Reviewerly/exHarmony huggingface dataset:
128-Bit LEA Block Encryption Architecture to Improve the Security of IoT Systems with Limited Resources and Area

The LEA block encryption algorithm is an architecture suitable for IoT systems with limited resources and space. It was developed by the National Security Technology Research Institute in 2013 and established as an international standard for cryptography by the International Electrotechnical Commission in 2019, drawing much attention from developers. In this paper, the 128-bit LEA block encryption algorithm was light weighted and implemented in a hardware environment. All modules share and reuse registers and are designed and implemented in a bottom area through the resource sharing function. As a result of synthesis using Xilinx ISE 14.7 Virtex-5 as a design environment, the maximum frequency achieved 190.88 MHz and has a processing speed of up to 128 Mbps. Compared to the previously designed architecture, we present a bottom-level hardware design with a 128-bit LEA algorithm implemented with a 49.8% reduction in Flip-Flop, 18.8% reduction in LUTs, and 67.6% reduction in Slices.
Application of Chaotic Increasing Linear Inertia Weight and Diversity Improved Particle Swarm Optimization to Predict Accurate Software Cost Estimation

Nowadays usage of software products is increases exponential in different areas in society, accordingly, the development of software products as well increases by the software organizations, but they are unable to focus to predict effective techniques for planning resources, reliable design, and estimation of time, budget, and high quality at the preliminary phase of the development of the product lifecycle. Consequently, it delivered improper software products. Hence, a customer loses the money, time, and not belief on the company as well as effort of teamwork will be lost. We need an efficient and effective accurate effort estimation procedure. In the past, several authors have introduced different methods for effort of estimation of the software. Particle Swarm Optimization is a most popular optimization technique. Maintaining diversity in particle swarm optimization is the main challenging one and in this paper, we propose chaotic linear increasing inertia weight and diversity improved mechanism to enhance the diversity. Seven standard data sets were employed to analyze of performance of the proposed technique, and it is outperformed compared to the previous techniques.
Turning to a Teacher for Timestamp Supervised Temporal Action Segmentation

Temporal action segmentation in videos has drawn much attention recently. Timestamp supervision is a cost-effective way for this task. To obtain more information to optimize the model, the existing method generated pseudo frame-wise labels iteratively based on the output of a segmentation model and the timestamp annotations. However, this practice may introduce noise and oscillation during the training, and lead to performance degeneration. To address this problem, we propose a new framework for timestamp supervised temporal action segmentation by introducing a teacher model parallel to the segmentation model to help stabilize the process of model optimization. The teacher model can be seen as an ensemble of the segmentation model, which helps to suppress the noise and to improve the stability of pseudo labels. We further introduce a segmentally smoothing loss, which is more focused and cohesive, to enforce the smooth transition of the predicted probabilities within action instances. The experiments on three datasets show that our method outperforms the state-of-the-art method and performs comparably against the fully-supervised methods at a much lower annotation cost.
Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised Approach

Medical image synthesis has attracted increasing attention because it could generate missing image data, improve diagnosis, and benefits many downstream tasks. However, so far the developed synthesis model is not adaptive to unseen data distribution that presents domain shift, limiting its applicability in clinical routine. This work focuses on exploring domain adaptation (DA) of 3D image-to-image synthesis models. First, we highlight the technical difference in DA between classification, segmentation, and synthesis models. Second, we present a novel efficient adaptation approach based on a 2D variational autoencoder which approximates 3D distributions. Third, we present empirical studies on the effect of the amount of adaptation data and the key hyper-parameters. Our results show that the proposed approach can significantly improve the synthesis accuracy on unseen domains in a 3D setting. The code is publicly available at https://github.com/WinstonHuTiger/2D_VAE_UDA_for_3D_sythesis .
Speedup of entanglement generation in hybrid quantum systems through linear driving

The hybrid quantum system in which interactions between superconducting (SC) qubits and atomic qubits are mediated by an auxiliary cavity mode is a promising architecture for quantum information processing. However, compared to the strong coupling ${g}_{1}$ between the SC qubits and the cavity, the coupling ${g}_{2}$ between the atomic qubits and the cavity is often extremely weak, which limits the operation speed and suffers more from dissipations. Here we propose a built-in fault-tolerant geometric operation to speed up the generation of entanglement between SC qubits and atomic qubits only by linearly driving the qubits. The operation speed is proportional to $\sqrt{{g}_{1}{g}_{2}}$ instead of the weaker coupling ${g}_{2}$. Comparing with traditional double-swap method, the speedup can reach around 10 times and the fidelity keeps high under current experimental parameters. This speedup technique can be used in various boson-mediated quantum platforms even if parametric driving or modulation of the boson mode is impossible, enabling exploration of fast quantum information processing.
Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science

The exploration and analysis of multidimensional data can be pretty complex tasks, requiring sophisticated tools able to transform large amounts of data bearing multiple parameters into helpful information. Multidimensional projection techniques figure as powerful tools for transforming multidimensional data into visual information according to similarity features. Integrating this class of methods into a framework devoted to data sciences can contribute to generating more expressive means of visual analytics. Although the Principal Component Analysis (PCA) is a well-known method in this context, it is not the only one, and, sometimes, its abilities and limitations are not adequately discussed or taken into consideration by users. Therefore, knowing in-depth multidimensional projection techniques, their strengths, and the possible distortions they can create is of significant importance for researchers developing knowledge-discovery systems. This research presents a comprehensive overview of current state-of-the-art multidimensional projection techniques and shows example codes in Python and R languages, all available on the internet. The survey segment discusses the different types of techniques applied to multidimensional projection tasks from their background, application processes, capabilities, and limitations, opening the internal processes of the methods and demystifying their concepts. We also illustrate two problems, from a genetic experiment (supervised) and text mining (non-supervised), presenting solutions through multidimensional projection application. Finally, we brought elements that reverberate the competitiveness of multidimensional projection techniques towards high-dimension data visualization, commonly needed in data sciences solutions.
OS-MSL: One Stage Multimodal Sequential Link Framework for Scene Segmentation and Classification

Scene segmentation and classification (SSC) serve as a critical step towards the field of video structuring analysis. Intuitively, jointly learning of these two tasks can promote each other by sharing common information. However, scene segmentation concerns more on the local difference between adjacent shots while classification needs the global representation of scene segments, which probably leads to the model dominated by one of the two tasks in the training phase. In this paper, from an alternate perspective to overcome the above challenges, we unite these two tasks into one task by a new form of predicting shots link: a link connects two adjacent shots, indicating that they belong to the same scene or category. To the end, we propose a general One Stage Multimodal Sequential Link Framework (OS-MSL) to both distinguish and leverage the two-fold semantics by reforming the two learning tasks into a unified one. Furthermore, we tailor a specific module called DiffCorrNet to explicitly extract the information of differences and correlations among shots. Extensive experiments on a brand-new large scale dataset collected from real-world applications, and MovieScenes are conducted. Both the results demonstrate the effectiveness of our proposed method against strong baselines. The code is made available.
An efficient lymphocytic leukemia detection based on EfficientNets and ensemble voting classifier

Abstract Early detection of leukemia increases the chances of a speedier recovery. If a patient exhibits any symptoms, doctors would often examine a blood sample slide under a microscope to detect hematological malignancies. Manually categorizing leukocytes as normal or abnormal requires examining the many characteristics of the cells, which is time‐consuming and error‐prone. This research aims to create a transfer learning‐based Acute Lymphocytic Leukemia (ALL) detection system that is both efficient and easy. To overcome the critical challenges associated with feature extraction, we used EfficientNet, the most recent and most substantial deep learning model. In this article, eight EfficientNets variations are used to extract features and are compared based on classification accuracy. This work uses an ensemble of three sophisticated classifiers, namely Support Vector Machine (SVM), Random Forest, and Logistic Regression, which achieves a classification accuracy of 98.5%.
AdvTraffic: Obfuscating Encrypted Traffic with Adversarial Examples

Website fingerprinting can reveal which sensitive website a user visits over encrypted network traffic. Obfuscating encrypted traffic, e.g., adding dummy packets, is considered as a primary approach to defend against website fingerprinting. How-ever, existing defenses relying on traffic obfuscation are either ineffective or introduce significant overheads. As recent website fingerprinting attacks heavily rely on deep neural networks to achieve high accuracy, producing adversarial examples could be utilized as a new way to obfuscate encrypted traffic. Unfortunately, existing adversarial example algorithms are designed for images and do not consider unique challenges for network traffic.In this paper, we design a new method, named AdvTraffic, which can customize perturbations produced by any existing adversarial example algorithm on images and derive adversarial examples over encrypted traffic. Our experimental results show that the integration of AdvTraffic, particularly with Generative Adversarial Networks, can effectively mitigate the accuracy of website fingerprinting from 95.0% to 10.2%, even if an attacker retrains a classifier with defended traffic. Compared to other defenses, our method outperforms most of them in mitigating attack accuracy and offers the lowest bandwidth overhead.
An intelligent fast controller for autonomous wheeled robot path navigation in challenging environments

Purpose This paper aims to incorporate one intelligent particle swarm optimization (IPSO) controller to realize an optimum path in unknown environments. In this paper, the fitness function of IPSO is designed with intelligent design parameters, solving the path navigation problem of an autonomous wheeled robot towards the target point by avoiding obstacles in any unknown environment. Design/methodology/approach This controller depends on randomly oriented positions with all other position information and a fitness function. Evaluating the position’s best values, this study gets the local best values, and finally, the global best value is updated as the current value after comparing the local best values. Findings The path navigation of the proposed controller has been compared with particle swarm optimization algorithm, BAT algorithm, flower pollination algorithm, invasive weed algorithm and genetic algorithm in multiple challenging environments. The proposed controller shows the percent deviation in path length near 14.54% and the percent deviation in travel time near 4% after the simulation. IPSO is applied to optimize said parameters for path navigation of the wheeled robot in different simulation environments. Originality/value A hardware model with a 32-bit ARM board interfaced with a global positioning system (GPS) module, an ultrasonic module and ZigBee wireless communication module is designed to implement IPSO. In real-time, the IPSO controller shows the percent deviation in path length near 9%.
Denial-of-Service Attacks on Shared Resources in Intel’s Integrated CPU-GPU Platforms

In this paper, we study the effectiveness of denial-of-service (DoS) attacks on Intel’s heterogeneous multicore system-on-chips with integrated GPU (iGPU) in which the last level cache (LLC) and the main memory subsystem are shared between the multicore CPU and the iGPU. Using two Intel processors with iGPU, we evaluate four different DoS attacks, three CPU based and one iGPU based, and show they can induce very high degree of shared resource contention and thus dramatically slowdown the victim’s execution time. We further evaluate the effectiveness of Intel’s recent hardware based shared resource isolation mechanisms, namely Intel Cache Allocation Technology (CAT) and Graphics Technology Class of Service (GT COS), which provide shared LLC partitioning capability for the CPU cores and the iGPU, respectively, in defending against these DoS attacks. Using both synthetic and real-world benchmarks, we find that hardware based LLC partitioning mechanisms does provide spatial LLC space isolation but does not necessarily provide temporal isolation.
KETCH

Health thread recommendation methods aim to suggest the most relevant existing threads for a user. Most of the existing methods tend to rely on modeling the post contents to retrieve relevant answers. However, some posts written by users with different clinical conditions can be lexically similar, as unrelated diseases (e.g., Angina and Osteoporosis) may have the same symptoms (e.g., back pain), yet irrelevant threads to a user. Therefore, it is critical to not only consider the connections between users and threads, but also the descriptions of users' symptoms and clinical conditions. In this paper, towards this problem of thread recommendation in online healthcare forums, we propose a knowledge graph enhanced Threads Recommendation (KETCH) model, which leverages graph neural networks to model the interactions among users and threads, and learn their representations. In our model, the users, threads and posts are three types of nodes in a graph, linked through their associations. KETCH uses the message passing strategy by aggregating information along with the network. In addition, we introduce a knowledge-enhanced attention mechanism to capture the latent conditions and symptoms. We also apply the method to the task of predicting the side effects of drugs, to show that KETCH has the potential to complement the medical knowledge graph. Comparing with the best results of seven competing methods, in terms of MRR, KETCH outperforms all methods by at least 0.125 on the MedHelp dataset, 0.048 on the Patient dataset and 0.092 on HealthBoards dataset, respectively. We release the source code of KETCH at: https://github.com/cuilimeng/KETCH.
DrAsync

Promises and async/await have become popular mechanisms for implementing asynchronous computations in JavaScript, but despite their popularity, programmers have difficulty using them. This paper identifies 8 anti-patterns in promise-based JavaScript code that are prevalent across popular JavaScript repositories. We present a light-weight static analysis for automatically detecting these anti-patterns. This analysis is embedded in an interactive visualization tool that additionally relies on dynamic analysis to visualize promise lifetimes and instances of anti-patterns executed at run time. By enabling the user to navigate between promises in the visualization and the source code fragments that they originate from, problems and optimization opportunities can be identified.
VPPlus: Exploring the Potentials of Video Processing for Live Video Analytics at the Edge

Edge-assisted video analytics is gaining momentum. In this work, we tackle an important problem to compress video content live streamed from the device to the edge without scarifying accuracy and timeliness of its video analytics. We find that on-device processing can be tuned over a larger configuration space for more video compression, which was largely overlooked. Inspired by our pilot study, we design VPPlus to fulfill the potentials to compress the video as much as we can, while preserving analytical accuracy. VPPlus incorporates two core modules – offline profiling and online adaptation – to generate proper feedback automatically and quickly to tune on-device processing. We validate the effectiveness and efficiency of VPPlususing five object detection tasks over two popular datasets; VPPlus outperforms the state-of-art approaches in almost all the cases.
Relevance under the Iceberg

In the era of big data, eXtreme Multi-label Classification (XMC) has already become one of the most essential research tasks to deal with enormous label spaces in machine learning applications. Instead of assessing every individual label, most XMC methods rely on label trees or filters to derive short ranked label lists as prediction, thereby reducing computational overhead. Specifically, existing studies obtain ranked label lists with a fixed length for prediction and evaluation. However, these predictions are unreasonable since data points have varied numbers of relevant labels. The greatly small and large list lengths in evaluation, such as [email protected] and [email protected], can also lead to the ignorance of other relevant labels or the tolerance of many irrelevant labels. In this paper, we aim to provide reasonable prediction for extreme multi-label classification with dynamic numbers of predicted labels. In particular, we propose a novel framework, Model-Agnostic List Truncation with Ordinal Regression (MALTOR), to leverage the ranking properties and truncate long ranked label lists for better accuracy. Extensive experiments conducted on six large-scale real-world benchmark datasets demonstrate that MALTOR significantly outperforms statistical baseline methods and conventional ranked list truncation methods in ad-hoc retrieval with both linear and deep XMC models. The results of an ablation study also shows the effectiveness of each individual component in our proposed MALTOR.
A Music Teaching Resource Management Model Based on Fuzzy Clustering Algorithm

This study develops and implements a music instructional resource platform based on fuzzy clustering algorithm knowledge in order to make the management of music teaching quality more scientific and standardised. This study presents the construction scheme of a music instructional resource management system in a higher education institution through requirements analysis, function design, system development and implementation, and system testing of the software system. In this study, a system is developed that uses computational intelligence to calculate the content and topics contained in resources based on semantic similarity, generate category labels, and cluster educational resources in order to integrate educational resources and implement effective management. It not only meets the needs of users who want to access the platform system via mobile terminals but also increases the platform system’s utilisation rate. Experiments show that this system’s stability can reach 95.37%. The retrieval accuracy is 96.24%, which is 8.94% better than the traditional method. It has the ability to effectively provide high-quality music teaching resources. Using the instructional resource system proposed in this study will improve teaching effectiveness, reduce learners’ learning burden, and increase learning efficiency.
Change is the only constant

Software systems must be updated regularly to address changing requirements and urgent issues like security-related bugs. Traditionally, updates are performed by shutting down the system to replace certain components. In modern software organizations, updates are increasingly frequent---up to multiple times per day---hence, shutting down the entire system is unacceptable. Safe dynamic software updating (DSU) enables component updates while the system is running by determining when the update can occur without causing errors. Safe DSU is crucial, especially for long-running or frequently executed asynchronous transactions (workflows), e.g., user-interactive sessions or order fulfillment processes. Unfortunately, previous research is limited to synchronous transaction models and does not address this case.
A bit-vector differential model for the modular addition by a constant and its applications to differential and impossible-differential cryptanalysis

ARX algorithms are a class of symmetric-key algorithms constructed by Addition, Rotation, and XOR. To evaluate the resistance of an ARX cipher against differential and impossible-differential cryptanalysis, the recent automated methods employ constraint satisfaction solvers to search for optimal characteristics or impossible differentials. The main difficulty in formulating this search is finding the differential models of the non-linear operations. While an efficient bit-vector differential model was obtained for the modular addition with two variable inputs, no differential model for the modular addition by a constant has been proposed so far, preventing ARX ciphers including this operation from being evaluated with automated methods. In this paper, we present the first bit-vector differential model for the n-bit modular addition by a constant input. Our model contains O(log2(n)) basic bit-vector constraints and describes the binary logarithm of the differential probability. We describe an SMT-based automated method that includes our model to search for differential characteristics of ARX ciphers including constant additions. We also introduce a new automated method for obtaining impossible differentials where we do not search over a small pre-defined set of differences, such as low-weight differences, but let the SMT solver search through the space of differences. Moreover, we implement both methods in our open-source tool ArxPy to find characteristics and impossible differentials of ARX ciphers with constant additions in a fully automated way. As some examples, we provide related-key impossible differentials and differential characteristics of TEA, XTEA, HIGHT, LEA, SHACAL-1, and SHACAL-2, which achieve better results compared to previous works.
Axiomatic Retrieval Experimentation with ir_axioms

Axiomatic approaches to information retrieval have played a key role in determining basic constraints that characterize good retrieval models. Beyond their importance in retrieval theory, axioms have been operationalized to improve an initial ranking, to "guide" retrieval, or to explain some model's rankings. However, recent open-source retrieval frameworks like PyTerrier and Pyserini, which made it easy to experiment with sparse and dense retrieval models, have not included any retrieval axiom support so far.