Reviewerly/exHarmony huggingface dataset:
Decentralized Data Aggregation: A New Secure Framework Based on Lightweight Cryptographic Algorithms

Blockchain has become very popular and suitable to the Internet of Things (IoT) field due to its nontamperability and decentralization properties. The number of IoT devices and leaders (who own IoT devices) is increased exponentially, and thus, data privacy and security are undoubtedly significant concerns. In this paper, we summarize some issues for the BeeKeeper system, a blockchain-based IoT system, proposed by Zhou et al., and then aim for presenting an improved solution for decentralized data aggregation (DDA) on IoT. Firstly, we formally state the security requirements of DDA. Secondly, we propose our basic DDA system by using secret sharing to improve its efficiency and smart contracts as the computing processors. Moreover, the proposed full-fledged system achieves data sharing (e.g., a leader to access data of others’ devices), which is realized by using local differential privacy and cryptographic primitives such as token-based encryption. Finally, to show the feasibility, we provide some implementations and experiments for the DDA systems.
Multiple Watermarking Scheme for Video &amp; Image for Authentication &amp; Copyright Protection

Abstract Watermarking has been used frequently to authenticate the accuracy and security of the image and video files. In the world of computer technology, several watermarking strategies have developed during the past 20 years. Integrate a picture of identity that is not always concealed, such that no detail is not possible to delete. A monitoring code can also be used to deter unauthorized recording equipment. Another application is the watermark copyright control, which works at stopping the creator of the image from stealing photos unlawfully. A watermark is a promising option for the copyright transcendence of multi-media files, as embedded messages are still included. Due to the limits of fidelity, a watermark may be implemented in a small multimedia data space. There is no proof that Automated Watermarking technologies will fulfil the ultimate purpose of the cleaners of all sorts of copyright security operations to gather knowledge from the data they obtain. Relevant situations may be deemed more fairly expected with the usage of automated copyright marking technologies. A perfect device will not be able to add a digital watermark without the limit, which does not supply the whole object with details. In this work, a modern technique for watermarking includes injecting two or more messages or photographs into a single picture for protection purposes and repeating a similar procedure for N-frames for authentication in the film.
Hard Decorrelated Centralized Loss for fine-grained image retrieval

Although there is abundant of investigations on fine-grained image retrieval, it is still an extremely challenging task in the field of computer vision, due to the character of small diversity in inter-class but large diversity within intra-class. To handle this task, loss functions are critical to the performance of a deep convolutional neural network in extracting the discriminative feature of the fine-grained image for retrieval. Recent studies showed that the global structure loss functions help to extract more discriminative features. In this paper, we introduce a novel global structure loss function, named Hard Decorrelated Centralized Loss, for further improving the representation for fine-grained image retrieval. The proposed loss is available in extracting the discriminative feature for dividing the most similar categories. In our experiments, we employ the proposed loss to train the convolutional neural network, which shows state-of-the-art performances on six classical fine-grained image retrieval benchmarks, e.g. CUB-200-2011 and Stanford Cars.
Bookworm Game: Automatic Discovery of LTE Vulnerabilities Through Documentation Analysis

In the past decade, the security of cellular networks has been increasingly under scrutiny, leading to the discovery of numerous vulnerabilities that expose the network and its users to a wide range of security risks, from denial of service to information leak. However, most of these findings have been made through ad-hoc manual analysis, which is inadequate for fundamentally enhancing the security assurance of a system as complex as the cellular network. An important observation is that the massive amount of technical documentation of cellular network can provide key insights into the protection it puts in place and help identify potential security flaws. Particularly, we found that such documentation often contains hazard indicators (HIs) – the statement that describes a risky operation (e.g., abort an ongoing procedure) when a certain event happens at a state, which can guide a test on the system to find out whether the operation can indeed be triggered by an unauthorized party to cause harm to the cellular core or legitimate users’ equipment. Based upon this observation, we present in this paper a new framework that makes the first step toward intelligent and systematic security analysis of cellular networks. Our approach, called Atomic, utilizes natural-language processing and machine learning techniques to scan a large amount of LTE documentation for HIs. The HIs discovered are further parsed and analyzed to recover state and event information for generating test cases. These test cases are further utilized to automatically construct tests in an LTE simulation environment, which runs the tests to detect the vulnerabilities in the LTE that allow the risky operations to happen without proper protection. In our research, we implemented Atomic and ran it on the LTE NAS specification, including 549 pages with 13,598 sentences and 283,850 words. In less than 5 hours, our prototype reported 42 vulnerabilities from 192 HIs discovered, including 10 never reported before, under two threat models. All these vulnerabilities have been confirmed through end-to-end attacks, which lead to unauthorized disruption of the LTE service a legitimate user’s equipment receives. We reported our findings to authorized parties and received their confirmation that these vulnerabilities indeed exist in major commercial carriers and $2,000 USD reward from Google.
Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

The numerical simulation of quantum circuits is an indispensable tool for development, verification, and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. We present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) that serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators that perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment that can scale from laptops to future exascale platforms. We report initial benchmarks of our framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.
Author topic model for co-occurring normal documents and short texts to explore individual user preferences

The investigation of user preferences through user comments has attracted significant attention. Although topic models have been verified as useful tools to facilitate the understanding of textual contents, they cannot be directly applied to accomplish this task because of two problems. The first problem is the severe data sparsity suffered by user comments because they are generally short. The second problem is the mixture of opinions from both user comments and the original documents the users commented on. To simultaneously solve the data sparsity problem and explore clean user preferences, we propose an author co-occurring topic model (AOTM) for normal documents and their short user comments. By considering authorship, AOTM allows each author of short texts to have a probability distribution over a set of topics represented only short texts. Accordingly, the individual user preferences can be investigated based on these author-level distributions. We verify the performance of AOTM using two news article datasets and one e-commerce dataset. Extensive experiments demonstrate that the AOTM outperforms several state-of-the-art methods in topic learning and topic representation of documents. The potential usage of AOTM in exploring individual user preferences is further illustrated by drawing user portraits and predicting user posting behaviors.
Towards Federated Learning with Attention Transfer to Mitigate System and Data Heterogeneity of Clients

Federated learning is a method of training a global model on the private data of many devices. With a growing spectrum of devices, some slower than smartphones, such as IoT devices, and others faster, such as home data boxes, the standard Federated Learning (FL) method of distributing the same model to all clients is starting to break down-- slow clients inevitably become strugglers. We propose a FL approach that spores different size models, each matching the computational capacity of the client system. There is still a global model, but for the edge tasks, the server trains different size student models with attention transfer, each chosen for a target client. This allows clients to perform enough local updates and still meet the round cut-off time. Client models are used as the source of attention transfer after their local update, to refine the global model on the server. We evaluate our approach on non-IID data to find that attention transfer can be paired with training on metadata brought from the client side to boost the performance of the server model even on previously unseen classes. Our FL with attention transfer opens the opportunity for smaller devices to be included in the Federated Learning training rounds and to integrate even more extreme data distributions.
State Observer based Elman Recurrent Neural Network for Electric Drive of Optical-Mechanical Complexes

This paper proposes an application of Elman recurrent neural networks as state observer to estimate electromechanical variable coordinates in electric drive control system of the optical-mechanical complex. The mathematical description of electric drive of the optical-mechanical complex is developed in the form of a two-mass elastic system. These elastic vibrations can be damped by using additional feedback signals from the elastic moment and the load velocity. The architecture of dynamic recurrent neural networks-based Elman scheme in investigated in the form of vector-matrix model, which allows approximating a wide class of nonlinear dynamic systems. During computer simulation in the MATLAB/Simulink environment, the comparison of the root-mean-square error between different learning algorithms for Elman's recurrent neural networks was carried out to study their accuracy estimates coordinates in a closed loop control system of optical-mechanical complex.
Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement

With the advent of drones, new potential applications have emerged for the unconstrained analysis of images and videos from aerial view cameras. Despite the tremendous success of the generic object detection methods developed using ground-based photos, a considerable performance drop is observed when these same methods are directly applied to images captured by Unmanned Aerial Vehicles (UAVs). Usually, most of the work goes into improving the performance of the detector in aspects such as design loss, training sample selection, feature enhancement, and so forth. This paper proposes a detection framework based on an anchor-free detector with several modules, including a sample balance strategies module and super-resolved generated feature module, to improve performance. We proposed the sample balance strategies module to optimize the imbalance among training samples, especially the imbalance between positive and negative, and easy and hard samples. Due to the high frequencies and noisy representation of the small objects in images captured by drones, the detection task is extraordinarily challenging. However, when compared with other algorithms of this kind, our method achieves better results. We also propose a super-resolved generated GAN (Generative Adversarial Network) module with center-ness weights to effectively enhance the local feature map. Finally, we demonstrate our method’s effectiveness with the proposed modules by carrying out a state-of-the-art performance on Visdrone2020 benchmarks.
Part-Relation-Aware Feature Fusion Network for Person Re-Identification

The research of part-based methods has been proven as an effective way in person re-identification (Re-ID) task. However, in existing part-based Re-ID methods, the informative interactions and potential associations among parts are neglected, which demands further study. To fill this gap, we propose a novel Part-relation-aware Feature Fusion Network (PFFN) which achieves a part-level feature fusion and enhances the discrimination of part features by fully employing helpful information from associations among parts. More specifically, a Dual-stage Attention (DA) module, consisting of spatial and part-based channel attention, is proposed to exploit complementary benefits of two kinds of attention information, thereby facilitating model with learning more discriminative features. Furthermore, Part-relation Exploitation (PE) module is proposed to learn relation-aware part features where correlative information among parts are fully employed, thereby bringing a noticeable improvement in performance. Extensive experiments are conducted on four mainstream Re-ID datasets to verify the superiority of PFFN. Compared with baseline model, PFFN has gained rank-1 accuracy improvement of 18.2% on MSMT17-v2, 12.1% on the CUHK03-Labeled, 5.6% on DukeMTMC-reid and 1.3% on Market1501, compellingly validating its effectiveness.
A Unified Method for Robust Self-Calibration of 3-D Field Sensor Arrays

Self-calibrating an array of 3-D field sensors, such as three-axis magnetometers and accelerometers, requires estimation of two variable sets—each sensor’s intrinsic model that maps its input field to the corresponding measurement and each sensor’s coordinates relative to a common frame of reference within the array. In this work, we propose the first unified self-calibration method for arrays of same-type 3-D field sensors, which is robust to anomalous sensor measurements unlike previous algorithms. The method breaks down the array calibration task into three steps of more easily subproblems, first estimating the intrinsic variables of each sensor independently, second computing the sensor coordinates with respect to a common reference frame, and last refining both these intrinsics and orientations jointly to minimize physically meaningful sensor estimation errors. Each stage has been carefully designed to maintain robustness to anomalies without compromising estimation quality. The performance of our method is compared against other state-of-the-art algorithms on both simulation and real data from a magnetometer array and accelerometer array, demonstrating significant improvements in accuracy and precision of the estimated array variables in versatile real-world self-calibration environments.
Making of Mobile SunSmart: Co-designing a Just-in-Time Sun Protection Intervention for Children and Parents

In this study, we describe a participatory design process to develop a technology-based intervention for sun protection for children and their parents. Our methodology embraces and leverages the expert knowledge of the target users, children and their parents, about their sun protection practices to directly influence the design of our mobile just-in-time adaptive intervention (JITAI). The objectives of this paper are to describe our research procedures and summarize primary findings incorporated into developing our JITAI modules. We conducted 3 rounds of iterative co-design workshops with design expert KidsTeam UW children (N: 11–12) and subject expert children and their parents from local communities in California (N: 22–48). Iteratively, we thematically coded the qualitative data generated by participants in the co-design sessions to directly inform design specifications. Three themes emerged: (1) preference for non-linear educational format with less structure,; (2) situations not conducive for prioritizing sun protection; and (3) challenges, barriers, and ambiguity relating to sun protection to protect oneself and one's family. Based on the design ideas and iterative participant feedback, three categories of modules were developed: personalized and interactive data intake module, narrative-education module with augmented reality experiment, person/real-time tailored JITAI, and assessment modules. This is one of the first projects that maximally engage children and parents as co-designers to build a technology to improve sun protection with iterative and intentional design principles. Our scalable approach to design a mobile JITAI to improve sun protection will lay the foundation for future public health investigators with similar endeavors.
Automatic Summarization of Legal Bills: A Comparative Analysis of Classical Extractive Approaches

A Legal Document is typically very long and structurally rich, which makes a quick understanding of the document very difficult. One way to deal with this difficulty is to manually summarize these documents with the help of legal experts. However, this is a costly and time consuming process. Design of automatic summarization approaches could be the key, to achieve a more time and cost effective solution to this problem. In this work, a detailed comparative analysis of multiple classical extractive summarization techniques is presented, with respect to Recall-Oriented Understudy for Gisting Evaluation metrics (ROUGE), Bilingual evaluation understudy metrics (BLEU) and Cosine Similarity scores, which are widely used metrics for evaluating automatic summarization approaches. The experimental analysis is performed on a publicly available benchmark dataset. From the experimental results, it has been observed that graph based summarization techniques perform well in general, across all the evaluation metrics. Another important observation is that, in addition to word frequency, considering other key contextual information can boost the performance of automatic summarization techniques. This comparative analysis work can serve as a baseline on the benchmark legal dataset, which is expected to be helpful for further research in this domain.
Pedestrian Counting Using Yolo V3

Object detection is the process of determining the presence, location, and type or class of at least one object using a bounding box. The person detection process produces a bounding box and allot a class label as a person based on YOLO v3. In YOLO v3 the features are learned, divides the image cells and each cell says a bounding box and entity classification directly. There could be more than one bounding box per person, but the system makes use of non-maximum suppression to reduce the number of bounding boxes to one per person. Finally, the number of persons in the image and video are calculated using the count of the bounding boxes. The dataset used for static pedestrian detection is the INRIAdataset and ShanghaiTech dataset. Yolo_Mark is used for marking bounding boxes of persons and gets its annotation files using 243 images from the INRIA dataset. Darknet is used as the framework for implementing YOLOv3. From INRIA Dataset 120 images are used for testing purposes. Testing on the INRIA dataset resulted in an accuracy of 96.1%. From the Shanghai tech-B, dataset 56 images are used for testing. Testing resulted in an accuracy of 87.3%.
Head Pose Estimation Based Robust Multicamera Face Recognition

In recent times, multicamera based face recognition has become an active research domain. Face recognition is utilized in multiple areas of applications such as surveillance, security etc. Several limitations and challenges like occlusion, pose changes, different illumination conditions, resolution, motion blur and sometimes subject will not be cooperative with the current face recognition systems. Due to the changes in pose, it becomes very difficult to recognize the person exactly by using a single camera. To overcome some of these limitations and challenges, a new approach of face recognition using multiple camera-based head pose estimation is proposed. The main motivation of this approach is to efficiently recognize the person by estimating the head pose. This estimation is used to select the best camera based on the direction and orientation of human head by using a network of multiple cameras.
An Improved Model for Human Activity Recognition by Integrated feature Approach and Optimized SVM

Lately, human activity recognition has pulled in an expanding measure of consideration from research and industry networks. Human activity recognition is the consistently sprouting exploration zone as it finds magnificent utilizations in surveillance, healthcare, and many real-life problems. This paper presents a technique to automatically recognize human activity from a video sequence. An integrated features approach using Histogram of Gradient (HOG) local feature descriptor and Principal Component Analysis (PCA) as a global feature is proposed in this paper. Optimized Support Vector Machine(SVM), Artificial Neural Network (ANN) used as a classifier. The proposed model is trained and tested on Benchmark KTH dataset, results obtained are comparable with existing methods. The proposed technique achieved the activity recognition accuracy of 99.21 percent. The experimental results confirms that the embedded feature approach and optimization techniques for classifier improves the performance of human activity recognition.
Ruledger: Ensuring Execution Integrity in Trigger-Action IoT Platforms

Smart home IoT systems utilize trigger-action platforms, e.g., IFTTT, to manage devices from various vendors. These platforms allow users to define rules for automatically triggering operations on devices. However, they may be abused by triggering malicious rule execution with forged IoT devices or events violating the execution integrity and the intentions of the users. To address this issue, we propose a ledger based IoT platform called Ruledger, which ensures the correct execution of rules by verifying the authenticity of the corresponding information. Ruledger utilizes smart contracts to enforce verifying the information associated with rule executions, e.g., the user and configuration information from users, device events, and triggers in the trigger-action platforms. In particular, we develop three algorithms to enable ledger-wallet based applications for Ruledger and guarantee that the records used for verification are stateful and correct. Thus, the execution integrity of rules is ensured even if devices and platforms in the smart home systems are compromised. We prototype Ruledger in a real IoT platform, i.e., IFTTT, and evaluate the performance with various settings. The experimental results demonstrate Ruledger incurs an average of 12.53% delay, which is acceptable for smart home systems.