Reviewerly/exHarmony huggingface dataset:
SoK: Static Configuration Analysis in Infrastructure as Code Scripts

This SoK paper presents findings from a survey conducted on the current state of tools and techniques used in the static configuration analysis of Infrastructure as Code (IaC). Our findings highlight the increasing importance of ensuring the quality of IaC scripts through techniques such as detecting code and security smells. Our findings reveal that regular expressions are widely used, but this may not be a long-term or fully automated solution for detecting smells. Additionally, our study found that the majority of the tools and techniques are developed for infrastructure provisioning, rather than configuration management and image building. This raises concerns because configuring software is a high-risk task, with malicious actors constantly targeting software systems. Therefore, it is crucial for researchers to develop efficient and advanced techniques for detecting defects in configuration management and image building. The aim of this paper is to provide a detailed overview of the current state of research in this field, and to identify areas for future development.
HSchain: Anonymous Permissioned Blockchain with Enhanced Auditability

Anonymity and auditability of transactions are two important but conflicting requirements for many blockchain-based applications. Technologies proposed to realize anonymous transactions, such as coin mixing and ring signature, make it hard to identify the participants of transactions. In this paper, we propose a system called HSchain, which enables the regulator to identify the participants while keeping the identity information hidden from nodes in the blockchain network. The proposed system uses the ring signature and the one-time public key to hide the sender and the receiver of a transaction respectively. By running a secret handshake protocol with the regulator, the sender/receiver generates a tag which is attached to the transaction to make it auditable. We carefully design the structure of the tag so that only the regulator can determine if a user has participated in a specific transaction. Simulation results demonstrate that adding such a tag to an anonymous transaction does not incur much overhead.
New Version, New Answer: Investigating Cybersecurity Static-Analysis Tool Findings

Automated detection of vulnerabilities and weaknesses in binary code is a critical need at the frontier of cybersecurity research. Cybersecurity static-analysis tools aim to detect and enumerate vulnerabilities and weaknesses. Two popular tools are CVE Binary Tool (cve-bin-tool) and cwe-checker. Cve-bin-tool reports vulnerabilities using Common Vulnerabilities and Exposures (CVE) whereas cwe-checker reports weaknesses using Common Weakness Enumeration (CWE). Despite widespread use, the consistency with which these tools report vulnerabilities and weaknesses (herein, “findings”) was unaddressed. We conducted a systematic investigation of 660 unique binaries taken from a Kali Linux distribution, evaluated each binary with multiple versions of the static-analysis tools, and investigated how the findings changed according to the version of the static-analysis tool used. We expected some variation in findings commensurate with the software-development life cycle. However, the number and magnitude of the changes in findings reported across versions were substantial. New versions gave new answers.
BPCluster: An Anomaly Detection Algorithm for RFID Trajectory Based on Probability

Indoor public places are facing more and more security risks, and need to be monitored to find potential anomalies. Benefiting from the advantages of low cost and high privacy, RFID is widely used in indoor monitoring. At present, it has become a common solution to construct the RFID raw data into time sequence trajectory, and then perform preprocessing and cluster analysis. However, there are redundant and uncertain factors in the RFID raw data, which affect the efficiency of anomaly detection. In this paper, we propose BPCluster, a probabilistic-based RFID trajectory anomaly detection algorithm for indoor RFID trajectories. The algorithm incorporates a probabilistic trajectory model, which reduces the redundancy and uncertainty through the context information of trajectories, and then clusters trajectories by the improved LCS algorithm to find abnormal trajectories. Experiments show that BPCluster has better performance in effectiveness and environmental adaptability, and the average accuracy in various environments reaches 91%.
Model Agnostic Approach for NLP Backdoor Detection

Poisoning training datasets by inserting backdoors into Natural Language Processing (NLP) models can result in model misclassifications with potential adverse impacts such as evasion of toxic content detection systems, fake news publication. A majority of the NLP backdoor defenses focus on model specific defenses. The current work proposes a model agnostic approach for NLP backdoor detection. To this end two metrics are developed to successfully distinguish between clean and poisoned text data samples.
Design of a Blockchain-Based Secure Storage Architecture for Resource-Constrained Healthcare

The Internet of Things (IoT) paradigm can improve a broad range of applications, such as medical sensors, smart cities, industrial monitoring, and so on. In healthcare specifically, these devices can aid in management tasks and improve the quality of life of patients in intensive care. However, securing medical IoT devices and its data is a crucial task. Not only do they handle Electronic Medical Records (EMR) and physiological information, but in some cases, disruption can affect a patient's treatment. Blockchain technologies applied in the healthcare context can provide privacy, immutability, decentralization, and easier access and sharing of medical data. Despite the emergence of applications aiming to solve security issues in the Healthcare IoT (HIoT) scenario using blockchain, there is still much to be addressed, mainly regarding throughput, storage of data, and efficient use of resources. This work proposes a blockchain-based storage architecture for HIoT, using a private network and distributed data storage to achieve integrity, accountability, and availability of medical data.
FACC: Flow-Size-Aware Congestion Control in Data Center Networks

The distribution of traffic shows a characteristic of different flow sizes in Data Center Networks (DCNs), which requires diverse demands for data transmission. However, most existing congestion control schemes treat all the flows equivalently and have a consistent control logic, which cannot meet the diverse demands of applications. In this paper, we propose FACC, a flow-size-aware congestion control scheme. In FACC, we design a distinguished congestion control logic to assign the transmission demands of different kinds of flows in the network. To meet the diverse demands, FACC provides an adaptable congestion window (cwnd) adjustment by assigning customized weights with a well-designed flow-size-aware reward function. Simulation results show that FACC can reduce the average FCT and the 99- th percentile FCT slowdown of short flows by 35% and 23% compared to the state-of-the-art congestion control schemes in DCNs, respectively.
Performance Enhancement of Stream-Based Decompression Process by Notifying Compression Buffer Size

This paper focuses on a performance enhancement of communication performance by compressing data stream. ASE coding is an effective lossless data compression method for data stream. The software implementation of the coding/decoding method inevitably meets a performance mismatch in memory and storage devices. In the compressor side, it is predictable to decide the size of an original data block and is available to process a flexible buffer memory. However, the decompressor is not able to predict the buffer size because the original data size is not obvious before the decompression. This causes a performance mismatch in the filesystem level. This paper proposes a novel method to address the performance mismatch by applying a notification mechanism of compression size from the compressor. This paper describes the mechanism focusing on the system call usage. Through experimental evaluations, we show the performance improvement of the decompression performance for handling data stream.
DepthWise Attention: Towards Individual Channels Attention

Human keypoints detection require the capture of long-range spatial constraints and the fusion of channels information. Many studies adopt attention mechanisms to generate feature weights, thereby enhancing the information interaction capability and improving the accuracy of keypoints detection. However, most attention mechanisms currently in use redun-dantly fuse information across all channel levels, which not only increases the computational cost of the network but also weakens the feature differences between different keypoints, affecting the prediction of heatmaps. In this study, we propose a plug-and play attention module based on separate convolution, called DWA module, which avoids the redundant use of information from different channels and improves the network's ability to capture long-range spatial relationships. Additionally, we adopt a novel feature compression method to reduce errors in single-dimensional compression. Experimental results indicate that our DWA model performs well on COCO and MPII datasets, achieving good accuracy improvements with relatively small computational costs
Improved Flaky Test Detection with Black-Box Approach and Test Smells

Flaky tests can pose a challenge for software development, as they produce inconsistent results even when there are no changes to the code or test. This leads to unreliable results and makes it difficult to diagnose and troubleshoot any issues. In this study, we aim to identify flaky test cases in software development using a black-box approach. Flaky test cases are unreliable indicators of code quality and can cause issues in software development. Our proposed model, Fast-Flaky, achieved the best results in the cross-validation results. In the per-project validation, the results showed an overall increase in accuracy but decreased in other metrics. However, there were some projects where the results improved with the proposed pre-processing techniques. These results provide practitioners in software development with a method for identifying flaky test cases and may inspire further research on the effectiveness of different pre-processing techniques or the use of additional test smells.
Collaborative Fuzzy Clustering Approach for Scientific Cloud Workflows

Cloud computing has allowed the sharing of applications with a lot of data, like scientific workflows. Using scientific workflows to process big data is expensive regarding data transfer, execution time, and bandwidth costs. A data placement method based on fuzzy sets is used to cut these costs. It helps optimize data placement and reduce the costs of processing big data. This paper presents a new method for scientific cloud workflow data placement involving fuzzy sets to realize collaborative clustering. The proposed method explores each data center's datasets through data dependencies, clusters them by the clustering algorithm Fuzzy C-Means (FCM), and re-clusters them based on the data collaboration. Our suggested method of using fuzzy sets to realize collaborative clustering can help cope with uncertainties in data and thus reduce the overall data placement amounts, with better results than previous approaches.
A Hybrid Algorithm for Service Bursting Based on GA and BPSO in Hybrid Clouds

Companies need to be creative and flexible, especially regarding customer-specific web applications, because there is a lot of competition, and the market is changing quickly. The hybrid cloud is now a popular choice for businesses that want to make the most of their resources and get things done faster by combining private and public cloud implementations. Some parts of the new apps would be set aside for the private cloud option, while others would be set aside for the public cloud option when the apps were being used. For this, a hybrid algorithm based on GA and BPSO is suggested, which can help keep the successful optimization of service bursting in the hybrid cloud platform. Based on the IBM benchmark, the experiment results show that our advanced algorithm-scored results were less cost-effective than related works experiments.
Cluster, Reconstruct and Prune: Equivalent Filter Pruning for CNNs without Fine-Tuning

Network pruning is effective in reducing memory usage and time complexity. However, current approaches face two common limitations. 1) Pruned filters cannot contribute to the final outputs, resulting in severe performance drops, especially at large pruning rates. 2) It requires time-consuming and computationally expensive fine-tuning to recover accuracy. To address these limitations, we propose a novel filter pruning method called Cluster Pruning (CP). Instead of directly deleting filters, CP reconstructs them based on their intra-similarity and removes them using the proposed channel addition operation. CP preserves all learned features and eliminates the need for fine-tuning. Specifically, each filter is distinguished by clustering and reconstructed as the centroid to which it belongs. Reconstructed filters are updated to prevent erroneous selections. After convergence, filters can be safely removed through the channel addition operation. Experiments on various datasets show that CP achieves the best trade-off between performance and complexity compared with other algorithms.
F<sub>2</sub>-HPCC: Achieve Faster Convergence and Better Fairness for HPCC

In recent years, Remote Direct Memory Access (RDMA) has been widely deployed in data centers to provide low-latency and high-bandwidth services. To ensure high performance in RDMA networks, congestion control manages queue depth on switches to minimize queueing delays. Although HPCC, the state-of-the-art scheme, can significantly reduce the flow completion time (FCT) of short flows, it still suffers from slow convergence and unfairness, which will affect the tail FCT of large flows. In this paper, we first analyze the causes of these defects and then propose an improved scheme called F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -HPCC, which introduces a self-adjusting additive increase algorithm to accelerate converging and a sliding window algorithm to improve fairness. In our evaluation, F2- HPCC achieves faster convergence and fairer allocations without sacrificing queue length and shortens the tail FCT of large flows by up to 33% under real data center workloads.
An Enhanced Vulnerability Detection in Software Using a Heterogeneous Encoding Ensemble

Detecting vulnerabilities in source code is essential to prevent cybersecurity attacks. Deep learning-based vulnerability detection is an active research topic in software security. However, existing deep learning-based vulnerability detectors (VD) are limited to using either serialization-based or graph-based methods, which do not combine serialized global and structured local information at the same time. As a result, a single method cannot perform well for semantic information that exists in complex source code, leading to low detection accuracy. In this paper, we present EL-VDetect, a stacked ensemble learning approach for vulnerability detection that eliminates these issues. EL-VDetect enhances feature selection techniques to represent the best relevant vulnerability features with the slice code and subgraphs, reducing redundant information of vulnerabilities. Our model combines serialization-based and graph-based neural networks to successfully capture the global and local context information of source code, effectively understands code semantics, and focuses on vulnerable nodes based on the attention mechanism to accurately detect vulnerabilities. To evaluate EL-VDetect's effectiveness, we crawl a real-world dataset from CVEDetails, consisting of functions for eight applications. A comprehensive performance analysis of the real-world dataset shows that EL-VDetect achieves 90.72% accuracy, outperforming baseline deep learning models by 1.75-26.21 %. Our proposed model can better identify vulnerabilities in software than other existing vulnerability detection models.
A Deep Learning Approach to Remotely Monitor People's Frailty Status

With the progressive aging of the population, monitoring the state of frailty of a person becomes increasingly important to prevent risk factors, which can lead to loss of autonomy and to hospitalization. Hygiene care, in particular, represents a wake-up call to detect a decline in physical and mental well-being. With the assistance of both environmental and localized sensors, measurements of hygiene-related activities can be made quickly and consistently over time. We here propose to remotely monitor these activities using a fixed camera and deep learning algorithms. In particular, three activities are considered, i.e., washing face, brushing teeth and arranging hair, together with the non-action class. Considering a dataset consisting of 11 healthy subjects of different age and sex, we show that using a Long-Short Term Memory (LSTM) neural network the selected activities can be distinguished with an accuracy of more than 92%, thus proving the validity of the proposed approach.
MESCAL: Malicious Login Detection Based on Heterogeneous Graph Embedding with Supervised Contrastive Learning

Malicious logins via stolen credentials have become a primary threat in cybersecurity due to their stealthy nature. Recent malicious login detection methods based on graph learning techniques have made progress due to their ability to capture interconnected relationships among log entries. However, limited malicious samples pose a critical challenge to the detection performance of existing methods. In this paper, we propose MESCAL, a novel approach based on heterogeneous graph embedding with supervised contrastive learning to solve this challenge. Concretely, we construct authentication heterogeneous graphs to represent multiple and interconnected log events. Then, we pretrain a feature extractor with supervised contrastive learning to capture rich semantics on the graphs from limited malicious samples. Based on this, cost-sensitive learning is adopted to distinguish malicious logins on imbalanced data. Extensive evaluations show that the F1 score of MESCAL based on the imbalance dataset is 94.63%, which outperforms state-of-the-art approaches.
RL-CEALS: Reinforcement Learning for Collaborative Edge Assisted Live Streaming

Crowdsourced live streaming services (CLS) present significant challenges due to massive data size and dynamic user behavior. Service providers must accommodate personalized QoE requests, while managing computational burdens on edge servers. Existing CLS approaches use a single edge server for both transcoding and user service, potentially overwhelming the selected node with high computational demands. In response to these challenges, we propose the Reinforcement Learning-based-Collaborative Edge-Assisted Live Streaming (RL-CEALS) framework. This innovative approach fosters collaboration between edge servers, maintaining QoE demands and distributing computational burden cost-effectively. By sharing tasks across multiple edge servers, RL-CEALS makes smart decisions, efficiently scheduling serving and transcoding of CLS. The design aims to minimize the streaming delay, the bitrate mismatch, and the computational and bandwidth costs. Simulation results reveal substantial improvements in the performance of RL-CEALS compared to recent works and baselines, paving the way for a lower cost and higher quality of live streaming experience.
A Covert TLS Encryption Transmission Method Based on Network Covert Channel

TLS 1.2 protocol, as one of the most essential secure communication protocols, is widely used for web services. However, it has been exposed to many vulnerabilities so far. In order to exploit these vulnerabilities to carry out attacks, the attacker must possess the necessary information. Based on this principle, we proposed a covert TLS encryption transmission method in this paper, which uses a storage network covert channel to transmit important handshake information. The network covert channel hides the true TLS handshake information, thereby improving the security of the entire transmission process. We conducted extensive experiments to evaluate its performance. The experimental results show that our covert channel can guarantee high covertness without delay. Meanwhile, vulnerability testing shows that our scheme can resist most attacks.
Dynamic PBFT with Active Removal

The Practical Byzantine Fault Tolerance (PBFT) consensus in permissioned blockchains is known for its strong consistency and engineering feasibility. However, research on dynamic node join/leave processes and robustness is limited. This study presents an active removal dynamic PBFT algorithm, utilizing atomic broadcast technology for a provably secure dynamic broadcast primitive and a K-Nearest Neighbors (KNN) based malicious node classification and removal protocol. This enables PBFT consensus to accommodate dynamic requirements while mitigating voting power attacks and malicious nodes. Results show maintained system performance during dynamic node changes, accurate malicious behavior detection, and prompt node removal, addressing current PBFT limitations and enhancing blockchain network robustness.
Bayesian-Based Symptom Screening for Medical Dialogue Diagnosis

In a medical dialogue diagnosis system, the selection of symptoms for inquiry has a significant impact on diagnostic accuracy and dialogue efficiency. In a typical diagnosis process, the symptoms initially reported by users are often insufficient to support an accurate diagnosis, making it necessary to ask users about other symptoms through dialogue to form a conclusive diagnosis. In this paper, we propose a disease diagnosis algorithm based on Bayesian, which simulates the process of doctor's inquiry and diagnosis by dynamically updating the list of diseases to increase the interpretability of diagnosis results. For the symptom interrogation, we propose a symptom screening algorithm based on the difference of symptom sets to exclude diseases with low probability. Through the intersection and union of disease symptom sets, we can screen out the symptoms that can distinguish diseases in fewer inquiring rounds. The experimental results demonstrate the proposed method performs more efficiently than existing state-of-the-art algorithms.
Multiple Information Extraction and Interaction for Emotion Recognition in Multi-Party Conversation

Emotion recognition in multi-party conversation (ERMC) has garnered attention in the field of natural language processing (NLP) due to its wide range of applications. Its objective is to identify the emotion of each utterance. Existing models mainly focus on context modeling, while ignoring the emotional interaction and dependency between utterances. In this work, we put forward a Multiple Information Extraction and Interaction network (MIEI) for ERMC that captures emotions by integrating emotional interaction, speaker-aware context, and discourse dependency in a conversation. Emotional interaction is simulated by proposed commonsense emotion modeling. Speaker-aware context is obtained by proposed speaker-aware context modeling with muti-head attention. Discourse dependency is improved to better depict the discourse structures. We verify the superiority of our proposed method by comparing it with existing models and validate the effectiveness of each module through ablation experiments.