Reviewerly/exHarmony huggingface dataset:
PQ-FLAT: A New Quantum-Resistant And Lightweight Authentication Approach for M2M Devices

It is believed that traditional asymmetric cryptosystems are compromised and symmetric cryptography can be used in the post-quantum world. In this paper, a new model based on post-quantum a set of FLAT (PQ-FLAT) protocol is proposed to ensure security in the machine to machine communication with post-quantum cryptography and it is inspired by the federated lightweight authentication of things (FLAT) protocol, which works effectively for resource-constrained devices in machine to machine (M2M) communication systems. The proposed model includes resource-constrained devices, certificate provider (CP), and service provider (SP). Communication of the certificate provider between resource-constrained devices is encrypted with AES. Instead of the asymmetric cryptography system between the certificate provider and the service provider, a lattice-based encryption mechanism, secure in the post-quantum world, is used. Thus, the FLAT protocol for resource-constrained devices is made resistant to post-quantum changes.
Researching the Usage of Blockchain in Higher Education

Trends in the development of the modern digital economy have determined the practical digital transformation of all aspects of human activity, including both production and social spheres. Currently, technologies are dynamically developing and under their influence; the volume of received, transmitted and stored information is constantly increasing. Accordingly, demand for Big Data technology is growing, since it allows you to work with large amounts of data, which determines the growing popularity of blockchain technology. This article presents a model of the workings of blockchain technology in an educational organization and the procedures for its use. It also highlights several directions for the development of educational institutions and processes that are based on the use of blockchain technology. These include confirmation of the authenticity of documentation, accreditation of an educational organization, confirmation of intellectual property and identification of students. In addition, the article discusses the problem of the attitude of the academic community towards blockchain technologies, which can significantly reduce the intensity of implementation. This paper presents a study of why professionals in the educational field are afraid of a significant digitalization in education. Thus, a balanced approach is the optimal strategy for introducing blockchain technologies into educational processes.
A new classification tree method with interaction detection capability

A new classification tree algorithm is presented. It has a novel variable selection algorithm that can effectively detect interactions. The algorithm uses a look-ahead approach that considers not only the significance at the current node, but also the significance at child nodes to detect the interaction. It is also different from other classification tree methods in that it finds the splitting point using the odds ratio. To evaluate the predictive performance of the newly proposed tree algorithm, an empirical study of 27 real or artificial data sets is performed. As a result of the experiment, the proposed algorithm shows at least similar or significantly better performance than the well-known and successful decision tree methods: Ctree, CART and CRUISE.
Hybrid Measuring the Similarity Value Based on Genetic Algorithm for Improving Prediction in A Collaborative Filtering Recommendation System.

In recent years, the Recommendation System (RS) has a wide range of applications in several fields, like Education, Economics, Scientific Researches and other related fields. The Personalized Recommendation is effective in increasing RS's accuracy, based on the user interface, preferences and constraints seek to predict the most suitable product or services. Collaborative Filtering (CF) is one of the primary applications that researchers use for the prediction of the accuracy rating and recommendation of objects. Various experts in the field are using methods like Nearest Neighbors (NN) based on the measures of similarity. However, similarity measures use only the co-rated item ratings while calculating the similarity between a pair of users or items. The two standard methods used to measure similarities are Cosine Similarity (CS) and Person Correlation Similarity (PCS). However, both are having drawbacks, and the present piece of the investigation will approach through the optimized Genetic Algorithms (GA) to improve the forecast accuracy of RS using the merge output of CS with PCS based on GA methods. The results show GA's superiority and its ability to achieve more correct predictions than CS and PCS.
Analysis of Sentimental Behaviour over Social Data Using Machine Learning Algorithms

A person’s sentiment is rigorously influenced by his emotional feelings which is evoked from every single incident, occurring every day in his surroundings. In this case, the decision that he makes is greatly affected by his sentiment rather than facts. Sentimental behavior can be applied to many applications in health, business, education, etc. This paper proposes and develops a sentimental behavior based on machine learning algorithms for pre-processing feature selection, and classification that helps identify, extract, quantify the feelings to twitter social dataset. Based on the Sentimental behavior, an analytical prediction has been developed that can be used to understand the behavior of the customers or users. Conducting the testing process for three state-of-the-art algorithms, the unique methodology is devised based on these proposed algorithms (support vector machine (SVM), logistic regression (LR), and XGboost), our extensive experiments show that our approach has high accuracy. Based on the testing results, the accurate sentimental behavior detection algorithm is identified and recommended to be used for textual data in the future.
Attention-guided Progressive Mapping for Profile Face Recognition

The past few years have witnessed great progress in the domain of face recognition thanks to advances in deep learning. However, cross pose face recognition remains a significant challenge. It is difficult for many deep learning algorithms to narrow the performance gap caused by pose variations; the main reasons for this relate to the intra-class discrepancy between face images in different poses and the pose imbalances of training datasets. Learning pose-robust features by traversing to the feature space of frontal faces provides an effective and cheap way to alleviate this problem. In this paper, we present a method for progressively transforming profile face representations to the canonical pose with an attentive pair-wise loss. First, to reduce the difficulty of directly transforming the profile face features into a frontal one, we propose to learn the feature residual between the source pose and its nearby pose in a block-by-block fashion, and thus traversing to the feature space of a smaller pose by adding the learned residual. Second, we propose an attentive pair-wise loss to guide the feature transformation progressing in the most effective direction. Finally, our proposed progressive module and attentive pair-wise loss are light-weight and easy to implement, adding only about 7.5% extra parameters. Evaluations on the CFP and CPLFW datasets demonstrate the superiority of our proposed method. Code is available at https://github.com/hjy1312/AGPM.
A Theoretical Insight Into the Effect of Loss Function for Deep Semantic-Preserving Learning

Good generalization performance is the fundamental goal of any machine learning algorithm. Using the uniform stability concept, this article theoretically proves that the choice of loss function impacts the generalization performance of a trained deep neural network (DNN). The adopted stability-based framework provides an effective tool for comparing the generalization error bound with respect to the utilized loss function. The main result of our analysis is that using an effective loss function makes stochastic gradient descent more stable which consequently leads to the tighter generalization error bound, and so better generalization performance. To validate our analysis, we study learning problems in which the classes are semantically correlated. To capture this semantic similarity of neighboring classes, we adopt the well-known semantics-preserving learning framework, namely label distribution learning (LDL). We propose two novel loss functions for the LDL framework and theoretically show that they provide stronger stability than the other widely used loss functions adopted for training DNNs. The experimental results on three applications with semantically correlated classes, including facial age estimation, head pose estimation, and image esthetic assessment, validate the theoretical insights gained by our analysis and demonstrate the usefulness of the proposed loss functions in practical applications.
SealPK: Sealable Protection Keys for RISC-V

With the continuous increase in the number of software-based attacks, there has been a growing effort towards isolating sensitive data and trusted software components from untrusted third-party components. Recently, Intel introduced a new hardware feature for intra-process memory isolation, called Memory Protection Keys (MPK). The limited number of unique domains (16) provided by Intel MPK prohibits its use in cases where a large number of domains are required. Moreover, Intel MPK suffers from the protection key use-after-free vulnerability. To address these shortcomings, in this paper, we propose an efficient intra-process isolation technique for the RISC-V open ISA, called SeaIPK, which supports up to 1024 unique domains. Additionally, we devise three novel sealing features to protect the allocated domains, their associated pages, and their permissions from modifications or tampering by an attacker. We demonstrate the efficiency of SealPK by leveraging it to implement an isolated secure shadow stack on an FPGA prototype.
Rate-Distortion-Time Cost Aware CNN Training for Fast VVC Intra-Picture Partitioning Decisions

This paper presents a new method for fast VVC intra-picture encoding using a CNN. The CNN operates on the original samples of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{32}\times \mathbf{32}$</tex> blocks. Given a current block, it derives for each of the block's multi-type trees (MTTs), which are nested in quad-tree (QT) nodes, a parameter pair. The parameter pairs constrain the minimum width and height of the sub-blocks in their MTTs. This enables the CNN to control the number of tested MTT splits with fine granularity. To skip modes while maintaining the rate-distortion (RD) performance, we train the CNN considering the Lagrangian rate-distortion-time (RDT) cost caused by the derived parameters. First, we generate training data by encoding; when reaching a quad-tree node in a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{32}\times \mathbf{32}$</tex> block, we encode the associated MTT with varying parameter pair values and record the resulting the RD and time cost. Then, when the CNN outputs parameters in training, we estimate the related RDT cost of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{32}\times \mathbf{32}$</tex> block using the recorded data. For this, we model the dependency between RDT cost and the parameters by emulating the encoder's RD optimization process. This way, we train the CNN while considering the RDT cost with an accuracy that is sufficient to outperform existing approaches. The approach achieves an encoding time reduction of 50% with a bit rate increase of only 0.7% for VTM-10.2.
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence

Variational Inference (VI) is a popular alternative to asymptotically exact sampling in Bayesian inference. Its main workhorse is optimization over a reverse Kullback-Leibler divergence (RKL), which typically underestimates the tail of the posterior leading to miscalibration and potential degeneracy. Importance sampling (IS), on the other hand, is often used to fine-tune and de-bias the estimates of approximate Bayesian inference procedures. The quality of IS crucially depends on the choice of the proposal distribution. Ideally, the proposal distribution has heavier tails than the target, which is rarely achievable by minimizing the RKL. We thus propose a novel combination of optimization and sampling techniques for approximate Bayesian inference by constructing an IS proposal distribution through the minimization of a forward KL (FKL) divergence. This approach guarantees asymptotic consistency and a fast convergence towards both the optimal IS estimator and the optimal variational approximation. We empirically demonstrate on real data that our method is competitive with variational boosting and MCMC.
Indoor Panorama Planar 3D Reconstruction via Divide and Conquer

Indoor panorama typically consists of human-made structures parallel or perpendicular to gravity. We leverage this phenomenon to approximate the scene in a 360-degree image with (H)orizontal-planes and (V)ertical-planes. To this end, we propose an effective divide-and-conquer strategy that divides pixels based on their plane orientation estimation; then, the succeeding instance segmentation module conquers the task of planes clustering more easily in each plane orientation group. Besides, parameters of V-planes depend on camera yaw rotation, but translation-invariant CNNs are less aware of the yaw change. We thus propose a yaw-invariant V-planar reparameterization for CNNs to learn. We create a benchmark for indoor panorama planar reconstruction by extending existing 360 depth datasets with ground truth H&V-planes (referred to as "PanoH&V" dataset) and adopt state-of-the-art planar reconstruction methods to predict H&V-planes as our baselines. Our method outperforms the baselines by a large margin on the proposed dataset.
Redefining the Digital Paradigm for Virtual Museums

The COVID-19 pandemic has greatly accelerated the digitization of services. As physical spaces become harder to access, there is a growing shift towards the use of virtual spaces for remote work, education and entertainment. In 2020, brick-and-mortar spaces like museums, art exhibits and galleries were especially affected by a lack of visitors. Shifting to a virtual medium would allow these entities to reach out and retain visitors more effectively. However, the use of virtual spaces to support these kinds of services is still quite under-explored. Presence, engagement and a real connection are difficult to establish through virtual exhibits. To explore these challenges, we partnered with the Liberation War Museum in Bangladesh and created a web-based 3D virtual museum to represent three of their historical galleries. Each virtual gallery has a different presentation modality - "self-guided", "avatar-guided" and "game-based". We sought to explain which of these artifact presentation modes led to the best performance in learnability, usability and engagement by conducting a user study. Our findings and user feedback are presented in this paper. We hope that these findings will be useful for designing virtual experiences that can allow users to learn and engage with virtual artifacts as effectively as they would with real-world ones.
Challenges in Single Sign-On

Abstract Single sign-on uses a centralized session concept, and it is a service that authenticates one and one-on-one designated platform. Later, with this, we can use various services and products without logging in every time. However, there are sure demanding situations in implementing SSO. Business enterprise no longer is most effective and needs to integrate user network logon with a nearby application and software program (SaaS) cloud offering. Simultaneously, in an enterprise, SSO’s centralized solution does not include all the users, applications, devices, and equipment. Another undertaking from the records angle is that the user can log on outside SSO answers. Its user identification may also exist in exceptional forms. Simultaneously, the enterprise desires to cope with bequest desktop programs that do not coordinate with users’ listing logins which may be the frail hyperlink that reasons SSO to fail in the company. With the efficiently applied SSO and designing framework keeping in mind the challenges, we can enable and disable users to enter more than one system, platform, apps, and different resources. Also, it may effectively resolve and dispose of the problem of password-related downtime and reset expenses. Simultaneously, it removes the danger of threat from an insider, improves authentication processes and user experience, delivers authority to the company, and simultaneously provides them with the right of entry to firmly on top of the user’s things.
Closed-loop Approach to Perception in Autonomous System

Currently, functional tasks within Autonomous Systems are balkanized into several sub-systems such as object detection, tracking, motion planning, multi-sensor fusion etc. which are developed and tested in isolation. In recent times, deep learning is used in the perception systems for improved accuracy, but such algorithms are not adaptive to the transient real-world requirements of an Autonomous System such as latency and energy. These limitations are critical for resource constrained systems such as autonomous drones. Therefore, a holistic closed-loop system design is required for building reliable and efficient perception systems for autonomous drones. The closed-loop perception system creates a focus-of-attention based feedback from end-task such as motion planning to control computation within the deep neural networks (DNNs) used in early perception tasks such as object detection. We observe that this closed-loop perception system improves resource utilization of resource hungry DNNs within perception system with minimal impact on motion planning.
Towards Understanding of the Behavior of Web Streaming

We study the behavior of a modern-era adaptive streaming system delivering videos embedded in web-pages. In such an application, the size of videos rendered on the screen may depend on user preferences, such as the position and size of a browser window. Moreover, the stream selection logic in such a system appears to be influenced not only by the available network bandwidth but also by the output video size, which, in many cases, limits the selection of higher quality streams. To explain this behavior, in this paper we introduce a simple analytical model of a client adapting to both bandwidth and player size. Using this model, we then compute stream selection probabilities and show that they are sufficiently close to respective statistics observed in practical experiments. Possible uses of this proposed client model are also suggested. Specifically, we show how it can be used to derive formulae for the average performance parameters of the system and also for posing related optimization problems.
Algorithm selection on a meta level

Abstract The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework, essentially combining ideas of meta learning and ensemble learning. In an extensive experimental evaluation, we demonstrate that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to form the new state of the art in algorithm selection.
Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration

In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation. Most existing MOT methods track multiple objects using a singular RGB camera, which are prone to camera field-of-view and suffer tracking failures in complex scenarios due to background clutters and poor light conditions. To meet these challenges, we propose a MultiModality PAnoramic multi-object Tracking framework (MMPAT), which takes both 2D panorama images and 3D point clouds as input and then infers target trajectories using the multimodality data. The proposed method contains four major modules, a panorama image detection module, a multimodality data fusion module, a data association module and a trajectory inference model. We evaluate the proposed method on the JRDB dataset, where the MMPAT achieves the top performance in both the detection and tracking tasks and significantly outperforms state-of-the-art methods by a large margin (15.7 and 8.5 improvement in terms of AP and MOTA, respectively).
Data Augmentation via Face Morphing for Recognizing Intensities of Facial Emotions

Being able to recognize emotional intensity is a desirable feature for a facial emotional recognition (FER) system. However, the development of such a feature is hindered by the paucity of intensity-labeled data for model training. To ameliorate the situation, the present study proposes using face morphing as a way of data augmentation to synthesize faces that express different degrees of a designated emotion. Such an approach has been successfully validated on humans and machines. Specifically, humans indeed perceived different levels of intensified emotions in these parametrically synthesized faces, and FER systems based on neural networks indeed showed improved sensitivities to intensities of different emotions when additionally trained on the synthesized faces. Overall, the proposed data augmentation method is not only simple and effective but also useful for building FER systems that recognize facial expressions of mixed emotions.
Lightweight CNN-based Models for Masked Face Recognition

Covid-19 has become one of the most threatening diseases in the world. Therefore, governments and health organizations asked people to wear masks in order to control the spread of such threatening diseases. During this epidemic, people must obey the rules of wearing masks that play a vital role in controlling the outbreak of COVID-19. In order to identify the persons who do not wear the mask, an automated recognition system is discussed in this paper. This model automatically detects masked faces using deep learning techniques; TensorFlow, and Keras. The proposed method effectively differentiates between masked faces and unmasked faces to help governments, companies, and organizations monitoring and detecting who broke the rule of wearing masks. The implementation of this model is based on three pre-trained models; MobileNetV2, DenseNet, and NASNetMobile. In terms of accuracy, MobilenetV2 reached 0.9859, while DenseNets and NASNetMobile reached 0.9852 and 0.9758 respectively. The contribution of this article is to present the feasibility of using a lightweight model (i.e. NasNetMobile) that can be implemented even on low-resource devices (i.e. mobile) to recognize masked faces efficiently.