Reviewerly/exHarmony huggingface dataset:
Fall of Giants: How popular text-based MLaaS fall against a simple evasion attack

The increased demand for machine learning applications made companies offer Machine-Learning-as-a-Service (MLaaS). In MLaaS (a market estimated 8000M USD by 2025), users pay for well-performing ML models without dealing with the complicated training procedure. Among MLaaS, text-based applications are the most popular ones (e.g., language translators). Given this popularity, MLaaS must provide resiliency to adversarial manipulations. For example, a wrong translation might lead to a misunderstanding between two parties. In the text domain, state-of-the-art attacks mainly focus on strategies that leverage ML models' weaknesses. Unfortunately, not much attention has been given to the other pipeline' stages, such as the indexing stage (i.e., when a sentence is converted from a textual to a numerical representation) that, if manipulated, can significantly affect the final performance of the application. In this paper, we propose a novel text evasion technique called “Zero-Width attack” (ZeW) that leverages the injection of human non-readable characters, affecting indexing stage mechanisms. We demonstrate that our simple yet effective attack deceives MLaaS of “giants” such as Amazon, Google, IBM, and Microsoft. Our case study, based on the manipulation of hateful tweets, shows that out of 12 analyzed services, only one is resistant to our injection strategy. We finally introduce and test a simple input validation defense that can prevent our proposed attack.
Deepfake Portraits in Augmented Reality for Museum Exhibits

In a collaboration with the Georgia Peanut Commission’s Education Center and museum in Georgia, USA, we developed an augmented reality app to guide visitors through the museum and offer immersive educational information about the artifacts, exhibits, and artwork displayed therein. Notably, our augmented reality system applies the First Order Motion Model for Image Animation to several portraits of individuals influential to the Georgia peanut industry to provide immersive animated narration and monologue regarding their contributions to the peanut industry. [4]
The DISRPT 2021 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification

In 2021, we organized the second iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task (Discourse Relation Parsing and Treebanking).Adding to the 2019 tasks on Elementary Discourse Unit Segmentation and Connective Detection, this iteration of the Shared Task included for the first time a track on discourse relation classification across three formalisms: RST, SDRT, and PDTB.In this paper we review the data included in the Shared Task, which covers nearly 3 million manually annotated tokens from 16 datasets in 11 languages, survey and compare submitted systems and report on system performance on each task for both annotated and plain-tokenized versions of the data.
Dynamic Wind Turbine Blade Inspection Using Micro-Polarisation Spatial Phase Shift Digital Shearography

Shearography, as a novel non-destructive evaluation technique, has shown notable ability in the detection of composite materials. However, in current shearography practices, the phase shifting and loading methods applied are mainly static. For instance, vacuum hood or force loading facilities are often used in phase-shifting shearography, and these are hard to realise with robotic control, especially for on-board inspection. In this study, a dynamic process for detecting defects in the subsurface of a wind turbine blade (WTB) using spatial phase shift with dynamic thermal loading was proposed. The WTB sample underwent a dynamic thermal loading operation, and its status is captured by a Michelson interferometric-based spatial phase shift digital shearography system using a pixelated micro-polarisation array sensor. The captured images were analysed in a 2D frequency domain and low-pass filtered for phase map acquisition. The initial phase maps underwent a window Fourier filtering process and were integrated to produce a video sequence for realisation of visualising the first derivative of the displacement in the process of thermal loading. The approach was tested in experimental settings and the results obtained were presented and discussed. A comparative assessment of the approach with shearography fringe pattern analysis and temporal phase shift technique is also presented and discussed.
Source inclusion in synthesis writing: an NLP approach to understanding argumentation, sourcing, and essay quality

Synthesis writing is widely taught across domains and serves as an important means of assessing writing ability, text comprehension, and content learning. Synthesis writing differs from other types of writing in terms of both cognitive and task demands because it requires writers to integrate information across source materials. However, little is known about how integration of source material may influence overall writing quality for synthesis tasks. This study examined approximately 900 source-based essays written in response to four different synthesis prompts which instructed writers to use information from the sources to illustrate and support their arguments and clearly indicate from which sources they were drawing (i.e., citation use). The essays were then scored by expert raters for holistic quality, argumentation, and source use/inferencing. Hand-crafted natural language processing (NLP) features and pre-existing NLP tools were used to examine semantic and keyword overlap between the essays and the source texts, plagiarism from the source texts, and instances of source citation and quoting. These variables along with text length and prompt were then used to predict essays scores. Results reported strong models for predicting human ratings that explained between 47 and 52% of the variance in scores. The results indicate that text length was the strongest predictor of score but also that more successful writers include stronger, semantically-related information from the source, provide more citations and do so later in the text, and copy less from the text. This work introduces the use of NLP techniques to assess source integration, provides details on the types of source integration used by writers, and highlights the effects of source integration on writing quality.
GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems

Intelligent Transportation Systems (ITSs) technology has advanced during the past years, and it is now used for several applications that require vehicles to exchange real-time data, such as in traffic information management.Traditionally, road traffic information has been collected using on-site sensors.However, crowd-sourcing traffic information from onboard sensors or smartphones has become a viable alternative.Stateof-the-art solutions currently follow a centralized model where only the service provider has complete access to the collected traffic data and represent a single point of failure and trust.In this paper, we propose GOLIATH, a blockchain-based decentralized framework that runs on the In-Vehicle Infotainment (IVI) system to collect real-time information exchanged between the network's participants.Our approach mitigates the limitations of existing crowd-sourcing centralized solutions by guaranteeing trusted information collection and exchange, fully exploiting the intrinsic distributed nature of vehicles.We demonstrate its feasibility in the context of vehicle positioning and traffic information management.Each vehicle participating in the decentralized network shares its position and neighbors' ones in the form of a transaction recorded on the ledger, which uses a novel consensus mechanism to validate it.We design the consensus mechanism resilient against a realistic set of adversaries that aim to tamper or disable the communication.We evaluate the proposed framework in a simulated (but realistic) environment, which considers different threats and allows showing its robustness and safety properties.
Haar Cascade and Convolutional Neural Network Face Detection in Client-Side for Cloud Computing Face Recognition

Faces are often used as authentication in security systems, such as facial recognition in running programs or facial recognition for room entry. One important reason is that the human face consists of structures and characteristics that differ from one person to another. In the authentication system, the face will be recognized as a person who has permission access. Many methods have been created and developed to perform image face detection. Two popular methods used today are Haar Cascade and Convolutional Neural Network (CNN). This paper proposed the usage of Haar Cascade and CNN for face detection. Haar Cascade is an algorithm that is used to detect a face quickly and in real-time. At the same time, CNN utilizes the convolution process by moving a convolution (filter) kernel of a specific size to the next image from the result of multiplying the image with the filter used. The results obtained indicate that the system has succeeded in detecting faces based on the specified algorithm. In the experiments, CNN gives better detection accuracy than Haar Cascade. However, for the client application face detection in some specific devices, haar cascade can be used wider than CNN face detection. In mobile, wearable, and Raspberry Pi devices, CNN is hardly used since its GPU requirements. Depends on the This means that the designed application can work and can be used for the next steps for developing the Cloud-based face recognition application.
Secure Complaint-Enabled Source-Tracking for Encrypted Messaging

While the end-to-end encryption properties of popular messaging schemes such as Whatsapp, Messenger, and Signal guarantee privacy for users, these properties also make it very difficult for messaging platforms to enforce any sort of content moderation. This can lead to the unchecked spread of malicious content such as misinformation on such platforms. In 2019, Tyagi et al. developed message traceback, which addresses this issue by allowing a messaging platform to recover the path of a forwarded message after a user reports it for malicious content. This paper presents an alternative to message traceback that offers more privacy to users and requires less platform-side storage. We term this approach source-tracking for encrypted messaging schemes. Source-tracking enables messaging platforms to provide the privacy guarantees expected from standard end-to-end encryption, but also helps hold the sources of malicious messages accountable: if malicious content is reported by a user, the source can be identified. We formalize security goals for source-tracking schemes and design and implement two source-tracking schemes with different security and performance tradeoffs.
Enabling Robust DRL-Driven Networking Systems via Teacher-Student Learning

The past few years have witnessed a surge of interest towards deep reinforcement learning (DRL) in computer networks. With extraordinary ability of feature extraction, DRL has the potential to re-engineer the fundamental resource allocation problems in networking without relying on pre-programmed models or assumptions about dynamic environments. However, such black-box systems suffer from poor robustness, showing high performance variance and poor tail performance. In this work, we propose a unified Teacher-Student learning framework that harnesses rich domain knowledge to improve robustness. The domain-specific algorithms, less performant but more trustable than DRL, play the role of teachers providing advice at critical states; the student neural network is steered to maximize the expected reward as usual and mimic the teacher’s advice meanwhile. The Teacher-Student method comprises of three modules where the confidence check module locates wrong decisions and risky decisions, the reward shaping module designs a new updating function to stimulate the learning of student network, and the prioritized experience replay module to effectively utilize the advised actions. We further implement our Teacher-Student framework in existing video streaming (Pensieve), load balancing (DeepLB), and TCP congestion control (Aurora). Experimental results manifest that the proposed approach reduces the performance standard deviation of DeepLB by 37%; it improves the 90th, 95th, and 99th tail performance of Pensieve by 7.6%, 8.8%, and 10.7% respectively; and it accelerates the growth rate of Aurora by 2x at the initial stage, and achieves a more stable performance in dynamic environments.
Natural Image Stitching With Layered Warping Constraint

Stitching images with parallax for naturalness remains a challenging problem. This paper proposes an image stitching method which preserves the flatness of planes in the scene for a natural look. Our method formulates the alignment of images as the camera parameters and the normal vectors of planes. Given a set of feature point matches, a process of grouping points into different layers and rejecting outliers is introduced. According to the epipolar constraint of corresponding points in two images, the focal length and the pose change of the camera are recovered simultaneously. Then, the normal vectors are estimated from the point pairs. To achieve good alignment and guide the warping of images, the model is combined with the mesh deformation as a global similarity constraint. In addition, bundle adjustment is adopted to maintain the consistency for stitching multiple images. Experiment shows that the proposed approach outperforms some state-of-the-art warps on real-world scenes.
Lagrange interpolation-driven access control mechanism: Towards secure and privacy-preserving fusion of personal health records

A personal health record (PHR) contains health information on a specific individual and incorporates medical data from cloud databases (e.g., diagnosis and treatment records). Because of the use of medical applications on smart mobile devices, security mechanisms must be implemented. Secure access control enables convenient information sharing under secure conditions. To effectively implement secure access control for PHRs in cloud databases, we propose a Lagrange-interpolation-driven access control mechanism (LIDACM) that ensures the security and confidentiality of healthcare information. The LIDACM strictly controls privacy settings and access authority; it also prevents unauthorised individuals from accessing PHRs, thereby increasing the difficulty of hacking the database and stealing private medical information. The present analysis indicates that the LIDACM effectively protects PHR information and enables secure information sharing among multiple medical institutions. Because a user’s private key is generated randomly through the proposed mechanism and no relationships exist between private keys, cracking the private key system is difficult. The LIDACM supports dynamic modifications (e.g., user addition and deletion and document or user inquiry) and can deliver files to specific users when they use a private key. Each user has specific access rights pertaining to retrieval of confidential data. The LIDACM helps guarantee the privacy and security of personal medical information within an information-sharing system.
HCBST: An Efficient Hybrid Sampling Technique for Class Imbalance Problems

Class imbalance problem is prevalent in many real-world domains. It has become an active area of research. In binary classification problems, imbalance learning refers to learning from a dataset with a high degree of skewness to the negative class. This phenomenon causes classification algorithms to perform woefully when predicting positive classes with new examples. Data resampling, which involves manipulating the training data before applying standard classification techniques, is among the most commonly used techniques to deal with the class imbalance problem. This article presents a new hybrid sampling technique that improves the overall performance of classification algorithms for solving the class imbalance problem significantly. The proposed method called the Hybrid Cluster-Based Undersampling Technique (HCBST) uses a combination of the cluster undersampling technique to under-sample the majority instances and an oversampling technique derived from Sigma Nearest Oversampling based on Convex Combination, to oversample the minority instances to solve the class imbalance problem with a high degree of accuracy and reliability. The performance of the proposed algorithm was tested using 11 datasets from the National Aeronautics and Space Administration Metric Data Program data repository and University of California Irvine Machine Learning data repository with varying degrees of imbalance. Results were compared with classification algorithms such as the K-nearest neighbours, support vector machines, decision tree, random forest, neural network, AdaBoost, naïve Bayes, and quadratic discriminant analysis. Tests results revealed that for the same datasets, the HCBST performed better with average performances of 0.73, 0.67, and 0.35 in terms of performance measures of area under curve, geometric mean, and Matthews Correlation Coefficient, respectively, across all the classifiers used for this study. The HCBST has the potential of improving the performance of the class imbalance problem, which by extension, will improve on the various applications that rely on the concept for a solution.
Disappearing Cryptography in the Bounded Storage Model

In this work, we study disappearing cryptography in thebounded storage model. Here, a component of the transmission, say a ciphertext, a digital signature, or even a program, is streamed bit by bit. The stream is too large for anyone to store in its entirety, meaning the transmission effectively disappears once the stream stops.
Mask Aware Network for Masked Face Recognition in the Wild

Face recognition is one of the most important research topics for intelligence security system, especially in the COVID-19 era. Medical research has proven that wearing a mask is the most efficient way to avoid the risk of COVID-19. Nevertheless, classic face recognition systems often fail when dealing with masked faces. So it is essential to design a method that is robust to Masked Face Recognition (MFR). In this paper, to relieve the degraded performance of MFR, we propose Mask Aware Network (MAN) including a mask generation module and a loss function searching module. The mask generation module utilizes the face landmarks to obtain more realistic and reliable masked faces for training. The loss function searching module tries to match the most suitable loss for face recognition. On ICCV MFR challenge, our team victor-2021 achieves 5 first places (including 3 champions in standard face recognition and 2 champions in masked face recognition) and 1 third place by 3rd August 2021. These results demonstrate the robustness and generalization of our method in both standard or masked face recognition task.
Multi-Stage Visual Tracking With Siamese Anchor-Free Proposal Network

The austere challenge of visual object tracking is to find the target to be tracked in various noise interference and obtain its accurate bounding box coordinates. Recently, the object tracking technology based on the Siamese network has made great breakthroughs, and more and more Siamese network trackers have been proposed with superior performance. They still have some shortcomings. To this end, a new Multi-Stage visual tracking algorithm with Siamese Anchor-Free Proposal Network (MS-SiamAFPN) is proposed in this paper. The algorithm is a three-stage Siamese network tracker composed of Feature Extraction and Fusion (FEF) sub-network, Classification and Regression (CR) sub-network, Validation and Regression (VR) sub-network in series. Firstly, the Anchor-Free Proposal Network (AFPN) module is designed in the CR stage, which can make full use of positive and negative samples for training while reducing neural network parameters. Secondly, aim to achieve better robustness and recognizability in the VR stage, on the one hand, a novel Feature Purification (FP) module is designed, which can automatically select the important channels, and extract the features of irregular regions on the input fusion features, so as to strengthen the representation ability of image features. On the other hand, the target recognition and position regression are regarded as different processing tasks, and the recognition score and position fine-tuning of candidate targets are obtained by newly designing the Dual-Branch Network (DBN) structure, thereby avoiding feature ambiguity. Due to the synergy of the above these innovations, MS-SiamAFPN has obtained a large performance improvement, and achieved SOTA performance in multiple public dataset benchmarks.
POAT-Net: Parallel Offset-Attention Assisted Transformer for 3D Object Detection for Autonomous Driving

3D object detection is playing a key role in the perception process of autonomous driving and industrial robots automation. Inherent characteristics of point cloud raise an enormous challenge to both spatial representation and association analysis. Unordered point cloud spatial data structure and density variations caused by gradually varying distances to LiDAR make accurate and robust 3D object detection even more difficult. In this paper, we present a novel transformer network POAT-Net for 3D point cloud object detection. Transformer is credited with the great success in Natural Language Processing (NLP) and exhibiting inspiring potentials in point cloud processing. Our method POAT-Net is inherently insensitive to element permutations within the unordered point cloud. The associations between local points contribute significantly to 3D object detection or other 3D tasks. Parallel offset-attention is leveraged to highlight and capture subtle associations between local points. To overcome the non-uniform density distribution of different objects, we exploit Normalized multi-resolution Grouping (NMRG) strategy to enhance the non-uniform density adaptive ability for POAT-Net. Quantitative experimental results on KITTI3D dataset demonstrate our method achieves the state-of-the-art performance.
Non-Gaussian operations in measurement-device-independent quantum key distribution

Non-Gaussian operations in continuous variable (CV) quantum key distribution (QKD) have been limited to photon subtraction on squeezed vacuum states only. This is mainly due to the ease of calculating the covariance matrix representation of such states. In this paper we study the effects of general non-Gaussian operations corresponding to photon addition, catalysis, and subtraction on squeezed coherent states on CV measurement-device-independent (MDI) QKD. We find that non-Gaussianity coupled with coherence can yield significantly longer transmission distances than without. Particularly we observe that zero photon catalysis on the two-mode squeezed coherent state (TMSC) is an optimal choice for CV MDI QKD, while single photon subtraction is also a good candidate; both of them offer nearly 70 km of transmission distances. We also derive a single generalized covariance matrix for the aforementioned states which will be useful in several other aspects of CV quantum information processing.