Reviewerly/exHarmony huggingface dataset:
Multiple Object Tracking Based on YOLOv5 and Optimized DeepSORT Algorithm

Abstract As a widely discussed issue in academic and industrial fields, multiple object tracking (MOT) has a huge impact on various aspects, such as video surveillance, human-machine interaction, viral reality and autonomous driving. Among all the MOT algorithms, DeepSORT enjoys a high reputation for its speed, accuracy and robustness to frequent occlusion circumstance. With the help of DeepSORT, the main objective of this research work is to propose an algorithm with a better performance on frequent occlusion and long-time occlusion issues. To accomplish such a mission, an improved deep appearance descriptor was constructed and trained off-line. To achieve a higher performance, the object detector YOLOv5, which has been proved to be faster than previous detectors, is employed in this research as a substitution of the original detector Faster R-CNN.
The Level of Classroom Teacher Digital Literacy in the Technology Dimension of the Instant Digital Competence Assessment (IDCA)

This study aims to describe the level of digital literacy of elementary school teachers, especially in the technological dimension. Based on the Law of the Republic of Indonesia No. 14 of 2005 concerning Teachers and Lecturers, article 20 verse 2, teachers are required to be able to use technology. However, in reality, many teachers still have a low ability in using technology. The method used in this research is a descriptive qualitative method with a case study approach. The subjects in this study were teachers of grades 1 to 6 coming from six different elementary schools. The classroom teacher's digital literacy level was measured using a test technique. The results of the study showed that teachers of grade 1, grade 2, grade 3, and grade 4 had low digital literacy categories, while teachers of grade 5 had moderate digital literacy categories, and teachers of grade 6 had a very low category of digital literacy. Based on these results, it can be concluded that there is no equality in the digital literacy abilities of classroom teachers in elementary schools.
Can Engineers Build AI/ML Systems? Analysis and an Alternative Approach

Although Artificial Intelligence (AI) and Machine Learning (ML) are attracting a lot of scientific and engineering attention nowadays, nothing up to now has been achieved to reach the level of building machines that possess human-like intelligence.Though, the engineering community continuously claims that several engineering problems are solved using AI or ML.Here, it is argued that engineers are not being able to build intelligent machines, implying that the systems claimed to have AI/ML belong to different engineering domains.The base of the syllogism is the existence of four main obstacles on which extensive elucidation is performed.In addition, attempt to clear out the developed confusion, mis-use and ab-use of the phrases "Artificial Intelligence" and "Machine Learning" by scientists and engineers is carried out.Furthermore, mathematical, and philosophical approaches are also mentioned that strengthen the argument against AI implementability as part of the whole syllogism.Finally, an alternative approach (not being unique) is suggested and discussed for performing research on AI and ML by the engineers.It is based on complexity theory and non-linear adaptive systems and provides the benefit of eliminating the before mentioned pragmatic and philosophical obstacles that engineers are facing and ignoring, without creating confusion on this scientific endeavor.
Comprehensive Analysis of Online Social Network Frauds

Over the past few years, the popularity of online social networks (OSN) has been tremendous, and OSN has become a part of everyone's lives. People uses OSN services to stay connected with each other for social interactions, business opportunities, entertainment, and career. The open nature and popularity of OSN have resulted in a number of frauds and misappropriations. Online Social Network Fraud (OSNF) is a growing threat in cyberspace through OSN sites. The risk is even higher when its targets are adults, children, and females. This chapter focuses on comprehensive analysis of different types of OSN fraud based on the key intention of fraud by describing the nature of the fraud and its potential harm to users, statistics of OSNF and the interrelationship between OSN frauds, threats, and attacks. In addition, we analyzed the most recent and cutting-edge findings from literature for detection of OSNF using machine learning algorithms. Although the machine learning algorithms are capable of detecting the OSNF still there are many challenges due to the complex nature of the OSN.
ESSA: Explanation Iterative Supervision via Saliency-guided Data Augmentation

Explanation supervision is a technique in which the model is guided by human-generated explanations during training. This technique aims to improve both the interpretability and predictability of the model by incorporating human understanding into the training process. Since explanation supervision requires a large scale of training data, the data augmentation technique is necessary to be applied to increase the size and diversity of the original dataset. However, data augmentation on sophisticated data like medical images is particularly challenging due to the following: 1) scarcity of data in training the learning-based data augmenter, 2) difficulty in generating realistic and sophisticated images, and 3) difficulty in ensuring the augmented data indeed boosts the performance of explanation-guided learning. To solve these challenges, we propose an Explanation Iterative Supervision via Saliency-guided Data Augmentation (ESSA) framework for conducting explanation supervision and adversarial-trained image data augmentation via a synergized iterative loop that handles the translation from annotation to sophisticated images and the generation of synthetic image-annotation pairs with an alternating training strategy. Extensive experiments on two datasets from the medical imaging domain demonstrate the effectiveness of our proposed framework in improving both the predictability and explainability of the model.
Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models

Existing anomaly detection models for time series are primarily trained with normal-point-dominant data and would become ineffective when anomalous points intensively occur in certain episodes. To solve this problem, we propose a new approach, called DiffAD, from the perspective of time series imputation. Unlike previous prediction- and reconstruction-based methods that adopt either partial or complete data as observed values for estimation, DiffAD uses a density ratio-based strategy to select normal observations flexibly that can easily adapt to the anomaly concentration scenarios. To alleviate the model bias problem in the presence of anomaly concentration, we design a new denoising diffusion-based imputation method to enhance the imputation performance of missing values with conditional weight-incremental diffusion, which can preserve the information of observed values and substantially improves data generation quality for stable anomaly detection. Besides, we customize a multi-scale state space model to capture the long-term dependencies across episodes with different anomaly patterns. Extensive experimental results on real-world datasets show that DiffAD performs better than state-of-the-art benchmarks.
Hierarchical Invariant Learning for Domain Generalization Recommendation

Most cross-domain recommenders require samples on target domains or source-target overlaps to carry out domain adaptation. However, in many real-world situations, target domains are lack of such knowledge. Few works discuss this problem, whose essence is domain generalization recommendation. In this paper, we figure out domain generalization recommendation with a clear symbolized definition and propose corresponding models. Moreover, we illustrate its strong connection with zero-shot recommendation, pretrained recommendation and cold-start recommendation, distinguishing it from content-based recommendation. By analyzing its properties, we propose HIRL^+ and a series of heuristic methods to solve this problem. We propose hierarchical invariant learning to expel the specific patterns in both domain-level and environment-level, and find the common patterns in generalization space. To make the division of environments flexible, fine-grained and balanced, we put forward a learnable environment assignment method. To improve the robustness against distribution shifts inside domain generalization, we present an adversarial environment refinement method. In addition, we conduct experiments on real-word datasets to verify the effectiveness of our models, and carry out further studies on the domain distance and domain diversity. To benefit the research community and promote this direction, we discuss the future of this field.
Towards Graph-level Anomaly Detection via Deep Evolutionary Mapping

Graph-level anomaly detection aims at capturing anomalous individual graphs in a graph set. Due to its significance in various real-world application fields, e.g., identifying rare molecules in chemistry and detecting potential frauds in online social networks, graph-level anomaly detection has received great attention recently. In distinction from node- and edge-level anomaly detection that is devoted to identifying anomalies on a single graph, graph-level anomaly detection faces more significant challenges because both the intra- and inter- graph structural and attribute patterns need to be taken into account to distinguish anomalies that exhibit deviating structures, rare attributes or the both. Although deep graph representation learning shows effectiveness in fusing high-level representations and capturing characters of individual graphs, most of the existing works are defective in graph-level anomaly detection because of their limited capability in exploring information across graphs, the imbalanced data distribution of anomalies, and low interpretability of the black-box graph neural networks (GNNs). To overcome these limitations, we propose a novel deep evolutionary graph mapping framework named GmapAD1, which can adaptively map each graph into a new feature space based on its similarity to a set of representative nodes chosen from the graph set. By automatically adjusting the candidate nodes using a specially designed evolutionary algorithm, anomalies and normal graphs are mapped to separate areas in the new feature space where a clear boundary between them can be learned. The selected candidate nodes can therefore be regarded as a benchmark for explaining anomalies because anomalies are more dissimilar/similar to the benchmark than normal graphs. Through our extensive experiments on nine real-world datasets, we demonstrate that exploring both intra- and inter- graph structural and attribute information is critical to spot anomalous graphs, and our method has achieved statistically significant improvements compared to the state of the art in terms of precision, recall, F1 score, and AUC.
Few-shot Low-resource Knowledge Graph Completion with Multi-view Task Representation Generation

Despite their capacity to convey knowledge, most existing knowledge graphs (KGs) are created for specific domains using low-resource data sources, especially those in non-global languages, and thus unavoidably suffer from the incompleteness problem. The automatic discovery of missing triples for KG completion is thus hindered by the challenging long-tail relations problem in low-resource KGs. Few-shot learning models trained on rich-resource KGs are unable to tackle this challenge due to a lack of generalization. To alleviate the impact of the intractable long-tail problem on low-resource KG completion, in this paper, we propose a novel few-shot learning framework empowered by multi-view task representation generation. The framework consists of four components, i.e., few-shot learner, perturbed few-shot learner, relation knowledge distiller, and pairwise contrastive distiller. The key idea is to utilize the different views of each few-shot task to improve and regulate the training of the few-shot learner. For each few-shot task, instead of augmenting it by complicated task designs, we generate its representation of different views using the relation knowledge distiller and perturbed few-shot learner, which are obtained by distilling knowledge from a KG encoder and perturbing the few-shot learner. Then, the generated representation of different views is utilized by the pairwise contrastive distiller based on a teacher-student framework to distill the knowledge of how to represent relations from different views into the few-shot learner and facilitate few-shot learning. Extensive experiments conducted on several real-world low-resource KGs validate the effectiveness of our proposed method.
Metaverse: Evolving role in healthcare delivery and implications

Metaverse, or the immersive internet, is touted as the next considerable technology-led disruption on the horizon, which can potentially disrupt clinician-patient interactions, patient experience, innovation and research and development processes. Metaverse is still in the evolution phase, and its firm definition framework is a work in progress. Surgical training allows the trainees to mimic procedures and simulate cross-collaborations within time and space, shortening the learning cycles. Similarly, patient experiences can be built by creating unique experiences replicating the real world without constraints. At the same time, the care providers can foster greater empathy while serving the patients with regular or special specific needs per disease conditions.
OpenICL: An Open-Source Framework for In-context Learning

In recent years, In-context Learning (ICL) has gained increasing attention and emerged as the new paradigm for large language model (LLM) evaluation.Unlike traditional fine-tuning methods, ICL instead adapts the pre-trained models to unseen tasks without any parameter updates.However, the implementation of ICL is sophisticated due to the diverse retrieval and inference methods involved, as well as the varying pre-processing requirements for different models, datasets, and tasks.A unified and flexible framework for ICL is urgently needed to ease the implementation of the aforementioned components.To facilitate ICL research, we introduce OpenICL, an open-source toolkit for ICL and LLM evaluation.OpenICL is research-friendly with a highly flexible architecture that users can easily combine different components to suit their needs.It also provides various state-of-the-art retrieval and inference methods to streamline the process of adapting ICL to cutting-edge research.The effectiveness of OpenICL has been validated on a wide range of NLP tasks, including classification, QA, machine translation, and semantic parsing.As a side-product, we found OpenICL to be an efficient yet robust tool for LLMs evaluation.OpenICL is released at https: //github.com/Shark-NLP/OpenICL.
Generalizing Graph ODE for Learning Complex System Dynamics across Environments

Learning multi-agent system dynamics have been extensively studied for various real-world applications, such as molecular dynamics in biology, multi-body system prediction in physics, and particle dynamics in material science. Most of the existing models are built to learn single system dynamics, which learn the dynamics from observed historical data and predict the future trajectory. In practice, however, we might observe multiple systems that are generated across different environments, which differ in latent exogenous factors such as temperature and gravity. One simple solution is to learn multiple environment-specific models, but it fails to exploit the potential commonalities among the dynamics across environments and offers poor prediction results where per-environment data is sparse or limited. Here, we present GG-ODE (Generalized Graph Ordinary Differential Equations), a machine learning framework for learning continuous multi-agent system dynamics across environments. Our model learns system dynamics using neural ordinary differential equations (ODE) parameterized by Graph Neural Networks (GNNs) to capture the continuous interaction among agents. We achieve the model generalization by assuming the dynamics across different environments are governed by common physics laws that can be captured via learning a shared ODE function. The distinct latent exogenous factors learned for each environment are incorporated into the ODE function to account for their differences. To improve model performance, we additionally design two regularization losses to (1) enforce the orthogonality between the learned initial states and exogenous factors via mutual information minimization; and (2) reduce the temporal variance of learned exogenous factors within the same system via contrastive learning. Experiments over various physical simulations show that our model can accurately predict system dynamics, especially in the long range, and can generalize well to new systems with few observations.
Adaptive Disentangled Transformer for Sequential Recommendation

Sequential recommendation aims at mining time-aware user interests through modeling sequential behaviors. Transformer, as an effective architecture designed to process sequential input data, has shown its superiority in capturing sequential relations for recommendation. Nevertheless, existing Transformer architectures lack explicit regularization for layer-wise disentanglement, which fails to take advantage of disentangled representation in recommendation and leads to suboptimal performance. In this paper, we study the problem of layer-wise disentanglement for Transformer architectures and propose the Adaptive Disentangled Transformer (ADT) framework, which is able to adaptively determine the optimal degree of disentanglement of attention heads within different layers. Concretely, we propose to encourage disentanglement by requiring the independence constraint via mutual information estimation over attention heads and employing auxiliary objectives to prevent the information from collapsing into useless noise. We further propose a progressive scheduler to adaptively adjust the weights controlling the degree of disentanglement via an evolutionary process. Extensive experiments on various real-world datasets demonstrate the effectiveness of our proposed ADT framework.
A Causality Inspired Framework for Model Interpretation

This paper introduces a unified causal lens for understanding representative model interpretation methods. We show that their explanation scores align with the concept of average treatment effect in causal inference, which allows us to evaluate their relative strengths and limitations from a unified causal perspective. Based on our observations, we outline the major challenges in applying causal inference to model interpretation, including identifying common causes that can be generalized across instances and ensuring that explanations provide a complete causal explanation of model predictions. We then present CIMI, a Causality-Inspired Model Interpreter, which addresses these challenges. Our experiments show that CIMI provides more faithful and generalizable explanations with improved sampling efficiency, making it particularly suitable for larger pretrained models.
SketchPolymer: Estimate Per-item Tail Quantile Using One Sketch

1Estimating the quantile of distribution, especially tail distribution, is an interesting topic in data stream models, and has obtained extensive interest from many researchers. In this paper, we propose a novel sketch, namely SketchPolymer to accurately estimate per-item tail quantile. SketchPolymer uses a technique called Early Filtration to filter infrequent items, and another technique called VSS to reduce error. Our experimental results show that the accuracy of SketchPolymer is on average 32.67 times better than state-of-the-art techniques. We also implement our SketchPolymer on P4 and FPGA platforms to verify its deployment flexibility. All our codes are available at GitHub.[1]
Domain-Specific Risk Minimization for Domain Generalization

Domain generalization (DG) approaches typically use the hypothesis learned on source domains for inference on the unseen target domain. However, such a hypothesis can be arbitrarily far from the optimal one for the target domain, induced by a gap termed ''adaptivity gap.'' Without exploiting the domain information from the unseen test samples, adaptivity gap estimation and minimization are intractable, which hinders us to robustify a model to any unknown distribution. In this paper, we first establish a generalization bound that explicitly considers the adaptivity gap. Our bound motivates two strategies to reduce the gap: the first one is ensembling multiple classifiers to enrich the hypothesis space, then we propose effective gap estimation methods for guiding the selection of a better hypothesis for the target. The other method is minimizing the gap directly by adapting model parameters using online target samples. We thus propose Domain-specific Risk Minimization (DRM). During training, DRM models the distributions of different source domains separately; for inference, DRM performs online model steering using the source hypothesis for each arriving target sample. Extensive experiments demonstrate the effectiveness of the proposed DRM for domain generalization. Code is available at: https://github.com/yfzhang114/AdaNPC.
A knowledge-based system for supporting the soundness of digital forensic investigations

Performing a technically and legally sound digital forensic investigation leads to digital evidence that can be used in courts of law. However, there is no single model of a standardized procedure that investigators should abide by. This paper presents a knowledge-based system that formally specifies information about investigative procedures in accordance with standards and guidelines such as ISO/IEC 27037, ISO/IEC 27041, ISO/IEC 27042, ISO/IEC 27043, NIST’s Guide to Integrating Forensic Techniques into Incident Response and Interpol’s Guidelines for Digital Forensics First Responders. The knowledge base is created in a description logic and it represents an ontological model. The model unifies concepts from different standards and guidelines, thus enabling the system to aid investigators in executing investigative procedures that will result in admissible digital evidence. The paper uses network forensics as a case study, but it can be customized to other digital forensics domains.
Multi-tower heliostat field optimization by means of adiabatic quantum computer

In this work, the possible use of quantum annealers for the optimization of multi-tower systems is studied. These systems are constituted by multiple solar towers and a set of mirrors (heliostats) reflecting the incident sun radiation toward a receiver on top of a tower of choice. The optimization consists in choosing on which tower each of the heliostats should focus the reflected radiation in order to optimize the global collected energy and, therefore, is inherently discrete. In this work, we formulated it in Quadratic Unconstrained Binary Optimization (QUBO) form and solved it with the use of quantum annealers. Results are shown for systems ranging from 2 to 9 towers and from tenths to thousands of heliostats. It is demonstrated that quantum annealers, used in hybrid mode, are effective in finding good solutions. The results are compared with the ones obtained with classical annealers showing comparable performances.