Reviewerly/exHarmony huggingface dataset:
COSMOS: Coordinated Management of Cores, Memory, and Compressed Memory Swap for QoS-Aware and Efficient Workload Consolidation for Memory-Intensive Applications

With the rapid growth in memory demands, the slowdown of DRAM scaling, and the DRAM price fluctuations, DRAM has become one of the critical resources in cloud computing systems and datacenters. The compressed memory swap (CMS) is a promising technique that improves the effective memory capacity of the underlying computer system by compressing and storing a subset of pages in memory instead of the disk swap. While prior works have extensively investigated resource management techniques for workload consolidation, they lack the capability of dynamically allocating cores, memory, andCMSto the consolidated applications in a controlled and efficient manner. To bridge this gap, this work presents the in-depth characterization of the impact of cores, memory, and CMS on the QoS and throughput of the consolidated latency-critical (LC) and batch applications. Guided by the characterization results, we propose COSMOS, a software-based runtime system for coordinated management of cores, memory, and CMS for QoS-aware and efficient workload consolidation for memory-intensive applications. COSMOS dynamically collects the runtime data from the consolidated applications and the underlying system and allocates the resources to the consolidated applications in a way that achieves high throughput with strong QoS guarantees. Our quantitative evaluation based on a real system and widely-used memory-intensive benchmarks demonstrates the effectiveness of COSMOS in that it robustly satisfies the QoS and achieves high throughput across all the evaluatedworkload mixes and scenarios and significantly reduces the number of explored system states.
Blockchain-Assisted Authentication and Key Agreement Protocol for Cloud-Edge Collaboration

In response to the urgent need for scalable, lightweight, low-latency, anonymous, and highly secure identity authentication and key agreement protocol schemes in cloud-edge collaborative environments, we propose a blockchain-assisted lightweight authentication and key agreement protocol. In this protocol, a distributed environment based on a consortium blockchain is created with the assistance of cloud servers and edge nodes, reducing reliance on a single trusted authority. Anonymous real identity authentication and blockchain storage mechanisms ensure the credibility of identity information and the security of privacy information. In addition, most of the computational overhead of the authentication and key agreement process is transferred to the edge nodes and cloud services, reducing latency and reducing the computational burden on IoT devices. Security analysis shows that the protocol is secure against common attacks, and performance comparison analysis with similar protocols shows that our protocol is more secure and efficient, making it more suitable for resource-limited IoT devices.
Privacy Protection Multi-copy Provable Data Possession Supporting Data Reliability

With the development of technology, a massive amount of data continues to emerge, and there is still a strong demand for data storage. Outsourcing data to cloud service providers has become a common practice. Although cloud storage offers the advantage of location-independent data access and reduces burdens such as hardware and software maintenance, this service still faces some challenges in terms of privacy and data reliability. To address this issue, we present Privacy-preserving Multi-copy Provable Data Possession supporting data Reliability (PMPDP-R). Our proposal ensures the integrity, reliability, and freshness of data by designing Timestamp-based Double-layer Merkle Hash Tree (TD-MHT), a novel dynamic structure for multicopy storage. Furthermore, our approach uses random masking techniques to develop unique replica blocks and safeguard privacy. We’ve also ensured data reliability by enabling PMPDP-R to locate damaged data blocks to aid recovery. The experimental analysis proves that our scheme is both practical and effective.
Semantic-Aware Attack and Defense on Deep Hashing Networks for Remote-Sensing Image Retrieval

Deep hashing networks have been successful in retrieving interesting images from massive remote sensing images. There is no doubt that security and reliability are critical in remote sensing image retrieval. Recent studies about natural image retrieval have shown the vulnerability of deep hashing networks to adversarial examples, but there do not exist any researches about the attack and defense on deep hashing networks in remote sensing image retrieval. Due to the large intra-class difference and high inter-class similarity of remote sensing images, the attack and defense methods on deep hashing networks for natural images cannot be directly applied to the remote sensing images. Different from the widely adopted instance-aware hash codes which often present the suboptimum performance of the attack and defense on deep hashing networks, this paper recommends the usage of semantic-aware hash codes, which take into account multiple samples in the given semantic categories, in both attack and defense. To pursue the strongest attack on remote sensing image retrieval, a novel semantic-aware attack with weights via multiple random initialization (RWC) is proposed. To alleviate the retrieval degradation caused by adversarial attacks, a new adversarial training defense method on deep hashing networks with the adversarial semantic-aware consistency constraint (ACN) is proposed. Extensive experiments on three typical open remote sensing image datasets (i.e., UCM, AID, NWPU-RESISC45) show the proposed attack and defense methods on various deep hashing networks achieve better performance compared with the state-of-the-art methods. The source code will be made publicly available along with this paper.
Deploying Machine Learning in Resource-Constrained Devices for Human Activity Recognition

Machine Learning (ML) has proven to be highly effective in solving complex tasks such as human activity and speech recognition. However, the introduction of accuracy-driven ML models has brought new challenges in terms of their applicability in resource-constrained systems. In Human Activity Recognition (HAR), current state-of-the-art approaches often rely on complex multilayer LSTM (Long Short Term Memory) networks once they are well suited to handle temporal series data, a crucial aspect of HAR, but presenting a high computational cost associated with running the inference phase. In HAR, low-power IoT devices, such as wearable sensor arrays, are frequently used as data-gathering devices. However, we observed a limited effort to deploy ML technology directly on these devices, most commonly using edge or cloud computing services, which can be unavailable in some situations. This work aims to provide a Convolutional Neural Network (CNN) tuned for resource-constrained embedded systems. After tuning the CNN model in the Pytorch framework, we present an equivalent C model and employ optimization techniques. The results show that, compared to the reference CNN, the optimized model reduced the CNN model 2.34 times, does not require floating-point units (FPUs), and improved accuracy from 74.9% to 85.2%. These results show the feasibility of running the proposed CNN on resource-constrained devices.
Optimizing Single-Source Graph Execution on NUMA Machines

Graphs are data structures capable of representing problems from different domains, such as logistics and social networks. However, these massive graphs stored in high-performance computing (HPC) servers start processing from distinct source vertices (i.e., single-source: such as a different user or message in a social network). Therefore, the amount of vertices and the structure of the sub-graphs to be processed will also change depending on the source, highly influencing the graph algorithm behavior and performance. In this paper, we propose GraphNroll, a framework to extract the full potential of these single-source graph algorithms by exploiting the fact that the majority of the HPC servers are on Non-Uniform Memory Access (NUMA) architectures and, therefore, highly dependent of thread and data mapping. GraphNroll adjusts the thread and data mapping on NUMA machines by using only the embeddings of the source vertices (i.e., vector representations that encode the graph topology) to find the best configuration in terms of thread/data mapping. For that, GraphNroll learns over such embeddings to build a machine learning model capable of predicting the mapping solutions for a new algorithm execution without any profiling. Our results show that GraphNroll is, on average, 8% (up to 18%) faster than the Linux OS Default and Random solutions on three NUMA machines while reducing energy consumption.
ARTERIAL: A Natural Language Processing Model for Prevention of Information Leakage from Electronic Health Records

Over the past decade, there has been a steady increase in health security breaches. Therefore, healthcare organizations must protect their sensitive information such as test results, diagnoses, prescriptions, research, and customer personal information. A leak of sensitive data can result in significant economic loss and damage to the organization’s image. In this sense, Data Leakage Prevention (DLP) systems can help to identify, monitor, protect, and reduce the risks of leaking sensitive data. However, state-of-the-art DLP solutions only use signature comparisons and static comparisons. Therefore, we propose to develop the ARTERIAL model based on Natural Language Processing (NLP), Entity Recognition (NER), and Artificial Neural Networks (ANN) to be more assertive in extracting information and recognizing entities from Electronic Health Records (EHR). Different from the current literature, ARTERIAL considers semantic features present in the EHR. Three approaches were implemented and tested, two based on ANN and the following based on machine learning algorithms. As a result, the approach taken in its implementation using a machine learning algorithm reached 98.0% of Precision, 86.0% of Recall, and 91.0% of F1-Score.
RDSF: Everything at Same Place All at Once - A Random Decision Single Forest

Random Forest is a widely-used machine learning approach. This work presents a novel graph representation called Random Decision Single Forest (RDSF) for Random Forests (RF). RDSF utilizes binary decision diagrams (BDD) to overcome challenges in RF implementations. It provides improved scalability, reduced execution time, and control over input data order compared to previous methods. The paper outlines the proposed mapping flow and experimental results, demonstrating the efficiency of RDSF for both numerical and categorical datasets. The RDSF significantly decreases generation time by up to two orders of magnitude and reduces inference time by one order of magnitude, as compared to the ADD-based approach.
Compact lensless optoelectronic convolutional neural network for image classification

Recently, free-space optical neural networks (ONNs) have gained extensive interest as emerging machine learning platforms for implementing artificial intelligence tasks, such as image classification. Despite various optical implementations of electronic neural networks (ENNs), the bulky volume of optical components remains challenging to deploy edge devices, such as Internet of Things peripherals, wearable devices, and camera. To address this problem, we propose a compact lensless optoelectronic convolutional neural network (LOE-CNN) architecture with a lensless optical analog processor utilizing a single optimized diffractive phase mask (DPM) to perform convolution operations without Fourier lens. Comparing the processor with a commercially available NVIDIA A100 Tensor Core GPU in terms of speed and power, indicates the optical computing platform enables to replace the electronic processor in latency reduction and energy savings. Furthermore, we compare the LOE-CNN with two all-electronic neural networks (i.e., fully connected neural network [FC-NN] and convolutional neural network [CNN]) over the Modified National Institute of Standards and Technology (MNIST) dataset and Fashion-MNIST dataset, respectively, and demonstrate that the LOE-CNN can be functionally comparable to existing electronic counterparts in classification performance. My study not only opens up new application prospects for free-space ONNs based on compact lensless single-chip convolution processor, but also facilitates the development of ONNs-based smart devices.
A General Smart Contract Vulnerability Detection Framework with Self-attention Graph Pooling

In recent years, the increasing development of Web 3.0 has generated growing attention toward blockchain and smart contracts. However, due to their immutability, smart contracts still exhibit various vulnerabilities that hackers can exploit, resulting in significant losses. Numerous smart contracts on various blockchains, including Ethereum, have been attacked due to various vulnerabilities. The inefficiency of detecting these vulnerabilities has become a major bottleneck in advancing blockchain and smart contracts. Although detecting smart contract vulnerabilities has attracted much attention, most existing machine learning-based methods rely on adequate expert knowledge and target only specific known vulnerabilities via binary classification models. To address this limitation, our proposed approach introduced a general vulnerability detection method that can be applied to identify various common vulnerabilities via a uniform framework. We leveraged the Abstract Syntax Trees (AST) and self-attention-based graph pooling models to generate topological graphs from smart contract code analysis. We adopted Graph Neural Networks for vulnerability detection. Experimental results demonstrated that the proposed approach exhibited satisfactory performance in detecting multiple and unseen vulnerabilities compared to traditional methods.
Long-Term Blockchain Transactions Spanning Multiplicity of Smart Contract Methods

Research on blockchains addresses multiple issues, with one being writing smart contracts. In developing smart contracts, blockchains have complex constraints that complicate the design and development of smart contracts, software that controls the blockchain transactions. Blockchain applications that involve collaborations of multiplicity of actors, such as in trade of goods and services, give rise to additional complexity in that transaction activities tend to be long term, perhaps hours or days, and thus spanning multiplicity of methods of a smart contract. The problem is that a block-chain only supports the concept of a transaction within the scope of an execution of a single smart contract method. We compare the requirements and properties of long-term transactions and blockchain transactions and examine their mismatch. We then propose how to represent long-term blockchain transactions in smart contracts, and we describe a mechanism to support such transactions using patterns. Finally, we evaluate the proposed mechanism in terms of supporting transactional properties and its cost of execution.
SC-Chain: A Multi-modal Collaborative Storage System for Medical Resources

At present, collaborative healthcare faces a series of problems such as inconsistent standards, prominent information silos, and inadequate information security management. Because of this, the application of a blockchain-based collaborative healthcare system has been born, and blockchain has become the main driving force for the development of collaborative healthcare. However, the existing blockchain HIS (Hospital Management Information System) has the disadvantages of low node access requirements, weak storage capacity, and no support for SQL query indexing that cannot meet the demand for collaborative sharing of massive multi-modal medical resources. To this end, based on Ceph and Tendermint, this paper constructs a low-cost, high-performance, and highly reliable on-chain and off-chain collaborative storage system SC-Chain for secure storage and efficient sharing of massive multimodal medical resources and reducing system storage pressure. In addition, we designed a relational state database, a high and low time data update policy, and an authorized access control policy based on electronic health codes to guarantee further the secure storage and reliable query of medical resources. Finally, the multi-modal medical data read and write performance analysis experiments found that the storage performance of SC-Chain is significantly better than that of traditional blockchain, especially the latency of Tendermint reaches 4 orders of magnitude of SC-Chain when reading and writing CT (Computed Tomography) data.
Collaborative Writing Skills Enhancement through the Use of Story Jumper

The present research is about improving collaborative writing skills enhancement through the use of StoryJumper. The work aims to know the impact of using StoryJumper as a technological resource implemented to develop the EFL students’ collaborative writing skills. The sample was 27 English students who were enrolled in the second year of Bachillerato in a public institution of Loja. These students were divided randomly into two groups; the experimental and the control group. It was an experimental study aided by a qualitative and quantitative analysis through the use of pre-tests, post-tests, class observations, and student surveys. The instruments used to gather the data were a writing placement test, a writing achievement test, a rubric, an observation sheet, and a questionnaire. The data obtained helped to know the effectiveness of using StoryJumper in collaborative narrative writing and the students’ perceptions of using this platform. Finally, the findings revealed that the students reached a good improvement in the development of their narrative writing skills in addition to their remarkable motivation for the practice and development of their collaborative writing skills after the use of the StoryJumper.
A new unsupervised outlier detection method

Information amount has been shown to be one of the most efficient methods for measuring uncertainty. However, there has been little research on outlier detection using information amount. To fill this void, this paper provides a new unsupervised outlier detection method based on the amount of information. First, the information amount in a given information system is determined, which offers a thorough estimate of the uncertainty of this information system. Then, the relative information amount and the relative cardinality are proposed. Following that, the degree of outlierness and weight function are shown. Furthermore, the information amount-based outlier factor is constructed, which determines whether an object is an outlier by its rank. Finally, a new unsupervised outlier detection method called the information amount-based outlier factor (IAOF) is developed. To validate the effectiveness and advantages of IAOF, it is compared to five existing outlier identification methods. The experimental results on real-world data sets show that this method is capable of addressing the problem of outlier detection in categorical information systems.
DSLR–: A low-overhead data structure layout randomization for defending data-oriented programming

By developing a Turing-complete non-control data attack to bypass existing defenses against control flow attacks, Data-Oriented Programming (DOP) has gained significant attention from researchers in recent years. While several defense techniques have been proposed to mitigate DOP attacks, they often introduce substantial overhead due to the blind protection of a large range of data objects. To address this issue, we focus on selecting and protecting the specific target data that are of interest to DOP attackers, rather than securing the entire non-control data in the program. In this regard, we perform static analysis on 20 real-world applications and identify the target data, verifying that they constitute only a small percentage of the overall program, averaging around 3%. Additionally, we propose a semi-automated tool to analyze how to chain operations on the target data in these 20 applications to achieve Turing-complete attacks. Furthermore, we introduce DSLR-: a low-overhead Data Structure Layout Randomization (DSLR) method, which modifies the existing DSLR technique to only randomize the selected target data for DOP. Experimental results demonstrate that DSLR- effectively mitigates DOP attacks, reducing performance overhead by 71.2% and memory overhead by 82.5% compared to the original DSLR technique.
Outlier detection using conditional information entropy and rough set theory

Outlier detection is critically important in the field of data mining. Real-world data have the impreciseness and ambiguity which can be handled by means of rough set theory. Information entropy is an effective way to measure the uncertainty in an information system. Most outlier detection methods may be called unsupervised outlier detection because they are only dealt with unlabeled data. When sufficient labeled data are available, these methods are used in a decision information system, which means that the decision attribute is discarded. Thus, these methods maybe not right for outlier detection in a a decision information system. This paper proposes supervised outlier detection using conditional information entropy and rough set theory. Firstly, conditional information entropy in a decision information system based on rough set theory is calculated, which provides a more comprehensive measure of uncertainty. Then, the relative entropy and relative cardinality are put forward. Next, the degree of outlierness and weight function are presented to find outlier factors. Finally, a conditional information entropy-based outlier detection algorithm is given. The performance of the given algorithm is evaluated and compared with the existing outlier detection algorithms such as LOF, KNN, Forest, SVM, IE, and ECOD. Twelve data sets have been taken from UCI to prove its efficiency and performance. For example, the AUC value of CIE algorithm in the Hayes data set is 0.949, and the AUC values of LOF, KNN, SVM, Forest, IE and ECOD algorithms in the Hayes data set are 0.647, 0.572, 0.680, 0.676, 0.928 and 0.667, respectively. The advantage of the proposed outlier detection method is that it fully utilizes the decision information.
Mining high average utility itemsets using artificial fish swarm algorithm with computed multiple minimum average utility thresholds

It is obvious that the problem of Frequent Itemset Mining (FIM) is very popular in data mining, which generates frequent itemsets from a transaction database. An extension of the frequent itemset mining is High Utility Itemset Mining (HUIM) which identifies itemsets with high utility from the transaction database. This gains popularity in data mining, because it identifies itemsets which have more value but the same was not identified as frequent by Frequent Itemset Mining. HUIM is generally referred to as Utility Mining. The utility of the items is measured based on parameters like cost, profit, quantity or any other measures preferred by the users. Compared to high utility itemsets (HUIs) mining, high average utility itemsets (HAUIs) mining is more precise by considering the number of items in the itemsets. In state-of-the-art algorithms that mines HUIS and HAUIs use a single fixed minimum utility threshold based on which HAUIs are identified. In this paper, the proposed algorithm mines HAUIs from transaction databases using Artificial Fish Swarm Algorithm (AFSA) with computed multiple minimum average utility thresholds. Computing the minimum average utility threshold for each item with the AFSA algorithm outperforms other state-of-the-art HAUI mining algorithms with multiple minimum utility thresholds and user-defined single minimum threshold in terms of number of HAUIs. It is observed that the proposed algorithm outperforms well in terms of execution time, number of candidates generated and memory consumption when compared to the state-of-the-art algorithms.