Reviewerly/exHarmony huggingface dataset:
Survey of Cross-Modal Person Re-Identification from a Mathematical Perspective

Person re-identification (Re-ID) aims to retrieve a particular pedestrian’s identification from a surveillance system consisting of non-overlapping cameras. In recent years, researchers have begun to focus on open-world person Re-ID tasks based on non-ideal situations. One of the most representative of these is cross-modal person Re-ID, which aims to match probe data with target data from different modalities. According to the modalities of probe and target data, we divided cross-modal person Re-ID into visible–infrared, visible–depth, visible–sketch, and visible–text person Re-ID. In cross-modal person Re-ID, the most challenging problem is the modal gap. According to the different methods of narrowing the modal gap, we classified the existing works into picture-based style conversion methods, feature-based modality-invariant embedding mapping methods, and modality-unrelated auxiliary information mining methods. In addition, by generalizing the aforementioned works, we find that although deep-learning-based models perform well, the black-box-like learning process makes these models less interpretable and generalized. Therefore, we attempted to interpret different cross-modal person Re-ID models from a mathematical perspective. Through the above work, we attempt to compensate for the lack of mathematical interpretation of models in previous person Re-ID reviews and hope that our work will bring new inspiration to researchers.
Progressive Hybrid-Modulated Network for Single Image Deraining

Rainy degeneration damages an image’s visual effect and influences the performance of subsequent vision tasks. Various deep learning methods for single image deraining have been proposed, obtaining appropriate recovery results. Unfortunately, most existing methods ignore the interaction between rain-layer and rain-free components when extracting relevant features, leading to undesirable results. To break the above limitations, we propose a progressive hybrid-modulated network (PHMNet) for single image deraining based on the two-branch and coarse-to-fine framework. Specifically, a hybrid-modulated module (HMM) with a two-branch framework is proposed to blend and modulate the feature of rain-free layers and rain streaks. After cascading several HMMs in the coarsest reconstructed stage of the PHMNet, a multi-level refined module (MLRM) is adopted to refine the final deraining results in the refined reconstructed stage. By being trained using loss functions such as contrastive learning, the PHMNet can obtain satisfactory deraining results. Extended experiments on several datasets and downstream tasks demonstrate that our method performs favorably against state-of-the-art methods in quantitative evaluation and visual effects.
Robot Bionic Eye Motion Posture Control System

This paper mainly studies the structure and system of robot bionic eye. Given that most robots usually run on battery power, the STM32L053C8T6 with high efficiency and super low power consumption was selected as the main control. By carrying IMU, the bionic eye attitude data can be acquired quickly and accurately, and the measurement data of accelerometer and gyroscope can be fused by the algorithm to obtain stable and accurate bionic eye attitude data. Thus, the precise control of the motor can be realized through the drive control system equipped with PCA9685, which can enhance the motion control precision of robot bionic eye. In the present study, three kinds of IMU sensors, MPU6050, MPU9250, and WT9011G4K, were selected to carry out experiments. Finally, MPU9250 with better power consumption and adaptability is selected. This is the attitude acquisition device of bionic eye. In addition, three different filters, CF, GD, and EKF, were used for data fusion and comparison. The experimental result showed that the dynamic mean errors of CF, GD, and EKF are 0.62°, 0.61°, and 0.43°, respectively, and the static mean errors are 0.1017°, 0.1001°, and 0.0462°, respectively. The result showed that, after the use of EKF, the robot bionic eye system designed in this paper can significantly reduce the attitude angle error and effectively improve the image quality. It ensures accuracy and reduces power consumption and cost, which has lower requirements on hardware and is easier to popularize.
A Thorough Reproducibility Study on Sentiment Classification: Methodology, Experimental Setting, Results

A survey published by Nature in 2016 revealed that more than 70% of researchers failed in their attempt to reproduce another researcher’s experiments, and over 50% failed to reproduce one of their own experiments; a state of affairs that has been termed the ‘reproducibility crisis’ in science. The purpose of this work is to contribute to the field by presenting a reproducibility study of a Natural Language Processing paper about “Language Representation Models for Fine-Grained Sentiment Classification”. A thorough analysis of the methodology, experimental setting, and experimental results are presented, leading to a discussion of the issues and the necessary steps involved in this kind of study.
Simplified High-Performance Cost Aggregation for Stereo Matching

Applying edge preservation filters for cost aggregation has been a leading technique in generating dense disparity maps. However, traditional approaches usually require intensive calculations, and their design parameters must be tuned for different scenarios to obtain the best performance. This paper shows that a simple texture-independent aggregation approach can achieve similar high performance. The proposed algorithm is equivalent to a sequence of matrix multiplications involving two weighting matrices and a primary matching cost. Notably, the weighting matrices are constant for image pairs with the same resolution. For higher matching accuracy, we integrate the algorithm with a multi-scale scheme to fully exploit the spatial distribution of textures in the image pairs. The resultant hybrid approach is efficient and accurate enough to surpass most existing approaches in stereo matching. The performance of the proposed approach is verified by extensive simulation results using the Middlebury (3rd Edition) benchmark stereo database.
Human Pose Estimation via Dynamic Information Transfer

This paper presents a multi-task learning framework, called the dynamic information transfer network (DITN). We mainly focused on improving the pose estimation with the spatial relationship of the adjacent joints. To benefit from the explicit structural knowledge, we constructed two branches with a shared backbone to localize the human joints and bones, respectively. Since related tasks share a high-level representation, we leveraged the bone information to refine the joint localization via dynamic information transfer. In detail, we extracted the dynamic parameters from the bone branch and used them to make the network learn constraint relationships via dynamic convolution. Moreover, attention blocks were added after the information transfer to balance the information across different granularity levels and induce the network to focus on the informative regions. The experimental results demonstrated the effectiveness of the DITN, which achieved 90.8% PCKh@0.5 on MPII and 75.0% AP on COCO. The qualitative results on the MPII and COCO datasets showed that the DITN achieved better performance, especially on heavily occluded or easily confusable joint localization.
Relevant epistemic logic with public announcements and common knowledge

Abstract We study a version of public announcement logic with common knowledge based on the relevant logic $\textsf {R}$. Public announcements, as represented in our framework, are not necessarily truthful and accepted by all agents, nor is it assumed that beliefs are preserved under announcements. We establish a completeness result with respect to a relational semantics, and we show that an alternative semantics based on information states is dual to the relational one. We add a question-forming inquisitive disjunction operator to the language and prove a completeness result with respect to the information semantics.
News to me: far-right news sharing on social media

News sharing on social media is ubiquitous and extends across demographics, platforms, and political ideologies. Online far-right communities are no exception, with a barrage of embedded news stories populating the social media feeds of far-right users. This has potentially profound implications for liberal democracy, especially given the propensity for far-right communities to spread disinformation via 'fake news'. Yet, we do not know enough about how news content is embedded into far-right discourses, nor the extent to which the practice may contribute to the digital transmission of far-right ideologies. Accordingly, this paper investigates how far-right communities share news media to promulgate illiberalism on social media platforms. The paper aims to uncover the dominant discursive devices deployed to integrate news media reports into far-right discourses. To achieve this aim, this paper applies critical discourse analysis alongside conceptualisations of legitimacy and far-right ideology to an original dataset of social media posts in an Australian context. It finds that news sharing in far-right online circles may legitimise and reify far-right ideology through the juxtaposition of mainstream news media indicating the validity of far-right grievances. The paper also introduces a prototype model of news sharing legitimisation.
HuffDuff: Stealing Pruned DNNs from Sparse Accelerators

Deep learning models are a valuable “secret sauce” that confers a significant competitive advantage. Many models are never visible to the user and even publicly known state-of-the-art models are either completely proprietary or only accessible via access-controlled APIs. Increasingly, these models run directly on the edge, often using a low-power DNN accelerator. This makes models particularly vulnerable, as an attacker with physical access can exploit side channels like off-chip memory access volumes. Indeed, prior work has shown that this channel can be used to steal dense DNNs from edge devices by correlating data transfer volumes with layer geometry.
VDom: Fast and Unlimited Virtual Domains on Multiple Architectures

Hardware memory domain primitives, such as Intel MPK and ARM Memory Domain, have been used for efficient in-process memory isolation. However, they can only provide a limited number of memory domains (16 domains), which cannot satisfy the compelling need for more isolated domains inside the address space of a process. Existing solutions to virtualize memory domains are either intrusive (need the modification to existing hardware), or incur a large performance overhead.
AfterImage: Leaking Control Flow Data and Tracking Load Operations via the Hardware Prefetcher

Research into processor-based side-channels has seen both a large number and a large variety of disclosed vulnerabilities that can leak critical, private data to malicious attackers. While most previous works require speculative execution and the use of cache primitives to transmit data, our new approach, called AfterImage, requires neither, capitalizing on vulnerabilities in Intel's IP-stride prefetcher to both expose and transmit victim data. By training this prefetcher with attacker-known values, and watching for changes to the prefetcher state when execution returns to the attacker, it is now possible to monitor and leak critical data from a large number of common userspace applications and kernel routines without speculation and additional cache accesses. To demonstrate the novel capabilities of AfterImage, we (1) present proof-of-concept attacks that leak data across different isolation levels, (2) present an end-to-end attack that leaks an entire RSA key from a modern, timing-balanced algorithm, and also (3) show how AfterImage can significantly improve the effectiveness of other attacks, such as power side-channel attacks, by using this technique as a high-precision marker.
Lucid: A Non-intrusive, Scalable and Interpretable Scheduler for Deep Learning Training Jobs

While recent deep learning workload schedulers exhibit excellent performance, it is arduous to deploy them in practice due to some substantial defects, including inflexible intrusive manner, exorbitant integration and maintenance cost, limited scalability, as well as opaque decision processes. Motivated by these issues, we design and implement Lucid, a non-intrusive deep learning workload scheduler based on interpretable models. It consists of three innovative modules. First, a two-dimensional optimized profiler is introduced for efficient job metric collection and timely debugging job feedback. Second, Lucid utilizes an indolent packing strategy to circumvent interference. Third, Lucid orchestrates resources based on estimated job priority values and sharing scores to achieve efficient scheduling. Additionally, Lucid promotes model performance maintenance and system transparent adjustment via a well-designed system optimizer. Our evaluation shows that Lucid reduces the average job completion time by up to 1.3× compared with state-of-the-art preemptive scheduler Tiresias. Furthermore, it provides explicit system interpretations and excellent scalability for practical deployment.
Multi-feature based Hazy Image Classification for Vision Enhancement

Deteriorating contrast, low glow, restricted dynamic range, poor resolution details, non-bright natural landscape colours, and reduced saturation of an image are subjected to various degrees of influence and deterioration in hazy weather conditions. Dehazing haze degraded images becomes challenging if they are not classified as hazy or clear, given that image dehazing techniques can only be used with hazy images. The competence to differentiate between hazy and clear images can not be left to human perception; hence a robust model is needed that classifies the input image into hazy and clear. Thus, we propose a nine unique features-based image classification framework based on K-Nearest Neighbour (KNN), which can accurately classify hazy and clear images. Experimental results demonstrate that the proposed method can efficiently classify the hazy and clear images, with an accuracy of 92%, a precision of 0.90, recall of 0.96, and F1 score of 0.93 for a benchmark dataset, which has both theoretical and practical implications.
A multi stage approach to handle class imbalance:An ensemble method

An imbalanced learning problem is a challenge that has grabbed the interest of academics and industry. Class imbalance is a widespread problem in machine learning due to the unavailability of data in a specific category. Several under-sampling and oversampling methods handle class imbalance; however, finding the correct pattern heavily depends on the sub-sampling. This paper proposes a two-stage approach with a data sampling method where Mutually Disjoint Data Sets(MDS) created from majority samples and binded with minority samples. A set of heterogeneous machine learning algorithms were applied on these binded sub-samples to create ensemble models. MDS ensembles give importance to the minority samples with a double voting method. The proposed method has an improvement of 3.78, 13.78 percent of average recall on the train and test data compared to best-base model results
Attention Over Attention: An Enhanced Supervised Video Summarization Approach

Now a days, Video Summarization is highly explored research area by the research community, which has off late gained a lot of interest from researchers globally. It comprises extraction of important frames from the big size video input in order to extract the important events from the video into a much smaller yet comprehensively summarized form. Towards effective video summarization, a novel methodology is proposed in this paper, that works on AoA (attention over attention) strategy. The proposed deep model is based on multiple attention modules including spatial, channel, and multi-headed attention. The AoA enables us to capture inter spatial and inter channel relationship between the features effectively. The proposed attention module is applied over the set of existing features from video summarization datasets. Progressively applying attention ensures to highlight the most important contents from the input frames, thereby producing more effective key frames. Several ablation studies have also been performed to analyze the position spatial and channel attention to determine the best possible architecture based on experiments alongside the theoretical proofs of AoA architecture. For the experimental work, two benchmark dataset have been used and compared the performance with existing methods.
Toward human-level tonal and modal melody harmonizations

This paper considers the problem of automatic music generation in the form of chord voicings, which is an important part of melody harmonization, with the use of Evolutionary Algorithms (EA), based on the rules derived from music theory and practice. The rules, whose role is to ensure the fulfillment of both the formal requirements of chord voicings and its less-formalized aesthetic requirements are encoded in the fitness function of EA. The fitness function is composed of several modules, each of which consists of smaller parts corresponding to the implemented music rules. The above modular design allows for flexible modification and extension of this function. The way the fitness function is constructed and tuned for better chord voicings quality is discussed in the context of music theory and technical EA implementation. In particular, we show how could generated voices be modeled by means of adjusting the relevance of particular fitness function components or extended by adding new components to the fitness function. The proposed algorithm is tested on two types of music: tonal and modal. Although tonal and modal chord voicings are significantly different, the achieved results (assessed by a human expert) indicate that the obtained solutions are both technically and aesthetically correct (i.e. they adhere to the theoretical rules and are nice to listen to). This study extends our previously published conference paper (Mycka et al., 2022).
An investigational FW-MPM-LSTM approach for face recognition using defective data

Facial recognition systems are based on the features and traits of the face, since the systems are classified as biometric systems. Additionally, they are founded on the image processing, machine vision and machine learning principles. From images, imperfect information is considered by face recognition systems. A variety of image reconstruction mechanisms is vital in this situation in order to match faces. The proposed method calls for image enhancement at the pre-processing stage. Following the image segmentation and reconstruction stage, the best facial features are extracted using features such the eyes, cheeks, face area and lips. By means of fractal model and wavelet transform the operation is performed. Using the Moore Penrose Matrix, the LSTM neural network is then improved also known as the MPM-LSTM, to train and test the system. From experimental results, the outcomes show that the proposed methodology performs better than the contemporary techniques.
Effective enhancement of isolation Forest method based on Minimal Spanning tree clustering

Modern technologies let researchers and practitioners explore large datasets. Anomaly detection methods applied to fix or delete unwanted records are of great importance here. One of the fastest and the most effective algorithms of anomaly detection is Isolation Forest. This solution is based on building isolation binary trees by randomly splitting the dataset elements. In this manuscript, we propose an innovative approach modifying this technique. In particular, we replace random divisions in the base mechanism with divisions based on Minimal Spanning Tree clustering. Additionally, we improve the evaluation process by introducing a two-component score function. The first component is related to the level of the test element in the isolation tree. The second term is calculated as the distance between specific points in the last split node. Namely, between the value of the evaluated attribute and the partition center stored in the node. In a series of comprehensive experiments, the proposed approach was compared with other Isolation Forest-based algorithms as well as state-of-the-art competing solutions. Our enhancement has proved its advantage in classification quality. In addition, the implementation operation times of selected solutions were measured. The results clearly demonstrate high effectiveness of the proposed approach.
Industrial Internet Sensor Node Construction and System Construction Based on Blockchain Technology

The present industrial Internet platform (IIP) is subjected to numerous complex security issues, and thus technologies that focus on achieving data security of the IIP has become top priority. The data security crisis of IIP gets mainly reflected in the malicious deletion and theft of data, the random access of terminal devices, and the low security of traditional security authentication methods. Blockchain technology adopts distributed network architecture, wherein through asymmetric encryption mechanism, it helps to solve problems associated with single point of downtime and privacy data leakage of central management of industrial Internet. In addition, considering the accept of random access and deletion of industrial interconnection terminal equipment, this paper uses the role access control mechanism based on Hyperledger, thereby regulating user access according to the user’s role in the platform and management of terminal equipment through chain code. The paper includes the following contributions: (1) firstly, it expounds the research background and significance of the IIP and introduces the research status of the industrial Internet and blockchain at home and abroad. (2) Secondly, the IIP architecture is designed based on blockchain technology and implements a sensor node management and monitoring system. (3) Thirdly, the paper uses the gateway to preprocess the collected data, thereby reducing the data transmission delay and improving the real-time and effectiveness of the data. The paper finally implements neural network to evaluate the construction quality of industrial Internet sensor nodes and obtains promising test results.
Multi-objective genetic model for co-clustering ensemble

Co-clustering ensemble establishes a consensus co-clustering over the data, and the ensemble process can be described as an optimization problem that can be solved by genetic algorithms. However, co-clustering ensemble methods based on genetic models are very few, in which fuzzy clustering and hard clustering is not combined. In this paper, a multi-objective genetic model for co-clustering ensemble (GMCCE) is proposed, and the corresponding objective function is designed. First, to process fuzzy samples and general samples more appropriately, bilateral fuzzy clustering and hard co-clustering are combined organically. Then, chromosomes are encoded as the membership of rows and columns, and after evolution process, the best chromosome is the consensus result. Finally, the proposed model is used to design a GMCCE algorithm. To evaluate the potential of GMCCE, extensive experiments are carried out, including comparison with base co-clustering algorithms and state-of-the-art algorithms. The results demonstrate that the GMCCE algorithm outperforms other algorithms.