OpenRaiser/Intern-Atlas huggingface dataset:
Learning Compact Neural Networks with Deep Overparameterised Multitask Learning
Venue: arXiv cs.LG 2023
Year: 2023

Compact neural network offers many benefits for real-world applications. However, it is usually challenging to train the compact neural networks with small parameter sizes and low computational costs to achieve the same or better model performance compared to more complex and powerful architecture. This is particularly true for multitask learning, with different tasks competing for resources. We present a simple, efficient and effective multitask learning overparameterisation neural network design by overparameterising the model architecture in training and sharing the overparameterised model parameters more effectively across tasks, for better optimisation and generalisation. Experiments on two challenging multitask datasets (NYUv2 and COCO) demonstrate the effectiveness of the proposed method across various convolutional networks and parameter sizes.
Transforming the Output of Generative Pre-trained Transformer: The Influence of the PGI Framework on Attention Dynamics
Venue: arXiv cs.AI 2023
Year: 2023

This paper presents a novel approach named Persona-Grouping-Intelligence (PGI), which has been crafted to tackle the challenges posed by GPT models when applied to real-world business issues. PGI leverages the inherent capabilities of the GPT model to comprehend intricate language structures and generate responses that are contextually relevant. The experiment occurred in a business scenario where human intelligence was being underutilized due to less optimized business processes. The primary objective of this approach is to leverage GPT models to reduce the workload on humans in tasks that are extensive, monotonous, and repetitive. Instead, the focus is redirected toward decision-making activities. Remarkably, the experiment yielded an accuracy rate of 93.81% in validating 4,000 responses generated by the model, underscoring the effectiveness of the PGI strategies. Effectively addressing the issue of underutilized human intelligence, this paradigm shift aligns business environments with dynamic machine intelligence, enabling them to navigate the intricacies of real-world challenges. This approach facilitates the practical utilization of these models to tackle actual problems. The methodology offers an opportunity to reshape the fundamental structure of business processes by seamlessly integrating human decision-making with adaptable machine intelligence. Consequently, this optimization enhances operational efficiency and elevates strategic decision-making across diverse business contexts.
TriGait: Aligning and Fusing Skeleton and Silhouette Gait Data via a Tri-Branch Network
Venue: arXiv cs.CV 2023
Year: 2023

Gait recognition is a promising biometric technology for identification due to its non-invasiveness and long-distance. However, external variations such as clothing changes and viewpoint differences pose significant challenges to gait recognition. Silhouette-based methods preserve body shape but neglect internal structure information, while skeleton-based methods preserve structure information but omit appearance. To fully exploit the complementary nature of the two modalities, a novel triple branch gait recognition framework, TriGait, is proposed in this paper. It effectively integrates features from the skeleton and silhouette data in a hybrid fusion manner, including a two-stream network to extract static and motion features from appearance, a simple yet effective module named JSA-TC to capture dependencies between all joints, and a third branch for cross-modal learning by aligning and fusing low-level features of two modalities. Experimental results demonstrate the superiority and effectiveness of TriGait for gait recognition. The proposed method achieves a mean rank-1 accuracy of 96.0% over all conditions on CASIA-B dataset and 94.3% accuracy for CL, significantly outperforming all the state-of-the-art methods. The source code will be available at https://github.com/feng-xueling/TriGait/.
Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan
Venue: arXiv cs.CV 2023
Year: 2023

PURPOSE: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN), a form of deep learning, are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing the initial CT scan on a CNN based algorithm. METHODS: Retrospective multicentric study of a consecutive cohort of patients with SAH between 2011-2022. Demographic, clinical and radiological variables were analyzed. Pre-processed baseline CT scan images were used as the input for training a CNN using AUCMEDI Framework. Our model's architecture leverages the DenseNet-121 structure, employing transfer learning principles. The output variable was mortality in the first three months. Performance of the model was evaluated by statistical parameters conventionally used in studies involving artificial intelligence methods. RESULTS: Images from 219 patients were processed, 175 for training and validation of the CNN and 44 for its evaluation. 52%(115/219) of patients were female, and the median age was 58(SD=13.06) years. 18.5%(39/219) were idiopathic SAH. Mortality rate was 28.5%(63/219). The model showed good accuracy at predicting mortality in SAH patients exclusively using the images of the initial CT scan (Accuracy=74%, F1=75% and AUC=82%). CONCLUSION: Modern image processing techniques based on AI and CNN make possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients resulting in better training, development and performance on tasks which are beyond the skills of conventional clinical knowledge.
Using Visual and Vehicular Sensors for Driver Behavior Analysis: A Survey
Venue: arXiv cs.LG 2023
Year: 2023

Risky drivers account for 70% of fatal accidents in the United States. With recent advances in sensors and intelligent vehicular systems, there has been significant research on assessing driver behavior to improve driving experiences and road safety. This paper examines the various techniques used to analyze driver behavior using visual and vehicular data, providing an overview of the latest research in this field. The paper also discusses the challenges and open problems in the field and offers potential recommendations for future research. The survey concludes that integrating vision and vehicular information can significantly enhance the accuracy and effectiveness of driver behavior analysis, leading to improved safety measures and reduced traffic accidents.
Scientific knowledge production of blockchain: A bibliometric and lexicometric review
Venue: arXiv cs.DL 2023
Year: 2023

While recent reviews of blockchain technology have focused on the latest developments in cryptocurrency and their derivative impacts, less attention has been given to analysing their knowledge paths and boundaries based on past research to guide their development. To address this gap, we conducted both a bibliometric study of 2525 articles and a lexicometric study of 123 articles. The bibliometric study provided holistic insights into the evolution and distribution of blockchain research, including influential researchers and countries, discipline composition, knowledge development trends, and emerging frontiers. The lexicometric study identified the boundary concept structure with a quantitative textual approach, extracting the strongest signifying epistemic communities. Our findings indicate that blockchain research draws from four major disciplines, making it a multidisciplinary field. With the increasing maturity and development of technological infrastructure, the application and management of blockchain become increasingly relevant issues. Our analysis suggests that blockchain can be considered more of a boundary object than a disruptive change from knowledge perspectives. Therefore, this paper proposes a comprehensive understanding of the development path and epistemic concepts of blockchain research.
The Poison of Alignment
Venue: arXiv cs.CL 2023
Year: 2023

From the perspective of content safety issues, alignment has shown to limit large language models' (LLMs) harmful content generation. This intentional method of reinforcing models to not respond to certain user inputs seem to be present in many modern open-source instruction tuning datasets such as OpenAssistant or Guanaco. We introduce a novel insight to an instruction-tuned model's performance affected by the presence of alignment in supervised fine-tuning dataset. To be specific, we noticed that alignment acts as if it is poisoning the instruction dataset. Experimentally, we demonstrate that aligned answers significantly worsen the performance of the resulting fine-tuned model's on various reasoning benchmarks such as Big Bench (BBH), Massive Multitask Language Understanding (MMLU), Human Eval, and Discrete Reasoning Over Paragraphs (DROP), performing worse than the counterpart tuned without alignment by 4-33%.
On the Practicality of Dynamic Updates in Fast Searchable Encryption
Venue: arXiv cs.CR 2023
Year: 2023

Searchable encrypted (SE) indexing systems are a useful tool for utilizing cloud services to store and manage sensitive information. However, much of the work on SE systems to date has remained theoretical. In order to make them of practical use, more work is needed to develop optimal protocols and working models for them. This includes, in particular, the creation of a working update model in order to maintain an encrypted index of a dynamic document set such as an email inbox. I have created a working, real-world end-to-end SE implementation that satisfies these needs, including the first empirical performance evaluation of the dynamic SE update operation. In doing so, I show a viable path to move from the theoretical concepts described by previous researchers to a future production-worthy implementation and identify issues for follow-on investigation.
MRNAV: Multi-Robot Aware Planning and Control Stack for Collision and Deadlock-free Navigation in Cluttered Environments
Venue: arXiv cs.RO 2023
Year: 2023

Multi-robot collision-free and deadlock-free navigation in cluttered environments with static and dynamic obstacles is a fundamental problem for many applications. We introduce MRNAV, a framework for planning and control to effectively navigate in such environments. Our design utilizes short, medium, and long horizon decision making modules with qualitatively different properties, and defines the responsibilities of them. The decision making modules complement each other and provide the effective navigation capability. MRNAV is the first hierarchical approach combining these three levels of decision making modules and explicitly defining their responsibilities. We implement our design for simulated multi-quadrotor flight. In our evaluations, we show that all three modules are required for effective navigation in diverse situations. We show the long-term executability of our approach in an eight hour long wall time (six hour long simulation time) uninterrupted simulation without collisions or deadlocks.
Attending Generalizability in Course of Deep Fake Detection by Exploring Multi-task Learning
Venue: arXiv cs.CV 2023
Year: 2023

This work explores various ways of exploring multi-task learning (MTL) techniques aimed at classifying videos as original or manipulated in cross-manipulation scenario to attend generalizability in deep fake scenario. The dataset used in our evaluation is FaceForensics++, which features 1000 original videos manipulated by four different techniques, with a total of 5000 videos. We conduct extensive experiments on multi-task learning and contrastive techniques, which are well studied in literature for their generalization benefits. It can be concluded that the proposed detection model is quite generalized, i.e., accurately detects manipulation methods not encountered during training as compared to the state-of-the-art.
Training and Meta-Evaluating Machine Translation Evaluation Metrics at the Paragraph Level
Venue: arXiv cs.CL 2023
Year: 2023

As research on machine translation moves to translating text beyond the sentence level, it remains unclear how effective automatic evaluation metrics are at scoring longer translations. In this work, we first propose a method for creating paragraph-level data for training and meta-evaluating metrics from existing sentence-level data. Then, we use these new datasets to benchmark existing sentence-level metrics as well as train learned metrics at the paragraph level. Interestingly, our experimental results demonstrate that using sentence-level metrics to score entire paragraphs is equally as effective as using a metric designed to work at the paragraph level. We speculate this result can be attributed to properties of the task of reference-based evaluation as well as limitations of our datasets with respect to capturing all types of phenomena that occur in paragraph-level translations.
Optimizing Hierarchical Queries for the Attribution Reporting API
Venue: arXiv cs.DS 2023
Year: 2023

We study the task of performing hierarchical queries based on summary reports from the {\em Attribution Reporting API} for ad conversion measurement. We demonstrate that methods from optimization and differential privacy can help cope with the noise introduced by privacy guardrails in the API. In particular, we present algorithms for (i) denoising the API outputs and ensuring consistency across different levels of the tree, and (ii) optimizing the privacy budget across different levels of the tree. We provide an experimental evaluation of the proposed algorithms on public datasets.
Implicit ZCA Whitening Effects of Linear Autoencoders for Recommendation
Venue: arXiv cs.IR 2023
Year: 2023

Recently, in the field of recommendation systems, linear regression (autoencoder) models have been investigated as a way to learn item similarity. In this paper, we show a connection between a linear autoencoder model and ZCA whitening for recommendation data. In particular, we show that the dual form solution of a linear autoencoder model actually has ZCA whitening effects on feature vectors of items, while items are considered as input features in the primal problem of the autoencoder/regression model. We also show the correctness of applying a linear autoencoder to low-dimensional item vectors obtained using embedding methods such as Item2vec to estimate item-item similarities. Our experiments provide preliminary results indicating the effectiveness of whitening low-dimensional item embeddings.
Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Dataset
Venue: arXiv cs.IR 2023
Year: 2023

The selection of features for text classification is a fundamental task in text mining and information retrieval. Despite being the sixth most widely spoken language in the world, Bangla has received little attention due to the scarcity of text datasets. In this research, we collected, annotated, and prepared a comprehensive dataset of 212,184 Bangla documents in seven different categories and made it publicly accessible. We implemented three deep learning generative models: LSTM variational autoencoder (LSTM VAE), auxiliary classifier generative adversarial network (AC-GAN), and adversarial autoencoder (AAE) to extract text features, although their applications are initially found in the field of computer vision. We utilized our dataset to train these three models and used the feature space obtained in the document classification task. We evaluated the performance of the classifiers and found that the adversarial autoencoder model produced the best feature space.
Dance with You: The Diversity Controllable Dancer Generation via Diffusion Models
Venue: arXiv cs.HC 2023
Year: 2023

Recently, digital humans for interpersonal interaction in virtual environments have gained significant attention. In this paper, we introduce a novel multi-dancer synthesis task called partner dancer generation, which involves synthesizing virtual human dancers capable of performing dance with users. The task aims to control the pose diversity between the lead dancer and the partner dancer. The core of this task is to ensure the controllable diversity of the generated partner dancer while maintaining temporal coordination with the lead dancer. This scenario varies from earlier research in generating dance motions driven by music, as our emphasis is on automatically designing partner dancer postures according to pre-defined diversity, the pose of lead dancer, as well as the accompanying tunes. To achieve this objective, we propose a three-stage framework called Dance-with-You (DanY). Initially, we employ a 3D Pose Collection stage to collect a wide range of basic dance poses as references for motion generation. Then, we introduce a hyper-parameter that coordinates the similarity between dancers by masking poses to prevent the generation of sequences that are over-diverse or consistent. To avoid the rigidity of movements, we design a Dance Pre-generated stage to pre-generate these masked poses instead of filling them with zeros. After that, a Dance Motion Transfer stage is adopted with leader sequences and music, in which a multi-conditional sampling formula is rewritten to transfer the pre-generated poses into a sequence with a partner style. In practice, to address the lack of multi-person datasets, we introduce AIST-M, a new dataset for partner dancer generation, which is publicly availiable. Comprehensive evaluations on our AIST-M dataset demonstrate that the proposed DanY can synthesize satisfactory partner dancer results with controllable diversity.
Large Language Models in Analyzing Crash Narratives -- A Comparative Study of ChatGPT, BARD and GPT-4
Venue: arXiv cs.CL 2023
Year: 2023

In traffic safety research, extracting information from crash narratives using text analysis is a common practice. With recent advancements of large language models (LLM), it would be useful to know how the popular LLM interfaces perform in classifying or extracting information from crash narratives. To explore this, our study has used the three most popular publicly available LLM interfaces- ChatGPT, BARD and GPT4. This study investigated their usefulness and boundaries in extracting information and answering queries related to accidents from 100 crash narratives from Iowa and Kansas. During the investigation, their capabilities and limitations were assessed and their responses to the queries were compared. Five questions were asked related to the narratives: 1) Who is at-fault? 2) What is the manner of collision? 3) Has the crash occurred in a work-zone? 4) Did the crash involve pedestrians? and 5) What are the sequence of harmful events in the crash? For questions 1 through 4, the overall similarity among the LLMs were 70%, 35%, 96% and 89%, respectively. The similarities were higher while answering direct questions requiring binary responses and significantly lower for complex questions. To compare the responses to question 5, network diagram and centrality measures were analyzed. The network diagram from the three LLMs were not always similar although they sometimes have the same influencing events with high in-degree, out-degree and betweenness centrality. This study suggests using multiple models to extract viable information from narratives. Also, caution must be practiced while using these interfaces to obtain crucial safety related information.
DARWIN Series: Domain Specific Large Language Models for Natural Science
Venue: arXiv cs.CL 2023
Year: 2023

Emerging tools bring forth fresh approaches to work, and the field of natural science is no different. In natural science, traditional manual, serial, and labour-intensive work is being augmented by automated, parallel, and iterative processes driven by artificial intelligence-based experimental automation and more. To add new capabilities in natural science, enabling the acceleration and enrichment of automation of the discovery process, we present DARWIN, a series of tailored LLMs for natural science, mainly in physics, chemistry, and material science. This series relies on open-source LLM, incorporating structured and unstructured scientific knowledge from public datasets and literature. We fine-tuned the models using over 60,000 instruction data points, emphasizing factual correctness. During the fine-tuning, we introduce the Scientific Instruction Generation (SIG) model, automating instruction generation from scientific texts. This eliminates the need for manual extraction or domain-specific knowledge graphs and efficiently injects scientific knowledge into the model. We also explore multi-task training strategies, revealing interconnections between scientific tasks. DARWIN series not only achieves state-of-the-art results on various scientific tasks but also diminishes reliance on closed-source AI models. Our research showcases the ability of LLM in the scientific domain, with the overarching goal of fostering prosperity within the broader AI for science community.
Implementing Snort Intrusion Prevention System (IPS) for Network Forensic Analysis
Venue: arXiv cs.CR 2023
Year: 2023

The security trade confidentiality, integrity and availability are the main pillar of the information systems as every organization emphasize of the security. From last few decades, digital data is the main asset for every digital or non-digital organization. The proliferation of easily accessible attack software on the internet has lowered the barrier for individuals without hacking skills to engage in malicious activities. An Industrial organization operates a server that (Confluence) serves as a learning platform for newly hired employees or Management training officers, thereby making it vulnerable to potential attacks using readily available internet-based software. To mitigate this risk, it is essential to implement a security system capable of detecting and preventing attacks, as well as conducting investigations. This research project aims to develop a comprehensive security system that can detect attack attempts, initiate preventive measures, and carry out investigations by analyzing attack logs. The study adopted a survey methodology and spanned a period of four months, from March 1, 2023, to June 31, 2023. The outcome of this research is a robust security system that effectively identifies attack attempts, blocks the attacker's IP address, and employs network forensic techniques for investigation purposes. The findings indicate that deploying Snort in IPS mode on PfSense enables the detection of attacks targeting e-learning servers, triggering automatic preventive measures such as IP address blocking. The alerts generated by Snort facilitate investigative actions through network forensics, allowing for accurate reporting on the detrimental effects of the attacks.