Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION - Surplus resources to Private Cloud

Cloud computing offers a unique set of features and provides a highly efficient and scalable solution to hosting. All business companies and educational institutions use some or the other hosting platform to host their software and applications on the Cloud. Moreover, many such institutions or businesses have unused or surplus computing resources even though they are working perfectly but have less computing power or resources to run as a regular workstation, hence resulting in e-waste. This paper presents an approach to integrating these surplus computing resources to form one single datacenter Private Cloud, which can run software, containers and virtual machines and then test its efficacy for different types of hypervisors. An experimental setup is formalised to achieve the results and study the theoretical and practical usable resource values for this approach, and formula is derived to find them. The single-core performance of the Virtual Machines and the Containers are compared for the various operating system for different hypervisors.
Challenges of experimenting with Virtual Reality

This manuscript describes challenges that we experienced during experiments employing virtual reality headsets. Its main focus is on technical issues that occurred when participants were transported from the environment they were physically occupying to its virtual replica displayed on a virtual reality headset. A range of hardware, software and experimental procedure issues and recommendations are presented, including issues such as the impact of mounting the virtual reality headset by participants on their Skin Conductance and Heart rate, and unexpected virtual reality headset features accidentally turned on by the participants. The method employed for creation of the virtual replica of the physical environment used in this study is described. We also discuss alternative methods that were considered, and which in some circumstances might produce better results than we achieved. This paper is written to share the lessons that we have learned during the beta-testing process and analysis of the data collected during our experiments using virtual reality headsets. The goal of describing these lessons learned is to aid other researchers in adopting techniques to improve the quality and replicability of experiments involving virtual reality headsets and biometric devices.
A fast k-means algorithm based on multi-granularity

The k-means algorithm has been widely used since it was proposed, but the standard k-means algorithm does not perform well in terms of efficiency when dealing with large-scale data. To solve this problem, in this paper, we propose a fast kmeans algorithm based on multiple granularities. First, from the coarse-grained perspective, we use the clustering distribution information to narrow the search range of sample points, which makes the proposed algorithm very advantageous on large k. Second, from the fine-grained perspective, we use the rules of upper and lower bounds to reduce the number of sample points involved in the distance calculation, thus reducing many unnecessary distance calculations. Finally, we evaluate the proposed k-means algorithm on several real-world datasets, and the experimental results show that the proposed algorithm converges hundreds of times faster than standard k-means on average with the accuracy loss controlled at about three percent, and the speedup of the algorithm is more obvious when the dataset size is larger and the dimensionality of the dataset is higher.
Artana: Art and Knowledge about Anamorphosis

For the 2022 IEEE VR 3DUI contest on the topic of &#x201C;Arts, Science, Information and Knowledge - visualization and interactions&#x201D;, we present Artana, an immersive application proposing to explore anamorphosis in various interactive ways. Through an unexpected experience, the user is transported to a museum in which he/she will have access to the history of anamorphosis before being able to experience seven examples of them. A teleportation and a spinning wheel interaction techniques were implemented in Artana to ease interaction without altering the experience.
Multicore implementation of a multichannel parallel graphic equalizer

Abstract Numerous signal processing applications are emerging on mobile computing systems. These applications are subject to responsiveness constraints for user interactivity and, at the same time, must be optimized for energy efficiency. Many current embedded devices are composed of low-power multicore processors that offer a good trade-off between computational capacity and low power consumption. In this context, equalizers are widely used in multiple mobile-based applications such as “Music streaming” to adjust the levels of bass and treble in sound reproduction. In this study, we evaluate a graphic equalizer from audio, computational capacity, and energy efficiency perspectives, as well as the execution of multiple real-time equalizers running on an embedded quad-core processor of a mobile device. To this end, we experiment with the working frequencies as well as the parallelism that can be extracted from a quad-core ARM Cortex-A57. Results show that using high CPU frequencies and three or four cores, our parallel algorithm is able to equalize more than five channels per watt in real time with an audio buffer of 4096 samples, which implies a latency of 92.8 ms at the standard sample rate of 44.1 kHz.
Exploring Corpora through Quantitative Measures

This is a ‘quantitative methods’ chapter. It explains the consequences of more advanced developments in quantitative corpus techniques. Two themes are developed. Firstly, some advanced measures improve the range of options available to researchers studying, for example, collocation or keyness. This shows that different measures may give different results. Secondly, some applications of statistical techniques allow the contents of a corpus to be rearranged, leading to novel insights. It is argued that these bottom-up approaches amount to quantitative ‘corpus-driven’ methods. Topics discussed in the chapter are keyness, collostructions, text clustering using lexical bundles and multidimensional analysis, and word clustering using principal component analysis and topic modelling.
Integral fault analysis of Midori64 lightweight cipher

Midori is a low energy block cipher proposed at ASIACRYPT 2015. It is composed of two variants: Midori64 and Midori128, with block sizes equal to 64 and 128 bits respectively. These two ciphers provide the function of both encryption and decryption for security communication and storage with minimal area and energy overhead. In this paper, the immunity of Midori64 against integral fault attacks is investigated. Based on integral properties of Midori64 and fault model, a method to broken this cipher is presented. By injecting random faults in the 5<sup>th</sup> round backwards in the encryption process, the faulty ciphertexts are obtained. Based on 4-round integral distinguisher of Midori64, the last round key is recovered by flittering the wrong key values. The experimental simulation results show that the key candidate values can be reduced from 2<sup>64</sup> to 2<sup>31.2</sup> by 16 sets faults injection. Using the last round key to decrypt the ciphertexts by one round, a same method can be used to recover the penultimate round key and thus the master key can be recovered by using the key expansion algorithm.
Complete classification of cyclic codes of length 3ps over 𝔽pm + u𝔽pm + u2𝔽pm

In this paper, we use a method inspired by the valuation theory to classify completely cyclic codes of length [Formula: see text] over [Formula: see text]. Then, we compute their separation parameters which was useful to compute the Hamming distance in [8].
Load Balancing and Parallel Computation Model for Performance and Accuracy over the Cluster of Nodes

Cloud Computing can be online based network engineering which contributed with a rapid advancement at the progress of communication technological innovation by supplying assistance to clients of assorted conditions with aid from online computing sources. It&#x0027;s terms of hardware and software apps together side software growth testing and platforms applications because tools. Large-scale heterogeneous distributed computing surroundings give the assurance of usage of a huge quantity of computing tools in a comparatively low price. As a way to lessen the software development and setup onto such complicated surroundings, high speed parallel programming languages exist which have to be encouraged by complex operating techniques. There are numerous advantages for consumers in terms of cost and flexibility that come with Cloud computing&#x0027;s anticipated uptake. Building on well-established research in Internet solutions, networks and utility computing, virtualization et cetera Service-Oriented Architectures and the Internet of Services (IoS) have implications for a wide range of technological issues such as parallel computing and load balancing as well as high availability and scalability. Effective load balancing methods are essential to solving these issues. Since such systems&#x0027; size and complexity make it impossible to concentrate job execution on a few select servers, a parallel distributed solution is required. Adaptive task load model is the name of the method wesuggest in our article for balancing the workload (ATLM). We developed an adaptive parallel distributed computing paradigm as a result of this (ADPM). While still maintaining the model&#x0027;s integrity, ADPM employs a more flexible synchronization approach to cut down on the amount of time synchronous operations use. As well as the ATLM load balancing technique, which solves the straggler issue caused by the performance disparity between nodes, ADPM also applies it to ensure model correctness. The results indicate that combining ADPM and ATLM improves training efficiency without compromising model correctness.
Composition Under Distributive Natural Transformations: Or, When Predicate Abstraction is Impossible

Abstract Natural language semanticists have often found it useful to assume that all expressions denote sets of values. The approach is most prominent in the study of questions and prosodic focus, but also common in work on indefinites, disjunction, negative polarity, and scalar implicature. However, the most popular compositional implementation of this idea is known to face technical obstacles in the presence of object-language binding constructs, including, chiefly, lambda abstraction. The problem has been well-described on several occasions in the literature, and in fact several solutions have been explored. This paper seeks to formalize the challenge of defining an indeterminate semantics for binding operators, and to formally establish the intuition that the challenge is in fact insurmountable. The primary benefit to this exercise is that it offers an abstract characterization of what it means to lift an operation from one semantic space to another, a notion which may be applied to domains having nothing to do with sets of alternatives.
A Decentralized Service Control Framework for Decentralized Applications in Cloud Environments

Effectively managing decentralized applications in cloud environments using a decentralized control paradigm is essential, as current cloud providers usually only offer a control interface for monitoring cloud infrastructures. This study proposes a decentralized service control framework for implementing the control across various organizations and coordinating collaboration among operators in a decentralized application. The proposed framework allows a consortium of organizations to control a shared distributed cloud infrastructure decentralized reliably. A consensus mechanism within the framework enables mutual coordination between the operators. This mechanism also uses an incentive protocol to enforce pro-active behavior and collaboration. We implement the framework with Hyperledger Fabric, and our experiments demonstrate its usability, reliability, and acceptable performance.
Adding Difference Flow between Virtual and Actual Motion to Reduce Sensory Mismatch and VR Sickness while Moving

Enjoying Virtual Reality in vehicles presents a problem because of the sensory mismatch and sickness. While moving, the vestibular sense perceives actual motion in one direction, and the visual sense, visual motion in another. We propose to zero out such physiological mismatch by mixing in motion information as computed by the difference between those of the actual and virtual, namely, &#x201C;Difference&#x201D; flow. We present the system for computing and visualizing the difference flow and validate our approach through a small pilot field experiment. Although tested only with a low number of subjects, the initial results are promising.
Image Cryptography: A Systematic Review

In recent years due to the observed growth of online multimedia applications, communication, and computer technologies, image security has been an essential demand. This survey paper summarizes existing image security approaches, their merits and demerits, and the area of future work. Along with the survey, the authors present a brief discussion of different multimedia data, as well as the attacks that affect the image transmission, general concepts of multimedia security, primary requirements, and recent applications. Multimedia security is classified into cryptography and data hiding techniques, including digital watermarking, steganography, and hybridization. Recent research work for cryptography techniques is discussed in the following sections.
A Neural Network to Estimate Isolated Performance from Multi-Program Execution

When multiple applications are running on a platform with shared resources like multicore CPUs, the behaviour of the running application can be altered by the co-runners. In this case, the system resources need to be managed (e.g. by repartitioning the cache space, re-schedule applications in distinct cores, modifying the prefetcher configuration, etc.) to reduce the inter-application interference in order to minimize the performance losses over isolated execution. In this context, a main challenge in different computing scenarios like the public cloud or soft real-time systems is knowing the performance impact of a given management action on each application with respect to its isolated execution. With this aim, in this work we present a neural network-based approach that estimates the performance an application would have had in isolation from multi-program executions. Experimental results show that the proposal dynamically adapts to changes in application behavior. On average, the predicted performance presents an error deviation by 11.7% and 2.3% for MAPE and MSE respectively.
Development of Fuzzy Controller Using 8-Bit Microcontroller for Switch Mode DC Servo Motor Control

This paper describes the challenges and solutions for low cost control of DC-DC converter with robust fuzzy logic based controller for robotic/servo applications. Robotic applications need design of compact and versatile controllers for its manipulator. Control of the DC servomotors as actuators for robotic arm is done recently by DC-DC converters with PWM control methods as a single processor multi-motor system. Design approaches for PWM controller is often investigated because of its nonlinear nature due to inherent switching in DC-DC converters. Robust controller for this system can be designed using fuzzy logic which is difficult to implement on low cost digital processors. The proposed approach makes it possible to design the fuzzy controller with low cost microcontroller for PWM based control of DC servomotor.
Scale-Adaptive Selection Network with Dynamic Focal IoU Loss

Abstract Feature pyramid network is wildly applied to predict the targets at different scales in many one-stage detectors. However, most of them treat all feature levels without level-wise attention. As a result, different levels may capture the same object and predict totally different labels, which lead to lower AP on evaluation. Besides, many classic detectors aim at relieving the imbalance between hard and easy samples on classification task, while we find the fact that hard-easy imbalance also exists in localization. To general detection task, bboxes regressed at high IoU are much less than those at low IoU so that detector can easily be biased towards suboptimal bboxes and localization accuracy will be harmed. Based on these two points, in this paper, we propose a Scale-Adaptive Selection Network (SASNet) with a novel Dynamic Focal IoU (DF-IOU) loss. The Scale-Adaptive Selection Network introduces multi-scale attention mechanism into feature pyramid so as to assign attention weight for feature maps on each level, which enables the network to select the dominant levels for prediction and alleviate the prediction conflicts between different levels. Furthermore, we design Dynamic Focal IoU loss to increase loss contribution of easy targets so that the coordinates of these easy targets can regress better and the bounding box will fit tighter. Our experimental results show that our SASNet with DF-IoU loss can increase average precision of objects at small, medium and large scale on the MS COCO and CCPD dataset.
PFCA: An Intelligent Method of Multidimensional User Profile Construction and Application

User profile plays an important role in analyzing high influencers in the era of big data. For example, the profile can help the political strategists to scrutinize the performance of a candidate or party and overcome their weakness long before election day. In this work, we propose a novel profile construction and application model (PFCA) that aims to discover the overall character of high influencers, and apply PFCA to Donald Trump, one of the most influential people recently, to identify Trump&#x2019;s comprehensive personalities. We first collect and preprocess profile data from static and dynamic aspects of multi-source data, and then categorize multi-source data topics and emotions. Next, we attempt to construct the profile from four aspects: fundamental characteristic, behavior characteristic, value characteristic and situation characteristic, to explore the correlation among these characteristics. Finally, PFCA is verified by the outcomes of the 2016 and 2020 U.S. presidential elections, and is evaluated by relevant experts, which proves that PFCA is relatively effective and efficient to profile high influencers. Moreover, PFCA can be further applied to related analysis and prediction in the 2024 U.S. presidential election.
Research on the realization method of the docking function between Wechat payment system and supermarket cashier system

Friendly docking various supermarket cashier systems with the WeChat payment system (WPS) achieve WPS at a supermarket. In addition to DLL (Dynamic Link Library) modules developed by multi-threading technology, it adopts hook and module injection to be introduced into and captures messages in the process message queue of the host program. Via matching the message strings with the bar code rules of WeChat, if satisfied, WPS will be switched to the client-side of the system and in-focus. After processing WeChat payment, WPS runs in the backend. Otherwise, the messages would be delayed and transmitted into the cashier system. WPS injects DLL files into the process of the Cashier system to achieve the docking functionality. This design scheme implements the docking ideally between two systems and supports all kinds of cashier systems with a positive user experience. Six hundred thousand commercial tenants are employing WPS in the market.
Defense against Adversarial Attacks Using Image Label and Pixel Guided Sparse Denoiser

The Deep Neural Networks models have been extensively applied to many real-world areas due to their outstanding performance, especially to safety-critical applications such as autonomous vehicles, healthcare and face recognition. However, many types of research showed that they are easily disturbed by some carefully crafted tiny perturbations, which are called adversarial examples. Therefore, in this paper, a novel method is proposed as image preprocessing. When preprocessing, without discriminating the input images clean images or adversarial examples, the adaptive parameters adjust the images of label loss and pixel-level loss to jointly guide the model to generate reconstructed images and eliminate adversarial perturbation without affecting the images quality and classification result. Moreover, the model uses an architecture of a sparsity constraint that ensures that a neuron fires only for meaningful patterns, limiting the influence of adversarial perturbation on clean images. The defense proposed against attacks of FGSM, MI-FGSM, C&#x0026;W, PGD, RAND+FGSM under the MNIST and CIFAR-10 datasets. The experimental results show that the target model can reach the average accuracy of 98% and 92% separately when the MNIST and CIFAR-10 datasets are attacked through the defense proposed, demonstrating the effectiveness of the proposed method.
Automatic inventory system of librarian books based on a deep learning algorithm with EAST and CRNN

Although the researchers have made great progress in the field of text detection and text recognition, text detection and text recognition are still facing great challenges because of the differences of text fonts and the complexity of backgrounds. Traditional text detection and recognition methods rely on artificial designed features and rules, thus the methods usually requires higher text layout and text resolution. Aiming at the problem of automatic book inventory in library, the paper proposes a new method based on an EAST (Efficient and Accurate Scene Text Detector) detection and an CRNN (Continuous Recurrent Neural Network) recognition. In this method, the library book titles are detected by EAST to get the text area on the side of the books and also to output coordinates. Then, the content of the text area is further identified by the CRNN. Finally, through the comparison of the database, we know whether books of libraries are on corresponding shelves or not. The experimental results show that this method can quickly and accurately realize the task of automatic book title recognitions, and it can still effectively detect the text area and accurately recognize the book title in the case of dark light. Therefore, the method effectively solves the problem of manual inventory of books in existing libraries, which is time-consuming and laborious, and has a certain engineering application prospect.
Band Overdrive: A Multi-Instrument Virtual Reality Music Rhythm Game

Music rhythm games have a tremendous audience in the modern game industry. However, most of these games have been deployed for mobile devices, desktop computers, or game consoles. Although users can still experience the excitement of playing music by tapping on-screen buttons or using controllers, the user experience still has more room for improvement. We therefore developed a network-based multiuser virtual reality music rhythm game in which users can choose their preferred instrument or sing as a band while also collecting points based on their performance.