Reviewerly/exHarmony huggingface dataset:
Explainable AI: A Hybrid Approach to Generate Human-Interpretable Explanation for Deep Learning Prediction

With massive computing power and data explosion as catalysts, Artificial Intelligence (AI) has finally come out of research labs to become a ground-breaking technology. Businesses are seeing its value in a wide range of applications and therefore looking for ways to make AI an integral part of their decision-making processes. However, to trust an AI model prediction or to take downstream action based on a prediction outcome, one needs to understand the reasons for the prediction. With deep neural networks increasingly becoming the algorithm of choice for models, generation of such reasons has become more challenging. Deep neural networks are highly nested non-linear models that learn patterns in the data through complex combinations of inputs. Their complex architecture makes it very difficult to decipher the exact reasons for their prediction. Due to this lack of transparency, businesses are not able to utilize this technology in many applications. To increase the adoption of deep learning models, explainability is critical in building trust in the solution and in guiding downstream actions in business applications. In this paper we aim to create human-interpretable explanations for predictions from deep learning models. We propose a hybrid of two prior approaches, integrating clustering of the network's hidden layer representation [2] with TREPAN decision tree [1], both of which uniquely deconstruct a neural network. Our aim is to visualize flow of information within the deep neural network using factors that make sense to humans, even if the underlying model uses more complex factors. This enables generation of human interpretable explanations (or, reasons codes) for each model outcome at an individual instance level. We demonstrate the new approach on credit card default prediction given by a deep feed forward neural network model. We compare and contrast this new integrated approach with three different approaches, based on the results we obtained from experimentation.
Data mining fool’s gold

The scientific method is based on the rigorous testing of falsifiable conjectures. Data mining, in contrast, puts data before theory by searching for statistical patterns without being constrained by prespecified hypotheses. Artificial intelligence and machine learning systems, for example, often rely on data-mining algorithms to construct models with little or no human guidance. However, a plethora of patterns are inevitable in large data sets, and computer algorithms have no effective way of assessing whether the patterns they unearth are truly useful or meaningless coincidences. While data mining sometimes discovers useful relationships, the data deluge has caused the number of possible patterns that can be discovered relative to the number that are genuinely useful to grow exponentially—which makes it increasingly likely that what data mining unearths is likely to be fool’s gold.
A Smart Contract-based agent marketplace for the J-Park Simulator - a knowledge graph for the process industry

The chemical industry is increasingly relying on agents for data acquisition, optimization, and simulation. In order to enable efficient management of agents, Knowledge Graphs (KG) together with agent composition frameworks are therefore applied. However, a method to assess the reliability of agents for such systems is absent. Therefore, this paper proposes a Smart Contract-based agent marketplace for composition frameworks to estimate the reliability of agents. In this agent marketplace, we improved the feedback-based reputation system by leveraging Smart Contracts to eliminate fraudulent ratings and to enable automation. The marketplace incorporates a rating-dependent payment mechanism as well, to further enhance trust. The paper also illustrates how this marketplace is integrated into the J-Park Simulator (JPS) agent composition framework for the automated agent selection and transaction.
BiMorphing: A Bi-Directional Bursting Defense against Website Fingerprinting Attacks

Network traffic analysis has been increasingly used in various applications to either protect or threaten people, information, and systems. Website fingerprinting is a passive traffic analysis attack which threatens web navigation privacy. It is a set of techniques used to discover patterns from a sequence of network packets generated while a user accesses different websites. Internet users (such as online activists or journalists) may wish to hide their identity and online activity to protect their privacy. Typically, an anonymity network is utilized for this purpose. These anonymity networks such as Tor (The Onion Router) provide layers of data encryption which poses a challenge to the traffic analysis techniques. Although various defenses have been proposed to counteract this passive attack, they have been penetrated by new attacks that proved the ineffectiveness and/or impracticality of such defenses. In this work, we introduce a novel defense algorithm to counteract the website fingerprinting attacks. The proposed defense obfuscates original website traffic patterns through the use of double sampling and mathematical optimization techniques to deform packet sequences and destroy traffic flow dependency characteristics used by attackers to identify websites. We evaluate our defense against state-of-the-art studies and show its effectiveness with minimal overhead and zero-delay transmission to the real traffic.
Robust Image Segmentation Using Fuzzy C-Means Clustering With Spatial Information Based on Total Generalized Variation

Fuzzy c-means clustering (FCM) has proved highly successful in the manipulation and analysis of image information, such as image segmentation.However, the effectiveness of FCM-based technique is limited by its poor robustness to noise and edge-preserving during the segmentation process.To tackle these problems, a new objective function of FCM is developed in this work.The main innovation work and results of this paper are outlined as follows.First, a regularization operation performed by total generalized variation (TGV) is used to guarantee noise smoothing and detail preserving.Second, a weight factor incorporated into the spatial information term is designed to form nonuniform membership functions, which can contribute to the assignment of each pixel for the highest membership value.In addition, a regularization parameter is used to balance the respective importance of penalty between whole image and each neighborhood.The main advantage of this technique over conventional FCM-based methods is that it can reconstruct image patterns in heavy noise with only a small loss.We perform experiments on both synthetic and real images.Compared to state-of-the art FCM-based methods, the proposed algorithm exhibits a very good ability to noise and edge-preserving in image segmentation.
All-Optical WDM Recurrent Neural Networks With Gating

Neuromorphic photonics came to the fore promising neural networks (NNs) with orders of magnitude higher computational speeds compared to electronic counterparts. In this direction, research efforts have been mainly concentrated on the development of spiking, convolutional and Feed-Forward (FF)-NN architectures, aiming to solve complex cognitive problems. However, in order to solve time-series classification and prediction complex tasks, state-of-the-art deep-learning models require in most cases the employment of Recurrent-NNs (RNNs) along with their gated variants, such as Long-Short-Term-Memories (LSTMs) and Gated-Recurrent-Units (GRUs). Herein, we experimentally demonstrate the first, to the best of our knowledge, all-optical RNN with a gating mechanism, laying the foundations for all-optical LSTMs and GRUs. The proposed layouts exploit a Semiconductor-Optical-Amplifier (SOA)-based sigmoid activation within a fiber loop and were validated using asynchronous Wavelength--(WDM) signals with 100psec optical pulses. A SOAMach-Zehnder-Interferometer (SOA-MZI) gate was employed in the Gated-RNN version, with the RNN output defining the input signal fraction that is desired to enter the RNN. Finally, a complex NN architecture was trained using the FI-2010 financial dataset exploiting the proposed non-gated and gated-RNNs, showcasing in an outstanding F1 score of 41.68% and 41.85%, respectively, outperforming the Multi-Layer Perceptron (MLP) based models by 6.49% in average.
Generic model implementation of deep neural network activation functions using GWO-optimized SCPWL model on FPGA

The implementation of non-linear Activation Functions (AFs) within the Artificial Neural Network (ANN) on the Field Programmable Gate Array (FPGA) is substantial due to the various applications it performs. Accuracy, speed and complexity are the most crucial factors considered in this implementation. Building non-linear AFs in a reconfigurable ANN requires either sequential operations and/or additional complexity. In this paper, a generic model for three types of non-linear AFs (Logistic sigmoid (LogSig), Tan sigmoid (TanSig) and Radial Basis Function (RBF)) has been designed based on Simplicial Canonical Piecewise Linear (SCPWL) model that is optimized using Grey Wolf Optimizer (GWO(Algorithm. The designed model has been achieved by nine segments of the SCPWL model. The input of the AFs is ranging from (−8 to 8). Matlab has been deployed to design, optimize, simulate and validate this model. The maximum errors were 5.2e−3, 15.4e−3 and 7e−3 for LogSig, TanSig and RBF respectively. And, the Mean Square Error (MSE) were 1.81e−6, 1.22e−5 and 1.42e−5 for LogSig, TanSig and RBF respectively. The Matlab/HDL Coder has been used to generate the VHDL codes. The Xilinx Arty A7 (Xc7a35ticsg324-1L) FPGA kit is used to validate the designed model on Vivado Design Suite software. It has been noticed that it takes 581 Look-Up Tables (LUTs), nine DSP slices and a delay of (35.346 ns) to implement the nine SCPWL segments for any linear and non-linear AF. For validation, a complete ANN has been built with three hidden layers, each layer contain with one of the proposed AF models.
An A*-based Bacterial Foraging Optimisation Algorithm for Global Path Planning of Unmanned Surface Vehicles

The bacterial foraging optimisation (BFO) algorithm is a commonly adopted bio-inspired optimisation algorithm. However, BFO is not a proper choice in coping with continuous global path planning in the context of unmanned surface vehicles (USVs). In this paper, a grid partition-based BFO algorithm, named AS-BFO, is proposed to address this issue in which the enhancement is contributed by the involvement of the A* algorithm. The chemotaxis operation is redesigned in AS-BFO. Through repeated simulations, the relative optimal parameter combination of the proposed algorithm is obtained and the most influential parameters are identified by sensitivity analysis. The performance of AS-BFO is evaluated via five size grid maps and the results show that AS-BFO has advantages in USV global path planning.
Performance Analysis of Blockchain Systems With Wireless Mobile Miners

In this letter, a novel framework that uses wireless mobile miners (MMs) for computation purposes in a blockchain system is proposed. To operate blockchains over such a wireless mobile network with minimum forking events, it is imperative to maintain low-latency wireless communications between MMs and communication nodes (CNs) that store the blockchain ledgers. To analyze the sensitivity of the system to latency, the probability of occurrence of a forking event is theoretically derived. Also, in mobile blockchain using MMs, minimizing energy consumption required for networking and computation is essential to extend the operation time of MMs. Hence, the average energy consumption of an MM is derived as a function of the system parameters such as the number of MMs and power consumed by the computing, transmission, and mobility processes of the MMs. Simulation results verify the analytical derivations and show that using a larger number of MMs can reduce the energy consumption by up to 95% compared to a blockchain system with a single MM.
Exploring the Vulnerabilities and Advantages of SWIPE or Pattern authentication in Virtual Reality (VR)

Virtual reality applications are carving out a new niche within the entertainment and business user-sphere, therefore reliable security and usability are essential to achieving consumer confidence. In this paper, we are exploring (1) the suitability of porting the popular SWIPE mobile device authentication system for use within virtual reality (VR) by observing the advantages and vulnerabilities. (2) The effects of the interaction devices such as the hand-held-controller (HHC), the LeapMotion sensor, EyeTracker and the head-mounted-display (HMD). Our study is in three-folds, a web study (N=219) to collect and analyze possible SWIPE password patterns, then a mobile device study (N=15) and a VR study (N = 15) to evaluate the speed, login errors, usability of the SWIPE authentication system in both environment for comparison. We are also interested in the effectiveness of shoulder-surfing within VR as it is known to be a weakness in mobile devices.
Global-and-Local Context Network for Semantic Segmentation of Street View Images

Semantic segmentation of street view images is an important step in scene understanding for autonomous vehicle systems. Recent works have made significant progress in pixel-level labeling using Fully Convolutional Network (FCN) framework and local multi-scale context information. Rich global context information is also essential in the segmentation process. However, a systematic way to utilize both global and local contextual information in a single network has not been fully investigated. In this paper, we propose a global-and-local network architecture (GLNet) which incorporates global spatial information and dense local multi-scale context information to model the relationship between objects in a scene, thus reducing segmentation errors. A channel attention module is designed to further refine the segmentation results using low-level features from the feature map. Experimental results demonstrate that our proposed GLNet achieves 80.8% test accuracy on the Cityscapes test dataset, comparing favorably with existing state-of-the-art methods.
Photoshopping Colonoscopy Video Frames

The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such detection system is built using a large annotated data set of frames with and without polyps, which is expensive to be obtained. In this paper, we introduce a new system that detects frames containing polyps as anomalies from a distribution of frames from exams that do not contain any polyps. The system is trained using a one-class training set consisting of colonoscopy frames without polyps - such training set is considerably less expensive to obtain, compared to the 2-class data set mentioned above. During inference, the system is only able to reconstruct frames without polyps, and when it tries to reconstruct a frame with polyp, it automatically removes (i.e., photoshop) it from the frame - the difference between the input and reconstructed frames is used to detect frames with polyps. We name our proposed model as anomaly detection generative adversarial network (ADGAN), comprising a dual GAN with two generators and two discriminators. To test our framework, we use a new colonoscopy data set with 14317 images, split as a training set with 13350 images without polyps, and a testing set with 290 abnormal images containing polyps and 677 normal images without polyps. We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems.
Hierarchical Temporal Memory Based Machine Learning for Real-Time, Unsupervised Anomaly Detection in Smart Grid: WiP Abstract

Micro-phasor measurement unit (μPMU) sensors in smart electric grids provide measurements of voltage and current at microsecond timescale across the network and have great potential value for grid diagnostics. In this work, we propose a novel neuro-cognitive inspired architecture based on Hierarchical Temporal Memory (HTM) for real-time anomaly detection in smart grid using μPMU data. The key technical idea is that the HTM learns a sparse distributed temporal representation of sequential data that turns out to be very useful for anomaly detection in real-time.Our numerical results show that the proposed HTM architecture can predict anomalies with 96%, 96%, and 98% accuracy for three different application profiles namely, Standard, Reward Few False Positive, Reward Few False Negative, respectively. The performance is compared with three state-of-the-art real-time anomaly detection algorithms and HTM demonstrates competitive score for real-time anomaly detection in μPMU data.
Eras: Improving the quality control in the annotation process for Natural Language Processing tasks

The increasing amount of valuable, unstructured textual information poses a major challenge to extract value from those texts. We need to use NLP (Natural Language Processing) techniques, most of which rely on manually annotating a large corpus of text for its development and evaluation. Creating a large annotated corpus is laborious and requires suitable computational support. There are many annotation tools available, but their main weaknesses are the absence of data management features for quality control and the need for a commercial license. As the quality of the data used to train an NLP model directly affects the quality of the results, the quality control of the annotations is essential. In this paper, we introduce ERAS, a novel web-based text annotation tool developed to facilitate and manage the process of text annotation. ERAS includes not only the key features of current mainstream annotation systems but also other features necessary to improve the curation process, such as the inter-annotator agreement, self-agreement and annotation log visualization, for annotation quality control. ERAS also implements a series of features to improve the customization of the user’s annotation workflow, such as: random document selection, re-annotation stages, and warm-up annotations. We conducted two empirical studies to evaluate the tool’s support to text annotation, and the results suggest that the tool not only meets the basic needs of the annotation task but also has some important advantages over the other tools evaluated in the studies. ERAS is freely available at https://github.com/grosmanjs/eras.
Research on Multistep Time Series Prediction Based on LSTM

LSTM (Long Short-Term Memory) is a neural network model that can effectively predict time series. This paper studies the problem of LSTM multi-step time series prediction. By studying and comparing two methods of multi-step input and seq2vec, the paper provides reference for LSTM in the field of time prediction. Firstly, the basic theory of LSTM is introduced. Then the LSTM model is used to analyze and predict the selected data set. The result proves that LSTM can effectively predict seasonal time series data in multiple steps. At the same time, the paper summarizes and presents the advantages and disadvantages of the two methods.
Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis

We present research on how the perception of intelligent systems can be influenced by early experiences of machine performance, and how explainability potentially helps users develop an accurate understanding of system capabilities. Using a custom video analysis system with AI-assisted activity recognition, we studied whether presenting explanatory information for system outputs affects user perception of the system. In this experiment, some participants encountered AI weaknesses early, while others encountered the same limitations later in the study. The difference in ordering had a significant impact on user understanding of the system and the ability to detect AI strengths and weaknesses, and the addition of explanations was not enough to counteract the strong effects of early impressions. The results demonstrate the importance of first impressions with intelligent systems and motivate the need for improved methods of intervention to combat automation bias.
Memory through Design: Supporting Cultural Identity for Immigrants through a Paper-Based Home Drafting Tool

Current research in HCI with immigrants predominantly focuses on their practical needs and little attention is given to their cultural identities. As such, we aim to understand how newcomers reflect their cultural values within domestic settings. We explore this by provoking memories immigrants associate with physical spaces inside their homes. Hence, we built "Our Home Sketcher": a paper-based home drafting tool that allows novice users to design their homes by sketching and implicitly expressing their space, light, and privacy preferences. The collected drawings are then fed into a computer algorithm that produces 3D models of the sketched houses. This process of design acts as an artifact-driven storytelling for heritage sharing and rapport building within migrant communities. We engage 13 Middle Eastern newcomers in Canada with the tool and use Halbwachs' [44] theory of collective memory to frame how home sketching provokes former experiences. Our findings show a strong longing for reclaiming the past, narrating space-related oral history, and designing beyond current limitations.
Adversarial Deep Embedded Clustering: On a Better Trade-off Between Feature Randomness and Feature Drift

To overcome the absence of concrete supervisory signals, deep clustering models construct their own labels based on self-supervision and pseudo-supervision. However, applying these techniques can cause Feature Randomness and Feature Drift. In this paper, we formally characterize these two new concepts. On one hand, Feature Randomness takes place when a considerable portion of the pseudo-labels is deemed to be random. In this regard, the trained model can learn non-representative features. On the other hand, Feature Drift takes place when the pseudo-supervised and the reconstruction losses are jointly minimized. While penalizing the reconstruction loss aims to preserve all the inherent data information, optimizing the embedded-clustering objective drops the latent between-cluster variances. Due to this compromise, the clustering-friendly representations can be easily drifted. In this context, we propose ADEC (Adversarial Deep Embedded Clustering) a novel autoencoder-based clustering model, which relies on a discriminator network to reduce random features while avoiding the drifting effect. Our new metrics <inline-formula><tex-math notation="LaTeX">$\Delta _{FR}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\Delta _{FD}$</tex-math></inline-formula> allows to, respectively, assess the level of Feature Randomness and Feature Drift. We empirically demonstrate the suitability of our model on handling these problems using benchmark real datasets. Experimental results validate that our model outperforms state-of-the-art autoencoder-based clustering methods.
Deciding Differential Privacy for Programs with Finite Inputs and Outputs

Differential privacy is a de facto standard for statistical computations over databases that contain private data. Its main and rather surprising strength is to guarantee individual privacy and yet allow for accurate statistical results. Thanks to its mathematical definition, differential privacy is also a natural target for formal analysis. A broad line of work develops and uses logical methods for proving privacy. A more recent and complementary line of work uses statistical methods for finding privacy violations. Although both lines of work are practically successful, they elide the fundamental question of decidability.