Reviewerly/exHarmony huggingface dataset:
A Feature-Based On-Line Detector to Remove Adversarial-Backdoors by Iterative Demarcation

This paper proposes a new defense against neural network backdooring attacks that are maliciously trained to mispredict in the presence of attacker-chosen triggers. Our defense is based on the intuition that the feature extraction layers of a backdoored network embed new features to detect the presence of a trigger and the subsequent classification layers learn to mispredict when triggers are detected. Therefore, to detect backdoors, the proposed defense uses two synergistic anomaly detectors trained on clean validation data: the first is a novelty detector that checks for anomalous features, while the second detects anomalous mappings from features to outputs by comparing with a separate classifier trained on validation data. The approach is evaluated on a wide range of backdoored networks (with multiple variations of triggers) that successfully evade state-of-the-art defenses. Additionally, we evaluate the robustness of our approach on imperceptible perturbations, scalability on large-scale datasets, and effectiveness under domain shift. This paper also shows that the defense can be further improved using data augmentation.
A Smart Internet-Wide Port Scan Approach for Improving IoT Security Under Dynamic WLAN Environments

The Internet of Things (IoT) has created acute network security concerns owing to their weak protocols and limited system resources. Vulnerable IoT devices increase the risk of compromising other devices connected to the network. Hence, vulnerability and risk assessments are necessary for IoT devices. Correspondingly, the Internet-wide port scan (IWPS) technique has garnered significant attention for its ability to discover and probe Internet-wide connected IoT devices. However, IWPS performance depends on the end wireless local area network (WLAN) state (e.g., congestion and signal-to-interference-plus-noise ratio). Scans oblivious to such dynamic WLAN factors can cause probe packet loss while increasing port-scan delays, which reduces the discovery rate, which misses the point of network security. Therefore, in this study, we propose a novel holistic approach to identifying WLAN environmental states based on round-trip time and probe-packet responses. To demonstrate the effectiveness of the proposed approach, we perform extensive experiments on real WLAN environments with various devices. The accuracy of the estimated states was validated at greater than 90% by analyzing the captured probe data at each WLAN.
Transformer-based Multi-target Regression on Electronic Health Records for Primordial Prevention of Cardiovascular Disease

Machine learning algorithms have been widely used to capture the static and temporal patterns within electronic health records (EHRs). While many studies focus on the (primary) prevention of diseases, primordial prevention (preventing the factors that are known to increase the risk of a disease occurring) is still widely under-investigated. In this study, we propose a multi-target regression model leveraging transformers to learn the bidirectional representations of EHR data and predict the future values of 11 major modifiable risk factors of cardiovascular disease (CVD). Inspired by the proven results of pre-training in natural language processing studies, we apply the same principles on EHR data, dividing the training of our model into two phases: pre-training and fine-tuning. We use the fine-tuned transformer model in a "multi-target regression" theme. Following this theme, we combine the 11 disjoint prediction tasks by adding shared and target-specific layers to the model and jointly train the entire model. We evaluate the performance of our proposed method on a large publicly available EHR dataset. Through various experiments, we demonstrate that the proposed method obtains a significant improvement (12.6% MAE on average across all 11 different outputs) over the baselines.
Integrating morphologic and molecular histopathological features through whole slide image registration and deep learning

Modern molecular pathology workflows in neuro-oncology heavily rely on the integration of morphologic and immunohistochemical patterns for analysis, classification, and prognostication. However, despite the recent emergence of digital pathology platforms and artificial intelligence-driven computational image analysis tools, automating the integration of histomorphologic information found across these multiple studies is challenged by large files sizes of whole slide images (WSIs) and shifts/rotations in tissue sections introduced during slide preparation.To address this, we develop a workflow that couples different computer vision tools including scale-invariant feature transform (SIFT) and deep learning to efficiently align and integrate histopathological information found across multiple independent studies. We highlight the utility and automation potential of this workflow in the molecular subclassification and discovery of previously unappreciated spatial patterns in diffuse gliomas.First, we show how a SIFT-driven computer vision workflow was effective at automated WSI alignment in a cohort of 107 randomly selected surgical neuropathology cases (97/107 (91%) showing appropriate matches, AUC = 0.96). This alignment allows our AI-driven diagnostic workflow to not only differentiate different brain tumor types, but also integrate and carry out molecular subclassification of diffuse gliomas using relevant immunohistochemical biomarkers (IDH1-R132H, ATRX). To highlight the discovery potential of this workflow, we also examined spatial distributions of tumors showing heterogenous expression of the proliferation marker MIB1 and Olig2. This analysis helped uncover an interesting and unappreciated association of Olig2 positive and proliferative areas in some gliomas (r = 0.62).This efficient neuropathologist-inspired workflow provides a generalizable approach to help automate a variety of advanced immunohistochemically compatible diagnostic and discovery exercises in surgical neuropathology and neuro-oncology.
Improved Person Counting Performance Using Kalman Filter Based on Image Detection and Tracking

People detection and counting is becoming of great relevance in many application fields which range from the video surveillance of shopping centres to the management of public services such as bus stops. Various computer vision applications exploit Deep Learning technology for people recognition which recently showed great achievements. A very important task along with object/people detection is their tracking which is of fundamental importance especially when counting has to be performed. In this research work, a specific application for people counting at a bus stop is proposed, taking advantage of NVIDIA DeepStream SDK 5.0 detection and tracking algorithms. The final number of people is obtained through a Kalman Filter which aims at avoiding occlusion issues which are typical of people detection-tracking applications. The experimental results show the effectiveness of this approach in three scenarios with different complexity, significantly reducing the counting error with respect to pure detection counting.
Test Script Generation Based on Hidden Markov Models Learning From User Browsing Behaviors

User interface (UI) testing is a necessary process to evaluate whether the developed web applications meet the software requirements defined by the end-users. This study proposes an e-business workflow that automates UI testing utilizing Hidden Markov Models. The proposed workflow is designed to generate test scripts based on user browsing behaviors. In particular, it is able to generate browsing patterns that are unseen in the historical web usage data. To demonstrate the usability of the proposed testing framework, we provide a prototype implementation. Furthermore, to facilitate testing of the prototype implementation, we conduct experimental studies that investigate the quality of newly generated test scripts. In order to achieve this, we analyze the similarity between the actual browsing behaviors and the newly generated browsing behaviors. The results show that the proposed testing workflow is able to learn from the user-browsing behaviors extracted from the historical web usage log data. The results also show that the workflow is able to generate new and unseen user-browsing behaviors that have high similarities with the actual user-browsing behaviors. In turn, this indicates that the proposed system is able to generate high-quality new test scripts that can provide comprehensive testing of web applications.
A Multi-task Model for Sentiment Aided Cyberbullying Detection in Code-Mixed Indian Languages

With the expansion of digital sphere and advancement of technology, cyberbullying has become increasingly common, especially among teenagers. In this work, we have created a benchmark Hindi-English code-mixed corpus called BullySent, annotated with bully and sentiment labels for investigating how sentiment label information helps to identify cyberbully in a better way. For a vast portion of India, both of these languages constitute the primary means of communication, and language mixing is common in everyday speech. A multi-task framework called MT-BERT+VecMap based on two different embedding schemes for the efficient representations of code-mixed data, has been developed. Our proposed multi-task framework outperforms all the single-task baselines with the highest accuracy values of 81.12(+/−1.65)% and 77.46(+/−0.99)% for the cyberbully detection task and sentiment analysis task, respectively.
How Reliable and Valid are the Evaluations of Digital Competence in Higher Education: A Systematic Mapping Study

Evaluating digital competencies has become a topic of growing interest in recent years. Although several reviews and studies have summarized the main elements of progress and shortcomings in this area, some issues are yet to be explored. Very little information is available about the ways of ensuring the validity and reliability of the instrument used. This study addresses this issue through systematic mapping covering the period from January 2015 to July 2020. Based on 88 primary studies, we conclude that a growing number of studies have emerged over the years; most of them are based on European university students in social science programs; the quality of the journals where they were published is low; and the instruments used are mostly questionnaires and ad-hoc surveys that measure the various dimensions of digital competence based on participants’ perceptions. Out of the eight possible types of quality assessment, studies commonly report only four (one in the case of reliability and three in the case of validity). More than 50% of the studies do not provide evidence of having assessed the reliability and validity at the same time. Although participant discipline resulted in a significantly association with the practice of reporting reliability and validity assessments, no specific dimension explained such association. A non-parametric multivariate analysis reveals, among other interesting patterns, that the practice of not reporting quality assessments is more present in studies where participants belong to the fields of Engineering and Technology, Health Sciences, and Humanities.
A Survey On Feature Selection Method For Product Review

The internet becomes a lot more accessible with the help of wireless technology.a great site to learn online, share ideas, and read reviews for the purpose of a product or service. It may be tough to keep track of and comprehend consumer feedback when there are millions of internet reviews for a product or service.Usersatisfaction can be improved by analyzing the sentiment of a huge number of user reviews on e-commerce sites. A discourse analysis, due to the use of official speech, errors and shorter versions of words in short, results in increasing heterogeneity and sparseness, is hard in short texts. Sentiment analysis uses a set of features derived from meaningful words taken from data. A selection of subsets is the strategy for selecting a subset of important characteristics from a big corpus to improve classification accuracy.In machine learning or pattern recognition applications, feature selection approaches allow us to reduce computation time, improve prediction performance, and gain a deeper knowledge of the data. We offer an overview of some of the methods used in the literature in this study. In this study, we used a few feature selection approaches such as the genetic algorithm, Principal Component Analysis, Forest Optimization Algorithm and particle swarm optimization.
Adaptive Regional Multiple Features for Large-Scale High-Resolution Remote Sensing Image Registration

The efficient and accurate registration of multitemporal images is essential for many remote sensing applications. With the increase in the imaging resolution and field in satellites, the acquired large-scale remote sensing images have brought serious challenges, since there exist parallax shifts and large background variations among different local regions of the acquired images. To address these issues, this article proposes an adaptive regional multiple features (ARMF) matching method for the registration of multitemporal large-scale high-resolution remote sensing images. Specifically, since large background variations in fixed-size regions of multitemporal images will cause insufficient features and the failure of features matching, the ARMF introduces an adaptive regions searching strategy, which utilizes the pyramid amplification technique to adaptively select the regions that can find the sufficient matched features. Then, the ARMF extracts multiple types of features (i.e., gradient feature, phase feature, and line feature) from the adaptive searched region that can more effectively represent the characteristics of the large regions. Finally, we utilize the feature matching error as the rule to adaptively select the suitable features as the descriptors of the region. The experimental results on large-scale multitemporal image data obtained from Google Earth demonstrated the proposed method can outperform several state-of-the-arts remote sensing registration approaches.
Personalized On-Device E-Health Analytics With Decentralized Block Coordinate Descent

Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating. Nowadays, enhancement on medical diagnosis via machine learning models has been highly effective in many aspects of e-health analytics. Nevertheless, in the classic cloud-based/centralized e-health paradigms, all the data will be centrally stored on the server to facilitate model training, which inevitably incurs privacy concerns and high time delay. Distributed solutions like Decentralized Stochastic Gradient Descent (D-SGD) are proposed to provide safe and timely diagnostic results based on personal devices. However, methods like D-SGD are subject to the gradient vanishing issue and usually proceed slowly at the early training stage, thereby impeding the effectiveness and efficiency of training. In addition, existing methods are prone to learning models that are biased towards users with dense data, compromising the fairness when providing E-health analytics for minority groups. In this paper, we propose a Decentralized Block Coordinate Descent (D-BCD) learning framework that can better optimize deep neural network-based models distributed on decentralized devices for E-health analytics. As a gradient-free optimization method, Block Coordinate Descent (BCD) mitigates the gradient vanishing issue and converges faster at the early stage compared with the conventional gradient-based optimization. To overcome the potential data scarcity issues for users’ local data, we propose similarity-based model aggregation that allows each on-device model to leverage knowledge from similar neighbor models, so as to achieve both personalization and high accuracy for the learned models. Benchmarking experiments on three real-world datasets illustrate the effectiveness and practicality of our proposed D-BCD, where additional simulation study showcases the strong applicability of D-BCD in real-life E-health scenarios.
An “order of data acquisition” for digital forensic investigations

Data acquisition is a fundamental stage of the digital forensic workflow, where without it, it may not be possible to conduct many criminal inquiries effectively. While any investigative team may want access to all digital data available, it is no longer an approach that is considered justifiable or proportionate in all cases. There is now an increasing narrative highlighting the invasiveness of digital data acquisition processes and their impact upon privacy, with calls to ensure greater scrutiny is placed upon their use. This work proposes the "Order of Data Acquisition" which defines 10 digital data acquisition methods that are available to practitioners as a part of a forensic examination, derived from a review of existing literature and best practice acquisition approaches, and arranged by their "invasiveness." Each method is discussed with examples provided in order to clarify and formalize the process of determining a suitable acquisition method in every case while acknowledging privacy invasion concerns. Finally, conclusions are drawn.
Integrating Prior Translation Knowledge Into Neural Machine Translation

Neural machine translation (NMT), which is an encoder-decoder joint neural language model with an attention mechanism, has achieved impressive results on various machine translation tasks in the past several years. However, the language model attribute of NMT tends to produce fluent yet sometimes unfaithful translations, which hinders the improvement of translation capacity. In response to this problem, we propose a simple and efficient method to integrate prior translation knowledge into NMT in a universal manner that is compatible with neural networks. Meanwhile, it enables NMT to consider the crossing language translation knowledge from the source-side of the training pipeline of NMT, thereby making full use of the prior translation knowledge to enhance the performance of NMT. The experimental results on two large-scale benchmark translation tasks demonstrated that our approach achieved a significant improvement over a strong baseline.
Training-Free Multi-objective Evolutionary Neural Architecture Search via Neural Tangent Kernel and Number of Linear Regions

A newly introduced training-free neural architecture search (TE-NAS) framework suggests that candidate network architectures can be ranked via a combined metric of expressivity and trainability. Expressivity is measured by the number of linear regions in the input space that can be divided by a network. Trainability is assessed based on the condition number of the neural tangent kernel (NTK), which affects the convergence rate of training a network with gradient descent. These two measurements have been found to be correlated with network test accuracy. High-performance architectures can thus be searched for without incurring the intensive cost of network training as in a typical NAS run. In this paper, we suggest that TE-NAS can be incorporated with a multi-objective evolutionary algorithm (MOEA), in which expressivity and trainability are kept separate as two different objectives rather than being combined. We also add the minimization of floating-point operations (FLOPs) as the third objective to be optimized simultaneously. On NAS-Bench-101 and NAS-Bench-201 benchmarks, our approach achieves excellent efficiency in finding Pareto fronts of a wide range of architectures exhibiting optimal trade-offs among network expressivity, trainability, and complexity. Network architectures obtained by our approach on CIFAR-10 also show high transferability on CIFAR-100 and ImageNet.
Learning to Coordinate via Multiple Graph Neural Networks

The collaboration between agents has gradually become an important topic in multi-agent systems. The key is how to efficiently solve the credit assignment problems. This paper introduces MGAN for collaborative multi-agent reinforcement learning, a new algorithm that combines graph convolutional networks and value-decomposition methods. MGAN learns the representation of agents from different perspectives through multiple graph networks, and realizes the proper allocation of attention between all agents. We show the amazing ability of the graph network in representation learning by visualizing the output of the graph network, and therefore improve interpretability for the actions of each agent in the multi-agent system.
Evaluating Model Robustness to Adversarial Samples in Network Intrusion Detection

Adversarial machine learning, a technique which seeks to deceive machine learning (ML) models, threatens the utility and reliability of ML systems. This is particularly relevant in critical ML implementations such as those found in Network Intrusion Detection Systems (NIDS). This paper considers the impact of adversarial influence on NIDS and proposes ways to improve ML based systems. Specifically, we consider five feature robustness metrics to determine which features in a model are most vulnerable, and four defense methods. These methods are tested on six ML models with four adversarial sample generation techniques. Our results show that across different models and adversarial generation techniques, there is limited consistency in vulnerable features or in effectiveness of defense method.
Efficient methods for deep learning

Deep neural networks (DNNs) have achieved unprecedented success in computer vision. However, their superior performance comes at the considerable cost of computational complexity, which greatly hinders their applications in many resource-constrained devices, such as mobile phones and Internet of Things (IoT) devices. Therefore, methods and techniques that can lift the efficiency bottleneck while preserving the high accuracy of DNNs are in great demand to enable numerous edge AI applications. This chapter provides an overview of efficient deep learning methods. We start from introducing popular model compression methods, including pruning, factorization, and quantization. We then describe compact model design techniques including efficient convolution layers and representative efficient CNN architectures. Finally, to reduce the large design cost of these manual solutions, we discuss the AutoML framework for each of them, such as neural architecture search (NAS) and automated pruning and quantization.
Methodology for Introducing Creativity in Requirements Engineering

The increase of global competitiveness, the ability of organizations to effectively use information technologies, and to focus on innovation and creativity are recognized as being important. In this context, the hypothesis of resorting to known creativity techniques or adaptations to help innovation in the field of Software Engineering appears to be challenging. This paper proposes a methodology for introducing creativity and innovation techniques in the Requirements Engineering process in order to build more agile and efficient Information Systems. The method uses a variety of creative techniques that are thought to be appropriate to the different stages of the process and is inspired by existing creative problem-solving methods and techniques, in particular in the Creative Problem-Solving Process, Productive Thinking Model and the Creative Potentiation Method. The study of the method allowed its application, through the use of various creativity techniques, in a real context in a social institution - the Social Center for Support to the Community of São Domingos. The application of the methodology allowed the identification of new opportunities that allowed the organization to devise service delivery strategies that were more suited to the needs of people.