Reviewerly/exHarmony huggingface dataset:
Feedback Learning for Improving the Robustness of Neural Networks

Recent research studies revealed that neural networks are vulnerable to adversarial attacks. State-of-the-art defensive techniques add various adversarial examples in training to improve models' adversarial robustness. However, these methods are not universal and can't defend unknown or non-adversarial evasion attacks. In this paper, we analyze the model robustness in the decision space. A feedback learning method is then proposed, to understand how well a model learns and to facilitate the retraining process of remedying the defects. The evaluations according to a set of distance-based criteria show that our method can significantly improve models' accuracy and robustness against different types of evasion attacks. Moreover, we observe the existence of inter-class inequality and propose to compensate for it by changing the proportions of examples generated in different classes.
Optimal Path Planning for Image Based Visual Servoing

Visual servoing is a control technique that uses image data as feedback in a motion control loop. This technique is useful in tasks that require robots or other automated motion systems to automatically inspect parts or structures. One specific method of visual servoing is Image Based Visual Servoing (IBVS), a method that simply minimizes the differences between an observed image orientation and a desired one. This method works well for orientations where these differences are small, but in the case where the desired orientation is more difficult to reach, the motion system can follow non-optimal trajectories and requires more computation time. In this paper we attempt to address this large difference issue by applying Dynamic Programming, an optimal control method that can determine an optimal trajectory by partitioning possible trajectories into multiple smaller trajectories. Using a cost function to penalize undesirable subtrajectories, the optimal overall trajectory can be determined. We present the method in an application of a camera mounted to a servo positioning system for part inspection.
Adversarial autoencoder for visualization and classification of human activity: Application to a low-cost commercial force plate

The ability to visualize and interpret high dimensional time-series data will be critical as wearable and other sensors are adopted in rehabilitation protocols. This study proposes a latent space representation of high dimensional time-series data for data visualization. For that purpose, a deep learning model called Adversarial AutoEncoder (AAE) is proposed to perform efficient data dimensionality reduction by considering unsupervised and semi-supervised adversarial training. Eighteen subjects were recruited for the experiment and performed two sets of exercises (upper and lower body) on the Wii Balance Board. Then, the accuracy of the latent space representation is evaluated on both sets of exercises separately. Data dimensionality reduction with conventional Machine Learning (ML) and supervised Deep Learning (DL) classification are also performed to compare the efficiency of AAE approaches. The results showed that AAE can outperform conventional ML approaches while providing close results to DL supervised classification. AAE approaches for data visualization are a promising approach to monitor the subject’s movements and detect adverse events or similarity with previous data, providing an intuitive way to monitor the patient’s progress and provide potential information for rehabilitation tracking.
Impact of Side Channel Attack in Information Security

Side channel attacks are such attacks that are being carried out on the application devices of a cryptosystem. A cryptosystem consists three algorithms as one for key generation, second for encryption and third for decryption purpose. Generally, the attacker tries to access sensitive data from the application part instead of using the brute force attack. Crucial information may be accessed through leaked data from software, hardware or protocol layers. Through monitoring the power flow or power consumption, timing information and electromagnetic radiation, we may extract the sensitive data from information technology devices to break the system is thus termed as side channel attack. The proposed countermeasures will enable the application device to generate cipher text at the same data rate and same power dissipation by the system. This paper will focus on types of side channel attack, vulnerabilities in side channels as well as its destructive effect and then will try to evaluate the proposed countermeasures.
Context-Aware Autonomous Driving Using Meta-Reinforcement Learning

Reinforcement learning (RL) methods achieved major advances in multiple tasks surpassing human performance. However, most of RL strategies show a certain degree of weakness and may become computationally intractable when dealing with high-dimensional and non-stationary environments. In contrast, human are more comfortable with learning from little experience and adapting to unexpected perturbations. These differences are shaping the current research intending to guide agent policies and eschewing the above limits. In this paper, we build a meta-reinforcement learning (MRL) method embedding an adaptive neural network (NN) controller for efficient policy iteration in changing task conditions. Our main goal is to extend RL application to the challenging task of urban autonomous driving in CARLA simulator. The proposed approach yields higher performance and faster learning capabilities than conventionally pre-trained and randomly initialized RL algorithms.
No "Love" Lost: Defending Hate Speech Detection Models Against Adversaries

Although current state-of-the-art hate speech detection models achieve praiseworthy results, these models have shown themselves to be vulnerable to attacks. Easy-to-execute lexical evasion schemes such as removal of whitespace from a given text creates significant issues for word-based hate speech detection models. In this paper, we reproduce the results of five cutting-edge models as well as four significant evasion schemes from prior work. These schemes are required to maintain readability which enables us to recreate the original data. We present several new defenses that leverage this need for maintained meaning and readability, and these schemes perform on par with or exceed the results of adversarial retraining. Furthermore, we demonstrate that each lexical attack or evasion scheme can be overcome with our new defense mechanisms with some reducing the effectiveness of the scheme to a mere .1 to .01 drop in F-1 score. We also propose a new evasion scheme that outperforms those in previous work along with a corresponding defense. Using our results as a foundation, we contend that hate speech detection models can be defended against lexically morphed data without the need for significant retraining. Our work suggests that by utilizing the requirement for preserved meaning, one can create a suitable defense against evasion schemes with a high reversal rate.
A Hybrid Approach for Path Planning of Multiple AUVs

The autonomous underwater vehicles (AUVs) are the independent marine robots employed in many undersea operations starting from the field of underwater research to commercial oil industry surveys. The path planning of a team of AUVs is necessary as it is required to find a safe route from the start to endpoints for the team of AUVS to accomplish any mission goal. The group of AUVs has to trace a time or energy optimal locus in order to reduce the cost of any operation. This paper proposed a hybrid grey wolf optimization (h-GWO) algorithm for path planning of multiple AUVs by integrating features of genetic algorithm (GA) and grey wolf optimization (GWO). This allows a team of AUVs to move in an obstacle rich environment without colliding with obstacles and with each other. Here, path cost is a summation of distance travelled and penalty of collision. Simulation results are obtained for three AUVs in a three-dimensional grid map with static obstacles by applying GA, GWO, and h-GWO algorithm. A comparative study of obtained results made on the basis of computational time, path length and path cost. The results of the hybrid algorithm overcome the disadvantages of GA and GWO and provided a cost-optimized path for operations that are not time-sensitive.
Anonymous Privacy-Preserving Scheme for Big Data Over the Cloud

This paper introduces an anonymous privacy-preserving scheme for big data over the cloud. The proposed design helps to enhance the encryption/decryption time of big data by utilizing the MapReduce framework. The Hadoop distributed file system and the secure hash algorithm are employed to provide the anonymity, security and efficiency requirements for the proposed scheme. The experimental results show a significant enhancement in the computational time of data encryption and decryption.
Translational and Rotational Arrow Cues (TRAC) Navigation Method for Manual Alignment Tasks

Many tasks in image-guided surgery require a clinician to manually position an instrument in space, with respect to a patient, with five or six degrees of freedom (DOF). Displaying the current and desired pose of the object on a 2D display such as a computer monitor is straightforward. However, providing guidance to accurately and rapidly navigate the object in 5-DOF or 6-DOF is challenging. Guidance is typically accomplished by showing distinct orthogonal viewpoints of the workspace, requiring simultaneous alignment in all views. Although such methods are commonly used, they can be quite unintuitive, and it can take a long time to perform an accurate 5-DOF or 6-DOF alignment task. In this article, we describe a method of visually communicating navigation instructions using translational and rotational arrow cues (TRAC) defined in an object-centric frame, while displaying a single principal view that approximates the human’s egocentric view of the physical object. The target pose of the object is provided but typically is used only for the initial gross alignment. During the accurate-alignment stage, the user follows the unambiguous arrow commands. In a series of human-subject studies, we show that the TRAC method outperforms two common orthogonal-view methods—the triplanar display, and a sight-alignment method that closely approximates the Acrobot Navigation System—in terms of time to complete 5-DOF and 6-DOF navigation tasks. We also find that subjects can achieve 1 mm and 1° accuracy using the TRAC method with a median completion time of less than 20 seconds.
Hypergraph Link Prediction: Learning Drug Interaction Networks Embeddings

Graph neural networks (GNNs) have revolutionized deep learning on non-Euclidean data domains, and are extensively used in fields such as social media and recommendation systems. However, complex relational data structures such as hypergraphs, pose challenges for GNNs in terms of their ability to model, embed, and learn relational complexities of multigraphs. Most GNNs focus on capturing flat local neighborhoods of a node thus failing to account for structural properties of multi-relational graphs. This paper introduces Hypergraph Link Prediction (HLP), a novel approach of encoding the multilink structure of graphs. HLP allows pooling operations to incorporate a 360 degrees overview of a node interaction profile, by learning local neighborhood and global hypergraph structure simultaneously. Global graph information is injected into node representations, such that unique global structural patterns of every node are encoded at the node level. HLP leverages the augmented hypergraph adjacency matrix to incorporate the depth of the hypergraph in the convolutional layers. The model is applied to the task of predicting multi-drug interactions, by modeling relations between pairs of drugs as a hypergraph. The existence and the type of drug interactions between the same pair of drugs are mapped as multiple edges, and can be inferred by learning the multigraph local and global structure concurrently. To account for molecular graph properties of a drug, additional drug chemical graph structural fingerprints are included as node attributes.
Feature Changes in Source Code for Commit Classification Into Maintenance Activities

Software maintenance plays an important role during software development and life cycle. Indeed, previous works show that maintenance activities consume most of the software budget. Therefore, understanding how these activities are performed can help software managers to previously plan and allocate resources in projects. Despite previous works, there is still a lack in accurate models to classify developers commits into maintenance activities. In the present article, we propose improvements in a state-of-the-art approach used to classify commits. Particularly, we include three additional features in the classification model and we use XGBoost, a boosting tree learning algorithm, for classification. Experimental results show that our approach outperforms the state-of-the-art baseline achieving more than 77% of accuracy and more than 64% in Kappa metric.
Human abnormal behavior detection method based on T-TINY-YOLO

Aiming at the problem of human abnormal behavior detection in video surveillance, an abnormal target detection method based on T-TINY-YOLO network model is proposed. First, the type of abnormal behavior is defined according to the requirements of the monitoring scenario. Then the calibrated abnormal behavior data is trained through the YOLO network model to achieve end-to-end abnormal behavior classification, thereby achieving abnormal target detection for specific application scenarios. For the characteristics of a large number of zero-valued weight parameters in YOLO's network weights, a convolutional neural network tailoring scheme is proposed to improve the network model, accelerate the algorithm, and improve the real-time performance of the system. Finally, the method is implemented on the embedded platform NVIDIA JetsonTX2. The experimental results are good, the detection speed reaches 12 frames/s, and the recall rate is 80.87%.
Detecting Adversarial Examples for Deep Neural Networks via Layer Directed Discriminative Noise Injection

Deep learning is a popular powerful machine learning solution to the computer vision tasks. The most criticized vulnerability of deep learning is its poor tolerance towards adversarial images obtained by deliberately adding imperceptibly small perturbations to the clean inputs. Such negatives can delude a classifier into wrong decision making. Previous defensive techniques mostly focused on refining the models or input transformation. They are either implemented only with small datasets or shown to have limited success. Furthermore, they are rarely scrutinized from the hardware perspective despite Artificial Intelligence (AI) on a chip is a roadmap for embedded intelligence everywhere. In this paper we propose a new discriminative noise injection strategy to adaptively select a few dominant layers and progressively discriminate adversarial from benign inputs. This is made possible by evaluating the differences in label change rate from both adversarial and natural images by injecting different amount of noise into the weights of individual layers in the model. The approach is evaluated on the ImageNet Dataset with 8-bit truncated models for the state-of-the-art DNN architectures. The results show a high detection rate of up to 88.00% with only approximately 5% of false positive rate for MobileNet. Both detection rate and false positive rate have been improved well above existing advanced defenses against the most practical noninvasive universal perturbation attack on deep learning based AI chip.
A Super-Resolution Image Reconstruction using Triangulation Interpolation in Feature Extraction for automatic sign language recognition

This paper presents the performance of the super-resolution visualization by Triangular Interpolation algorithm to extract characteristic in sign language recognition. By compares performance from sign language image files in the experiment, the results showed that the generation of the super-resolution image by improved Triangulation Interpolation technique can provide the best results when evaluating image performance using PSNR which has a similarity value between the original image and the high-resolution image. These use the PSNR method for measuring image quality. The PSNR value of sign language image is 40.6081 or has more efficiency at 13.15 percent when compared with the SRCNN techniques which are closest to the original image. For measuring performance by SSIM, which is a structured similarity measurement techniques, Triangulation Interpolation method can get the results of generating the super-resolution image next below the SRCNN technique. But in case of the real-time process, Triangulation Interpolation methods can process faster.
Image Segmentation using Convolutional Neural Network for Image Annotation

Due to rapid advancement in the digital display, communication and storage devices with effective techniques are needed to organize, index, retrieve and annotate a large image database. Image segmentation finds application in various areas of image processing and computer vision. Inferring of low level features from the given image is a challenging task in unstructured regions. Most of the annotation and retrieval algorithms fail to consider region semantics. This paper proposes convolutional neural network (CNN) based image segmentation for image annotation application. The proposed CNN includes pixel based prediction of the regions that are applied to obtain low level image features. The algorithm uses image region information based on the precise color distribution within the image. Experimental results demonstrate better results of image segmentation using CNN.
Beyond observation and interaction: Augmented Reality through the lens of constructivism and constructionism

This is the final draft, after peer-review, of a manuscript published in British Journal of Educational Technology. The published version is available online at https://doi.org/10.1111/bjet.12908
Nighttime Haze Removal with Glow Decomposition Using GAN

In this paper, we investigate the problem of a single image haze removal in the nighttime. Glow effect is inherently existing in nighttime scenes due to multiple light sources with various colors and prominent glows. As the glow obscures the color and the shape of objects nearby light sources, it is important to handle the glow effect in the nighttime haze image. Even the convolutional neural network has brought impressive improvements for daytime haze removal, it has been hard to train the network in supervised manner in the nighttime because of difficulty in collecting training samples. Towards this end, we propose a nighttime haze removal algorithm with a glow decomposition network as a learning-based layer separation technique. Once the generative adversarial network removes the glow effect from the input image, the atmospheric light and the transmission map are obtained, then eventually the haze-free image. To verify the effectiveness of the proposed method, experiments are conducted on both real and synthesized nighttime haze images. The experiment results show that our proposed method produces haze-removed images that have better quality and less artifacts than ones from previous studies.
An Assistive Learning Workflow on Annotating Images for Object Detection

We present an end-to-end workflow to generate annotated image datasets for object detection semi-automatically, thereby reducing manual annotation need. With this workflow, which we call assistive learning, we are able to reduce manual annotation time on two experimental datasets by approximately 80%. The experimental results of this work show three contributions of the assistive learning workflow: (1) Savings on human annotation time; (2) generalizability to variable dataset sizes, domains and convolutional neural network (CNN) models; and (3) faster CNN training with limited amount of labeled data using a novel contextual sampling method, thereby a reduction in human workload early on in the assistive learning process. In addition, we wrap the workflow in an interactive annotation interface, allowing annotators without any machine learning experience to speed up the annotation process for training the CNN models.
Metaheuristic Clustering Algorithms

Metaheuristic algorithms are well-known optimization tools which have been used in many applications including those in data mining. In particular, these algorithms are well suited for solving nonconvex clustering problems since they are able to find global solutions whereas the traditional clustering algorithms such as k-means can only guarantee convergence to a local solution. The metaheuristic algorithms applied to solve clustering problems include the tabu search and the simulated annealing algorithms as well as evolutionary algorithms like the genetic algorithm, the artificial bee colony optimization, the particle swarm optimization, and the ant colony optimization algorithms. The description of these algorithms and their flowcharts are given in this chapter.
Model for Practice Badminton Basic Skills by using Motion Posture Detection from Video Posture Embedding and One-Shot Learning Technique

Badminton is a sport that is very popular in Asia for players of all gender and all ages. The practice of basic badminton skill such as badminton posture practice is essential because the correct posture help to avoid injury and help for improving player skill. In this article, we propose The Model for Practice Badminton Basic Skills. We have created the video posture embedding by using the Triplet-Loss technique and develop the badminton player's motion posture detection by using the One-Shot Learning technique. The motion posture detection consists 8 badminton's postures and 4 other postures instances 1) Forehand Clear 2) Backhand Clear 3) Forehand Drop Shot 4) Backhand Drop Shot 5) Forehand Smash 6) Backhand Smash 7) Forehand Serve 8) Backhand Serve 9) Raise the right hand 10) Raise the left hand 11) Stand/Walk and 12) Run. The performance of the model present the Precision is 0.856, Recall is 0.845, F-Measure is 0.847 and the Accuracy is 0.845
Toward Owners’ Control in Digital Data Markets

Finding a good balance between the availability of data for analysis and the control that individuals should exercise over their own data is a key requirement for generating innovation and growth in our data-driven society. A promising direction is the development of data market platforms where individuals can directly provide their own data and monetize them by making them selectively available to data consumers. Data market platforms are often based on the cloud paradigm and can be managed by parties that may be not fully trusted, or even be malicious, thus introducing new data security and privacy issues. In this article, we discuss the issues and challenges toward empowering individuals to use a data market platform for trading their data while keeping control over them. We also discuss how existing techniques can be possibly adapted to address these issues and highlight aspects that still need to be investigated.