Reviewerly/exHarmony huggingface dataset:
Anomaly Detection for a Water Treatment System Based on One-Class Neural Network

The prevalence of Internet-of-Things (IoT) technologies and the ubiquity of networked sensors and actuators in many industrial control systems (ICS) have led to the exposure of critical infrastructure in our society to malicious activities and cyber threats.Programmable logic controllers (PLCs) are embedded devices that automate ICS processes.PLCs, which serve as the heart of ICS, are vulnerable to attacks and system malfunctions like other embedded devices.Because PLCs are widely used to control ICS physical processes, attacks against PLCs can cause irreparable damage to enterprises and even loss of life.However, due to the unique and proprietary architecture of PLCs, it is not easy to apply traditional tools and techniques for PLC protection.This work presents an unsupervised learning approach for anomaly detection in ICS based on neural networks with one class objective function and additional regularization term.This technique combines the abilities of neural networks to learn complex relationships with a one-class objective function and a regularization term for separating normal conditions from anomalous operations.The newly introduced regularization term provides a model-tuning mechanism based on specific industrial requirements and performance metrics of interest (i.e., precision or recall).The model was evaluated on a recent real-world ICS dataset: the Secure Water Treatment (SWaT) dataset.The proposed technique's performance is compared with previous work, showing improvements in terms of scalability and attack detection capability, proving that the proposed technique is suitable for use in real ICS scenarios.The proposed method with the regularization term demonstrated superior recall values in 15 out of the 36 attack scenarios in the SWaT dataset, which is the largest of any published methods in the literature.A qualitative analysis of the proposed technique on the SWaT security showdown event data further proves the technique's high anomaly detection ability on real-time injected attacks.
A study of generative large language model for medical research and healthcare

There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text. We train GatorTronGPT using a GPT-3 architecture with up to 20 billion parameters and evaluate its utility for biomedical natural language processing (NLP) and healthcare text generation. GatorTronGPT improves biomedical natural language processing. We apply GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models trained using synthetic text generated by GatorTronGPT outperform models trained using real-world clinical text. Physicians' Turing test using 1 (worst) to 9 (best) scale shows that there are no significant differences in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights into the opportunities and challenges of LLMs for medical research and healthcare.
Predicting the future of disruptive technologies: The method of alternative histories

With digital technologies shaping competition in many industries, predicting the future of potentially disruptive technologies becomes an essential task of business leaders concerned with the survival and success of their organizations. Looking into the future of disruptive technologies requires a philosophical stance and a practical method that accommodates the inherent uncertainty and nonlinearity of the path of disruptive innovations. Unfortunately, much of the current thinking in relation to adoption and diffusion of innovations is rather linear and deterministic. This article proposes a set of philosophical principles, together with a practical brainstorming method, for glimpsing into the future of disruptive technologies. The method of alternative histories is based on the traditional brainstorming techniques and the philosophical ideas of Imre Lakatos, Roy Bhaskar, Bruno Latour, and Nassim Taleb.
Efficient learning with robust gradient descent

Minimizing the empirical risk is a popular training strategy, but for learning tasks where the data may be noisy or heavy-tailed, one may require many observations in order to generalize well. To achieve better performance under less stringent requirements, we introduce a procedure which constructs a robust approximation of the risk gradient for use in an iterative learning routine. Using high-probability bounds on the excess risk of this algorithm, we show that our update does not deviate far from the ideal gradient-based update. Empirical tests using both controlled simulations and real-world benchmark data show that in diverse settings, the proposed procedure can learn more efficiently, using less resources (iterations and observations) while generalizing better.
A fully trainable network with RNN-based pooling

Pooling is an important component in convolutional neural networks (CNNs) for aggregating features and reducing computational burden. Compared with other components such as convolutional layers and fully connected layers which are completely learned from data, the pooling component is still handcrafted such as max pooling and average pooling. This paper proposes a learnable pooling function using recurrent neural networks (RNN) so that the pooling can be fully adapted to data and other components of the network, leading to an improved performance. Such a network with learnable pooling function is referred to as a fully trainable network (FTN). Experimental results demonstrate that the proposed RNN based pooling can well approximate the existing pooling functions with just one neuron, thus making it appropriate to be used as pooling function in a network with its rich representation capability. Furthermore, experiments have shown that the proposed FTN can achieve better performance than the existing pooling methods under similar network architectures for image classification.
Sensitivity Analysis for Non-Interactive Differential Privacy: Bounds and Efficient Algorithms

Differential privacy (DP) has gained significant attention lately as the state of the art in privacy protection. It achieves privacy by adding noise to query answers. We study the problem of privately and accurately answering a set of statistical range queries in batch mode (i.e., under non-interactive DP). The noise magnitude in DP depends directly on the sensitivity of a query set, and calculating sensitivity was proven to be NP-hard. Therefore, efficiently bounding the sensitivity of a given query set is still an open research problem. In this work, we propose upper bounds on sensitivity that are tighter than those in previous work. We also propose a formulation to exactly calculate sensitivity for a set of COUNT queries. However, it is impractical to implement these bounds without sophisticated methods. We therefore introduce methods that build a graph model G based on a query set Q, such that implementing the aforementioned bounds can be achieved by solving two well-known clique problems on G. We make use of the literature in solving these clique problems to realize our bounds efficiently. Experimental results show that for query sets with a few hundred queries, it takes only a few seconds to obtain results.
How to Extract Image Features Based on Co-Occurrence Matrix Securely and Efficiently in Cloud Computing

High-dimensional feature extraction based on co-occurrence matrix improves the detection performance of steganalysis, but it is difficult to be realized for massive image data by an analyzer with limited computational ability. We solve this problem by verifiable outsourcing computation, which allows a computationally weak client to outsource the evaluation of a function to a powerful but untrusted server. In this paper, we propose a verifiable outsourcing scheme of feature extraction based on co-occurrence matrix with single untrusted cloud server. The original images are protected from the server by using a projection of one to many with trapdoor, which can be realized by a symmetric probabilistic encryption scheme we present. The analyzer can obtain true results of feature extraction and detect any failure with a probability of 1 if the server misbehaves. Finally, we provide the simulations on the outsourcing of extracting ccJRM features in cloud computing. The theory analysis and experiment result also show that the proposed outsourcing scheme could greatly decrease the computation cost of the analyzer without exposure of the original images and extraction results.
User perception of sentiment-integrated critiquing in recommender systems

Critiquing in recommender systems has been accepted as an effective feedback mechanism that allows users to incrementally refine their preferences for product attributes, especially in complex decision environments and high-investment product domains where users’ initial preferences are usually uncertain and incomplete. However, the traditional critiquing methods are limited in that they are only based on static attribute values (such as a digital camera’s screen size, effectiveness pixels, optical zoom). Considering product reviews contain other customers’ sentiments (also called opinions) expressed on some features, in this manuscript, we propose a sentiment-integrated critiquing approach, for helping users to formulate and refine their preferences. Through both before-after and within-subjects experiments, we find that the incorporation of feature sentiments into the critiquing interface can significantly improve users’ product knowledge, preference certainty, decision confidence, perceived information usefulness, and purchase intention. The results can hence be constructive for enhancing current critiquing-based recommender systems.
Biomechanics Imaging and Analysis

This article presents the common imaging techniques that are used in biomechanics and movement sciences. The two main optical motion capture techniques that are discussed are marker-based and markerless systems. The marker-based systems rely on the recording and analysis of images of multiple distinguishable markers placed on the moving objects. In contrast, the markerless systems employ image processing and machine learning to first detect the object of interest in the captured images and then use such information to calculate the movement of the objects. This article also discusses the common workflow to analyze the movement of body segments, which can be used in the clinical and biomechanical analysis of movements.
Usability Attributes for Mobile Applications: A Systematic Review

The usability of mobile applications (apps) is an emerging area of research because of the increasing use of mobile devices around the world. App development is challenging because each application has its own purpose, and each individual user has different needs and expectations from the apps. There are various apps available for each purpose, and the success of the application depends on its usefulness. This paper presents a systematic review of some of the most contemporary apps and highlights their usability attributes. It discusses usability models, frameworks and guidelines outlined in previous research for designing apps with enhanced usability characteristics. Based on this research, comprehensive guidelines for mobile apps’ usability can then be provided.
Integrating Technical Debt Management and Software Quality Management Processes: A Normative Framework and Field Tests

Despite the increasing awareness of the importance of managing technical debt in software product development, systematic processes for implementing technical debt management in software production have not been readily available. In this paper we report on the development and field tests of a normative process framework that systematically incorporates steps for managing technical debt in commercial software production. The framework integrates processes required for technical debt management with existing software quality management processes prescribed by the project management body of knowledge (PMBOK), and it contributes to the further development of software-specific extensions to the PMBOK. We partnered with three commercial software product development organizations to implement the framework in real-world software production settings. All three organizations, irrespective of their varying software process maturity levels, were able to adopt the proposed framework and integrate the prescribed technical debt management processes with their existing software quality management processes. Our longitudinal observations and case-study interviews indicate that the organizations were able to accrue economic benefits from the adoption and use of the integrated framework.
Learning With Mobile Devices

The concept of learning with small portable computers was developed by Alan Kay in 1972. Since that early conception, scholars, such as Traxler, Sharples, and Soloway are the pioneering scholars who paved the way to a better understanding of the philosophical, pedagogical, and conceptual underpinnings of mobile learning today. In this chapter, an overview is provided to explicate the initial foundations of the emerging field of mobile learning (mlearning). Next, current scientific knowledge is delineated with explicit references to the early scholars. This chapter concludes with a preview of the future research directions in mobile learning.
An Approach to Optimise Resource Provision with Energy-Awareness in Datacentres by Combating Task Heterogeneity

Cloud workloads are increasingly heterogeneous such that a single Cloud job may encompass one to several tasks, and tasks belonging to the same job may behave distinctively during their actual execution. This inherent task heterogeneity imposes increased complexities in achieving an energy efficient management of the Cloud jobs. The phenomenon of a few proportions of tasks characterising increased resource intensity within a given job usually lead the providers to over-provision all the encompassed tasks, resulting in majority of the tasks incurring an increased proportions of resource idleness. To this end, this paper proposes a novel analytics framework which integrates a resource estimation module to estimate the resource requirements of tasks a priori, a straggler classification module to classify tasks based on their resource intensity, and a resource optimisation module to optimise the level of resource provision depending on the task nature and various runtime factors. Performance evaluations conducted both theoretically and through practical experiments prove that the proposed methodology performs better than the compared statistical resource estimation methods and existing models of straggler mitigation, and further demonstrate the effectiveness of the proposed methodology in achieving energy conservation by postulating appropriate level of resource provisioning for task execution.
Continuous Visual World Modeling for Autonomous Robot Manipulation

Service robots need to handle a variety of everyday manipulation tasks to accomplish household chores such as cooking and cleaning. Successful execution of these tasks is highly dependent on how reliable the robot perceives its environment through noisy sensing. We present a visual world modeling system for service robots to generate and maintain accurate models of their environments for continuous scenarios. This system is designed to provide a generic platform for both humanoid and ground manipulation robots using different types of vision sensors and algorithms. In our particular implementation, visual data processed by different perception algorithms are used for building and continuously updating a world model of the environment. We evaluate our system on a variety of object manipulation scenarios and show that the system produces consistent perception outcomes suitable for different manipulation tasks.
Design and Analysis of a Neural Network Inference Engine Based on Adaptive Weight Compression

Neural networks generally require significant memory capacity/bandwidth to store/access a large number of synaptic weights. This paper presents design of an energy-efficient neural network inference engine based on adaptive weight compression using a JPEG image encoding algorithm. To maximize compression ratio with minimum accuracy loss, the quality factor of the JPEG encoder is adaptively controlled depending on the accuracy impact of each block. With 1% accuracy loss, the proposed approach achieves 63.4× compression for multilayer perceptron (MLP) and 31.3× for LeNet-5 with the MNIST dataset, and 15.3× for AlexNet and 10.2× for ResNet-50 with ImageNet. The reduced memory requirement leads to higher throughput and lower energy for neural network inference (3× effective memory bandwidth and 22× lower system energy for MLP).
Data Mining: Prediction Methods

In this article will be introduced some classification methods, such as k-nearest-neighbor, case-based reasoning, genetic algorithms. Moreover, prediction methods will be explored, in particular referring to linear and nonlinear regression. Finally, we discuss two cases of generalized linear models: logistic and Poisson regression.
Dimensionality reduction via preserving local information

Dimensionality reduction is an important technique for multimedia data in smart cities. As a significant type of multimedia data, images are being processed frequently using such technique. It is worth noting that finding neighbors is an integral part of graph embedding algorithms in dimensionality reduction. In this paper, we present an effective and efficient metric function, named local similarity preserving (LSP), which can preserve the similarity information of each sample to its homogeneous and heterogeneous neighbors. The proposed LSP function is helpful to enhance the discriminative capability of subsequent feature extractors. Based on LSP function, we also propose two novel algorithms, local similarity preserving discriminant (LSPD) algorithm, which can preserve the local similarity information and LSPD+ algorithm, which can preserve the local similarity and geometric structure information, respectively. The experimental results on digit visualization and face recognition demonstrate the effectiveness and efficiency of the proposed algorithms.
Mobile Application Quality Assurance

Mobile applications have become highly pervasive in recent years. Their quality is essential since application failures can lead to serious consequences, such as damage of corporate reputation or financial loss. The goal of this work is to identify and expose approaches that address the issue of quality assurance for mobile applications. In order to drive our systematic mapping study, we derived eight research questions based on the stated goal. Ultimately, we systematically identified 311 articles based on 4607 captured records. We created clustered views to answer the research questions and used existing surveys to complement our overview of current challenges. The results show an overall upward trend of publications since 2003. Hot topics include automation of GUI tests and assurance of nonfunctional qualities. Aspects of future research could be the integration of review techniques into existing approaches and focusing more strongly on defects addressing the specific characteristics of mobile applications.
Error-tolerant graph matching in linear computational cost using an initial small partial matching

Error-tolerant graph matching has been demonstrated to be an NP-problem, therefore, its exact computation has an exponential computational cost and several sub-optimal algorithms have been presented with the aim of making the runtime acceptable in some applications. Some well-known sub-optimal algorithms have sixth, cubic or quadratic computational costs with respect to the order of the graphs. Although these computational costs could be considered very low, when applications deal with large graphs (for instance in social networks), the quadratic cost continues to be unacceptable. For this reason, we present an error-tolerant graph-matching algorithm that has a O(d3.5 · ) computational cost, d being the number of output edges per node and n the order of the graphs. Note that, usually, in social networks, it holds that d ≪ n and for this reason we consider the cost to be linear, in other words O(k · ), k being a low constant. Our method needs an initial seed, which is composed of one or several node-to-node mappings. The algorithm has been applied to analyse the evolution of social networks.
Misinformation on the Internet regarding Ablative Therapies for Prostate Cancer

To evaluate the quality of web-based information on ablative therapies for prostate cancer.The 2 most common search engines (Google and Bing) were queried for the following terms: "prostate cancer" + "HIFU" and "cryotherapy," respectively. The top 50 websites for each were obtained. Websites were characterized and analyzed regarding their accuracy and completeness of information using criteria determined a priori. Academic papers were excluded.Of "HIFU" search results, 17% were advertisements, 13% and 29% were academic and private practice websites, respectively. Erroneous information on oncological efficacy was presented in 15% and 41% of academic and private practice websites, respectively. Criteria for treatment were mentioned in 31% and 66% of academic and private practice websites, respectively. Of "cryotherapy" search results, 18% were advertisements, 15% academic sites, and 11% private practices. Erroneous information was presented in 73% of both academic and private practice websites. Criteria for treatment were mentioned in 27% and 18% of these sites, respectively. Seventy eight percent and 75% of HIFU and cryotherapy sites, respectively, mentioned general side effects.There is substantial inaccurate and incomplete information on the Internet regarding ablative treatments for prostate cancer from academic and private practice websites. Selection criteria are uncommonly discussed. More attention to accuracy of information is needed to ensure patients are not misled about the data behind these treatments.