling is unfeasible.This paper introduces an advanced server system design with 3D VC (3-Dimensional Vaper Chamber) heat sink for power constraint data center deployment. Compared with the traditional air-cooled heat sink solutions, 3D VC heat sink has better temperature uniformity and cost advantages. With the 3D VC thermal solution in server system design, CPU thermal performance can achieve 2°C improvement vs. the solution of traditional heat pipes and VC plates heat sinks, and the TCO saving is up to 15% to 20%.
Adversarial Network for Photographic Image Synthesis from Fine-grained Captions

Automatic synthesis of realistic images from a given caption of text is a fascinating research idea and useful in many applications such as image in-painting, photo-editing, computer-aided design, etc. Current AI techniques are still exploring in this direction. However, researchers are currently developing text-to-image synthesis networks focused on the learning of discriminative text and image features using continuous and generalized robust neural network architectures. Many applications use deep convolution Generative Adversarial Networks (GANs) to construct extremely persuasive representations of explicit categories like faces, album covers, birds, creatures, and room interiors, among others. With the progress of generative models, neural networks can not only recognize images but are used to generate audio and realistic images as well. In the proposed work, the authors have used GAN-CLS architecture to create images from given text descriptions/captions. The experiment uses the CUB-200 dataset, which contains 11,788 bird images from 200 categories, as well as the Oxford-102 dataset, which contains 8,189 flower images from 102 categories. The proposed system's performance is assessed and compared to that of other systems.
The Use of Instagram in the Sports Biomechanics Classroom

Students in higher education habitually use mobile devices such as smartphones and tablets, specifically using social networks on them for staying updated on current affairs, communication between one another, and entertainment. However, its use as a potential medium for learning especially in times of distance learning has not been studied in depth. Instagram is the fastest growing social network in the world and its graphical interface makes it a useful learning tool for a theoretical-practical subject such as biomechanics, where movement is analyzed. The main objective was to evaluate the change in perception of students in the use of Instagram as a learning medium for qualitative biomechanical analysis as a part of their undergraduate sports science program. 171 students filled out a questionnaire on the use of smartphones, social networks, and specifically Instagram, before and after doing an assignment of qualitative analysis that was carried. The results indicated a positive change in the perspective of the students when asked if class assignments can be done on mobile devices ( p = 0.002) and social media ( p &amp;lt; 0.001). The students also indicated a greater interest in doing assignments via social networks ( p &amp;lt; 0.001), especially in the subject of biomechanics ( p &amp;lt; 0.001). They also cited that Instagram is a useful source for information on sports biomechanics ( p = 0.015) and could be used to make observations of sporting movements ( p = 0.043). The results indicated that an introduction of familiar devices in undergraduate teaching could produce a positive change in perception of using such methodologies and facilitate learning. The students, who use smartphones very frequently in their daily lives, and specifically, many use them for browsing social networks, find the platform to be very useful in finding and sharing information related to sports and specifically in sports biomechanics. The visual aspect of social networks like Instagram can help engage them with learning strategies in a subject like biomechanics.
Interacting with Humanoid Robots: Affective Robot Motion Design with 3D Squash and Stretch Using Japanese Jo-ha-kyu Principles in Bunraku

The Bunraku puppets’ affective motions are often praised as “one of the most beautiful motions in the world” by UNESCO. We characterize 3D “squash and stretch” motion in Bunraku puppet plays and realize them in a real life-size robot with unique mechanical structures. Our results reveal that the music tempos and the puppet movements of “squash and stretch” follow the principle so-called “Jo-Ha-Kyū,” which is artistic modulations in traditional Japanese performances. Our research reveals that the affective robot motion design with the 3D “squash and stretch” and Jo-Ha-Kyū principle is one of the keys in affective human-robot interactions.
Planning Parcel Relay Manoeuvres for Quadrotors

In this paper, a strategy for planning aggressive collision-free parcel relay manoeuvres for quadrotors is proposed. The method relies on the generation of optimal polynomial trajectories with acceleration constraints in order to coordinate the attitude of the vehicles during the package exchange. The problem is formulated as a mixed-integer quadratic program where the integer constraints ensure collision avoidance. The manoeuvre is divided into three phases and the dynamical model of the robots is considered to ensure the vehicles keep a suitable relative orientation during the parcel transfer. Simulation results demonstrate the success of the presented strategy.
PSCPAC: Post-quantum secure certificateless public auditing scheme in cloud storage

To reduce the local storage burden and enjoy the conveniently ubiquitous access, an increasing number of individuals and enterprises prefer to outsource their data to the cloud server. Due to the loss of physical control over data, how to guarantee the cloud server stores user’s data honestly becomes an important security challenge. Meanwhile, most of existing public auditing schemes based on traditional public key cryptosystem either suffer from bearing the heavy burden of certificate management or possess an inherent key escrow drawback. Even worse, with the advent of powerful quantum computers, most of them could be cracked quickly by a large-scale quantum computer in polynomial time in the near future. To this end, we propose a novel post-quantum secure certificateless public auditing scheme in cloud storage (PSCPAC) from lattice assumptions. Compared with the existing schemes, our novel scheme enjoys the following promising merits: (1) removing the requirement for complicated certificate management in traditional PKI-based public auditing schemes; (2) eliminating the inherent key escrow problem in ID-based public auditing schemes; (3) providing resistance against quantum computers attacks. Correctness, security and performance analysis demonstrate that PSCPAC is provably secure, highly efficient and more practical for post-quantum security in cloud storage public auditing.
Some Remarks on ANFIS for Forest Fires Prediction

In this paper, we introduce a variant of the Adaptive Network-based Fuzzy Inference System (ANFIS). The proposed variant does not use backpropagation and grid partitioning. Scatter partitioning is employed by complementing the least-squares method with Tikhonov regularization, both in standard and fractional version. The application example is the prediction of the burnt area in forest fires. We used two publicly available datasets for the numerical experiments. The results encourage further investigations.
Modeling multi-prototype Chinese word representation learning for word similarity

Abstract The word similarity task is used to calculate the similarity of any pair of words, and is a basic technology of natural language processing (NLP). The existing method is based on word embedding, which fails to capture polysemy and is greatly influenced by the quality of the corpus. In this paper, we propose a multi-prototype Chinese word representation model (MP-CWR) for word similarity based on synonym knowledge base, including knowledge representation module and word similarity module. For the first module, we propose a dual attention to combine semantic information for jointly learning word knowledge representation. The MP-CWR model utilizes the synonyms as prior knowledge to supplement the relationship between words, which is helpful to solve the challenge of semantic expression due to insufficient data. As for the word similarity module, we propose a multi-prototype representation for each word. Then we calculate and fuse the conceptual similarity of two words to obtain the final result. Finally, we verify the effectiveness of our model on three public data sets with other baseline models. In addition, the experiments also prove the stability and scalability of our MP-CWR model under different corpora.
An Improved YOLOv3 Object Detection Network for Mobile Augmented Reality

With the spread of mobile devices such as mobile phones, MAR(Mobile augmented reality), which is a technology that realizes augmented reality on mobile devices, is becoming one of the most popular directions in augmented reality research. In MAR, the capturing and positioning of target objects, that is, tracking and registration technology is a crucial problem. In mobile devices, tracking registration technologies that use cam-eras as tracking sensors are divided into hardware sensor-based and computer vision-based tracking registration technologies. Compared with the former, the latter has the characteristics of low hardware equipment requirements and high accuracy. However, traditional computer vision-based tracking registration technologies are susceptible to factors such as background environment, distance, and angle. To overcome the weakness, our research combines the development of deep learning in the field of object detection and lightens YOLOV3 network, which includes simplifying the network structure, improving multi-scale feature fusion detection, optimizing the dimensions of candidate frames through clustering, and optimizing the loss function, so that the object detection network can be used on mobile devices with guaranteed accuracy, and reduces the influence of background environment and other factors on the visual tracking registration technology. Our research realizes a mobile augmented reality system based on the IOS system, which achieves state-of-the-art performance.
High-Resolution MRI Brain Inpainting

Analyzing diseased brain images is often challenging as the presence of abnormal brain tissues hinders several image processing tasks, like tissue segmentation and non-rigid registration, leading to distorted or biased results. This paper presents a solution to this problem by replacing abnormal brain tissues with healthy ones using free-form image inpainting to restore spatial consistency. Inspired by the recent success of Gated Convolution, we propose a user-guided deep adversarial inpainting model to fill irregularly shaped holes in high-resolution T1-weighted MR brain images. Besides, we propose a novel linear-time algorithm to generate irregular masks on the fly for training images. Comparative qualitative and quantitative analyses demonstrated that the proposed model outperforms recently proposed deep learning methods for brain lesion filling and significantly reduces blurriness and boundary artifacts. Moreover, the proposed network shows promising performance in inpainting brain tumors and improving tissue segmentation outputs.
Multi-Source Domain Adaptation with Fuzzy-Rule based Deep Neural Networks

Unsupervised domain adaptation provides a variety of methods to leverage the previously gained knowledge from a labeled source domain to help complete a task from a similar unlabeled target domain. Many existing methods focus on transferring knowledge across single source and single target domains, while few studies deal with multi-source domain adaptation, which is more realistic and challengeable. Existing multi-source domain adaptation methods rarely consider the uncertainty of the transformed knowledge resulting from limited information in target domain. A fuzzy system allows imprecision and ambiguity within transfer, thus it can deal with problems with uncertainty. This work proposes a multi-source domain adaptation method with fuzzy-rule based deep neural networks (MDAFuz). The proposed method first extracts multi-view adapted features and pre-trains source classifiers. Using the learned features and classifiers, training samples are then split into multiple clusters, hence fuzzy rules can be built to learn new classifiers. At the same time, the cluster discriminator is trained to define the membership. Finally, by measuring the similarities among source and target domains using the pseudo target labels and a domain discriminator, the target task is completed by combining all source classifiers with regard to the learned weights. The experiment results on real-world visual datasets show the superiority of the proposed method.
Blockchain for the Scalable Issuance and Verification of Private Academic Information

Blockchain technologies are attracting the interest of different actors in various sectors and, among them, the education field, which is also studying how to apply these technologies to improve the traceability, accountability, and integrity of its information. Although different initiatives and projects were launched and are being developed, there are still issues not fully resolved, such as privacy, compliance with data protection regulations and scalability. This research proposes a novel design of a system to issue, store and verify different types of academic information, which allows complying with applicable regulations, protecting the privacy of users' personal data, as well as addressing the problem of scalability and performance.
VisHash: visual similarity preserving image hashing for diagram retrieval

We present VisHash to address the problem of retrieving copies of images, particularly drawings and diagrams used in technical documents. While these images convey important technical information, it is difficult to search for these images. Recent advances in computer vision using deep learning methods have significantly advanced our ability to analyze and retrieve natural images, yet most of these advances do not directly apply to drawings and diagrams due to the very different nature of low-level features of the images. We find that classic computer vision techniques on patch-based extraction of image features such as relative brightness work better on drawings and diagrams compared to frequency-based similarity-preserving perceptual hashes; yet existing relative-brightness signatures often fail to calculate any meaningful signature due to the sparsity of information in technical drawings. We take advantage of the effectiveness of the relative-brightness signature and extend the approach to develop VisHash, a visual similarity preserving signature that works well on technical diagrams. Importantly, we demonstrate the high level of precision of VisHash for image retrieval compared with competing image hashes of large sets of real drawings from patents and technical images from the web. VisHash is available as open-source code to incorporate into image search and indexing workflows.
Towards super resolution in the compressed domain of learning-based image codecs

Learning-based image coding has shown promising results during recent several years. Unlike the traditional approaches for image compression, learning-based codecs exploit deep neural networks for reducing dimensionality of the input at the stage where a linear transform would be typically applied previously. The signal representation after this stage, called latent space, caries the information in such a format that it can be interpreted by other deep neural networks without the need of decoding it. One of the tasks that can benefit from the above-mentioned possibility is super resolution. In this paper, we explore the possibilities and propose an approach for super resolution that is applied in the latent space. We focus on two types of architectures: fixed compression model and enhanced compression model. Additionally, we assess the performance of the proposed solutions.