Reviewerly/exHarmony huggingface dataset:
Safety Helmet Detection Method Based on Faster R-CNN

In order to reduce the probability of safety accidents caused by non-wearing helmet, we proposed an improved Safety helmet wearing detection method based on Faster R-CNN. We use Faster R-CNN as the basic algorithm for Safety helmet detection. This paper uses VGG16 feature extraction network to extract the characteristics of the helmet. Region Proposal Network is used to extract detection regions and to predict positions and categories. We use the video monitoring at the substation entrance and exit as the data set to determine the target frame parameters. Finally, We use euclidean distance to compare the relationship between the return position of the human head and the return position of the helmet. A method to determine whether the helmet is worn correctly by comparing the regression position of the head with the helmet. Experiments show that the improved algorithm not only satisfies the real-time detection task in helmet wear detection, but also has high detection accuracy.
Robust Blind Watermarking Algorithm Based on Contourlet Transform with Singular Value Decomposition

In this Letter, we propose a blind and robust multiple watermarking scheme using Contourlet transform and singular value decomposition (SVD). The host image is first decomposed by Contourlet transform. Singular values of Contourlet coefficient blocks are adopted to embed watermark information, and a fast calculation method is proposed to avoid the heavy computation of SVD. The watermark is embedded in both low and high frequency Contourlet coefficients to increase the robustness against various attacks. Moreover, the proposed scheme intrinsically exploits the characteristics of human visual system and thus can ensure the invisibility of the watermark. Simulation results show that the proposed scheme outperforms other related methods in terms of both robustness and execution time.
Semantic Definition of Anonymity in Identity-Based Encryption and Its Relation to Indistinguishability-Based Definition

In this paper we point out an overlooked subtlety in providing proper security definitions of anonymous identity-based encryption (anonymous IBE) and its applications such as searchable encryption. Namely, we find that until now there is no discussion whether the widely used indistinguishability-based notion of anonymity for IBE implies simulation-based definition of anonymity, which directly captures the intuition that recipients’ IDs are not leaked from ciphertexts. We compensate this undesirable situation by providing a simulation-based notion, which requires that a ciphertext can be simulated without knowing the associated ID, by specializing the anonymity notion defined for more generalized notion of attribute-based encryption in previous work to the setting of IBE and then proving that this definition is equivalent to the conventional indistinguishability-based definition. We note that while the final result is something one would expect, our proof is not completely trivial. In particular, previous proofs that show the equivalence between semantic security and indistinguishability-based one in the setting where the security of payload is the main concern do not work immediately in our setting due to the difference between the semantics of identities and messages and the existence of the key extraction oracles.
Finding a planted clique by adaptive probing

We consider a variant of the planted clique problem where we are allowed unbounded computational time but can only investigate a small part of the graph by adaptive edge queries. We determine (up to logarithmic factors) the number of queries necessary both for detecting the presence of a planted clique and for finding the planted clique. Specifically, let $G \sim G(n,1/2,k)$ be a random graph on $n$ vertices with a planted clique of size $k$. We show that no algorithm that makes at most $q = o(n^2 / k^2 + n)$ adaptive queries to the adjacency matrix of $G$ is likely to find the planted clique. On the other hand, when $k \geq (2+\epsilon) \log_2 n$ there exists a simple algorithm (with unbounded computational power) that finds the planted clique with high probability by making $q = O( (n^2 / k^2) \log^2 n + n \log n)$ adaptive queries. For detection, the additive $n$ term is not necessary: the number of queries needed to detect the presence of a planted clique is $n^2 / k^2$ (up to logarithmic factors).
Segmentation of Cerebrovascular Anatomy from TOF-MRA Using Length-Strained Enhancement and Random Walker

Cerebrovascular rupture can cause a severe stroke. Three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) is a common method of obtaining vascular information. This work proposes a fully automated segmentation method for extracting the vascular anatomy from TOF-MRA. The steps of the method are as follows. First, the brain is extracted on the basis of regional growth and path planning. Next, the brain’s highlighted connected area is explored to obtain seed point information, and the Hessian matrix is used to enhance the contrast of image. Finally, a random walker combined with seed points and enhanced images is used to complete vascular anatomy segmentation. The method is tested using 12 sets of data and compared with two traditional vascular segmentation methods. Results show that the described method obtains an average Dice coefficient of 90.68%, and better results were obtained in comparison with the traditional methods.
Event clustering within news articles

This paper summarizes our group’s efforts in the event sentence coreference identification shared task, which is organized as part of the Automated Extraction of Socio-Political Events from News (AESPEN) Workshop. Our main approach consists of three steps. We initially use a transformer based model to predict whether a pair of sentences refer to the same event or not. Later, we use these predictions as the initial scores and recalculate the pair scores by considering the relation of sentences in a pair with respect to other sentences. As the last step, final scores between these sentences are used to construct the clusters, starting with the pairs with the highest scores. Our proposed approach outperforms the baseline approach across all evaluation metrics.
Development of a Wheelchair Automator

The first wheelchair model could be traced to the 5th, 6th and later in 18th century. However, tremendous development and great achievements in this field started mid of 20th century. Since that time, varieties of models have been designed, leading to wide range of products. This work involves the development of an automator (ergonomically attachment) to convert a manual wheelchair into an electric wheelchair for domestic use by Nigerian physically disabled people who could not pay the high cost of a ready-made motorized or an electric-powered wheelchair. A gear motor and drive system has been integrated in this automator for convenience and easy control for the user. Two units of 12v batteries and breaking system has also been integrated into this work and by this, obtained an automatic wheelchair which can be driven with the possibility of avoiding obstacles by the use of the obstacle detection components. In addition, obstacle detection component is also incorporated to the system to serve as added alert feature in case the wheelchair is been used by a blind user. The automator which is attached to the wheelchair has been developed to enhance easy and long distance movement. It helped the disabled move around without external interference. A manual wheelchair does not give sufficient mobility, it has a limited range and is very difficult to use on rough and harsh terrain. This work is designed as a solution to this problem by combining the comfort and maneuverability of a wheelchair with the capability and efficiency to move about. This work will benefit the physically challenged people.Keywords—Automator, obstacle detection, electric-powered wheelchair, wheel chair, mobility.
Adaptive Context Encoding Module for Semantic Segmentation

The object sizes in images are diverse, therefore, capturing multiple scale context information is essential for semantic segmentation. Existing context aggregation methods such as pyramid pooling module (PPM) and atrous spatial pyramid pooling (ASPP) employ different pooling size or atrous rate, such that multiple scale information is captured. However, the pooling sizes and atrous rates are chosen empirically. Rethinking of ASPP leads to our observation that learnable sampling locations of the convolution operation can endow the network learnable fieldof- view, thus the ability of capturing object context information adaptively. Following this observation, in this paper, we propose an adaptive context encoding (ACE) module based on deformable convolution operation where sampling locations of the convolution operation are learnable. Our ACE module can be embedded into other Convolutional Neural Networks (CNNs) easily for context aggregation. The effectiveness of the proposed module is demonstrated on Pascal-Context and ADE20K datasets. Although our proposed ACE only consists of three deformable convolution blocks, it outperforms PPM and ASPP in terms of mean Intersection of Union (mIoU) on both datasets. All the experimental studies confirm that our proposed module is effective compared to the state-of-the-art methods.
Naive

Automatic dialect identification is a more challengingctask than language identification, as it requires the ability to discriminate between varieties of one language. In this paper, we propose an ensemble based system, which combines traditional machine learning models trained on bag of n-gram fetures, with deep learning models trained on word embeddings, to solve the Discriminating between Mainland and Taiwan Variation of Mandarin Chinese (DMT) shared task at VarDial 2019. Our experiments show that a character bigram-trigram combination based Naive Bayes is a very strong model for identifying varieties of Mandarin Chinense. Through further ensemble of Navie Bayes and BiLSTM, our system (team: itsalexyang) achived an macro-averaged F1 score of 0.8530 and 0.8687 in two tracks.
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning

The impressive performance exhibited by modern machine learning models hinges on the ability to train such models on a very large amounts of labeled data. However, since access to large volumes of labeled data is often limited or expensive, it is desirable to alleviate this bottleneck by carefully curating the training set. Optimal experimental design is a well-established paradigm for selecting data point to be labeled so to maximally inform the learning process. Unfortunately, classical theory on optimal experimental design focuses on selecting examples in order to learn underparameterized (and thus, non-interpolative) models, while modern machine learning models such as deep neural networks are overparameterized, and oftentimes are trained to be interpolative. As such, classical experimental design methods are not applicable in many modern learning setups. Indeed, the predictive performance of underparameterized models tends to be variance dominated, so classical experimental design focuses on variance reduction, while the predictive performance of overparameterized models can also be, as is shown in this paper, bias dominated or of mixed nature. In this paper we propose a design strategy that is well suited for overparameterized regression and interpolation, and we demonstrate the applicability of our method in the context of deep learning by proposing a new algorithm for single shot deep active learning.
A Holistic Approach to Cyber Physical Systems Security and Resilience

A critical need exists for collaboration and action by government, industry, and academia to address cyber weaknesses or vulnerabilities inherent to embedded or cyber physical systems (CPS). These vulnerabilities are introduced as we leverage technologies, methods, products, and services from the global supply chain throughout a system's lifecycle. As adversaries are exploiting these weaknesses as access points for malicious purposes, solutions for system security and resilience become a priority call for action. The SAE G-32 Cyber Physical Systems Security Committee has been convened to address this complex challenge. The SAE G-32 will take a holistic systems engineering approach to integrate system security considerations to develop a Cyber Physical System Security Framework. This framework is intended to bring together multiple industries and develop a method and common language which will enable us to more effectively, efficiently, and consistently communicate a risk, cost, and performance trade space. The standard will allow System Integrators to make decisions utilizing a common framework and language to develop affordable, trustworthy, resilient, and secure systems.
Format Wars Hampering Crisis Response – The Case of Contact Tracing Apps During COVID-19

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The Basic Problem of Contemporary Water Resources Environment Research: Translation and Analysis from The English Practice Report on Water Resources

Qu, Y.J. and Liu, H., 2020. The basic problem of contemporary water resources environment research: translation and analysis from the English practice report on water resources. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 106–110. Coconut Creek (Florida), ISSN 0749-0208.Water resources have become an important resource for people's life and production, and with the rapid development of modern economy and society, the problem of water pollution and shortage is becoming more and more serious. China has a large land and a large population, and the amount of water resources per capita is difficult to meet people's real needs. Therefore, combined with the importance of water resources, strengthen the basic problems of environmental research on water resources, not only to strengthen the use and development of water resources, but also to do a good job of sewage, water conservation environmental protection strategies. To achieve the ecological environment and economic development can be balanced and healthy development. With the concept of “scientific, rational and sustainable development”, the research and analysis of water resources environmental protection should be carried out, so that the specific details of the work can be put into practice, so as to effectively protect the water resources environment and realize the maximum benefit of water resources. The study of water resources environment in the English practice report on water resources also clearly proposes to solve its basic problems.
Image-Based Visual Servo Control for Ground Target Tracking Using a Fixed-Wing UAV with Pan-Tilt Camera

This paper proposes a control framework to achieve the tracking of the moving target by a fixed-wing unmanned aerial vehicle (UAV) with a monocular pan-tilt camera. This control framework is based on the image-based visual servoing (IBVS) method, which takes the target feature point on the captured image as the input and outputs the control signals directly with the aid of image Jacobian matrix. However, the image is affected by the attitude of both the UAV and the pan-tilt, and the attitude of the pan-tilt is coupled with that of the UAV simultaneously. To solve this problem, we present an "Ideal State" as the reference state, and make sure the coordinates of the feature point in the state are only affected by the change of the yaw angle of the UAV. In this way, we can integrate the attitude control of the UAV and the pan-tilt. By using this control framework, the fixed-wing UAV can track the ground target continuously on the one hand, and the target will tend to locate at image center on the other hand. This prevents the target from moving toward to the edge of the image or even disappearing. Besides, we prove the controller is exponentially convergent by the Lyapunov method. In order to evaluate the performance of our controller, we build a hardware-in-the-loop (HIL) simulation platform and a prototype platform. Based on these platforms, extensive experiments including simulations and real flight tests are conducted. The results show that our controller can achieve continuous and robust tracking of the target with a speed of 20km/h when the speed of the UAV is 16m/s.
Accounting Implications of Blockchain: A Hyperledger Composer Use Case for Intangible Assets

ABSTRACT Blockchain technology, commonly associated with bitcoin cryptocurrency, attracted large amounts of investment, attention, and analysis. In addition to attracting the attention of investors, speculators, and regulators, implications for financial practitioners and organizations are increasingly apparent. Financial transactions, property transfers, audit and attestation services, supply chains, and numerous other areas of industry are continuing to integrate blockchains into operations. This paper bridges the gap between the technical concept of blockchain and relevance to the accounting field. By distilling technical components of blockchain into understandable components, practitioners and other users of this research are poised to better understand, explain, and apply salient concepts. This paper proposes a Hyperledger Composer use case method through which practitioners and researchers can familiarize themselves with blockchain concepts using an interactive demonstration of an accounting integration for intangible assets. Addressing the growing need for increased awareness, this paper builds a necessary skillset with blockchain technology and applications.
On Text Tiling for Documents: A Neural-Network Approach

Segmenting documents or conversation threads into semantically coherent segments have been one of the challenging tasks in natural language processing. In this work, we introduce three new text segmentation models that employ BERT for post-training. Extensive experiments are conducted based on benchmark datasets to demonstrate that our BERT-based models show significant improvements over the state-of-the-art text segmentation algorithms.
Combined Distillation Pose

Human keypoint detection is a challenging task, especially under blurry and crowded conditions. However, the existing network for human keypoint detection has become increasingly deeper. When backpropagating, the final supervision information of the network often cannot effectively guide the training of the entire network. Therefore, how to guide the deep network to train effectively is a subject worth discussing. In this paper, the knowledge distillation method is used to make the network predictions results act as supervision information, then a multi-stage supervision training framework is designed from shallow to deep layers. Besides, to further improve the feature expression ability and enhance the receptive field of the network, we also design a new convolution module, which can model the channel and spatial features separately. Finally, our method increased from AP49 to AP55 on the HiEve human keypoint detection dataset[1], which demonstrates the superior performance and effectiveness of our method.
Contrast and Classify: Alternate Training for Robust VQA.

Recent Visual Question Answering (VQA) models have shown impressiveperformance on the VQA benchmark but remain sensitive to small linguisticvariations in input questions. Existing approaches address this by augmentingthe dataset with question paraphrases from visual question generation models oradversarial perturbations. These approaches use the combined data to learn ananswer classifier by minimizing the standard cross-entropy loss. To moreeffectively leverage the augmented data, we build on the recent success incontrastive learning. We propose a novel training paradigm (ConCAT) thatalternately optimizes cross-entropy and contrastive losses. The contrastiveloss encourages representations to be robust to linguistic variations inquestions while the cross-entropy loss preserves the discriminative power ofthe representations for answer classification. We find that alternatelyoptimizing both losses is key to effective training. VQA models trained withConCAT achieve higher consensus scores on the VQA-Rephrasings dataset as wellas higher VQA accuracy on the VQA 2.0 dataset compared to existing approachesacross a variety of data augmentation strategies.
Path Planning Obstacle Avoidance Algorithm Based on Wheeled Robot

There are many obstacles and movements in the indoor environment. Indoor robots need to cope with the changing environment. This paper studies the obstacle avoidance problem of wheeled robots moving in an unknown environment. Firstly, the dynamic path planning algorithm for robot autonomous obstacle avoidance is studied, and the algorithm is implemented in C# language. Then use the Unity3D game engine to simulate the algorithm. The innovations of this algorithm are as follows: 1. Vectorize the path of the robot; 2. Summarize the motion state of the obstacle and the robot into six cases. During the movement process, the obstacle movement state is continuously judged, and the speed and direction of the obstacle are analyzed. The judgment result must belong to six situations. The experiment proves that the algorithm can solve the obstacle avoidance problem when encountering obstacles of different speeds and sizes, and has stronger applicability.
An Integrated Geophysical and Geochemical Investigation of an Open Dumpsite in a Sedimentary Formation

The extents of migration of leachate plumes and the probabilistic assessment of human health risks were carried out on Oke-Diya dumpsite, Sagamu, Southwestern Nigeria. Geophysical methods involving Very Low Frequency-Electromagnetic (VLF-EM) and Electrical Resistivity Topography (ERT) techniques, and Atomic Absorption Spectrometric (AAS) geochemical method were used to map and detect the presence and extent of leachate migration. The VLFEM pseudo-sections revealed conductive zones of responses 25 - 50 to a depth of about 15 m and approximated distance 50 m from the study area. The 2D and 3D ERT models delineated high conductive zones with very low resistivity values ranging from 0.551-6.54 and 2.4respectively. These conductive regions were observed to depths above 13.4 m and approximated distance 60 m from the study area in the 2D models. The 3D sections comprising of the horizontal depth slices, vertical planes and volumetric displays revealed regions with higher conductivity to a depth of about 21.9 m and approximated distance 50 m from the dumpsite. The mean concentrations of heavy metals Pb, Ni, Cd, Zn, Fe and Cu in the soil samples were higher than the concentrations in the control samples. The human health risk assessment indicated unacceptable values for non-carcinogenic adverse effects for both children and adults. The probabilistic cancer risks were within the acceptable range for Pb and unacceptable for Cd for both the children and adults. Generally, this study had identified areas of concentrated plumes accumulation and migration pathways, which were in agreement with earlier studies in sedimentary terrain.