Reviewerly/exHarmony huggingface dataset:
Quantum secure data transfer with pulse shape encoded optical qubits

Quantum secure data transfer is an important topic for quantum cyber security. We propose a scheme to realize quantum secure data transfer in the basis of quantum secure direct communication (QSDC). In this proposal, the transmitted data is encoded in the pulse shape of a single optical qubit, which is emitted from a trapped atom owned by the sender and received by the receiver with another trapped atom. The encoding process can be implemented with high fidelity by controlling the time-dependent driving pulse on the trapped atom to manipulate the Rabi frequency in accordance with the target pulse shape of the emitted photons. In the receiving process, we prove that, the single photon can be absorbed with arbitrary probability by selecting appropriate driving pulse. We also show that, based on the QSDC protocol, the data transfer process is immune to the individual attacks.
Network Cost-Aware Geo-Distributed Data Analytics System

Many geo-distributed data analytics (GDA) systems have focused on the network performance-bottleneck: inter-data center network bandwidth to improve performance. Unfortunately, these systems may encounter a <i>cost-bottleneck</i> ( <inline-formula><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula> ) because they have not considered data transfer cost ( <inline-formula><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula> ), one of the most expensive and heterogeneous resources in a multi-cloud environment. In this article, we present <i>Kimchi</i> , a network cost-aware GDA system to meet the cost-performance tradeoff by exploiting data transfer cost heterogeneity to avoid the cost-bottleneck. Kimchi determines cost-aware task placement decisions for scheduling tasks given inputs including data transfer cost, network bandwidth, input data size and locations, and desired cost-performance tradeoff preference. In addition, Kimchi is also mindful of data transfer cost in the presence of dynamics. Kimchi has been applied to two common GDA MapReduce models: synchronous barrier and asynchronous push-based shuffle. A Kimchi prototype has been implemented on Spark, and experiments show that it reduces cost by 5% <inline-formula><tex-math notation="LaTeX">$\scriptstyle \sim$</tex-math></inline-formula> 24% without impacting performance and reduces query execution time by 45% <inline-formula><tex-math notation="LaTeX">$\scriptstyle \sim$</tex-math></inline-formula> 70% without impacting cost compared to other baseline approaches centralized, vanilla Spark, and bandwidth-aware (e.g., Iridium). More importantly, Kimchi allows applications to explore a much richer cost-performance tradeoff space in a multi-cloud environment.
Mapping Cyber Threat Intelligence to Probabilistic Attack Graphs

As cyber threats continue to grow and expertise resources are limited, organisations need to find ways to evaluate their resilience efficiently and take proactive measures against an attack from a specific adversary before it occurs. Threat modelling is an excellent method of assessing the resilience of ICT systems, forming Attack (Defense) Graphs (ADGs) that illustrate an adversary’s attack vectors. Cyber Threat Intelligence (CTI) is information that helps understand the current cyber threats, but has little integration with ADGs. This paper contributes with an approach that resolves this problem by using CTI feeds of known threat actors to enrich ADGs under multiple reuse. This enables security analysts to take proactive measures and strengthen their ICT systems against current methods used by any threat actor that is believed to pose a threat to them.
Towards Fast, Accurate and Compact Online Handwritten Chinese Text Recognition

Although great success has been achieved in online handwritten Chinese text recognition (OLHCTR), most existing methods based on over-segmentation or long short-term memory are inefficient and not parallelizable. Moreover, n-gram language models and beam search algorithm were commonly adopted by many existing systems as a part of post-processing, resulting in extremely low speed and large footprint. To this end, we propose a fast, accurate and compact approach for OLHCTR. The proposed method consists of a global and local relationship network (GLRNet) and a Transformer-based language model (TransLM). A novel feature extraction mechanism, which alternately learns global and local dependencies of input trajectories, is proposed in GLRNet for the recognition of online texts. Based on the output of GLRNet, TransLM captures contextual information through Transformer encoder and further improves the recognition accuracy. The recognition and language modelling are always treated as two separate parts. However, the two components of our methods are jointly optimized, which ensures the optimal performance of the whole model. Furthermore, the non-recurrence design improves the parallelization and efficiency of our method, and the parameterized TransLM avoids the large footprint to store the probabilities of n-grams. The experiments on CASIA-OLHWDB2.0-2.2 and ICDAR2013 competition dataset show that our method achieves state-of-the-art performances with the fastest speed and the smallest footprint. Especially in the situation with language model, our method exhibits 2 times to 130 times acceleration compared with existing methods.
Speech Disorder Classification Using Extended Factorized Hierarchical Variational Auto-Encoders

Objective speech disorder classification for speakers with communication difficulty is desirable for diagnosis and administering therapy. With the current state of speech technology, it is evident to propose neural networks for this application. But neural network model training is hampered by a lack of labeled disordered speech data. In this research, we apply an extended version of Factorized Hierarchical Variational Auto-encoders (FHVAE) for representation learning on disordered speech. The FHVAE model extracts both content-related and sequence-related latent variables from speech data, and we utilize the extracted variables to explore how disorder type information is represented in the latent variables. For better classification performance, the latent variables are aggregated at the word and sentence level. We show that an extension of the FHVAE model succeeds in the better disentanglement of the content-related and sequence-related related representations, but both representations are still required for best results on disorder type classification.
Application of Internet of Things intelligent image-positioning studio classroom in English teaching

The purpose is to minimize color overflow and color patch generation in intelligent images and promote the application of the Internet of Things (IoT) intelligent image-positioning studio classroom in English teaching. Here, the Convolutional Neural Network (CNN) algorithm is introduced to extract and classify features for intelligent images. Then, the extracted features can position images in real-time. Afterward, the performance of the CNN algorithm is verified through training. Subsequently, two classes in senior high school are selected for experiments, and the influences of IoT intelligent image-positioning studio classroom on students’ performance in the experimental class and control class are analyzed and compared. The results show that the introduction of the CNN algorithm can optimize the intelligent image, accelerate the image classification, reduce color overflow, brighten edge color, and reduce color patches, facilitating intelligent image editing and dissemination. The feasibility analysis proves the effectiveness of the IoT intelligent image-positioning studio classroom, which is in line with students’ language learning rules and interests and can involve students in classroom activities and encourage self-learning. Meanwhile, interaction and cooperation can help students master learning strategies efficiently. The experimental class taught with the IoT intelligent positioning studio has made significant progress in academic performance, especially, in the post-test. In short, the CNN algorithm can promote IoT technologies and is feasible in English teaching.
Blockchain-Based Fog Computing Model (BFCM) for IoT Smart Cities

Over the past few years, the necessity of smart city has grown considerably. The smart city infrastructure grasps several technologies like Internet of Things, blockchain, and fog and cloud computing to form sustainable development of innovative applications for intellectual data processing, control systems, and communications between heterogeneous network infrastructures. Some of the smart city’s initiatives are green energy, water and pollution management, smart transportation, wireless Internet services, and promotion of local commerce that incorporate technologies in infrastructure to extenuate many of the entanglements. Blockchain provides decentralized architecture with the list of records called blocks, linked using cryptography for built-in security to strengthen the trust and integrity of data transactions. Fog computing acts as a significant place to analyze the IoT data gathered from the near sensor devices at the edge of the network. The network edge performs the data processing for smart city applications and reduces the energy consumption for communication in IoT environment. But, the downside of the technology’s implementation is it faces multiple challenges like security, data privacy, data integrity, platform dependencies, and resource management. This chapter discusses a conceptual framework with the dimensions (1) technology, (2) human, and (3) organization and reconnoitring the challenges of digital technologies with the support of blockchain-based fog computing model which reduces energy consumption and latency in data communication and improves security features. At the end of this chapter, the authors present suggestions to get rid of the gap and challenges in connecting the cities and citizens to governance, education, and regulation.
Which Hype for My New Task? Hints and Random Search for Echo State Networks Hyperparameters

In learning systems, hyperparameters are parameters that are not learned but need to be set a priori. In Reservoir Computing, there are several parameters that needs to be set a priori depending on the task. Newcomers to Reservoir Computing cannot have a good intuition on which hyperparameters to tune and how to tune them. For instance, beginners often explore the reservoir sparsity, but in practice this parameter is not of high influence on performance for ESNs. Most importantly, many authors keep doing suboptimal hyperparameter searches: using grid search as a tool to explore more than two hyperparameters, while restraining the spectral radius to be below unity. In this short paper, we give some suggestions, intuitions, and give a general method to find robust hyperparameters while understanding their influence on performance. We also provide a graphical interface (included in ReservoirPy) in order to make this hyperparameter search more intuitive. Finally, we discuss some potential refinements of the proposed method.
Demystifying Blockchain Technology for Resource-Constrained IoT Devices: Parameters, Challenges and Future Perspective

One of the most promising enablers for the secure distributed operation of the Internet of Things (IoT) systems could be based on a mathematical construct widely known as blockchain that aims to neglect the system's centralization and scalability properties.This paper aims to map the requirements and features of both systems, highlight the main integration challenges, and technological candidates for smoother integration of IoT and blockchain, as well as highlight the standartization outlook.This work has identified an architectural approach to an integrated solution based on classic literature review methodology aiming to consider the IoT versus blockchain characteristics mapping and outlining related integration challenges.Potential integration challenges of the proposed integrated approach are also identified and classified.Critical solutions to address the integration bottlenecks include moving from Proof-of-Work (PoW) to Distributed Proof-of-Stake (DPoS) consensus, adding a Fog overlay to the architecture model, and leveraging the synergies combining the benefits of blockchain and IoT technology are highlighted.
Deep-Learning Approach with DeepXplore for Software Defect Severity Level Prediction

Fixing the defects of earlier releases and working on fast and efficient fixing of those software defects is detrimental for the release of further versions. Bug tracking systems like Bugzilla get thousands of software defect reports every day. Manually handling those report to assign severity to the defects is not feasible. Earlier traditional Machine Learning methods have been used to predict the severity level from the defect description. This paper presents different deep learning models to predict defect severity level. Furthermore, the deep neural network was tested using a framework developed similar to that DeepXplore. Different word-embedding techniques, feature-selection techniques, sampling techniques and deep learning models are analyzed and compared for this study. In this paper, we have considered Descriptive statistics, Box-plot, and Significant tests to compare the developed models for defect severity level prediction. The three performance metrics used for testing the models are AUC, Accuracy and Neuron Coverage. This is a preliminary study on DNN testing on this dataset. Thus, the paper focuses on DeepXplore DNN testing technique. However further studies would be undertaken on comparative analysis of different DNN testing techniques on this dataset.
Attacking the linear congruential generator on elliptic curves via lattice techniques

Abstract In this paper we study the linear congruential generator on elliptic curves from the cryptographic point of view. We show that if sufficiently many of the most significant bits of the composer and of three consecutive values of the sequence are given, then one can recover the seed and the composer (even in the case where the elliptic curve is private). The results are based on lattice reduction techniques and improve some recent approaches of the same security problem. We also estimate limits of some heuristic approaches, which still remain much weaker than those known for nonlinear congruential generators. Several examples are tested using implementations of ours algorithms.
Design and Development of Retrieval-Based Chatbot Using Sentence Similarity

Chatbots or the well-known automated conversational agents have become a raging trend among all the sectors of businesses as a result of the rapid transition happening towards automation in processes. They are already being used extensively and will spread their wings to newer horizons shortly. The basic model of Chatbots is to interact with the user to answer their questions using various modes like text messages, voice replies, or any other predefined suitable interface. This paper discusses the development of a Chatbot for the college, Prasad V Potluri Siddhartha Institute of technology, to answer various questions related to the college like the facilities, procedures, policies, etc. This is a web-based software application implemented using Flask framework. This model is designed to capture text inputs from the user through a console and outputs the response in text format using machine learning concepts. A retrieval approach is implemented to process the input and to respond with an appropriate answer using logic adapters. The performance of this model is analyzed using a questionnaire that uses various parameters like performance, humanity, effect, and accessibility. This paper presents the overall approach used to design the Chatbot and compares the web application as-is study with the to-be website when the Chatbot is incorporated. The web application along with the Chatbot showed a 20% improvement in performance and 5% increase in accessibility by analyzing the performance metrics.
Investigating Remote Pair Programming In Part-Time Distance Education

Pair programming promotes immediate, informal collaboration over coding activities. The driving developer writes the code and controls the keyboard and mouse; the navigating developer checks the code as it is written by the driver; the developers swap their roles frequently. In agile development, programmers often code in pairs, in order to detect errors faster, produce higher code quality and discover better solutions.
VIA: Analyzing Device Interfaces of Protected Virtual Machines

Both AMD and Intel have presented technologies for confidential computing in cloud environments. The proposed solutions — AMD SEV (-ES, -SNP) and Intel TDX — protect VMs (VMs) against attacks from higher privileged layers through memory encryption and integrity protection. This model of computation draws a new trust boundary between virtual devices and the VM, which in so far lacks thorough examination. In this paper, we therefore present an analysis of the virtual device interface and discuss several attack vectors against a protected VM. Further, we develop and evaluate VIA, an automated analysis tool to detect cases of improper sanitization of input recieved via the virtual device interface. VIA improves upon existing approaches for the automated analysis of device interfaces in the following aspects: (i) support for virtualization relevant buses, (ii) efficient Direct Memory Access (DMA) support and (iii) performance. VIA builds upon the Linux Kernel Library and clang’s libfuzzer to fuzz the communication between the driver and the device via MMIO, PIO, and DMA. An evaluation of VIA shows that it performs 570 executions per second on average and improves performance compared to existing approaches by an average factor of 2706. Using VIA, we analyzed 22 drivers in Linux 5.10.0-rc6, thereby uncovering 50 bugs and initiating multiple patches to the virtual device driver interface of Linux. To prove our findings’ criticality under the threat model of AMD SEV and Intel TDX, we showcase three exemplary attacks based on the bugs found. The attacks enable a malicious hypervisor to corrupt the memory and gain code execution in protected VMs with SEV-ES and are theoretically applicable to SEV-SNP and TDX.
Improved Scientific Workflow Scheduling Algorithm with Distributed Heft Ranking and TBW Scheduling Method

Scheduling is a process that manages the workflow tasks during execution on different resources. Virtual infrastructure is a dynamic mapping of system resources to applications in order to maximize its utilization. In today's technological world, cloud has taken a long stride on the success towards maximum throughput as well as highest qualitative services to its consumers. Yet, approaches for maximizing the utilization of cloud resources are at peak demand. Each cloud service provider focuses on maximum utilization with minimum consumption of cloud resources, although managing and providing computational resources to maximum number of users and to execute such huge applications is a challenging one. In this paper, a scheduling algorithm with name TBW (Tabu Bayesian Whale Optimization) has been proposed. Basically, the algorithm is used to target the improvement in scheduling of scientific workflows. The complete framework has firstly used a ranking algorithm named distributed HEFT ranking and then applied TBW algorithm on ranked tasks of input workflows. The work has been executed for five scientific workflows LIGO, MONTAGE, Epigenomics, SIPHT and Cybershake. TBW is using tabu method on workflow tasks for fast local search in cloud system, and Bayesian Optimization is used to find out best possible combinations of resources where tasks are mapped and then whale optimization maps the tasks on the resources in a smart way. In the whole process, total execution time and cost parameters are minimized under deadline constraints.
Link Prediction on Knowledge Graph by Rotation Embedding on the Hyperplane in the Complex Vector Space

The large-scale exploitation of knowledge graphs has promoted research efforts on graph construction and completion in many organizations such as Google, Apple. The problem of predicting the missing links in the knowledge graph often depends heavily on the method of embedding the vertices into a low-dimensional space, mostly considering the relations as a translation. Recently, there is an approach based on rotation embedding, which can improve efficiency remarkably. Therefore, in this paper, we propose an approach towards rotation embedding entities on a low-dimensional vector. Specifically, we start by projecting the entities onto the relation-specific hyperplanes before rotating them so that the head entities are as close as possible to the tail entities. Based on that, each relation is a rotation from the head entities to the tail entities on the hyperplane in complex vector space. Experiments on well-known datasets show the improvement of the proposed model compared to other models.
Digital Twin of Npps: Simulation Systems and Verification

The article discusses the approach conformity with which the flexible software package (FMSP) is the kernel of the digital twin of an NPP power unit. The models that are part of the FMSP cover the main and a number of auxiliary technological systems of the power unit, automatic control systems. The developed flexible modeling software package is designed to simulate dynamic processes occurring in NPP power units with VVER-1000 reactors. The FMSP allows considering various types of violations in the operation of safety systems and energy imbalances in the reactor core. The results of verification of a flexible modeling complex based on stationary neutron-physical calculations of the VVER-1000 reactor core using the BIPR-7 program and NPP operational data are presented.
A model-based reinforcement learning method based on conditional generative adversarial networks

Deep reinforcement learning (DRL) integrates the advantages of the perception of deep learning and enables reinforcement learning scale to problems with high dimensional state and action spaces that were previously intractable. The success of DRL primarily relies on the high level representation ability of deep learning. To obtain a good performed representation model, excessive training samples and training time are necessary. However, collecting a large number of samples in real world is extremely expensive and time consuming. To mitigate the sample inefficiency problem, we propose a novel model-based reinforcement learning method by combining conditional generative adversarial networks (CGAN-MbRL) with the state-of-the-art policy learning method. The proposed CGAN-MbRL can directly deal with the high dimensional state, and mitigate the problem of sample inefficiency to some extent. Finally, the effectiveness of the proposed method is demonstrated through the illustrative data and the RL benchmark.
The Application of VR Technology in Preschool Education Professional Teaching

While China’s scientific and technological power is constantly increasing, relevant experts are also studying how to closely combine science and technology with preschool education. Under the continuous practical exploration efforts of people from all walks of life, the application of VR in preschool education professional teaching has also achieved gratifying results. This paper first introduces the information of VR technology, identifies the problems in the teaching of preschool education, and proposes the application of VR technology in preschool education.