Reviewerly/exHarmony huggingface dataset:
Uncovering the Deceptions: An Analysis on Audio Spoofing Detection and Future Prospects

Audio has become an increasingly crucial biometric modality due to its ability to provide an intuitive way for humans to interact with machines. It is currently being used for a range of applications including person authentication to banking to virtual assistants. Research has shown that these systems are also susceptible to spoofing and attacks. Therefore, protecting audio processing systems against fraudulent activities such as identity theft, financial fraud, and spreading misinformation, is of paramount importance. This paper reviews the current state-of-the-art techniques for detecting audio spoofing and discusses the current challenges along with open research problems. The paper further highlights the importance of considering the ethical and privacy implications of audio spoofing detection systems. Lastly, the work aims to accentuate the need for building more robust and generalizable methods, the integration of automatic speaker verification and countermeasure systems, and better evaluation protocols.
Fighting against Organized Fraudsters Using Risk Diffusion-based Parallel Graph Neural Network

Medical insurance plays a vital role in modern society, yet organized healthcare fraud causes billions of dollars in annual losses, severely harming the sustainability of the social welfare system. Existing works mostly focus on detecting individual fraud entities or claims, ignoring hidden conspiracy patterns. Hence, they face severe challenges in tackling organized fraud. In this paper, we proposed RDPGL, a novel Risk Diffusion-based Parallel Graph Learning approach, to fighting against medical insurance criminal gangs. In particular, we first leverage a heterogeneous graph attention network to encode the local context from the beneficiary-provider graph. Then, we devise a community-aware risk diffusion model to infer the global context of organized fraud behaviors with the claim-claim relation graph. The local and global representations are parallel concatenated together and trained simultaneously in an end-to-end manner. Our approach is extensively evaluated on a real-world medical insurance dataset. The experimental results demonstrate the superiority of our proposed approach, which could detect more organized fraud claims with relatively high precision compared with state-of-the-art baselines.
Neuro-Symbolic Class Expression Learning

Models computed using deep learning have been effectively applied to tackle various problems in many disciplines. Yet, the predictions of these models are often at most post-hoc and locally explainable. In contrast, class expressions in description logics are ante-hoc and globally explainable. Although state-of-the-art symbolic machine learning approaches are being successfully applied to learn class expressions, their application at large scale has been hindered by their impractical runtimes. Arguably, the reliance on myopic heuristic functions contributes to this limitation. We propose a novel neuro-symbolic class expression learning model, DRILL, to mitigate this limitation. By learning non-myopic heuristic functions with deep Q-learning, DRILL efficiently steers the standard search procedure in a quasi-ordered search space towards goal states. Our extensive experiments on 4 benchmark datasets and 390 learning problems suggest that DRILL converges to goal states at least 2.7 times faster than state-of-the-art models on all learning problems. The results of our statistical significance test confirms that DRILL converges to goal states significantly faster (p-value &lt;1%) than state-of-the-art models on all benchmark datasets. We provide an open-source implementation of DRILL, including pre-trained models, training and evaluation scripts.
Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++

Reliable outlier detection is critical for real-world deployment of deep learning models. Although extensively studied, likelihoods produced by deep generative models have been largely dismissed as being impractical for outlier detection. First, deep generative model likelihoods are readily biased by low-level input statistics. Second, many recent solutions for correcting these biases are computationally expensive, or do not generalize well to complex, natural datasets. Here, we explore outlier detection with a state-of-the-art deep autoregressive model: PixelCNN++. We show that biases in PixelCNN++ likelihoods arise primarily from predictions based on local dependencies. We propose two families of bijective transformations -- ``stirring'' and ``shaking'' -- which ameliorate low-level biases and isolate the contribution of long-range dependencies to PixelCNN++ likelihoods. These transformations are inexpensive and readily computed at evaluation time. We test our approaches extensively with five grayscale and six natural image datasets and show that they achieve or exceed state-of-the-art outlier detection, particularly on datasets with complex, natural images. We also show that our solutions work well with other types of generative models (generative flows and variational autoencoders) and that their efficacy is governed by each model's reliance on local dependencies. In sum, lightweight remedies suffice to achieve robust outlier detection on image data with deep generative models.
XFormer: Fast and Accurate Monocular 3D Body Capture

We present XFormer, a novel human mesh and motion capture method that achieves real-time performance on consumer CPUs given only monocular images as input. The proposed network architecture contains two branches: a keypoint branch that estimates 3D human mesh vertices given 2D keypoints, and an image branch that makes prediction directly from the RGB image features. At the core of our method is a cross-modal transformer block that allows information flow across these two branches by modeling the attention between 2D keypoint coordinates and image spatial features. Our architecture is smartly designed, which enables us to train on various types of datasets including images with 2D/3D annotations, images with 3D pseudo labels, and motion capture datasets that do not have associated images. This effectively improves the accuracy and generalization ability of our system. Built on a lightweight backbone (MobileNetV3), our method runs blazing fast (over 30fps on a single CPU core) and still yields competitive accuracy. Furthermore, with a HRNet backbone, XFormer delivers state-of-the-art performance on Huamn3.6 and 3DPW datasets.
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search

Search spaces hallmark the advancement of Neural Architecture Search (NAS). Large and complex search spaces with versatile building operators and structures provide more opportunities to brew promising architectures, yet pose severe challenges on efficient exploration and exploitation. Subsequently, several search space shrinkage methods optimize by selecting a single sub-region that contains some well-performing networks. Small performance and efficiency gains are observed with these methods but such techniques leave room for significantly improved search performance and are ineffective at retaining architectural diversity. We propose LISSNAS, an automated algorithm that shrinks a large space into a diverse, small search space with SOTA search performance. Our approach leverages locality, the relationship between structural and performance similarity, to efficiently extract many pockets of well-performing networks. We showcase our method on an array of search spaces spanning various sizes and datasets. We accentuate the effectiveness of our shrunk spaces when used in one-shot search by achieving the best Top-1 accuracy in two different search spaces. Our method achieves a SOTA Top-1 accuracy of 77.6% in ImageNet under mobile constraints, best-in-class Kendal-Tau, architectural diversity, and search space size.
Discriminative-Invariant Representation Learning for Unbiased Recommendation

Selection bias hinders recommendation models from learning unbiased user preference. Recent works empirically reveal that pursuing invariant user and item representation across biased and unbiased data is crucial for counteracting selection bias. However, our theoretical analysis reveals that simply optimizing representation invariance is insufficient for addressing the selection bias — recommendation performance is bounded by both representation invariance and discriminability. Worse still, current invariant representation learning methods in recommendation neglect even hurt the representation discriminability due to data sparsity and label shift. In this light, we propose a new Discriminative-Invariant Representation Learning framework for unbiased recommendation, which incorporates label-conditional clustering and prior-guided contrasting into conventional invariant representation learning to mitigate the impact of data sparsity and label shift, respectively. We conduct extensive experiments on three real-world datasets, validating the rationality and effectiveness of the proposed framework. Code and supplementary materials are available at: https://github.com/HungPaan/DIRL.
Prediction with Incomplete Data under Agnostic Mask Distribution Shift

Data with missing values is ubiquitous in many applications. Recent years have witnessed increasing attention on prediction with only incomplete data consisting of observed features and a mask that indicates the missing pattern. Existing methods assume that the training and testing distributions are the same, which may be violated in real-world scenarios. In this paper, we consider prediction with incomplete data in the presence of distribution shift. We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i.e., mask distribution may shift agnostically between training and testing. To achieve generalization, we leverage the observation that for each mask, there is an invariant optimal predictor. To avoid the exponential explosion when learning them separately, we approximate the optimal predictors jointly using a double parameterization technique. This has the undesirable side effect of allowing the learned predictors to rely on the intra-mask correlation and that between features and mask. We perform decorrelation to minimize this effect. Combining the techniques above, we propose a novel prediction method called StableMiss. Extensive experiments on both synthetic and real-world datasets show that StableMiss is robust and outperforms state-of-the-art methods under agnostic mask distribution shift.
Unsupervised Deep Subgraph Anomaly Detection (Extended Abstract)

Effectively mining anomalous subgraphs in networks is crucial for various applications, including disease outbreak detection, financial fraud detection, and activity monitoring in social networks. However, identifying anomalous subgraphs poses significant challenges due to their complex topological structures, high-dimensional attributes, multiple notions of anomalies, and the vast subgraph space within a given graph. Classical shallow models rely on handcrafted anomaly measure functions, limiting their applicability when prior knowledge is unavailable. Deep learning-based methods have shown promise in detecting node-level, edge-level, and graph-level anomalies, but subgraph-level anomaly detection remains under-explored due to difficulties in subgraph representation learning, supervision, and end-to-end anomaly quantification. To address these challenges, this paper introduces a novel deep framework named Anomalous Subgraph Autoencoder (AS-GAE). AS-GAE leverages an unsupervised and weakly supervised approach to extract anomalous subgraphs. It incorporates a location-aware graph autoencoder to uncover anomalous areas based on reconstruction mismatches and introduces a supermodular graph scoring function module to assign meaningful anomaly scores to subgraphs within the identified anomalous areas. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of our proposed method.
Video Object Segmentation in Panoptic Wild Scenes

In this paper, we introduce semi-supervised video object segmentation (VOS) to panoptic wild scenes and present a large-scale benchmark as well as a baseline method for it. Previous benchmarks for VOS with sparse annotations are not sufficient to train or evaluate a model that needs to process all possible objects in real-world scenarios. Our new benchmark (VIPOSeg) contains exhaustive object annotations and covers various real-world object categories which are carefully divided into subsets of thing/stuff and seen/unseen classes for comprehensive evaluation. Considering the challenges in panoptic VOS, we propose a strong baseline method named panoptic object association with transformers (PAOT), which associates multiple objects by panoptic identification in a pyramid architecture on multiple scales. Experimental results show that VIPOSeg can not only boost the performance of VOS models by panoptic training but also evaluate them comprehensively in panoptic scenes. Previous methods for classic VOS still need to improve in performance and efficiency when dealing with panoptic scenes, while our PAOT achieves SOTA performance with good efficiency on VIPOSeg and previous VOS benchmarks. PAOT also ranks 1st in the VOT2022 challenge. Our dataset and code are available at https://github.com/yoxu515/VIPOSeg-Benchmark.
Social Motivation for Modelling Other Agents under Partial Observability in Decentralised Training

Understanding other agents is a key challenge in constructing artificial social agents. Current works focus on centralised training, wherein agents are allowed to know all the information about others and the environmental state during training. In contrast, this work studies decentralised training, wherein agents must learn the model of other agents in order to cooperate with them under partially-observable conditions, even during training, i.e. learning agents are myopic. The intrinsic motivation for artificial agents is modelled on the concept of human social motivation that entices humans to meet and understand each other, especially when experiencing a utility loss. Our intrinsic motivation encourages agents to stay near each other to obtain better observations and construct a model of others. They do so when their model of other agents is poor, or the overall task performance is bad during the learning phase. This simple but effective method facilitates the processes of modelling others, resulting in an improvement of the performance in cooperative tasks significantly. Our experiments demonstrate that the socially-motivated agent can model others better and promote cooperation across different tasks.
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations

Using reinforcement learning for automated theorem proving has recently received much attention. Current approaches use representations of logical statements that often rely on the names used in these statements and, as a result, the models are generally not transferable from one domain to another. The size of these representations and whether to include the whole theory or part of it are other important decisions that affect the performance of these approaches as well as their runtime efficiency. In this paper, we present NIAGRA; an ensemble Name InvAriant Graph RepresentAtion. NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving. Our experimental evaluation shows state-of-the-art performance on multiple datasets from different domains with improvements up to 10% compared to the best learning-based approaches. Furthermore, transfer learning experiments show that our approach significantly outperforms other learning-based approaches by up to 28%.
Examining the Impact of Training Practices and Organizational Culture on Police Misconduct in Law Enforcement Agencies in the Regional Training Center 5

This study investigates the relationship between training practices, organizational culture, incidents of abuse of authority, and unsafe training practices within law enforcement agencies. The main purpose of the study is to identify factors contributing to police misconduct and to provide recommendations for reforms to ensure the safety and well-being of trainees, prevent similar incidents in the future, and restore public faith in law enforcement agencies. This study used a combination of qualitative and quantitative methodology. The quantitative method was used to get the data from surveys coming from the participants and official records from the involved agency and organization. The qualitative method was used for the interviews and case studies. The sample includes trainees, officers, and supervisors from various law enforcement agencies. Results reveal that abuse of authority and unsafe training practices are associated with a negative organizational culture, inadequate ethical training, and a lack of effective leadership. Furthermore, the fear experienced by trainees in complying with excessive demands contributes to the risk of harm. The study also found that such incidents can damage the reputation of the police force, leading to a loss of public trust and confidence. In conclusion, the findings emphasize the need for a comprehensive approach to address these issues, including implementing ethical training, strong leadership, transparency, accountability measures, and evidence-based recruitment practices. These measures can promote a positive organizational culture, enhance the safety and well-being of trainees, and restore public faith in law enforcement agencies.
Forecasting COVID-19 Cases in Indonesia, Malaysia, Philippines, and Vietnam Using ARIMA and LSTM

COVID-19 has severely impacted the global economy, including ASEAN countries. Various plans and strategies are still needed during the pandemic-to-epidemic transition period to minimize the risk of COVID-19 transmission. The research focuses on the total number of confirmed cases of COVID-19 in Indonesia, Malaysia, the Philippines, and Vietnam, which are among the ASEAN countries with the highest number of cases in Southeast Asia. Those countries have cultural similarities, where gathering with friends and family is an important part of social life. This research evaluates the ability of ARIMA and LSTM to predict COVID-19 cases in each country, using daily data from January 23, 2020 to October 22, 2022. Datasets published by Johns Hopkins University (JHU) and Our World in Data (OWID) are used, which are accessible through Github. Compared to ARIMA with R2 of 0,8883 for Indonesia, 0,8353 for Malaysia, 0.97291 for the Philippines, and -3.105 for Vietnam, LSTM model can predict better in the four sampled ASEAN countries, with an R2 of 0.9996 for Indonesia, 0.9707 for Malaysia, 0.97291 for the Philippines, and 0.9200 for Vietnam.
The Empowerment of Blockchain Technology to Traditional Joint Action

Due to the transparency and distributed ledger technology that blockchain can provide, large-scale information collection and sharing becomes possible. In traditional joint lawsuits, regardless of the area of law involved, there is the problem of how to collect the consequences of damages and how to distribute them to large groups of people. Blockchain offers possible solutions to this problem. The open and transparent recording capability on the blockchain and the automatic execution capability of smart contracts can significantly improve the efficiency of traditional joint action. The essence of blockchain-enabled joint action is the integration of disadvantaged individuals into one large individual with a relatively unified mind and will to improve the position of the disadvantaged. Blockchain’s ability therefore effectively creates a decentralisation in joint action, i.e., the aggregation of a large number of dispersed individuals into a collective legal will. Blockchain can help to erase the status differences in traditional joint action.
Experimental work as one aspect of the environmental education activities for rural schools in Republic of Kazakhstan

The experimental work is one of actual directions of modern ecological education modernization. Worldwide we can often see the modern comfortable and functional campuses and buildings that harmonize with the environment around. The Republic of Kazakhstan Government accepted new National project “Green Kazakhstan” which describes the creation favorable environment for the population, sustainable development, biological education and modernizing the ecological mind of population. The environmental education plays a special role in the practical activities in school, helps to form necessary competences for students they can use in their life. Every school in Kazakhstan has its own experimental site (plot) where teacher and students can realize different environmental experiments and observe the natural objects etc. Authors consider this method of work the most reasonable for ecological breeding, as well as these activities are one of the most interesting for students. Experimental work may serve as a specific indicator for the environmental education result.
Unpacking P-Hacking and Publication Bias

We use unique data from journal submissions to identify and unpack publication bias and p-hacking. We find that initial submissions display significant bunching, suggesting the distribution among published statistics cannot be fully attributed to a publication bias in peer review. Desk-rejected manuscripts display greater heaping than those sent for review i.e. marginally significant results are more likely to be desk rejected. Reviewer recommendations, in contrast, are positively associated with statistical significance. Overall, the peer review process has little effect on the distribution of test statistics. Lastly, we track rejected papers and present evidence that the prevalence of publication biases is perhaps not as prominent as feared.
Learning Model for Autistic and Dyslexic Children

The purpose of the project is to create a learning software for autistic and dyslexic children. The goal of the project is to make the children choose a language and then using a custom speech -to- text classifier make the children repeat and pictographically learn various words. Teaching autistic and dyslexic is a very difficult task as they have a difficulty in learning and differentiating between different languages. We aim to create the model and categorize words from different languages, mainly English and Hindi. Currently, with models present, the problem in the models is that they are unable to detect the accent in which native Indian speakers speak. Thus, creating a model which can be used naturally over the whole country is a difficult task. The created model would be able to differentiate and understand a handful of words, and with a much powerful engine and dataset, it would be able to act as a modern-day text to speech and a teaching tool for the autistic and dyslexic children.