Reviewerly/exHarmony huggingface dataset:
Traffic Sign Detection using Feature Fusion and Contextual Information

Traffic sign detection based on image and video data is critical, which captures real-time traffic road information for autonomous vehicle. With the rapid development of CNN, more and more CNN-based detectors have promoted general object detection. However, these mainstream detectors still suffer from small object detection task because of small size and fuzzy representation. Traffic signs are representative small object on road scenes causing a rigid challenge for autonomous driving perception system. In this paper, traffic sign detection (TSD) is regard as a small object detection task. We propose a feature fusion method via cross-connection to enhance feature representation. In addition, contextual information searched by dilated convolution is also used to support small traffic sign detection. We have implemented our modules into Faster R-CNN and evaluated effectiveness of proposed method on Tsinghua-Tencent 100K dataset. Our experimental results prove that the feature fusion method via cross connection and contextual information improve detection result of small traffic sign.
V—The Linguistic Approach to Ontology

Abstract What are the prospects for a linguistic approach to ontology? Given that it seems that there are true subject-predicate sentences containing empty names, traditional linguistic approaches to ontology appear to be flawed. I argue that in order to determine what there is, we need to determine which sentences ascribe properties (and relations) to objects, and that there does not appear to be any formal criterion for this. This view is then committed to giving an account of what predicates do in sentences when they do not ascribe properties. I sketch an approach to the varieties of predication.
Adversarial LSTM-Based Sequence-to-Sequence Model for Drug-Related Reactions Understanding

Social networks become an extensive source used to automatically report treatment and drug issues. Few approaches have been proposed in this matter, namely multi-word medical concept identification and drug-related descriptions that denoted vast connections for understanding patients’ shared experiences. Therefore, the paper explores the problem of mutual forms of drug interactions including beneficial drug effects. We developed an LSTM-based encoder–decoder model that leverages and memorizes concepts’ details and relationships to form a drug description that compactly with highly informed embeddings seeks multi-word drug-related descriptions from online generated narratives. Meanwhile, we apply adversarial training to regularize sequential embeddings of distributed biomedical n-grams representation from controlled medical vocabulary such as PubMed. It consists of a semi-supervised featuring medical concepts from online generated narratives at sentence-level, especially to create a feature descriptor to select possible drug-related descriptions. The model achieves an accuracy of 86% in assessing false positives and negatives of patients’ statements. Whereby the model shows a well-understanding of real-world medication-related descriptions with significant improvements over baseline neural network approaches.
Blockchain Interoperability Using Hash Time Locks

The recent escalation of individual, independent blockchain networks has engendered the need for a robust solution to communication between the various token networks. The solution must conform to industry standards and must be universally endorsed. This research paper explores various studies and implementations conducted under the topic of interoperability between different blockchain networks. Discovering various aspects of a blockchain system, comparing existing solutions and proposing a solution for communication between any two token blockchain networks are the steps effectuated. The proposed solution allows for a secure transfer of tokens between homogeneous blockchains using the concept of hash time locked contracts. Involving a self-enlisted middleman in the transaction eliminates the need for an external interface or a centralised authority for transaction, and reinforces trust. As a result, a safe and reliable transfer of tokens without the involvement of a centralised third party takes place. The solution warrants a mass adoption for further testing, however, it executes in a stable environment between two homogeneous token blockchain networks.
Object detection with noisy annotations in high-resolution remote sensing images using robust EfficientDet

A large number of accurate annotations of targets is a prerequisite for efficient and accurate object detection. However, to obtain such annotated samples for completing detection model training is time-consuming, laborious, and difficult to achieve. Usually, the training samples often contain noisy annotation, including mislabeled class and inaccurate bounding box. These noisy annotations reduce the classification and detection performance. In order to solve this problem and efficiently and accurately detect interested targets from remote sensing images, we propose a robust object detection method, called robustEfficientDet. In this method, firstly, with the help of EfficientDet's powerful ability of deep feature fusion, the feature representation with higher classification performance is extracted from the image. Secondly, the “Active passive losses (APL)” function is introduced into the calculation process of the classification loss to deal with the noisy annotations. In addition, in the bounding box regression, a new Focal- EIOU (short for effective Efficient Intersection over Union) loss function is introduced to reduce the positioning loss caused by inaccurate bounding box. Finally, the robustEfficientDet is constructed to improve the performance of object detection in remote sensing images. The results of several experiments show that the proposed method can achieve better detection results with noisy annotations.
Neural Text Generation with Artificial Negative Examples to Address Repeating and Dropping Errors

Neural text generation models that are conditioned on a given input (e.g., machine translation and image captioning) are typically trained through maximum likelihood estimation of the target text. However, models trained in this manner often suffer from various types of errors when making subsequent inferences. In this study, we propose suppressing an arbitrary type of error by training the text generation model in a reinforcement learning framework; herein, we use a trainable reward function that can discriminate between references and sentences, containing the targeted type of errors. We create such negative examples by artificially injecting the targeted errors into the references. In the experiments, we focus on two error types; repeated and dropped tokens in model-generated text. The experimental results demonstrate that our method can suppress generation errors, and achieves significant improvements on two machine translation and two image captioning tasks.
CoEvo-Net: Coevolution Network for Video Highlight Detection

Video highlight detection (VHD) has emerged as a pressing task due to the unprecedentedly increasing amount of video data, such as those from e-commerce live-broadcasting platforms. Many approaches focus on exploiting text data, in the form of video description or time-sync comments, to facilitate the VHD task. Despite the promising results, they have largely overlooked the noises inherent in the text data and have mostly relied on isolating the feature of text and video. In this paper, we introduce a novel model to handle VHD, termed Coevolution Network (CoEvo-Net), that allows us to account for joint learning of the language and video features explicitly via a coevolution paradigm, in which features from the two data modalities progressively refine each other. This is achieved by a dedicated CoEvo-Cell that takes language and video together as inputs, extracts cross-modality, and filters the undesired parts of the input, such as words in a sentence. Furthermore, we release a large-scale dataset of e-commerce for VHD, in which each video is coupled with a sentence for description, to benchmark the sentence-based VHD approaches. Extensive experiments on the released dataset demonstrate that CoEvo-Net achieves state-of-the-art performance. Our dataset and code will be made publicly available.
Triple-Input-Unsupervised neural Networks for deformable image registration

Deep-learning frameworks have been widely used in deformable image registration(DIR), which is a fundamental step in medical image analysis. In traditional deep-learning-based DIR methods, pairwise registration is conducted for each image, cooperated with a dual-input structure. They only considered that the registered images need to be as similar as possible to the target image, while ignored that all registered images to the same target image should have similar appearance too. Therefore, in this paper we add the deformation constraints between different registered images to the registration network in order to enhance the appearance consistency of the registered images and improve the registration accuracy. Specifically, a Triple-Input-Unsupervised neural Network (TIUNet) is proposed to register two source images to a common target image at the same time. Then, a novel deformation-strengthened loss is designed to simulate the deformation constraint between the two warped source images. Finally, the deformation constraints between the source images and the target image are jointly optimized to obtain the deformation field model. The experimental results on the available tissue segmentation database demonstrate that our proposed TIUNet method shows a superior performance of deformation field model over several other advanced deformable image registration algorithms. Moreover, benefited from the triple-input structure, our proposed TIUNet method could expand the available training sets naturally and is suitable for the registration task with fewer training data.
Probability-to-Goal and Expected Cost Trade-Off in Stochastic Shortest Path

Markov Decision Processes (MDPs) model problems where a decision-maker makes sequential decisions and the effect of decisions is probabilistic. A particular formulation of MDPs is the Shortest Stochastic Path (SSP), in which the agent seeks to accomplish a goal while reducing the cost of the path to it. Literature introduces some optimality criteria; most of them consider a priority of maximizing probability to accomplish the goal while minimizing some cost measure; such criteria allow a unique trade-off between probability-to-goal and path cost for a decision-maker. Here, we present algorithms to make a trade-off between probability-to-goal and expected cost; based on the Minimum Cost given Maximum Probability (MCMP) criterion, we propose to treat such a trade-off under three different methods: (i) additional constraints for probability-to-goal or expected cost; (ii) a Pareto’s optimality by finding non-dominated policies; and (iii) an efficient preference elicitation process based on non-dominated policies. We report experiments on a toy problem, where probability-to-goal and expected cost trade-off can be observed.
In Your Face: Sentiment Analysis of Metaphor with Facial Expressive Features

Metaphor plays an important role in human communication, which often conveys and evokes sentiments.Numerous approaches to sentiment analysis of metaphors have thus gained attention in natural language processing (NLP).The primary focus of these approaches is on linguistic features and text rather than other modal information and data.However, visual features such as facial expressions also play an important role in expressing sentiments.In this paper, we present a novel neural network approach to sentiment analysis of metaphorical expressions that combines both linguistic and visual features and refer to it as the multimodal model approach.For this, we create a Chinese dataset, containing textual data from metaphorical sentences along with visual data on synchronized facial images.The experimental results indicate that our multimodal model outperforms several other linguistic and visual models, and also outperforms the state-of-the-art methods.The contribution is realized in terms of novelty of the approach and creation of a new, sizeable, and scarce dataset with linguistic and synchronized facial expressive image data.The dataset is particularly useful in languages other than English and the approach addresses one of the most challenging NLP issue: sentiment analysis in metaphor.
CNN-Based Mobile Device Detection Using Still Images

Image forgery has been increased enormously due to the development of software and introduction of new cheaper digital devices like cameras and cellular phones. The introduction of new devices leads to inadvertent capturing of images. A lot of misuse of these devices led to the need for their identification. Source camera identification deals with the issue of identification of the mobile device using their camera through which image has been taken. This empowers the scientific agent to spot mobile device that has been used for capturing the specific image during the study. This is significant as this alphanumeric content is considered as a inaudible observer. In this proposed work, we are reviewing the various approaches for SCI which depend on classical machine learning algorithms feature extraction. Then, a CNN model is proposed for identification of mobile device using vision dataset. High accuracy of 92% is achieved for vision dataset.
Complexity of Nonemptiness in Control Argumentation Frameworks

Control argumentation frameworks (CAFs) [] extend the standard model of argumentation framework (AF) due to Dung [] in a way that takes unquantified uncertainty regarding arguments and attacks into account. Complementing recent work by Skiba et al. [] for incomplete argumentation frameworks, we study the (nonempty) existence problem for CAFs and fully analyze its computational complexity for the most common semantics.
Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns

Recent studies demonstrated the vulnerability of control policies learned through deep reinforcement learning against adversarial attacks, raising concerns about the application of such models to risk-sensitive tasks such as autonomous driving. Threat models for these demonstrations are limited to (1) targeted attacks through real-time manipulation of the agent's observation, and (2) untargeted attacks through manipulation of the physical environment. The former assumes full access to the agent's states/observations at all times, while the latter has no control over attack outcomes. This paper investigates the feasibility of targeted attacks through visually learned patterns placed on physical objects in the environment, a threat model that combines the practicality and effectiveness of the existing ones. Through analysis, we demonstrate that a pre-trained policy can be hijacked within a time window, e.g., performing an unintended self-parking, when an adversarial object is present. To enable the attack, we adopt an assumption that the dynamics of both the environment and the agent can be learned by the attacker. Lastly, we empirically show the effectiveness of the proposed attack on different driving scenarios, perform a location robustness test, and study the tradeoff between the attack strength and its effectiveness Code is available at https://github.com/ASU-APG/ Targeted-Physical-Adversarial-Attacks-on-AD
Data-Driven Online Recommender Systems with Costly Information Acquisition

In numerous recommender systems, collecting useful information from users is costly, implying that the recommender system has to make active choices by simultaneously learning the observations of the features' states to make useful recommendations to users among available products and services. This paper integrates information acquisition decisions into recommender system. To solve the aforementioned dual learning problem, we propose two different algorithms, namely Sim-OOS and Seq-OOS, where observations are made simultaneously and sequentially, respectively. We prove that both algorithms guarantee a sub linear regret. The developed recommender system can be applied to a variety of real-world applications, including medical informatics, smart transportation, finance, and cyber-security where collecting information before making decisions results in an excessive cost. We validate and evaluate our proposed policies in a medical decision-support system that recommends tests and treatments for breast cancer patients.
A Comprehensive Study on SQL Injection Attacks, Their Mode, Detection and Prevention

SQL injection indicates the type of attack that exploits vulnerability and security prevailing in the database systems concerned with any application. This vulnerability is mostly encountered within web pages having dynamic contents. In this study, for any kind of vulnerability, we discuss the way how attackers of these types could get benefit out of such vulnerability and execute vulnerable codes along with the strategy to countermeasure such negative impacts associated with database systems. In this context, web operations are commonly employed for online administrations spanning from high range of informal communication to administering transaction accounts, while dealing with private user information which is confidential. However, the underlying problem is that, this information is vulnerable to attacks owing to unauthorized access, where the attackers avail access into the system through different hacking and cracking techniques with surprisingly negative intentions. The attacker can employ some more well-versed queries and some novel strategies to bypass the authentication while conjointly attaining complete control over the web application as well as the server. A lot of novel algorithms have been developed till now to encrypt data query for preventing such attacks by framing desired query change plan. In this paper, we will be collaborating on the background of injection attack, types of injection attack, various case studies and preventive measures associated with SQL injection attack along with a suitable illustration.
V-CRYPT: A Secure Visual Cryptography System

Nowadays, peoples are very concerned about their data privacy. Hence, all the current security methods should be improved to stay relevant in this fast-growing technology world. Visual Cryptography (VC) is a cryptographic technique that using the image processing method. The implementation of VC can be varying and flexible to be applied to the system that requires an extra security precaution as it is one of the effective solutions in securing the data exchange between two or more parties. The main purpose of the development of V-CRYPT System is to improve the current VC technique and make it more complex in the encryption and decryption process. V-CRYPT system will let the user enter the key, then select the image that they want to encrypt, and the system will split the image into four shares: share0, share1, share2, share3. Each pixel of the image will be splatted into a smaller block of subpixels in each of the four shares and encrypted as two subpixels in each of the shares. The decryption will work only when the user selects all the shares, and the correct text key is entered. The system will superimpose all the shares and producing one perfect image. If the incorrect key is entered, the resulted image will be unidentified. The results show that V- CRYPT is a valuable alternative to existing methods where its security level is higher in terms of adding a secure key and complexity.
How and when to stop the co-training process

Co-training is a semi-supervised learning approach used when only a small set of the data that is available for training is labeled. By using multiple classifiers, the co-training process utilizes the small set of labeled data in order to label an additional set of samples. During this process, the classifiers gradually augment the training data in an iterative process in which a new co-training model is derived and used for labeling the unlabeled samples in each iteration. A few of the newly labeled samples are added in each iteration to the training dataset to improve the performance of the classifiers. The main challenge in applying co-training is to make sure that the co-trainer assigns accurate labels to the unlabeled samples. Many empirical studies showed that the performance (accuracy) of the co-trainer could not be further improved when a certain number of iterations was reached, and in some cases, the performance even declined if the process (i.e., labeling) continued. Despite this, no general solution has been suggested for identifying the optimal final co-training model or number of iterations before this decline. In this work, we propose a novel method aimed at selecting the near-optimal final co-training model among all models created in the various iterations according to a predefined measurement based solely on the unlabeled data. Experiments on nine open, publicly available and real-life datasets demonstrate that the proposed method outputs a near-optimal final co-training model compared to other co-training models created in the various iterations.
Restricted Bi-pattern Mining

Bi-pattern mining has been previously introduced to mine attributed networks in which nodes may have two types or two roles. In particular a bi-partite network have two vertex sets and attributes describing node labels depends then on the node type, still some attributes may be relevant to describe both kind of nodes. It is then natural to consider bi-patterns made of two pattern components. In directed networks there is only one vertex set, but we may consider the out and in roles of a node. In both cases it may be interesting to enforce both components of a bi-pattern to share part of their description. We discuss how to impose such a restriction in closed bi-pattern mining.
Detecting Offensive Content on Social Media During Anti-Lockdown Protests in Michigan

Hateful and offensive speech on online social media platforms has been exacerbated by the turbulent and chaotic circumstances brought on by the coronavirus pandemic. A particularly contentious issue was lockdown orders issued by state governments designed to keep citizens safe by controlling the spread of the virus. To compel the government to relax these orders and restore normalcy, antilockdown protests were organized in many states. The economic, ideological, political, and health concerns related to the lockdowns and the associated protests were debated vigorously on social media platforms, many times using offensive content. Detecting such insulting and humiliating content is especially important during tumultuous times, when tensions are high, because such expressions online can quickly precipitate violence in the physical world. This paper presents an approach to detect hateful and offensive content from Twitter feeds collected after anti-lockdown protests in Lansing, Michigan. These tweets were labeled using a comprehensive definition of what constitutes offensive content based on its potential to trigger and incite people. Linguistic and auxiliary features were extracted from these labeled tweets. These features were further processed through feature selection and dimensionality reduction techniques. The preprocessed feature set was used to train machine learning models, which detect offensive content with an accuracy of around 84%. Our approach demonstrates the feasibility of identifying and tagging offensive content in politically motivated situations, even when such speech is dominated by contextual and circumstantial information. It can thus be used to mitigate the damage caused by widespread dissemination of offensive content.