Reviewerly/exHarmony huggingface dataset:
Meet-in-the-middle differential fault analysis on Midori

&lt;abstract&gt;&lt;p&gt;Midori is a lightweight block cipher designed by Banik et al. and presented at the ASIACRYPT 2015 conference. According to the block size, it consists of two algorithms, denoted as Midori-64 and Midori-128. Midori generates 8-bit S-Boxes from 4-bit S-Boxes and applies almost MDS matrices instead of MDS matrices. In this paper, we introduce the meet-in-the-middle fault attack model in the 4-round cell-oriented fault propagation trail and reduce the key space in the last round by $ 2^{45.71} $ and $ 2^{39.86} $ for Midori-64 and Midori-128, respectively. For Midori-64, we reduce the time complexity from $ 2^{80} $ to $ 2^{28} $, $ 2^{32} $ and $ 2^{56} $ for the different single fault injection approaches. For Midori-128, we provide a 4-round fault attack method, which slightly increases the complexity compared to previous attacks. Our results indicate that the first and last four rounds of Midori must be protected to achieve its security.&lt;/p&gt;&lt;/abstract&gt;
Colon histology slide classification with deep-learning framework using individual and fused features

&lt;abstract&gt;&lt;p&gt;Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively.&lt;/p&gt;&lt;/abstract&gt;
INTELLIGENT SYSTEM BASED ON A CONVOLUTIONAL NEURAL NETWORK FOR IDENTIFYING PEOPLE WITHOUT BREATHING MASKS

The COVID-19 pandemic is having a huge impact on people and communities. Many organizations face significant disruptions and issues that require immediate response and resolution. Social distancing, breathing masks and eye protection as preventive measures against the spread of COVID-19 in the absence of an effective antiviral vaccine play an important role. Banning unmasked shopping in supermarkets and shopping malls is mandatory in most countries. However, with a large number of buyers, the security is not able to check the presence of breathing masks on everyone. It is necessary to introduce intelligent automation tools to help the work of security. In this regard, the paper proposes an up-to-date solution – an intelligent system for identifying people without breathing masks. The proposed intelligent system works in conjunction with a video surveillance system. A video surveillance sys-tem has a structure that includes video cameras, recorders (hard disk drives) and monitors. Video cameras shoot sales areas and transmit the video image to recording devices, which, in turn, record what is happening and display the video from the cameras di-rectly on the monitor. The main idea of the proposed solution is the use of an intelligent system for classifying images periodically received from cameras of a video surveillance system. The developed classifier divides the image stream into two classes. The first class is “a person in a breathing mask” and the second is “a person without a breathing mask”. When an image of the second class appears, that is, a person who has removed a breathing mask or entered a supermarket without a breathing mask, the security service will immediately receive a message indicating the problem area. The intelligent system for image classification is based on a convo-lution neural network VGG-16. In practice, this architecture shows good results in the classification of images with great similarity. To train the neural network model, the Google Colab cloud service was used – this is a free service based on Jupyter Notebook. The trained model is based on an open source machine learning platform TensorFlow. The effectiveness of the proposed solution is con-firmed by the correct processing of the practically obtained dataset. The classification accuracy is up to 90 %.
Robust and Secure Evidence Management in Digital Forensics Investigations Using Blockchain Technology

The chapter proposes a framework for evidence management in digital forensic investigations that leverages blockchain technology to ensure integrity and authenticity of the chain of custody process. The framework utilizes smart contracts, nodes, and consensus algorithms to create a tamper-proof record of the entire chain of custody process from evidence collection to presentation. A contract that facilitates transfer of ownership of the forensic report from the forensic laboratory to the investigation department is implemented. Using this approach, the authors ensure integrity and security of the forensic report, thereby mitigating any potential risks of tampering or compromise through unethical means. A robust framework to safeguard credibility of forensic report to maintain the chain of custody, instilling confidence in the reliability of the investigative process is established. Traditional methods of evidence management, showing that the proposed framework offers a secure, reliable, and transparent solution for managing digital evidence in digital forensics investigations has been proved.
Comparison of Machine Learning Approaches for Detecting COVID-19-Lockdown-Related Discussions During Recovery and Lockdown Periods

Ever since COVID-19 was declared a pandemic, governments around the world have implemented numerous phases of lockdown measures to curb the spread of the virus. These lockdown tactics manifest themselves in the form of widespread fear and panic driven by social media discussions. Given that individuals hold diverse opinions about these lockdown measures during and after their completion, positive and negative lockdown-related discussions should be differentiated to further understand the major related issues and to make appropriate messaging and policy choices in the future. We conduct a sentiment analysis (SA) of COVID-19-lockdown-related tweets by using different machine learning (ML) classifiers and then evaluate their performance before and after using the synthetic minority oversampling technique (SMOTE). This research is performed in five phases, starting with data collection and followed by pre-processing the dataset, preparing the dataset by annotation, applying SMOTE and using ML classifiers. We observe an improvement in accuracy ( ) as confirmed by the Matthew correlation coefficient ( ) across most classifiers, except for the k-nearest neighbour (KNN), whose Acc decreased from 0.82 to 0.59 and MCC decreased from 0.544 to 0.279 before and after SMOTE was applied. Despite the potential of SMOTE with some classifiers, this technique cannot be considered an ultimate solution, especially with other classifiers and datasets. The study provides insights into the need to evaluate and benchmark the integration of data balancing approaches with ML classifiers in addition to considering additional metrics, such as MCC, for binary classification problems, especially in SA.
Enhancing Security and Reliability of Information Systems through Blockchain Technology: A Case Study on Impacts and Potential

In the digital age, ensuring the security and reliability of information systems is a paramount concern for organizations. This research investigates the potential of blockchain technology to enhance information system security and reliability within the dynamic landscape of Indonesian start-up companies. By employing a mixed-methods approach, combining qualitative interviews and quantitative surveys, this study explores the current state of information system security practices, assesses the challenges faced by start-ups, and evaluates perceptions regarding the adoption of blockchain technology. The qualitative findings highlight the existing security measures, challenges, and potential benefits associated with blockchain. The quantitative results provide insights into security practices and willingness to adopt blockchain. Through the integration of these findings, the study offers practical recommendations for enhancing information system security and reliability in the context of start-ups, while considering the challenges of blockchain adoption. This research contributes to the understanding of the symbiotic relationship between technology adoption and information security, offering guidance for start-ups, policymakers, and researchers.
Student Classroom Behavior Detection Based on YOLOv7+BRA and Multi-model Fusion

Accurately detecting student behavior in classroom videos can aid in analyzing their classroom performance and improving teaching effectiveness. However, the current accuracy rate in behavior detection is low. To address this challenge, we propose the Student Classroom Behavior Detection system based on YOLOv7+BRA (YOLOv7 with Bi-level Routing Attention). We identified eight different behavior patterns, including standing, sitting, talking, listening, walking, raising hands, reading, and writing. We constructed a dataset, which contained 11,248 labels and 4,001 images, with an emphasis on the common behavior of raising hands in a classroom setting (Student Classroom Behavior dataset, SCB-Dataset). To improve detection accuracy, we added the biformer attention module to the YOLOv7 network. Finally, we fused the results from YOLOv7 CrowdHuman, SlowFast, and DeepSort models to obtain student classroom behavior data. We conducted experiments on the SCB-Dataset, and YOLOv7+BRA achieved an mAP@0.5 of 87.1%, resulting in a 2.2% improvement over previous results. Our SCB-dataset can be downloaded from: https://github.com/Whiffe/SCB-dataset .
Leveraging GPT-4 for food effect summarization to enhance product-specific guidance development via iterative prompting

Food effect summarization from New Drug Application (NDA) is an essential component of product-specific guidance (PSG) development and assessment, which provides the basis of recommendations for fasting and fed bioequivalence studies to guide the pharmaceutical industry for developing generic drug products. However, manual summarization of food effect from extensive drug application review documents is time-consuming. Therefore, there is a need to develop automated methods to generate food effect summary. Recent advances in natural language processing (NLP), particularly large language models (LLMs) such as ChatGPT and GPT-4, have demonstrated great potential in improving the effectiveness of automated text summarization, but its ability with regard to the accuracy in summarizing food effect for PSG assessment remains unclear. In this study, we introduce a simple yet effective approach, iterative prompting, which allows one to interact with ChatGPT or GPT-4 more effectively and efficiently through multi-turn interaction. Specifically, we propose a three-turn iterative prompting approach to food effect summarization in which the keyword-focused and length-controlled prompts are respectively provided in consecutive turns to refine the quality of the generated summary. We conduct a series of extensive evaluations, ranging from automated metrics to FDA professionals and even evaluation by GPT-4, on 100 NDA review documents selected over the past five years. We observe that the summary quality is progressively improved throughout the iterative prompting process. Moreover, we find that GPT-4 performs better than ChatGPT, as evaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 35%). Importantly, all the FDA professionals unanimously rated that 85% of the summaries generated by GPT-4 are factually consistent with the golden reference summary, a finding further supported by GPT-4 rating of 72% consistency. Taken together, these results strongly suggest a great potential for GPT-4 to draft food effect summaries that could be reviewed by FDA professionals, thereby improving the efficiency of the PSG assessment cycle and promoting generic drug product development.
Predicting object properties based on movement kinematics

In order to grasp and transport an object, grip and load forces must be scaled according to the object's properties (such as weight). To select the appropriate grip and load forces, the object weight is estimated based on experience or, in the case of robots, usually by use of image recognition. We propose a new approach that makes a robot's weight estimation less dependent on prior learning and, thereby, allows it to successfully grasp a wider variety of objects. This study evaluates whether it is feasible to predict an object's weight class in a replacement task based on the time series of upper body angles of the active arm or on object velocity profiles. Furthermore, we wanted to investigate how prediction accuracy is affected by (i) the length of the time series and (ii) different cross-validation (CV) procedures. To this end, we recorded and analyzed the movement kinematics of 12 participants during a replacement task. The participants' kinematics were recorded by an optical motion tracking system while transporting an object, 80 times in total from varying starting positions to a predefined end position on a table. The object's weight was modified (made lighter and heavier) without changing the object's visual appearance. Throughout the experiment, the object's weight (light/heavy) was randomly changed without the participant's knowledge. To predict the object's weight class, we used a discrete cosine transform to smooth and compress the time series and a support vector machine for supervised learning from the achieved discrete cosine transform parameters. Results showed good prediction accuracy (up to [Formula: see text], depending on the CV procedure and the length of the time series). Even at the beginning of a movement (after only 300 ms), we were able to predict the object weight reliably (within a classification rate of [Formula: see text]).
On the benefits of knowledge compilation for feature-model analyses

Abstract Feature models are commonly used to specify the valid configurations of product lines. As industrial feature models are typically complex, researchers and practitioners employ various automated analyses to study the configuration spaces. Many of these automated analyses require that numerous complex computations are executed on the same feature model, for example by querying a SAT or SATsolver. With knowledge compilation, feature models can be compiled in a one-time effort to a target language that enables polynomial-time queries for otherwise more complex problems. In this work, we elaborate on the potential of employing knowledge compilation on feature models. First, we gather various feature-model analyses and study their computational complexity with regard to the underlying computational problem and the number of solver queries required for the respective analysis. Second, we collect knowledge-compilation target languages and map feature-model analyses to the languages that make the analysis tractable. Third, we empirically evaluate publicly available knowledge compilers to further inspect the potential benefits of knowledge-compilation target languages.
Towards Serverless Sky Computing: An Investigation on Global Workload Distribution to Mitigate Carbon Intensity, Network Latency, and Cost

The high demand for energy consumption and the resulting carbon footprint of the cloud pose significant sustainability challenges, as cloud data centers consume vast amounts of energy. The emergence of serverless cloud computing platforms has opened up new avenues for more sustainable cloud computing. Serverless Function-as-a-Service (FaaS) cloud computing platforms facilitate deploying applications as decoupled microservices to leverage automatic rapid scaling, high availability, fault tolerance, and on-demand pricing. The absence of always-on hosting costs associated with virtual machines enables serverless functions to be deployed with many different function configurations and cloud regions to achieve high performance, low network latency, and reduced costs. In this paper, we investigate the utility of a global sky computing platform where serverless resources are aggregated between up to 19 distinct cloud regions. We prototype a serverless load distribution system to distribute client requests across serverless aggregations to minimize performance objectives, including network latency, runtime, hosting costs, and carbon footprint. To evaluate our serverless distribution system's ability to meet performance objectives, we continuously executed large experiments across 19 regions around the world from November 2022 through March 2023. Our serverless load distribution approach using aggregated resources reduced the carbon intensity of a globally distributed serverless application by up to 99.8%, network latency by 65%, or hosting costs by 58% by optimizing function routing to deployments with optimal hardware configurations.
Retraction Notice: VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation

A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data distribution. Despite its recent success in image synthesis, applying DPMs to video generation is still challenging due to high-dimensional data spaces. Previous methods usually adopt a standard diffusion process, where frames in the same video clip are destroyed with independent noises, ignoring the content redundancy and temporal correlation. This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. The denoising pipeline employs two jointly-learned networks to match the noise decomposition accordingly. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation. We further show that our decomposed formulation can benefit from pre-trained image diffusion models and well-support text-conditioned video creation.
Seeing double with a multifunctional reservoir computer

Multifunctional biological neural networks exploit multistability in order to perform multiple tasks without changing any network properties. Enabling artificial neural networks (ANNs) to obtain certain multistabilities in order to perform several tasks, where each task is related to a particular attractor in the network's state space, naturally has many benefits from a machine learning perspective. Given the association to multistability, in this paper, we explore how the relationship between different attractors influences the ability of a reservoir computer (RC), which is a dynamical system in the form of an ANN, to achieve multifunctionality. We construct the "seeing double" problem in order to systematically study how a RC reconstructs a coexistence of attractors when there is an overlap between them. As the amount of overlap increases, we discover that for multifunctionality to occur, there is a critical dependence on a suitable choice of the spectral radius for the RC's internal network connections. A bifurcation analysis reveals how multifunctionality emerges and is destroyed as the RC enters a chaotic regime that can lead to chaotic itinerancy.
A Scalable Sharding Protocol Based on Cross-Shard Dynamic Transaction Confirmation for Alliance Chain in Intelligent Systems

Applying sharding protocol to address scalability challenges in alliance chain is popular. However, inevitable cross-shard transactions significantly hamper performance even at low ratios, negating scalability benefits when they dominate as shard scale grows. This article proposes a new sharding protocol suitable for alliance chain that reduces cross-shard transaction impact, improving system performance. It adopts a directed acyclic graph ledger, enabling parallel transaction processing, and employs dynamic transaction confirmation consensus for simplicity. The protocol's sharding process and node score mechanism can deter malicious behavior. Experiments show that compared with mainstream sharding protocols, the protocol performs better when affected by cross-shard transactions. Moreover, its throughput has shown improvement compared to high-performance protocols without cross-shard transactions. This solution suits systems requiring high throughput and reliability, maintaining a stable performance advantage even as cross-shard transactions increase to the usual maximum ratio.
Weak measurements and quantum-to-classical transitions in free electron–photon interactions

Abstract How does the quantum-to-classical transition of measurement occur? This question is vital for both foundations and applications of quantum mechanics. Here, we develop a new measurement-based framework for characterizing the classical and quantum free electron–photon interactions and then experimentally test it. We first analyze the transition from projective to weak measurement in generic light–matter interactions and show that any classical electron-laser-beam interaction can be represented as an outcome of weak measurement. In particular, the appearance of classical point-particle acceleration is an example of an amplified weak value resulting from weak measurement. A universal factor, $$\exp \left(-{\Gamma }^{2}/2\right)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>exp</mml:mi> <mml:mrow> <mml:mfenced> <mml:mrow> <mml:mo>−</mml:mo> <mml:msup> <mml:mrow> <mml:mi>Γ</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:mfenced> </mml:mrow> </mml:mrow> </mml:math> , quantifies the measurement regimes and their transition from quantum to classical, where $$\Gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Γ</mml:mi> </mml:math> corresponds to the ratio between the electron wavepacket size and the optical wavelength. This measurement-based formulation is experimentally verified in both limits of photon-induced near-field electron microscopy and the classical acceleration regime using a DLA. Our results shed new light on the transition from quantum to classical electrodynamics, enabling us to employ the essence of the wave-particle duality of both light and electrons in quantum measurement for exploring and applying many quantum and classical light–matter interactions.