Reviewerly/exHarmony huggingface dataset:
Single Image 3D Beard Face Reconstruction Approaches

3D face and 3D hair reconstruction are interesting and emerging applications within the fields of computer vision, computer graphics, and cyber-physical systems. It is a difficult and challenging task to reconstruct the 3D facial model and 3D facial hair from a single photo due to arbitrary poses, facial beard, non-uniform illumination, expressions, and occlusions. Detailed 3D facial models are difficult to reconstruct because every algorithm has some limitations related to profile view, beard face, fine detail, accuracy, and robustness. The major problem is to develop 3D face with texture of large, beard, and wild poses. Mostly algorithms use convolution neural networks and deep learning frameworks to develop 3D face and 3D hair. The latest and state-of-the-art 3D facial reconstruction and 3D face hair approaches are described. Different issues, problems regarding 3D facial reconstruction, and their proposed solutions have been discussed.
A Novel Mobile Instant Messaging System

The IMS/SIP protocol currently adopted by Mobile instant messaging system is too heavy due to the text-based design, and the transmission efficiency on the air interface is not high. In order to solve this problem, this paper analyzes the use of lightweight MQTT protocol as the underlying transmission protocol of instant messaging system, furthermore designs and implements a novel mobile instant messaging system. Test results are given to show better performance when compared with systems based on XMPP and SIP/SIMPLE protocols.
Adapting VST AR X-Ray Vision Techniques to OST AR

When merging physical and virtual objects with optical see-through augmented reality there is little research that has focused on x-ray vision visualizations considering depth perception. We investigate partial occlusion visualizations when merging visual cues and real-world objects to explore the effect of the visualizations with a proce-dural placement task. We adapted existing x-ray visualization techniques designed for Video See-Through (VST) Augmented Reality to operate on Optical See-Through (OST) devices and investigated how these techniques affect accuracy in a placement task within arms reach. We evaluate the visualizations' impact on accuracy when user movement is unrestricted and report the perceived usability and mental load of each visualization. Our findings indicate that although the type of x-ray visualization is important, the presence of other virtual objects in the scene appears to have a stronger impact on the users' accuracy in placing objects and user experience.
Zero-Knowledge Range Arguments for Signed Fractional Numbers from Lattices

Range proofs introduced by Brickell et al. at CRYPTO 1988, allow a prover to convince a verifier that the committed value belongs to an interval without revealing anything else. It has become an essential building block in various modern cryptographic protocols such as distributed ledgers, anonymous transactions, e-cash, e-voting, auction protocols, privacy-preserving certificate transparency, and many more. In this paper, we provide a zero-knowledge range argument system showing that a committed value is in a public or hidden range by constructing a zero-knowledge argument system to prove inequalities between signed fractional numbers as well as non-negative integers in the standard lattice settings. The complexity of our range arguments is only logarithmic in the size of the range. Negative numbers and fractional numbers play an important role in our everyday life, especially in financial loss, medical data, bank account balances, GPA and tax records, etc. It would be desirable to handle them in a privacy-preserving manner. Prior to this work, all the lattice-based zero-knowledge range argument systems only address a range of non-negative integers, whereas our range arguments can handle signed fractional numbers and fill an interesting research gap in the literature.
The Evaluation of Gait-Free Locomotion Methods with Eye Movement in Virtual Reality

As VR becomes increasingly popular in the entertainment industry, VR locomotion, a technique that allows users to navigate virtual environments beyond the spatial confines of the real world, is being increasingly studied by developers and researchers. Previous work has examined the effects of locomotion methods on various aspects of users, such as user experience, motion sickness, and task performance. However, how locomotion methods affect users' eye movements that might indicate cognitive load has not yet been in-vestigated, although several relevant works have addressed these effects as being important to study. To contribute to this area of research, in this work we investigate the evaluation of five common gait-free locomotion methods using eye movements during VR nav-igation. The results show that locomotion methods significantly affect participants' eye movement behavior (i.e., blinks, fixations, and saccades), suggesting that different cognitive responses were elicited with different locomotion methods. Our research provides a viable tool for future studies evaluating locomotion methods, thus providing further in-depth insights for developing more effective and enjoyable VR locomotion methods.
Scalable Resource Provisioning For Multi-tenant SaaS With Cloud Functions

SaaS cloud services support the sharing of software resources among multiple tenants, but the utilization of computing resources by different tenants is different. Some tenants with large business volumes and large amounts of data tend to occupy more CPU and database resources, especially when the SaaS platform provides computing-intensive services such as machine learning, which will cause more serious resource overhead and seriously affect tenants' experience. Therefore, this paper designs and implements a scalable resource provisioning scheme based on cloud functions for multi-tenant architecture. We present a self-defined scheduling protocol, scheduling center, and distributed nodes to alleviate the central server pressure, rationally scheduling and allocating tenant requests by running functional tenant services across nodes.
Automatic segmentation of breast tumors in mammography using fuzzy clustering

Image segmentation is the process via which the digital image understudy is partitioned into multiple segments. The purpose of the segmentation is to convert the representation of the image into something more significant and easier to examine. The primary aim of image segmentation in mammography is to find the hazardous objects (lesions, dense breast tissues, and micro calcifications) and boundaries of the mammograms. Segmentation allocates a tag to every individual pixel in an image in such a routine that the pixels with the similar tag share certaintopographies. The research paper elaborates a proposed method for performing segmentation of the mammograms. The MIAS dataset have been used to implement the research on 16 specific mammogram images. The proposed model is detailed via a flowchart followed by an elaborated explanation specifying the adopted process for conducting the segmentation. The techniques used are GCC “Green Channel Complement”, CLAHE “Contrast Limited Adaptive Histogram Equalization”, Morphological operations, and FCM “Fuzzy C-Means”. The values of 15 performance evaluation parameters for 16 different mammograms have been obtained to prove the worthiness of the conducted research. The GUIis designed in such a manner that it makes implementation easy and contented.
Unifying Visual Contrastive Learning for Object Recognition from a Graph Perspective

Recent contrastive based unsupervised object recognition methods leverage a Siamese architecture, which has two branches composed of a backbone, a projector layer, and an optional predictor layer in each branch. To learn the parameters of the backbone, existing methods have a similar projector layer design, while the major difference among them lies in the predictor layer. In this paper, we propose to Unify existing unsupervised Visual Contrastive Learning methods by using a GCN layer as the predictor layer (UniVCL), which deserves two merits to unsupervised learning in object recognition. First, by treating different designs of predictors in the existing methods as its special cases, our fair and comprehensive experiments reveal the critical importance of neighborhood aggregation in the GCN predictor. Second, by viewing the predictor from the graph perspective, we can bridge the vision self-supervised learning with the graph representation learning area, which facilitates us to introduce the augmentations from the graph representation learning to unsupervised object recognition and further improves the unsupervised object recognition accuracy. Extensive experiments on linear evaluation and the semi-supervised learning tasks demonstrate the effectiveness of UniVCL and the introduced graph augmentations.
A Machine Learning Technique for Rice Blast Disease Severity Prediction Using K-Means SMOTE Class Balancing

Rice blast disease is strongly dependent on environmental and climate factors. This paper demonstrates the integration of a rice blast disease severity prediction model based on climate factors, providing a decision-support framework for farmers to overcome these problems. The major contribution of the proposed study is to predict the severity of rice blast disease using the linear SVM model. Prediction of rice blast disease severity is divided into four classes: 0, 1, 2, and 3. Data imbalance is the most challenging problem in multi-class classification. This study has efficiently handled imbalanced data using k-means SMOTE and SMOTE oversampling techniques to balance training and testing data. Finally, cross-location and cross-year models are developed using a linear support vector machine and predict the severity of rice blast disease to the classes 0, 1, 2, 3, respectively. Cross-year and cross-location models are cross-validated using five-fold cross-validation.
Decentralized Computation Market for Stream Processing Applications

While cloud computing is the current standard for outsourcing computation, it can be prohibitively expensive for cities and infrastructure operators to deploy services. At the same time, there are underutilized computing resources within cities and local edge-computing deployments. Using these slack resources may enable significantly lower pricing than comparable cloud computing; such resources would incur minimal marginal expenditure since their deployment and operation are mostly sunk costs. However, there are challenges associated with using these resources. First, they are not effectively aggregated or provisioned. Second, there is a lack of trust between customers and suppliers of computing resources, given that they are distinct stakeholders and behave according to their own interests. Third, delays in processing inputs may diminish the value of the applications. To resolve these chal-lenges, we introduce an architecture combining a distributed trusted computing mechanism, such as a blockchain, with an efficient messaging system like Apache Pulsar. Using this architecture, we design a decentralized computation market where customers and suppliers make offers to deploy and host applications. The proposed architecture can be realized using any trusted computing mechanism that supports smart contracts, and any messaging framework with the necessary features. This combination ensures that the market is robust without incurring the input processing delays that limit other blockchain based solutions. We evaluate the market protocol using game-theoretic analysis to show that deviation from the protocol is discouraged. Finally, we assess the performance of a prototype implementation based on experiments with a streaming computer-vision application.
Comparison of conversion matrices for a compact AES–CTR defined over an isomorphic field

AES is a widely used block cipher that adopts inverse operation over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{2^{8}}$</tex> in the nonlinear operation, called SubBytes. The inverse operation is usually implemented by a lookup table considering its time-consuming nature. In this paper, the authors introduce an isomorphic field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{(2^{4})^{2}}$</tex> and compare several conversion matrices by counting the number of required logic gates (AND and XOR) to reduce the circuit size.
3A comparative Study on Sentiment Analysis for Bengali Language Based on LSTM in F-Commerce

LSTMs are a type of RNN cell that regulates incoming and outgoing information using text-processing-specific gates. Online retailers, as we all know, have been extremely popular in recent years. Many social media sites, such as Facebook and Twitter, are increasingly used by online shops. As a result, market monitoring is critical. We can monitor user reviews and satisfaction levels using machine learning and deep learning methods. Many projects have been completed in various languages in the past. We use LSTM to work with Bangla text reviews in our research. We acquire an accuracy of 88 percent with this model.
End-to-End Transformer-Based Architecture for Text Recognition from Document Images

Text recognition from degraded historical document images of books presents several challenges to solve within the vision and document analytics community. The accuracy of existing recognition sharply drops with pitfalls in image quality as well as usage of decades old vocabulary and fonts. In order to overcome such challenges, we introduce a Transformer [39]-based Recognition engine fused with Bidirectional Encoder Representations from Transformers (BERT) Language Model [10] which is preceded by deep back projection network (DBPN) along with cascaded segmentation carried out using the U-net framework. In essence, end-to-end segmentation framework is cascaded with the end-to-end Transformer Network for performing recognition. We have intensively tested our framework on Odia literature documents provided by Odia Virtual Academy. Significant improvements in results are observed and the proposed methodology empirically outperformed the state of the art for Odia Script.
Speed Control of Brushless DC Motor by Using Particle Swarm Optimization Algorithm

Over the years, the applications of variable speed drive system have been increases very vast in the field of automobile industries and many more household applications. The non-polluted electronics are implemented in order to use for energy consumption of various devices. This leads to the evolution of brushless DC motor. BLDC motor provides the dominancy over the conventional DC motor with respect to high torque-to-weight ratio, more torque per watt, and many more. This paper represents the speed control of BLDC motor using particle swarm optimization algorithm. The PSO algorithm is used to tune PI controller in order to get fast convergence to optimum values. Compared with traditional Ziegler–Nichol’s method, PSO algorithm provides an improved time response performance with lower overshoot and effective speed control. The model is implemented in MATLAB Simulink, and simulation results are presented.
The Roles of Instructional Agents in Human-Agent Interaction Within Serious Games

Automated agent technology in video games is a form of artificial intelligence (AI) that interacts with humans, and thus can provide interactive skills training via human-agent interaction. Having a good framework for agent types could help industry agent designers make best use of agents’ capabilities. This research extends the previously established Human-Agent Team Game Analysis Framework from traditional video games to serious games focused on learning by clarifying the instructional agent types based on characteristics from the literature. Using the characteristics of (1) interaction timing, (2) level of autonomy, and (3) memory, agents of type Planner, Advisor, Critic, Companion, Actor, and Player were analyzed across eight different serious games. The new clarification of agent types could help agent designers decide the best agent type appropriate for their applications.
Unconfirmed Transactions in Cryptocurrency: Reasons, Statistics, and Mitigation

Blockchain has emerged to be a pioneer fundamental technology for distributed applications. Not only it is limited to financial sector, but it also has extended in the fields of health & medicare, managing logistics of goods through effective supply chain management etc. Although there are numerous applications of blockchain, cryptocurrencies remains at the top, in terms of popularity and cryptographic security it provides in maintenance of digital assets. Miner(s) in a cryptocurrency is/are an individual/group of individuals who benefit after per-forming Proof-of-Work for validating a transaction. The top two cryptocurrencies according to market cap value are Bitcoin and Ether. Millions of transactions happen on their blockchain on a daily basis, but not all of them result in success. Some are also marked as failed/unconfirmed, even if they are less compared to the confirmed transactions. Some of the reasons for this behavior could be too many transactions present in mempool of miners or insufficient fees is provided as the incentive to the miners of the network. Though the number of transactions that go unconfirmed per day is very small compared to the ones getting confirmed, still the area of failed cryptocurrency transactions remain unexplored. This paper focuses on statistics of failed cryptocurrency transactions, some primary reasons of failure in a cryptocurrency transaction. Furthermore, it also presents existing approaches to minimize the failure of transactions.
On the Implementation of a Fragile Medical Image Watermarking Technique Using Saliency and Phase Congruency

The recent developments of enormous computer networks have invoked insecurities related to copyright theft of digital media. To be precise, the virtual sharing of medical images over networks with a novel desire of improved medical diagnosis has led to the tampering of sensitive patient identity information. In this chapter, the authors have exemplified the need of watermarking with fragile medical image watermarking using saliency and phase congruency. Initially, the saliency and phase congruency methodologies are applied on the original medical image to highlight the object features. Based on the feature map, a mask is generated which segregates the area of interest from the portions containing visual medical information. An encrypted text, containing identity of the patient, is embedded into the area of interest of the image. The results of imperceptibility and fragility criteria are satisfactory towards the implementation of a fragile watermark as the extracted watermark is found to be corrupted upon unfaithful image processing modifications.
A Novel Clock Randomization Method Against Side-channel Attack

Side-Channel Attack (SCA) has become a major concern in the protection of secret data processed in cryptographic devices. As a main direction of hardware countermeasures against SCA, clock randomization hides the relation between the leakage information at a particular time and the intermediate values processed by the cryptographic core. In this paper, an improved random clock generation method is proposed. By designing an Adaptive Feedback Ring (AFR), the random clock is generated. In our experiment, it shows that the output clock frequencies are approximately uniformly distributed in the range of 320 MHz–450 MHz. According to the method of computing distinct execution times for a specific cryptographic core, e.g., for AES–128, AFR has 20,030,010 different delay times, which is much higher than results from related works. In fact, the higher number of distinct execution times, the more attacking difficulty for adversaries.
A Visual Contour-based Detection Method for Orthodontic Archwire during Robotic Bending

With the development of robot technology, the bending of orthodontic archwire is becoming automated. Online visual measurement of archwire parameters during bending is beneficial for guaranteeing high robotic bending precision. The traditional feature point detection method is unsuitable for the visual detection of the archwire due to the lack of texture features. Aimed to realize the visual detection of this kind of tiny curved object, this paper proposes a measurement method based on contour positioning and binocular vision. Firstly, the stereo correction was performed on the original archwire image. Then the thinning algorithm was used to extract the archwire skeleton. A multi-layer contour detection method was developed to extract the bending point of the archwire. Lastly, the normalized cross-correlation coefficient is used to obtain parallax information and complete the parameter measurement. The experimental results showed that the detection method successfully extracted all feature points while the SIFT algorithm and literature [15] method did not make it. Maximum measurement error not more than 0.4mm, which is practicable for justifying the bending quality and close-loop controlling the robotic bending precision.
Copy-Move and Image Splicing Forgery Detection based on Convolution Neural Network

Digital images plays a very significant role in fields like journalism, medical imaging, criminal and forensic investigations and many more. Because of the easily available photo editing tools and software, images can be manipulated easily, that can disturb the contents of the images. Due to this, authenticity of the image gets lost and these can be misused by any person. The techniques that are commonly used for creating forged images are copy-move, image splicing and image enhancement forgery. Many techniques were developed to detect forgery, but these techniques are not robust against the structural changes occurred due to forgery in the images. In this paper, Convolution Neural Network(CNN) based image forgery detection method is proposed. In this method, Patch Sampling and Modulus LBP will be used to pretrain the neural network for Feature Learning and Feature Extraction. Then finally these features will be fed to SVM classifier that will help to detect forged images.Evaluation of the proposed method is done based on the parameters like precision, recall and accuracy, which shows that the proposed method is robust and insensitive against different operations as well as there is the improvement in the accuracy of the proposed method as compared to existing method.
RichCast - A Voice-Driven Interactive Digital Narrative Authoring System

We present RichCast a platform for conversational audio Interactive Digital Narrative (IDN). RichCast includes an accessible 'No-code' authoring tool, a community driven library of works, and voice interactive medium for interactive storytelling.
Runners for Interleaving Algebraic Effects

We study a model of interleaving concurrency for higher-order functional programs with algebraic effects, which can function as a basis for notions of behavioural equivalence of effectful programs within concurrent processes. Using the category of relations to describe nondeterministic functions, we model runs of programs using trace semantics. These traces have actions describing possible program-environment interactions. The functor of traces forms both a monad and a comonad in the category of relations, allowing us to describe programs as both active computations and passive background processes. We adapt traditional concurrent interleaving semantics for traces as an operation in the category of relations, merging two traces into a set of interwoven traces. These semantics give rise to a runner for the monad, and in some cases form a monad-comonad interaction law. With this, we can simulate an environment of concurrent background processes, and describe the behaviour of a program within such an environment. This runner for interleaving concurrency is readily composable with runners for algebraic effects, allowing us to describe a wide variety of different concurrent effectful scenarios.