Reviewerly/exHarmony huggingface dataset:
A Reinforcement Learning Approach to Reduce Serverless Function Cold Start Frequency

Serverless computing is an event-driven cloud computing architecture for processing requests on-demand, using light weight function containers and a micro-services model. A variety of applications like Internet of Things (IoT) services, edge computing, and stream processing have been introduced to the serverless paradigm. These applications are characterized by their stringent response time requirements, therefore expecting a quick and fault tolerant feedback from the application. The serverless, or Function-as-a-Service (FaaS), paradigm suffers from function `cold start' challenges, where the serverless platform takes time to set up the dependencies, prepare the runtime environment and code for execution before serving the incoming workload. Most of the current works address the problem of cold start by (1) reducing the start-up or preparation time of function containers, or (2) reducing the frequency of function cold starts on the platform. Recent industrial research has identified that factors such as runtime environment, CPU and memory settings, invocation concurrency, and networking requirements, affect the cold start of a function. Therefore, we propose a Reinforcement Learning (Q-Learning) agent setting, to analyze the identified factors such as function CPU utilization, to ascertain the function-invocation patterns and reduce the function cold start frequency by preparing the function instances in advance. The proposed Q-Learning agent interacts with the Kubeless serverless platform by discretizing the environment states, actions and rewards with the use of per-instance CPU utilization, available function instances and success or failure rate of response, respectively. The workload is replicated using the Apache JMeter non-GUI toolkit and our agent is evaluated against the baseline default auto-scale feature of Kubeless. The agent demonstrates the capability of learning the invocation pattern, make informed decisions by preparing the optimal number of function instances over the period of learning, under controlled environment settings.
Enhanced Data Security of Communication System Using Combined Encryption and Steganography

&lt;h2&gt;Data security has become a paramount necessity and more obligation in daily life. Most of our systems can be hacked, and it causes very high risks to our confidential files inside the systems. Therefore, for various security reasons, we use various methods to save as much as possible on this data, regardless of its different forms, texts, pictures, videos, etc. In this paper, we mainly rely on storing the basic image which should be protected in another image after changing its formal to composites using the DWT wavelet transform. The process of zeroing sites and storing their contents technique is used to carry the components of the main image. Then process them mathematically by using the exponential function. The result of this process is to obtain a fully encrypted image. The image required to be protected from detection and discrimination is hidden behind the encrypted image. The proposed system contains two algorithms. the first algorithm is used for encryption and hiding, but the second algorithm is designed for returning and decoding the main image to its original state with very efficiently.&lt;/h2&gt;
Innovative Teacher Education with the Augmented Reality Device Microsoft HoloLens—Results of an Exploratory Study and Pedagogical Considerations

Augmented Reality (AR) tools are increasingly finding their way into education settings. Although their use is still not widespread in educational contexts, the research literature indicates their potential and effectiveness. However, overall and specifically for the education sector there are still numerous research gaps. This study investigates how the use of head-mounted AR displays such as the Microsoft HoloLens can change learning and what needs to be considered from a didactic perspective. The researched sample consists of 18 student teachers with a nature and technology teaching profile of a German-speaking university of teacher education. The data collection included a written questionnaire, video recordings of a teaching unit with HoloLens examining molecular structures, and one-to-one semi-structured interviews. The results of questionnaires and interviews presented in this paper show that all students were highly motivated to work with this technology in teacher education. The usability of the HoloLens was rated very satisfactory, although many students expressed minor problems. Most students attributed a positive impact on learning to the AR device and stated that the usage of the devices increased their motivation for learning the topic. Overall, the results show that the use of AR in teacher education is considered very valuable and should be increasingly employed in the future.
A method to integrate and classify normal distributions

Univariate and multivariate normal probability distributions are widely used when modeling decisions under uncertainty. Computing the performance of such models requires integrating these distributions over specific domains, which can vary widely across models. Besides some special cases where these integrals are easy to calculate, there exist no general analytical expressions, standard numerical methods, or software for these integrals. Here we present mathematical results and open-source software that provide (a) the probability in any domain of a normal in any dimensions with any parameters; (b) the probability density, cumulative distribution, and inverse cumulative distribution of any function of a normal vector; (c) the classification errors among any number of normal distributions, the Bayes-optimal discriminability index, and relation to the receiver operating characteristic (ROC); (d) dimension reduction and visualizations for such problems; and (e) tests for how reliably these methods may be used on given data. We demonstrate these tools with vision research applications of detecting occluding objects in natural scenes and detecting camouflage.
A quantum key distribution testbed using a plug&amp;play telecom-wavelength single-photon source

Deterministic solid state quantum light sources are considered key building blocks for future communication networks. While several proof-of-principle experiments of quantum communication using such sources have been realized, most of them required large setups—often involving liquid helium infrastructure or bulky closed-cycle cryotechnology. In this work, we report on the first quantum key distribution (QKD) testbed using a compact benchtop quantum dot single-photon source operating at telecom wavelengths. The plug&amp;play device emits single-photon pulses at O-band wavelengths (1321 nm) and is based on a directly fiber-pigtailed deterministically fabricated quantum dot device integrated into a compact Stirling cryocooler. The Stirling is housed in a 19 in. rack module including all accessories required for stand-alone operation. Implemented in a simple QKD testbed emulating the BB84 protocol with polarization coding, we achieve an multiphoton suppression of g(2)(0)=0.10±0.01 and a raw key rate of up to (4.72 ± 0.13) kHz using an external pump laser. In this setting, we further evaluate the performance of our source in terms of the quantum bit error ratios, secure key rates, and tolerable losses expected in full implementations of QKD while accounting for finite key size effects. Furthermore, we investigate the optimal settings for a two-dimensional temporal acceptance window applied on the receiver side, resulting in predicted tolerable losses up to 23.19 dB. Not least, we compare our results with previous proof-of-concept QKD experiments using quantum dot single-photon sources. Our study represents an important step forward in the development of fiber-based quantum-secured communication networks exploiting sub-Poissonian quantum light sources.
White knight in dark days? Supply chain finance firms, blockchain, and the COVID-19 pandemic

• We study the impact of blockchain on supply chain finance firms’ value in the COVID-19 era. • We use an event-study-based methodology to study this phenomenon. • We compute CAAR using four expected return models and CAAV of sample firms. • Regression analysis is used to study the impact of various firm characteristics on its value. We investigate the impact of the announcement of the COVID-19 pandemic on the market value and trading volume of supply chain finance (SCF) firms. Using an event study, we observe a significant valuation loss and higher trading volume of SCF firms. However, blockchain-enabled SCF firms are protected from such valuation loss and volatility in trading. We find that higher research and development (R&D) and capital expenditures by firms prevent the loss. Moreover, the firm value of blockchain-enabled SCF firms is impacted by their membership in a blockchain consortium and progress in blockchain implementation. Investors’ confidence in blockchain reduces the market uncertainty.
A novel adaptive L-SHADE algorithm and its application in UAV swarm resource configuration problem

In order to further improve the performance of L-SHADE, one of the most competitive variants of differential evolution (DE), a novel adaptive L-SHADE algorithm named AL-SHADE is proposed in the study. Two main parts have been modified for L-SHADE. In one part, a novel mutation strategy current-to-Amean/1 is added to the mutation process to improve the exploitation ability and make full use of population information. In another part, a selection strategy with adaptation scheme for mutation strategies is proposed to tune the exploitation and exploration. The performance of AL-SHADE is evaluated using CEC 2018 and CEC 2014 test suites comparing with L-SHADE, and its state-of-the-art variants, i.e., DbL-SHADE, EB-LSHADE, ELSHADE-SPACMA, jSO, and mL-SHADE. The statistical results demonstrate that AL-SHADE outperforms other competitors in terms of convergence efficiency and accuracy. Finally, AL-SHADE is applied to solve the problem of UAV swarm resource configuration, and the promising performance of AL-SHADE for solving constrained optimization problem are demonstrated by the experimental results. The source code of AL-SHADE can be downloaded from https://github.com/Yintong-Li/AL-SHADE.
Improved YOLOv5 network method for remote sensing image-based ground objects recognition

High-resolution remote sensing images have the characteristics of complex background environment, clustering of objects, etc., the complex background makes the remote sensing image contain a large number of irrelevant ground objects with a high similarity or overlap, which makes the edge and texture of the objects not clear enough, and this leads to low recognition accuracy of ground objects such as airports, dams, and golf field, although the size of this object is large. Based on this problem, this paper proposes a remote sensing image object detection method based on the YOLOv5 network. By improving the backbone extraction network, the network structure can be deepened to get more information about large objects, and the detection effect can be improved by adding an attention mechanism and adding an output layer to enhance feature extraction and feature fusion. The pre-training weight is obtained by transfer learning and used as the training weight of the improved YOLOv5 to speed up the network convergence. The experiment is carried out on the DIOR dataset, the results show that the improved YOLOv5 network can significantly improve the accuracy of large object recognition compared with the YOLO series network and the EfficientDet model on DIOR dataset, and the mAP of the improved YOLOv5 network is 80.5%, which is 2% higher than the original YOLOv5 network.
A novel deep learning‐based single shot multibox detector model for object detection in optical remote sensing images

Abstract Remote sensing image object detection is widely used in civil and military fields. The important task is to detect objects such as ships, planes, airports, harbours and so on, and then it can obtain object category and position information. It is of great significance to use remote sensing images to observe the densely arranged and directional targets such as cars and ships parked in parking lots and harbours. The object detection task mainly includes object localization and classification. Remote sensing images contain large number of small objects and dense scenes due to the long shooting distance and wide coverage. Small objects occupy few pixels in the image, and they are easily miss‐detected. In dense scenes, the overlapping part of each object is large, so it is easy to detect objects repeatedly. The traditional small object detection methods deliver low accuracy and take long time. Therefore, object detection is very challenging. We put forward a novel deep learning‐based single shot multibox detector (SSD) model for object detection. First, we propose an improved inception network to optimize SSD to strengthen the small object feature extraction ability (FEA) in the shallow network. Second, the feature pyramid network is modified to enhance the fusion effect. Third, the deep feature fusion module is designed to improve the FEA of the deep network. Finally, the extracted image features are matched with candidate boxes with different aspect ratios to perform object detection and location with different scales. Experiments on DOTA show that the proposed algorithm can adapt to the remote sensing object detection in different backgrounds, and effectively improve the detection effect of remote sensing objects in complex scenes.
Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study

Substance misuse is a heterogeneous and complex set of behavioural conditions that are highly prevalent in hospital settings and frequently co-occur. Few hospital-wide solutions exist to comprehensively and reliably identify these conditions to prioritise care and guide treatment. The aim of this study was to apply natural language processing (NLP) to clinical notes collected in the electronic health record (EHR) to accurately screen for substance misuse.The model was trained and developed on a reference dataset derived from a hospital-wide programme at Rush University Medical Center (RUMC), Chicago, IL, USA, that used structured diagnostic interviews to manually screen admitted patients over 27 months (between Oct 1, 2017, and Dec 31, 2019; n=54 915). The Alcohol Use Disorder Identification Test and Drug Abuse Screening Tool served as reference standards. The first 24 h of notes in the EHR were mapped to standardised medical vocabulary and fed into single-label, multilabel, and multilabel with auxillary-task neural network models. Temporal validation of the model was done using data from the subsequent 12 months on a subset of RUMC patients (n=16 917). External validation was done using data from Loyola University Medical Center, Chicago, IL, USA between Jan 1, 2007, and Sept 30, 2017 (n=1991 adult patients). The primary outcome was discrimination for alcohol misuse, opioid misuse, or non-opioid drug misuse. Discrimination was assessed by the area under the receiver operating characteristic curve (AUROC). Calibration slope and intercept were measured with the unreliability index. Bias assessments were performed across demographic subgroups.The model was trained on a cohort that had 3·5% misuse (n=1 921) with any type of substance. 220 (11%) of 1921 patients with substance misuse had more than one type of misuse. The multilabel convolutional neural network classifier had a mean AUROC of 0·97 (95% CI 0·96-0·98) during temporal validation for all types of substance misuse. The model was well calibrated and showed good face validity with model features containing explicit mentions of aberrant drug-taking behaviour. A false-negative rate of 0·18-0·19 and a false-positive rate of 0·03 between non-Hispanic Black and non-Hispanic White groups occurred. In external validation, the AUROCs for alcohol and opioid misuse were 0·88 (95% CI 0·86-0·90) and 0·94 (0·92-0·95), respectively.We developed a novel and accurate approach to leveraging the first 24 h of EHR notes for screening multiple types of substance misuse.National Institute On Drug Abuse, National Institutes of Health.
A survey of adaptive large neighborhood search algorithms and applications

This article provides a survey on the highly popular metaheuristic framework, the adaptive large neighborhood search (ALNS). The basic concepts of ALNS are discussed in this paper. Based on a simple taxonomy, the analysis of publication intensity, application areas, and the variant of ALNS features are executed on 252 scientific publications to synthesize the state-of-the-art of ALNS research. Finally, some discussions on the future research of ALNS are provided.
Spatial-temporal interaction learning based two-stream network for action recognition

Two-stream convolutional neural networks have been widely applied to action recognition. However, two-stream networks are usually adopted to capture spatial information and temporal information separately, which normally ignore the strong complementarity and correlation between spatial and temporal information in videos. To solve this problem, we propose a Spatial-Temporal Interaction Learning Two-stream network (STILT) for action recognition. Our proposed two-stream (i.e., a spatial stream and a temporal stream) network has a spatial–temporal interaction learning module, which uses an alternating co-attention mechanism between two streams to learn the correlation between spatial features and temporal features. The spatial–temporal interaction learning module allows the two streams to guide each other and then generates optimized spatial attention features and temporal attention features. Thus, the proposed network can establish the interactive connection between two streams, which efficiently exploits the attended spatial and temporal features to improve recognition accuracy. Experiments on three widely used datasets (i.e., UCF101, HMDB51 and Kinetics) show that the proposed network outperforms the state-of-the-art models in action recognition.
Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention

With the rapid development of high-performance computing technology, data-driven models, especially deep learning models, are being used increasingly for solar radiation prediction. However, the characteristics of the black box model lead to a lack of interpretability in their prediction results. This limits the application of the model in final optimization scenarios (such as model predictive control), as operation managers might not fully trust models lacking explanatory results. In our study, models were proposed based on the prediction model of the recurrent neural network. We hope to improve the interpretability of the models through the design and improvement of the model structure, thereby increasing the credibility of the model results. The interpretability in time and spatial dependencies of the prediction process were studied by the attention mechanism and graph neural network, respectively. Our results showed that the deep learning model, with attention, could effectively shift the attention mechanism to adapt to varying prediction target hours. The graph neural network expresses the most relevant variables in the dataset related to solar radiation through a self-learning graph structure. The results showed that solar radiation is connected directly with month, hour, temperature, penetrating rainfall, water vapor pressure, and radiation time.
Formulaic language identification model based on GCN fusing associated information

Formulaic language is a general term for ready-made structures in a language. It usually has fixed grammatical structure, stable language expression meaning and specific use context. The use of formulaic language can coordinate sentence generation in the process of writing and communication, and can significantly improve the idiomaticity and logic of machine translation, intelligent question answering and so on. New formulaic language is generated almost every day, and how to accurately identify them is a topic worthy of research. To this end, this article proposes a formulaic language identification model based on GCN fusing associated information. The innovation is that each sentence is constructed into a graph in which the nodes are part-of-speech features and semantic features of the words in the sentence and the edges between nodes are constructed according to mutual information and dependency syntactic relation. On this basis, the graph convolutional neural network is adopted to extract the associated information between words to mine deeper grammatical features. Therefore, it can improve the accuracy of formulaic language identification. The experimental results show that the model in this article is superior to the classical formulaic language identification model in terms of accuracy, recall and F1-score. It lays a foundation for the follow-up research of formulaic language identification tasks.
A neural network boosting regression model based on XGBoost

The boosting model is a kind of ensemble learning technology, including XGBoost and GBDT, which take decision trees as weak classifiers and achieve better results in classification and regression problems. The neural network has an excellent performance on image and voice recognition, but its weak interpretability limits on developing a fusion model. By referring to principles and methods of traditional boosting models, we proposed a Neural Network Boosting (NNBoost) regression, which takes shallow neural networks with simple structures as weak classifiers. The NNBoost is a new ensemble learning method, which obtains low regression errors on several data sets. The target loss function of NNBoost is approximated by the Taylor expansion. By inducing the derivative form of NNBoost, we give a gradient descent algorithm. The structure of deep learning is complex, and there are some problems such as gradient disappearing, weak interpretability, and parameters difficult to be adjusted. We use the integration of simple neural networks to alleviate the gradient vanishing problem which is laborious to be solved in deep learning, and conquer the overfitting of a learning algorithm. Finally, through testing on some experiments, the correctness and effectiveness of NNBoost are verified from multiple angles, the effect of multiple shallow neural network fusion is proved, and the development path of boosting idea and deep learning is widened to a certain extent.