Reviewerly/exHarmony huggingface dataset:
Using XAI for Deep Learning-Based Image Manipulation Detection with Shapley Additive Explanation

In the arena of image forensics, detecting manipulations in an image is extremely significant because of the use of images in different fields. Various detection techniques have been suggested in the literature that are based on digging out the features from images to unveil the traces left by manipulation operations. In this paper, a deep learning-based approach is proposed in which a residual network is used to learn deep, complex features from preprocessed images for classification into authentic and forged images. There is statistical symmetry in similar types of images and asymmetry in different types of images. The proposed scheme can highlight the statistical asymmetry between authentic and forged images. In the proposed scheme, firstly, an RGB image is analyzed for different JPEG compression levels. The obtained difference between the error levels is used to extract enhanced LBP code. Then, the scale- and direction-invariant LBP (SD-LBP) code is transformed into SD-LBP feature maps to feed to a deep residual network. Next, the concept of explainable artificial intelligence (XAI) is used to help provide explanations and interpret the output, thereby raising the credibility of the proposed approach. The unique feature selection approach employed is the kernel SHAP method, which is focused on the Shapley values. This technique is used to pinpoint the specific characteristics that are responsible for the aberrant behavior of the forged images dataset. Later, the deep learning-based model is trained and validated using these feature sets. A pre-activation version of ResNet-50 architecture is used that achieved an accuracy of 99.31%, 99.52%, 98.05%, and 99.10% on CASIA v1, CASIA v2, IMD 2020, and DVMM datasets, respectively. The capability of the pretrained residual network and rich textural features, which are scale- and direction-invariant, helps to expand the detection accuracy of the proposed approach. The results confirmed that the method either produced competitive results or outperformed existing methods.
SMS Spam Classification Using Machine Learning Techniques

Almost every person today owns a mobile phone with at least the most basic facilities like messaging and calling. Spam calls are already infamous for the constant ringing of cell phones for promotional or fraudulent pitching to innocent customers. With the reducing costs of bulk messaging services from network providers, a massive base of these spam calls has shifted to messaging. SMS, standing for a short messaging service, has become a dumping ground of unwanted product descriptions and scam offers. Here, in this scenario, classification becomes a necessity. Classification in this context occurs as separating spam messages from &#x2018;ham&#x2019; or legitimate messages. For this aforementioned purpose, we used natural language processing techniques and machine learning algorithms in this paper. We used four simple classification models on a dataset from UCI Machine Learning Repository. We compared the accuracies at the end, which pointed towards the most suitable model being SVM, with an accuracy of 98.797&#x0025;.
What Makes Fiat–Shamir zkSNARKs (Updatable SRS) Simulation Extractable?

We show that three popular universal zero-knowledge SNARKs (Plonk, Sonic, and Marlin) are updatable SRS simulation extractable NIZKs and signatures of knowledge (SoK) out-of-the-box avoiding any compilation overhead. Towards this we generalize results for the Fiat–Shamir (FS) transformation, which turns interactive protocols into signature schemes, non-interactive proof systems, or SoK in the random oracle model (ROM). The security of the transformation relies on rewinding to extract the secret key or the witness, even in the presence of signing queries for signatures and simulation queries for proof systems and SoK, respectively. We build on this line of work and analyze multi-round FS for arguments with a structured reference string (SRS). The combination of ROM and SRS, while redundant in theory, is the model of choice for the most efficient practical systems to date. We also consider the case where the SRS is updatable and define a strong simulation extractability notion that allows for simulated proofs with respect to an SRS to which the adversary can contribute updates. We define three properties (trapdoor-less zero-knowledge, rewinding-based knowledge soundness, and a unique response property) that are sufficient for argument systems based on multi-round FS to be also simulation extractable in this strong sense. We show that Plonk, Sonic, and Marlin satisfy these properties, and conjecture that many other argument systems such as Lunar, Basilisk, and transparent variants of Plonk fall within the reach of our main theorem.
Third Law of Thermodynamics and the Scaling of Quantum Computers

The third law of thermodynamics, also known as the Nernst unattainability principle, puts a fundamental bound on how close a system, whether classical or quantum, can be cooled to a temperature near to absolute zero. On the other hand, a fundamental assumption of quantum computing is to start each computation from a register of qubits initialized in a pure state, i.e., at zero temperature. These conflicting aspects, at the interface between quantum computing and thermodynamics, are often overlooked or, at best, addressed only at a single-qubit level. In this Letter, we argue how the existence of a small but finite effective temperature, which makes the initial state a mixed state, poses a real challenge to the fidelity constraints required for the scaling of quantum computers. Our theoretical results, carried out for a generic quantum circuit with $N$-qubit input states, are validated by test runs performed on a real quantum processor.
Short-term solar radiation forecast using total sky imager via transfer learning

Ground-based sky cameras, which capture hemispherical images, have been extensively used for localized monitoring of clouds. This paper proposes a short-term forecasting approach based on transfer learning using Total Sky-Imager (TSI) images of the Southern Great Plains (SGP) site obtained from the Atmospheric Radiation Measurement (ARM) dataset. An accurate estimation of solar irradiance using TSI is key for short-term solar energy generation forecasting and optimal energy consumption planning. We make use of deep neural network architectures such as AlexNet and ResNet-101 to extract the underlying deep convolution features from TSI images and then train using an ensemble learning approach to model and forecast solar radiation. We demonstrate the performance of the proposed approach by showcasing the best and worst cases. Thus, the transfer learning approach significantly reduces the time and resources required for modeling solar radiation. We outperform with reference to another state-of-art technique for solar modeling using TSI images at different forecast lead times.
Photovoltaic power prediction for solar micro-grid optimal control

In a solar micro-grid, a hybrid renewable energy system generates electricity for a building’s onsite use. The battery storage and the main power grid connection are used to facilitate the matching between the demand and production. To control energy flows optimally, an accurate day-ahead prediction of the photovoltaic (PV) panels output is required. However, this is a challenging task due to the fluctuating nature of solar radiation availability. The accuracy of the prediction is influenced by the modelling method and input parameters. In this study, the measured power and weather data is gathered from an experimental installation of PV panels to predict PV output for a 24-hours horizon in 15 min intervals. The multiple linear regression (MLR) and artificial neural network (ANN) methods are considered in the prediction modelling and compared using performance indicators. The micro-inverter technology is used to gather the individual PV panel output in addition to the overall system output. The results show that the modelling methods have different accuracy performances and the ANN model built with the individual PV output data results in the highest accuracy. Utilizing the micro-inverter technology leads to an advantage of having more accurate PV prediction for the control purpose.
babble: Learning Better Abstractions with E-Graphs and Anti-unification

Library learning compresses a given corpus of programs by extracting common structure from the corpus into reusable library functions. Prior work on library learning suffers from two limitations that prevent it from scaling to larger, more complex inputs. First, it explores too many candidate library functions that are not useful for compression. Second, it is not robust to syntactic variation in the input. We propose library learning modulo theory (LLMT), a new library learning algorithm that additionally takes as input an equational theory for a given problem domain. LLMT uses e-graphs and equality saturation to compactly represent the space of programs equivalent modulo the theory, and uses a novel e-graph anti-unification technique to find common patterns in the corpus more directly and efficiently. We implemented LLMT in a tool named babble. Our evaluation shows that babble achieves better compression orders of magnitude faster than the state of the art. We also provide a qualitative evaluation showing that babble learns reusable functions on inputs previously out of reach for library learning.
Self-Supervised Equivariant Learning for Oriented Keypoint Detection

Detecting robust keypoints from an image is an integral part of many computer vision problems, and the characteristic orientation and scale of keypoints play an important role for keypoint description and matching. Existing learning-based methods for keypoint detection rely on standard translation-equivariant CNNs but often fail to detect reliable keypoints against geometric variations. To learn to detect robust oriented keypoints, we introduce a self-supervised learning framework using rotation-equivariant CNNs. We propose a dense orientation alignment loss by an image pair generated by synthetic transformations for training a histogram-based orientation map. Our method outperforms the previous methods on an image matching benchmark and a camera pose estimation benchmark.
Predictive Hybrid Autoscaling for Containerized Applications

One of the main challenges in deploying container service is providing the scalability to satisfy the service performance and avoid resource wastage. To deal with this challenge, Kubernetes provides two kinds of scaling mode: vertical and horizontal. Several existing autoscaling methods make efforts to improve the default autoscalers in Kubernetes; however, most of these works only focus on one scaling mode at the same time, which results in some limitations. Only horizontal scaling may lead to low utilization of containers due to the fixed amount of resources for each instance, especially in the low-request period. In contrast, only vertical scaling may not ensure the quality of service (QoS) requirements in case of bursty workload due to reaching the upper limit. Besides, it is also necessary to provide burst identification for auto-scalers to guarantee service performance. This paper proposes a hybrid autoscaling method with burst awareness for containerized applications. This new approach considers a combination of both vertical and horizontal abilities to satisfy the QoS requirement while optimizing the utilization of containers. Our proposal uses a predictive method based on the machine learning technique to predict the future demand of the application and combines it with a burst identification module, which makes scaling decisions more effective. Experimental results show an enhancement in maintaining the response time below the QoS constraint whereas remaining high utilization of the deployment compared with existing baseline methods in single scaling mode.
CMD: Self-supervised 3D Action Representation Learning with Cross-Modal Mutual Distillation

In 3D action recognition, there exists rich complementary information between skeleton modalities. Nevertheless, how to model and utilize this information remains a challenging problem for self-supervised 3D action representation learning. In this work, we formulate the cross-modal interaction as a bidirectional knowledge distillation problem. Different from classic distillation solutions that transfer the knowledge of a fixed and pre-trained teacher to the student, in this work, the knowledge is continuously updated and bidirectionally distilled between modalities. To this end, we propose a new Cross-modal Mutual Distillation (CMD) framework with the following designs. On the one hand, the neighboring similarity distribution is introduced to model the knowledge learned in each modality, where the relational information is naturally suitable for the contrastive frameworks. On the other hand, asymmetrical configurations are used for teacher and student to stabilize the distillation process and to transfer high-confidence information between modalities. By derivation, we find that the cross-modal positive mining in previous works can be regarded as a degenerated version of our CMD. We perform extensive experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD II datasets. Our approach outperforms existing self-supervised methods and sets a series of new records. The code is available at: https://github.com/maoyunyao/CMD .
Incentivizing the Spatiotemporal Flexibility of Data Centers Toward Power System Coordination

As data centers emerge as the information backbone of an increasingly digital world, the associated service demand has been rising rapidly. The operational nature of the data center brings great flexibility potential for energy systems. However, there remains an open question as to how to capture the flexibility potentials of geographically dispersed data centers and incentivize their flexibility provision for system operation. In this paper, we propose electricity–heat coordinated operation to capture the flexibility potential of data centers, i.e., temporal, spatial and integrated energy flexibility. To better depict the interaction between the system operator and the data centers, the centralized coordinated operation model is decomposed into a bilevel model, and an incentivized profit-sharing mechanism is designed to suitably motivate the flexibility provision of data centers. Case studies based on real-world datasets show that incorporating data centers at the system level can effectively improve operation efficiency and facilitate renewable energy integration. Hopefully, our work can provide novel insights into the coordination of data networks and power grids as well as the flexibility potential activation of data centers.
Comparative Performance of LSTM and ARIMA for the Short-Term Prediction of Bitcoin Prices

This research assesses the prediction of Bitcoin prices using the autoregressive integrated moving average (ARIMA) and long-short-term memory (LSTM) models. We forecast the price of Bitcoin for the following day using the static forecast method, with and without re-estimating the forecast model at each step. We take two different training and test samples into consideration for the cross-validation of forecast findings. In the first training sample, ARIMA outperforms LSTM, but in the second training sample, LSTM exceeds ARIMA. Additionally, in the two test-sample forecast periods, LSTM with model re-estimation at each step surpasses ARIMA. Comparing LSTM to ARIMA, the forecasts were much closer to the actual historical prices. As opposed to ARIMA, which could only track the trend of Bitcoin prices, the LSTM model was able to predict both the direction and the value during the specified time period. This research exhibits LSTM's persistent capacity for fluctuating Bitcoin price prediction despite the sophistication of ARIMA.
The Secure E-Wallet Powered by Blockchain and Distributed Ledger Technology

Last decade had been worst for humanity as it faced Covid-19. The pandemic invited many things unknown to humans such as lockdown, compulsory mask and no contact with other people or things. The ‘No Contact’ initiated much awaited progress in payment from being entity exchange to being digital. The digitization of payment became biggest transformation; every common citizen realized the use of digital payment. The evolution of cash payment to internet banking and internet banking to e-wallet has made transaction easier for all the services without exhibiting physical form. Payment Gateways, Digital Wallet, Internet Banking use has risen as being fast and instant. Though the recent payments are secure on theft front with the use of digital money by many users at many times online payment system might face problem on digital money transfer with issues such as wrong payment, link failure or single point failure. There might be more problems such as insider problem and transaction being transparent. For such situations more secure and private path towards security is needed as insufficiency will give rise to risk mitigation. The paper recommends a payment system which will be based on privacy and permission laid by blocks to blocks for use in financial sector. The architecture proposed integrates the digital wallet with different banks to give foundation of Blockchain for secure transactions. The peer to peer network will share transactions as well share load to minimize load on central banking system keep the load distributed and secure overall data centres.
Twin-Field Quantum Key Distribution without Phase Locking

Twin-field quantum key distribution (TF-QKD) has emerged as a promising solution for practical quantum communication over long-haul fiber. However, previous demonstrations on TF-QKD require the phase locking technique to coherently control the twin light fields, inevitably complicating the system with extra fiber channels and peripheral hardware. Here, we propose and demonstrate an approach to recover the single-photon interference pattern and realize TF-QKD without phase locking. Our approach separates the communication time into reference frames and quantum frames, where the reference frames serve as a flexible scheme for establishing the global phase reference. To do so, we develop a tailored algorithm based on fast Fourier transform to efficiently reconcile the phase reference via data postprocessing. We demonstrate no-phase-locking TF-QKD from short to long distances over standard optical fibers. At 50-km standard fiber, we produce a high secret key rate (SKR) of 1.27 Mbit/s, while at 504-km standard fiber, we obtain the repeaterlike key rate scaling with a SKR of 34 times higher than the repeaterless secret key capacity. Our work provides a scalable and practical solution to TF-QKD, thus representing an important step towards its wide applications.
Joined up Thinking: Aggregating archaeological datasets at an international scale

The archaeological research community was an early adopter of digital tools for data acquisition, organisation, analysis, and presentation of research results of individual projects. However, the provision of e-infrastructure and services for data sharing, discovery, access, and reuse has lagged behind. The ARIADNE Research Infrastructure has sought to address this situation. Developed with European funding, ARIADNE has created an e-infrastructure that enables data providers to register and provide access to their digital resources through the ARIADNE data portal, facilitating discovery, access, and research. ARIADNE has aggregated resources from over 45 data providers, spanning over 40 countries and 4 continents. The portal now provides online access to over 3.9 million research resources. It is based upon Linked Open Data technologies and is underpinned by a flexible and extensible architecture, enabling multiple combinations and presentations of the same underpinning data. We have been keen not to 'make a great heap' of all the data and, learning from previous data aggregation projects, we have defined a subset of the CIDOC CRM to be used as a strict ontology and paid close attention to data standards and controlled vocabularies to achieve a high degree of interoperability. This article discusses some of the challenges of large-scale data integration and describes the approaches adopted to ensure that the ARIADNE Knowledge Base is an effective tool for archaeological heritage management and research at a national and international level.
Strategic Role of Trust in Digital Communication: Critical Insights into Building Organizational Sustainability

The recent trends of managing the way on how to achieve the communication with safe cyberspace paid a particular attention among the scholarly worldwide. The strategic initiative on having an active arrangement of communication pathway is required for further scholarly elaboration in assisting the scenario on leading to safe community in the cyber space. One of the challenges as an example is like the issue of trust in information or questionable point referring to lack of detail and clarity. This is of course being a dubious circumstance which needs to expand the situation on addressing the critical point of trust-based information quality. In order to respond such undeniable situation, this paper aims to critically explore the insightful value of trust in information as the quality assurance in enabling the individual capacity and accountability in achieving the communication management for safe cyberspace. The critical review of current literature was made through peer-reviewed articles from journals, books, proceedings and chapters related to the topic of communication management for safe cyberspace. The finding revealed that strategic approach of empowering communication management for safe cyberspace is actualized through advancing the extent of trust-based information quality. This paper is supposed to contribute in enhancing the additional information of communication management for safe cyberspace through addressing the information quality with the trust basis.
Robust Adaptive Median Binary Pattern for Noisy Texture Classification and Retrieval

Texture is an important characteristic for different computer vision tasks and applications. Local binary pattern (LBP) is considered one of the most efficient texture descriptors yet. However, LBP has some notable limitations, in particular its sensitivity to noise. In this paper, we address these criteria by introducing a novel texture descriptor, robust adaptive median binary pattern (RAMBP). RAMBP is based on a process involving classification of noisy pixels, adaptive analysis window, scale analysis, and a comparison of image medians. The proposed method handles images with highly noisy textures and increases the discriminative properties by capturing microstructure and macrostructure texture information. The method was evaluated on popular texture datasets for classification and retrieval tasks and under different high noise conditions. Without any training or prior knowledge of the noise type, RAMBP achieved the best classification compared to state-of-the-art techniques. It scored more than 90% under 50% impulse noise densities, more than 95% under Gaussian noised textures with a standard deviation σ = 5 , more than 99% under Gaussian blurred textures with a standard deviation σ = 1.25 , and more than 90% for mixed noise. The proposed method yielded competitive results and proved to be one of the best descriptors in noise-free texture classification. Furthermore, RAMBP showed high performance for the problem of noisy texture retrieval providing high scores of recall and precision measures for textures with high noise levels. Finally, compared with the state-of-the-art methods, RAMBP achieves a good running time with low feature dimensionality.