Reviewerly/exHarmony huggingface dataset:
Interacting Multiple Model-Based Adaptive Trajectory Prediction for Anticipative Human Following of Mobile Industrial Robot

In smart manufacturing, the introduction of mobile industrial robot has facilitated efficient and flexible production. Mobile industrial robot has the capability of following human operators to coordinate with them to complete complicated operations. In this context, following target person robustly is a significant prerequisite for mobile robot offering assistance. However, the mobile robot with limited field of view may lose the dynamic target during following, which easily leads to following failure. In this paper, therefore, an anticipative human following approach is adopted. This method is appropriate for omnidirectional mobile industrial robot equipped with a visual sensor with limited perception range. We propose a novel Interacting Multiple Model-based adaptive trajectory prediction algorithm. The algorithm integrates two physics-based motion models and adaptively adjusts model parameters for increasing accuracy of prediction. Based on the prediction, mobile robot plans its path and configuration during following in advance to avoid obstacles and achieve robust following. Comparison results demonstrate that the proposed approach can predict human trajectory more accurately, and reduce the deviation between the human and the center of view of the robot.
Edge-Gan: Edge Conditioned Multi-View Face Image Generation

Reconstructing photorealistic multi-view images from an image with an arbitrary view has a wide range of applications in the field of face generation. However, most current pixel-based generation models cannot generate sufficiently realistic enough images. To address this problem, we propose an edge-conditioned multi-view image generation model called Edge-GAN. Edge-GAN utilizes edge information to guide the image generation based on the perspective of the target view while the details of the input image are used to influence the target image. Edge-GAN combines the input image with the target pose information to generate a coarse image with an approximate target outline which is then refined to a better quality using adversanal training. Experiments conducted show that our Edge-GAN is able to generate high-quality images of people with convincing details.
Blind natural image deblurring with edge preservation based on L0-regularized gradient prior

L0 regularization is highly suitable for estimating the latent image in the blur kernel estimation step of the two-step process of blind image deblurring. To tackle L0 regularization problem, the method randomly selects gradient truncation threshold or only depends on the experience of repeated experiments, which often leads to the damage of edges in the latent image and inaccurate blur kernel. The proposed blind deblurring model based on edge-preserving of L0-regularized gradient (EP-L0RG) prior has the great adaptability to the parameters selection for retaining the edges. The experimental results state that the proposed method not only reduces the blindness and difficulty of the parameter settings, but also estimates a more accurate kernel and improves the deblurring performance. Comparative experiments demonstrated that the proposed method provides the optimal and quantitative results.
Grounding Language to Landmarks in Arbitrary Outdoor Environments

Robots operating in outdoor, urban environments need the ability to follow complex natural language commands which refer to never-before-seen landmarks. Existing approaches to this problem are limited because they require training a language model for the landmarks of a particular environment before a robot can understand commands referring to those landmarks. To generalize to new environments outside of the training set, we present a framework that parses references to landmarks, then assesses semantic similarities between the referring expression and landmarks in a predefined semantic map of the world, and ultimately translates natural language commands to motion plans for a drone. This framework allows the robot to ground natural language phrases to landmarks in a map when both the referring expressions to landmarks and the landmarks themselves have not been seen during training. We test our framework with a 14-person user evaluation demonstrating an end-to-end accuracy of 76.19% in an unseen environment. Subjective measures show that users find our system to have high performance and low workload. These results demonstrate our approach enables untrained users to control a robot in large unseen outdoor environments with unconstrained natural language.
Hybrid Attack Detection Framework for Industrial Control Systems using 1D-Convolutional Neural Network and Isolation Forest

Industrial control systems (ICSs) are used in various infrastructures and industrial plants for realizing their control operation and ensuring their safety. Concerns about the cybersecurity of industrial control systems have raised due to the increased number of cyber-attack incidents on critical infrastructures in the light of the advancement in the cyber activity of ICSs. Nevertheless, the operation of the industrial control systems is bind to vital aspects in life, which are safety, economy, and security. This paper presents a semi-supervised, hybrid attack detection approach for industrial control systems by combining Isolation Forest and Convolutional Neural Network (CNN) models. The proposed framework is developed using the normal operational data, and it is composed of a feature extraction model implemented using a One-Dimensional Convolutional Neural Network (1D-CNN) and an isolation forest model for the detection. The two models are trained independently such that the feature extraction model aims to extract useful features from the continuous-time signals that are then used along with the binary actuator signals to train the isolation forest-based detection model. The proposed approach is applied to a down-scaled industrial control system, which is a water treatment plant known as the Secure Water Treatment (SWaT) testbed. The performance of the proposed method is compared with the other works using the same testbed, and it shows an improvement in terms of the detection capability.
Pair-Wise and Group-Wise Deformation Consistency in Deep Registration Network

Ideally the deformation field from one image to another should be invertible and smooth to register images bidirectionally and preserve topology of anatomical structures. In traditional registration methods, differential geometry constraints could guarantee such topological consistency but are computationally intensive and time consuming. Recent studies showed that image registration using deep neural networks is as accurate as and also much faster than traditional methods. Current popular unsupervised learning-based algorithms aim to directly estimate spatial transformations by optimizing similarity between images under registration; however, the estimated deformation fields are often in one direction and do not possess inverse-consistency if swapping the order of two input images. Notice that the consistent registration can reduce systematic bias caused by the order of input images, increase robustness, and improve reliability of subsequent data analysis. Accordingly, in this paper, we propose a new training strategy by introducing both pair-wise and group-wise deformation consistency constraints. Specifically, losses enforcing both inverse-consistency for image pairs and cycle-consistency for image groups are proposed for model training, in addition to conventional image similarity and topology constraints. Experiments on 3D brain magnetic resonance (MR) images showed that such a learning algorithm yielded consistent deformations even after switching the order of input images or reordering images within groups. Furthermore, the registration results of longitudinal elderly MR images demonstrated smaller volumetric measurement variability in labeling regions of interest (ROIs).
Integrating “Random Forest” with Indexing and Query Processing for Personalized Search

The internet has become an integral part of at least 4.4 billion lives. An average person looks at their device at least 20 times a day. One can only imagine the amount of queries a search engine gets on a daily basis. With the help of all the data acquired over the years, the internet updates us with all the biggest trends and live events happening all over the world. A search engine is able to provide query suggestions based on the number of times a keyword has been searched for or the current query relates to a certain trend. All these trends are updated to every device internationally or locally. This concept is generalized throughout all devices that use any kind of search engine on any application. Through this paper we intend to propose to use Random Forest as a predictive model to be integrated with the indexing process of the search engine to produce query suggestions that a user would want to search, contrary to the query suggestions that are usually displayed based on hyped trends and fashion.
Problem Solving with Hopfield Networks and Adiabatic Quantum Computing

Our goal with this paper is to elucidate the close connection between Hopfield networks and adiabatic quantum computing. Focusing on their use in problem solving, we point out that the energy functions minimized by Hopfield networks are essentially identical to those minimized by adiabatic quantum computers. To practically illustrate this, we consider a simple textbook problem, namely the k-rooks problem, and discuss how to set it up for solution via a Hopfield network or adiabatic quantum computing.
Noisy Localization Annotation Refinement For Object Detection

The production of finely annotated datasets for object detection tasks is labor-intensive, therefore, cloud sourcing is often used to create datasets, which leads to these datasets tending to contain incorrect annotations such as inaccurate localization bounding boxes. In this study, we highlight a problem of object detection with noisy bounding box annotations and show that these noisy annotations are harmful to the performance of deep neural networks. To solve this problem, we further propose a framework to allow the network to modify the noisy datasets by alternating refinement. The experimental results demonstrate that our proposed framework can significantly alleviate the influences of noise on model performance.
A modular and scalable computational framework for interactive immersion into imaging data with a holographic augmented reality interface

Modern imaging scanners produce an ever-growing body of 3D/4D multimodal data requiring image analytics and visualization of fused images, segmentations, and information. For the latter, augmented reality (AR) with head-mounted displays (HMDs) has shown potential. This work describes a framework (FI3D) for interactive immersion with data, integration of image processing and analytics, and rendering and fusion with an AR interface. The FI3D was designed and endowed with modules to communicate with peripherals, including imaging scanners and HMDs, and to provide computational power for data acquisition and processing. The core of FI3D is deployed to a dedicated computational unit that performs the computationally demanding processes in real-time, and the HMD is used as a display output peripheral and an input peripheral through gestures and voice commands. FI3D offers user-made processing and analysis dedicated modules. Users can customize and optimize these for a particular workflow while incorporating current or future libraries. The FI3D framework was used to develop a workflow for processing, rendering, and visualization of CINE MRI cardiac sets. In this version, the data were loaded from a remote database, and the endocardium and epicardium of the left ventricle (LV) were segmented using a machine learning model and transmitted to a HoloLens HMD to be visualized in 4D. Performance results show that the system is capable of maintaining an image stream of one image per second with a resolution of 512 × 512. Also, it can modify visual properties of the holograms at 1 update per 16 milliseconds (62.5 Hz) while providing enough resources for the segmentation and surface reconstruction tasks without hindering the HMD. We provide a system design and framework to be used as a foundation for medical applications that benefit from AR visualization, removing several technical challenges from the developmental pipeline.
Analysis of activation maps through global pooling measurements for texture classification

We analyzed the effects of global pooling measurements on extracting relevant texture information from a given set of activation maps. Initially, using a layer-by-layer approach (GP-CNN), we experimentally demonstrated that layers at various depth levels could provide high-quality texture information. Based on this finding, we developed RankGP-CNN, a method that performs multi-layer feature extraction. More specifically, RankGP-CNN treats every CNN model as a vast collection of deep composite functions, where each function computes a 2D activation map for every input image. A feature ranking approach then assigns a score to each deep composite function by processing the activation maps generated for a particular dataset bank. Eventually, RankGP-CNN uses the top-ranked deep composite functions to compute feature vectors for different texture datasets. Experiments on a dedicated classifier showed that RankGP-CNN achieves good results and can adapt to different texture problems. Finally, we present RankGP-3M-CNN as the version of RankGP-CNN that considers multiple CNN models. Overall, RankGP-3M-CNN achieves promising results with the advantage of only using the default scale of the input images.
Data Collection Techniques for Forensic Investigation in Cloud

Internet plays a vital role in providing various services to people all over the world. Its usage has been increasing tremendously over the years. In order to provide services efficiently at a low cost, cloud computing has emerged as one of the prominent technologies. It provides on-demand services to the users by allocating virtual instances and software services, thereby reducing customer's operating cost. The availability of massive computation power and storage facilities at very low cost motivates a malicious individual or an attacker to launch attacks from machines either from inside or outside the cloud. This causes high resource consumption and also results in prolonged unavailability of cloud services. This chapter surveys the systematic analysis of the forensic process, challenges in cloud forensics, and in particular the data collection techniques in the cloud environment. Data collection techniques play a major role to identify the source of attacks by acquiring evidence from various sources such as cloud storage (Google Drive, Dropbox, and Microsoft SkyDrive), cloud log analysis, Web browser, and through physical evidence acquisition process.
TAMBUS: A novel authentication method through covert channels for securing industrial networks

Nowadays, many companies still use old and insecure protocols in Industrial Control Systems (ICSs). An example of such protocols is Modbus, one of the most employed industrial protocols. Also, companies are moving to Modbus/TCP when there are TCP devices involved in the facility. While remaining insecure, this migration also disrupts the assumption of air-gapped industrial networks, opening more attack surface to previously isolated systems. Due to legacy and efficiency constraint, the replacement of Modbus/TCP with secure protocols is not possible, generating big security issues. In this paper, we present TAMBUS (Transmitter Authentication and packet integrity in Modbus/TCP). This method is the first that at the same time: is not implemented in a secure by obscurity design and keeps the Modbus/TCP protocol compatible with legacy devices. TAMBUS allows detecting attacks with high statistical confidence, by leveraging two covert channels as a mean of providing security: 1) Storage-based, that hides authentication messages into the Modbus/TCP protocol fields; 2) Timing-based, that considers the inter-arrival time of packets. We demonstrate the feasibility and effectiveness of our method through a prototype implementation and testing in an industrial testbed environment. Our experiments confirm that TAMBUS introduces only a small overhead, negligible in most application, and it preserves the regular functioning of industrial systems. In particular, considering the storage-based covert channel, TAMBUS introduces an error into transmitted values of only 1.19×10−5%, without traffic overhead. On the other hand, TAMBUS can transmit correct security information through the timing-based covert channel with an accuracy of more than 99.99%.
Adaptively Secure Inner Product Encryption from LWE

Attribute-based encryption (ABE) is an advanced form of encryption scheme allowing for access policies to be embedded within the secret keys and ciphertexts. By now, we have ABEs supporting numerous types of policies based on hardness assumptions over bilinear maps and lattices. However, one of the distinguishing differences between ABEs based on these two breeds of assumptions is that the former can achieve adaptive security for quite expressible policies (e.g., inner-products, boolean formula) while the latter can not. Recently, two adaptively secure lattice-based ABEs have appeared and changed the state of affairs: a non-zero inner-product (NIPE) encryption by Katsumata and Yamada (PKC'19) and an ABE for t-CNF policies by Tsabary (CRYPTO'19). However, the policies supported by these ABEs are still quite limited and do not embrace the more interesting policies that have been studied in the literature. Notably, constructing an adaptively secure inner-product encryption (IPE) based on lattices still remains open. In this work, we propose the first adaptively secure IPE based on the learning with errors (LWE) assumption with sub-exponential modulus size (without resorting to complexity leveraging). Concretely, our IPE supports inner-products over the integers $$\mathbb {Z}$$ with polynomial sized entries and satisfies adaptively weakly-attribute-hiding security. We also show how to convert such an IPE to an IPE supporting inner-products over $$\mathbb {Z}_p$$ for a polynomial-sized p and a fuzzy identity-based encryption (FIBE) for small and large universes. Our result builds on the ideas presented in Tsabary (CRYPTO'19), which uses constrained pseudorandom functions (CPRF) in a semi-generic way to achieve adaptively secure ABEs, and the recent lattice-based adaptively secure CPRF for inner-products by Davidson et al. (CRYPTO'20). Our main observation is realizing how to weaken the conforming CPRF property introduced in Tsabary (CRYPTO'19) by taking advantage of the specific linearity property enjoyed by the lattice evaluation algorithms by Boneh et al. (EUROCRYPT'14).
Deep Lightening Network for Low-Light Image Enhancement

We propose a Deep Lightening Network (DLN) for low-light image enhancement. Inspire by the domain transfer study, we propose a novel cycle learning structure to learn the mapping relationship between low- and normal-light images. Each DLN consists of several Lightening Back-Projection (LBP) blocks that learn the residual between low- and normal-light images. To efficiently estimate the local and global information, we fuse the features from different LBP results. Experimental results on different datasets show that our proposed DLN approach outperforms other approaches in all objective and subjective measures.
Classification of Cyberbullying Text in Arabic

The increase in electronic devices and social media use has allowed face-to-face bullying integrate the cyber space. Cyberbullying is an increasing problem that affects its victims worldwide both mentally and physically. Acting upon this phenomenon is of highly importance. Several researches were conducted on cyberbullying classification in English language and less on Arabic. In this paper, we conducted a series of experiments using neural network models (Convolutional and Recurrent Neural Networks) and pre-trained word embeddings in an attempt to classify cyberbullying instances on an Arabic channel news comments dataset. Best models achieved 0.84 F1-score on a balanced version of the aforementioned dataset.
Finding client-side business flow tampering vulnerabilities

The sheer complexity of web applications leaves open a large attack surface of business logic. Particularly, in some scenarios, developers have to expose a portion of the logic to the client-side in order to coordinate multiple parties (e.g. merchants, client users, and third-party payment services) involved in a business process. However, such client-side code can be tampered with on the fly, leading to business logic perturbations and financial loss. Although developers become familiar with concepts that the client should never be trusted, given the size and the complexity of the client-side code that may be even incorporated from third parties, it is extremely challenging to understand and pinpoint the vulnerability. To this end, we investigate client-side business flow tampering vulnerabilities and develop a dynamic analysis based approach to automatically identifying such vulnerabilities. We evaluate our technique on 200 popular real-world websites. With negligible overhead, we have successfully identified 27 unique vulnerabilities on 23 websites, such as New York Times, HBO, and YouTube, where an adversary can interrupt business logic to bypass paywalls, disable adblocker detection, earn reward points illicitly, etc.
A Highly Efficient Power Model for Correlation Power Analysis (CPA) of Pipelined Advanced Encryption Standard (AES)

We evaluate the vulnerability of a pipelined Advanced Encryption Standard (AES) against Correlation Power Analysis (CPA) Side-Channel Attack (SCA). We identify that the registers in pipelined AES are most vulnerable against CPA SCA and propose a new power model targeting the switching activities of the registers. The proposed power model is constructed based on the Hamming Distance (HD) between the intermediate values stored in the registers in two consecutive clock cycles. Then, we analyze the vulnerability of pipelined AES under two scenarios. First, during regular pipeline operation where the device is performing AES pipeline operation. Second, in non-pipeline operation where we assume the adversaries can insert delay to the input of the device to increase the signal to noise ratio of the physical leakage information. The simulation results show that under regular pipelined operation, our proposed power model can reveal all the 16 key bytes in less than 4,900 traces, resulting in 4.7× more effective than the conventional power models. Under non-pipelined operation, our proposed power model requires only 590 traces to reveal all the 16 key bytes, which is 5.9× more effective than other power models.