Reviewerly/exHarmony huggingface dataset:
Accelerating Falcon on ARMv8

Falcon is one of the promising digital-signature algorithms in NIST’s ongoing Post-Quantum Cryptography (PQC) standardization finalist. Computational efficiency regarding software and hardware is also the main criteria for PQC standardization. In this paper, we present an efficient Falcon software implementation on ARMv8 environment. Until now, most of the software optimization on PQC algorithms have been conducted on 32-bit ARM (Cortex-M4) and typical CPUs (Intel and AMD CPUs). However, ARMv8 including Cortex-A30, 50, and 70 series have been widely used for various IoT (Internet of Things) applications, Edge computing devices, and OBUs (On Board Units) in autonomous driving cars. For optimizing the performance of Falcon, we take full advantage of NEON engine which is a kind of parallel processing unit in ARMv8 MCU. The main computation in Falcon belongs to polynomial multiplications in Complex number domain and Integer domain. Typically, FFT (Fast Fourier Transformation)-based multiplication method and NTT (Number Theoriteic Transform)-based multiplication method have been widely used for efficient polynomial multiplications in Complex number domain and Integer domain, respectively. Thus, in order to enhance the overall performance of Falcon, we improve the FFT-based multiplication method and NTT-based multiplication method by utilizing NEON engine in ARMv8. Specifically, we parallelize the overall process (FFT/NTT transformation, pointwise multiplication, and inverse FFT/NTT transformation) of FFT-based polynomial multiplication method and NTT-based polynomial multiplication method with strategically utilizing the NEON engine and vector instructions. Furthermore, we minimize the number of redundant memory accesses during FFT/NTT-based polynomial multiplication by making the most of available registers in NEON engine. Through the proposed parallel FFT/NTT-based polynomial multiplications, the proposed Falcon software provides 15.1% (resp. 18.1%), 16.5% (resp. 17.1%), and 65.4% (resp. 69.4%) of performance improvement in keypair generation, signing, and verification at security level 1 (resp. 5) compared with the reference Falcon implementation submitted to the final round of NIST PQC competition. Furthermore, as far as we know, this is the first optimized implementation of Falcon on ARMv8 environment.
Towards Knowledge-Aware Video Captioning via Transitive Visual Relationship Detection

Video captioning can be enhanced by incorporating the knowledge, which is usually represented as relationships of objects. However, the previous methods construct only superficial or static object relationships, and often introduce noise into the task through irrelevant common sense or fixed syntax templates. These problems mitigate the model interpretability and lead to the undesirable consequence. To overcome these limitations, we propose to enhance video captioning with deep-level object relationships that are adaptively explored during training. Specifically, we present a Transitive Visual Relationship Detection (TVRD) module in which we estimate the actions of the visual objects, and construct an Object-Action Graph (OAG) to describe the shallow relationship between the objects and actions. Then we bridge the gap between the objects via the actions to transitively infer an Object-Object Graph (OOG) which reflects the deep-level relationship. We further feed the OOG to a graph convolutional network to refine the object representation by deep-level relationships. With the refined representation, we capitalize on an LSTM-based decoder for caption generation. Experimental results on two benchmark datasets: MSVD, MSR-VTT demonstrate that the proposed method achieves state-of-the-art performance. Lastly, we present comprehensive ablation studies as well as visualization of visual relationships to demonstrate the effectiveness and interpretability of our model.
Opinion Mining Using Sentiment Analysis: A Case Study of Readers’ Response on Long Litt Woon’s The Way Through the Woods in Goodreads

In recent years, advancements in computer mediated technologies, such as internet, have greatly impacted disciplines like literary studies. Previous studies have shown that on-line discussion spaces have helped readers across the globe to co-construct their interpretations of a text. The increasing popularity of digital writing has prompted the book industry—that is publishers, book seller and libraries—to take close heed of readers' writings and responses in order to improve book suggestions, offer personalized book recommendations and increase their sales. The purpose of this study is to propose an analytical model to examine the dominant feeling in public's reaction to a text. To achieve this goal, the study focuses on readers' feedback in Goodreads website on Long Litt Woon's memoir The Way Through the Woods: Of Mushrooms and Mourning (2017). To carry out this study, this project uses a text mining technique that employs sentiment analysis using VADER and TextBlob. The results of opinion mining shows 89 positive sentiments, 5 neutral, and 28 negative sentiments using VADER whereas TextBlog results depicts 99, 5, 18 for positive, neutral, and negative sentiments respectively.
Automated RBC Morphology Counting and Grading Using Image Processing and Support Vector Machine

Red Blood Cell (RBC) morphology such as Target Cells and Elliptocytes characterize early pathognomonic determinants in certain diseases like Iron Deficiency Anemia and Thalassemia. Significant amounts of target cells or elliptocytes in a blood sample can be used to grade the existence of Blood Related Disease. In the Philippines, 37.6% of Filipinos have Iron Deficiency Anemia (IDA) and 27.8% suffer from Thalassemia. This study automates the classification, counting, and grading of RBC morphology using image processing techniques and SVM classification. The researchers acquired PBS samples and designed a prototype capable of analyzing these with a Raspberry Pi computer. The device classified, counted, graded and provided associated disease considerations of the sample PBS test. Comparison of the machine and hematologist’s reading of the normal red blood cells, target cells and elliptocytes samples gave an average accuracy of 95.77%.
MEDT: Using Multimodal Encoding-Decoding Network as in Transformer for Multimodal Sentiment Analysis

Multimodal sentiment analysis is a challenging task in the field of natural language processing (NLP). It uses multimodal signals (natural language, facial gestures, and acoustic behavior) in videos to generate emotional understanding. However, the importance of single modality data in the video to emotional outcomes is not static. With the extension of the time dimension, the emotional attributes of a specific natural language will be affected by non-natural language data, resulting in a vector shift in the feature space. At the same time, long-term dependencies within a specific modality and long-term dependencies between multiple modalities that are &#x201C;unaligned&#x201D; need to be considered. In response to the above problems, this paper proposes Multimodal Encoding-Decoding Network with Transformer. The network model encodes multimodal data through a Bidirectional Encoder Representations from Transformers (BERT) network and Transformer encoder to resolve long-term dependencies within modalities. And the network reconstructs the Transformer decoder to solve the weight problem of multimodal data in an iterative way. The network fully considers the long-term dependencies between modalities and the offset effect of non-natural language data on natural language data. Under the same experimental conditions, we validated our model on general multimodal sentiment analysis datasets. Compared with state-of-the-art models, the network achieves good progress and strong stability.
A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning

Coronavirus born COVID-19 disease has spread its roots in the whole world. It is primarily spread by physical contact. As a preventive measure, proper crowd monitoring and management systems are required to be installed in public places to limit sudden outbreaks and impart improved healthcare. The number of new infections can be significantly reduced by adopting social distancing measures earlier. Motivated by this notion, a real-time crowd monitoring and management system for social distance classification is proposed in this research paper. In the proposed system, people are segregated from the background using the YOLO v4 object detection technique, and then the detected people are tracked by bounding boxes using the Deepsort technique. This system significantly helps in COVID-19 prevention by social distance detection and classification in public places using surveillance images and videos captured by the cameras installed in these places. The performance of this system has been assessed using mean average precision (mAP) and frames per second (FPS) metrics. It has also been evaluated by deploying it on Jetson Nano, a low-cost embedded system. The observed results show its suitability for real-time deployment in public places for COVID-19 prevention by social distance monitoring and classification.
Adaptive Blockchain Technology for a Cybersecurity Framework in IIoT

This article presents the problem regarding the assurance of the information that circulates in the devices that are connected to the internet, due to the multiple threats to which they are exposed, running the risk of losing or leaking information; However, with the passage of time, new technologies are emerging that encourage organizations to maintain continuous improvement of their applications and tools, but at the same time the emergence of new technologies brings with it cybersecurity risks that organizations must face when carrying out an integration with them. Therefore, through the action research methodology, two phases were defined, one of them reviewing Blockchain technologies and the second comparing Blockchain technologies to finally determine the most adaptable technology to a cybersecurity Framework focused on IIoT, capable of managing the integrity of the information and achieving a consensus among all the nodes belonging to a certain network.
Large-Scale Crowdsourced Subjective Assessment of Picturewise Just Noticeable Difference

The picturewise just noticeable difference (PJND) for a given image, compression scheme, and subject is the smallest distortion level that the subject can perceive when the image is compressed with this compression scheme. The PJND can be used to determine the compression level at which a given proportion of the population does not notice any distortion in the compressed image. To obtain accurate and diverse results, the PJND must be determined for a large number of subjects and images. This is particularly important when experimental PJND data are used to train deep learning models that can predict a probability distribution model of the PJND for a new image. To date, such subjective studies have been carried out in laboratory environments. However, the number of participants and images in all existing PJND studies is very small because of the challenges involved in setting up laboratory experiments. To address this limitation, we develop a framework to conduct PJND assessments via crowdsourcing. We use a new technique based on slider adjustment and a flicker test to determine the PJND. A pilot study demonstrated that our technique could decrease the study duration by 50% and double the perceptual sensitivity compared to the standard binary search approach that successively compares a test image side by side with its reference image. Our framework includes a robust and systematic scheme to ensure the reliability of the crowdsourced results. Using 1,008 source images and distorted versions obtained with JPEG and BPG compression, we apply our crowdsourcing framework to build the largest PJND dataset, KonJND-1k (Konstanz just noticeable difference 1k dataset). A total of 503 workers participated in the study, yielding 61,030 PJND samples that resulted in an average of 42 samples per source image. The KonJND-1k dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://database.mmsp-kn.de/konjnd-1k-database.html</uri>
Dove Swarm Optimization Algorithm

Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm Optimization (DSO), that adopts the foraging behaviors of doves to have six benchmark functions expressing DSO performance. By considering competition for forage, DSO is designed to ensure the most satisfied dove as well as optimization, then compared with 15 popular optimization algorithms using random initial and lattice initial values. The results reveal that DSO performs the best in time efficiency and well in both convergences for these functions in a reasonable region from 1 to 3 seconds, and population diversity for the initialization method from less than 1 second to 9 seconds dependent on the population size. As a result, DSO is indeed a time-efficient and effective algorithm in solving optimization problems.
Modeling and simulation of all-optical diffractive neural network based on nonlinear optical materials

In this Letter, we propose an all-optical diffractive deep neural network modeling method based on nonlinear optical materials. First, the nonlinear optical properties of graphene and zinc selenide (ZnSe) are analyzed. Then the optical limiting effect function corresponding to the saturation absorption coefficient of the nonlinear optical materials is fitted. The optical limiting effect function is taken as the nonlinear activation function of the neural network. Finally, the all-optical diffractive neural network model based on nonlinear materials is established. The numerical simulation results show that the model can effectively improve the nonlinear representation ability of the all-optical diffractive neural network. It provides a theoretical support for the further realization of a photonic artificial intelligence chip based on nonlinear optical materials.
Blockchain networks as constitutional and competitive polycentric orders

Abstract Institutional economists have analyzed permissionless blockchains as a novel institutional building block for voluntary economic exchange and distributed governance, with their unique protocol features such as automated contract execution, high levels of network and process transparency, and uniquely distributed governance. But such institutional analysis needs to be complemented by polycentric analysis of how blockchains change. We characterize such change as resulting from internal sources and external sources. Internal sources include constitutional (protocol) design and collective-choice processes for updating protocols, which help coordinate network participants and users. External sources include competitive pressure from other cryptocurrency networks. By studying two leading networks, Bitcoin and Ethereum, we illustrate how conceptualizing blockchains as competing and constitutional polycentric enterprises clarifies their processes of change.
SPG-VTON: Semantic Prediction Guidance for Multi-Pose Virtual Try-on

Image-based virtual try-on is challenging in fitting a target in-shop clothes onto a reference person under diverse human poses. Previous works focus on preserving clothing details ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> texture, logos, patterns) when transferring desired clothes onto a target person under a fixed pose. However, the performances of existing methods significantly dropped when extending existing methods to multi-pose virtual try-on. In this paper, we propose an end-to-end Semantic Prediction Guidance multi-pose Virtual Try-On Network (SPG-VTON), which can fit the desired clothing into a reference person under arbitrary poses. Specifically, SPG-VTON is composed of three sub-modules. First, a Semantic Prediction Module (SPM) generates the desired semantic map. The predicted semantic map provides more abundant guidance to locate the desired clothing region and produce a coarse try-on image. Second, a Clothes Warping Module (CWM) warps in-shop clothes to the desired shape according to the predicted semantic map and the desired pose. Specifically, we introduce a conductible cycle consistency loss to alleviate the misalignment in the clothing warping process. Third, a Try-on Synthesis Module (TSM) combines the coarse result and the warped clothes to generate the final virtual try-on image, preserving details of the desired clothes and under the desired pose. In addition, we introduce a face identity loss to refine the facial appearance and maintain the identity of the final virtual try-on result at the same time. We evaluate the proposed method on the most massive multi-pose dataset (MPV) and the DeepFashion dataset. The qualitative and quantitative experiments show that SPG-VTON is superior to the state-of-the-art methods and is robust to data noise, including background and accessory changes, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , hats and handbags, showing good scalability to the real-world scenario.
Spreading Fine-Grained Prior Knowledge for Accurate Tracking

With the widespread use of deep learning in single object tracking task, mainstream tracking algorithms treat tracking as a combined classification and regression problem. Classification aims at locating an arbitrary target, and regression aims at estimating the corresponding bounding box. In this paper, we focus on regression and propose a novel box estimation network, which consists of a transformer encoder target pyramid guide (TPG) and transformer decoder target pyramid spread (TPS). Specifically, the transformer encoder TPG is designed to generate fine-grained prior knowledge with explicit representation for template targets. In contrast to the raw transformer encoder, we capture the visual dependence through local-global self-attention and deem the multi-scale target regions as the “local” region. Using this fine-grained prior knowledge, we design the transformer decoder TPS to spread it to the subsequent search regions with high affinity to accurately estimate the bounding boxes. Considering that self-attention fails to model information interaction across channels between the template target and search regions, we develop a channel-wise cross-attention block within the TPS as compensation. Extensive experiments on the OTB100, UAV123, NFS, VOT2020, VOT2021, LaSOT, LaSOT_ext, TrackingNet and GOT-10k benchmarks show that the proposed box estimation network outperforms most existing box estimation methods. Furthermore, our trackers based on this estimation network exhibit a competitive performance against state-of-the-art trackers.
Mitigating Cold Start Problem in Serverless Computing: A Reinforcement Learning Approach

Serverless computing has revolutionized the world of cloud-based and event-driven applications with the introduction of Function as a Service (FaaS) as the latest cloud computing model. This computational model increases the level of abstraction from the infrastructure and breaks the program into small units called functions. Thus, it brings benefits, such as ease of development, saving resources, and reducing product launch time for enterprises and developers. Thanks to the scale-to-zero feature of this computational model, idle functions with no traffic will be depreciated from memory. However, this cost-saving approach adversely impacts delay leading to the cold start problem. Unfortunately, the existing solutions to alleviate the cold start delay are not resource efficient as they follow a fixed policy over time. Thereby, this article proposes a novel two-layer adaptive approach to tackle this issue. The first layer utilizes a holistic reinforcement learning algorithm to discover the function invocation patterns over time for determining the best time to keep the containers warm. The second layer is designed based on a long short-term memory (LSTM) to predict the function invocation times in the future to determine the required prewarmed containers. The experimental results on the Openwhisk platform show that the proposed approach reduces the memory consumption by 12.73% and improves the execution invocations on prewarmed containers by 22.65% compared to the Openwhisk platform.
A Novel Differential Chaos Shift Keying Scheme With Transmit Diversity

A novel differential chaos shift keying scheme with transmit diversity, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TD-DCSK scheme</i> , is proposed in this letter. The proposed TD-DCSK scheme can achieve full diversity and high data rate by advisably designing the space-time (ST) block of the transmitted signal. With the designed ST block, the transmitter with multiple transmit antennas in the TD-DCSK scheme requires only one radio frequency chain, which is particularly important for keeping low hardware complexity. We derive the theoretical expression of bit-error rate (BER) and diversity order of the TD-DCSK scheme over multipath Rayleigh fading channels, and analyze its hardware complexity. Simulation results illustrate the superiority of the proposed scheme and validate the accuracy of the theoretical derivation. The proposed TD-DCSK scheme stands out as a promising solution for low-power and low-cost short-range wireless communications.
Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks

Clustered Federated Multitask Learning (CFL) was introduced as an efficient scheme to obtain reliable specialized models when data is imbalanced and distributed in a non-i.i.d. (non-independent and identically distributed) fashion amongst clients. While a similarity measure metric, like the cosine similarity, can be used to endow groups of the client with a specialized model, this process can be arduous as the server should involve all clients in each of the federated learning rounds. Therefore, it is imperative that a subset of clients is selected periodically due to the limited bandwidth and latency constraints at the network edge. To this end, this paper proposes a new client selection algorithm that aims to accelerate the convergence rate for obtaining specialized machine learning models that achieve high test accuracies for all client groups. Specifically, we introduce a client selection approach that leverages the devices' heterogeneity to schedule the clients based on their round latency and exploits the bandwidth reuse for clients that consume more time to update the model. Then, the server performs model averaging and clusters the clients based on predefined thresholds. When a specific cluster reaches a stationary point, the proposed algorithm uses a greedy scheduling algorithm for that group by selecting the clients with less latency to update the model. Extensive experiments show that the proposed approach lowers the training time and accelerates the convergence rate by up to 50% while imbuing each client with a specialized model that is fit for its local data distribution.