Reviewerly/exHarmony huggingface dataset:
Automated Kernel Search for Gaussian Processes on Data Streams

Gaussian Processes offer non-parametric, probabilistic models that can be used in numerous fields of data analysis. One major drawback is their lack of adjustability in case of drifting and evolving streaming data, where inherent kernels need to be adapted in an efficient manner. To counteract this issue, we propose a novel automated kernel search method that allows us to incrementally adapt Gaussian Process models to evolving IoT data streams. Our approach, denoted as Adjusting Kernel Search (AKS), offers an efficient alternative to searching for suitable kernels from scratch. We evaluate the AKS algorithm on several IoT datasets and show that our approach is able to achieve higher accuracy with lower run-times compared to previous approaches.
CellDet: Dual-Task Cell Detection Network for IHC-Stained Image Analysis

Cell detection on immunohistochemistry stained (IHC-stained) images plays an essential role in computer assisted prediction of tumor progression and treatment response. Currently available cell detection datasets provide either point level or bounding box level annotations for deep object detection network training. And these widely used networks usually employ standard pyramid structured multi-scale feature fusion. However, we find that these methods have obvious limitation when facing large amounts of cells in similar scale with severe overlapping. To address this problem, we propose a novel CellDet network with (1) Scale Consistency Feature Fusion Module (SCFFM) and (2) Dual Task Detection Module to simultaneously exploit the complementary information from both point and bounding box annotations. In order to verify the effectiveness our proposed method, We make efforts to relabel the public SHIDC-B-Ki-67 dataset with bounding box annotations. Extensive experimental results show that the proposed CellDet outperforms other state-of-the-art cell detection methods with a remarkable margin. We will release our source code and dataset in https://github.com/JiweiMaster/celldet.
Energy Efficient Adiabatic Logic Against Power Analysis Attacks for IOT Applications

This paper presents an efficient way of protecting the cryptographic circuits against the power analysis attacks(P AA) using positive feedback adiabatic logic. The conventional circuit is improvised by adding a dummy transistor circuit which provides a symmetric optimized path for the current flow in the circuit. The EE-CPO-SPFAL logic is been proposed, designed and simulation results are provided by using Hspice-A 2008.03. From the simulation reports of the proposed logic, the current fluctuations are reduced by 41 % and also the energy consumption fluctuations are being reduced by 76% compared to the previous logic CPO-SPGAL. Thereby ensuring more protection against the power analysis attacks compared to the existing logic theories.
Fundamental Categories of Artificial Neural Networks

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.
Collective unitary evolution with linear optics by Cartan decomposition

Unitary operation is an essential step for quantum information processing. We first propose an iterative procedure for decomposing a general unitary operation without resorting to controlled-NOT gate and single-qubit rotation library. Based on the results of decomposition, we design two compact architectures to deterministically implement arbitrary two-qubit polarization-spatial and spatial-polarization collective unitary operations, respectively. The involved linear optical elements are reduced from 25 to 20 and 21 to 20, respectively. Moreover, the parameterized quantum computation can be flexibly manipulated by wave plates and phase shifters. As an application, we construct the specific quantum circuits to realize two-dimensional quantum walk and quantum Fourier transformation. Our schemes are simple and feasible with the current technology.
The Usage of Artificial Intelligence in Arab Financial Institutions

The global banking sector has undergone a radical transformation as a result of the implications of the rapid digital development imposed by the twenty-first century. Artificial intelligence is emerging as one of the most effective technologies that have a strong and tangible impact that will make a comprehensive change in the global business environment. The Study revealed that the Arab world is witnessing a major shift towards digital banks in the coming years in the Arab markets, especially the Gulf, with the world’s tendency towards artificial intelligence, which may open the door for the disappearance of traditional banks, especially since digital transformation will reduce spending by reducing traditional bank branches.
Using Local Phrase Dependency Structure Information in Neural Sequence-to-Sequence Speech Synthesis

We introduce end-to-end text-to-speech synthesis (TTS) with prosodic symbols that represent phrase components based on local syntactic dependency structures for synthesizing Japanese speech with natural prosody. We propose two TTS models: 1) one with prosodic symbols representing the syntactic dependency distance at the phrase boundaries and 2) another with prosodic symbols that reflect a superimposed model of the phrase and accent components based on an F0 generation control mechanism. Using these two models, we observed 1) pause insertion that indicates the phrase boundary and 2) F0 resetting at the right-branching boundaries. To verify the effectiveness of these two proposed models against the conventional model using only accent components, we conducted an AB test as a subjective evaluation. Our result confirmed that synthetic speech with natural prosody, which reflects the corresponding intention to the utterance, was generated using the local phrase dependency information of sentences and the F0 generation model in a Japanese end-to-end TTS.
Most Favorable Results for Forecasting Methods for Natural Gas and Photovoltaic Energy Consumption

This article contrasts three forecasting methods (Statistical Method, Stochastic Method and Optimal Control Method) for calculating forecasts for energy consumption from natural gas and photovoltaic. Accurate forecasting is important to ensure network stability and reliability. The advantages and disadvantages of each method are summarized based on historical data, together with the forecast horizons and input parameters. A comparison is made of the effectiveness of the proposed three forecasting methods to help researchers choose the best approach to forecasting for future research.
Game with Central Symmetry on a Rectangular Board

A game with central symmetry on a rectangular board, created with Unity3D, is presented. It is available for both PC and Android with mixed reality. The purpose of creating the game is to provide conditions for the development of digital and mathematical competence in students through play in both formal and non-formal learning. Four options are described, depending on the presence or absence of animation, as well as the limitation of working time.
Asset Modeling using Serverless Computing

Assets in the domain of Internet of Things (IoT) generate time-series data such as sensor readings and alerts. In addition, the assets have associated static data such as the make, model and other manufacturing information. The sensors in the asset components may have implicit relationships with each other, which are not interpretable without domain knowledge. Many problems exist which involve computation of relationships between sensors or subsystems in the asset components. Typically, the number of sensors in a real world asset may range anywhere from tens to thousands of sensors - and in this case, finding relationships between them becomes a highly computationally intensive task. In this paper, we study one such problem of anomaly detection in industrial data based on the functioning of the sensors and their interrelationships in both normal and abnormal conditions. We further demonstrate the issue of run-time and performance complexity in this problem, and present a speed-up strategy using Serverless Computing for parallelization, and demonstrate the usefulness of this method by comparing the speed-up achieved.
Adjustable mode ratio and focus boost search strategy for cat swarm optimization

&lt;abstract&gt;&lt;p&gt;Evolutionary algorithm is one of the optimization techniques. Cat swarm optimization (CSO)-based algorithm is frequently used in many applications for solving challenging optimization problems. In this paper, the tracing mode in CSO is modified to reduce the number of user-defined parameters and weaken the sensitivity to the parameter values. In addition, a &lt;italic&gt;mode ratio&lt;/italic&gt; control scheme for switching individuals between different movement modes and a search boosting strategy are proposed. The obtained results from our method are compared with the modified CSO without the proposed strategy, the original CSO, the particle swarm optimization (PSO) and differential evolution (DE) with three commonly-used DE search schemes. Six test functions from IEEE congress on evolutionary competition (CEC) are used to evaluate the proposed methods. The overall performance is evaluated by the average ranking over all test results. The ranking result indicates that our proposed method outperforms the other methods compared.&lt;/p&gt;&lt;/abstract&gt;
Building a Trust Model for Secure Data Sharing (TM-SDS) in Edge Computing Using HMAC Techniques

With the rapid growth of Internet of Things (IoT) based models, and the lack amount of data makes cloud computing resources insufficient. Hence, edge computing-based techniques are becoming more popular in present research domains that makes data storage, and processing effective at the network edges. There are several advanced features like parallel processing and data perception are available in edge computing. Still, there are some challenges in providing privacy and data security over networks. To solve the security issues in Edge Computing, Hash-based Message Authentication Code (HMAC) algorithm is used to provide solutions for preserving data from various attacks that happens with the distributed network nature. This paper proposed a Trust Model for Secure Data Sharing (TM-SDS) with HMAC algorithm. Here, data security is ensured with local and global trust levels with the centralized processing of cloud and by conserving resources effectively. Further, the proposed model achieved 84.25% of packet delivery ratio which is better compared to existing models in the resulting phase. The data packets are securely transmitted between entities in the proposed model and results showed that proposed TM-SDS model outperforms the existing models in an efficient manner.
Sample Weight-Based Domain Adaptation Network for General Fault Diagnosis

Fault diagnosis based on machine learning has been widely used in the Industrial Internet of Things (IIoT). However, due to variation of operating conditions, training and testing data are drawn from different distributions. Existing methods usually need prior knowledge of the relationship between label space of the source and target domains and suffer the negative transfer problem if the knowledge is unavailable. To handle these issues, a novel transferability weighted universal domain adaptation network (TWUAN) is proposed for the universal domain adaptation problem. TWUAN consists of a weighted adversarial module and a weighting learning module. Among them, a transferability measure is embedded into the adversarial domain adaptation network, which helps TWUAN classifies samples in the common label space correctly and identifies unknown classes. Experiments on the CWRU dataset demonstrate that the proposed TWUAN can bridge the distribution discrepancy and achieve satisfactory diagnosis accuracy in different settings.
Efficient Application of the Residue Number System in Elliptic Cryptography

The article is devoted to the study of the efficiency of arithmetic operations with points of an elliptic curve using the residue number system. Based on the obtained data, it was found that the use of the residue number system with moduli of a special type from the operations with an elliptic curve from NIST FIPS 186 allows gaining 7.72% for the operation of addition and 7.50% for the operation of doubling points of an elliptic curve on average.
A Novel Trust-based Model for Collaborative Filtering Recommendation Systems using Entropy

With the proliferation of false redundant information on various e-commerce platforms, ineffective recommendations and other untrustworthy behaviors have seriously hindered the healthy development of e-commerce platforms. Modern recommendation systems often use side information to alleviate these problems and also increase prediction accuracy. One such piece of side information, which has been widely investigated, is trust. However, it is difficult to obtain explicit trust relationship data, so researchers infer trust values from other methods, such as the user-to-item relationship. In this paper, addressing the problems, we proposed a novel trust-based recommender model called UITrust, which uses user-item relationship value to improve prediction accuracy. With the improvement the traditional similarity measures by employing the entropies of user and item history ratings to reflect the global rating behavior on both. We evaluate the proposed model using two real-world datasets. The proposed model performs significantly better than the baseline methods. Also, we can use the UITrust to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.
Is Noninferior Not Inferior?

Is Noninferior Not Inferior? What is a non-inferiority trial margin? To answer this, ask whether you have ever weighed a tradeoff between the best version of something and an acceptable alternative. Restated, in a choice between two decisions, is one option not inferior to the other? Watch an animated video that explores the basis for non-inferiority trials, the meaning of non-inferiority margins, and the interpretation of a non-inferiority trial's results.
Epistemic Network Analysis Visualization

Visualization plays an important role in Epistemic Network Analysis (ENA), not only in graphical representation but also to facilitate interpretation and communicate research findings. However, there is no published description of the design features behind ENA network graphs. This paper provides this description from a graphic design perspective, focusing on the design principles that make ENA network graphs aesthetically pleasing and intuitive to understand. By reviewing graphic design principles and examining other extant network visualizations, we show how the current ENA network graphs highlight the most important network characteristics and facilitate sense-making.
MLLP-VRAIN Spanish ASR Systems for the Albayzín-RTVE 2020 Speech-to-Text Challenge: Extension

This paper describes the automatic speech recognition (ASR) systems built by the MLLP-VRAIN research group of Universitat Politècnica de València for the Albayzín-RTVE 2020 Speech-to-Text Challenge, and includes an extension of the work consisting of building and evaluating equivalent systems under the closed data conditions from the 2018 challenge. The primary system (p-streaming_1500ms_nlt) was a hybrid ASR system using streaming one-pass decoding with a context window of 1.5 seconds. This system achieved 16.0% WER on the test-2020 set. We also submitted three contrastive systems. From these, we highlight the system c2-streaming_600ms_t which, following a similar configuration as the primary system with a smaller context window of 0.6 s, scored 16.9% WER points on the same test set, with a measured empirical latency of 0.81 ± 0.09 s (mean ± stdev). That is, we obtained state-of-the-art latencies for high-quality automatic live captioning with a small WER degradation of 6% relative. As an extension, the equivalent closed-condition systems obtained 23.3% WER and 23.5% WER, respectively. When evaluated with an unconstrained language model, we obtained 19.9% WER and 20.4% WER; i.e., not far behind the top-performing systems with only 5% of the full acoustic data and with the extra ability of being streaming-capable. Indeed, all of these streaming systems could be put into production environments for automatic captioning of live media streams.
Adam Optimized Pruning Method for Few-Shot Learning with GAN Data Augmentation

Weight pruning is widely used for model compression and acceleration. In this work, a novel Adam optimization method for few-shot learning with GAN data augmentation is proposed. The first-order Taylor series is implemented to evaluate parameters' importance toward the loss function. And with the given compression ratio, parameters with importance above the threshold are updated by the Adam optimizer with momentum-accelerated weight decay, while others have negative updates as the penalization. After continuous iterations, the model enables to achieve corresponding sparsity ratio, with the influence of the redundant parameters reducing to a low extent. Experiments demonstrate that this method is effective on ResNet with CUB and ISIC-2018 datasets. Note that CUB and ISIC-2018 are datasets about birds and skin deceases, respectively, which represents the generalization of our method on cross-domain classification issues. As a result, the pruned model is able to retain the accuracy with high model sparse ratios. And in some specific compress ratio, like 10× for CUB dataset and 3 × for ISIC-2018 dataset, the pruned model even outperforms the origin model by 3.15% and 1.16%, respectively.
Construction of Integrated Protection System for Industrial Control System Based on Trusted Computing

Industrial control system requires high availability. In principle, security measures should not adversely affect the basic functions of high availability industrial control system. The method of building a secure and trusted PLC are proposed based on trusted computing. Under the premise of ensuring business security, the security protection system of industrial control system is built through trusted computing, and hardware solutions are provided on the security master controller. In order to prevent the attack from the industrial control system network, the encryption technology is used to ensure the confidentiality and integrity of the communication session, which are between the field of control equipment layer and the process monitoring equipment layer, so as to make the industrial control system in a stable and reliable state.
Vision based Semantic Runway Segmentation from Simulation with Deep Convolutional Neural Networks

Manned flight crew rely upon optical imagery to make sense of the world and carry out high level guidance, navigation \& control tasks. To advance autonomous aircraft's capabilities and safety, programmes need to be developed that aim to achieve piloted human-level perception. We designed a simulation environment to train deep Convolutional Neural Networks (CNNs) to semantically segment objects and then test the trained network on imagery collected from the same location on a real aircraft. This approach is capable of achieving state of the art performance on the task of runway segmentation along with providing a proof of concept to rapidly generate training sets on simulation capable of being used on fixed-wing aircraft.
Hybrid Dynamic Scheduling of MapReduce and Spark Services Based on the Profit Model in the Cloud Computing Platform

The big data technology has been efficient in carrying out analysis and processing of massive data. Many service providers build cloud platforms based on big data technology to provide users with computational requirements. The goal of this paper is to, targeting users who are uncertain about service deadlines and who have urgent needs to complete their services, develop an efficient, two-type, offline service scheduling strategy that enables the service provider to achieve maximum profit, at the same time determines relatively exact deadlines for the user, and empowers the resource of the cloud platform to reach optimum utilization rate, thus realizing mutual benefit between service providers and users. In this paper, we first propose a Profit Model with reward and penalty, as a criterion of profit maximization. This paper then put forward the MapReduce and Spark services hybrid scheduler (MRSHS) targeting Profit Model, which contains the determining Round Number (MRN) algorithm facing MapReduce service as well as the Hybrid Dynamic Scheduling (HDS) algorithm facing MapReduce and Spark services. Finally, a large number of experiments prove that, compared with existing schedulers, MRSHS put forward in this paper reaches a fairly high level of efficiency and availability in terms of service performance, the profit for service providers, and platform resource utilization.
Cyber Forensic Case Study of Waste Water Treatment Plant

Recent attacks on SCADA systems such as Stuxnet, Havex, BlackEnergy demand forensic investigation to determine the cause of intrusion and also to prevent cyber-attack. Sometimes due to the criticality of the system forensic investigation needs to be carried out on a live analysis. This paper provides a case study of an infected wastewater treatment plant (WTP)where live memory dump acquisition FTK Imager was used. Features are extracted from memory dump using bulk extract forensic carving method here some features such as AES files, URL services, PCAP files, IP files, Email files, CCN files, etc are found. PCAP files are open in Wireshark network forensic tool that detects DoS was detected in WTP plant and Finally, importance and need of IT forensic tools in ICS environment discussed.
Twine Stack: A Hybrid Mechanism Achieving Less Cost for Return Address Protection

Return-oriented programming(ROP) is a prevalent technique that targets return addresses to hijack control flow. To prevent such attack, researchers mainly focus on either Shadow Stack or MAC-based mechanisms(message code authentication). But Shadow Stack suffers from additional memory overhead and information leakage, while MAC-based mechanisms(e.g. Zipper Stack) impose high runtime overhead for MAC calculations.In this paper, we propose Twine Stack, a hybrid and efficient return address protection mechanism with lightweight hardware extension. It utilizes a tiny hardware shadow stack to realize a new multi-chain Zipper Stack. Specifically, each entry in the shadow stack stores a return address and its MAC in each chain, allowing queueing calculation with just one hash module. At meantime, some return address verifications could be done by comparison with the hardware shadow stack, instead of calculation again. We implemented Twine Stack on RISC-V architecture, and evaluated it on FPGA board. Our experiments show that Twine Stack reduces over 95% hash verifications, and imposes merely 1.38% performance overhead with an area overhead of 974 LUTs and 726 flip flops. The result demonstrates that our hybrid scheme mitigates the drawbacks of each separate scheme.