Reviewerly/exHarmony huggingface dataset:
Evaluation of Household Waste Image Identification Using Convolutional Neural Network

Waste identification has been widely carried out using machine learning with various methods. One method that is often used is the Convolutional Neural Network (CNN). Waste identification has a function as machine learning that helps and accelerates the waste management process, especially the waste sorting process. This research was conducted by collecting data by downloading a dataset from Kaggle. The data were then be labeled in a folder according to their type. The dataset folder was entered into a Python machine learning system equipped with the TensorFlow module. The data was preprocessed so that it became simplified and the machine could read the data. The data was trained to be able to distinguish between the types of waste. After that, the CNN method was implemented in machine learning so that the machine could classify the waste image. The results of the CNN implementation show that the system can classify images with the greatest accuracy of 99.83% with a validation accuracy of 76.1% and the smallest loss percentage of about 0.04% at epoch 18. The results show that there was overfitting in the dataset classification process caused by the average accuracy value of the training data and validation data being too large, around 20%.
The Active Use of Reading the Textbook is the Crucial tool of Learning for College Students

The textbook is a crucial part of learning that works like momentum for college students. It can enhance meaningful learning through understanding verbal and nonverbal circumstances. Reading does not provide only a sense of knowing the vocabulary and pronunciation. It introduces the real-life consequences, convinces the reader to read thoroughly, listen, think, interact with his mind, allows reliable acceptance by heart, and communicates and shares the peers' thoughts and comments. It discovers a new perspective and ideas beyond the textbook. Almost all teachers recommend the handout and introduce the different website resources for reading and writing to accomplish their assigned tasks at the college level. The individuals gain a lot of knowledge and awareness by working on it. They have a massive collection of required data but cannot read it. They spend much time collecting the updated and required material instead of reading the text. It is like chaos because they do not confidently critically analyze the text and cannot share their thoughts and perspectives through reading and writing. The textbook works like momentum. It increases the speed of learning. The individual focuses on thorough reading, negotiates the inductive and deductive ideas, follows the explicit and implicit instructions, and critically analyzes the text. It discovers the new perspectives that move him beyond the textbook and helps converge his thoughts into academic writing. Yes, it is all concerned with the active use of the book.
Maximum Euclidean Norm Loss-Based Unsupervised Domain Adaptation for Semantic Segmentation

Abstract — Although deep convolutional neural networks have made great progress in semantic segmentation tasks, the training of the network requires a large amount of accurate annotated data, and it is difficult to obtain better performance by training the network with easily obtained synthetic images. Furthermore, an imbalance in target domain categories will have an impact on network performance. To address the first and second issues, we propose an unsupervised domain adaptation method based on the Euclidean norm and an image-level fine-grained category balancing method, respectively. We propose the use of the Euclidean norm to calculate the uncertainty value of the predictions for all class probability values in the image by analyzing the relationship between the uncertainty of the predictions and the distance between categories. At the same time, to avoid the predictions from being dominated by high-confidence categories, the maximum Euclidean norm loss is proposed based on the Euclidean norm, which improves the training gradient of low-probability categories during the training process and increases the network’s generalization ability of low-probability categories. Furthermore, we propose an image-level fine-grained class balance method that computes the class balance factor based on predictions. The proposed method’s performance was validated using two synthetics to real domain adaptation tasks. The VGG-16 and ResNet-101 backbone networks grew by 5.7% and 2.9%, respectively, when compared to the related SOTA method. Experiments showed that the proposed method outperforms other SOTA methods.
Laplacian Nonlinear Logistic Stepwise and Gravitational Deep Neural Classification for Facial Expression Recognition

Facial expression recognition is the paramount segment of non-verbal communication and one frequent procedures of human communication. Facial expression recognition, as one of the foremost evolution regulations of human-computer interaction, enhances the fluency and accuracy of relationship. Over the past few years, deep learning using feature selection of face image data based on machine and deep learning has become increasingly in demand, it has achieved progress, however due to different facial expression and attaining accuracy still remains major issue to be focused. Their accessibility shoots from their potentiality to select righteous features from face image data, via feature selection to achieve high performance at minimum time. In this work a method called, Laplacian Non-linear Logistic Regression and Gravitational Deep Learning (LNLR-GDL) for facial expression recognition is proposed. The proposed method is split into three sections, namely, preprocessing, feature selection and classification. In the first section, preprocessing is conducted with the face recognition dataset where noise reduced preprocessed face images are obtained by employing Unsharp Masking Laplacian Non-linear Filter model. Second with the preprocessed face images, computationally efficient relevant features are selected by means of Logistic Stepwise Regression-based feature selection model. Finally, Gravitational Deep Neural Classification model is applied to the selected features for robust recognition of facial expressions. The proposed LNLR-GDL method along with two state-of-the-art methods is implemented on corrective re-annotation of images for facial expression recognition. The databases comprise of consist of seven emotions viz., anger, contempt, disgust, fear, happiness, neutrality, sadness and surprise. The proposed method is compared with existing methods using three evaluation metrics namely, facial expression recognition accuracy, facial expression recognition time and PSNR. The obtained results demonstrate that the proposed LNLR-GDL method outperforms the state-of-the-art methods.
A Fully Automatic Framework for Evaluating Cosmetic Results of Breast Conserving Therapy

The breast cosmetic outcome after undergoing breast cancer therapy is an essential aspect of evaluating breast treatment as well as one of the determinants for remedy selection. This prompts the need of objective and efficient methods for breast cosmesis evaluations. However, current evaluation methods rely on ratings from a small group of physicians, making the processes time-consuming and their results inconsistent. Previous attempts (e.g., BCCT.core, an existing intelligent medical system for breast cosmetic evaluation) tried to address this issue with semi-automated pipelines by requiring manual annotations on breasts and limiting its efficiency in terms of time and accuracy. To solve the problem, in this study, we proposed: 1. a fully-automaticÂ Machine Learning Breast Cosmetic evaluation algorithm leveraging the state-of-the-art Deep Learning algorithms for breast detection and contour annotation, 2. a novel set of Breast Cosmesis features, 3. a new Breast Cosmetic dataset consisting 3k+ images from three clinical trials with human annotations on both breast components and their cosmesis scores. We show our fully-automatic framework can achieve comparable performance to BCCT.core without the need of human inputs, leading to a more objective, low-cost and scalable solution for breast cosmetic evaluation in breast cancer treatment.
The Telework Virus: How COVID-19 has Affected Telework and Exposed Its Implications for Privacy

The COVID-19 pandemic has shifted millions of people from working at their workplace to teleworking from home, generating a new normal: remote work. Remote work will be an integral part of the future of work and has positive implications for the employee, employer, and society. However, as this article aims to demonstrate, remote work also involves hidden, problematic implications for workers’ rights to privacy that arise from its hybrid, technology-based nature. Remote work creates a new hybrid workspace for employees: the home-office. The home-office combines the logic and structure of the traditional workplace with those of the private sphere of the employee. As a result, the home-office creates an ongoing dilemma with respect to the privacy rights of the employee and anyone near her, especially her family. Because the work is conducted outside the physical workplace, employers are motivated to use various monitoring tools to ensure that the worker actually works. These programs give the employer a foothold in the private life of the employee that continually infringes on the privacy rights of the worker and her family. As this article shows, this is possible because of the state of the law and the increasing evolution of dubious monitoring programs in the tech industry. Against this background, this article suggests ways to begin solving the privacy difficulty. Because of the hybrid nature of the home-office, the article argues that such solutions need to be applicable both in the workplace context and the tech industry. The article then elaborates three leading principles to solve the privacy dilemma. The first is the proportionality approach. The current state of things leads to the “victory” of the employer’s interests over the employee’s right to privacy. The proportionality approach can rebalance this equation to ensure that the interests of both sides are considered. To ensure that such a balance is being achieved, the second principle is involving a representative of the employee in the process of evaluating the two parties’ needs and rights such that they jointly generate a privacy policy tailored for that specific workplace. Finally, since monitoring companies are what enable the massive intrusion into the employee’s private sphere, the last principle is incorporating privacy considerations throughout the process of engineering tracking programs, by following the privacy by design approach.
Pattern Matching of Positive and Negative DNA Sequences with General Gaps and One-Off Constraints

The symmetrical DNA double-helix structure stores a large amount of complex and valuable biological information. Pattern matching of DNA sequence can count the location and number of gene fragments, which is beneficial to state tracking and targeted therapy of pathogenic genes. However, most sequence pattern matching methods target the entire sequence rather than frequent sequence patterns, which increase the computational complexity. In addition, current research methods ignore gene fragments with missing bases, and there is no unified analysis method for negative sequence pattern. In this case, the accuracy and efficiency of biological information analysis is significantly influenced. In view of the low efficiency and the small number of DNA sequence matching the paths obtained in the existing analysis methods, in this paper, we propose a method for pattern matching of positive and negative DNA sequences with general gaps and one-off constraints. First of all, the mined frequent sequence patterns are taken as patterns in the matching process, in order to make pattern matching more practical. Then, a detection mechanism for two duplicate elements is proposed, which not only effectively prunes the matching paths that do not meet the one-off constraint, but also accurately matches all information and eliminates redundancy. Experiments on the DNA sequences of 10 real species show that the algorithm can effectively deal with negative sequence patterns and obtain more matching paths, with the running efficiency to be improved.
Study of Ageing in Complex Interface Interaction Tasks: Based on Combined Eye-Movement and Hrv Bioinformatic Feedback

With design as a grip, human-computer interaction tends to be intelligent and driven by technological innovation. However, there are a digital divide and usage barriers for older users when interacting with complex tasks. To better help elderly users efficiently complete complex task interactions, a smart home operating system interface is used as an example to explore the usage characteristics of elderly users of different genders. This study uses multi-signal physiological acquisition as a criterion. The results of the study showed that: 1) Older users are more attracted to iconic information than textual information. 2) When searching for complex tasks, female users are more likely to browse the whole page before locating the job. 3) Female users are more likely to browse from top to bottom when searching for complex tasks. 4) Female users are more likely to concentrate when performing complex tasks than male users. 5) Men are more likely to be nervous than women when performing complex tasks.
Blockchain Based Security Framework for Sharing Digital Images Using Reversible Data Hiding and Encryption

Christo Ananth et al. emphasized that Security is an important issue in current and next-generation networks. Blockchain will be an appropriate technology for securely sharing information in next-generation networks. Digital images are the prime medium attacked by cyber attackers. In this paper, a blockchain based security framework is proposed for sharing digital images in a multi user environment. The proposed framework uses reversible data hiding and encryption as component techniques. A novel high capacity reversible data hiding scheme is also proposed to protect digital images. Reversible data hiding in combination with encryption protects the confidentiality, integrity and authentication of digital images. In the proposed technique, the digital image is compressed first to create room for data hiding, then the user signature is embedded; afterwards the whole image is encrypted. For compression, JPEG lossy compression is used to create high capacity. For encryption, any symmetric block cipher or stream cipher can be used. Experimental results show that the proposed blockchain based framework provides high security and the proposed reversible data hiding scheme provides high capacity and image quality.
Deep Realistic Facial Editing Via Label-Restricted Mask Disentanglement

With the rapid development of GAN (Generative Adversarial Network), recent years have witnessed an increase number of tasks on reference-guided facial attributes transfer. Most state-of-art methods consist of facial information extraction, latent space disentanglement and target attribute manipulation. However, they either adopt reference- guided translation methods for manipulation or monolithic module for diverse attributes exchange, which cannot accurately disentangle the exact facial attributes with specific style from the reference image. In this paper, we propose an deep realistic facial editing method (termed LMGAN) based on target region focusing and dual label constraint. The proposed method, manipulating target attribute by latent space exchange, consists of sub-networks for every individual attribute. Each sub-network exerts label restriction on both target attribute exchanging stage and training process aimed at optimizing generative quality and reference style correlation. Our method performs greatly on disentangled representation and transferring the target attribute's style accurately. A global discriminator is introduced to combine the generated editing regional image with other non-editing area of the source image. Both qualitative and quantitative results on the CelebA dataset verify the ability of the proposed LMGAN.
Lightweight Certificateless Privacy-Preserving Integrity Verification with Conditional Anonymity for Cloud-Assisted Medical Cyber-Physical Systems

Cloud-assisted medical cyber-physical systems (MCPSs) leverage the power of cloud computing for scalable storage and management of outsourced medical data. Based on the crucial outsourced medical data, guardians could know patient's health condition timely, doctors can make accurate diagnoses of patients, and medical research centers could also conduct medical big data analysis. However, medical data is highly sensitive and demands strong protection. Meanwhile, the integrity of outsourced medical data is vulnerable to hardware failures, software bugs or human errors. To date, extensive data privacy-preserving integrity auditing schemes have been presented, but most of them are faced with the problems of public key certificate management or key escrow. In this paper, we propose a lightweight certificateless privacy-preserving integrity verification (LCPPIV) with conditional anonymity for cloud-assisted MCPSs by developing an elliptic-curve digital signature, which can achieve the functionalities of medical data confidentiality, integrity verification of outsourced medical data, conditional anonymity, and secure compensation mechanism, simultaneously. Theoretical security analysis demonstrates that our scheme is provably secure in the random oracle model, with resistance to the public-key-replacement attack, malicious-but-passive-KGC attack, and response auditing proofs forgery. The performance evaluations indicate that LCPPIV is much more practical for cloud-assisted MCPSs compared with state-of-art data auditing schemes
On the robustness of self-supervised representations for multi-view object classification

It is known that representations from self-supervised pre-training can perform on par, and often better, on various downstream tasks than representations from fully-supervised pre-training. This has been shown in a host of settings such as generic object classification and detection, semantic segmentation, and image retrieval. However, some issues have recently come to the fore that demonstrate some of the failure modes of self-supervised representations, such as performance on non-ImageNet-like data, or complex scenes. In this paper, we show that self-supervised representations based on the instance discrimination objective lead to better representations of objects that are more robust to changes in the viewpoint and perspective of the object. We perform experiments of modern self-supervised methods against multiple supervised baselines to demonstrate this, including approximating object viewpoint variation through homographies, and real-world tests based on several multi-view datasets. We find that self-supervised representations are more robust to object viewpoint and appear to encode more pertinent information about objects that facilitate the recognition of objects from novel views.
Learning-based Side-Channel Analysis on PIPO

As the global IoT market increases, the importance of security in the IoT environment is growing. So, studies on lightweight cipher techniques are actively underway for limited environments. In ICISC 2020, PIPO, a bitslice lightweight cipher that can effectively apply a countermeasure considering side-channel analysis, was proposed. In this paper, we propose Deep Learning-based profiled and non-profiled Side-Channel Analysis for PIPO. In profiled attack, we use an 8-bit model instead of 1-bit model that considered the bitslice characteristic of S-Box output. Although an each bit of S-Box output is distributed across the power trace, the 8-bit model has shown high training performance with 98% accuracy, and was able to derive right key successfully. In non-profiled attack, we propose a labeling technique suitable for the bitslice characteristic and show the excellence of our proposed labeling through experiments. Also, we expect that these characteristics will apply to other bitslice block ciphers as well as PIPO.
A Preimage Attack on Reduced Gimli-Hash

In CHES 2017, Bernstein et al. proposed Gimli, a 384-bit permutation with 24 rounds, which aims to provide high performance on various platforms. In 2019, the full-round (24 rounds) Gimli permutation was used as an underlying primitive for building AEAD Gimli-Cipher and hash function Gimli-Hash. They were submitted to the NIST Lightweight Cryptography Standardization process and selected as one of the second-round candidates. In ToSC 2021, Liu et al. presented a preimage attack with a divide-and-conquer method on round-reduced Gimli-Hash, which uses 5-round Gimli. In this paper, we present preimage attacks on a round-reduced variant of Gimli-Hash, in which the message absorbing phase uses 5-round Gimli and the squeezing phase uses 9-round Gimli. We call this variant as 5-9-round Gimli-Hash. Our first preimage attack on 5-9-round Gimli-Hash requires $$2^{96.44}$$ time complexity and $$2^{97}$$ memory complexity. This attack requires the memory for storing several precomputation tables in Gimli SP-box operations. In our second preimage attack, we take a time-memory trade-off approach, reducing memory requirements for precomputation tables but increasing computing time for solving SP-box equations by SAT solver. This attack requires $$2^{66.17}$$ memory complexity and $$2^{96+\epsilon }$$ time complexity, where $$\epsilon $$ is a time complexity for solving SP-box equations. Our experiments using CryptoMiniSat SAT solver show that the maximum time complexity for $$\epsilon $$ is about $$2^{20.57}$$ 9-round Gimli.
Differential Fault Attack on Rocca

Rocca is the first dedicated cipher for 6G systems accepted in ToSC 2021 and will be presented at FSE 2022. In this paper we show that Rocca is susceptible to differential fault attack under the nonce reuse scenario. The attack outcome results in a complete internal state recovery by injecting $$ 4\times 48 $$ faults in three out of eight internal state registers. Since the round update function of Rocca is reversible it also allows for key recovery. To the best of our knowledge this is the first third party analysis of Rocca.
Efficient Quantum Circuit of Proth Number Modular Multiplication

The efficient quantum circuit of Post Quantum Cryptography (PQC) impacts both performance and security because Grover’s algorithm, upon which various attacks are based, also requires a circuit. Therefore, the implementation of cryptographic operations in a quantum environment is considered to be one of the main concerns for PQC. Most lattice-based cryptography schemes employ Number Theoretic Transform (NTT). Moreover, NTT can be efficiently implemented using the modulus $$p=k\cdot 2^{m}+1$$ , called Proth number, and there is a need to elaborate on the quantum circuit for a modular multiplication over p. However, to the best of our knowledge, only quantum circuits for modular multiplication of the general odd modulus have been proposed, and quantum circuits for specific odd modulus are not presented. Thus, this paper addresses this issue and presents a new optimized quantum circuit for Proth Number Modular Multiplication (PNMM) which is faster than Rines et al.’s modular multiplication circuit. According to the evaluation with commonly used modulus parameters for lattice-based cryptography, our circuit requires an approximately 22%–45% less T-depth than that of Rines et al.’s.