Reviewerly/exHarmony huggingface dataset:
Continuous dynamical decoupling of optical 171Yb+ qudits with radiofrequency fields

The use of multilevel quantum information carriers, also known as qudits, has attracted significant interest as a way of further scaling quantum computing devices. However, such multilevel systems usually express shorter coherence time than their two-level counterparts, which limits their computational potential. We thus propose and experimentally demonstrate two approaches for realizing the continuous dynamical decoupling of magnetic-sensitive states with m F = ±1 for qudits encoded in optical transition of trapped 171 Yb + ions. We improve the coherence time of qudit levels by an order of magnitude (more than 9 ms) without any magnetic shielding, revealing the potential advantage of the symmetry of the 171 Yb + ion energy structure for counteracting magnetic field noise. Our results are a step toward realizing qudit-based algorithms using trapped ions.
An Image Encryption Algorithm Based on Improved Hilbert Curve Scrambling and Dynamic DNA Coding

As an effective method for image security protection, image encryption is widely used in data hiding and content protection. This paper proposes an image encryption algorithm based on an improved Hilbert curve with DNA coding. Firstly, the discrete wavelet transform (DWT) decomposes the plaintext image by three-level DWT to obtain the high-frequency and low-frequency components. Secondly, different modes of the Hilbert curve are selected to scramble the high-frequency and low-frequency components. Then, the high-frequency and low-frequency components are reconstructed separately using the inverse discrete wavelet transform (IDWT). Then, the bit matrix of the image pixels is scrambled, changing the pixel value while changing the pixel position and weakening the strong correlation between adjacent pixels to a more significant correlation. Finally, combining dynamic DNA coding and ciphertext feedback to diffuse the pixel values improves the encryption effect. The encryption algorithm performs the scrambling and diffusion in alternating transformations of space, frequency, and spatial domains, breaking the limitations of conventional scrambling. The experimental simulation results and security analysis show that the encryption algorithm can effectively resist statistical attacks and differential attacks with good security and robustness.
Using Augmented Reality Toward Improving Social Skills: Scoping Review

Augmented reality (AR) has emerged as a promising technology in educational settings owing to its engaging nature. However, apart from applications aimed at the autism spectrum disorder population, the potential of AR in social-emotional learning has received less attention.This scoping review aims to map the range of AR applications that improve social skills and map the characteristics of such applications.In total, 2 independent researchers screened 2748 records derived from 3 databases in December 2021-PubMed, IEEE Xplore, and ACM Guide to Computing Literature. In addition, the reference lists of all the included records and existing reviews were screened. Records that had developed a prototype with the main outcome of improving social skills were included in the scoping review. Included records were narratively described for their content regarding AR and social skills, their target populations, and their outcomes. Evaluation studies were assessed for methodological quality.A total of 17 records met the inclusion criteria for this study. Overall, 10 records describe applications for children with autism, primarily teaching about reading emotions in facial expressions; 7 records describe applications for a general population, targeting both children and adults, with a diverse range of outcome goals. The methodological quality of evaluation studies was found to be weak.Most applications are designed to be used alone, although AR is well suited to facilitating real-world interactions during a digital experience, including interactions with other people. Therefore, future AR applications could endorse social skills in a general population in more complex group settings.
SoK: Cryptographic Neural-Network Computation

We studied 53 privacy-preserving neural-network papers in 2016-2022 based on cryptography (without trusted processors or differential privacy), 16 of which only use homomorphic encryption, 19 use secure computation for inference, and 18 use non-colluding servers (among which 12 support training), solving a wide variety of research problems. We dissect their cryptographic techniques and "love-hate relationships" with machine learning alongside a genealogy highlighting noteworthy developments. We also re-evaluate the state of the art under WAN. We hope this can serve as a go-to guide connecting different experts in related fields.
ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks

With the increased usage of AI accelerators on mobile and edge devices, on-device machine learning (ML) is gaining popularity. Thousands of proprietary ML models are being deployed today on billions of untrusted devices. This raises serious security concerns about model privacy. However, protecting model privacy without losing access to the untrusted AI accelerators is a challenging problem. In this paper, we present a novel on-device model inference system, ShadowNet. ShadowNet protects the model privacy with Trusted Execution Environment (TEE) while securely outsourcing the heavy linear layers of the model to the untrusted hardware accelerators. ShadowNet achieves this by transforming the weights of the linear layers before outsourcing them and restoring the results inside the TEE. The non-linear layers are also kept secure inside the TEE. ShadowNet’s design ensures efficient transformation of the weights and the subsequent restoration of the results. We build a ShadowNet prototype based on TensorFlow Lite and evaluate it on five popular CNNs, namely, MobileNet, ResNet-44, MiniVGG, ResNet-404, and YOLOv4-tiny. Our evaluation shows that ShadowNet achieves strong security guarantees with reasonable performance, offering a practical solution for secure on-device model inference.
IYOLO-NL: An improved you only look once and none left object detector for real-time face mask detection

<h2>Abstract</h2> Object detection is a fundamental task in computer vision that aims to locate and classify objects in images or videos. The one-stage You Only Look Once (YOLO) models are popular approaches to object detection. Real-time monitoring of mask wearing is necessary, especially for preventing the spread of the COVID-19 virus. While YOLO detectors facing challenges include improving the robustness of object detectors against occlusion, scale variation, handling false detection and false negative, and maintaining the balance between higher precision detection and faster inference time. In this study, a novel object detection model called Improved You Only Look Once and None Left (IYOLO-NL) based on YOLOv5 was proposed for real-time mask wearing detection. To fulfill the requirement of real-time detection, the lightweight IYOLO-NL was developed by using novel CSPNet-Ghost and SSPP bottleneck architecture. To prevent any missed correct results, IYOLO-NL integrates the proposed PANet-SC with a multi-level prediction scheme. To achieve high precision and handle sample allocation properly, the proposed global dynamic-k label assignment strategy was utilized in an anchor-free manner. A large dataset of face masks (FMD) was created, consisting of 6130 images, for use in conducting experiments on IYOLO-NL and other models. The experiment results show that IYOLO-NL surpasses other state-of-the-art (SOTA) methods and achieves 98.8% accuracy while maintaining 130 FPS.
Integrating color cues to improve multimodal sentiment analysis in social media

Multimodal sentiment analysis is an emerging and critical area of research, particularly in the context of social media where users express their emotions through both text and images. However, existing methods that learn common semantic features between visual and textual modalities for multimodal sentiment analysis often overlook the importance of color information, which plays a crucial role in sentiment expression, according to psychology and art theory. This paper proposes a novel model, named ICCI, which aims to enhance multimodal sentiment analysis in social media by integrating color cues, in order to address the limitation in existing approaches. The proposed model leverages both the semantic information from image-text pairs and the color cues from images to improve the accuracy of sentiment analysis. The model comprises a feature extraction module that extracts semantic features from both images and text, as well as color features from images. Furthermore, the feature interaction module employs a cross-attention mechanism to enable the interaction of information between semantic features and color features. Finally, the label prediction module integrates all attention features to enhance multimodal sentiment analysis. The proposed model was evaluated through experiments on two widely used benchmark datasets, namely MVSA-Single and MVSA-Multiple datasets, demonstrating its effectiveness in outperforming existing methods. On MVSA-Single, ICCI achieved an accuracy of 79.33%. Similarly, on MVSA-Multiple, ICCI achieved an accuracy of 73.29%. The results underscore the importance of integrating color information for more accurate sentiment analysis in social media.
TRUSTEE: Towards the creation of secure, trustworthy and privacy-preserving framework

Digital transformation is a method where new technologies replace the old to meet essential organisational requirements and enhance the end-user experience. Technological transformation often improvises the manner in which a facility or resources are delivered to the recipient. Data is one of the key assets of every organisation which influences significantly reaching the long-term objective. Thus, the entities, as well as technologies involved in the data management process, have a significant role to play to secure different data types. However, the traditional data governance process often follows a centralised approach and thus resulting in various cyber attacks, whereas the distributed approaches are mostly research prototypes and often comprise various security challenges. Security incidents such as data theft fabricate the integrity of confidential data and thus the consequences are often disastrous. To address the challenges, we introduce TRUSTEE, a data-driven platform which aims to provide a secure and privacy-by-design framework to empower companies, organisations, and individuals to access different data domains, use and re-use the data and metadata to extract knowledge with trust and confidentiality. In this paper, we assess the effectiveness of the platform by reviewing the potential challenges and threats associated with the incorporated technologies. Our research emphasises the efficacy of distributed technologies to indicate their significance in data integrity and security.
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank

Understanding why a model makes certain predictions is crucial when adapting it for real world decision making. LIME is a popular model-agnostic feature attribution method for the tasks of classification and regression. However, the task of learning to rank in information retrieval is more complex in comparison with either classification or regression. In this work, we extend LIME to propose Rank-LIME, a model-agnostic, local, post-hoc linear feature attribution method for the task of learning to rank that generates explanations for ranked lists. We employ novel correlation-based perturbations, differentiable ranking loss functions and introduce new metrics to evaluate ranking based additive feature attribution models. We compare Rank-LIME with a variety of competing systems, with models trained on the MS MARCO datasets and observe that Rank-LIME outperforms existing explanation algorithms in terms of Model Fidelity and Explain-NDCG. With this we propose one of the first algorithms to generate additive feature attributions for explaining ranked lists.
Dense Retrieval Adaptation using Target Domain Description

In information retrieval (IR), domain adaptation is the process of adapting a retrieval model to a new domain whose data distribution is different from the source domain. Existing methods in this area focus on unsupervised domain adaptation where they have access to the target document collection or supervised (often few-shot) domain adaptation where they additionally have access to (limited) labeled data in the target domain. There also exists research on improving zero-shot performance of retrieval models with no adaptation. This paper introduces a new category of domain adaptation in IR that is as-yet unexplored. Here, similar to the zero-shot setting, we assume the retrieval model does not have access to the target document collection. In contrast, it does have access to a brief textual description that explains the target domain. We define a taxonomy of domain attributes in retrieval tasks to understand different properties of a source domain that can be adapted to a target domain. We introduce a novel automatic data construction pipeline that produces a synthetic document collection, query set, and pseudo relevance labels, given a textual domain description. Extensive experiments on five diverse target domains show that adapting dense retrieval models using the constructed synthetic data leads to effective retrieval performance on the target domain.
Modeling and validating temporal rules with semantic Petri net for digital twins

The semantic web has become an important resource of domain knowledge in the construction industry, and there is a need to model the temporal states and check the transition rules of digital twins in the semantic web. Related studies have added timestamps to describe an RDF graph that varies in time, but digital twin applications require a formal representation of the temporal states and transition rules other than simple timestamps. Related studies also focused on the interaction between temporal and semantic models, but there are still challenges in the two-way sharing of knowledge in the runtime of the temporal model. In this paper, the Semantic Petri Net (SPN) is proposed as a method to represent the temporal states and rules in RDF and SPARQL so that a runnable temporal model can be implemented on a common SPARQL engine, and the state change rules can be checked with access to the rich domain knowledge provided by the semantic web. An application case is presented for modeling and simulating the constraints in the process of a construction project.
Comparison of Hybrid Models for PV Power Output Forecasting — Application to Oryahovo, Bulgaria

The paper presents a comparison of different hybrid models used for PV power output forecasting. The models are hybrid, because they use combination of solar radiation forecasting model and power output estimation model. The prediction precision is studied on two time periods – three and six days with different type of solar radiation. The relative root mean square error is used for estimation of the precision. The investigated models show good correlation to the real PV power and can be applied in the exploitation of a PV plant according to the available data.
Patent image retrieval using transformer-based deep metric learning

Intellectual property work covers a wide range of areas. In particular, prior art literature searching in the patent field requires finding documents that can be used to determine novelty and inventive steps from a vast amount of past literature. Concerning this search practice, research and development of a drawing search technology that directly searches drawings, and essential information about inventions, has long been desired. However, patent drawings are usually described as black-and-white abstract drawings, except in some countries, and their modal characteristics are very different from those of natural images, so they have yet to be explored. This study achieved higher accuracy than the previous ones by introducing InfoNCE and ArcFace in the DeepPatent (Kucer et al., 2022) dataset instead of the conventional Triplet. In addition, we developed an application that enables users to search for patent drawings using any images. Our architecture can be applied to patent drawings and many other modal-like drawings, such as mechanical drawings, design patents, trademarks, diagrams, and sketches.
On the Fusion Strategies for Federated Decision Making

We consider the problem of information aggregation in federated decision making, where a group of agents collaborate to infer the underlying state of nature without sharing their private data with the central processor or each other.We analyze the non-Bayesian social learning strategy in which agents incorporate their individual observations into their opinions (i.e., soft-decisions) with Bayes rule, and the central processor aggregates these opinions by arithmetic or geometric averaging.Building on our previous work, we establish that both pooling strategies result in asymptotic normality characterization of the system, which, for instance, can be utilized in order to give approximate expressions for the error probability.We verify the theoretical findings with simulations and compare both strategies.
Implementation of Smart Security System in Agriculture fields Using Embedded Machine Learning

Tiny Machine Learning (TinyML), a branch of machine learning that focuses on the effectiveness of machine learning on extremely constrained edge machines, is flourishing. Deep learning techniques are being used more frequently lately in a variety of data-intensive and time-sensitive Internet of Things (IoT) apps. Because MCUs lack resources like RAM, deploying new methods like Deep Neural Networks (DNN) models on them has proven challenging. However, recent developments in the TinyML space promise to create a completely new class of peripheral apps. By eliminating the need for the cloud’s omnipresent computing support, which uses power and presents risks to data security and privacy, TinyML paves the way for the development of original apps and services. Traditional machine learning needs a lot of processing capacity to predict a scenario. This computational capacity will be moved from high-end systems to low-end devices thanks to the TinyML method for machine learning on small devices. To keep the precision of the learning models, enable resource-efficient small edge devices to manage the training and deployment process, maximize computing capacity, and enhance dependability are some of the challenges presented by this change. Here in this paper, we propose a efficient method to detect animals near farmland for security purposes using TinyML and compared with many algorithms and their effectiveness.
Rewind &amp; Discard: Improving Software Resilience using Isolated Domains

Well-known defenses exist to detect and mitigate common faults and memory safety vulnerabilities in software. Yet, many of these mitigations do not address the challenge of software resilience and availability, i.e., whether a system can continue to carry out its function and remain responsive, while being under attack and subjected to malicious inputs. In this paper we propose secure rewind and discard of isolated domains as an efficient and secure method of improving the resilience of software that is targeted by run-time attacks. In difference to established approaches, we rely on compartmentalization instead of replication and checkpointing. We show the practicability of our methodology by realizing a software library for Secure Domain Rewind and Discard (SDRaD) and demonstrate how SDRaD can be applied to real-world software.
Tabby: Automated Gadget Chain Detection for Java Deserialization Vulnerabilities

Java is one of the preferred options of modern developers and has become increasingly more prominent with the prevalence of the open-source culture. Thanks to the serialization and deserialization features, Java programs have the flexibility to transmit object data between multiple components or systems, which significantly facilitates development. However, the features may also allow the attackers to construct gadget chains and lead to Java deserialization vulnerabilities. Due to the highly flexible and customizable nature of Java deserialization, finding an exploitable gadget chain is complicated and usually costs researchers a great deal of effort to confirm the vulnerability. To break such a dilemma, in this paper, we introduced Tabby, a highly accurate framework that leverages the Soot framework and Neo4j graph database for finding Java deserialization gadget chains. We leveraged Tabby to analyze 248 Jar files, found 80 practical gadget chains, and received 7 CVE-IDs from Xstream and Apache Dubbo. They both improved the security design to deal with potential security risks.
An Imbalanced Discriminant Alignment Approach for Domain Adaptive SAR Ship Detection

Synthetic aperture radar (SAR) imaging has round-the-clock data acquisition capability regardless of light and climate constraints, so it has been widely used for ship detection. However, SAR images usually suffer lower imaging quality, which may result in indistinct contours and non-negligible noise. Therefore, the manual labeling for SAR images is expensive, leading to a lack of training data in the task of ship detection. In this paper, we propose a route by utilizing domain adaptive methods to transfer information from labeled visible images (source domain) to unlabeled SAR images (target domain) for ship detection. To address the distribution mismatch between domains, we develop a novel imbalanced discriminant alignment (IDA) approach to improve the discriminant ability of the network and prevent negative migration. The core of the IDA approach is applying a new loss function called imbalanced prediction consistency (IPC) loss to describe the domain classifier consistency, and we further provide theoretical analysis for the effectiveness of the IPC loss. IDA ensures consistency at the image level and instance level, and focuses on the consistency of the source domain to enhance the feature extraction capability of the adversarial network. The theoretical discussion has proven that a necessary and sufficient condition for convergence of the IPC loss is that the two discriminant probabilities converge to 0 at the discriminant distance we define. Experimental results have indicated the advantage of IDA when compared with other domain adaptation SAR ship detection methods.