Reviewerly/exHarmony huggingface dataset:
Better than Composition: How to Answer Multiple Relational Queries under Differential Privacy

Answering relational queries under differential privacy has attracted a lot of attention in recent years due to growing concerns on personal privacy, and instance-optimal mechanisms have been developed for a single query. However, most real-world data analytical tasks require multiple queries to be answered under a total privacy budget. The standard solution to extend the single-query mechanism to multiple queries is via privacy composition. However, we observe that this may yield an error bound that could be a d0.5-factor worse from the optimal, where d is the number of queries. In this paper, we present a different, more holistic approach that closes this gap. In addition to theoretical optimality, our new mechanism also significantly outperforms privacy composition in practice, especially on more skewed data and large d.
GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning

Training data pre-processing pipelines are essential to deep learning (DL). As the performance of model training keeps increasing with both hardware advancements (e.g., faster GPUs) and various software optimizations, the data pre-processing on CPUs is becoming more resource-intensive and a severe bottleneck of the pipeline. This problem is even worse in the cloud, where training jobs exhibit diverse CPU-GPU demands that usually result in mismatches with fixed hardware configurations and resource fragmentation, degrading both training performance and cluster utilization. We introduce GoldMiner, an input data processing service for stateless operations used in pre-processing data for DL model training. GoldMiner decouples data pre-processing from model training into a new role called the data worker. Data workers facilitate scaling of data pre-processing to anywhere in a cluster, effectively pooling the resources across the cluster to satisfy the diverse requirements of training jobs. GoldMiner achieves this decoupling in a fully automatic and elastic manner. The key insight is that data pre-processing is inherently stateless, thus can be executed independently and elastically. This insight guides GoldMiner to automatically extract stateless computation out of a monolithic training program, efficiently disaggregate it across data workers, and elastically scale data workers to tune the resource allocations across jobs to optimize cluster efficiency. We have applied GoldMiner to industrial workloads, and our evaluation shows that GoldMiner can transform unmodified training programs to use data workers, accelerating individual training jobs by up to 12.1x. GoldMiner also improves average job completion time and aggregate GPU utilization by up to 2.5x and 2.1x in a 64-GPU cluster, respectively, by scheduling data workers with elasticity.
Efficient GPU-Accelerated Subgraph Matching

Subgraph matching is a basic operation in graph analytics, finding all occurrences of a query graph Q in a data graph G. A common approach is to first filter out non-candidate vertices in G, and then order the vertices in Q to enumerate results. Recent work has started to utilize the GPU to accelerate subgraph matching. However, the effectiveness of current GPU-based filtering and ordering methods is limited, and the result enumeration often runs out of memory quickly. To address these problems, we propose EGSM, an efficient approach to GPU-based subgraph matching. Specifically, we design a data structure Cuckoo trie to support dynamic maintenance of candidates for filtering, and order query vertices based on estimated numbers of candidate vertices on the fly. Furthermore, we perform a hybrid breadth-first and depth-first search with memory management for result enumeration. Consequently, EGSM significantly outperforms the state-of-the-art GPU-accelerated algorithms, including GSI and CuTS.
Computing the probability density function of a random compartmental model to describe the dynamics of HIV. Application to real-world data<sup>*</sup>

In this contribution, we incorporate randomness into a previous deterministic fractional HIV model to study the propagation of uncertainty with aim of better understanding the disease dynamic. To this end, we use the Random Variable Transformation Technique to compute the first probability density function of the solution stochastic process of the randomized fractional HIV model. Then, we assign judicious parametric probability distributions for some model parameters and we then find out the best values to describe the real data obtained from the Hospital Pedro Hispano in Portugal. These preliminary results may be useful to better understand the dynamics of HIV and provide valuable information for public health policymakers.
Design and Practice of Programming Teaching Platform Based on Cloud Computing and Streaming Media Technology

In order to improve innovation, promote the combination of theoretical teaching and experimental teaching, and give full play to the practical application of cloud computing and streaming media technology in course teaching, a C language programming online teaching and experimental environment construction method based on cloud computing and streaming media technology was designed. Build Linux, FreeBSD, Windows and other operating systems on the basis of KVM virtual machine technology, extend the C language experimental environment from single-machine mode to the cloud with JupyterLab, and realize the data monitoring of teaching cloud platform experimental environment with Webservice and other technologies. RTMP protocol is used to transmit streaming media online video teaching data. This environment can meet the daily cloud learning needs of teachers and students, improve the teaching effect of C language programming, and realize the expansion of the experimental environment from single PC to Android, iPad, iPhone and other mobile terminals, so as to facilitate students to practice in fragmented time in their spare time. This will help to achieve the goal of cultivating applied talents for computer majors.
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning

Recently using machine learning (ML) based techniques to optimize the performance of modern database management systems (DBMSs) has attracted intensive interest from both industry and academia. With an objective to tune a specific component of a DBMS (e.g., index selection, knobs tuning), the ML-based tuning agents have shown to be able to find better configurations than experienced database administrators (DBAs). However, one critical yet challenging question remains unexplored -- how to make those ML-based tuning agents work collaboratively. Existing methods do not consider the dependencies among the multiple agents, and the model used by each agent only studies the effect of changing the configurations in a single component. To tune different components for DBMS, a coordinating mechanism is needed to make the multiple agents be cognizant of each other. Also, we need to decide how to allocate the limited tuning budget (e.g., time and resources) among the agents to maximize the performance. Such a decision is difficult to make since the distribution of the reward (i.e., performance improvement) corresponding to each agent is unknown and non-stationary. In this paper, we study the above question and present a unified coordinating framework to efficiently utilize existing ML-based agents. First, we propose a message propagation protocol that specifies the collaboration behaviors for agents and encapsulates the global tuning messages in each agent's model. Second, we combine Thompson Sampling, a well-studied reinforcement learning algorithm with a memory buffer so that our framework can allocate the tuning budget judiciously in a non-stationary environment. Our framework defines the interfaces adapted to a broad class of ML-based tuning agents, yet simple enough for integration with existing implementations and future extensions. Based on extensive evaluations, we show that this framework can effectively utilize different ML-based agents and find better configurations with 1.4~14.1x speedups on the workload execution time compared with baselines.
Prerequisite-driven Fair Clustering on Heterogeneous Information Networks

This paper studies the problem of fair clustering on heterogeneous information networks (HINs) by considering constraints on structural and sensitive attributes. We propose a Prerequisite-driven Fair Clustering (PDFC ) algorithm to solve this problem. Specifically, we define the structural constraint on the connection among nodes in HINs by combining meta-paths and prerequisite meta-paths and introduce Fairlets as the balance constraint. Under two constraints, we learn node embeddings based on graph models and perform theCholesky decomposition to obtain their orthogonal embeddings. We fuse node embeddings under constraints, define the loss function of PDFC, and perform k-means to achieve clustering. In addition, we design an update strategy of the adjacency matrix to achieve dynamic PDFC over time. Compared with several fair clustering algorithms on three real-world datasets, our experimental results verify the effectiveness and efficiency of PDFC.
Measuring Re-identification Risk

Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification risk in such user representations. Our framework, based on hypothesis testing, formally bounds the probability that an attacker may be able to obtain the identity of a user from their representation. As an application, we show how our framework is general enough to model important real-world applications such as the Chrome's Topics API for interest-based advertising. We complement our theoretical bounds by showing provably good attack algorithms for re-identification that we use to estimate the re-identification risk in the Topics API. We believe this work provides a rigorous and interpretable notion of re-identification risk and a framework to measure it that can be used to inform real-world applications.
Deep Active Alignment of Knowledge Graph Entities and Schemata

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.
A novel on-chip linear and switching mixed regulation against power analysis attacks

On-chip voltage converters such as low drop-out (LDO) regulators are capable of generating fast and scaled voltage levels against power analysis attacks (PAAs) due to the high bandwidths of feedback loops. Unfortunately, LDO regulators suffer from much power loss when they are utilized for outputting scaled voltage levels. In contrast, buck converters may achieve high power conversion efficiencies but are unable to generate output voltage with fast scaling patterns. In this paper, a novel hybrid voltage regulator that is consist of an LDO regulator and a buck converter connected in parallel is proposed to address the issues related with security against PAAs and power loss. The LDO regulator offers fast scaled voltage levels while the buck converter provides major load current. The result shows that the measurement-to-disclosure (MTD) value of a cryptographic circuit (CC) with the proposed hybrid voltage regulator maintains over 2 million with a 4.3% power loss.
TaintGuard: Preventing implicit privilege leakage in smart contract based on taint tracking at abstract syntax tree level

Solidity smart contracts play an important role in enabling interactions and collaborations among users in a decentralized blockchain-based community, and their security has received much attention in the industry. One of the most serious security issues faced by Solidity smart contracts is that the use of delegatecall to make cross-contract calls will tamper with contract privilege, which is difficult to detect. To improve the robustness of smart contract privilege control mechanisms at the level of source code, this paper designs and implements a new cross-contract static analysis tool called TaintGuard based on the abstract syntax tree of Solidity code and by combining taint analysis and instrumentation monitoring. TaintGuard uses solc to obtain the abstract syntax tree of the contract source code and then traverses the nodes of the tree to filter call relations that use delegatecall for cross-contract calls. Then, TaintGuard obtains the control flow graph within the target function, determines whether there are problematic paths in the control flow graph that may cause implicit leakage of contract privileges through static taint analysis, and instruments monitoring code in the corresponding position of the program. In this way, the contract status in the runtime stage is monitored to prevent the contract privileges from being tampered with by malicious callers. Finally, TaintGuard is evaluated against existing static analysis tools for Solidity smart contracts. The evaluation results indicate that TaintGuard outperforms other tools in terms of execution efficiency. Also, it can effectively fix the contract code with privilege leaks with a acceptable overhead.
On the Crucial Role of Information Theory in the Metaverse

In recent years, the development of social media and virtual worlds, such as the Metaverse, has transformed human interaction and created new opportunities for digital experiences. As the Metaverse becomes more complex and immersive, a vast amount of data will be transmitted and processed in real time, making information theory crucial to its development. This paper explores the key concepts in information theory and investigates the potential applications of information theory in the Metaverse. The applications discussed include privacy protection, security, data storage, transmission, visualization, and user behavior. Continued research is essential in each of these areas to develop a more comprehensive information-theoretic framework that can effectively address the diverse challenges in the Metaverse.
6G Technology as Enabler of Psychophysical Well-being &amp; Social Inclusion Services for Citizen 4.0

Virtual Reality and Augmented Reality technologies are increasingly being adopted in the healthcare field. Their use not only involves physicians as end users, but also the patients themselves. VR is “consumer ready” and ubiquitous, making it easy for patients to find and use self-management AR/VR tools for conditions such as non-pharmacological pain management. The limited dimensions and wearability of the VR/AR devices make it possible to benefit from them not just in healthcare facilities, but nearly everywhere, making it particularly relevant for people who are forced to stay home due to significant illness. This paper explores the possibilities of social inclusion, psychological benefits and non-pharmacological pain management derived from the routine use of VR and AR, opportunely integrated with other 6G technologies, by isolated individuals that suffer from chronic and acute conditions.
A Distributed Cybersecurity Solution in an IoMT Network Using a Multi-target Federated Learning

Most cybersecurity solutions are based on building a single logical entity to tackle one or multiple cyber threats. Such solutions have proven effective but can be less advantageous in a situation where cyber threats are so many, such that building a detection mechanism will imply writing a lot of logical expressions. Such solutions can be less effective on a specific threat and resource exhaustive on multiple threats. This can be risky to implement in delicate environments such as the medical IoT environment, where an optimal threat detection precision is required. In this paper, we introduce a distributed cybersecurity solution where the logical entities (agents) are distributed in federation along a threat profile, and their individual security operations are merged to produce a globalized security solution over a larger threat profile. We did experiment on this proposed solution and compare the results with those of conventional cybersecurity solution.
Adaptive feature aggregation based multi-task learning for uncertainty-guided semi-supervised medical image segmentation

Automatic segmentation of medical images is a necessary prerequisite for diagnosing related diseases. Magnetic resonance imaging (MRI) is a widely used non-invasive method in clinical practice, but obtaining reliable labeled training data for deep learning models is time-consuming and challenging. In this study, we propose an adaptive feature aggregation-based multi-task learning for uncertainty-guided semi-supervised image segmentation, dubbed AFAM-Net. The framework leverages a reconstruction task to capture anatomical information from raw images and assist the segmentation task. Moreover, we propose an adaptive feature aggregation strategy that selectively transfers useful features while filtering out irrelevant information, considering the associations between the two tasks. To better utilize unlabeled data, we incorporate dual uncertainty-aware methods to improve the segmentation performance. We evaluate the proposed AFAM-Net on clinical liver, 3D heart, and Cine cardiac MRI datasets. According to the experimental results, our AFAM-Net significantly outperformed other state-of-the-art semi-supervised medical image segmentation algorithms.
FSformer: Fast-Slow Transformer for video action recognition

Two-stream networks have achieved good results on action recognition datasets by modeling the interdependence of various motions. However, previous two-stream networks focus on action modeling but ignore concentrating on the difference in importance between different short-term actions, causing the limitation of the model's action modeling capabilities between different short-term actions. Therefore, we propose a Short-term Action Differentiated Attention (SADA) module based on the two-stream structure with different temporal resolution inputs. We embed the SADA module in a novel two-stream transformer architecture called Fast-Slow Transformer (FSformer). The SADA module dramatically pays attention to the difference between the importance of different short-term actions. It can: (i) deploy attention from the video frames to learn the differentiated knowledge of the importance of different short-term action feature information for action recognition, (ii) fuse rich importance difference knowledge and context information through a novel Fast-Slow Attention mechanism. Overall, the SADA module significantly focuses on the difference in importance of short-term actions and improves action recognition performance. We evaluate our method's effectiveness on three challenging densely-labeled action datasets and achieve results over the state-of-the-art.
Deteriorated image classification model for malayalam palm leaf manuscripts

The method for document image classification presented in this paper mainly focuses on six different Malayalam palm leaf manuscripts categories. The proposed approach consists of three phases: dataset analysis, building a bag of words repository followed by recognition and classification using a voting approach. The palm leaf manuscripts are initially subject to pre-processing and subjective analysis techniques to create a bag of words repository during the dataset analysis phase. Next, the textual components from the manuscripts are extracted for recognition using Tesseract 4 OCR with default and self-adapted training sets and a deep-learning algorithm. The Bag of Words approach is used in the third phase to categorize the palm leaf manuscripts based on textual components recognized by OCR using a voting process. Experimental analysis was done to analyze the proposed approach with and without the voting techniques, varying the size of the Bag of Words with default/self-adapted training datasets using Tesseract OCR and a deep learning model. Experimental analysis proves that the proposed approach works equally well with/ without voting with a bag of words technique using Tesseract OCR. It is noticed that, for document classification, an overall accuracy of 83% without voting and 84.5% with voting is achieved with an F-score of 0.90 in both cases using Teserract OCR. Overall, the proposed approach proves to be high generalizable based on trial wise experiments with Bag of Words, offering a reliable way for classifying deteriorated Malayalam handwritten palm manuscripts.
The mediating effect of knowledge management in the relationship between the HPO framework and performance

Purpose The study examines the mediating effect of knowledge management (KM) in the relationship between the high-performance organisation (HPO) framework and high performance in financial institutions (FIs) in Uganda. The paper aims to develop a framework that promotes high performance in the FIs. Design/methodology/approach The conceptual model was tested on a sample of 28 financial instituitions using structural equation model. Findings The findings revealed that the high-performance framework is significantly related to high performance and KM is related high performance. KM mediates the relationship between the high-performance framework and high performance. Research limitations/implications The findings revealed that the high-performance framework is significantly related to high performance and KM is related high performance. KM mediates the relationship between the high-performance framework and high performance. Practical implications The findings revealed that the high-performance framework is significantly related to high performance and KM is related high performance. KM mediates the relationship between the high-performance framework and high performance. Originality/value This study makes several empirical and theoretical contributions, addressing the gap in the literature about the role of the HPO framework in strategic management. This study tests the relationship between the HPO and the firm's performance by taking the mediating effects of KM. The designed model highlights a significant organisational performance approach that can influence the finance sector positively.