Reviewerly/exHarmony huggingface dataset:
Assessment of malignant risk stratification for microcalcifications interpreted as “amorphous” morphology on mammography: A study based on the 5th edition of breast Imaging Reporting and Data System

To investigate the malignant risk stratification of microcalcifications interpreted as amorphous morphology on mammography according to the coexistence of punctate microcalcifications based on the 5th edition of the Breast Imaging Reporting and Data System.Between March 2013 and September 2020, 367 microcalcifications interpreted as amorphous morphology on mammography with surgical biopsies were included. The amorphous microcalcifications were classified into a predominantly punctate group (A, <50% of amorphous), a predominantly amorphous group (B, >50% of amorphous), and an only amorphous group (C, 100% of amorphous). The distribution was classified into diffuse, regional, grouped, and linear/segmental. The reference standard was the pathology. The positive predictive values (PPV) were calculated and compared using the Chi-square's test or Fisher's exact test and Kruskal-Wallis test.The overall PPV of microcalcifications interpreted as having an amorphous morphology was 5.2%. The PPV across groups significantly increased in proportion to the amorphous morphology, with 1.0% in group A, 5.6% in group B, and 23.3% in group C (p <.001). Furthermore, the PPV between group A and groups B plus C (10.1%) and groups A plus B (2.8%) and group C were significantly different (p <.001). The PPV of distribution was 0% for diffuse, 4.9% for regional, 5.0% for grouped, and 11.1% for linear/segmental distributions, without statistical significance.Pure amorphous microcalcifications are suitable for category 4B. However, when they coexist with punctate morphology, the malignant risk decreases suitable for category 4A or lower. When amorphous microcalcifications coexist with a predominantly punctate morphology, follow-up should be considered.
A Parallel Hybrid Feature Selection Approach Based on Multi-Correlation and Evolutionary Multitasking

Particle swarm optimization (PSO) has been successfully applied to feature selection (FS) due to its efficiency and ease of implementation. Like most evolutionary algorithms, they still suffer from a high computational burden and poor generalization ability. Multifactorial optimization (MFO), as an effective evolutionary multitasking paradigm, has been widely used for solving complex problems through implicit knowledge transfer between related tasks. Based on MFO, this study proposes a PSO-based FS method to solve high-dimensional classification via information sharing between two related tasks generated from a dataset using two different measures of correlation. To be specific, two subsets of relevant features are generated using symmetric uncertainty measure and Pearson correlation coefficient, then each subset is assigned to one task. To improve runtime, the authors proposed a parallel fitness evaluation of particles under Apache Spark. The results show that the proposed FS method can achieve higher classification accuracy with a smaller feature subset in a reasonable time.
The Application of Micro-Video Technology in the Promotion of Digital Reading in Children's Libraries Under the Background of Media Fusion

Digital reading is now an important way for children to read. This article defines the concept of children's digital reading based on the current situation, reading needs, and research progress of reading promotion methods. Aiming at the problem that only simple matching method is used for entity extraction in BAG model, this paper proposes an entity extraction method based on reference words and an entity graph construction method based on problem-related entities. Experiments on data sets verify the effectiveness of the model. The results show that the BAG model is improved by 2.4% on the verification set and 4.1% on the test set, which proves the effectiveness of the improved method. Based on the graph convolution algorithm of the central node, the local subgraph convolution and the global graph convolution are connected in the entity graph behind the central node, so that the model can obtain local and global information at the same time and enhance the reasoning ability of the model.
Blockchain-Based IoT E-Healthcare

Numerous industries, including e-healthcare, are capitalizing on and using blockchain and internet of things (IoT) technology. IoT devices may collect patient vitals and other sensory information in real-time, which medical professionals can then examine. All information gathered from the internet of things is stored, processed, and computed in one place. Such concentration raises concerns since it increases the likelihood of a catastrophic failure, distrust, tampering with data, and even the circumvention of privacy protections. By offering decentralized processing and storage for IoT data, blockchain has the potential to address these critical issues. As a result, designing a decentralized IoT-based e-healthcare system that incorporates IoT and blockchain technology might be a viable option. First, the authors provide some context about blockchain in this essay. The viability of blockchain systems for the internet of things-based e-healthcare is then assessed.
An efficient framework for outfit compatibility prediction towards occasion

Abstract Nowadays, in a communicating society, fashion is an integral part of a human life, and it is more comfortable and confident when people dress well. Outfit compatibility is not only a combination of different items but also regarding various aspects, such as style, user preferences, and specific occasions. Most of the existing works lead to address the outfit compatibility concerning only style or user preferences, and have no regard for occasions. In this paper, we propose an efficient method for both outfit compatibility and the fill-in-the-blank tasks according to specific occasions. To this end, we utilized an auxiliary classification branch to learn the significantly important features regarding specific occasions. Besides, a sequence to sequence approach is also applied to learn the relationship of different items along with a visual semantic space, which is able to learn the connection between visual features and their semantic presentation. To demonstrate the effectiveness of the proposed method, we conduct experiments on our newly collected Shoplook-Occasion dataset. The experimental results indicate that our proposed method improved the AUC metric from 0.02 to 0.15% and from 0.5 to 4% on accuracy, compared with other approaches for outfit compatibility problem conditioning on specific occasions.
Know Your Enemy To Save Cloud Energy: Energy-Performance Characterization of Machine Learning Serving

The proportion of machine learning (ML) inference in modern cloud workloads is rapidly increasing, and graphic processing units (GPUs) are the most preferred computational accelerators for it. The massively parallel computing capability of GPUs is well-suited to the inference workloads but consumes more power than conventional CPUs. Therefore, GPU servers contribute significantly to the total power consumption of a data center. However, despite their heavy power consumption, GPU power management in cloud-scale has not yet been actively researched. In this paper, we reveal three findings about energy efficiency of ML inference clusters in the cloud. ❶ GPUs of different architectures have comparative advantages in energy efficiency to each other for a set of ML models. ❷ The energy efficiency of a GPU set may significantly vary depending on the number of active GPUs and their clock frequencies even when producing the same level of throughput. ❸ The service level objective(SLO)-blind dynamic voltage and frequency scaling (DVFS) driver of commercial GPUs maintain an immoderately high clock frequency. Based on these implications, we propose a hierarchical GPU resource management approach for cloud-scale inference services. The proposed approach consists of energy-aware cluster allocation, intra-cluster node scaling, intra-node GPU scaling and GPU clock scaling schemes considering the inference service architecture hierarchy. We evaluated our approach with its prototype implementation and cloud-scale simulation. The evaluation with real-world traces showed that the proposed schemes can save up to 28.3% of the cloud-scale energy consumption when serving five ML models with 105 servers having three different kinds of GPUs.
Leveraging Domain Information for the Efficient Automated Design of Deep Learning Accelerators

Deep learning accelerators are important tools for feeding the growing demand for deep learning applications. The automated design of such accelerators—which is important for reducing development costs—can be viewed as a search over a vast and complex design space that consists of all possible accelerators and all the possible software that could run on them. Unfortunately, this search is complicated by the existence of many ordinal and categorical values, which are critical to explore for the ultimate design but are not handled well by existing search techniques.This paper presents a technique for efficiently searching this space by injecting domain information—in this case information about hardware/software (HW/SW) co-design—into the automated search process. Specifically, this paper introduces a novel Bayesian optimization framework called daBO (domain-aware BO) that accepts domain information as input, including those describing ordinal and categorical values.This paper also introduces Spotlight, a design tool based on daBO, and this paper empirically shows that Spotlight produces accelerator designs and software schedules that are orders of magnitude better than those created by the state-of-the-art. For example, for the ResNet-50 deep learning model, Spotlight produces a HW/SW configuration that reduces delay by 135× over the configuration produced by ConfuciuX, a state-of-the-art HW/SW co-design tool, and Spotlight reduces energy-delay product (EDP) by 44× over an Eyeriss-like accelerator, which is an edge-scale hand-designed accelerator. In the realm of cloud-scale accelerators, Spotlight reduces the EDP of a scaled-up Eyeriss-like accelerator by 23×. Our evaluation shows that Spotlight benefits from the efficiency of daBO, which allows Spotlight to identify accelerator designs and software schedules that prior work cannot identify.
Logical/Physical Topology-Aware Collective Communication in Deep Learning Training

Training is an important aspect of deep learning to enable network models to be deployed. To scale training, multiple GPUs are commonly used with data parallelism to exploit the additional GPU compute and memory capacity. However, one challenge in scalability is the collective communication between GPUs. In this work, we propose to accelerate the AllReduce collective. AllReduce communication is often based on a logical topology (e.g., ring or tree algorithms) that is mapped to a physical topology or the physical connectivity between the nodes. In this work, we propose a logical/physical topology-aware collective communication that we refer to as C-Cube architecture – Chaining Collective Communication with Computation. C-Cube exploits the opportunity to overlap or chain different phases of collective communication as well as forward computation in a tree algorithm AllReduce. We exploit the communication pattern in a logical tree topology to overlap the different phases of communication. Since ordering is maintained in the tree collective algorithm, we propose gradient queuing to enable chaining of communication with forward computation to accelerate overall performance while having no impact on training accuracy. We also exploit the physical topology characteristics to further improve the performance, including proposing detour connections for collective communication while leveraging the additional connectivity to enable a double-tree C-Cube implementation. We implement a C-Cube proof-of-concept on a real system (8-GPU NVIDIA DGX-1) and show C-Cube results in performance improvement in communication performance compared to non-overlapped tree algorithms as well as overall performance.
SpecFaaS: Accelerating Serverless Applications with Speculative Function Execution

Serverless computing has emerged as a popular cloud computing paradigm. Serverless environments are convenient to users and efficient for cloud providers. However, they can induce substantial application execution overheads, especially in applications with many functions.In this paper, we propose to accelerate serverless applications with a novel approach based on software-supported speculative execution of functions. Our proposal is termed Speculative Function-as-a-Service (SpecFaaS). It is inspired by out-of-order execution in modern processors, and is grounded in a characterization analysis of FaaS applications. In SpecFaaS, functions in an application are executed early, speculatively, before their control and data dependences are resolved. Control dependences are predicted like in pipeline branch prediction, and data dependences are speculatively satisfied with memoization. With this support, the execution of downstream functions is overlapped with that of upstream functions, substantially reducing the end-to-end execution time of applications. We prototype SpecFaaS on Apache OpenWhisk, an open-source serverless computing platform. For a set of applications in a warmed-up environment, SpecFaaS attains an average speedup of 4.6×. Further, on average, the application throughput increases by 3.9× and the tail latency decreases by 58.7%.
HyQSAT: A Hybrid Approach for 3-SAT Problems by Integrating Quantum Annealer with CDCL

Propositional satisfiability problem (SAT) is represented in a conjunctive normal form with multiple clauses, which is an important non-deterministic polynomial-time (NP) complete problem that plays a major role in various applications including artificial intelligence, graph colouring, and circuit analysis. Quantum annealing (QA) is a promising methodology for solving complex SAT problems by exploiting the parallelism of quantum entanglement, where the SAT variables are embedded to the qubits. However, the long embedding time fundamentally limits existing QA-based methods, leading to inefficient hardware implementation and poor scalability.In this paper, we propose HyQSAT, a hybrid approach that integrates QA with the classical Conflict-Driven Clause Learning (CDCL) algorithm to enable end-to-end acceleration for solving SAT problems. Instead of embedding all clauses to QA hardware, we quantitatively estimate the conflict frequency of clauses and apply breadth-first traversal to choose their embedding order. We also consider the hardware topology to maximize the utilization of physical qubits in embedding to QA hardware. Besides, we adjust the embedding coefficients to improve the computation accuracy under qubit noise. Finally, we present how to interpret the satisfaction probability based on QA energy distribution and use this information to guide the CDCL search. Our experiments demonstrate that HyQSAT can effectively support larger-scale SAT problems that are beyond the capability of existing QA approaches, achieve up to 12.62X end-to-end speedup using D-Wave 2000Q compared to the classic CDCL algorithm on Intel E5 CPU, and considerably reduce the QA embedding time from 17.2s to 15.7µs compared to the D-Wave Minorminer algorithm [11].
Market Mechanism-Based User-in-the-Loop Scalable Power Oversubscription for HPC Systems

Significant power consumption is one of the major challenges for current and future high-performance computing (HPC) systems. All the while, HPC systems generally remain power underutilized, making them a great candidate for applying power oversubscription to reclaim unused capacity. However, an oversubscribed HPC system may occasionally get overloaded. In this paper, we propose MPR (Market-based Power Reduction), a scalable market-based approach where users actively participate in reducing the HPC system’s power consumption to mitigate overloads. In MPR, HPC users bid to supply, in exchange for incentives, the resource reduction required for handling the overloads. Using several real-world trace-based simulations, we extensively evaluate MPR and show that, by participating in MPR, users always receive more rewards than the cost of performance loss. At the same time, the HPC manager enjoys orders of magnitude more resource gain than her incentive payoff to the users. We also demonstrate the real-world effectiveness of MPR on a prototype system.
Forecasting chaotic weather variables with echo state networks and a novel swing training approach

Physical systems like weather variables, the behavior of oceanic bodies, economic variables, and similar systems possess a considerable amount of chaos, and deducing predictive patterns from them is almost not feasible. Forecasting weather parameters has always been a major objective due to several reasons, but observably not all weather variables are equally influential. Parameters like temperature, humidity, and radiation, though easier to predict due to obvious patterns, are found to be less impactful in practical circumstances. But variables that are more turbulent and difficult to model, like wind flow, cloud cover, sea level pressure, etc. are very influential in the context of managing disasters like cyclones, estimating trades, estimating power generation and many other causes. In this study, the aim is to accurately forecast the horizontal wind velocity field which individually considers the longitudinal and latitudinal components of velocity and sea level pressure over a certain region. Initial attempts in this domain used to be purely mathematical with deterministic results, but predicting real phenomena by overpowering the chaos required enormous computational overhead. The results obtained were not satisfactory. With the evolution of statistical modeling, machine learning and deep learning, these systems were majorly formulated as spatiotemporal forecasting problems. Modified deep learning approaches like ConvLSTM, transformer-based models, CNN-GRU models, and others were applied on the spatiotemporal prediction problem producing more accurate results as compared to the primary methods. But the introduction of reservoir computing framework once again invoked the possibilities of improvements in this domain. An instance of reservoir computing, known as the echo state Network (ESN) was found to simulate and learn the complex dynamics of physical systems with significantly less effort. This boiled down to be a major motivation behind this paper. In this study, we have selected two regions over the Indian peninsula and the Mexican gulf and collected hourly data of horizontal wind velocity and sea level pressure with a spatial granularity of 0.25 degrees on these regions. This data has been used to train our proposed model which is an echo state network and convolution-based encoder–decoder architecture. The model forecasts the same parameters over the same spatial zone for some forecast horizons. The results have been rigorously compared with potential baselines where the ESN was replaced by conventional recurrent networks, and it was observed that our proposed approach outperforms all the other baselines.
Loki – the semantic wiki for collaborative knowledge engineering

We present Loki, a semantic wiki designed to support the collaborative knowledge engineering process with the use of software engineering methods. Designed as a set of DokuWiki plug-ins, it provides a variety of knowledge representation methods, including semantic annotations, Prolog clauses, and business processes and rules oriented to specific tasks. Knowledge stored in Loki can be retrieved via SPARQL queries, in-line Semantic MediaWiki-like queries, or Prolog goals. Loki includes a number of useful features for a group of experts and knowledge engineers developing the wiki, such as knowledge visualization, ontology storage, or code hint and completion mechanism. Reasoning unit tests are also introduced to validate knowledge quality. The paper is complemented by the formulation of the collaborative knowledge engineering process and the description of experiments performed during Loki development to evaluate its functionality. Loki is available as free software at https://loki.re.
Structured encryption for triangle counting on graph data

Triangle counting is an important metric on graph analysis for graph data such as social networks. To protect the privacy of graph data, researchers have proposed differential privacy-based triangle counting methods, but the counting results are flawed by estimation errors. A recent study proposes approximate triangle counting on encrypted graph data to achieve triangle counting of the entire graph, but cannot calculate the number of triangles formed between each individual node and its neighbors. In addition, triangle counting on graph is not fully investigated based on other privacy-preserving techniques, such as structured encryption for triangle counting on graph data. To improve the accuracy of triangle counting on graph data, we proposed two types of structured encryption for triangle counting on graph data (STE-TC) with various efficiency-security tradeoffs and combined it with homomorphic encryption to achieve triangle counting between each individual node and its neighbors that can evaluate the importance of this node. We proposed two triangle counting methods in both asymmetric key and symmetric key settings to satisfy different sharing requirements in the cloud. The security analysis and experimental results show that the proposed methods are feasible and practical.
Enhancing privacy for automatically detected quasi identifier using data anonymization

The fast advancement of information technology has resulted in more efficient information storage and retrieval. As a result, most organizations, businesses, and governments are releasing and exchanging a large amount of micro data among themselves for commercial or research purposes. However, incorrect data exchange will result in privacy breaches. Many methods and strategies have been developed to address privacy breaches, and Anonymization is one of them that many companies use. In order to perform anonymization, identification of the Quasi Identifier (QI) is significant. Hence this paper proposes a method called Quasi Identification Based on Tree (QIBT) for automatic QI identification. The proposed method derives the QI, based on the relationship between the numbers of distinct values assumed by the set of attributes. So, it uses the tree data structure to derive the unique and infrequent attribute values from the entire dataset with less computational cost. The proposed method consists of four phases: (i) Unique attribute value computation (ii) Tree construction and (iii) Computation of quasi-identifier from the tree (iv) Applying Anonymization Technique to the identified QI. Attributes with high risk of disclosure are identified using our proposed algorithm. Synthetic data are created exclusively for the detected QI using a partial synthetic data generating technique to improve usefulness. The suggested method’s efficiency is tested with a subset of the UCI machine learning dataset and produces superior results when compared to other current approaches.