Reviewerly/exHarmony huggingface dataset:
How is a “Kitchen Chair” like a “Farm Horse”? Exploring the Representation of Noun-Noun Compound Semantics in Transformer-based Language Models

Authors:
raw_author_name: Barry Devereux
raw_affiliation_string: Queen's University Belfast, ECIT, Queens Road, Belfast, United Kingdom
raw_author_name: Jesús Martínez del Rincón
raw_affiliation_string: Queen's University Belfast, ECIT, Queens Road, Belfast, United Kingdom

Abstract: Abstract Despite the success of Transformer-based language models in a wide variety of natural language processing tasks, our understanding of how these models process a given input in order to represent task-relevant information remains incomplete. In this work, we focus on semantic composition and examine how Transformer-based language models represent semantic information related to the meaning of English noun-noun compounds. We probe Transformer-based language models for their knowledge of the thematic relations that link the head nouns and modifier words of compounds (e.g., kitchen chair: a chair located in a kitchen). Firstly, using a dataset featuring groups of compounds with shared lexical or semantic features, we find that token representations of six Transformer-based language models distinguish between pairs of compounds based on whether they use the same thematic relation. Secondly, we utilize fine-grained vector representations of compound semantics derived from human annotations, and find that token vectors from several models elicit a strong signal of the semantic relations used in the compounds. In a novel “compositional probe” setting, where we compare the semantic relation signal in mean-pooled token vectors of compounds to mean-pooled token vectors when the two constituent words appear in separate sentences, we find that the Transformer-based language models that best represent the semantics of noun-noun compounds also do so substantially better than in the control condition where the two constituent works are processed separately. Overall, our results shed light on the ability of Transformer-based language models to support compositional semantic processes in representing the meaning of noun-noun compounds.
IoUformer: Pseudo-IoU prediction with transformer for visual tracking

Authors:
raw_author_name: Huayue Cai
raw_affiliation_string: Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China
raw_author_name: Long Lan
raw_affiliation_string: Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China
raw_author_name: Jing Zhang
raw_affiliation_string: School of Computer Science, University of Sydney, Sydney, Australia
raw_author_name: Xiang Zhang
raw_affiliation_string: Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China; Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology, Changsha, China
raw_author_name: Yibing Zhan
raw_affiliation_string: JD Explore Academy, Beijing, China
raw_author_name: Zhigang Luo
raw_affiliation_string: Science and Technology on Parallel and Distributed Laboratory, College of Computer, National University of Defense Technology, Changsha, China

Abstract: 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

Authors:
raw_author_name: Purab Nandi
raw_affiliation_string: BITS Pilani, K K Birla, Goa Campus, Goa 403726, India
raw_author_name: K.R Anupama
raw_affiliation_string: BITS Pilani, K K Birla, Goa Campus, Goa 403726, India
raw_author_name: Kishan Patel
raw_affiliation_string: BITS Pilani, K K Birla, Goa Campus, Goa 403726, India

Abstract: 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.

Authors:
raw_author_name: Martin Rolfs
raw_affiliation_string: Department of Psychology, Humboldt-Universitat zu Berlin.

Abstract: 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

Authors:
raw_author_name: Chang Niu
raw_affiliation_string: Department of Modern Physics & Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences, Hefei, 230026, CHINA
raw_author_name: Sixia Yu
raw_affiliation_string: Department of Modern Physics & Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences, Hefei, 230026, CHINA

Abstract: 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

Authors:
raw_author_name: Fernando Ibarra-Torres
raw_affiliation_string: Universidad Nacional de La Plata, UNLP, La Plata, Buenos Aires,1900, Argentina
raw_author_name: Matias Urbieta
raw_affiliation_string: Universidad Nacional de La Plata, UNLP, La Plata, Buenos Aires,1900, Argentina
raw_author_name: Nuria Medina-Medina
raw_affiliation_string: University of Granada, ETSIIT, CITIC-UGR, Granada,18071, Spain

Abstract: 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

Authors:
raw_author_name: Sanaa Kaddoura
raw_affiliation_string: Zayed University, Abu Dhabi, UAE
raw_author_name: Safaa Henno
raw_affiliation_string: Zayed University, Abu Dhabi, UAE

Abstract: 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

Authors:
raw_author_name: Constantine Doumanidis
raw_affiliation_string: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
raw_author_name: George K. Karagiannidis
raw_affiliation_string: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

Abstract: 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

Authors:
raw_author_name: Xianfeng Yuan
raw_affiliation_string: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China
raw_author_name: Baojiang Yang
raw_affiliation_string: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China
raw_author_name: Yong Song
raw_affiliation_string: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China
raw_author_name: Qingyang Xu
raw_affiliation_string: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China
raw_author_name: Fengyu Zhou
raw_affiliation_string: School of Control Science and Engineering, Shandong University, Jinan, China
raw_author_name: Weihua Sheng
raw_affiliation_string: School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA

Abstract: 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

Authors:
raw_author_name: Kepeng Qiu
raw_affiliation_string: Department of Precision Instrument, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China
raw_author_name: Luo Tian
raw_affiliation_string: Department of Precision Instrument, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China

Abstract: 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.
2-D Compressive Sensing-Based Visually Secure Multilevel Image Encryption Scheme

Authors:
raw_author_name: Lixiang Li
raw_affiliation_string: Information Security Center, the State Key Laboratory of Networking and Switching Technology, and the National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Haipeng Peng
raw_affiliation_string: Information Security Center, the State Key Laboratory of Networking and Switching Technology, and the National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Fenghua Tong
raw_affiliation_string: Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

Abstract: 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

Authors:
raw_author_name: Xianhua Zeng
raw_affiliation_string: College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
raw_author_name: Tianxing Liao
raw_affiliation_string: College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
raw_author_name: Liming Xu
raw_affiliation_string: College of Computer Science, China West Normal University, Nanchong, Sichuan, 637000, China
raw_author_name: Zhiqiang Wang
raw_affiliation_string: College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Abstract: 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.