Reviewerly/exHarmony huggingface dataset:
Estimation of subtle facial emotion changes from expressionless images

Subtle changes in emotional expressions occur more frequently compared to rich ones, which makes the evaluation of the emotional response of an individual challenging. In this study, we focus on the near-expressionless facial images, indicated with low arousal and valence value. We investigated the facial landmarks which are crucial in estimating subtle emotion through a novel feature selection method named Random Combination Selection with Iterative Step (RACSIS)1 . By combining appearance and geometrical features, while reducing the feature points up to 93.8%, the Mean Absolute Error (MAE) for Arousal = [-4 8], Valence = [-7 6], was reduced to 54.95% and 46.39% for the full emotional spectrum and the subtle emotion, respectively. We then tested the performance of the RACSIS to estimate the emotional response of participants undertaking audio-visual activities. We conclude that: 1. Appearance features played a greater role in reducing the MAE. 2. Feature selection (FS) by RACSIS achieved lower MAE values compared to correlation.
A Simulation Model of a Blockchain-Based Decentralized Patient Information Exchange System for Parkinson’s Disease Patients

Parkinson’s disease (PD) is a progressive disorder of slow progress of the nervous system produced by the absence of levels of dopamine, which can incite unrestrained instinctive movements of the body and psychological affections. For the development of a practical, low-cost, and general diagnosis system of the symptoms to support PD patients, the implementation of an IoT health monitoring system that uses smartphones for data collection is necessary. However, data can be processed in Cloud Computing (CC) for analysis and comparison, but to reduce the latency of retrieving data from sensitive applications, Fog Computing (FC) plays a vital role. Nevertheless, these technologies IoT, CC, and FC have several limitations and are vulnerable to security threats. Blockchain technology enhances IoT challenges in a network in terms of security and availability. This chapter implemented a Decentralized IoT Fog-based Solutions and Blockchain using Ethereum Smart Contract for the authentication system. The smart contract is programmed using Solidity to allow Things to communicate with each other automatically without intermediaries and to store data in a public/private blockchain. The validation of the system was simulated them using the simulations tools Cisco Packet Tracer, iFogSim, and Remix Ethereum. The obtained results proved the feasibility of the proposed system.
Blockchain for Cyber-Physical Systems

Cyber-physical systems (CPSs) are the intelligent systems that offer an interaction among computational, software, and networking resources in a continuous and dynamic fashion. Future systems are likely to be created and developed using CPSs, which have been recognized as a significant area of research. The electric power grid, energy systems, body area networks, modern vehicles, smart homes, cooperative robotics, and smart transportation are the examples for CPS. The security aspects of CPSs can be enhanced with blockchain (BC) technology. For instance, with the combination of CPSs and blockchain, a peer-to-peer energy market is made possible where machines may automatically buy and sell energy based on parameters specified by the user. In this chapter, we summarize recent developments in the creation and applications of CPS, the state-of-the-art and pertinent concepts, numerous CPS applications that have employed blockchain, relevant solutions, and open challenging issues.
Linear Layer Architecture Based on Cyclic Shift and XOR

One of the nodes of a block symmetric encryption algorithm is represented by a linear layer, the purpose of which is to distribute the mutual influence of bits within the processed data block. Several methods exist for constructing a linear layer, the most common of which are matrix multiplication operations and the permutation of bits. Both approaches have high computational complexity and are not equally effective for both hardware and software implementations. This paper presents an approach for constructing linear functions for block symmetric encryption algorithms utilizing cyclic shift, and bitwise addition operations are formulated. We provide a preliminary assessment of certain properties of such functions, including the branch number. This linear operation can accommodate binary words of any length, allowing for the design of an optimal linear layer for software or hardware architectures with any word size. Furthermore, the developed architecture allows for balancing the laboriousness of linear operations and related branch numbers. The proposed novel linear layer architecture facilitates the creation of fast lightweight encryption algorithms as well as robust classical algorithms with a high level of cryptographic strength. For efficient implementation on software and hardware platforms, no additional optimizations are required, as the proposed linear layer allows for achieving high performance in both cases.
Sunsetting martineve.com

This is a quick note to say that, in the near future, I will be abandoning the martineve.com domain name. For quite some time now, the primary address for this blog and site has been https://eve.gd. I know that [cool URLs don't change](https://www.w3.org/Provider/Style/URI). But I guess I am not totally cool. Perhaps most importantly: the email address martin@martineve.com will very soon not belong to me. Please use martin@eve.gd or martin.eve@bbk.ac.uk to contact me. So long trusty domain,...
Enhanced key-point detection for plenoptic imaging

Standard imaging techniques do not get as much information from a scene as light-field imaging. Light-field (LF) cameras can measure the light intensity reflected by an object and, most importantly, the direction of its light rays. This information can be used in different applications, such as depth estimation, in-plane focusing, creating full-focused images, etc. However, standard key-point detectors often employed in computer vision applications cannot be applied directly to plenoptic images due to the nature of raw LF images. This work presents an approach for key-point detection dedicated to plenoptic images. Our method allows using of conventional key-point detector methods. It forces the detection of this key-point in a set of micro-images of the raw LF image. Obtaining this important number of key-points is essential for applications that require finding additional correspondences in the raw space, such as disparity estimation, indirect visual odometry techniques, and others. The approach is set to the test by modifying the Harris key-point detector.
Symmetric Color Image Encryption Using a Novel Cross–Plane Joint Scrambling–Diffusion Method

Motivated by the pursuit of both encryption efficiency and security, this paper presents a symmetric color image encryption algorithm using a novel cross–plane joint scrambling–diffusion method. To provide high–strength security, the color planes are firstly spliced horizontally and then vertically to be adequately encrypted by the joint scrambling–diffusion method. Therefore, minor changes in any pixel can cross every color plane that significantly influences the final cipher image. To achieve fast encryption speed, all employed chaotic systems have simple structures but robust properties to rapidly generate high–quality pseudorandom sequences. Additionally, the joint scrambling–diffusion method is vectorized to handle pixels in parallel for satisfactory execution efficiency. Security tests demonstrate the outstanding security performance of the proposed algorithm, with correlation coefficients and entropies of cipher images being close to 0 and greater than 7.99, respectively, and results of NPCR and UACI tests being similar to ideal cipher images, which can resist statistical, differential, exhaustive, and even chosen ciphertext/plaintext attacks. Moreover, because of the O(4W + 4H) time complexity of the novel scrambling–diffusion method, even a color image with a size of 1024 × 1024 only costs 0.26 s to be encrypted. Hence, the proposed algorithm can satisfy the efficiency and security requirements of color image encryption.
Borderless Optimization Method Genetic Algorithm

Genetic algorithms, a stochastic research method, are Darwin’s “survival of the best.” Development of biological systems based on the principle of “survival of the fittest” emerged with the adaptation of the process to the computer environment. In genetic algorithms, processes executed are stored in computer memory similar to natural populations performed on units. Today, there are many linear and nonlinear solutions for optimization problems. The method has been developed. Designing optimization problems with these method variables is assumed to be continuous. But this acceptance does not always valid. In some problems, there are too many design variables and constraints for traditional optimization in solving such problems. The use of this method sometimes gives erroneous results or the solution time is too long. Since genetic algorithms are an intuitive method, a given may not find the optimum result for the problem. However, it optimizes the problems that cannot be solved by known methods or the solution time of the problem is too long. Initial nonlinear optimization genetic algorithms applied to the problems of traveling salesman, workshop scheduling, and syllabus preparation, such as complex optimization problems, have been successfully implemented.
A Robust Sharding-Enabled Blockchain with Efficient Hashgraph Mechanism for MANETs

Blockchain establishes security and trust in mobile ad hoc networks (MANETs). Due to the decentralized and opportunistic communication characteristics of MANETs, hashgraph consensus is more applicable to the MANET-based blockchain. Sharding scales the consensus further through disjoint nodes in multiple shards simultaneously updating ledgers. However, the dynamic addition and deletion of nodes in a shard pose challenges regarding robustness and efficiency. Particularly, the shard is vulnerable to Sybil attacks and targeted attacks, and dishonest gossip reduces the efficiency of hashgraph consensus. Therefore, we proposed a behavior-based sharding hashgraph scheme. First, dishonest behaviors of nodes are recorded in a decentralized blacklist. Gossip information is sent to a reliable neighbor, and gossip information from another reliable neighbor is received. Second, a tree-assisted inter-sharding consensus is proposed to prevent Sybil attacks. The combination of shard recovery and reconfiguration based on node state is devised to prevent targeted attacks. Finally, we conducted the performance evaluation including security analysis and experimental evaluation to reveal the security and efficiency of the proposed scheme.
Using software metrics for predicting vulnerable classes in java and python based systems

ABSTRACTABSTRACT[Context:] Failure to predict vulnerability in the earlier stage of development can cause vulnerable code being written and deployed in the final software product. Vulnerability prediction using software metrics as features can support the discovery process by localizing vulnerable code. Existing studies have successfully employed metrics for vulnerability prediction for some platforms (C/C++ or Java projects). We propose that a comparative evaluation of how these metrics perform in projects of different languages can help the developers in deciding whether metrics-based prediction approach can be effective in their own project's context. [Objective:] The purpose of this research is to analyze/compare the performance of software metrics in vulnerability-prediction for different programming language contexts (Java vs. Python). [Method:] We conducted experiments on vulnerabilities reported for Apache Tomcat (releases 6 and 7), Apache CXF, and two Python projects (Django and Keystone). We applied machine learning for predicting a particular type of code component (Java and Python classes) as vulnerable/non-vulnerable. [Results:] We found that metrics-based prediction can predict Java vulnerable classes with higher recall and precision than the Python vulnerable classes. [Conclusion:] This study at class-level will help developers to predict vulnerabilities at the class-level and assist in secure coding in object-oriented programming.KEYWORDS: Machine learningsoftware metricssoftware securityvulnerability prediction Disclosure statementNo potential conflict of interest was reported by the author(s).
Management of Optimal Resource Allocation in the Cloud

The management of resource allocation in the cloud is a critical issue that has received significant attention in recent years due to the increasing demand for cloud-based services.The efficient allocation of resources is crucial to meet the requirements of different applications and to optimize the utilization of available resources.This research paper explores the concept of optimal management of resource allocation in the cloud.The paper analyzes different approaches to resource allocation and discusses the advantages and limitations of each approach.The research also examines various factors that affect resource allocation in the cloud, including workload, resource availability, and resource utilization.The paper proposes a novel approach to resource allocation that is based on machine learning algorithms.The approach uses historical data to predict resource utilization and allocate resources accordingly.The research also investigates the impact of different factors on the performance of the proposed approach and compares it with other existing approaches.The findings of this research paper provide insights into the optimal management of resource allocation in the cloud.The proposed approach is shown to be effective in improving resource utilization and meeting the requirements of different applications.The research also highlights the importance of considering different factors that affect resource allocation in the cloud to achieve optimal performance.
SeniorSentry: Correlation and Mutual Information-Based Contextual Anomaly Detection for Aging in Place

With the ever-growing reliance on IoT-enabled sensors to age in place, a need arises to protect them from malicious actors and detect malfunctions. In an IoT smart home, it is reasonable to hypothesize that sensors near one another can exhibit linear or nonlinear correlations. If substantiated, this property can be beneficial for constructing relationship trends between the sensors and, consequently, detecting attacks or other anomalies by measuring the deviation of their readings against these trends. In this work, we confirm the presence of correlations between co-located sensors by statistically analyzing two public smart-home datasets and a dataset we collected from our experimental setup. Additionally, we leverage the sliding window approach and supervised machine learning to develop a contextual-anomaly-detection model. This model reaches a true positive rate of 89.47% and a false positive rate of 0%. Our work not only substantiates the correlations but also introduces a novel anomaly-detection technique to enhance security in IoT smart homes.
Hybrid Threshold Denoising Framework Using Singular Value Decomposition for Side-Channel Analysis Preprocessing

The traces used in side-channel analysis are essential to breaking the key of encryption and the signal quality greatly affects the correct rate of key guessing. Therefore, the preprocessing of side-channel traces plays an important role in side-channel analysis. The process of side-channel leakage signal acquisition is usually affected by internal circuit noise, external environmental noise, and other factors, so the collected signal is often mixed with strong noise. In order to extract the feature information of side-channel signals from very low signal-to-noise ratio traces, a hybrid threshold denoising framework using singular value decomposition is proposed for side-channel analysis preprocessing. This framework is based on singular value decomposition and introduces low-rank matrix approximation theory to improve the rank selection methods of singular value decomposition. This paper combines the hard threshold method of truncated singular value decomposition with the soft threshold method of singular value shrinkage damping and proposes a hybrid threshold denoising framework using singular value decomposition for the data preprocessing step of side-channel analysis as a general preprocessing method for non-profiled side-channel analysis. The data used in the experimental evaluation are from the raw traces of the public database of DPA contest V2 and AES_HD. The success rate curve of non-profiled side-channel analysis further confirms the effectiveness of the proposed framework. Moreover, the signal-to-noise ratio of traces is significantly improved after preprocessing, and the correlation with the correct key is also significantly enhanced. Experimental results on DPA v2 and AES_HD show that the proposed noise reduction framework can be effectively applied to the side-channel analysis preprocessing step, and can successfully improve the signal-to-noise ratio of the traces and the attack efficiency.
FRDet: Few‐shot object detection via feature reconstruction

Abstract State‐of‐the‐art object detection models rely on large‐scale datasets for training to achieve good precision. Without sufficient samples, the model can suffer from severe overfitting. Current explorations in few‐shot object detection are mainly divided into meta‐learning‐based methods and fine‐tuning‐based methods. However, existing models do not focus on how feature maps should be processed to present more accurate regions of interest (RoIs), leading to many non‐supporting RoIs. These non‐supporting RoIs can increase the burden of subsequent classification and even lead to misclassification. Additionally, catastrophic forgetting is inevitable in both few‐shot object detection models. Many models classify directly in low‐dimensional spaces due to insufficient resources, but this transformation of the data space can confuse some categories and lead to misclassification. To address these problems, the Feature Reconstruction Detector (FRDet) is proposed, a simple yet effective fine‐tune‐based approach for few‐shot object detection. FRDet includes a region proposal network (RPN) based on channel attention and space attention called Multi‐Attention RPN (MARPN) and a head based on feature reconstruction called Feature Reconstruction Head (FRHead). MARPN utilizes channel attention to suppress non‐supporting classes and spatial attention to enhance support classes based on Attention RPN, resulting in fewer but more accurate RoIs. Meanwhile, FRHead utilizes support features to reconstruct query RoI features through a closed‐form solution, allowing for a comprehensive and fine‐grained comparison. The model was validated on the PASCAL VOC, MS COCO, FSOD, and CUB200 datasets and achieved better results.
Morpheme-Based Neural Machine Translation Models for Low-Resource Fusion Languages

Neural approaches, which are currently state-of-the-art in many areas, have contributed significantly to the exciting advancements in machine translation. However, Neural Machine Translation (NMT) requires a substantial quantity and good quality parallel training data to train the best model. A large amount of training data, in turn, increases the underlying vocabulary exponentially. Therefore, several proposed methods have been devised for relatively limited vocabulary due to constraints of computing resources such as system memory. Encoding words as sequences of subword units for so-called open-vocabulary translation is an effective strategy for solving this problem. However, the conventional methods for splitting words into subwords focus on statistics-based approaches that mainly conform to agglutinative languages. In these languages, the morphemes have relatively clean boundaries. These methods still need to be thoroughly investigated for their applicability to fusion languages, which is the main focus of this article. Phonological and orthographic processes alter the borders of constituent morphemes of a word in fusion languages. Therefore, it makes it difficult to distinguish the actual morphemes that carry syntactic or semantic information from the word’s surface form, the form of the word as it appears in the text. We, thus, resorted to a word segmentation method that segments words by restoring the altered morphemes. We also compared conventional and morpheme-based NMT subword models. We could prove that morpheme-based models outperform conventional subword models on a benchmark dataset.
Dependent Optics

A wide variety of bidirectional data accessors, ranging from mixed optics to functor lenses, can be formalized within a unique framework-dependent optics. Starting from two indexed categories, which encode what maps are allowed in the forward and backward directions, we define the category of dependent optics and establish under what assumptions it has coproducts. Different choices of indexed categories correspond to different families of optics: we discuss dependent lenses and prisms, as well as closed dependent optics. We introduce the notion of Tambara representation and use it to classify contravariant functors from the category of optics, thus generalizing the profunctor encoding of optics to the dependent case.
Data Warehouse, Detection and Transfer of Anomalies in Retail Data

In this article, we offer some suggestions for anomaly detection on the data received from the source to the Data warehouse. As a result, it is aimed to prevent the entry of dirty and noisy data into the data warehouse. We think that knowing that there is clean and healthy data in the data warehouse will be resistant to anomalies in the processed data used for data science. In order to reach our goal, studies were carried out on the data in the retail sector. We aimed to determine our theoretical thoughts from some topics such as user erroneous login data in the retail and energy industry, abnormal sales over employees during the campaign period, product stock abnormality, and incorrect pricing. When we examined many studies, we saw that they made anomaly detection after estimation. Before taking the data from the source to the data warehouse, we thought that anomaly detection would be more efficient and healthier. Analysis and results were evaluated on the data obtained in the wiseboard retail project of Gtech company.
Lite‐weight semantic segmentation with AG self‐attention

Abstract Due to the large computational and GPUs memory cost of semantic segmentation, some works focus on designing a lite weight model to achieve a good trade‐off between computational cost and accuracy. A common method is to combined CNN and vision transformer. However, these methods ignore the contextual information of multi receptive fields. And existing methods often fail to inject detailed information losses in the downsampling of multi‐scale feature. To fix these issues, we propose AG Self‐Attention, which is Enhanced Atrous Self‐Attention (EASA), and Gate Attention. AG Self‐Attention adds the contextual information of multi receptive fields into the global semantic feature. Specifically, the Enhanced Atrous Self‐Attention uses weight shared atrous convolution with different atrous rates to get the contextual information under the specific different receptive fields. Gate Attention introduces gating mechanism to inject detailed information into the global semantic feature and filter detailed information by producing “fusion” gate and “update” gate. In order to prove our insight. We conduct numerous experiments in common semantic segmentation datasets, consisting of ADE20 K, COCO‐stuff, PASCAL Context, Cityscapes, to show that our method achieves state‐of‐the‐art performance and achieve a good trade‐off between computational cost and accuracy.