Reviewerly/exHarmony huggingface dataset:
Depression Analysis from Social Media Data in Bangla Language using Long Short Term Memory (LSTM) Recurrent Neural Network Technique

Human emotions like depression are inner sentiments of human beings which expose actual behaviors of a person. Analyzing and determining these type of emotions from people's social activities in virtual world can be very helpful to understand their behaviors. Existing approaches may be useful for analyzing common sentiments, such as positive, negative or neutral expressions. However, human emotions, such as depression, are very critical and sometimes almost impossible to analyze using these approaches. In this work, we deployed Long Short Term Memory (LSTM) Deep Recurrent Network for depression analysis on Bangla social media data. We created a small dataset of Bangla tweets and stratified it. In this paper, we have shown the effects of hyper-parameter tuning and how it can be helpful for depression analysis on a small Bangla social media dataset. The result shows that 5 layered LSTM of size 128 with batch size 25, learning rate 0.0001 over 20 epochs, the depression detection accuracy is high for stratified dataset with repeated sampling. This result will help psychologists and other researchers to detect depression of individuals from their social activities in virtual world and help them to take necessary measures to prevent undesirable doings resulted from depression.
Explainable AI meets persuasiveness: Translating reasoning results into behavioral change advice

Explainable AI aims at building intelligent systems that are able to provide a clear, and human understandable, justification of their decisions. This holds for both rule-based and data-driven methods. In management of chronic diseases, the users of such systems are patients that follow strict dietary rules to manage such diseases. After receiving the input of the intake food, the system performs reasoning to understand whether the users follow an unhealthy behavior. Successively, the system has to communicate the results in a clear and effective way, that is, the output message has to persuade users to follow the right dietary rules. In this paper, we address the main challenges to build such systems: (i) the Natural Language Generation of messages that explain the reasoner inconsistency; and, (ii) the effectiveness of such messages at persuading the users. Results prove that the persuasive explanations are able to reduce the unhealthy users’ behaviors.
Broad Reinforcement Learning for Supporting Fast Autonomous IoT

The emergence of a massive Internet-of-Things (IoT) ecosystem is changing the human lifestyle. In several practical scenarios, IoT still faces significant challenges with reliance on human assistance and unacceptable response time for the treatment of big data. Therefore, it is very urgent to establish a new framework and algorithm to solve problems specific to this kind of fast autonomous IoT. Traditional reinforcement learning and deep reinforcement learning (DRL) approaches have abilities of autonomous decision making, but time-consuming modeling and training procedures limit their applications. To get over this dilemma, this article proposes the broad reinforcement learning (BRL) approach that fits fast autonomous IoT as it combines the broad learning system (BLS) with a reinforcement learning paradigm to improve the agent's efficiency and accuracy of modeling and decision making. Specifically, a BRL framework is first constructed. Then, the associated learning algorithm, containing training pool introduction, training sample preparation, and incremental learning for BLS, is carefully designed. Finally, as a case study of fast autonomous IoT, the proposed BRL approach is applied to traffic light control, aiming to alleviate traffic congestion in the intersections of smart cities. The experimental results show that the proposed BRL approach can learn better action policy at a shorter execution time when compared with competing approaches.
Noise Removal in the Presence of Significant Anomalies for Industrial IoT Sensor Data in Manufacturing

The emergence of the Industrial Internet of Things (IIoT) to enhance manufacturing and industrial processes allows data analysts to address significant problems such as predictive maintenance. For the purpose of accurate data analysis, cleansing noisy sensor data is one of the most fundamental and necessary steps. Without first removing the noise, the anomaly detection techniques are likely to give a large number of false positives. However, using traditional outlier detection methods directly for such analysis are not appropriate as both noise and significant anomalies might exist in the sensor data. This article introduces the new challenges and proposes a novel solution to address the issue of removing noise while preserving anomalies in the IIoT Data. It proposes an approach that measures both the rate of change and deviation to compute the noise score. It employs a sliding window technique to define the analysis unit of the contrast measure which is used in conjunction with statistical techniques. Extensive experiments demonstrate that the proposed approach outperforms the other state-of-the-art noise detection methods, providing a clean data set that preserves the anomalies on which one can effectively apply anomaly detection techniques.
Potential Augmented Reality Application Areas for Pilot Education: An Exploratory Study

The goal of carrying out this work was to identify potential application areas for augmented reality (AR) in pilot education by addressing gender preferences. Like the field of engineering, the aviation industry is dominated by men. Because the aviation industry forecasts a high demand for pilots, it is highly desirable to address gender diversity and improve teaching methods in pilot education. In this study, potential application areas for AR-supported pilot training were investigated by conducting a survey with 60 pilots and flight instructors (including 12 women). Typical AR use cases were presented in videos, and the pilots reported their preferences regarding similar or other AR applications used in different parts of the flight training program. AR navigation was the use case that was most frequently preferred by both female and male pilots. The majority of pilots agreed that AR could potentially be used in theoretical instruction, pre-flight aircraft inspection, and procedure training. In addition, both gender groups showed similar preferences for various gaming concepts that make learning more interesting and engaging, such as receiving positive feedback. However, a higher percentage of women than men reported that achieving a target or receiving points to successfully finish a task and answering questions during the game were satisfying. Including a story in the game to attract attention was preferred by a higher percentage of men than women. The results of this study can be used to design AR educational concepts that support gender diversity in pilot education and other technical domains.
Super Resolution Perception for Smart Meter Data

In this paper, we present the problem formulation and methodology framework of Super Resolution Perception (SRP) on smart meter data. With the widespread use of smart meters, a massive amount of electricity consumption data can be obtained. Smart meter data is the basis of automated billing and pricing, appliance identification, demand response, etc. However, the provision of high-quality data may be expensive in many cases. In this paper, we propose a novel problem - the SRP problem as reconstructing high-quality data from unsatisfactory data in smart grids. Advanced generative models are then proposed to solve the problem. This technology makes it possible for empowering existing facilities without upgrading existing meters or deploying additional meters. We first mathematically formulate the SRP problem under the Maximum a Posteriori (MAP) estimation framework. The dataset namely Super Resolution Perception Dataset (SRPD) is designed for this problem and released. A case study is then presented, which performs SRP on smart meter data. A network namely Super Resolution Perception Convolutional Neural Network (SRPCNN) is proposed to generate high-frequency load data from low-frequency data. Experiments demonstrate that our SRP models can reconstruct high-frequency data effectively. Moreover, the reconstructed high-frequency data can lead to better appliance identification results.
An adversarial attack on DNN-based black-box object detectors

Object detection models play an essential role in various IoT devices as one of the core components. Scientific experiments have proven that object detection models are vulnerable to adversarial examples. Heretofore, some attack methods against object detection models have been proposed, but the existing attack methods can only attack white-box models or a specific type of black-box models. In this paper, we propose a novel black-box attack method called Evaporate Attack, which can successfully attack both regression-based and region-based detection models. To perform an effective attack on different types of object detection models, we design an optimization algorithm, which can generate adversarial examples only utilizes the position and label information of the model's prediction. Evaporate Attack can hide objects from detection models without any interior information of the model. This scenario is much practical in real-world faced by the attacker. Our approach achieves an 84% fooling rate on regression-based YOLOv3 and a 48% fooling rate on region-based Faster R–CNN, under the premise that all objects are hidden.
Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram

Because digitized images are easily replicated or manipulated, copy-move forgery techniques are rendered possible with minimal expertise. Furthermore, it is difficult to verify the authenticity of images. Therefore, numerous efforts have been made to detect copy-move forgeries. In this paper, we present an improved region duplication detection algorithm based on the keypoints. The proposed algorithm utilizes the scale invariant feature transform (SIFT) and the reduced local binary pattern (LBP) histogram. The LBP values with 256 levels are obtained from the local window centered at the keypoint, which are then reduced to 10 levels. For a keypoint, a 138-dimensional is generated to detect copy-move forgery. We test the proposed algorithm on various image datasets and compare the detection accuracy with those of existing methods. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested copy-move forgery detection methods. Furthermore, the proposed method exhibits a uniform detection performance for various types of test datasets.
Optimizing a Neuro-Fuzzy System Based on Nature-Inspired Emperor Penguins Colony Optimization Algorithm

A neuro-fuzzy system is a learning machine that finds the parameters of a fuzzy system using approximate techniques of neural networks. Both neural network and fuzzy system have common features. These can solve problems that have no mathematical models. Adaptive neuro-fuzzy inference system (ANFIS) is an adaptive network that uses supervised learning on learning algorithm. To achieve effective results with ANFIS, selecting the optimization method in training is very important. Heuristics and metaheuristics algorithms attempt to find the best solution out of all possible solutions to an optimization problem. ANFIS training can be based on nonderivative algorithms. Heuristics and metaheuristics are nonderivative algorithms that can lead to better performance in ANFIS training. Most heuristic and metaheuristic algorithms are taken from the behavior of biological systems or physical systems in nature. The newly released emperor penguins colony (EPC) algorithm is a population-based and nature-inspired metaheuristic algorithm. This algorithm has much potential for solving various problems. In this article, an optimized ANFIS based on the new EPC algorithm is proposed. The optimized ANFIS is compared with other nonderivative algorithms on benchmark data sets. Eventually, the proposed algorithm is used to solve the classical inverted pendulum problem. The results show that the proposed ANFIS based on the EPC algorithm has less error and better performance than other state-of-the-art algorithms in both training and testing phase.
Achieving Accountable and Efficient Data Sharing in Industrial Internet of Things

In this article, we propose an accountable and efficient data sharing scheme for industrial IoT (IIoT), named an accountable and data sharing scheme (ADS), in which a data owner can pursue the responsibility of a data receiver if the latter leaks some sensitive shared data to the public for profits while without permission (i.e., accountability). Specifically, ADS is built upon an adaptive decentralized oblivious transfer protocol together with a zero-knowledge proof technique, which enables the data receiver's private key to be hidden from the data owner and yet correctly embedded into the shared data during the process of data sharing. Once data breaches occur, the private key can be automatically revealed to the data owner so as to achieve the accountability. In addition, with ADS, a group of sharing providers can also assist IIoT devices in handling heavy computational tasks via the secret sharing technique without sacrificing the security. Extensive performance evaluations are conducted, and the simulation results demonstrate that ADS has high computational efficiency, making it well fit for IIoT.
Generating behavior features for cold-start spam review detection with adversarial learning

Due to the wide applications, spam detection has long been a hot research topic in both academia and industry. Existing studies show that behavior features are effective in distinguishing the spam and legitimate reviews. However, it usually takes a long time to collect such features and thus is hard to apply them to cold-start spam review detection tasks. Recent advances leveraged the neural network to encode the various types of textual, behavior, and attribute information for this task. However, the inherent problem, i.e., lack of effective behavior features for new users who post just one review, is still unsolved. In this paper, we exploit the generative adversarial network (GAN) for addressing this problem. The key idea is to generate synthetic behavior features (SBFs) for new users from their easily accessible features (EAFs). Specifically, we first select six well recognized real behavior features (RBFs) existing for regular users. We then train a GAN framework including a generator to generate SBFs from their EAFs including text, rating, and attribute features, and a discriminator to discriminate RBFs and SBFs. We design a new implementation of generator and discriminator for effective training. The trained GAN is finally applied to new users for generating synthetic behavior features. We conduct extensive experiments on two Yelp datasets. Experimental results demonstrate that our proposed framework significantly outperforms the state-of-the-art methods.
The explanation game: a formal framework for interpretable machine learning

Abstract We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation(s) for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a (conditionally) optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions.
Exploring Pre-Training with Alignments for RNN Transducer Based End-to-End Speech Recognition

Recently, the recurrent neural network transducer (RNN-T) architecture has become an emerging trend in end-to-end automatic speech recognition research due to its advantages of being capable for online streaming speech recognition. However, RNN-T training is made difficult by the huge memory requirements, and complicated neural structure. A common solution to ease the RNN-T training is to employ connectionist temporal classification (CTC) model along with RNN language model (RNNLM) to initialize the RNN-T parameters. In this work, we conversely leverage external alignments to seed the RNN-T model. Two different pre-training solutions are explored, referred to as encoder pre-training, and whole-network pre-training respectively. Evaluated on Microsoft 65,000 hours anonymized production data with personally identifiable information removed, our proposed methods can obtain significant improvement. In particular, the encoder pre-training solution achieved a 10% and a 8% relative word error rate reduction when compared with random initialization and the widely used CTC+RNNLM initialization strategy, respectively. Our solutions also significantly reduce the RNN-T model latency from the baseline.
Model predictive control of a collaborative manipulator considering dynamic obstacles

Summary Collaborative robots have to adapt its motion plan to a dynamic environment and variation of task constraints. Currently, they detect collisions and interrupt or postpone their motion plan to prevent harm to humans or objects. The more advanced strategy proposed in this article uses online trajectory optimization to anticipate potential collisions, task variations, and to adapt the motion plan accordingly. The online trajectory planner pursues a model predictive control approach to account for dynamic motion objectives and constraints during task execution. The prediction model relates reference joint velocities to actual joint positions as an approximation of built‐in robot tracking controllers. The optimal control problem is solved with direct collocation based on a hypergraph structure, which represents the nonlinear program and allows to efficiently adapt to structural changes in the optimization problem caused by moving obstacles. To demonstrate the effectiveness of the approach, the robot imitates pick‐and‐place tasks while avoiding self‐collisions, semistatic, and dynamic obstacles, including a person. The analysis of the approach concerns computation time, constraint violations, and smoothness. It shows that after model identification, order reduction, and validation on the real robot, parallel integrators with compensation for input delays exhibit the best compromise between accuracy and computational complexity. The model predictive controller can successfully approach a moving target configuration without prior knowledge of the reference motion. The results show that pure hard constraints are not sufficient and lead to nonsmooth controls. In combination with soft constraints, which evaluate the proximity of obstacles, smooth and safe trajectories are planned.
CGAN-TM: A Novel Domain-to-Domain Transferring Method for Person Re-Identification

Person re-identification (re-ID) is a technique aiming to recognize person cross different cameras. Although some supervised methods have achieved favorable performance, they are far from practical application owing to the lack of labeled data. Thus, unsupervised person re-ID methods are in urgent need. Generally, the commonly used approach in existing unsupervised methods is to first utilize the source image dataset for generating a model in supervised manner, and then transfer the source image domain to the target image domain. However, images may lose their identity information after translation, and the distributions between different domains are far away. To solve these problems, we propose an image domain-to-domain translation method by keeping pedestrian's identity information and pulling closer the domains' distributions for unsupervised person re-ID tasks. Our work exploits the CycleGAN to transfer the existing labeled image domain to the unlabeled image domain. Specially, a Self-labeled Triplet Net is proposed to maintain the pedestrian identity information, and maximum mean discrepancy is introduced to pull the domain distribution closer. Extensive experiments have been conducted and the results demonstrate that the proposed method performs superiorly than the state-of-the-art unsupervised methods on DukeMTMC-reID and Market-1501.
Video Frame Interpolation Via Residue Refinement

Video frame interpolation achieves temporal super-resolution by generating smooth transitions between frames. Although great success has been achieved by deep neural networks, the synthesized images stills suffer from poor visual appearance and unsatisfactory artifacts. In this paper, we propose a novel network structure that leverages residue refinement and adaptive weight to synthesize in-between frames. The residue refinement technique is used for optical flow and image generation for higher accuracy and better visual appearance, while the adaptive weight map combines the forward and backward warped frames to reduce the artifacts. Moreover, all submodules in our method are implemented by U-Net with less depths, so the efficiency is guaranteed. Experiments on public datasets demonstrate the effectiveness and superiority of our method over the state-of-the-art approaches.
BBAND INDEX: A NO-REFERENCE BANDING ARTIFACT PREDICTOR

Banding artifact, or false contouring, is a common video compression impairment that tends to appear on large flat regions in encoded videos. These staircase-shaped color bands can be very noticeable in high-definition videos. Here we study this artifact, and propose a new distortion-specific no-reference video quality model for predicting banding artifacts, called the Blind BANding Detector (BBAND index). BBAND is inspired by human visual models. The proposed detector can generate a pixel-wise banding visibility map and output a banding severity score at both the frame and video levels. Experimental results show that our proposed method outperforms state-of-the-art banding detection algorithms and delivers better consistency with subjective evaluations.