Miracle-dz/UniFAR-FTUs huggingface dataset:
FDAP: Efficient Radio Map Reconstruction Using Sparse Signals

Indoor location-based services (LBS) hold significant potential for social and commercial value in smart city development. However, fingerprint-based indoor localization technologies face challenges due to the dynamic indoor environment. Updating the fingerprint database through dense site surveys is costly. Therefore, a method utilizing sparse samples (10%) is attractive. This work proposes the fingerprint database adaptation paradigm (FDAP), an adaptive update framework designed for sparse samples. The FDAP creates a swin transformer-based reconstruction model (STRM), effectively addressing the issue of inaccurate map reconstruction from sparse samples. Using the STRM significantly reduces the expenses associated with updating the fingerprint database. Additionally, FDAP evaluates the quality of the samples with a signal quality evaluation algorithm (SQEA), assessing the features of dispersion degree, signal strength, and fingerprint correlation from sparse samples. Lastly, FDAP provides a signal correction algorithm (SCA) based on partial least squares regression, which minimizes potential errors for unreliable reconstruction signals. Experiments conducted on a public dataset demonstrate that FDAP effectively accommodates radio map variations over time. It reduces the reconstruction error by 22.2 % compared to the current state-of-the-art method. The code is publicly available at https://github.com/Cen-Shaoqi/FDAP.
Zero Trust+: A Trusted-based Zero Trust architecture for IoT at Scale

The Zero Trust security architecture has been widely recognized for its efficacy in strengthening cybersecurity by rejecting the concept of implicit trust and adopting strict identity verification measures. However, as organizations face escalating demands for rapid and scalable access control, the performance limitations of traditional Zero Trust implementations have become a concern. In response to this challenge, we introduce a novel trust management-based solution that significantly improves performance without compromising security integrity. Through a streamlined trust provisioning approach, our solution offers a 20% enhancement in processing efficiency, making it highly suitable for dynamic and large-scale environments. This research paper presents the design, implementation, and evaluation of our trust management-based solution, showcasing its superiority over conventional Zero Trust architectures in addressing performance concerns while delivering the same robust access control capabilities.
An Approach to Detect Cardiomegaly, COVID-19, Pneumonia, Pneumothorax and Tuberculosis from CXR Images Using Ensembles of Deep Learning

Recent advancements in medical image analysis have harnessed the potential of deep learning, especially in diagnosing thoracic ailments using chest X-ray (CXR) images. This study introduces a novel approach to detecting a range of thoracic diseases, including COVID-19, pneumonia, pneumothorax, cardiomegaly, and tuberculosis, through ensembles of deep learning models. The development of a reliable and efficient diagnostic system is crucial given the increasing significance of early disease detection and the growing number of CXR images in medical databases. The proposed ensemble method combines features of multiple deep learning models to improve overall performance, leveraging their distinct strengths. The study includes extensive experimentation and comparative analyses on a diverse and extensive dataset to highlight the superior performance of this approach. The results demonstrate that the ensemble-based model outperforms individual models, achieving a higher accuracy of 98.37%, sensitivity of 0.9837, and precision of 0.9836, thus presenting significant potential for robust and multi-disease detection in CXR images. Our results hold promise for advancing healthcare and clinical decision-making, underlining the valuable contribution of this research. This work not only underscores the potency of deep learning in medical imaging but also highlights its pivotal role in elevating patient care.
Low-Power Approximate Multiplier Architecture for Error Resilient Applications

Approximate computing offers a solution since it is a potential technique to improve area, speed, and reduce power in cases when accurate calculation is not expected. In this work, an unsigned approximate multiplier architecture has been presented for error resilient applications. This architecture is divided into three segments: the least significant component remains constant and maintains the same accuracy, the middle portion, which is a partial product, is simplified using or gates, and the error is compensated utilizing an error correction module. According to results of 8-bit multiplier, 30.9% and 35.33% were reduced of power and power-delay products, respectively, compared state-of-the-art designs without sacrificing accuracy. Finally, proposed design is validated using error resilient applications.
Research on Discourse Structure Fusing Specific Features

Automatic essay scoring (AES) refers to the use of computers to automatically score essays without human intervention. Whether the discourse structure is reasonable is an important consideration dimension in narrative scoring. At present, the research on text structure at home and abroad is still in its infancy, and there is a lack of open corpus for training. In addition, most of the text structure evaluation models constructed in the past are trained by selecting features based on manual experience. To solve the above problems, this paper develops text composition standards and labeling norms, supplements the previously constructed Chinese narrative text structure assessment corpus, and puts forward a narrative text structure assessment model with the aid of text composition. The experimental results show that the model constructed in this paper can effectively identify text components, and the accuracy of the narrative text structure assessment model with the help of text components is better than that of the baseline model, with an accuracy of 76.8%.
Significance of Secure Internet of Things Device Certification – An Authentication Perspective

Internet of Things (IoT) devices are connected network smart elements, sensors, and software capabilities that allow for data acquisition and transact via the internet. These days, it's very difficult to secure the devices used in the IoT applications. This article talks about concise review of the security challenges in IoT devices, kinds of attacks, IoT security software tools and available directives for IoT system design. The main contribution is the change in emphasis from architecture to devices and the suggestion of technology advancements needed for IoT device security. This investigation also studies about standards and certifying procedures currently in vogue and suggests a route for further research. It also discusses the current testing techniques and technological advancements that will affect device certification in the future.
Leukemia Cancer Classification from Abnormal Cell Images Using a Deep Learning Approach: A Comprehensive Study

Leukemia, a malignant blood cancer, is characterized by the uncontrolled proliferation of abnormal white blood cells in the bone marrow and blood. It is a life-threatening disease with various subtypes, making precise classification crucial for effective treatment. However, accurate classification of leukemia subtypes from abnormal cell images poses a challenging problem due to the complex and subtle variations in cell morphology. In response to this challenge, this comprehensive study employs an innovative machine learning approach, utilizing the EfficientNet B3 architecture. With this model, we achieved an impressive accuracy rate of 98.95% and a minimal loss of 0.120, demonstrating its efficacy in classifying leukemia subtypes. To conduct our experiments, we utilized the Kaggle Leukemia cancer dataset, ensuring a robust and reliable evaluation of our proposed methodology. This research contributes significantly to the development of efficient tools for early diagnosis and personalized treatment strategies for leukemia.
Enabling Real-Time Video Analytics with Adaptive Sampling and Detection-Based Tracking in Edge Computing

With the popularization of visual machine learning, intelligent video analytics can automatically analyze and extract information from video streams, yet it brings heavy computing burdens. Edge computing can improve the processing experience by bringing computing resources near users. On top of this, various processing methods and settings have different resource requirements and output different user experiences. How to dynamically select the video processing configuration according to system states becomes a critical problem that remains to be addressed. In this paper, we propose an edge-assisted video analytic framework based on adaptive sampling and detection-based tracking. We design four functional modules to realize a cooperative computing processing flow. We consider two performance metrics, recognition accuracy and processing time to estimate the experience of real-time video analytics. Further, we design an online configuration method based on Double Deep Q-Network, which can adaptively select analytic configurations under the condition of system dynamics. Experimental results based on a real dataset demonstrate the superior performance of the proposed framework on reward, mean Intersection over Union (IoU), and processing time.
Knowledge-Based Systems

Collaborative filtering-based recommender systems are regarded as an important tool to predict the items that users will appreciate based on the historical usage of users. However, traditional recommendation solutions often pay more attentions to the accuracy of the recommended items while neglect the diversity of the final recommended list, which may produce partial redundant items in the recommended list and as a result, decrease the satisfaction degree of users. Moreover, historical usage data for recommendation decision-making often update frequently, which may lead to low recommendation efficiency as well as scalability especially in the big data environment. Considering these drawbacks, a novel method called DivRec_LSH is proposed in this paper to achieve diversified and efficient recommendations, which is based on the historical usage records and the Locality-Sensitive Hashing (LSH) technique. Finally, we compare our method with existing methods on the MovieLens dataset. Experiment results indicate that our proposal is feasible in addressing the triple dilemmas of recommender systems simultaneously, i.e., high efficiency, accuracy and diversity. © 2020ElsevierB
TEEM: Two-Factor Energy Evaluation Metric Toward Green Big Data System

Toward green Big Data System (BDS), one of the key requirements is to save energy consumption so that the system lifetime can be prolonged. Hence, the energy evaluation metric for the measurement of energy efficiency in green BDS, is very critical. Unfortunately, most current energy evaluation metrics are based on a single factor, which might be difficult to meet the diverse application requirements. In this paper, we propose a novel two-factor energy evaluation metric (TEEM) for green BDS. Specifically, the transmission distance and the modulation level are taken into account simultaneously, generating a metric named the bit-per energy consumption (BEC). Extensive simulation results demonstrate that the system performance in energy consumption can be more effectively evaluated by BEC.
The Relationships between Host Tree Characteristics and Liana Climbing Success at Mo Singto Forest Dynamics Plot, Khao Yai National Park, Thailand

Variations in climbing strategies of liana ramets allow them to successfully ascend to forest canopies. These diverse climbing strategies also depend on variations in sizes and bark textures of host trees. Characteristics of lianas and host trees at the Mo Singto Forest Dynamics Plot in the Khao Yai National Park in Thailand were examined. The total 1,560 ramets were randomly selected. Their sizes were positively correlated with host tree sizes. However, this relationship varied with liana climbing strategies. Most twiner, hook and tendril climbers tended to use small host trees while adventious root climbers used large host tree. The scrambler and the combination of twiner and scrambler ramets appeared to be independent of host tree sizes. Host trees with slightly rough bark texture showed high association with most ramets regardless of sizes and climbing strategies except the adventitious root climbers that were often found on trees with rough bark texture.
RWKV-based Encoder-Decoder Model for Code Completion

Intelligent code completion techniques, which offer code suggestions, are essential for assisting programmers in reducing errors and improving programming efficiency. Traditional Recurrent Neural Networks (RNNs) face challenges in terms of training efficiency, representation performance, and architecture extensibility. We introduce RWKV, the latest parallel trainable RNN architecture, to tackle these challenges. Additionally, we propose a new code completion model that leverages RWKV. The model utilizes an encoder-decoder framework to optimize the use of temporal and structural aspects of the code. It learns the mapping between the concrete syntax tree of the source code and the source code to identify exceptional correspondences. This approach leads to more accurate complementation outcomes. Based on experimental results, our model outperforms the baseline model in three metrics: Accuracy, EM, and Edit Sim.
Advancing SynergyAI: Enhancing Explainability and Decision Tree Optimization in Human-AI Pair Programming

This paper focuses on the optimization of SynergyAI. SynergyAI is a human-AI collaborative system. We addressed challenges related to transparency, efficiency, and advice function in AI programming model. We introduced a visualized decision tree to improve human-AI collaborative capability in terms of explainability. A comprehensive prediction algorithm was proposed to enhance prediction efficiency across data flow. The AI programmer’s advice function utilized scatter-plot matrices for intuitive data relationship visualization. The main contributions of the improved SynergyAI platform are streamlining AI-human collaboration and maximizing prediction accuracy and transparency.
Aigis: Full-Coverage And Low-Overhead Mitigating Against Amplified Reflection DDoS Attacks

In Internet Service Provider (ISP) networks, Amplified Reflection DDoS (AR-DDoS) attack is one of the main attack categories, which launches gigabytes of traffic with little effort and minimal cost. Thus, the mitigation of AR-DDoS attacks has been considered as a crucial part. In particular, such mitigation requires full coverage (i.e., mitigating AR-DDoS attacks launched from any location) and low overhead (i.e., mitigation should avoid high latency that degrades user experience). However, existing solutions suffer from either limited coverage or high overhead. In this paper, we propose Aigis, a distributed framework that offers full-coverage and low-overhead mitigation of AR-DDoS attacks. Our key idea is to co-design top-of-rack (ToR) switches and end-hosts, which offers line-rate packet processing performance and fine-grained view inherently, to jointly execute endpoint verification. Specifically, Aigis selectively offloads mitigation operations between ToR switches and end-hosts and implements a network-wide epoch synchronization mechanism to guarantee reliable verification. It efficiently coordinates ToR switches and end-hosts to execute the entire mitigation task. We have implemented Aigis on a testbed comprising 32×100 Gbps Tofino switches. Testbed experiments indicate that Aigis achieves complete full coverage and orders of magnitude lower host-side overhead compared to existing solutions.
Greenhouse Gas Emissions Under Different Drainage and Flooding Regimes of Cultivated Peatlands

Globally, approximately 10–20% of peatlands have been drained for agricultural purposes. A strategy to protect peatlands and mitigate carbon dioxide (CO2) emissions, while continuing agricultural production, is the use of intermittent flooding and drainage. A potential drawback of this strategy could be increases in methane (CH4) and nitrous oxide (N2O) emissions. The objective of this study was to compare greenhouse gas (GHG) emissions from peatlands under various flooding–drainage cycles. A laboratory study was performed using intact soil cores subjected to different durations of flooding and drainage for 6 months. Average daily emissions of CO2 and N2O were significantly higher (P < 0.001) under drained (667 ± 37 mg CO2–C m−2 d−1 and 135 ± 19 μg N2O–N m−2 d−1) than flooded conditions (86 ± 6 mg CO2–C m−2 d−1 and 48 ± 2 μg N2O–N m−2 d−1). Methane emissions were not influenced by drained/flooded conditions, with an average rate of 116 ± 11 μg CH4–C m−2 d−1. Peaks of CH4 and N2O emissions were observed after flooding events and lasted less than 24 h. The peak emissions were approximately 8 and 19 times higher than the mean CH4 and N2O emissions, respectively. Carbon dioxide was the dominant component of GHGs, irrespective of hydrologic regime, accounting for more than 92% of overall global warming potential. Global warming potential was inversely proportional to the flooding period, indicating that prolonging the flooding period of peatlands would help mitigate soil oxidation and GHG emissions and enhance sustainability of these agricultural peatlands.
A Matrix Decomposition Recommendation Algorithm Introducing Untrusted Information between Users

In response to the problems of poor recommendation performance caused by sparse data in traditional recommendation algorithms and slow convergence speed caused by the fact that the values of trust matrix elements can be any real number, this paper proposes a non negative matrix decomposition algorithm based on trust and non trust information, which comprehensively utilizes user historical information data and trust and non trust information between users. In the algorithm design, non negative constraints were applied to the potential factors of the rating matrix and trust matrix, while negative constraints were applied to the potential factors of the distrust matrix; At the same time, a novel trust regularization and distrust regularization method was also designed for model training. Experiments have shown that compared with SVD++ and TrustSVD, this algorithm not only overcomes the impact of sparse traditional data on recommendation quality to a certain extent, but also effectively enhances the robustness of the algorithm, greatly improving the accuracy of recommendations.
A Study on the Number of Composite Layers and Effectiveness of Metamorphic Relation

Metamorphic Testing is one of the main methods to alleviate the test Oracle problem, centered on metamorphic relation. Currently, the construction of the metamorphic relation mainly relies on manual analysis. Therefore, the systematic generation method has been the research concern. One of the construction methods is the composition of existing metamorphic relations. Research shows that the composited metamorphic relations have better fault detection capability than component ones. However, the more composite layers there are, the more time-consuming the computation becomes; even a combination explosion will be triggered. So, studying the mechanism of the number of composite layers on cost-effectiveness is very important. Firstly, combined operation is defined for the numerical expression type metamorphic relation. Then, experiments carry out the trend of the number of composite layers and cost-effectiveness and conclude that three is the most cost-effective, which is good guidance for the systematic generation of metamorphic relations.
GLS-NET: An ensemble framework for classification of images

Image classification stands as a fundamental task in computer vision, and Convolutional Neural Networks (CNNs) have emerged as highly proficient tools, demonstrating remarkable accuracy and performance. However, with the increasing complexity and diversity of image datasets, there is a growing need to improve the robustness and generalization of CNN-based classifiers. One promising approach to address this challenge is the ensembling of CNNs. Ensembling involves combining the outputs of multiple CNNs to enhance classification performance. This technique leverages the strength and diversity of individual models to achieve superior results compared to using a single model alone. Therefore, GLS-NET, an ensemble framework is proposed which uses three parallel ResNet50 CNNs and takes different features as input so as to induce the diversity in data which in turn can learn discriminative features to produce high accuracy. The proposed framework is evaluated on the most popular dataset, EMNIST, and achieved good performance improvement in accuracy. EMNIST is the most popular dataset used extensively in evaluating the performance of many deep learning techniques.
Proposal and Evaluation of Virtual Node Placement Algorithm in KVS (Key Value Store)

Distributed Key Value Stores (Distributed KVS) are used in large-scale cloud systems to manage huge amounts of data and to provide the required availability and fragmentation tolerance. In a KVS consisting of many physical nodes, the performance of each physical node is uniform at the beginning of the configuration, but during long-term operation of a cloud system causes variations in the performance of the physical nodes. Therefore, it was necessary to consider a load balancing placement scheme that takes heterogeneous performance into account. In previous studies, a vnode placement method has been proposed to create multiple virtual nodes (vnodes) on a physical node and then equalize the load when the performance of the physical node is mixed between 1x and 2x. However, the proposed method is limited to the case where the performance of physical nodes is mixed between 1x and 2x, and the performance of physical nodes that are added year by year cannot be limited to 1x and 2x. To solve this problem, this paper proposes a method of allocating physical nodes and vnodes when multiple physical nodes have multiple performances. A simulator is also created to evaluate the performance of the proposed method and to compare it with previous methods.
Classification of Politically Aggressive Bengali Texts Using Transformers

The widespread use of the Internet and social media has enabled unrestricted communication among individuals with diverse backgrounds. However, interactions between individuals from opposing political groups can lead to physical violence. In this study, we introduce a new Bengali dataset on political aggression, classifying texts into aggressive or non-aggressive. Aggressive texts are further categorized into inciting assassination, rebellion, or communal violence. By utilizing advanced BERT transformer models such as multilingual-BERT, distil-BERT, Bangla-BERT, and XLM-RoBERTa, our approach achieved an impressive 0.87 F1 score at the first and the second level using Bangla-BERT and Multilingual-BERT respectively. This research contributes to our comprehension of political aggression in online discourse, offering valuable insights for mitigating its real-world consequences.