Reviewerly/exHarmony huggingface dataset:
Support vector data description with kernel density estimation (SVDD-KDE) control chart for network intrusion monitoring

Abstract Multivariate control charts have been applied in many sectors. One of the sectors that employ this method is network intrusion detection. However, the issue arises when the conventional control chart faces difficulty monitoring the network-traffic data that do not follow a normal distribution as required. Consequently, more false alarms will be found when inspecting network traffic data. To settle this problem, support vector data description (SVDD) is suggested. The control chart based on the SVDD distance can be applied for the non-normal distribution, even the unknown distributions. Kernel density estimation (KDE) is the nonparametric approach that can be applied in estimating the control limit of the non-parametric control charts. Based on these facts, a multivariate chart based on the integrated SVDD and KDE (SVDD-KDE) is proposed to monitor the network's anomaly. Simulation using the synthetic dataset is performed to examine the performance of the SVDD-KDE chart in detecting multivariate data shifts and outliers. Based on the simulation results, the proposed method produces better performance in detecting shifts and higher accuracy in detecting outliers. Further, the proposed method is applied in the intrusion detection system (IDS) to monitor network attacks. The NSL-KDD data is analyzed as the benchmark dataset. A comparison between the SVDD-KDE chart with the other IDS-based-control chart and the machine learning algorithms is executed. Although the it has high computational cost, the results show that the IDS based on the SVDD-KDE chart produces a high accuracy at 0.917 and AUC at 0.915 with a low false positive rate compared to several algorithms.
Cybersecurity vulnerability analysis of medical devices purchased by national health services

Abstract The growing integration of software within medical devices introduces the potential for cybersecurity threats. How significant is this risk, and to what extent are citizens currently exposed? In this study, we adopt a new data-gathering methodology using datasets provided in Open Contracting Data Standard (OCDS). This allowed us to perform an extensive analysis across over 36 countries within a 12-year range, searching 92 million public administration purchase records for potentially vulnerable medical devices. The findings reveal a concerning landscape wherein numerous medical devices purchased by national health services possessed or still possess 661 distinct vulnerabilities—more than half of which are deemed critical or high-severity. These vulnerabilities enable relatively simple attacks to impact data confidentiality, integrity, and accessibility severely. Even if patches were applied immediately upon discovery, these vulnerabilities would still result in roughly 3.2 years of system exposure from the time a device is purchased until a software vulnerability is announced, with all classes of devices affected, including high-risk IIB and III devices which accounts for 74% of instances. While a full analysis requires interactivity, this noninvasive methodology enables a large-scale study, emphasizing the need to move faster from the safety to the security of medical devices.
Pool competition and centralization in PoS blockchain network

In Proof-of-Stake (PoS) blockchain network, stakeholders spend tokens for consumption while competing for network transaction fee with unspent tokens. Stakeholders are incentivized to form staking pools to compete with each other, but continued staking pool merge does not occur given fixed pool operating cost. Unlike other blockchain networks and traditional payment systems, token price in PoS blockchain network is a direct result of competition among service providers (staking pools) and is strongly positively correlated with network decentralization level. As an extension, we show that uneven wealth distribution or biased validator selection function enhances stake centralization and sabotage token price.
OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study

Background Electronic health records (EHRs) in unstructured formats are valuable sources of information for research in both the clinical and biomedical domains. However, before such records can be used for research purposes, sensitive health information (SHI) must be removed in several cases to protect patient privacy. Rule-based and machine learning–based methods have been shown to be effective in deidentification. However, very few studies investigated the combination of transformer-based language models and rules. Objective The objective of this study is to develop a hybrid deidentification pipeline for Australian EHR text notes using rules and transformers. The study also aims to investigate the impact of pretrained word embedding and transformer-based language models. Methods In this study, we present a hybrid deidentification pipeline called OpenDeID, which is developed using an Australian multicenter EHR-based corpus called OpenDeID Corpus. The OpenDeID corpus consists of 2100 pathology reports with 38,414 SHI entities from 1833 patients. The OpenDeID pipeline incorporates a hybrid approach of associative rules, supervised deep learning, and pretrained language models. Results The OpenDeID achieved a best F1-score of 0.9659 by fine-tuning the Discharge Summary BioBERT model and incorporating various preprocessing and postprocessing rules. The OpenDeID pipeline has been deployed at a large tertiary teaching hospital and has processed over 8000 unstructured EHR text notes in real time. Conclusions The OpenDeID pipeline is a hybrid deidentification pipeline to deidentify SHI entities in unstructured EHR text notes. The pipeline has been evaluated on a large multicenter corpus. External validation will be undertaken as part of our future work to evaluate the effectiveness of the OpenDeID pipeline.
How AI Assistants Could Help Answer Patients’ Messages—and Potentially Improve Their Outcomes

This Medical News article is an interview with John Ayers, PhD, MA, vice chief of innovation in the Division of Infectious Diseases &amp;amp;amp; Global Public Health at the University of California, San Diego, the lead author of a recent study on chatbot responses to patient questions.
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy

Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keeping their training data locally has received great attention recently and can protect privacy in comparison with the traditional centralized learning paradigm. However, sensitive information about the training data can still be inferred from model parameters shared in FL. Differential privacy (DP) is the state-of-the-art technique to defend against those attacks. The key challenge to achieving DP in FL lies in the adverse impact of DP noise on model accuracy, particularly for deep learning models with large numbers of parameters. This paper develops a novel differentially-private FL scheme named Fed-SMP that provides a client-level DP guarantee while maintaining high model accuracy. To mitigate the impact of privacy protection on model accuracy, Fed-SMP leverages a new technique called Sparsified Model Perturbation (SMP) where local models are sparsified first before being perturbed by Gaussian noise. We provide a tight end-to-end privacy analysis for Fed-SMP using Rényi DP and prove the convergence of Fed-SMP with both unbiased and biased sparsifications. Extensive experiments on real-world datasets are conducted to demonstrate the effectiveness of Fed-SMP in improving model accuracy with the same DP guarantee and saving communication cost simultaneously.
Quality Assurance Awareness in Open Source Software Projects on GitHub

Software engineers employ a variety of approaches to ensure the quality of software systems, including software testing, modern code review, automated static analysis, build automation, and continuous integration. To make effective decisions regarding quality assurance (QA), software engineers need to have an awareness of (1) the QA approaches that are in use in a project, and (2) how they are used. Through an exploratory, mixed-methods investigation we set out to better understand the awareness of software engineers in open-source software (OSS) development with regard to QA practices. This involved a largescale survey of 471 maintainers and contributors on GitHub. Our findings indicate that a high-level awareness among the respondents is common, but also that the respondents are less certain about how the practices are adopted; we further consider the perspective of both the contributor and the maintainer.
Mentor initialed multiparty hierarchical joint remote preparation of an arbitrary n-qudit state via generalized Bell states

Abstract We propose a protocol to Mentor initiated hierarchical joint remote prepare the arbitrary n-qudit state with generalized Bell states.The agents share generalized Bell states with the Mentor. The Mentor performs five-qudit projective measurements on his entangled particles to initiate the quantum channel shared by the agents. All the senders share the information of the prepared state and make quantum measurements on their particles based on the state to be prepared. The agent in the upper grade needs only the assistance of one of the agents in the lower grade to reconstruct the original state, while the agents in the lower grade need the assistance of all the other agents to recover the original state. It is more convenient in application than others since the agents in the protocol only requires two-particle entanglement for Mentor initiated hierarchical joint remote preparation of an arbitrary n-qudit state.
Randomly Punctured Reed-Solomon Codes Achieve the List Decoding Capacity over Polynomial-Size Alphabets

This paper shows that, with high probability, randomly punctured Reed-Solomon codes over fields of polynomial size achieve the list decoding capacity. More specifically, we prove that for any $\varepsilon \gt 0$ and $R \in(0,1)$, with high probability, randomly punctured Reed-Solomon codes of block length n and rate R are $(1-R-\varepsilon, O(1 / \varepsilon))$ list decodable over alphabets of size at least $2^{\text {poly }(1 / \varepsilon)} n^{2}$. This extends the recent breakthrough of Brakensiek, Gopi, and Makam (STOC 2023) that randomly punctured Reed-Solomon codes over fields of exponential size attain the generalized Singleton bound of Shangguan and Tamo (STOC 2020).
TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception

This work aims for transferring a Transformer-based image compression codec from human perception to machine perception without fine-tuning the codec. We propose a transferable Transformer-based image compression framework, termed TransTIC. Inspired by visual prompt tuning, TransTIC adopts an instance-specific prompt generator to inject instance-specific prompts to the encoder and task-specific prompts to the decoder. Extensive experiments show that our proposed method is capable of transferring the base codec to various machine tasks and outperforms the competing methods significantly. To our best knowledge, this work is the first attempt to utilize prompting on the low-level image compression task.
SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes

Multi-object tracking (MOT) in sports scenes plays a critical role in gathering players statistics, supporting further applications, such as automatic tactical analysis. Yet existing MOT benchmarks cast little attention on this domain. In this work, we present a new large-scale multi-object tracking dataset in multiple sports scenes, coined as SportsMOT, where all players on the court are supposed to be tracked. It consists of 240 video sequences, over 150K frames (almost 15x MOT17) and over 1.6M bounding boxes (3x MOT17) collected from 3 sports categories, including basketball, volleyball and football. Our dataset is characterized with two key properties: 1) fast and variable-speed motion and 2) similar yet distinguishable appearance. We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association. We benchmark several state-of-the-art trackers and reveal the key challenge of SportsMOT lies in object association. To alleviate the issue, we further propose a new multi-object tracking framework, termed as MixSort, introducing a MixFormer-like structure as an auxiliary association model to prevailing tracking-by-detection trackers. By integrating the customized appearance-based association with the original motion-based association, MixSort achieves state-of-the-art performance on SportsMOT and MOT17. Based on MixSort, we give an in-depth analysis and provide some profound insights into SportsMOT.
WaterMask: Instance Segmentation for Underwater Imagery

Underwater image instance segmentation is a fundamental and critical step in underwater image analysis and understanding. However, the paucity of general multiclass instance segmentation datasets has impeded the development of instance segmentation studies for underwater images. In this paper, we propose the first underwater image instance segmentation dataset (UIIS), which provides 4628 images for 7 categories with pixel-level annotations. Meanwhile, we also design WaterMask for underwater image instance segmentation for the first time. In Water-Mask, we first devise Difference Similarity Graph Attention Module (DSGAT) to recover lost detailed information due to image quality degradation and downsampling to help the network prediction. Then, we propose Multi-level Feature Refinement Module (MFRM) to predict foreground masks and boundary masks separately by features at different scales, and guide the network through Boundary Mask Strategy (BMS) with boundary learning loss to provide finer prediction results. Extensive experimental results demonstrates that WaterMask can achieve significant gains of 2.9, 3.8 mAP over Mask R-CNN when using ResNet-50 and ResNet-101. Code and Dataset are available at https://github.com/LiamLian0727/WaterMask.
Latent-OFER: Detect, Mask, and Reconstruct with Latent Vectors for Occluded Facial Expression Recognition

Most research on facial expression recognition (FER) is conducted in highly controlled environments, but its performance is often unacceptable when applied to real-world situations. This is because when unexpected objects occlude the face, the FER network faces difficulties extracting facial features and accurately predicting facial expressions. Therefore, occluded FER (OFER) is a challenging problem. Previous studies on occlusion-aware FER have typically required fully annotated facial images for training. However, collecting facial images with various occlusions and expression annotations is time-consuming and expensive. Latent-OFER, the proposed method, can detect occlusions, restore occluded parts of the face as if they were unoccluded, and recognize them, improving FER accuracy. This approach involves three steps: First, the vision transformer (ViT)based occlusion patch detector masks the occluded position by training only latent vectors from the unoccluded patches using the support vector data description algorithm. Second, the hybrid reconstruction network generates the masking position as a complete image using the ViT and convolutional neural network (CNN). Last, the expression-relevant latent vector extractor retrieves and uses expression-related information from all latent vectors by applying a CNN-based class activation map. This mechanism has a significant advantage in preventing performance degradation from occlusion by unseen objects. The experimental results on several databases demonstrate the superiority of the proposed method over state-of-the-art methods. This code is available at https://github.com/leeisack/Latent-OFER.
A medical image segmentation methods based on SOM and wavelet transforms

Image segmentation plays a crucial role in many medical imaging applications and is an important but inherently difficult problem. The paper discuses the method that classify unsupervised image using a Kohonen self-organizing map neural network. This method exits two problems: training time of the network is too long and the classified result and quantity were bigger influenced by the noise of image. Two-dimensional Discrete Wavelet Transforms (DWT) decompose MRI image into the small size and denoise approximation images. Kohonen self-organizing map neural network is trained with approximation image, then trained neural network classify pixels of original image. Training time of the network was notability decrease and the classified quality influenced by the noise of image was notability reduce. The technique presented here has shown a very encouraging level of performance for the problem of segmentation in MRI image of the head.
Planning with Always Preferences by Compilation into STRIPS with Action Costs

We address planning with always preferences in propositional domains, proposing a new compilation schema for translating a STRIPS problem enriched with always preferences (and possibly also soft goals) into a STRIPS problem with action costs. Our method allows many STRIPS planners to effectively address planning with always preferences and soft goals. An experimental analysis indicates that such basic planners are competitive with current planners using techniques specifically developed to handle always preferences.
Decision Fusion Using Fuzzy Logic Type 1 for Two Aviation Scenarios

In this paper we discuss some aspects of decision fusion using fuzzy logic of type 1 for two aviation scenarios. We study the fuzzy inference system/engine and briefly discuss the MATLAB-GUI based tool developed by the authors to evaluate any existing or new fuzzy implication functions. Two examples: i) formation flight, and ii) flying along an air lane are numerically evaluated and the performance of decision fusion systems incorporating fuzzy logic is illustrated. The results indicate that some existing as well as new implication functions, devised by the authors, work very well for the examples considered. This opens up new possibilities of applications of fuzzy logic based decision fusion to varieties of aerospace/aviation problems in data fusion, decision making, and decision fusion.
Resolution of Multimodal Object References using Conceptual Short Term Memory

This poster presents cognitive-motivated aspects of a technical system for the resolution of references to objects within an assembly-task domain. The research is integrated in the Collaborative Research Center SFB 360 which is concerned with situated artificial communicators. One application scenario consists of a task-oriented discourse between an instructor and a constructor who collaboratively build aggregates from a wooden toy kit (Baufix), or from generic CAD parts. In our current setting this scenario is embedded in a virtual reality (VR) installation, where the human user, taking the role of the instructor, guides the artificial constructor (embodied by the ECA Max) through the assembly process by means of multimodal task descriptions (see Figure 1). The system handles instructions like: Plug the left red screw from above in the middle hole of the wing and turn it this way. accompanied by coverbal deictic and mimetic gestures (see Latoschik, 2001).
Approximate String Matching Based on Bit Operations

The bit-parallelism is a speedup method for solving problems of string matching. The speedup by the bitparallelism depends on the performance of a computer and it is very significant in practice. In terms of time complexity based on a standard computational model, however, the performance cannot be represented explicitly. This paper introduces a parameter in a computational model to measure the performance of a computer, and explicitly analyses the time complexity of a bit-parallel algorithm for the match-count problem. The implementation of the algorithm and some test calculations are presented.
Blow Up and Blur Constructions in Algebraic Logic

Fix \(2<n<\omega \) and let \(\mathsf{CA}_n\) denote the class of cylindric algebras of dimension n. Roughly, \(\mathsf{CA}_n\) is the algebraic counterpart of the proof theory of first order logic restricted to the first n variables which we denote by \(L_n\). The variety \(\mathsf{RCA}_n\) of representable \(\mathsf{CA}_n\)s reflects algebraically the semantics of \(L_n\). We show using a so-called blow up and blur construction that several varieties (in fact infinitely many) containing and including the variety \(\mathsf{RCA}_n\) are not atom-canonical. A variety \(\mathsf V\) of Boolean algebras with operators is atom-canonical, if whenever \({\mathfrak {A}}\in \mathsf V\) is atomic, then its Dedekind–MacNeille completion, sometimes referred to as its minimal completion, is also in \(\mathsf V\). From our hitherto obtained algebraic results, we show, employing the powerful machinery of algebraic logic, that the celebrated Henkin–Orey omitting types theorem fails dramatically for \(L_n\) even if we allow certain generalized models that are only locally classical.
Model Acceptability and the Use of Bayes-Fiducial Methods for Validating Models

The use of significance tests for validating models is basically inadequate, since nonsignificant results only pertain to the compatibility of the data with the model. In this paper an alternative form of data analysis is proposed, whose objective is to assess the acceptability of the model given the data. The use of Bayes-fiducial inference is suggested in this connection; the approach is exemplified through the analysis of an experiment planned for investigating a model of successive stages of information processing in binary choice reaction.
Learning Transformation Rules by Examples

This paper presents an abstract for a general data transformation approach. Using programming by demonstration technique, we learn the transformation rules through user given examples. These transformation rules are automatically generated from a predefined grammar. Due to the grammar space is huge, we propose a grammar space reduction method to reduce the search space and a sketch of search algorithm is adopted to identify the rules that are consistent with the examples. The final experimental results show our approach achieves promising results on different transformation scenarios.
Improving Narrative Consistency in Planning-Based Interactive Storytelling

Interactive Storytelling (IS) systems are an emerging class of interactive entertainment applications with emphasis in narrative aspects. This paper deals with stories that have plots centered on a riddle, and describes a mechanism that can be introduced to planning-based IS in order to make the generated stories follow a specified tension arc. Results from experiments with users suggest that the proposed method is capable to bring improvements to these systems.
A TRANSFORMAÇÃO DIGITAL NO SETOR DE RECURSOS HUMANOS: UM ESTUDO DE CASO SOBRE O USO DA TECNOLOGIA NO PROCESSO DE RECRUTAMENTO E SELEÇÃO

This article aims to demonstrate how Digital Transformation (DT) can help an organization in its Recruitment and Selection (RS) process. The study has a qualitative approach, from which a case study was carried out to validate the theory. Semi-structured interviews were conducted with four people linked to RS, in order to follow the DT process and its benefits after implementation. As a result of this study, it was identified that DT brought several improvements, being possible to emphasize the analysis of profiles and resume through Artificial Intelligence (AI), automation and creation of mass emails, curriculum database, and indicators that help the decision making of the organization. This action was essential for the organization to focus its attention on other processes and to continue its transformation journey.