Reviewerly/exHarmony huggingface dataset:
TROT: A Three-Edge Ring Oscillator Based True Random Number Generator With Time-to-Digital Conversion

This paper introduces a new true random number generator (TRNG) based on a three-edge ring oscillator. Our design uses a new technique with a time-to-digital converter to effectively acquire jitter accumulated independently by each edge. As a part of the security evaluation, we present the stochastic model of the TRNG’s digital noise source and estimate a lower bound of the min-entropy per random bit. Starting from the obtained entropy bound, we propose a procedure for selecting and implementing an area-efficient and throughput-optimal post-processing function based on the best known linear codes that will increase the output min-entropy rate to more than 0.999. The proposed TRNG exquisitely balances low design effort and resource consumption with high throughput and a high min-entropy rate, making it more suitable for randomness-demanding and resource-constrained platforms than the state-of-the-art. The complete implementation of the TRNG digital noise source and the post-processing occupies 33 slices and achieves a throughput of 12.5 Mbps on Xilinx Zynq-7000 FPGAs. The min-entropy of the generated random bits is assessed by NIST SP 800-90B entropy estimators, and the tested sequences pass the AIS-31 test suit.
Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions

We study joint video and language (VL) pretraining to enable cross-modality learning and benefit plentiful downstream VL tasks. Existing works either extract low-quality video features or learn limited text embedding, while neglecting that high-resolution videos and diversified semantics can significantly improve cross-modality learning. In this paper, we propose a novel High-resolution and Diversified VIdeo-LAnguage pre-training model (HD-VILA) for many visual tasks. In particular, we collect a large dataset with two distinct properties: 1) the first high-resolution dataset including 371.5k hours of 720p videos, and 2) the most diversified dataset covering 15 popular YouTube categories. To enable VL pre-training, we jointly optimize the HD-VILA model by a hybrid Transformer that learns rich spatiotemporal features, and a multimodal Transformer that enforces interactions of the learned video features with diversified texts. Our pre-training model achieves new state-of-the-art results in 10 VL understanding tasks and 2 more novel text-to-visual generation tasks. For example, we outperform SOTA models with relative increases of 40.4% R@1 in zero-shot MSR-VTT text-to-video retrieval task, and 55.4% in high-resolution dataset LSMDC. The learned VL embedding is also effective in generating visually pleasing and semantically relevant results in text-to-visual editing and super-resolution tasks.
Adaptive encoding based lossless data hiding method for VQ compressed images using tabu search

A novel joint image coding and reversible data hiding method for vector quantization (VQ) compressed images is proposed in this paper. Since the original VQ indices often exhibit uncorrelated, a rearrangement of them by considering their correlations may benefit the prediction performance so as to reduce the required bitrate. In this study, the tabu search algorithm is employed to rearrange codewords by fully exploiting their neighboring correlations, yielding a moepressible rearranged indices. By combining more highly-correlated indices of a to-be-predicted index into prediction, the improved linear regression method is then applied to achieve a sharper prediction-error histogram and less required additional information. After prediction, an adaptive run-length encoding method is presented to encode prediction errors, thereby eliminating unnecessary indicators. Experimental results demonstrate that the proposed method effectively reduces the bitrate of the compressed image while providing a comparable hiding capacity.
Privacy Preserving using Enhanced Shadow Honeypot technique for Data Retrieval in Cloud Computing

Data has been essential to all facets of human life in the last decades. In recent years, the enormous growth of data has an outcome of the development of various applications. This data needs to be protected and stored in locked sites. Cloud computing is the technology that can be used to accumulate those immense amounts of data. . The rapid enlargement of this technology makes it more dangerous, therefore it has become imperative to secure records from attackers to preserve its reliability confidentiality, protection, privacy, and actions required for handle it. This paper wished for an Enhanced Shadow Honeypot Algorithm (ESHA) along with CNN based preprocessing segment of cryptography. The cloud server is responsible for establishing a code and validating it with the user for secure authentication whenever the file administrator seeks it. The file is decrypted using a superior Shadow Honeypot technique after the key is delivered, and the user might retrieve the decrypted file. Furthermore, a presentation analysis is performed, as well as a comparison of existing and suggested methodologies, in order to illustrate the future scheme's effectiveness. The parameters considered are precision, recall, accuracy, f-measure.
The Trend-Fuzzy-Granulation-Based Adaptive Fuzzy Cognitive Map for Long-Term Time Series Forecasting

One drawback of using the existing one-step forecasting models for long-term time series prediction is the cumulative errors caused by iterations. In order to overcome this shortcoming, this article proposes a trend-fuzzy-granulation-based adaptive fuzzy cognitive map (FCM) for long-term time series forecasting. Different from the original FCM-based forecasting models, a class of trend fuzzy information granules is built to represent the trend, fluctuation range, and trend persistence of various segments of time series, which are more instrumental and comprehensive than simple magnitude information. Thus, the proposed forecasting model is a granular model according to the form of its inputs and outputs. In an original FCM-based forecasting model, the causal relationships among concepts remain unchanged throughout the training of the whole dataset, however, in reality, the causal relationships may change with the state of concepts. Therefore, it is unreasonable to use the invariable causal relationships which often result in poor predictions. In view of this, we construct an adaptive FCM where different causal relationships are built to forecast concepts of different states. This is the first time to forecast trend fuzzy information granules using an adaptive FCM. Compared with the existing classical forecasting models, the proposed forecasting model achieves superior performance which is verified through a series of experimental studies.
Approximate Homomorphic Encryption with Reduced Approximation Error

The Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme is currently the most efficient method to perform approximate homomorphic computations over real and complex numbers. Although the CKKS scheme can already be used to achieve practical performance for many advanced applications, e.g., in machine learning, its broader use in practice is hindered by several major usability issues, most of which are brought about by relatively high approximation errors and the complexity of dealing with them. We present a reduced-error CKKS variant that removes the approximation errors due to the Learning With Errors (LWE) noise in the encryption and key switching operations. We propose and implement its Residue Number System (RNS) instantiation that has a lower error than the original CKKS scheme implementation based on multiprecision integer arithmetic. While formulating the RNS instantiation, we also develop an intermediate RNS variant that has a smaller approximation error than the prior RNS variant of CKKS. The high-level idea of our main RNS-related improvements is to remove the approximate scaling error using a novel procedure that computes level-specific scaling factors. The rescaling operations and scaling factor adjustments in our implementation are done automatically. We implement both RNS variants in PALISADE and compare their approximation error and efficiency to the prior RNS variant. Our results for uniform ternary secret key distribution, which is the most efficient setting included in the community homomorphic encryption security standard, show that the reduced-error CKKS RNS implementation typically has an approximation error that is 6 to 9 bits smaller for computations with multiplications than the prior RNS variant. The results for the sparse secret setting, which was used for the original CKKS scheme, imply that our reduced-error CKKS RNS implementation has an approximation error up to 12 bits smaller than the prior RNS variant.
Supervised Adaptive Similarity Matrix Hashing

Compact hash codes can facilitate large-scale multimedia retrieval, significantly reducing storage and computation. Most hashing methods learn hash functions based on the data similarity matrix, which is predefined by supervised labels or a distance metric type. However, this predefined similarity matrix cannot accurately reflect the real similarity relationship among images, which results in poor retrieval performance of hashing methods, especially in multi-label datasets and zero-shot datasets that are highly dependent on similarity relationships. Toward this end, this study proposes a new supervised hashing method called supervised adaptive similarity matrix hashing (SASH) via feature-label space consistency. SASH not only learns the similarity matrix adaptively, but also extracts the label correlations by maintaining consistency between the feature and the label space. This correlation information is then used to optimize the similarity matrix. The experiments on three large normal benchmark datasets (including two multi-label datasets) and three large zero-shot benchmark datasets show that SASH has an excellent performance compared with several state-of-the-art techniques.
Implementing a Chatbot on a Library Website

A library's website is a virtual point of contact for interacting with its patrons. Ensuring a library's website has easily findable content is critical for providing access to library resources and highlighting services and events. One tool for assisting with content findability is a chatbot, a form of artificial intelligence software. In this case study, Lehman College's Leonard Lief Library implemented Ivy, a proprietary educational software chatbot on its website, the first of its kind for an academic library. This chatbot functioned as a new tool that assisted users seeking information and provided insight to librarians about the kinds of topics students search for via the library website. This article provides the first detailed description in the literature of an implementation of a proprietary chatbot for an academic library.
Camouflaged Instance Segmentation In-the-Wild: Dataset, Method, and Benchmark Suite

This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged object segmentation) in terms of quantity and diversity. The new dataset substantially increases the number of images with hierarchical pixel-wise ground truths. We also provide a benchmark suite for the task of camouflaged instance segmentation. In particular, we present an extensive evaluation of state-of-the-art instance segmentation methods on our newly constructed CAMO++ dataset in various scenarios. We also present a camouflage fusion learning (CFL) framework for camouflaged instance segmentation to further improve the performance of state-of-the-art methods. The dataset, model, evaluation suite, and benchmark will be made publicly available on our project page.
AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning

Aerial object detection acts a pivotal role in searching and tracking applications. However, the large model, limited memory, and computing power of embedded devices restrict aerial pedestrian detection algorithms’ deployment on the UAV (unmanned aerial vehicle) platform. In this paper, an innovative method of aerial infrared YOLO (AIR-YOLOv3) is proposed, which combines network pruning and the YOLOv3 method. Firstly, to achieve a more appropriate number and size of the prior boxes, the prior boxes are reclustered. Then, to accelerate the inference speed on the premise of ensuring the detection accuracy, we introduced Smooth-L1 regularization on channel scale factors, and we pruned the channels and layers with less feature information to obtain a pruned YOLOv3 model. Meanwhile, we proposed the self-built aerial infrared dataset and designed ablation experiments to perform model evaluation well. Experimental results show that the AP (average precision) of AIR-YOLOv3 is 91.5% and the model size is 10.7 MB (megabyte). Compared to the original YOLOv3, its model volume compressed by 228.7 MB, nearly 95.5 %, while the model AP decreased by only 1.7%. The calculation amount is reduced by about 2/3, and the inference speed on the airborne TX2 has been increased from 3.7 FPS (frames per second) to 8 FPS.
Clifford-Valued Distributed Optimization Based on Recurrent Neural Networks

In this paper, we address the Clifford-valued distributed optimization subject to linear equality and inequality constraints. The objective function of the optimization problems is composed of the sum of convex functions defined in the Clifford domain. Based on the generalized Clifford gradient, a system of multiple Clifford-valued recurrent neural networks (RNNs) is proposed for solving the distributed optimization problems. Each Clifford-valued RNN minimizes a local objective function individually, with local interactions with others. The convergence of the neural system is rigorously proved based on the Lyapunov theory. Two illustrative examples are delineated to demonstrate the viability of the results in this article.
Personal Voice Assistant Security and Privacy—A Survey

Personal voice assistants (PVAs) are increasingly used as interfaces to digital environments. Voice commands are used to interact with phones, smart homes, or cars. In the United States alone, the number of smart speakers, such as Amazon&#x2019;s Echo and Google Home, has grown by 78&#x0025; to 118.5 million, and 21&#x0025; of the U.S. population own at least one device. Given the increasing dependency of society on PVAs, security and privacy of these have become a major concern of users, manufacturers, and policy makers. Consequently, a steep increase in research efforts addressing security and privacy of PVAs can be observed in recent years. While some security and privacy research applicable to the PVA domain predates their recent increase in popularity, many new research strands have emerged. This article provides a survey of the state of the art in PVA security and privacy. The focus of this work is on the security and privacy challenges arising from the use of the acoustic channel. Work that describes both attacks and countermeasures is discussed. We highlight established areas such as voice authentication (VA) and new areas such as acoustic Denial of Service (DoS) that deserve more attention. This survey describes research areas where the threat is relatively well understood but where countermeasures are lacking, for example, in the area of hidden voice commands. We also discuss work that looks at privacy implications; for example, work on management of recording consent. This survey is intended to provide a comprehensive research map for PVA security and privacy.
Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition

With the rapid development of smart medical care, copyright security for medical images is becoming increasingly important. To improve medical images storage and transmission safety, this paper proposes a robust zero-watermarking algorithm for medical images by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT). First, the low-frequency sub-band of the medical image is obtained through the DTCWT and MDCT. Then Hessenberg decomposition is used to construct the visual feature vector. Meanwhile, the encryption of the watermarking image by combining cryptographic algorithms, third-party concepts, and chaotic sequences enhances the algorithm’s security. In the proposed algorithm, zero-watermarking technology is utilized to assure the medical images’ completeness. Compared with the existing algorithms, the proposed algorithm has good robustness and invisibility and can efficiently extract the watermarking image and resist different attacks.
Solving Nonlinear Equations Systems with an Enhanced Reinforcement Learning Based Differential Evolution

Nonlinear equations systems (NESs) arise in a wide range of domains. Solving NESs requires the algorithm to locate multiple roots simultaneously. To deal with NESs efficiently, this study presents an enhanced reinforcement learning based differential evolution with the following major characteristics: (1) the design of state function uses the information on the fitness alternation action; (2) different neighborhood sizes and mutation strategies are combined as optional actions; and (3) the unbalanced assignment method is adopted to change the reward value to select the optimal actions. To evaluate the performance of our approach, 30 NESs test problems and 18 test instances with different features are selected as the test suite. The experimental results indicate that the proposed approach can improve the performance in solving NESs, and outperform several state-of-the-art methods.
Introduction to the special section on advances of machine learning in cybersecurity (VSI-mlsec)

As National parks are one of the best places of attraction to the tourists, the so-called abode of the animals has to be protected due to the frequent intervention of human beings. On the other hand, the lives of human beings have also to be insulated from the sudden and vulnerable attack of the animals. When it comes to ‘life’, it becomes a vital factor for humans and animals. Therefore, here arises a dire need to create a system which can protect the life and safety of human beings as well as animals. However, when it comes to mutual safety of both, there has not been an effective solution till date that can practically be implemented in a national park or a sanctuary. In this scenario, ‘Video Object Identifying’ using Machine Learning Technique has been proven to render promising results in terms of accuracy and performance. Classifying and identifying objects using Customized Convolutional Neural-Network Model in order to detect the multi-class objects (vehicle and animal) become the aim of this present research. In this research, five categories of data sets namely lion, elephant, tiger, car and motorcycle have been used for training and testing. The custom model for video object detection renders with an accuracy of 82% which proves its efficiency in detecting the multi class datasets.
Quantum Cryptography—A Simplified Undergraduate Experiment and Simulation

Quantum cryptography is a topic of considerable interest. A simple and robust experiment and theory for a senior level undergraduate investigation of quantum key distribution are described. In the proposed experiment, key principles from the BB84 protocol, used in quantum cryptography, are emulated using an optical apparatus and computational scripts independently.
EADN: An Efficient Deep Learning Model for Anomaly Detection in Videos

Surveillance systems regularly create massive video data in the modern technological era, making their analysis challenging for security specialists. Finding anomalous activities manually in these enormous video recordings is a tedious task, as they infrequently occur in the real world. We proposed a minimal complex deep learning-based model named EADN for anomaly detection that can operate in a surveillance system. At the model’s input, the video is segmented into salient shots using a shot boundary detection algorithm. Next, the selected sequence of frames is given to a Convolutional Neural Network (CNN) that consists of time-distributed 2D layers for extracting salient spatiotemporal features. The extracted features are enriched with valuable information that is very helpful in capturing abnormal events. Lastly, Long Short-Term Memory (LSTM) cells are employed to learn spatiotemporal features from a sequence of frames per sample of each abnormal event for anomaly detection. Comprehensive experiments are performed on benchmark datasets. Additionally, the quantitative results are compared with state-of-the-art methods, and a substantial improvement is achieved, showing our model’s effectiveness.
Automated Radiographic Report Generation Purely on Transformer: A Multicriteria Supervised Approach

Automated radiographic report generation is challenging in at least two aspects. First, medical images are very similar to each other and the visual differences of clinic importance are often fine-grained. Second, the disease-related words may be submerged by many similar sentences describing the common content of the images, causing the abnormal to be misinterpreted as the normal in the worst case. To tackle these challenges, this paper proposes a pure transformer-based framework to jointly enforce better visual-textual alignment, multi-label diagnostic classification, and word importance weighting, to facilitate report generation. To the best of our knowledge, this is the first pure transformer-based framework for medical report generation, which enjoys the capacity of transformer in learning long range dependencies for both image regions and sentence words. Specifically, for the first challenge, we design a novel mechanism to embed an auxiliary image-text matching objective into the transformer's encoder-decoder structure, so that better correlated image and text features could be learned to help a report to discriminate similar images. For the second challenge, we integrate an additional multi-label classification task into our framework to guide the model in making correct diagnostic predictions. Also, a term-weighting scheme is proposed to reflect the importance of words for training so that our model would not miss key discriminative information. Our work achieves promising performance over the state-of-the-arts on two benchmark datasets, including the largest dataset MIMIC-CXR.