Reviewerly/exHarmony huggingface dataset:
IoUformer: Pseudo-IoU prediction with transformer for visual tracking

Siamese tracking has witnessed tremendous progress in tracking paradigm. However, its default box estimation pipeline still faces a crucial inconsistency issue, namely, the bounding box decided by its classification score is not always best overlapped with the ground truth, thus harming performance. To this end, we explore a novel simple tracking paradigm based on the intersection over union (IoU) value prediction. To first bypass this inconsistency issue, we propose a concise target state predictor termed IoUformer, which instead of default box estimation pipeline directly predicts the IoU values related to tracking performance metrics. In detail, it extends the long-range dependency modeling ability of transformer to jointly grasp target-aware interactions between target template and search region, and search sub-region interactions, thus neatly unifying global semantic interaction and target state prediction. Thanks to this joint strength, IoUformer can predict reliable IoU values near-linear with the ground truth, which paves a safe way for our new IoU-based siamese tracking paradigm. Since it is non-trivial to explore this paradigm with pleased efficacy and portability, we offer the respective network components and two alternative localization ways. Experimental results show that our IoUformer-based tracker achieves promising results with less training data. For its applicability, it still serves as a refinement module to consistently boost existing advanced trackers.
Inertial measurement and heart-rate sensor-based dataset for geriatric fall detection using custom built wrist-worn device

This paper describes a dataset acquired from 41 volunteers performing 16 Activities of daily livings (ADLs) and 8 Falls repeated 5 times. This data was collected using a custom wrist-worn end device. The dataset has data collected from Inertial measurement unit (IMU) and heart-rate sensors. The end device is built using Qualcomm Snapdragon 820c System on Chip (SoC) interfaced to the sensors via Interconnect Integrated Circuit (I2C) protocol. The data was sampled for every activity at a rate of 20 Hz for the motion sensors and at a rate of 1 Hz for the heart-rate sensor. The motion sensor comprised of a triaxial accelerometer, triaxial gyroscope, triaxial magnetometer and a linear accelerometer. The heart-rate sensor was medical grade and all sensors were calibrated for the wrist -worn position. The dataset is available on this website https://shamanx86.github.io/fall_detection_data/ and https://doi.org/10.5281/zenodo.10013090.
Saccadic selection in visual working memory is robust across the visual field and linked to saccade metrics: Evidence from nine experiments and more than 100,000 trials.

Visual working memory and actions are closely intertwined. Memory can guide our actions, but actions also impact what we remember. Even during memory maintenance, actions such as saccadic eye movements select content in visual working memory, resulting in better memory at locations that are congruent with the action goal as compared to incongruent locations. Here, we further substantiate the claim that saccadic eye movements are fundamentally linked to visual working memory by analyzing a large data set (> 100k trials) of nine experiments (eight of them previously published). Using Bayesian hierarchical models, we demonstrate robust saccadic selection across the full range of probed saccade directions, manifesting as better memory performance at the saccade goal irrespective of its location in the visual field. By inspecting individual differences in saccadic selection, we show that saccadic selection was highly prevalent in the population. Moreover, both saccade metrics and visual working memory performance varied considerably across the visual field. Crucially, however, both idiosyncratic and systematic visual field anisotropies were not correlated between visual working memory and the oculomotor system, suggesting that they resulted from different sources (e.g., rely on separate spatial maps). In stark contrast, trial-by-trial variations in saccade metrics were strongly associated with memory performance: At any given location, shorter saccade latencies and more accurate saccades were associated with better memory performance, undergirding a robust link between action selection and visual memory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Holevo bound independent of weight matrices for estimating two parameters of a qubit

Holevo bound plays an important role in quantum metrology as it sets the ultimate limit for multi-parameter estimations, which can be asymptotically achieved. Except for some trivial cases, the Holevo bound is implicitly defined and formulated with the help of weight matrices. Here we report the first instance of an intrinsic Holevo bound, namely, without any reference to weight matrices, in a nontrivial case. Specifically, we prove that the Holevo bound for estimating two parameters of a qubit is equivalent to the joint constraint imposed by two quantum Cramér–Rao bounds corresponding to symmetric and right logarithmic derivatives. This weightless form of Holevo bound enables us to determine the precise range of independent entries of the mean-square error matrix, i.e., two variances and one covariance that quantify the precisions of the estimation, as illustrated by different estimation models. Our result sheds some new light on the relations between the Holevo bound and quantum Cramér–Rao bounds. Possible generalizations are discussed.
Applying Scrum to Knowledge Transfer Among Software Developers

In the teaching-learning processes, research and continuous innovation are encouraged. Now, when talking about innovation, the concept of adapting methodologies that have been successfully applied to speed up and increase the quality of software projects begins to emerge, is the case of agile software development methodologies. Scrum is one of the most widely used methodologies in the software development process, it increases productivity and deliverable quality of software development teams, but there is not much evidence of how to use it to structure learning content and its use to transfer new knowledge in training or software development contexts. To fill this gap, this research analyzes the applicability of Scrum for transferring new knowledge among software developers is developed. In this paper, we describe the details of how to start transferring knowledge using Scrum, guiding the reader through its standards, processes, phases, and objectives. We also report an experiment for improving students’ performance to support the approach’s benefits in the academy context and a case study performed inside the company context.
Dataset of Arabic spam and ham tweets

This data article provides a dataset of 132421 posts and their corresponding information collected from Twitter social media. The data has two classes, ham or spam, where ham indicates non-spam clean tweets. The main target of this dataset is to study a way to classify whether a post is a spam or not automatically. The data is in Arabic language only, which makes the data essential to the researchers in Arabic natural language processing (NLP) due to the lack of resources in this language. The data is made publicly available to allow researchers to use it as a benchmark for their research in Arabic NLP. The dataset was collected using the Twitter REST API between January 27, 2021, and March 10, 2021. An ad-hoc crawler was constructed using Python programming language to collect the data. Many scientists and researchers will benefit from this dataset in the domain of cybersecurity, NLP, data science and social networking analysis.
MM-Net: A Multi-Modal Approach Toward Automatic Modulation Classification

Automatic Modulation Classification (AMC) has become an important component in communication systems for both civil and defense applications. The shortcomings of traditional approaches to AMC have led researchers to develop complex machine learning (ML)-based approaches. In this work, inspired by multi-modal approaches for general Computer Vision tasks like Semantic Segmentation, we propose MM-Net, a multimodal approach to AMC that uses domain-specific features in the form of Higher Order Cumulants (HOCs) to improve classification performance. Furthermore, we explore the usage of HOCs in existing Deep Learning (DL)-based applications for AMC. Simulation results show that for eight modulation classification, MM-Net achieves high classification accuracy even at low SNRs, demonstrating the robustness of the multimodal approach even under challenging channel conditions, while existing methods are improved by utilizing HOCs, especially at low SNR values.
RP-SG: Relation Prediction in 3D Scene Graphs for Unobserved Objects Localization

The ability to search for objects is a fundamental prerequisite for mobile robots when addressing a wide range of automation tasks. However, how to effectively estimate the positions of unobserved objects in a continuously changing environment remains an open challenge. Previous works have utilized probabilistic models to estimate the co-occurrence property between the target object and the observed landmark objects in a scene. However, few approaches can predict the precise spatial relations between objects based on a specific scene configuration. In this letter, we propose a novel unobserved object localization framework that achieves context-specific relation prediction based on the particular configuration of a scene. First, we leverage a 3D scene graph as a compact representation of the environment and propose a relation prediction model based on graph neural networks. This model can effectively interpret the information provided by the 3D scene graph and make accurate relation predictions. Second, to address the challenge of a high number of non-existent links between objects in the scene graph, we introduce a novel loss function that can better address imbalanced training data. Additionally, we propose an evaluation framework to comprehensively assess whether the relation prediction model benefits object search tasks. Comprehensive evaluation results obtained on public datasets and real-world scenes reveal the superiority of our method over competing approaches.
A Novel Generalized Adaptive Domain Fusion Framework for Structured Light 3-D Measurement

As a three-dimensional measurement technology that combines simplicity, accuracy, and noncontact measurement, structured light has attracted much attention in industrial metrology. Fringe projection profilometry (FPP) is one of the most promising structured light techniques based on phase analysis. In FPP, the phase-height model is a key element that significantly impacts the precision of three-dimensional measurements. However, the performance of such models is sensitive to the unwrapped phase and lacks the ability for adaptive adjustment. Despite extensive research on improving the mapping relationship of phase-height models, effectively using pixel phase information to establish a more accurate and reliable phase-height model remains a challenging issue, especially in cases with limited numbers of calibration planes. To address this issue, we propose a generalized adaptive domain fusion (GADF) framework for FPP. The core principle of the GADF framework is to use a specially designed dual indexing criterion to mine the adaptive domain for each pixel, and then use the domain model fusion technology to improve the accuracy of the height measurement. Experiment results demonstrate that the GADF framework outperforms traditional phase-height models.
International Human Rights Law

Abstract Facebook’s Oversight Board upheld Donald Trump’s suspension not because he incited violence, which the First Amendment would not protect, but because he glorified violence, which the Board said is unprotected under international law, even though it is protected by the First Amendment. This chapter suggests that so-called content-moderation cases such as Facebook’s suspension of Trump should be government by the First Amendment, not international human rights law. The two are fundamentally different. International human rights law aims to protect individuals’ rights from interference by states, not from interference by other individuals or businesses. Adherence to it by private companies is voluntary and nonbinding. It is not a single, monolithic body of law. It is incomplete, fragmented, and inconsistent. Some of its elements allow speech suppression that is flatly incompatible with that permitted by the Constitution. The principal provisions of its seminal treaties are often vague and loophole-ridden. They have spawned state practice and customary international law that are confusing and contradictory. The net effect is a false universalism, a façade of consensus that masks indeterminacy and enables arbitrariness and cherry-picking.
2-D Compressive Sensing-Based Visually Secure Multilevel Image Encryption Scheme

Aiming at the problem of how to effectively protect the sensitive information of multiple images and ensure the security of their transmission, we propose a visually meaningful multilevel image encryption solution that combines 3D chaotic mapping, DCT and 2D compressed sensing. Firstly, the association relationship between related grayscale images is established by using different reversible relationship matrices. We then undersample multiple grayscale images with established relationships via 2D compressive sensing. Secondly, chaotic scrambling and index scrambling are performed on the obtained multiple measurement results. Finally, the visually meaningful steganographic image is obtained by embedding different measurement results into the selected color image via the discrete cosine transform. Compared with the existing methods, this approach solves the problem of multi-privacy protection by realizing multi-level encryption of multiple images. More importantly, it improves the encryption ability and transmission efficiency by establishing the correlation relationship between different images. Realize the secure transmission of ciphertext data by information hiding and chaotic encryption mechanisms. Provide multi-level image recovery quality to authorized users by providing different authorization keys. In short, the proposed scheme has the advantages of high encryption capability, high security and controllable access. It is especially suitable for compressive sampling, privacy protection and secure transmission of images in sensor networks with limited computing power and transmission bandwidth.
AERMNet: Attention-enhanced relational memory network for medical image report generation

The automatic generation of medical image diagnostic reports can assist doctors in reducing their workload and improving the efficiency and accuracy of diagnosis. However, among the most existing report generation models, there are problems that the weak correlation between generated words and the lack of contextual information in the report generation process. To address the above problems, we propose an Attention-Enhanced Relational Memory Network (AERMNet) model, where the relational memory module is continuously updated by the words generated in the previous time step to strengthen the correlation between words in generated medical image report. And the double LSTM with interaction module reduces the loss of context information and makes full use of feature information. Thus, more accurate disease information can be generated by AERMNet for medical image reports. Experimental results on four medical datasets Fetal heart (FH), Ultrasound, IU X-Ray and MIMIC-CXR, show that our proposed method outperforms some of the previous models with respect to language generation metrics (Cider improving by 2.4% on FH, Bleu1 improving by 2.4% on Ultrasound, Cider improving by 16.4% on IU X-Ray, Bleu2 improving by 9.7% on MIMIC-CXR). Conclusions: This work promotes the development of medical image report generation and expands the prospects of computer-aided diagnosis applications. Our code is released at https://github.com/llttxx/AERMNET.
SNLI Indo: A Recognizing Textual Entailment Dataset in Indonesian Derived from the Stanford Natural Language Inference Dataset

Recognizing textual entailment (RTE) is an essential task in natural language processing (NLP). It is the task of determining the inference relationship between text fragments (premise and hypothesis), of which the inference relationship is either entailment (true), contradiction (false), or neutral (undetermined). The most popular approach for RTE is neural networks, which has resulted in the best RTE models. Neural network approaches, in particular deep learning, are data-driven and, consequently, the quantity and quality of the data significantly influences the performance of these approaches. Therefore, we introduce SNLI Indo, a large-scale RTE dataset in the Indonesian language, which was derived from the Stanford Natural Language Inference (SNLI) corpus by translating the original sentence pairs. SNLI is a large-scale dataset that contains premise-hypothesis pairs that were generated using a crowdsourcing framework. The SNLI dataset is comprised of a total of 569,027 sentence pairs with the distribution of sentence pairs as follows: 549,365 pairs for training, 9,840 pairs for model validation, and 9,822 pairs for testing. We translated the original sentence pairs of the SNLI dataset from English to Indonesian using the Google Cloud Translation API. The existence of SNLI Indo addresses the resource gap in the field of NLP for the Indonesian language. Even though large datasets are available in other languages, in particular English, the SNLI Indo dataset enables a more optimal development of deep learning models for RTE in the Indonesian language.
Machine-Learning-Assisted Soft Fiber Optic Glove System for Sign Language Recognition

Sign language recognition devices are effective approaches to breaking the communication barrier between signers and non-signers and exploring human-machine interactions. Wearable gloves have been developed for gesture recognition and virtual reality applications by employing flexible sensors for motion detection and machine learning for data analysis. However, most existing wearable devices present limited sign language translating capacity due to the sensors' design and distribution. Here, we propose a cost-effective dual-hand soft fiber optic glove system consisting of multimode soft liquid-core fiber optic sensors, gyroscopes, wireless printed circuit boards, and batteries for sign language translation. In combination with different deep learning techniques and recognition strategies, the glove system can recognize static gestures and dynamic gestures of American Sign Language, and deduce the meaning of sentences by the sequence of gestures. The soft glove system exhibits a broad sign language range (10 numbers, 26 alphabets, 18 words, and 5 sentences meaning prediction), and high recognition accuracy (98.6% for static gestures, 95% for dynamic gestures). The results also present the recognizing capacity for high-correlated gestures (e.g., “M” and “N”). Finally, we demonstrate its application for controlling the motion of a virtual character through 7 discrete commands in the VR interface.
Pelican Whale Optimization Enabled Deep Learning Framework for Video Steganography Using Arnold Transform-Based Embedding

Steganography refers to hiding a secret message from various sources, such as images, videos, audio and so on. The advantage of steganography is to avoid data hacking in transmission medium during the transmission of information sources. Video steganography is superior to image steganography since the videos can hide a substantial quantity of secret messages more than the image. Hence, this research introduced the video stereography technique, Arnold Transform with SqueezeNet-based Pelican Whale Optimization Algorithm (AT[Formula: see text]SqueezeNet_PWOA), for concealing the secret image on the video. To hide the secret image on the video, the proposed method follows three steps: key frame and feature extraction, pixel prediction and embedding. The extraction of the key frame process is carried out by the Structural Similarity Index Measure (SSIM), and then the neighborhood features and convolutional neural network (CNN) features are extracted from the frame to improve the robustness of the embedding process. Moreover, the pixel prediction is completed by the SqueezeNet model, wherein the learning factors are tuned by the PWOA. In addition, the embedding process is completed by applying the Arnold transform on the predicted pixel, and the transformed regions are combined with the secret image using the embedding function. Likewise, the extraction process extracts the secret image from the embedded video by substituting the predicted pixel and Arnold transform on the embedded video. The proposed method is used to hide chunks of secret data in the form of video sequences and it improves the performance. The Arnold transform used in this work provides security by encrypting the data. The use of SqueezeNet makes the proposed model a simple design and this reduces the computational time. Thus, the AT[Formula: see text]SqeezeNet_PWOA attained better correlation coefficient (CC), peak signal-to-noise ratio (PSNR) and mean square error (MSE) of 0.908, 48.66 and 0.001 dB with the Gaussian noise.
TaSPM: Targeted Sequential Pattern Mining

Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated, but also increase the efficiency of users in performing related analysis. The current algorithms available for targeted sequence querying are based on specific scenarios and can not be extended to other applications. In this article, we formulate the problem of targeted sequential pattern mining and propose a generic algorithm, namely TaSPM. What is more, to improve the efficiency of TaSPM on large-scale datasets and multiple-item-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in the mining process. Totally four pruning strategies are designed in TaSPM, and hence TaSPM can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conducted extensive experiments on different datasets to compare the baseline SPM algorithm with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.
Hardware Accelerator for High Accuracy Sign Language Recognition with Residual Network Based on FPGAs

The ResNet series of networks has demonstrated powerful capabilities in the fields of object detection and image classification, garnering increasing attention from researchers. However, due to their deep network architectures, accelerator development based on FPGA faces challenges associated with limited on-chip resources and lengthy design cycles. This paper presents a versatile hardware acceleration system based on FPGA, achieving optimization through both hardware implementation and software inference architecture. The system reduces network complexity by employing techniques such as inter-layer fusion and dynamic quantization, while enhancing hardware resource utilization through channel parallelism and tightly-pipelined hardware design principles. By configuring and reusing computation units, the forward inference process of ResNet series networks can be rapidly deployed on FPGA, shortening the development and validation cycles. The proposed system is validated using the ResNet-18 model on a PYNQ-Z2 development board within a gesture recognition application scenario. The overall power consumption of the system is 2.136W, with hardware inference accuracy reaching 98.87%.