Reviewerly/exHarmony huggingface dataset:
HARR: Learning Discriminative and High-quality Hash Codes for Image Retrieval

This paper studies deep unsupervised hashing, which has attracted increasing attention in large-scale image retrieval. The majority of recent approaches usually reconstruct semantic similarity information, which then guides the hash code learning. However, they still fail to achieve satisfactory performance in reality due to two reasons. On the one hand, without accurate supervised information, these methods usually fail to produce independent and robust hash codes with semantics information well preserved, which may hinder effective image retrieval. On the other hand, due to discrete constraints, how to effectively optimize the hashing network in an end-to-end manner with small quantization errors remains to be a problem. To address these difficulties, we propose a novel unsupervised hashing method called HARR to learn discriminative and high-quality hash codes. To comprehensively explore semantic similarity structure, HARR adopts the Winner-Take-All (WTA) hash to model the similarity structure. Then similarity-preserving hash codes are learned under the reliable guidance of the reconstructed similarity structure. Additionally, we improve the quality of hash codes by a bit correlation reduction module, which forces the cross-correlation matrix between a batch of hash codes under different augmentations to approach the identity matrix. In this way, the generated hash bits are expected to be invariant to disturbances with minimal redundancy, which can be further interpreted as an instantiation of the Information Bottleneck principle. Finally, for effective hashing network training, we minimize the cosine distances between real-value network outputs and their binary codes for small quantization errors. Extensive experiments demonstrate the effectiveness of our proposed HARR.
Use of E-Library Services by Postgraduate Students at Mzuzu University

AbstractMzuzu University Library provides various e-library services to postgraduate students. However, a minimal amount is known regarding the use of these e-library services by postgraduate students. This mixed methods quantitative study aimed to investigate postgraduate students’ use of e-library services. The findings reveal that 95% of postgraduate students knew about e-library services. In comparison, 83% knew internet access, and 73% knew e-journals service from 10 e-library services that Mzuzu University offers. Further, 80% and 64% of postgraduate students used the internet and e-journals, respectively, while other e-library services were the least used. Additionally, 68% of users were satisfied with the internet and 64% were confident with e-journals. Factors such as usefulness, availability of support, and resources influenced postgraduate students’ use of e-library services. Inadequate infrastructure and staff support discouraged some users from effectively using e-library services. Training and marketing are highly recommended.Keywords: E-library servicespostgraduate studentsMzuzu UniversityMalawi Disclosure statementNo potential conflict of interest was reported by the author(s).
Decision Support System in Identification of Lesions in the Dense Breast on Digital Mammograms

Abstract Background The most common cancer affecting women globally is breast cancer. The most effective and extensively used tool for identifying breast abnormalities in the early stage is mammography screening. However, it is less effective when there is high breast density. Therefore, the radiologist considers a sonomammography in addition to mammography to identify and characterize the lesion. Therefore, we aim at modelling a decision support system to identify a lesion in a dense breast using a mammogram and thereby avoiding additional sonomammogram. Methods In this work, the image pixels were pre-processed to produce super-pixels that are very meaningful and easy to analyse. Texture features extracted using a rotation invariant local binary pattern (RI-LBP) approach from super-pixels. Feature Selection algorithm was used to extract the top 5 features. The performance of various machine learning models was studied, and the best model was used for the prediction of the presence or absence of lesions in the mammogram images. Results The findings suggest that breast masses localized in the dense background can be described by features produced from RI-LBP super-pixel patterns in very effective and efficient ways. The experiment showed that with 500 super-pixels and only 5 RI-LBP features, the Support Vector machine model with gaussian kernel yielded an accuracy value of 90.38% and an area under the receiver operating characteristic (ROC) curve score of 0.96 for the mammogram dataset.
A novel remote detection method for inner leak location in a tokamak: Theory and preliminary experimental validation

In this study, a detection scheme was investigated for small leaks in tokamak vacuum chambers, and the scheme was validated theoretically and experimentally. An ultra-high vacuum should be maintained in the tokamak chamber. However, traditional methods of locating leaks in the inner vacuum chamber are usually less efficient. A leak detection scheme in the vacuum chamber was proposed in this study. Locating leaks by scanning the vacuum chamber wall with a multi-degree-of-freedom robot arm. Moreover, a small vacuum chamber was adopted to perform a leak test at a small flow rate. The results were compared with those achieved through COMSOL, Molflow simulations, as well as theoretical calculations. Furthermore, the scenario of a small leak in the EAST vacuum chamber was simulated. Significant pressure value fluctuations were detected when the detection device passed through the area close to the leak at a reasonable detection distance.
N-LoLiGan: Unsupervised low-light enhancement GAN with an N-Net for low-light tunnel images

Because of low luminance in tunnels, target features in images are not salient, which makes the detection of tunnel surface defects challenging. Most deep-learning-based low-light enhancement methods suffer from contrast distortion and insufficient brightness in extremely dark scenes, as well as artifacts and overexposure in tunnel scenes with large light and dark differences. To alleviate these problems, we propose a novel unsupervised generative adversarial network (GAN), called the N-shape low-light enhancement GAN (N-LoLiGan), which can be trained using unpaired low-/normal-light images and is proven to generalize sufficiently on tunnel images with complex lighting. Experimental results demonstrate that the proposed N-LoLiGan outperforms several state-of-the-art low-light image enhancement methods in terms of visual quality and two no-reference image quality metrics. Additionally, when the proposed method is used as a preprocessing step for the tunnel multi-target detection task, it yields a higher detection rate (88.9% and 97.23%) than other methods.
Generating local oscillator locally in continuous variable quantum key distribution using optical injection phase locked loop: a theoretical approach

In this study, we investigate the feasibility of utilizing optical injection locking with phase-locked loop feedback to generate a true local oscillator for continuous-variable quantum key distribution (CV-QKD). We evaluate the noise and imperfections associated with this novel approach and compare our findings with existing CV-QKD schemes. Notably, our calculations demonstrate significantly lower noise levels compared to other locally generated local oscillator-based CV-QKD schemes. Furthermore, we assess the secure key rate and achievable distance under collective attack for our proposed scheme and compare it to other variants of CV-QKD protocols employing a true local oscillator. Our results align well with experimental data, highlighting the promise of our approach.
Multi-grained hypergraph interest modeling for conversational recommendation

Conversational recommender system (CRS) interacts with users through multi-turn dialogues in natural language, which aims to provide high-quality recommendations for user’s instant information need. Although great efforts have been made to develop effective CRS, most of them still focus on the contextual information from the current dialogue, usually suffering from the data scarcity issue. Therefore, we consider leveraging historical dialogue data to enrich the limited contexts of the current dialogue session. In this paper, we propose a novel multi-grained hypergraph interest modeling approach to capture user interest beneath intricate historical data from different perspectives. As the core idea, we employ hypergraph to represent complicated semantic relations underlying historical dialogues. In our approach, we first employ the hypergraph structure to model users’ historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations. Second, to alleviate the issue of data scarcity, we use an external knowledge graph and construct a knowledge-based hypergraph considering fine-grained, entity-level semantics. We further conduct multi-grained hypergraph convolution on the two kinds of hypergraphs, and utilize the enhanced representations to develop interest-aware CRS. Extensive experiments on two benchmarks ReDial and TG-ReDial validate the effectiveness of our approach on both recommendation and conversation tasks. Code is available at: https://github.com/RUCAIBox/MHIM.
Data augmentation using continuous conditional generative adversarial networks for regression and its application to improved spectral sensing

Machine learning-assisted spectroscopy analysis faces a prominent constraint in the form of insufficient spectral samples, which hinders its effectiveness. Meanwhile, there is a lack of effective algorithms to simulate synthetic spectra from limited samples of real spectra for regression models in continuous scenarios. In this study, we introduced a continuous conditional generative adversarial network (CcGAN) to autonomously generate synthetic spectra. The labels employed for generating the spectral data can be arbitrarily selected from within the range of labels associated with the real spectral data. Our approach effectively produced spectra using a small spectral dataset obtained from a self-interference microring resonator (SIMRR)-based sensor. The generated synthetic spectra were subjected to evaluation using principal component analysis, revealing an inability to discern them from the real spectra. Finally, to enhance the DNN regression model, these synthetic spectra are incorporated into the original training dataset as an augmentation technique. The results demonstrate that the synthetic spectra generated by CcGAN exhibit exceptional quality and significantly enhance the predictive performance of the DNN model. In conclusion, CcGAN exhibits promising potential in generating high-quality synthetic spectra and delivers a superior data augmentation effect for regression tasks.
Infer unseen from seen: Relation regularized zero-shot visual dialog

The Visual Dialog task requires retrieving the correct answer based on detected objects, a current question, and history dialogs. However, in real-world scenarios, most existing models face the hard-positive problem and are unable to reason about unseen features, which limits their generalization ability. To address this issue, we propose two Relation Regularized Modules (RRM) in this article. The first is the Visual Relation Regularized Module (VRRM), which seeks known visual features that have semantic relations with unknown visual features and leverages these known features to assist in understanding the unknown features. The second is the Text Relation Regularized Module (TRRM), which enhances the keywords in the answers to strengthen the understanding of unknown text features. To evaluate the effectiveness of these modules, we propose two zero-shot Visual Dialog splits for verification: Visual Zero-shot VisDial with unseen visual features and Text Zero-shot VisDial with unseen answers. Experimental results demonstrate that our proposed modules achieve state-of-the-art performance in zero-shot Visual Dialog with unseen visual features and unseen answers, while also producing comparable results on the benchmark VisDial v1.0 test dataset.
What I didn’t grow up with is dangerous: personal experience with a new technology or societal change reduces the belief that it corrupts youth

Throughout history, technological and societal changes consistently receive suspicion. Their influences appear damaging, corrupting, and potential precursors to societal downfall, with today's youth often portrayed as the primary victims. This study aims to explore an underlying reason for these perceptions and to investigate why society frequently perceives technological and societal transitions as detrimental to the younger generation.We conduct two studies across a total of 1,702 participants. In a pilot study, American adults generate a list of technological/societal innovations they believe to be especially problematic for youth in various ways. The second study maps beliefs that specific technological/societal shifts are corruptive, correlating with whether American adults experience them during their upbringing.People view recent technologies as particularly corrupting of today's youth. A notable within-person correlation exists between an individual's exposure to specific technologies during their youth and their belief that these technologies corrupt today's youth. Specifically, people are more inclined to view technological/societal shifts as corruptive if they don't experience them during their formative years (b = -0.09, p < 0.001, 95%CI = [-0.11, -0.09]). When reminded of their own exposure to a particular innovation during their upbringing, however, this relationship reduces.These findings suggest unfamiliarity currently stands as a pivotal factor in societal apprehensions regarding new technological and societal evolutions. As society welcomes new innovations, an enduring cycle emerges where those unacquainted changes seem corruptive to the newer generations. Recognizing this bias, primarily driven by mere unfamiliarity, may be crucial for more balanced evaluations of the inevitable technological and societal progress.
Evolutionary Approaches for Adversarial Attacks on Neural Source Code Classifiers

As the prevalence and sophistication of cyber threats continue to increase, the development of robust vulnerability detection techniques becomes paramount in ensuring the security of computer systems. Neural models have demonstrated significant potential in identifying vulnerabilities; however, they are not immune to adversarial attacks. This paper presents a set of evolutionary techniques for generating adversarial instances to enhance the resilience of neural models used for vulnerability detection. The proposed approaches leverage an evolution strategy (ES) algorithm that utilizes as the fitness function the output of the neural network to deceive. By starting from existing instances, the algorithm evolves individuals, represented by source code snippets, by applying semantic-preserving transformations, while utilizing the fitness to invert their original classification. This iterative process facilitates the generation of adversarial instances that can mislead the vulnerability detection models while maintaining the original behavior of the source code. The significance of this research lies in its contribution to the field of cybersecurity by addressing the need for enhanced resilience against adversarial attacks in vulnerability detection models. The evolutionary approach provides a systematic framework for generating adversarial instances, allowing for the identification and mitigation of weaknesses in AI classifiers.
Exploring Effective Approaches to the Risk Management Framework (RMF) in the Republic of Korea: A Study

As the world undergoes rapid digitalization, individuals and objects are becoming more extensively connected through the advancement of Internet networks. This phenomenon has been observed in governmental and military domains as well, accompanied by a rise in cyber threats consequently. The United States (U.S.), in response to this, has been strongly urging its allies to adhere to the RMF standard to bolster the security of primary defense systems. An agreement has been signed between the Republic of Korea and the U.S. to collaboratively operate major defense systems and cooperate on cyber threats. However, the methodologies and tools required for RMF implementation have not yet been fully provided to several allied countries, including the Republic of Korea, causing difficulties in its implementation. In this study, the U.S. RMF process was applied to a specific system of the Republic of Korea Ministry of National Defense, and the outcomes were analyzed. Emphasis was placed on the initial two stages of the RMF: ‘system categorization’ and ‘security control selection’, presenting actual application cases. Additionally, a detailed description of the methodology used by the Republic of Korea Ministry of National Defense for RMF implementation in defense systems is provided, introducing a keyword-based overlay application methodology. An introduction to the K-RMF Baseline, Overlay, and Tailoring Tool is also given. The methodologies and tools presented are expected to serve as valuable references for ally countries, including the U.S., in effectively implementing the RMF. It is anticipated that the results of this research will contribute to enhancing cyber security and threat management among allies.
Similarity-Based Framework for Unsupervised Domain Adaptation: Peer Reviewing Policy for Pseudo-Labeling

The inherent dependency of deep learning models on labeled data is a well-known problem and one of the barriers that slows down the integration of such methods into different fields of applied sciences and engineering, in which experimental and numerical methods can easily generate a colossal amount of unlabeled data. This paper proposes an unsupervised domain adaptation methodology that mimics the peer review process to label new observations in a different domain from the training set. The approach evaluates the validity of a hypothesis using domain knowledge acquired from the training set through a similarity analysis, exploring the projected feature space to examine the class centroid shifts. The methodology is tested on a binary classification problem, where synthetic images of cubes and cylinders in different orientations are generated. The methodology improves the accuracy of the object classifier from 60% to around 90% in the case of a domain shift in physical feature space without human labeling.
Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT

The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnection and communication among heterogeneous devices. Smart cities are established with sophisticated designs and control of seamless machine-to-machine (M2M) communication, to optimize resources, costs, performance, and energy distributions. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, this encompasses quite a few challenges for devices that lack a compatible and interoperable design. The conventional solutions are restricted to limited domains or rely on engineers designing and deploying translators for each pair of ontologies. This is a costly process in terms of engineering effort and computational resources. An issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontological meta-data and structural information. The model finds matches between two distinct ontologies using a natural language processing (NLP) approach to learn linguistic contexts. Then, by visualizing the ontological network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device's message formulation. Finally, the model can align entities of ontological graphs that are similar in context and structure.Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware resources.
Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer

Generative adversarial networks (GANs) can synthesize various feasible-looking images. We showed that a GAN, specifically a conditional GAN (CGAN), can simulate breast mammograms with normal, healthy appearances and can help detect mammographically-occult (MO) cancer. However, similar to other GANs, CGANs can suffer from various artifacts, e.g., checkerboard artifacts, that may impact the quality of the final synthesized image, as well as the performance of detecting MO cancer. We explored the types of GAN artifacts that exist in mammogram simulations and their effect on MO cancer detection.We first trained a CGAN using digital mammograms (FFDMs) of 1366 women with normal/healthy breasts. Then, we tested the trained CGAN on an independent MO cancer dataset with 333 women with dense breasts (97 MO cancers). We trained a convolutional neural network (CNN) on the MO cancer dataset, in which real and simulated mammograms were fused, to identify women with MO cancer. Then, a radiologist who was independent of the development of the CGAN algorithms evaluated the entire MO cancer dataset to identify and annotate artifacts in the simulated mammograms.We found four artifact types, including checkerboard, breast boundary, nipple-areola complex, and black spots around calcification artifacts, with an overall incidence rate over 69% (the individual incident rate ranged from 9% to 53%) from both normal and MO cancer samples. We then evaluated their potential impact on MO cancer detection. Even though various artifacts existed in the simulated mammogram, we found that it still provided complementary information for MO cancer detection when it was combined with the real mammograms.We found that artifacts were pervasive in the CGAN-simulated mammograms. However, they did not negatively affect our MO cancer detection algorithm; the simulated mammograms still provided complementary information for MO cancer detection when combined with real mammograms.
Emergent quantum probability from full quantum dynamics and the role of energy conservation

Abstract We propose and study a toy model for the quantum measurements that yield the Born’s rule of quantum probability. In this model, the electrons interact with local photon modes and the photon modes are dissipatively coupled with local photon reservoirs. We treat the interactions of the electrons and photons with full quantum mechanical description, while the dissipative dynamics of the photon modes are treated via the Lindblad master equation. By assigning double quantum dots setup for the electrons coupling with local photons and photonic reservoirs, we show that the Born’s rule of quantum probability can emerge directly from microscopic quantum dynamics. We further discuss how the microscopic quantities such as the electron–photon coupling, detuning, and photon dissipation rate affect the quantum dynamics. Surprisingly, in the infinite long time measurement limit, the energy conservation already dictates the emergence of the Born’s rule of quantum probability. For finite-time measurement, the local photon dissipation rate determines the characteristic time-scale for the completion of the measurement, while other microscopic quantities affect the measurement dynamics. Therefore, in genuine measurements, the measured probability is determined by both the local devices and the quantum mechanical wavefunction.