Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid Prediction and Evaluation of COVID-19 Epidemic in the United States Based on Feature Selection and Improved ARIMAX Model

Purpose: Through the short-term rapid prediction and evaluation of the COVID-19 epidemic in the United States, explore the development trend of the COVID-19 epidemic in the United States in the short term in the future, and provide an effective basis for the prediction, prevention and control of subsequent epidemic spread;Method: Using the feature selection method based on stepwise regression is to process theCOVID-19 epidemic data set fromJanuary 13,2020 to January 16,2021 in the United States, and data mining is carried out through computer programs for a large number of indicators that reflect the situation of the epidemic. After statistical testing, the ARIMA model and the improved ARIMAX model based on feature selection quickly solves the development trend of the COVID-19 epidemic in the United States in the short-term;Result: The implementation of the computer program shows that the traditional ARIMAmodel cannot predict the cumulative number of COVID-19 diagnoses in the United States well, and the improved ARIMAX model using features based on stepwise regression can accurately predict the scale of the COVID-19 epidemic in the United States under the 95% confidence interval. The U.S. epidemic will show a clear upward trend in the next 60 days, and in mid-March the cumulative number of confirmed diagnoses in the country is about 3,724,000, and the cumulative death toll is about 476,000, and the number of people in the ICU ward is about 22,118. © 2021 Association for Computing Machinery. All rights reserved.
Take the trash out... to the edge. Creating a Smart Waste Bin based on 5G Multi-access Edge Computing

The fifth generation of mobile cellular networks (5G) will bring many improvements to our everyday lives. In particular, the interplay between 5G and IoT technologies will pave the way for the realisation of the Smart City concept, providing humans with better, safer and cleaner cities. In this work, we describe the prototype of a Smart Waste Bin, a connected trash bin that automatically sorts garbage with the help of Convolutional Neural Networks algorithms. The bin intelligence runs on the edge of the 5G network exploiting a Mobile Edge Computing (MEC) architecture, enabling a low-latency and energy efficient operation. We describe the design, the implementation and the experimental validation of the prototype, also discussing possible future application scenarios.
Behavior and elimination of pesticide residues during supercritical carbon dioxide extraction of essential oils of spice plants and analysis of pesticides in high-lipid-content plant extracts

With the production of pesticide-free extracts as an objective, this research investigations commenced with the extraction of spices of various provenance. Firstly, by a conventional solvent extraction and ascertaining the level of pesticide contamination. This was followed by fluid CO2 extraction. The level of pesticide contamination for these extracts from both extraction methods were compared. The work proceeded with the investigation of various parameters influencing pesticides behavior during fluid carbon dioxide extraction. The knowledge gained therefrom, was utilized for an on-line reduction or eventual elimination of pesticide residues during the extraction procedure. Furthermore, a countercurrent based clean up methods was developed for the analysis of pesticide residues in high-lipid-content plant extracts.
DecNet-CNN Framework for Channel Estimation

Channel estimation is crucial in wireless communication. In this letter, we consider the pilot channel information as a 2D image and present a channel estimation scheme based on deep learning (DL), called as DecNet-CNN framework for channel estimation of orthogonal frequency division multiplexing (OFDM) systems. Specifically, based on the potential of deconvolution network in image process, we exploit a channel learning approach using the deconvolution neural network (DecNet), where the pilot channel information is enlarged by only fewer deconvolution layers. Furthermore, we adopt a denoising convolution neural network (DnCNN) to solve noise problem, which offers more accurate channel estimation. Compared with traditional methods, results show that the performance of DecNet-CNN framework is robust and superior.
Automatic Pavement Crack Detection Based on Hierarchical Feature Augmentation

As one of the most important infrastructures, road is the foundation of transportation. The detection of pavement cracks has become an important task. However, due to the complexity and diversity of crack morphology, detecting cracks in the road has become a challenging task. Poor detection effect and weak generalizability are the problems of most current crack detection methods. Inspired by deep learning related technologies, a crack detection network based on feature augmentation is proposed to extract crack areas in pavement images. The network uses parallel dilated convolution branches to capture different scales image information. The hierarchical feature augmentation module is proposed to combine key information from feature maps of different levels. And some side networks are introduced to perform prediction individually at each level. To demonstrate the performance of the proposed crack segmentation network, experiments are carried out on public datasets. Compared with other advanced crack detection methods, the proposed method can effectively predict the crack pixels in pavement image. The detection accuracy and generalization are improved.
Graft: general purpose raft consensus in Elixir

We present Graft, a generic tool for creating distributed consensus clusters running the Raft algorithm using state machines in Elixir. Our tool exhibits performance that is comparable to that of the Raft implementation supporting the original proposal, as well as the performance of other state-of-the-art Raft implementations running on the BEAM. The correctness of our tool is also ascertained through a number of complementary verification methods.
Underwater target detection system based on YOLO v4

In recent years, with the improvement of deep learning methods, underwater target recognition has made a certain degree of progress. However, there are still some challenges. Poor underwater imaging effects are accompanied by color distortion and noise. Underwater objects are usually small and unfavorable for recognition. Therefore, this paper uses the YOLO v4 model for underwater target recognition tasks. The backbone network of the deep convolutional neural network of the YOLO v4 algorithm uses the CSPDarknet 53 network. The CSPDarknet 53 network adopts the Cross Stage Partial (CSP) structure, which can improve the learning ability of CNN, and can maintain accuracy, reduce computing bottlenecks, and reduce memory costs while lightweight. At the same time, YOLO v4 adopts the Spatial Pyramid Pooling (SPP) structure, which increases the receptive field of CNN and improves the detection accuracy of small objects. In addition, YOLO v4 uses Weighted-Residual-Connection (WRC), Cross mini-Batch Normalization (CmBN), Mish loss, and a combination of methods to achieve better results. Finally, the mAP in the underwater data set is 41.2, and the recognition speed under RTX2080TI reaches 62FPS.
Design of Portable Physiological Data Detection System

The system used three kinds of light emitting diode with different wave bands in MAX30105 reflex optical sensor, and measured human heart rate and blood oxygen saturation through photoelectric plethysmography. It also utilized DHT11 digital temperature humidity sensor to measure body surface temperature and humidity by using resistance type humidity sensor and negative temperature coefficient (NTC) temperature measuring element. The measuring results were finally and real-timely shown by organic light emitting diode (OLED). After systematic tests and result analysis, the portable physiological data detection system worked steadily. It can relatively precisely and stably measure heart rate, blood oxygen saturation and body surface temperature and humidity, and can monitor real-time change of the data. What is more, the four physiological parameters measured can provide effective references for cardiovascular and cerebrovascular diseases, such as obstructive sleep apnea syndrome and coronary heart disease, and thus can help people to monitor their real-time body situation in their daily lives, in order to more effectively preventing diseases from attacking and worsening in time.
Health of smart ecosystems

Software is a core component of smart ecosystems, large ’system communities’ that emerge from the composition of autonomous, independent, and highly heterogeneous systems, like smart cities, smart grids, smart buildings. The systems that comprise smart ecosystems are not centrally owned, and mutually interact both explicitly and implicitly, leading to unavoidable contradictions and failures. The distinctive characteristics of smart ecosystems challenge software engineers with problems never addressed so far. In this paper we discuss the big challenge of defining a new concept of ’dependability’ and new approaches to reveal smart ecosystem failures.
Channel Estimation Based on Dual-path Reconstruction Algorithm

In the sparse channel estimation of Orthogonal Frequency Division Multiplexing (OFDM) systems, for any delay of the multipath channels, most of the channel estimation algorithms based on Compressed Sensing (CS) have only one estimate. In view of this situation, a Dual-path Reconstruction (DR) algorithm is proposed based on the Orthogonal Matching Pursuit with Binary-search Re-finement (OMPBR) algorithm. When locating a time delay in the actual channel, the algorithm provides two estimators to estimate the time delay. Ideally, the actual time delay is in the middle of the two estimators, and the errors of the two estimators cancel each other out. Through theoretical analysis and simulation results, it is confirmed that the accuracy of channel estimation achieved by the DR algorithm is higher than that achieved by the OMPBR algorithm.
Deep Learning based Street Parking Sign Detection and Classification for Smart Cities

Smart traffic management is essential for smart cities. Detection and classification of traffic signs is essential in autonomous driving and in helping with traffic navigation in general. Automated recognition of street parking signs, however, is a more challenging task, due to their small size and huge diversity. Identification of street parking signs is not only helpful in autonomous driving but also in notifying drivers of available parking spaces ahead, significantly alleviating traffic congestion. In this paper, we introduce a new street parking sign detection and classification method that is based on a deep learning network that receives a video feed from a car camera to accurately detect and classify different street parking signs in real-time. Performance evaluations showed that our model achieves mean average precision of 98.56%.
Persistent software transactional memory in Haskell

Emerging persistent memory in commodity hardware allows byte-granular accesses to persistent state at memory speeds. However, to prevent inconsistent state in persistent memory due to unexpected system failures, different write-semantics are required compared to volatile memory. Transaction-based library solutions for persistent memory facilitate the atomic modification of persistent data in languages where memory is explicitly managed by the programmer, such as C/C++. For languages that provide extended capabilities like automatic memory management, a more native integration into the language is needed to maintain the high level of memory abstraction. It is shown in this paper how persistent software transactional memory (PSTM) can be tightly integrated into the runtime system of Haskell to atomically manage values of persistent transactional data types. PSTM has a clear interface and semantics extending that of software transactional memory (STM). Its integration with the language’s memory management retains features like garbage collection and allocation strategies, and is fully compatible with Haskell's lazy execution model. Our PSTM implementation demonstrates competitive performance with low level libraries and trivial portability of existing STM libraries to PSTM. The implementation allows further interesting use cases, such as persistent memoization and persistent Haskell expressions.
KGAMD: an API-misuse detector driven by fine-grained API-constraint knowledge graph

Application Programming Interfaces (APIs) typically come with usage constraints. The violations of these constraints (i.e. API misuses) can cause significant problems in software development. Existing methods mine frequent API usage patterns from codebase to detect API misuses. They make a naive assumption that API usage that deviates from the most-frequent API usage is a misuse. However, there is a big knowledge gap between API usage patterns and API usage constraints in terms of comprehensiveness, explainability and best practices. Inspired by this, we propose a novel approach named KGAMD (API-Misuse Detector Driven by Fine-Grained API-Constraint Knowledge Graph) that detects API misuses directly against the API constraint knowledge, rather than API usage pat-terns. We first construct a novel API-constraint knowledge graph from API reference documentation with open information extraction methods. This knowledge graph explicitly models two types of API-constraint relations (call-order and condition-checking) and enriches return and throw relations with return conditions and exception triggers. Then, we develop the KGAMD tool that utilizes the knowledge graph to detect API misuses. There are three types of frequent API misuses we can detect - missing calls, missing condition checking and missing exception handling, while existing detectors mostly focus on only missing calls. Our quantitative evaluation and user study demonstrate that our KGAMD is promising in helping developers avoid and debug API misuses Demo Video: https://www.youtube. com/watch? v=TN4LtHJ-494 IntelliJ plug-in: https://github.com/goodchar/KGAMD
ICT Infrastructure Effects on Labor Productivity in the Chinese Hotel Sector: A Spatial Econometric Approach

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library. None of the papers from this Proceeding should be cited in the literature because of the questionable integrity of the peer review process for this Conference.
Context-aware and data-driven feedback generation for programming assignments

Recently, various techniques have been proposed to automatically provide personalized feedback on programming exercises. The cutting edge of which is the data-driven approaches that leverage a corpus of existing correct programs and repair incorrect submissions by using similar reference programs in the corpus. However, current data-driven techniques work under the strong assumption that the corpus contains a solution program that is close enough to the incorrect submission. In this paper, we present Cafe, a new data-driven approach for feedback generation that overcomes this limitation. Unlike existing approaches, Cafe uses a novel context-aware repair algorithm that can generate feedback even if the incorrect program differs significantly from the reference solutions. We implemented Cafe for OCaml and evaluated it with 4,211 real student programs. The results show that Cafe is able to repair 83 % of incorrect submissions, far outperforming existing approaches.
C2cd6-encoded CatSperτ targets sperm calcium channel to Ca2+ signaling domains in the flagellar membrane

In mammalian sperm cells, regulation of spatiotemporal Ca2+ signaling relies on the quadrilinear Ca2+ signaling nanodomains in the flagellar membrane. The sperm-specific, multi-subunit CatSper Ca2+ channel, which is crucial for sperm hyperactivated motility and male fertility, organizes the nanodomains. Here, we report CatSperτ, the C2cd6-encoded membrane-associating C2 domain protein, can independently migrate to the flagella and serve as a major targeting component of the CatSper channel complex. CatSperτ loss-of-function in mice demonstrates that it is essential for sperm hyperactivated motility and male fertility. CatSperτ targets the CatSper channel into the quadrilinear nanodomains in the flagella of developing spermatids, whereas it is dispensable for functional channel assembly. CatSperτ interacts with ciliary trafficking machinery in a C2-dependent manner. These findings provide insights into the CatSper channel trafficking to the Ca2+ signaling nanodomains and the shared molecular mechanisms of ciliary and flagellar membrane targeting. Highlights CatSperτ encoded by C2cd6 is a C2 membrane-associating domain containing protein CatSperτ loss-of-function impairs sperm hyperactivation and male fertility CatSperτ adopts ciliary trafficking machineries for flagellar targeting via C2 domain CatSperτ targets the CatSper channel into nanodomains of developing sperm flagella
Target-specific Sentiment Analysis of Dual-channel Interactive Attention Network

Aiming at most of the target sentiment analysis based on short text length, sparse content, insufficient text feature extraction and ignoring the impact of target words on context modeling, a dual-channel interactive attention network-specific target sentiment analysis model is proposed. First, fusion of part-of-speech information is based on the input of a single text word vector, and an internal attention is used to preprocess the text target; then, the text target and its context are separately input into Bi-LSTM and the position information is combined to obtain the deep semantics of the text Expression; Finally, the target and context are interactively learned through dual-channel interactive attention to focus on the most important emotional features in the text. Compared with INA and AOA specific target sentiment analysis models, the accuracy rates on the Restaurant and Lap-top datasets are increased by 2.7%, 1.6%, 3.0% and 2.5%, respectively, which effectively proves the effectiveness of this model on target
The β-cell primary cilium is an autonomous Ca2+ compartment for paracrine GABA signaling

The primary cilium is an organelle present in most adult mammalian cells and is thought of as an antenna for detection of a variety of signals. Here we use intact mouse pancreatic islets of Langerhans to investigate signalling properties of the primary cilium in β-cells. Using cilia-targeted Ca2+ indicators we find that the resting Ca2+ concentration in the cilium is lower than that of the cytosol, and we uncover a Ca2+ extrusion mechanism in the cilium that effectively insulates the cilium from changes in cytosolic Ca2+. Stimuli that give rise to pronounced cytosolic Ca2+ concentration increases, such as glucose- and depolarization-induced Ca2+ influx, and mobilization of Ca2+ from the ER, was accompanied by minor increases in cilia Ca2+ concentrations that were spatially restricted to a small compartment at the base. Conversely, we observe pronounced Ca2+ concentration changes in the primary cilia of islet β-cells that do not propagate into the cytosol and show that paracrine GABA signalling via cilia-localized GABA-B1-receptors is responsible for this Ca2+ signalling. Finally, we demonstrate that the cilia response to GABA involves ligand-dependent transport of GABA-B1 receptors into the cilium.
A Synthetic Genetic Reversible Feynman Gate in a Single E.coli Cell and its Application in Bacterial to Mammalian Cell Information Transfer

Reversible computing is a nonconventional form of computing where the inputs and outputs are mapped in a unique one-to-one fashion. Reversible logic gates in single living cells have not been demonstrated. Here, we created a synthetic genetic reversible Feynman gate in a single E.coli cell. The inputs were extracellular chemicals, IPTG and aTc and the outputs were two fluorescence proteins EGFP and E2-Crimson. We developed a simple mathematical model and simulation to capture the essential features of the genetic Feynman gate and experimentally demonstrated that the behavior of the circuit was ultrasensitive and predictive. We showed an application by creating an intercellular Feynman gate, where input information from bacteria was computed and transferred to HeLa cells through shRNAs delivery and the output signals were observed as silencing of native AKT1 and CTNNB1 genes in HeLa cells. Given that one-to-one input-output mapping, such reversible genetic systems might have applications in diagnostics and sensing, where compositions of multiple input chemicals could be estimated from the outputs.
Bacterial DNA on the skin surface overrepresents the viable skin microbiome

The skin microbiome provides vital contributions to human health. However, the spatial organization and viability of its bacterial components remain unclear. Here we apply culturing, imaging, and molecular approaches to human and mouse skin samples, and find that the skin surface is colonized by fewer viable bacteria than predicted by bacterial DNA levels. Instead, viable skin-associated bacteria are predominantly located in hair follicles and other cutaneous invaginations. Furthermore, we show that the skin microbiome has a uniquely low fraction of viable bacteria as compared to other human microbiome sites, indicating that most bacterial DNA on the skin surface is not associated with viable cells Additionally, a small number of bacterial families dominate each skin site and traditional sequencing methods overestimate both the richness and diversity of the skin microbiome. Finally, we performed an in-vivo skin microbiome perturbation-recovery study using human volunteers. Bacterial 16S rRNA gene sequencing revealed that, while the skin microbiome is remarkably stable even in the wake of aggressive perturbation, repopulation of the skin surface is driven by the underlying viable population. Our findings help explain the dynamics of skin microbiome perturbation, as bacterial DNA on the skin surface can be transiently perturbed but is replenished by a stable underlying viable population. These results address multiple outstanding questions in skin microbiome biology with significant implications for future efforts to study and manipulate it. Significance statement This study provides a crucial update to the skin microbiome paradigm by showing that viable bacteria of the skin microbiome are primarily localized to hair follicles and other sub-cutaneous structures rather than the skin surface. The native distribution of skin-associated bacteria has not been previously evaluated, and here we show that the skin surface has few intact bacteria while deeper structures are replete with bacterial contents. We used orthogonal approaches to evaluate the source of bacterial DNA and the dynamics of bacterial repopulation on the skin surface to develop an updated model of the skin microbiome. By adjusting the current understanding of the skin microbiome to match this model, we will be able to address outstanding questions in the field.