Reviewerly/exHarmony huggingface dataset:
Information Theory-based Evolution of Neural Networks for Side-channel Analysis

Profiled side-channel analysis (SCA) leverages leakage from cryptographic implementations to extract the secret key. When combined with advanced methods in neural networks (NNs), profiled SCA can successfully attack even those cryptocores assumed to be protected against SCA. Despite the rise in the number of studies devoted to NN-based SCA, a range of questions has remained unanswered, namely: how to choose an NN with an adequate configuration, how to tune the NN’s hyperparameters, when to stop the training, etc. Our proposed approach, “InfoNEAT,” tackles these issues in a natural way. InfoNEAT relies on the concept of neural structure search, enhanced by information-theoretic metrics to guide the evolution, halt it with novel stopping criteria, and improve time-complexity and memory footprint. The performance of InfoNEAT is evaluated by applying it to publicly available datasets composed of real side-channel measurements. In addition to the considerable advantages regarding the automated configuration of NNs, InfoNEAT demonstrates significant improvements over other approaches for effective key recovery in terms of the number of epochs (e.g.,x6 faster) and the number of attack traces compared to both MLPs and CNNs (e.g., up to 1000s fewer traces to break a device) as well as a reduction in the number of trainable parameters compared to MLPs (e.g., by the factor of up to 32). Furthermore, through experiments, it is demonstrated that InfoNEAT’s models are robust against noise and desynchronization in traces.
Medha: Microcoded Hardware Accelerator for computing on Encrypted Data

Homomorphic encryption enables computation on encrypted data, and hence it has a great potential in privacy-preserving outsourcing of computations to the cloud. Hardware acceleration of homomorphic encryption is crucial as software implementations are very slow. In this paper, we present design methodologies for building a programmable hardware accelerator for speeding up the cloud-side homomorphic evaluations on encrypted data.First, we propose a divide-and-conquer technique that enables homomorphic evaluations in the polynomial ring RQ,2N = ZQ[x]/(x2N + 1) to use a hardware accelerator that has been built for the smaller ring RQ,N = ZQ[x]/(xN + 1). The technique makes it possible to use a single hardware accelerator flexibly for supporting several homomorphic encryption parameter sets.Next, we present several architectural design methods that we use to realize the flexible and instruction-set accelerator architecture, which we call ‘Medha’. At every level of the implementation hierarchy, we explore possibilities for parallel processing. Starting from hardware-friendly parallel algorithms for the basic building blocks, we gradually build heavily parallel RNS polynomial arithmetic units. Next, many of these parallel units are interconnected elegantly so that their interconnections require the minimum number of nets, therefore making the overall architecture placement-friendly on the platform. As homomorphic encryption is computation- as well as data-centric, the speed of homomorphic evaluations depends greatly on the way the data variables are handled. For Medha, we take a memory-conservative design approach and get rid of any off-chip memory access during homomorphic evaluations.Finally, we implement Medha in a Xilinx Alveo U250 FPGA and measure timing performances of the microcoded homomorphic addition, multiplication, key-switching, and rescaling routines for the leveled fully homomorphic encryption scheme RNSHEAAN at 200 MHz clock frequency. For the large parameter sets (log Q,N) = (438, 214) and (546, 215), Medha achieves accelerations by up to 68× and 78× times respectively compared to a highly optimized software implementation Microsoft SEAL running at 2.3 GHz.
Exploration of the Impact of Cybersecurity Awareness on Small and Medium Enterprises (SMEs) in Wales Using Intelligent Software to Combat Cybercrime

Intelligent software packages have become fast-growing in popularity for large businesses in both developed and developing countries, due to their higher availability in detecting and preventing cybercrime. However, small and medium enterprises (SMEs) are showing prominent gaps in this adoption due to their level of awareness and knowledge towards cyber security and the security mindset. This is due to their priority of running their businesses over requiring using the right technology in protecting their data. This study explored how SMEs in Wales are handling cybercrime and managing their daily online activities the best they can, in keeping their data safe in tackling cyber threats. The sample collected consisted of 122 Welsh SME respondents in a collection of data through a survey questionnaire. The results and findings showed that there were large gaps in the awareness and knowledge of using intelligent software, in particular the uses of machine learning integration within their technology to track and combat complex cybercrime that perhaps would have been missed by standard cyber security software packages. The study’s findings showed that only 30% of the sampled SMEs understood the terminology of cyber security. The awareness of machine learning and its algorithms was also questioned in the implementation of their cyber security software packages. The study further highlighted that Welsh SMEs were unaware of what this software could do to protect their data. The findings in this paper also showed that various elements such as education and the size of SME made an impact on their choices for the right software packages being implemented, compared to elements such as age, gender, role and being a decision maker, having no impact on these choices. The study finally shares the investigations of various SME strategies to help understand the risks, and to be able to plan for future contingencies and preparation in keeping data safe and secure for the future.
EFFECT: Explainable framework for meta-learning in automatic classification algorithm selection

With the growing convergence of artificial intelligence and daily life scenarios, the application scenarios for intelligent decision methods are becoming increasingly complex. The development of various machine learning algorithms has benefited all disciplines of study, but choosing which algorithm is most suitable for a certain problem among a large number of algorithms is a challenge that every field must overcome. Another challenge at the practical application level is that machine learning algorithms currently trained with large amounts of data are primarily black-box and uninterpretable. This indicates that these methods pose potential risks and are difficult to rely on, thus hindering their application in sensitive fields such as finance and healthcare. The first challenge can be overcome by using meta-learning to combine data and prior knowledge to efficiently and automatically select the machine learning models. The second challenge remains to be addressed due to the lack of interpretability of traditional meta-learning techniques and deficiencies in transparency and fairness. Achieving the interpretability of meta-learning in autonomous algorithm selection for classification is crucial to balance the need for high accuracy and transparency of machine learning models in practical application scenarios. This paper proposes EFFECT, an interpretable meta-learning framework that can explain the recommendation results of meta-learning algorithm selection and provide a more complete and accurate explanation of the recommendation algorithm’s performance on specific datasets combined with business scenarios. Extensive experiments have demonstrated the validity and correctness of this framework.
Security and privacy in smart city: a secure e-voting system based on blockchain

In recent years, the internet of things (IoT) growth has brought about many technological changes, including the emergence of the notion of the smart city. The development of a smart city requires the integration of IoT devices and information and communication technologies to improve the quality of lives of citizens in many areas such as health, economy, business, agriculture, and transport. However, with this evolution, many cybersecurity risks and challenges have been raised, so it is necessary to develop these technologies in a protected way to avoid being compromised by attackers. Blockchain, being a new technology based on cryptographic principles, can play an important role in securing smart cities. In this survey, we discussed different applications of blockchain technology in smart cities and also studied how blockchain features (transparency, democracy, decentralization, and security) can help in the improvement of smart city services. This analysis will help us to implement an electronic voting model using a smart contract based on the Ethereum blockchain to highlight how blockchain technology can be implemented in smart cities to promote security.
FedCO: Communication-Efficient Federated Learning via Clustering Optimization

Federated Learning (FL) provides a promising solution for preserving privacy in learning shared models on distributed devices without sharing local data on a central server. However, most existing work shows that FL incurs high communication costs. To address this challenge, we propose a clustering-based federated solution, entitled Federated Learning via Clustering Optimization (FedCO), which optimizes model aggregation and reduces communication costs. In order to reduce the communication costs, we first divide the participating workers into groups based on the similarity of their model parameters and then select only one representative, the best performing worker, from each group to communicate with the central server. Then, in each successive round, we apply the Silhouette validation technique to check whether each representative is still made tight with its current cluster. If not, the representative is either moved into a more appropriate cluster or forms a cluster singleton. Finally, we use split optimization to update and improve the whole clustering solution. The updated clustering is used to select new cluster representatives. In that way, the proposed FedCO approach updates clusters by repeatedly evaluating and splitting clusters if doing so is necessary to improve the workers’ partitioning. The potential of the proposed method is demonstrated on publicly available datasets and LEAF datasets under the IID and Non-IID data distribution settings. The experimental results indicate that our proposed FedCO approach is superior to the state-of-the-art FL approaches, i.e., FedAvg, FedProx, and CMFL, in reducing communication costs and achieving a better accuracy in both the IID and Non-IID cases.
Chosen plaintext attack on JPEG image encryption with adaptive key and run consistency

A JPEG image encryption with the adaptive key and run consistency of MCUs is proposed. The chosen-plaintext attack (CPA) is given here on this encryption scheme. First, the adaptive key can be reproduced from the encrypted image, so that the plaintext images with the same adaptive key can be constructed. Second, the existence of run consistency of MCUs (RCM) between the original image and the encrypted image facilitates rapid estimation. In addition, the single swap for the runs of MCUs with RCM is designed for more accurate estimation. Detailed cryptanalytic results suggest that this encryption scheme can only be used to realize perceptual encryption but not to provide content protection for digital images. Furthermore, applications of the CPA to break other encryption schemes with RCM are presented.
A three-way clustering method based on improved density peaks algorithm and boundary detection graph

Density Peaks Clustering (DPC) is a classic density-based clustering algorithm that has been successfully applied in various areas. However, it assigns samples based on their nearest neighbors with higher density which may lead to an error propagation problem. Besides, it can not detect fringe and overlapping samples. To handle these defects, we improve the density measurement of DPC to make it more adaptive to different shapes and varying densities. Furthermore, we extend DPC to three-way clustering which means a sample in the positive region certainly belongs to the cluster, a sample in the boundary region belongs to the cluster partially and a sample in the negative region certainly does not belong to the cluster. In this paper, we propose a three-way clustering method called TW-RDPC. It mainly consists of three steps: (1) Identify cluster centers and assign other samples based on relative Cauchy kernel density to get initial clusters. (2) Detect potential boundary samples through boundary detection graph. (3) Determine whether potential boundary samples belong to multiple clusters based on the subordinate relationship to their k nearest neighbors. In order to validate TW-RDPC, we compare it to 7 algorithms on 10 synthetic datasets and 8 real-world datasets. Experimental results indicate that TW-RDPC is competitive with the compared 7 algorithms.
Feature Space of XRD Patterns Constructed by an Autoencoder

It would be a natural expectation that only major peaks, not all of them, would make an important contribution to the characterization of the XRD pattern. We developed a scheme that can identify which peaks are relavant to what extent by using auto-encoder technique to construct a feature space for the XRD peak patterns. Individual XRD patterns are projected onto a single point in the two-dimensional feature space constructed using the method. If the point is significantly shifted when a peak of interest is masked, then we can say the peak is relevant for the characterization represented by the point on the space. In this way, we can formulate the relevancy quantitatively. By using this scheme, we actually found such a peak with a significant peak intensity but low relevancy in the characterization of the structure. The peak is not easily explained by the physical viewpoint such as the higher-order peaks from the same plane index, being a heuristic finding by the power of machine-learning.
OG-SGG: Ontology-Guided Scene Graph Generation—A Case Study in Transfer Learning for Telepresence Robotics

Scene graph generation from images is a task of great interest to applications such as robotics, because graphs are the main way to represent knowledge about the world and regulate human-robot interactions in tasks such as Visual Question Answering (VQA). Unfortunately, its corresponding area of machine learning is still relatively in its infancy, and the solutions currently offered do not specialize well in concrete usage scenarios. Specifically, they do not take existing “expert” knowledge about the domain world into account; and that might indeed be necessary in order to provide the level of reliability demanded by the use case scenarios. In this paper, we propose an initial approximation to a framework called Ontology-Guided Scene Graph Generation (OG-SGG), that can improve the performance of an existing machine learning based scene graph generator using prior knowledge supplied in the form of an ontology; and we present results evaluated on a specific scenario founded in telepresence robotics.
Extension–contraction transformation network for pancreas segmentation in abdominal CT scans

Accurate and automatic pancreas segmentation from abdominal computed tomography (CT) scans is crucial for the diagnosis and prognosis of pancreatic diseases. However, the pancreas accounts for a relatively small portion of the scan and presents high anatomical variability and low contrast, making traditional automated segmentation methods fail to generate satisfactory results. In this paper, we propose an extension–contraction transformation network (ECTN) and deploy it into a cascaded two-stage segmentation framework for accurate pancreas segmenting. This model can enhance the perception of 3D context by distinguishing and exploiting the extension and contraction transformation of the pancreas between slices. It consists of an encoder, a segmentation decoder, and an extension–contraction (EC) decoder. The EC decoder is responsible for predicting the inter-slice extension and contraction transformation of the pancreas by feeding the extension and contraction information generated by the segmentation decoder; meanwhile, its output is combined with the output of the segmentation decoder to reconstruct and refine the segmentation results. Quantitative evaluation is performed on NIH Pancreas Segmentation (Pancreas-CT) dataset using 4-fold cross-validation. We obtained average Precision of 86.59±6.14% , Recall of 85.11±5.96%, Dice similarity coefficient (DSC) of 85.58±3.98%. and Jaccard Index (JI) of 74.99±5.86%. The performance of our method outperforms several baseline and state-of-the-art methods.
Data Hiding Scheme Based on Quad General Difference Expansion Cluster

For some periods, information technology has developed, but there are some issues with its data security. For this reason, exploring the data hiding method is relevant, which is an appropriate study in the information concealment paradigm. Several factors must be considered in its implementation, such as the capacity of the confidential data and the quality of the generated stego file. Nevertheless, it is difficult to solve those two problems simultaneously in actual application. We often need to choose either one of them, which is more suitable for a specific environment. In this research, those problems are approached by extending the Quad General Difference Expansion Cluster (QGDEC) method combined with fuzzy logic in the image environment. We use the cluster in the QGDEC, which aims to ensure that the pixel difference is not significant so that the quality of the stego can be maintained. The confidential data can be embedded in multiple layers based on several image characteristics, which can be processed using fuzzy logic. The result of the experiment denotes that the proposed method obtains about 20 dB higher than that of the previous ones regarding the Peak Signal to Noise Ratio (PSNR) for the same capacity of the secret. It depicts that the proposed method is more applicable than the previous ones by considering the specific message size and its respective characteristics.
Facial Expression Recognition Based on Dual-Channel Fusion with Edge Features

In the era of artificial intelligence, accomplishing emotion recognition in human–computer interaction is a key work. Expressions contain plentiful information about human emotion. We found that the canny edge detector can significantly help improve facial expression recognition performance. A canny edge detector based dual-channel network using the OI-network and EI-Net is proposed, which does not add an additional redundant network layer and training. We discussed the fusion parameters of α and β using ablation experiments. The method was verified in CK+, Fer2013, and RafDb datasets and achieved a good result.
Towards Ambient Assisted Living (AAL): Design of an IoT-based Elderly Activity Monitoring System

This paper presents a design and development of an IoT-based system to real-time track elders' physical activities using accelerometer sensor data.The objective behind conducting such research is to overcome the lack of ability to monitor physical activities.Especially with the development of the socio-economic sector, the number of elders who live in isolated areas such as elderly homes have increased rapidly.In such a case with declining cognitive abilities, the healthcare of these elderly personalities becomes vulnerable.This research project fulfilled the necessity of a system to capture the vital details about those people.The Internet of Things (IoT) and cloud-based applications have become a significant part of the Information and Technology sector.Realtime monitoring is a concept tightly coupled with IoT cloud cloud-native application for this application is an excellent example of that.Further, the requirement of a low-cost system was fulfilled by using hardware components such as NodeMCU and accelerometer sensors.The designed and developed system is composed of a cost-effective wrist-worn device capable of capturing hand movement on three different arises.Hence, the detected signals are transmitted to a master node to process and recognize the activity according to the detected signal.Another significant aspect of the project is using machine learning techniques to recognize the four different activities such as walking, sitting, sleeping, and standing.The use of supervised machine learning techniques is evaluated to overcome the barriers of real-time activity recognition.Further different supervised machine learning algorithms were used and evaluated, which were extracted from existing literature.The project was conducted while accomplishing the machine learning life cycle stages, and it has significantly benefitted from generating highly accurate final results for the overall system.Further different supervised machine learning algorithms were used and evaluated, which were extracted from existing literature.The supervised machine learning algorithm Decision Tree Classifier used for this study.Using the Decision Classifier Tree algorithm succeeded in gaining more than 80% of model accuracy.Since the research topic comes under a classification type-oriented problem, the testing process of the model has been done using the confusion matrix for the trained model.
Video game project management anti-patterns

Project Management anti-patterns are well-documented in the software-engineering literature, and studying them allows understanding their impacts on teams and projects. The video game development industry is known for its mismanagement practices, and therefore applying this knowledge would help improve game developers' productivity and well-being. In this paper, we map project management anti-patterns to anti-patterns reported by game developers in the gray literature. We read 440 postmortem problems, identified anti-pattern candidates, and related them with definitions from the software-engineering literature. We discovered that most anti-pattern candidates could be mapped to anti-patterns in the software-engineering literature, except for Feature Creep, Feature Cuts, Working on Multiple Projects, and Absent or Inadequate Tools. We discussed the impact of the unmapped candidates on the development process while also drawing a parallel between video games and traditional software development. Future works include validating the definitions of the candidates via survey with practitioners and also considering development anti-patterns.