Reviewerly/exHarmony huggingface dataset:
D-LORD: DYSL-AI Database for Low-Resolution Disguised Face Recognition

Face recognition in a low-resolution video stream captured from a surveillance camera is a challenging problem. The problem becomes even more complicated when the subjects appearing in the video wear disguise artifacts to hide their identity or try to impersonate someone. The lack of labeled datasets restricts the current research on low-resolution face recognition systems under disguise. With this paper, we propose a large-scale database, D-LORD, that will facilitate the research on face recognition. The proposed D-LORD consists of high-resolution mugshot images of 2,100 subjects and 14,098 low-resolution surveillance videos with over 1.2 million frames. In the videos, subjects’ faces are occluded by various disguise artifacts, such as face masks, sunglasses, wigs, hats, and monkey caps. To the best of our knowledge, D-LORD is the first database to address the complex problem of low-resolution face recognition with disguise variations. We also establish the benchmark results of several state-of-the-art face detectors, frame selection algorithms, face restoration, and face verification algorithms on well-defined experimental protocols. The results demonstrate GAR@1%FAR values in the range of 86.44% to 49.45% for varying disguises at varying distances. The dataset is publicly available to the research community at https://dyslai.org/datasets/D-LORD/.
Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data

Electronic health records have facilitated the extraction and analysis of a vast amount of data with many variables for clinical care and research. Conventional regression-based statistical methods may not capture all the complexities in high-dimensional data analysis. Therefore, researchers are increasingly using machine learning (ML)-based methods to better handle these more challenging datasets for the discovery of hidden patterns in patients’ data and for classification and predictive purposes. This article describes commonly used ML methods in structured data analysis with examples in orthopedic surgery. We present practical considerations in starting an ML project and appraising published studies in this field.
The impact of promotion on purchase intentions in Jordan: Video game industry

This study is done to identify the impact of promotion on the purchase intentions of video games in Jordan. The independent variables used to test promotion were trailers, discounts, influencers/streamers, and social media. The data were collected by using a survey designed on google forms sent or using a Quick Response (QR) code to random gamers in Jordan. 129 people responded to the survey. The data were coded on SPSS and reliability, validity, and correlation among variables were confirmed, then the hypotheses were tested by multiple regressions. The researcher found a statistically significant impact of promotional tools on purchase intentions of the video gaming industry in Jordan, where trailers have the highest significant positive effect on purchase intentions, followed by social media, then influencers/streamers, while discounts do not significantly affect purchase intentions of video games in Jordan. Some limitations and recommendations for future research have been provided at the end of the research.
Based on Text Augmentation Personalized Dialog Generation With Persona-Sparse Data

Introducing persona information in dialogue generation helps to make replies more human-like. However, how to solve the problem of ignoring the persona of the speakers in dialogue generation on persona-spare data, and only focusing on dialogue information is still a problem. In this paper, a text augmentation method for persona-sparse data is proposed. By performing text augmentation operations such as text generation on sparse persona information, the problem of ignoring persona in reply generation is improved. Two encoders are used to encode the context dialogue and character information respectively, and the decoder use multi-head attention mechanism to fully integrate dialogue context and persona. Experiments show, compared with the baseline model, for persona-spares data, the text augmentation method and dialogue generation model adopted in this paper have more advantages, and can generate responses that are consistent with the dialogue context and have personal information.
ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer

Machine unlearning can fortify the privacy and security of machine learning applications. Unfortunately, the exact unlearning approaches are inefficient, and the approximate unlearning approaches are unsuitable for complicated CNNs. Moreover, the approximate approaches have serious security flaws because even unlearning completely different data points can produce the same contribution estimation as unlearning the target data points. To address the above problems, we try to define machine unlearning from the knowledge perspective, and we propose a knowledge-level machine unlearning method, namely ERM-KTP. Specifically, we propose an entanglement-reduced mask (ERM) structure to reduce the knowledge entanglement among classes during the training phase. When receiving the un-learning requests, we transfer the knowledge of the non-target data points from the original model to the unlearned model and meanwhile prohibit the knowledge of the target data points via our proposed knowledge transfer and prohibition (KTP) method. Finally, we will get the un-learned model as the result and delete the original model to accomplish the unlearning process. Especially, our proposed ERM-KTP is an interpretable unlearning method because the ERM structure and the crafted masks in KTP can explicitly explain the operation and the effect of un-learning data points. Extensive experiments demonstrate the effectiveness, efficiency, high fidelity, and scalability of the ERM-KTP unlearning method. Code is available at https://github.com/RUIYUN-ML/ERM-KTP
TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models

Masked image modeling (MIM) performs strongly in pretraining large vision Transformers (ViTs). However, small models that are critical for real-world applications can-not or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.
Knowledge-enhanced Graph Topic Transformer for Explainable Biomedical Text Summarization

Given the overwhelming and rapidly increasing volumes of the published biomedical literature, automatic biomedical text summarization has long been a highly important task. Recently, great advances in the performance of biomedical text summarization have been facilitated by pre-trained language models (PLMs) based on fine-tuning. However, existing summarization methods based on PLMs do not capture domain-specific knowledge. This can result in generated summaries with low coherence, including redundant sentences, or excluding important domain knowledge conveyed in the full-text document. Furthermore, the black-box nature of the transformers means that they lack explainability, i.e. it is not clear to users how and why the summary was generated. The domain-specific knowledge and explainability are crucial for the accuracy and transparency of biomedical text summarization methods. In this article, we aim to address these issues by proposing a novel domain knowledge-enhanced graph topic transformer (DORIS) for explainable biomedical text summarization. The model integrates the graph neural topic model and the domain-specific knowledge from the Unified Medical Language System (UMLS) into the transformer-based PLM, to improve the explainability and accuracy. Experimental results on four biomedical literature datasets show that our model outperforms existing state-of-the-art (SOTA) PLM-based summarization methods on biomedical extractive summarization. Furthermore, our use of graph neural topic modeling means that our model possesses the desirable property of being explainable, i.e. it is straightforward for users to understand how and why the model selects particular sentences for inclusion in the summary. The domain-specific knowledge helps our model to learn more coherent topics, to better explain the performance.
Perception Versus Reality: How User Self-reflections Compare to Actual Data

One of the main promises of wearable sensing devices capable of physiological tracking is the potential that users can leverage the technology to make positive life changes. Now that these devices have sufficient accuracy, it is feasible that users could make decisions based on feedback from the device to change their habits and improve their well-being. A potential challenge to this, however, is whether users are able to recognize actual changes in their behavior compared to perceived changes. In this work, we look at participants who used the Oura ring as part of an in-the-wild study. Based on data analysis following 10–12 months of usage, we find that users who had positive perceptions of habit change towards sleep failed to show long-term improvement in their sleep quality, indicating a gap between perceived success and real-world impact. This work is important to informing the future design and implementation of health-tracking wearables.
Uncertain about ChatGPT: enabling the uncertainty evaluation of large language models

ChatGPT, OpenAI’s chatbot, has gained consider-able attention since its launch in November 2022, owing to its ability to formulate articulated responses to text queries and comments relating to seemingly any conceivable subject. As impressive as the majority of interactions with ChatGPT are, this large language model has a number of acknowledged shortcomings, which in several cases, may be directly related to how ChatGPT handles uncertainty. The objective of this paper is to pave the way to formal analysis of ChatGPT uncertainty handling. To this end, the ability of the Uncertainty Representation and Reasoning Framework (URREF) ontology is assessed, to support such analysis. Elements of structured experiments for reproducible results are identified. The dataset built varies Information Criteria of Correctness, Non-specificity, Self-confidence, Relevance and Inconsistency, and the Source Criteria of Reliability, Competency and Type. ChatGPT’s answers are analyzed along Information Criteria of Correctness, Non-specificity and Self-confidence. Both generic and singular information are sequentially provided. The outcome of this preliminary study is twofold: Firstly, we validate that the experimental setup is efficient in capturing aspects of ChatGPT uncertainty handling. Secondly, we identify possible modifications to the URREF ontology that will be discussed and eventually implemented in URREF ontology Version 4.0 under development.
A Dataflow Analysis for Comparing and Reordering Predicate Arguments

In this work, which is done in the context of a (moded) logic programming language, we devise a data-flow analysis dedicated to computing what we call argument profiles. Such a profile essentially describes, for each argument of a predicate, its functionality, i.e. the operations in which the argument can be involved during an evaluation of the predicate, as well as how the argument contributes to the consumption and/or construction of data values. While the computed argument profiles can be useful for applications in the context of program understanding (as each profile essentially provides a way to better understand the role of the argument), they more importantly provide a way to discern between arguments in a manner that is more fine-grained than what can be done with other abstract characterizations such as types and modes. This is important for applications where one needs to identify correspondences between the arguments of two or more different predicates that need to be compared, such as during clone detection. Moreover, since a total order can be defined on the abstract domain of profiles, our analysis can be used for rearranging predicate arguments and order them according to their functionality, constituting as such an essential ingredient for predicate normalization techniques.
The healthcare cyberpandemic: It’s time for an intervention

The healthcare sector is in crisis as Internet-based actors attack the digital infrastructure necessary for operations. The growing complexity of systems and events on the world stage have given rise to a dynamic threat landscape that includes nation-states affiliates. Challenging even private industry, healthcare systems and budgets already strained by COVID-19 are struggling to cope. A pandemic style response with new investment and legislation is needed.
Tighter monogamy inequalities of multiqubit entanglement

Abstract Multipartite entanglement holds great importance in quantum information processing. The distribution of entanglement among subsystems can be characterized by monogamy relations. Based on the β th power of concurrence and negativity, we provide two new monogamy inequalities. Through detailed examples, we demonstrate that these inequalities are tighter than previous results.
Cell projection plots: A novel visualization of bone marrow aspirate cytology

Deep models for cell detection have demonstrated utility in bone marrow cytology, showing impressive results in terms of accuracy and computational efficiency. However, these models have yet to be implemented in the clinical diagnostic workflow. Additionally, the metrics used to evaluate cell detection models are not necessarily aligned with clinical goals and targets. In order to address these issues, we introduce novel, automatically generated visual summaries of bone marrow aspirate specimens called cell projection plots (CPPs). Encompassing relevant biological patterns such as neutrophil maturation, CPPs provide a compact summary of bone marrow aspirate cytology. To gauge clinical relevance, CPPs were inspected by 3 hematopathologists, who decided whether corresponding diagnostic synopses matched with generated CPPs. Pathologists were able to match CPPs to the correct synopsis with a matching degree of 85%. Our finding suggests CPPs can represent clinically relevant information from bone marrow aspirate specimens and may be used to efficiently summarize bone marrow cytology to pathologists. CPPs could be a step toward human-centered implementation of artificial intelligence (AI) in hematopathology, and a basis for a diagnostic-support tool for digital pathology workflows.
High Compression Rate Architecture For Texture Padding Based on V-PCC

Video-based point cloud compression(V-PCC)is a point cloud compression standard formulated by the Moving Picture Experts Group(MPEG) organization. The concept of this standard is to project 3D point cloud information onto a 2D plane and generate 2D image, namely geometry map, texture map, and occupancy map. Overlap 2D images to form 3D depth from three images, combine color and position information, and then encode and decode using High Efficiency Video Coding (HEVC) compression. However, the projected image will have obvious holes. These holes are regarded as high-frequency signal in the image, which will have a bad impact on the subsequent compression rate. It is necessary to use image filling to smooth the image, reduce high-frequency signal, and facilitate subsequent compression processing. Therefore, the purpose of this research is to develop a series of anti-noise procedures to fill and smooth images with High-Efficiency Video Coding(HEVC), including mean filter, Smooth Pull Push Algorithm(SPP), etc. This algorithm has been implemented in mpeg-pcc-tmc2-release-v8.0 [1], and the obtained data proves that although PSNR needs to be sacrificed, it can effectively reduce the number of compressed bytes after texture map filling.
Low Trace-Count Template Attacks on 32-bit Implementations of ASCON AEAD

The recently adopted Ascon standard by NIST offers a lightweight authenticated encryption algorithm for use in resource-constrained cryptographic devices. To help assess side-channel attack risks of Ascon implementations, we present the first template attack based on analyzing power traces, recorded from an STM32F303 microcontroller board running Weatherley’s 32-bit implementations of Ascon-128. Our analysis combines a fragment template attack with belief-propagation and key-enumeration techniques. The main results are three-fold: (1) we reached 100% success rate from a single trace if the C compiler optimized the unmasked implementation for space, (2) the success rate was about 95% after three traces if the compiler optimized instead for time, and (3) we also attacked a masked version, where the success rate was over 90% with 20 traces of executions with the same key, all after enumerating up to 224 key candidates. These results show that suitably-designed template attacks can pose a real threat to Ascon implementations, even if protected by first-order masking, but we also learnt how some differences in programming style, and even compiler optimization settings, can significantly affect the result.
Surface Water Balance Based on the Water District Analysis in Lamong Watershed, East Java Province, Indonesia

The Kali Lamong Watershed is an area administratively located in Gresik, Lamongan, Mojokerto Regencies, and Surabaya City. The Kali Lamong is one of the water resources in these regions. Nowadays, the Kali Lamong is used for raw water, industry, and agriculture, so it has an essential value for society in this region. The average yearly rainfall in the Kali Lamong is 1,700 mm, however, the hydrology conditions in the Kali Lamong Watershed are critical. It is seen from the discharge condition in the rainy season that is moreover the river capacity, but in the dry season it experiences drought. This research intends to determine the solution to the problem with the Kali Lamong Watershed. There is a need for good understanding related to the condition of the Kali Lamong about water availability that is used for the ecosystem demand in this watershed. Therefore, this study aims to conduct the water balance analysis in the Kali Lamong Watershed depending on the water district. The methodology is based on water availability analysis in each water district and water demand analysis for domestic, agricultural, and industrial sectors. The water availability analysis is carried out through two approaches 1) based on the estimation of the discharge data record in one of the water recorders, and 2) using rainfall–runoff modeling. The water balance analysis result in each water district revealed a water deficit in the Lamong Hulu and Kalitemu water districts, where there are only two months periods of water surplus. Keywords: watershed, water balance, water district, Kali Lamong. https://doi.org/10.55463/issn.1674-2974.50.7.9
SDE-YOLO: A Novel Method for Blood Cell Detection

This paper proposes an improved target detection algorithm, SDE-YOLO, based on the YOLOv5s framework, to address the low detection accuracy, misdetection, and leakage in blood cell detection caused by existing single-stage and two-stage detection algorithms. Initially, the Swin Transformer is integrated into the back-end of the backbone to extract the features in a better way. Then, the 32 × 32 network layer in the path-aggregation network (PANet) is removed to decrease the number of parameters in the network while increasing its accuracy in detecting small targets. Moreover, PANet substitutes traditional convolution with depth-separable convolution to accurately recognize small targets while maintaining a fast speed. Finally, replacing the complete intersection over union (CIOU) loss function with the Euclidean intersection over union (EIOU) loss function can help address the imbalance of positive and negative samples and speed up the convergence rate. The SDE-YOLO algorithm achieves a mAP of 99.5%, 95.3%, and 93.3% on the BCCD blood cell dataset for white blood cells, red blood cells, and platelets, respectively, which is an improvement over other single-stage and two-stage algorithms such as SSD, YOLOv4, and YOLOv5s. The experiment yields excellent results, and the algorithm detects blood cells very well. The SDE-YOLO algorithm also has advantages in accuracy and real-time blood cell detection performance compared to the YOLOv7 and YOLOv8 technologies.
Adaptive Federated Learning With Negative Inner Product Aggregation

Federated Learning (FL) represents a distributed machine learning approach that leverages a centralized server to train models while keeping the data on edge devices isolated. FL has the benefits of preserving data privacy and improving model accuracy. However, the occurrence of unexpected device exits during model training can severely impact the performance of the models. To address the communication overhead issue and accelerate model convergence, a novel adaptive federated learning with negative inner product aggregation approach, namely NIPAFed is proposed in this paper. The NIPAFed leverages a congestion control algorithm inspired by TCP, known as additive multiplication subtraction strategy, to adaptively predict the workload of devices based on historical workload. So NIPAFed effectively mitigates the impact of stragglers on the training process. Additionally, to reduce communication overhead and latency, a negative inner product aggregation strategy is employed to accelerate model convergence and minimize the number of communication rounds required. The convergence of the model is also analyzed theoretically. The validity of NIPAFed is tested on federated public datasets and the NIPAFed is compared with some algorithms. The experimental results clearly demonstrate the superiority of the NIPAFed in terms of performance. By reducing device dropouts and minimizing the communication rounds, the NIPAFed effectively controls the communication overhead while the convergence is ensured.
Electronic health records and stratified psychiatry: bridge to precision treatment?

Abstract The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR data’s power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed.