Reviewerly/exHarmony huggingface dataset:
Nonbistable rectified linear unit-based gain-dissipative Ising spin network with stochastic resonance effect

The gain-dissipative Ising machine (GIM), comprising a nonlinear transfer function and spin interaction feedback, is one of the most promising quantum analog annealers. However, previous GIM constructions have been limited to only bistable nonlinear transfer functions with low noise intensity condition. To provide a new insight and broaden the potential design of GIMs, we construct a nonbistable rectified linear unit-based gain-dissipative Ising spin network (RISN). In contrast to the traditional GIM, spin dynamics analysis in the uncoupled state proves that RISN is nonbistable. Furthermore, it is demonstrated that the proposed RISN can effectively solve combinatorial optimization problems with a high noise level, which can be regarded as a stochastic resonance phenomenon. The proposed configuration, which does not comply with known conclusions, can become a stepping-stone for new GIM designs.
Retrieval Augmented Convolutional Encoder-decoder Networks for Video Captioning

Video captioning has been an emerging research topic in computer vision, which aims to generate a natural sentence to correctly reflect the visual content of a video. The well-established way of doing so is to rely on encoder-decoder paradigm by learning to encode the input video and decode the variable-length output sentence in a sequence-to-sequence manner. Nevertheless, these approaches often fail to produce complex and descriptive sentences as natural as those from human being, since the models are incapable of memorizing all visual contents and syntactic structures in the human-annotated video-sentence pairs. In this article, we uniquely introduce a Retrieval Augmentation Mechanism (RAM) that enables the explicit reference to existing video-sentence pairs within any encoder-decoder captioning model. Specifically, for each query video, a video-sentence retrieval model is first utilized to fetch semantically relevant sentences from the training sentence pool, coupled with the corresponding training videos. RAM then writes the relevant video-sentence pairs into memory and reads the memorized visual contents/syntactic structures in video-sentence pairs from memory to facilitate the word prediction at each timestep. Furthermore, we present Retrieval Augmented Convolutional Encoder-Decoder Network (R-ConvED), which novelly integrates RAM into convolutional encoder-decoder structure to boost video captioning. Extensive experiments on MSVD, MSR-VTT, Activity Net Captions, and VATEX datasets validate the superiority of our proposals and demonstrate quantitatively compelling results.
Multidisciplinary Data Fusion for THz Ray- Tracing

The human appearance can be interpreted as a disturbance, convenience or even necessity, for example, in technical applications with human-robot interaction and human-gesture detection. This implies the need for implementing a realistic human digital twin with its positions, gestures and individual movements in ray-tracing simulations. Thus, bringing harmony between simulation and measurement owing to the more realistic assumptions within the simulation environment. In this paper, the human-digital twin combo is primarily re-alized based on two rationales: Firstly, a 3D human model is generated whose individual segment movement is represented by individual trajectories. Secondly, the inertial measurement units (IMUs) are placed on the human body for capturing the human motion estimation. The data fusion between different human postures and ray-tracing is achieved using a self-written script applying the translation and rotation of joint movements to the corresponding segments of its digital twin. Investigations using terahertz (THz) ray-tracing simulations for in-room environment scenario are carried out for different human gaits and velocities at 300 GHz center frequency. Moreover, upon comparing ani-mated postures of this human model with its digital twin which executes the movements according to the measured data from the inertial sensors, the need to record human individual postures is stressed in order to simulate the interaction between humans and technical equipment whereby small changes in human movement might trigger a major impact.
Foreign direct investment and poverty in sub-Saharan African countries: The role of host absorptive capacity

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Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique

DNA microarray technologies enable the analysis of the expression of numerous genes in an individual experiment and become an important approach in the field of medicine and biology for investing genetic function, regulation, and interaction. Microarray images can be investigated well for obtaining the contained genetic data. But is it undesirable to retain the genetic data and avoid the microarray images? Due to considerable attention to DNA microarray and several experiments being performed under distinct conditions, a massive quantity of data gets produced over the globe. In order to store and share the microarray images, effective storage and communication models are needed in a natural way. Vector quantization (VQ) is a commonly utilized tool for compressing images, which mainly aims to produce effective codebooks comprising a collection of codewords. Therefore, this paper presents a manta ray foraging optimization (MRFO) with Linde-Buzo-Gray (LBG) based microarray image compression (MRFOLBG-MIC) technique. The LBG model is commonly utilized to design local optimal codebooks to compress images. The construction of codebooks can be defined as a nondeterministic polynomial time (NP) hard problem and can be resolved by the MRFO algorithm. The codebooks produced from LBG-VQ are optimized using the MRFO algorithm to attain optimum optimal codebooks. When the codebooks are produced by the MRFOLBG-MIC algorithm, Deflate model can be applied to compress the index tables. The design of the MRFO algorithm with LBG and Deflate based index table compression demonstrate the novelty of the work. For demonstrating the enhanced compression efficacy of the MRFOLBG-MIC model, a wide-ranging experimental validation process is performed using a benchmark dataset. The experimental outcomes inferred that the MRFOLBG-MIC model accomplished superior outcomes over the other existing models.
Induction of Secure Data Repository in Blockchain over IPFS

In blockchains, enormous records can't be put away effectively. From one perspective, the blockchain becomes swollen with information that should be dispersed all through the organization. Nonetheless, because the blockchain is repeated on numerous hubs, a lot of extra room is needed without serving a quick use, especially assuming the hub administrator doesn't have to get to each document saved money on the blockchain. Moreover, on the grounds that more information should be handled, traded, and kept, the expense of running blockchain hubs rises. IPFS is a document-sharing innovation that can be utilized to store and share colossal records all the more viably. It depends on cryptographic hashes, which might be put away effectively on a blockchain. IPFS, then again, doesn't permit clients to impart documents to explicit individuals. In the event that delicate or individual information is to be revealed. In this methodology there are ideas of blockchain, interplanetary document framework and awry key encryption included which are presented underneath.
Who is behind the Mask: Periocular Biometrics when Face Recognition Fails

The ongoing COVID-19 epidemic has emphasized the importance of hygienic and contactless identification au-thentication. On the other hand, the pandemic resulted in the widespread usage of face masks, which are still necessary to keep the infection under control. The consequence of wearing a mask on facial recognition is a sensitive yet understudied issue right now. Furthermore, the performance and reliability of contactless identity verification using facial recognition can indeed be affected by different types, sizes, and colors of masks. Therefore, this study evaluates the performance of face recognition system with various kinds of occlusions. Periocular region based authentication, especially when only a partial face is available, is proposed. Experimental results prove the reliability, robustness, and superiority of periocular recognition over the face under studied circumstances.
Integrated approach to evaluating the impact of feed-in tariffs on the life cycle economic performance of photovoltaic systems in China: A case study of educational facilities

The photovoltaic (PV) system in a building is being actively promoted; however, they have a high initial investment cost and take a long time to make money back. To solve this challenge, this study aimed to propose an integrated approach to evaluating how feed-in tariffs (FiT) affect the life cycle economic performance of PV systems in educational facilities, in which the impact of China's zonal FiT policy was investigated in various ways. The integrated approach consists of three steps: (i) data collection; (ii) technical analysis of PV systems by considering shading effects; and (iii) economic analysis of PV systems by considering a government financial supporting policy. The followings are main findings. If the self-consumed utilization plan (SC plan) and the grid-connected utilization plan (GC plan) could be mixed together and used as a single utilization plan, net present value at year 25 (NPV25), saving-to-investment ratio at year 25 (SIR25) and break-even point (BEP) were found to be 1423.03 USD/kW, 2.63, and 6.40 years, respectively. This study could be a promising approach for the managers of educational facilities to evaluate in advance whether PV systems would be feasible for their buildings from a long-term perspective with government policy (e.g., FiT upwards).
Nurse-in-the-loop smart home detection of health events associated with diagnosed chronic conditions: A case-event series

Telehealth and home-based care options significantly expanded during the SARS-CoV2 pandemic. Sophisticated, remote monitoring technologies now exist that support at-home care. Advances in the research of smart homes for health monitoring have shown these technologies are capable of recognizing and predicting health changes in near-real time. However, few nurses are familiar enough with this technology to use smart homes for optimizing patient care or expanding their reach into the home between healthcare touch points.The objective of this work is to explore a partnership between nurses and smart homes for automated remote monitoring and assessing of patient health. We present a series of health event cases to demonstrate how this partnership may be harnessed to effectively detect and report on clinically relevant health events that can be automatically detected by smart homes.25 participants with multiple chronic health conditions.Ambient sensors were installed in the homes of 25 participants with multiple chronic health conditions. Motion, light, temperature, and door usage data were continuously collected from participants' homes. Descriptions of health events and participants' associated behaviors were captured via weekly nursing telehealth visits with study participants and used to analyze sensor data representing health events. Two cases of participants with congestive heart failure exacerbations, one case of urinary tract infection, two cases of bowel inflammation flares, and four cases of participants with sleep interruption were explored.For each case, clinically relevant health events aligned with changes from baseline in behavior data patterns derived from sensors installed in the participant's home. In some cases, the detected event was precipitated by additional behavior patterns that could be used to predict the event.We found evidence in this case series that continuous sensor-based monitoring of patient behavior in home settings may be used to provide automated detection of health events. Nursing insights into smart home sensor data could be used to initiate preventive strategies and provide timely intervention.Nurses partnered with smart homes could detect exacerbations of health conditions at home leading to early intervention.
CDFKD-MFS: Collaborative Data-Free Knowledge Distillation via Multi-Level Feature Sharing

Recently, the compression and deployment of powerful deep neural networks (DNNs) on resource-limited edge devices to provide intelligent services have become attractive tasks. Although knowledge distillation (KD) is a feasible solution for compression, its requirement on the original dataset raises privacy concerns. In addition, it is common to integrate multiple pretrained models to achieve satisfactory performance. How to compress multiple models into a tiny model is challenging, especially when the original data are unavailable. To tackle this challenge, we propose a framework termed collaborative data-free knowledge distillation via multi-level feature sharing (CDFKD-MFS), which consists of a multi-header student module, an asymmetric adversarial data-free KD module, and an attention-based aggregation module. In this framework, the student model equipped with a multi-level feature-sharing structure learns from multiple teacher models and is trained together with a generator in an asymmetric adversarial manner. When some real samples are available, the attention module adaptively aggregates predictions of the student headers, which can further improve performance. We conduct extensive experiments on three popular computer visual datasets. In particular, compared with the most competitive alternative, the accuracy of the proposed framework is 1.18&#x0025; higher on the CIFAR-100 dataset, 1.67&#x0025; higher on the Caltech-101 dataset, and 2.99&#x0025; higher on the mini-ImageNet dataset.
An empirical study of IoT security aspects at sentence-level in developer textual discussions

IoT is a rapidly emerging paradigm that now encompasses almost every aspect of our modern life. As such, ensuring the security of IoT devices is crucial. IoT devices can differ from traditional computing, thereby the design and implementation of proper security measures can be challenging in IoT devices. We observed that IoT developers discuss their security-related challenges in developer forums like Stack Overflow(SO). However, we find that IoT security discussions can also be buried inside non-security discussions in SO. In this paper, we aim to understand the challenges IoT developers face while applying security practices and techniques to IoT devices. We have two goals: (1) Develop a model that can automatically find security-related IoT discussions in SO, and (2) Study the model output to learn about IoT developer security-related challenges. First, we download 53K posts from SO that contain discussions about IoT. Second, we manually labeled 5,919 sentences from 53K posts as 1 or 0. Third, we use this benchmark to investigate a suite of deep learning transformer models. The best performing model is called SecBot. Fourth, we apply SecBot on the entire posts and find around 30K security related sentences. Fifth, we apply topic modeling to the security-related sentences. Then we label and categorize the topics. Sixth, we analyze the evolution of the topics in SO. We found that (1) SecBot is based on the retraining of the deep learning model RoBERTa. SecBot offers the best F1-Score of 0.935, (2) there are six error categories in misclassified samples by SecBot. SecBot was mostly wrong when the keywords/contexts were ambiguous (e.g., gateway can be a security gateway or a simple gateway), (3) there are 9 security topics grouped into three categories: Software, Hardware, and Network, and (4) the highest number of topics belongs to software security, followed by network security.
Resource Based Automatic Calibration System (RBACS) Using Kubernetes Framework

Kubernetes, a container orchestrator for cloud-deployed applications, allows the application provider to scale automatically to match the fluctuating intensity of processing demand.Container cluster technology is used to encapsulate, isolate, and deploy applications, addressing the issue of low system reliability due to interlocking failures.Cloud-based platforms usually entail users define application resource supplies for eco container virtualization.There is a constant problem of over-service in data centers for cloud service providers.Higher operating costs and incompetent resource utilization can occur in a waste of resources.Kubernetes revolutionized the orchestration of the container in the cloud-native age.It can adaptively manage resources and schedule containers, which provide real-time status of the cluster at runtime without the user's contribution.Kubernetes clusters face unpredictable traffic, and the cluster performs manual expansion configuration by the controller.Due to operational delays, the system will become unstable, and the service will be unavailable.This work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes cluster.RBACS allocation pattern is analyzed with the Kubernetes VPA.To estimate the overall cost of RBACS, we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site relocation.The experiments ran on the simulations to show the method's effectiveness yielded high precision in the real-time deployment of resources in eco containers.Compared to the default baseline, Kubernetes results in much fewer dropped requests with only slightly more supplied resources.
Data-Driven Algorithms for Signal Processing with Trigonometric Rational Functions

Rational approximation schemes for reconstructing periodic signals from samples with poorly separated spectral content are described. These methods are automatic and adaptive, requiring no tuning or manual parameter selection. Collectively, they form a framework for fitting trigonometric rational models to data that is robust to various forms of corruption, including additive Gaussian noise, perturbed sampling grids, and missing data. Our approach combines a variant of Prony's method with a modified version of the adaptive Antoulas--Anderson algorithm. Using representations in both frequency and time space, a collection of algorithms is described for adaptively computing with trigonometric rationals. This includes procedures for differentiation, filtering, convolution, and more. A new MATLAB software system based on these algorithms is introduced. Its effectiveness is illustrated with synthetic and practical examples drawn from applications including biomedical monitoring, acoustic denoising, and feature detection.
Medical Text Classification Based on an Optimized Machine Learning and External Semantic Resource

Automatic classification of texts is a well-known topic in natural language processing (NLP), and it involves categorizing unstructured texts into specific groups. Classification algorithms for a given problem are searched for, as well as the appropriate features to use as input. These models have been used for analysis of data with a large number of sparse dimensions. In medical field, categorization of medical texts is a difficult task because these types of texts contain several terminologies that define medical ideas and words. Medical data also lack proper sentence structure and do not adhere to the standard rules of natural language grammar. The main problem in this classification task is to choose an appropriate representation as input. To solve the representation problem, we propose a new extension model for medical text personification. Our model starts with the preprocessing of medical text. Then, we use external terminology resources to expand the text. After that, we use a combination of different text vector representations. To speed up the process, we try to increase the accuracy while selecting the optimal classification parameters. In this topic, we use an adaptive particle swarm optimization (PSO) algorithm to select the best parameters of a machine learning model. The PubMed, Hallmarks and AIM dataset were used to test our model. Our categorization text model outperforms the competition and provides much higher retrieval accuracy than previous models.
Insights into the relationship between usability and willingness to use a robot in the future workplaces: Studying the mediating role of trust and the moderating roles of age and STARA

Human-robot collaboration is the key component of the fourth industrial revolution concept. Workers' willingness to collaborate with industrial robots is a basic requirement for an efficient and effective interaction. The roles of human-robot trust and technology affinity as mediators in the relationship between robot usability and worker willingness were analyzed in this study. As other critical variables, the mediator roles of Age and STARA were also calculated.This study included 400 workers from a car company who interacted with industrial robots in their daily work activities. After examining the questionnaires' validity and reliability, the main variables were determined to be willingness to use robots and robot usability. AMOS software also considered human-robot trust and worker technology affinity as mediators. The bootstrapping method was used to evaluate indirect relationships. A set of goodness-of-fit indices were presented to determine the adequacy of the goodness of fit between the proposed model and the data.Based on model fit indices, an overall satisfactory model fit was obtained for the direct/indirect relationship between robot usability and worker willingness to use it (with mediating role of human-robot trust). Workers' age and fear of Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA) were identified as moderators in the relationship between usability and willingness.Attention to the robot usability and the role of workers' trust in robots appears to be required to ensure workers' willingness to use robots and the success of human-robot collaboration in future workplaces. As the workers age and their fear of robots grows, usability can play a larger role in increasing their willingness to put robots to work.