Reviewerly/exHarmony huggingface dataset:
Reframe: An Augmented Reality Storyboarding Tool for Character-Driven Analysis of Security &amp; Privacy Concerns

While current augmented reality (AR) authoring tools lower the technical barrier for novice AR designers, they lack explicit guidance to consider potentially harmful aspects of AR with respect to security & privacy (S&P). To address potential threats in the earliest stages of AR design, we developed Reframe, a digital storyboarding tool for designers with no formal training to analyze S&P threats. We accomplish this through a frame-based authoring approach, which captures and enhances storyboard elements that are relevant for threat modeling, and character-driven analysis tools, which personify S&P threats from an underlying threat model to provide simple abstractions for novice AR designers. Based on evaluations with novice AR designers and S&P experts, we find that Reframe enables designers to analyze threats and propose mitigation techniques that experts consider good quality. We discuss how Reframe can facilitate collaboration between designers and S&P professionals and propose extensions to Reframe to incorporate additional threat models.
Use of an AI-powered Rewriting Support Software in Context with Other Tools: A Study of Non-Native English Speakers

Academic writing in English can be challenging for non-native English speakers (NNESs). AI-powered rewriting tools can potentially improve NNESs’ writing outcomes at a low cost. However, whether and how NNESs make valid assessments of the revisions provided by these algorithmic recommendations remains unclear. We report a study where NNESs leverage an AI-powered rewriting tool, Langsmith, to polish their drafted academic essays. We examined the participants’ interactions with the tool via user studies and interviews. Our data reveal that most participants used Langsmith in combination with other tools, such as machine translation (MT), and those who used MT had different ways of understanding and evaluating Langsmith’s suggestions than those who did not. Based on these findings, we assert that NNESs’ quality assessment in AI-powered rewriting tools is influenced by the simultaneous use of multiple tools, offering valuable insights into the design of future rewriting tools for NNESs.
Polarization image defogging based on detail recovery generative adversarial network

Most of the existing polarization image based defogging methods use a priori and assumptions to recover images, and although these methods have made great progress, the a priori or assumptions are not always reliable in practical scenarios, which limits the performance of defogging methods. In this paper, we design a two branch network to learn image features; the defogging network uses the fusion module to adaptively assign weights to different path features to ensure the defogging effect while focusing on the haze region; the detail recovery network uses convolutional layers and smoothly expanding convolution to expand the perceptual field and fully obtain local and global feature information; finally, the features obtained from the detail recovery network and the defogging network are fused to improve the defogging Finally, the features obtained from detail recovery network and defogging network are fused to improve the defogging effect. The experimental results show that this method can reconstruct clear images in foggy environment with 2db improvement in PSNR and 0.016 improvement in SSIM, and the quality of reconstructed images is better than existing algorithms.
Errors in Augmented Reality Interactions Affected Muscular Loads in the Neck and Shoulders

The use of augmented reality (AR) head-mounted displays is becoming increasingly prevalent due to their ability to provide interactive experiences with high degrees of freedom. However, AR interactions have been shown to be associated with increased biomechanical loads in the neck and shoulders. This repeated-measures study evaluated the effects of interaction errors on neck and shoulder biomechanical loads during AR tasks. Twenty participants performed two standardized AR tasks (omni-directional pointing and cube placing) with and without AR interaction errors. During the tasks, neck and shoulder angles and muscle activity were collected. The results showed that the presence of errors led to increased neck extension and shoulder flexion angles. Peak muscle activity in the shoulders (anterior and medial deltoids) also increased with errors. These findings highlight the importance of reducing interaction errors in AR interfaces to minimize risks of musculoskeletal discomfort and injuries in the neck and shoulders
PriSHAP: Prior-guided Shapley Value Explanations for Correlated Features

Among numerous explainable AI (XAI) methods proposed in recent years, model explanations based on Shapley values are widely accepted for their solid theoretical support from game theory. However, most existing methods approximate Shapley values based on a feature independence assumption considering the complexity of calculating exact Shapley values. This assumption could bring some counterfactual problems when interpreted features are highly correlated and result in explanations contrary to human intuition. In this paper, we propose PriSHAP to explicitly model the dependency relationship between correlated features and provide reasonable explanations for tabular data. Feature dependencies are analyzed and taken as prior information to guide the process of estimating Shapley values. Additionally, PriSHAP is free to be applied in popular Shapley value-based explainers to address counterfactual problems while providing more faithful explanations. A pipeline is given to apply PriSHAP in existing explainers with simple adjustments. Extensive experiments on both public datasets and artificial datasets are provided to demonstrate the effectiveness of our method.
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking

Retrieval-enhanced text generation has shown remarkable progress on knowledge-intensive language tasks, such as open-domain question answering and knowledge-enhanced dialogue generation, by leveraging passages retrieved from a large passage corpus for delivering a proper answer given the input query. However, the retrieved passages are not ideal for guiding answer generation because of the discrepancy between retrieval and generation, i.e., the candidate passages are all treated equally during the retrieval procedure without considering their potential to generate a proper answer. This discrepancy makes a passage retriever deliver a sub-optimal collection of candidate passages to generate the answer. In this paper, we propose the GeneRative Knowledge Improved Passage Ranking (GripRank) approach, addressing the above challenge by distilling knowledge from a generative passage estimator (GPE) to a passage ranker, where the GPE is a generative language model used to measure how likely the candidate passages can generate the proper answer. We realize the distillation procedure by teaching the passage ranker learning to rank the passages ordered by the GPE. Furthermore, we improve the distillation quality by devising a curriculum knowledge distillation mechanism, which allows the knowledge provided by the GPE can be progressively distilled to the ranker through an easy-to-hard curriculum, enabling the passage ranker to correctly recognize the provenance of the answer from many plausible candidates. We conduct extensive experiments on four datasets across three knowledge-intensive language tasks. Experimental results show advantages over the state-of-the-art methods for both passage ranking and answer generation on the KILT benchmark.
Anomaly and Novelty detection for Satellite and Drone systems (ANSD '23)

In recent times, there has been a notable surge in the amount of vision and sensing/time-series data obtained from drones and satellites. This data can be utilized in various fields, such as precision agriculture, disaster management, environmental monitoring, and others. However, the analysis of such data poses significant challenges due to its complexity, heterogeneity, and scale. Furthermore, it is critical to identify anomalies and maintain/monitor the health of drones and satellite systems to enable the aforementioned applications and sciences. This workshop presents an excellent opportunity to explore solutions that specifically target the detection of anomalies and novel occurrences in drones and satellite systems and their data. For more information, visit our website at https://sites.google.com/view/ansd23
Understanding the Multi-vector Dense Retrieval Models

While dense retrieval has become a promising alternative to the traditional text retrieval models, such as BM25, some recent studies show that multi-vector dense retrieval models are more effective than the single-vector method in retrieval tasks. However, due to a lack of interpretability, why the multi-vector method outperforms its single-vector counterpart has not been fully studied. To fill this research gap, in this work, we investigate and compare the behaviors of single-vector and multi-vector models in retrieval. Specifically, we analyze the vocabulary distribution of dense representations by mapping them back to the sparse, vocabulary space. Our empirical findings show that the multi-vector representation has more lexical overlaps between queries and passages. Additionally, we show that this feature of multi-vector representation can enhance its ranking performance when a given passage can fulfill different information needs and thus can be retrieved by different queries. These results shed light on the internal mechanisms of multi-vector representation and may provide new perspectives for future research.
HOVER: Homophilic Oversampling via Edge Removal for Class-Imbalanced Bot Detection on Graphs

As malicious bots reside in a network to disrupt network stability, graph neural networks (GNNs) have emerged as one of the most popular bot detection methods. However, in most cases these graphs are significantly class-imbalanced. To address this issue, graph oversampling has recently been proposed to synthesize nodes and edges, which still suffers from graph heterophily, leading to suboptimal performance. In this paper, we propose HOVER, which implements Homophilic Oversampling Via Edge Removal for bot detection on graphs. Instead of oversampling nodes and edges within initial graph structure, HOVER designs a simple edge removal method with heuristic criteria to mitigate heterophily and learn distinguishable node embeddings, which are then used to oversample minority bots to generate a balanced class distribution without edge synthesis. Experiments on TON IoT networks demonstrate the state-of-the-art performance of HOVER on bot detection with high graph heterophily and extreme class imbalance.
Chess Piece Image Recognition Using Transfer Learning, Simple Neural Network, and Convolutional Neural Network

Online chess has opened up a way for players to sharpen their skills through move analytics. Based on this feature, a support system called chess advisor can be utilized to assist players in a real-time match. However such system doesn't exist within the website itself, rather an involvement of 3rd party software is required. These kinds of software usually need assistance for input. This paper aims to create a system that is able to recognize chessboard state in real-time. We have proposed a chess piece image recognition system using transfer learning using ResNet50, a simple neural network, and a convolutional neural network (CNN). We collected our own 693 2D chess piece images dataset and performed data preprocessing to standardize the image dimensions. The proposed system detects and converts individual positions of each piece into a computer-readable output. This output can be used to facilitate the chess advisor in determining the best move. The transfer learning approach utilizing the ResNet50 model exhibited superior performance with an accuracy of 95.65%. This outperformed alternative methods such as the simple neural network and the CNN. The ResNet50 model's advantage stemmed from its pre-training on the ImageNet dataset, enabling effective pattern generalization. In contrast, the simple neural network and CNN achieved lower accuracies of 73.4% and 85%. These models encountered difficulties due to limited dataset quantity and quality, as well as the absence of regularization techniques. Nevertheless, the CNN displayed better consistency in generalizing patterns compared to the simple neural network.
Peer-to-Peer Federated Learning Based Anomaly Detection for Open Radio Access Networks

Open radio access network (O-RAN) has been recognized as a revolutionized architecture to support the multi-class wireless services required in fifth-generation (5G) and beyond 5G networks. The openness and the distributed nature of the O-RAN architecture have created new forms of threat surfaces than the conventional RAN architecture and require complex anomaly detection mechanisms. Moreover, with the introduction of RAN intelligent controllers (RICs), it is possible to utilize advanced Artificial Intelligence (AI)/ Machine Learning (ML) algorithms based on closed control loops to detect anomalies in a data-driven manner. In this paper, we particularly investigate the use of Federated Learning (FL) for anomaly detection in the O-RAN architecture, which can further preserve data privacy. We propose a peer-to-peer (P2P) FL-based anomaly detection mechanism for the O-RAN architecture and provide a comprehensive analysis of four variants of P2P FL techniques. Moreover, we simulate the proposed models using the UNSW-NB15 dataset.
Blockchain and Dew Computing-based Model for Secure Energy Trading in Smart Grids

The Peer-to-Peer (P2P) energy trading paradigm allows producers and consumers to buy or sell energy from each other directly. An increasing energy demand requires a robust P2P supply chain driven by digitization and decen-tralization. Blockchain technology offers security and financial transaction benefits for P2P energy trading. However, existing energy trading systems are struggling to preserve privacy along with fair economic profit to the prosumers and consumers. This paper proposes a secure and profitable P2P energy trading mechanism using federated learning, dew computing, and blockchain. Blockchain-enabled Dew Servers (BDS) are employed in microgrids to store the user's data and local management software. A cloud server aggregates local models of individual microgrid models to construct the global federated learning model. The proposed Blockchain-enabled Dew Computing and Federated Learning (BDC-FL) model ensures data privacy for prosumers/consumers and attempts for profit maximization. The proposed system predicts energy production and intelligently flips between energy sharing and trading with the prosumer's consent. The proposed model has been tested using benchmark datasets and demonstrates superior performance compared to state-of-the-art energy trading and sharing mechanisms. Experimental results indicate that our BDC-FL model can increase profits up to 18% for stable participants in the smart microgrid ecosystem.
UMLsecRT: Reactive Security Monitoring of Java Applications with Round-Trip Engineering

Today’s software systems tend to be long-living and often process security-critical data, so keeping up with ever-changing security measures, attacks, and mitigations is critical to maintaining their security. While it has become common practice to consider security aspects during the design of a system, OWASP still identifies insecure design as one of the top 10 threats to security. Furthermore, even if the planned design is secure, verifying that the planned security assumptions hold at run-time and investigating any violations that may have occurred is cumbersome. In particular, the configuration of run-time monitors such as the Java Security Manager, which could enforce design-time security assumptions, is non-trivial and therefore used in practice rarely. To address these challenges, we present UMLsecRT for automatically supporting model-based security engineering with run-time monitoring of design-time security specifications and round-trip engineering for propagating run-time observations to the design level. Following the established security-by-design approach UMLsec, security experts annotate system models with security properties that UMLsecRT automatically synchronizes with corresponding source code annotations for the automatic configuration of UMLsecRT’s run-time monitor. To this end, UMLecRT monitors these security properties at run-time without additional effort to specify monitoring policies. Developers can define mitigations for attacks detected at run-time in advance by adjusting the automatically synchronized annotations at implementation time. Triggered by a security violation, UMLsecRT can adapt the design-time models based on run-time findings to facilitate the investigation of security violations. We evaluated UMLsecRT concerning its effectiveness and applicability to security violations extracted from real-world attacks and the DaCapo benchmark, conducted user studies on the usability of the adapted models and the feasibility of UMLsecRT in practice, especially concerning countermeasures, and investigated the scalability of UMLsecRT. To study the applicability of the whole development process, we applied UMLsecRT in two case studies to the Eclipse Secure Storage and the electronic health record system iTrust.
VersatileFL: Volatility-Resilient Federated Learning in Wireless Edge Networks

In the era of artificial intelligence (AI), deep neural networks (DNNs) become larger using a massive amount of data, and thus, they are trained via cooperative computing devices (e.g., GPUs or servers) based on federated learning. As computation and data generation move to the edge due to privacy, latency, or bandwidth issue, DNN with edge devices has been investigated. However, edge devices are wirelessly connected and mostly incur fragile connectivity. We propose VersatileFL, a novel volatility-resilient deep learning framework under hostile environments. We address short-term and long-term volatility: 1) versatile distributed learning against short-term fluctuation by substituting the missing intermediate values with the past or approximated values and 2) model rearrangement with runtime connectivity diagnosis against long-term variation by adaptively adjusting the partitioned model for the impaired. We have demonstrated that VersatileFL has achieved 62.0% and 31.9% higher performance than hostile learning without a maintenance scheme against the short-term and long-term volatility, respectively.
A Comparative Analysis of Reference Alliances in Domestic and International Library Communities

Reference is one of the basic core services of libraries, and numerous reference alliances have been established in the library community at home and abroad to better promote the service development.Based on the basic objectives and actual scope of work of reference alliances, this paper divides them into three types of literature sharing, librarian sharing, and intelligence sharing, makes a detailed elaboration and comparative analysis of typical alliances of each type, and puts forward thoughts and suggestions for the subsequent development of domestic reference alliances.
IoT Prototype Measuring Plant Height in Real-Time

Physically, development of plants will experience physical changes, one of which is the height of the plant. Monitoring activities will certainly be effective with the help of technology. The utilization of IoT is a technological breakthrough in that multitasking can be used for monitoring and sharing data simultaneously. So to support growth observation activities, this study aims to implement an IoT-based prototype modeling tool with ultrasonic sensors embedded in the NodeMCU microcontroller which is integrated with the database server. Modeling is used to analyze the workings of measuring plant height, involving case studies of mustard plant growth which are interpreted descriptively based on sensor capture height which is compared with expert-validated original observations. The research method used is quantitative data processing in the form of comparison charts. The data obtained in real-time by accessing the website address is available so that the data records are stored in the database server for as much as 1071 data for 33 days with 5 storage sessions a day. The results of the IoT prototype modeling managed to capture altitude data, with a comparison presentation between the height of the original plant and the sensor capture of 54.7% valid data without any difference. The conclusion from this study, there are still quite a lot of differences in the comparison of the two. This is due to modeling with improper sensor placement, so that it is detected outside the boundaries between predetermined objects.
Expert Finding for Citizen Science

Citizen Science brings together scientists and public participants to collaborate on a wide range of applications and fields. With this approach, Citizen Science advances scientific research and communication while accounting for various stakeholders. Across many Citizen Science projects, digital discussion platforms play an essential role for self-governance and self-organisation. In order to increase the quality of the discussions held on these platforms, we propose a model that recommends users to new discussions in which they are likely to contribute meaningful content. Our model learns relevant user representations based on the quality of past interactions between users and discussion threads, as well as the text content of questions, using a ranking loss function, an approximation of the NDCG metric, and matrix factorization. We demonstrate that our approach is able to predict potential experts on unseen discussion threads and outperforms several baselines. Compared to state-of-the-art expert finding techniques, the architecture of our model is significantly less complex, while focusing on a mostly overlooked ranking loss function.
Efficient Bayesian Inference By Conjugate-Computation Variational Message Passing

Variational message passing is an efficient Bayesian inference method in factorized probabilistic models composed of conjugate factors from the exponential family (EF) distributions. In many applications, a more accurate model for the process under consideration can be obtained by inserting nonlinear deterministic factors in the model. Unfortunately, variational messages that pass through nonlinear nodes cannot be analytically computed in closed form. In this paper, we derive an efficient algorithm for passing variational messages through arbitrary deterministic factors. Our method is based on projecting outgoing messages onto an EF distribution. We implemented our algorithm in RxInfer, which is an open-source message passing-based inference package in Julia. The resulting implementation yields efficient message passing-based inference in arbitrary models composed of stochastic and deterministic factors. We compare our method to alternative state-of-the-art inference methods and find lower (i.e., better) free energy residuals for the proposed method.