Reviewerly/exHarmony huggingface dataset:
Using Machine Learning for Performance Classification and Early Fault Detection in Solar Systems

The steady increase in the world’s population has directly influenced global climate change, resulting in catastrophic environmental consequences. This has created an immediate need for scientists from interdisciplinary domains like clean technology innovation in solar energy and computer science to join in the effort to save the world for future generations. As such, the United Nations has set a goal to ensure global access to affordable, sustainable, and clean energy. As a leading influential G20 economy, Saudi Arabia has recently established the Green Saudi initiative to align with the UN goal for enhancing the use of green energy. However, research in this area is sparse and greater effort is still required. This work is among the first to address the issue of enhancing and expanding the use of clean energy by means of studying the data collected from solar plants around Saudi. We used machine learning-based methods to assess the energy output performance of solar plants and employed the collected data to train the models to make early detection of faults. Our models achieved the highest performance at an accuracy score of 98.85% and 0.98 weighted F-score using the J48 model trained on a publicly available dataset of 874 instances collected from 26 different sites across Saudi. We anticipate that the findings of this work to serve as testbed to facilitate further research in this area and enhance the early fault detection in solar energy stations.
Self-directed Robot for Car Driving Using Genetic Algorithm

The big issue with a human driving car is traffic, with the current continuous growth in the world population. The second big issue with the growing population is creating huge chaos, which leads to accidents. Every year nearly 1.35 million people lose their lives due to traffic crashes, and 20 to 50 million face serious injuries with some untreatable disability as of their road injury. Over 80% of accidents happen due to driver error. Other issues are the efficiency of the car as we are slowly transforming into the electric car. This paper introduced car with the self-driving feature using genetic algorithm to reduce the traffic with route optimization, and by reducing traffic, so that many problems related to driving can be solved. It minimizes the rate of an accident and also maximizes the efficiency of the car.
Fractional-order High-boost Filtering for Textural Improvement of Images using Relative Spatial Entropy Quartiles

Textural segmentation and its usage for region-wise image quality improvement unfold a new chapter for texture-dependent image processing in association with fractional order calculus (FOC). Along with intensity variation, texture variation is also equally important for human as well as machine vision to discriminate between surfaces and objects even having the same intensity. Most of the vision applications deal with intensity-wise segmented frames as their raw input. The power of textural analysis along with conventional intensity-based processing can enhance the system's capability in a remarkable manner. To address the textural nature of the image and for imparting texture-dependent image restoration or enhancement fractional-order high-boost filtering (FoHBF) the framework is essentially relevant irrespective of the image domain. Spatial entropy quantile-based textural segmentation and region-wise FoHBF is employed in this paper for imparting total quality enhancement, especially for remotely sensed images.
An Decentralized Anonymous Payment Confidential Transactions with Efficient Proofs and Scalability

With the popularity of blockchain application scenarios, the blockchain&#x0027;s privacy preserving is still a challenging job without sacrificing scalability. To address this problem, this paper proposes a Decentralized Anonymous Payment Scheme Via Confidential Transactions with Efficient Proofs and Scalability, DAPCT, which is short for &#x201C;queue masquerade&#x201D;. The core insight of DAPCT is that a new decentralization anonymous payment scheme reconstructed on the mini blockchain model and a proving system. In this work, mini blockchain enables us to have better scalability and this proving system enables us to have better anonymity by proving knowledge what uses a single set and a verifiable random function assessed. With soundness depending on a novel discre-logarithm, we use data from the Bitcoin to test DAPCT. The results show that it provide faster verification time than other confidential transaction models. The results demonstrate that DAPCT creates a great new privacy protection model.
Integrated Security Management of Public and Private Sector for Critical Infrastructures – Problem Investigation

The interaction between security management in public and private organisations includes complex challenges. In particular in critical infrastructure sectors, there is a need for instruments that enable the holistic and overarching management of private and public providers. Cross-organisational structures and processes should be defined, but are difficult to establish in federal governmental structures due to different legislative levels and scopes. The paper investigates this challenge using Germany and the Free Hanseatic City of Bremen as example. The study proposes the development of an “Enterprise Architecture Framework” integrating and overarching the organizational structurers for both, a federal state, its municipalities and the (private) critical infrastructure providers in these municipalities. The main contributions of this paper are based on the results of an interview study. The interview partners were representatives of enterprises and public bodies covered by the federal IT security regulations. The contribution of the paper is the identification of security management challenges for services of general interest and how to increase the resilience of public service providers. Cybersecurity management in the context of public institutions is in focus.
Make Consumers Happy by Defuzzifying the Service Level Agreements

A Service Level Agreement (SLA) is a special kind of legal contract that binds a vendor to its customers where the vendor commits to provide certain services in exchange for certain payments from the customers. However, when customers do not get the services that they have subscribed for, it becomes a laborious job for customers to contact or visit the company and claim the correct amount of compensation or service credits. On the other hand, a Smart Contract is a contract that is a computer program that also binds multiple parties into given agreements but is a set of precise rules and is self-enforceable and self-executable. In this paper, we have introduced a novel work where we use fuzzy logic inside the Ethereum-based smart contract for two significant objectives. The first objective is to make the claim of the compensation easier and faster for customers by translating the SLA into a smart contract. The second objective is to make the smart contract even smarter and intelligent by implementing fuzzy logic so that customers who have a hard time understanding the legal jargon and ambiguities of the legal contract and SLA to find out if the compensation amount they are getting when the service is poor is good enough. Since fuzzy logic models semantics of linguistic expressions by capturing vagueness in the fuzzy sets, it becomes easier to solve the problem of contractual ambiguities and expedite the process of claiming compensation when implemented in a Blockchain-based smart contract.
CNN Based Image Forgery Segmentation and Classification for Forensic Verification

The growth of digital images has improved because of the advent of a new camera and smart devices. The social networking websites like Facebook, Instagram, and Twitter have further contributed to their distribution. The images that are circulating over the social websites are easily manipulated with certain tools such as Photoshop Pixler, Snapseed, etc. Various techniques have proposed for the detection of a digital image based on artifacts from resampling, a filter of a color array, and so on. In the field of image identification, forensics comprise diverse region to predict a manipulated image that involves detection of resampling, detection of copy-move, splicing and removal of the object. The main objective of image forensic is to verify the integrity and authentication of the manipulated image. In this chapter, the manipulated images are identified with CNN model. The proposed system is designed using the MATLAB tool.
QuantMark: A Benchmarking API for VQE Algorithms

Thanks to the rise of quantum computers, many variations of the variational quantum eigensolver (VQE) have been proposed in recent times. This is a promising development for real quantum algorithms, as the VQE is a promising algorithm that runs on current quantum hardware. However, the popular method of comparing your algorithm versus a classical baseline in a small basis set is not meaningful in the big picture. Moreover, many papers use a different molecular representation or a different quantum computer to test their algorithms such that the used baselines are different between different papers. Thus, it is almost impossible to compare the different algorithms to each other. As a solution, we have built a benchmarking framework to standardize the VQE performance metrics, such that they can be analyzed more easily. Using our framework, any researcher working on the VQE can easily test their own algorithms against previous ones on the leaderboard without the need to reproduce previous work themselves.
Youtube Web-Projects: Path from Entertainment Web Content to Online Educational Tools

Digital technology helps to organize student learning regardless of the circumstances surrounding the COVID-19 epidemic. In the proposed article, the authors draw attention to the main conceptual approaches to teaching students using modern technologies, including the integration of video content into the learning process. The main attention is paid not only to the reproduction of the material, but also to the requirements for the creation of educational video content. Today, video content is a topical milestone in e-learning. The authors draw attention to the use of the largest video hosting on the Internet—YouTube and offer to pay attention to it not only as entertainment content, but also a certain educational platform. The focus is on the organizational and technical aspects of using YouTube in educational activities, the requirements for video content, including educational content posted on YouTube, the relationship of YouTube with social networks and related services that complement or expand its functionality.
An Institutional Approach to Communicating UML State Machines

Abstract We present a new approach on how to provide institution-based semantics for communicating UML state machines in form of a hybrid modal logic $$\mathcal {M}^{\downarrow }_{\mathcal {D}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mi>D</mml:mi> <mml:mo>↓</mml:mo> </mml:msubsup> </mml:math> . A theoroidal comorphism maps $$\mathcal {M}^{\downarrow }_{\mathcal {D}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mi>D</mml:mi> <mml:mo>↓</mml:mo> </mml:msubsup> </mml:math> into the $$\textsc {Casl}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>C</mml:mi> <mml:mstyle> <mml:mi>A</mml:mi> <mml:mi>S</mml:mi> <mml:mi>L</mml:mi> </mml:mstyle> </mml:mrow> </mml:math> institution. This allows for symbolic reasoning on communicating UML state machines.
Meta Mask Correction for Nuclei Segmentation in Histopathological Image

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings.
Event Detection via Context Understanding Based on Multi-task Learning

Event detection (ED) aims to identify events of interest described in the text. With the current explosive growth of text data on the internet, ED is increasingly practical and has gained many researchers’ attention. The existing works usually design ED as a token-level multi-class classification task. In this setting, given a sentence, ED models’ prediction for each token is relatively independent and thus cannot fully utilize sentence-level information and the association relations between multiple events in this sentence. To handle these situations, this paper proposes a multi-task learning based event detection model, which introduces an event type oriented text classification as an auxiliary task to improve the model’s understanding of sentence-level information. In addition, this model utilizes a Conditional Random Field (CRF) to explore the correlations between various event types and constrain the model’s output space. Experimental comparisons with state-of-the-art baselines on DuEE dataset demonstrate the model’s effectiveness.
Mélange fabric image retrieval based on soft similarity learning

Fabric image retrieval, a special case in Content Based Image Retrieval, has high potential application value in many fields. Compared with common image retrieval, fabric image retrieval has high requirements for results. To address the actual needs of the industry for Mélange fabric retrieval, we propose a novel framework for efficient and accurate fabric retrieval. We first introduce a quantified similarity definition, soft similarity, to measure the fine-grained pairwise similarity and design a CNN for fabric image representation. An objective function, which consists of three losses: soft similarity loss for preserving the similarity, cross-entropy loss for image representation, and quantization loss for controlling the quality of hash code, is used to drive the learning of the model. Experimental results demonstrate that the proposed method can not only achieve effective feature learning and hashing learning, but also effectively work on smaller datasets. Comparative experiments illustrate that the proposed method outperforms the compared methods.
Federated Learning Communication-Efficiency Framework via Corset Construction

Abstract Federated learning (FL) can learn a shared global model across multiple client devices without breaching privacy requirements. But an essential challenge is that devices in FL usually have limited network bandwidth, resulting in inefficient communication as an important bottleneck for FL implementation. Current studies try to overcome this shortcoming by compressing the number of model update bits uploaded by every client. But they did not explore the underlying reason why redundant parameters are generated. In this paper, we propose Corset-Based Federated Learning framework (CBFL) —a novel FL communication framework from the perspective of redundancy data. Instead of training full datasets on a regular network model, CBFL trains a much smaller evolution network model on extracted corset, which intrinsically reduces the overall transmission bits and obtains efficient computation while maintaining a desirable model accuracy. In CBFL, a novel Fedcorset construction algorithm at selected clients and a further distributed model evolution scheme to fit the constructed corset are included. The training model size is dynamically adapted to the corset, either removing a fraction of unimportant or adding important connections at each communication iteration. Experimental results show that CBFL transfers about 13% of communication bits and saves around 56% computing time while having only 2% destination in model accuracy.
Multilevel Security Framework based on An Onion Encryption in Public Cloud Network

Cloud computing has evolved as a well-known technology in the twenty-first century for delivering data service requirements at a low cost, with minimal effort, and with a high level of scalability. Because the Public Cloud has some passiveness, hackers can effortlessly get unauthorized admittance to data, launch fabrication attacks, and conduct a variety of other internal and external attacks. A multilevel level security framework based on Onion encryption was proposed in this paper. This framework generates session keys to maintain data confidentiality, authentication and mitigates interception, fabrication attacks, Onion encryption was used to ease any effort to transform or influence paths for gaining access, and a Blockchain-based decentralized framework was used to preserve data integrity.
Zero-Shot Ultrasound Nondestructive Testing Image Super-Resolution Based on Reflection Projection

In ultrasonic nondestructive testing, the low resolution of ultrasound images possibly lead to misinterpretation of defects in the image. At present, there is no special data set for ultrasonic nondestructive testing images in super-resolution, and the performance of numerous existing models depends on the learning of general data sets. In this paper, a zero-shot super-resolution network based on reflection projection units is proposed. Ultrasound images contain numerous image blocks with similar content, which are randomly extracted and down-sampled to form training samples. Then the image features are extracted through the reflection projection units in the network, and the information between the high and low-resolution image pairs is fully excavated. Finally, the feature channel is reduced by the attention mechanism, and the reconstructed image is output. Moreover, a combined loss function is used to optimize the network parameters. The compared experiments show that the proposed method performs better than the state of the art. Code is available at: https://github.com/BJUT-VIP/Zero-Shot-UNDTSR .
Modeling Network Traffic via Identifying Encrypted Packets to Detect Stepping-Stone Intrusion Under the Framework of Heterogonous Packet Encryption

As the capability to detect network intrusion has increased, so has attackers’ ability to avoid detection. Commonly, attackers use Secure Shell (SSH) to hide their identity. SSH securely connects two hosts together and encrypts their interactions. In stepping-stone attacks, one connection of SSH leads to another on a different host, and again until the attacker becomes untraceable from a victim host. In attempts to explore a method to detect stepping-stone attacks, we are pinned between two difficult issues. First, we desire to be able to detect a stepping-stone attack from any “stone” in the link from an attacker to a victim, which runs against our other issue; we must do this without being able to access any encrypted data. This means we only have the data “surrounding” the encrypted information. The previous work detecting a stepping-stone by viewing the lengths of encrypted packets assumed that the encryption algorithm used for both encryptions would remain the same. Our work is to determine whether we can use of the same effect, across algorithms, by focusing on the length sequences of incoming and outgoing packets. We expect that these sequences can be found when considering stream ciphers and comparing the length sequences coming in and out of a host. If this is the case, we will be able to develop a detection algorithm that can identify these sequences and determine if a host is being used as a stepping-stone to access another host.
Deep Convolutional Neural Network Approach for Classification of Poems

In this paper, we proposed an automatic convolutional neural network (CNN)-based method to classify poems written in Marathi, one of the popular Indian languages. Using this classification, a person unaware of Marathi Language can come to know what kind of emotion the given poem indicates. To the best of our knowledge, this is probably the first attempt of deep learning strategy in the field of Marathi poem classification. We conducted experiments with different models of CNN, considering different batch sizes, filter sizes, regularization methods like dropout, early stopping. Experimental results witness that our proposed approach outperforms both in effectiveness and efficiency. Our proposed CNN architecture for the classification of poems produces an impressive accuracy of 73%.
Detecting Micromanagement During Pair Programming

One of the biggest challenges hindering effective collaboration and therefore, pair programming, is micromanagement. In this paper, we investigated the capabilities of a pretrained transformer-based machine learning model to detect such micromanagement, following a text classification approach. We collected and labeled a dataset of 42,820 utterances from three course assignments involving 32 pair programmers in remote environments. This is the first successful automated detection of such micromanagement.
Response to UK IPO Open Consultation on AI and IP: Copyright and Patents

Submission on computer generated works in the Copyright Designs and Patents Act 1988 and the concept of inventorship in relation to AI innovations.
Cross-modal and Semantics-Augmented Asymmetric CycleGAN for Data-Imbalanced Anime Style Face Translation

Human face to anime face translation has attracted the attention of many researchers in recent years, and various works have achieved high-quality style transfer on conventional tasks. However, existing works often have fatal shortcomings when the target domain training data is heavily insufficient, which is named as imbalanced setting. Here the imbalanced (low-resource) task, generally means there is no sufficient data on the training dataset compared with the conventional task, e.g. the training data size is less than 100. To solve this problem, we propose a multi-modal translation model for a specific style. Based on the cyclic adversarial network and class activation map, we import semantic modality to enhance data information and attention modules, which help the model focus more on the discriminative areas between source and target domain. The experimental results show that our method has superiority in low-resource settings compared with similar existing work.