Miracle-dz/UniFAR-FTUs huggingface dataset:
Research about Tongue Image of Traditional Chinese Medicine(TCM) Based on Artificial Intelligence Technology

Artificial intelligence has successfully integrated the frontier research in many fields, which is gradually mature. Among them, the intelligent diagnosis of traditional Chinese medicine(TCM) has entered the initial stage. Tongue diagnosis is one of the traditional methods of clinical history collection in TCM, as one part of the “Four Diagnosis of Traditional Chinese Medicine”, playing an irreplaceable role in diagnosing. Due to the influence of external environment such as light and temperature, tongue diagnosis lacks objective, quantitative and standard evaluation and application. Based on the Yolo deep learning technology, this paper used a classification method to construct a multi task learning model of tongue image, which realized the simultaneous identification of tongue color, fur color, crack and tooth mark in traditional Chinese tongue diagnosis. The tongue images of 200 subjects were labeled by labelImg, including 160 training data sets and 40 testing data sets. The model effect was evaluated by precision rate(P), recall rate(R) and accuracy rate(A). It could reliably complete the task of tongue feature identification and had a good migration ability, providing a theoretical basis for the application of intelligent diagnosis technology in the medical field.
Poster: Word embedding for deployment descriptors in NFV

Virtualized networks such as NFV enable the automation of the network and decrease the need for human interventions. They ensure the availability of adequate compute, storage, and network resources for network services. To automate the network, the NFV orchestrator coordinates and manages automatically the resources to meet the requirement of network functions and services. To date, network function deployment descriptors are manually designed by service providers. Learning automatically from deployment descriptors is an important step toward fully automating the network management. In this work, we propose a word embedding approach that learns from a set of deployment descriptors a vector representation. This approach is shown to be useful for recommending deployment descriptors.
SCueU-Net: Efficient Damage Detection Method for Railway Rail

Automatic detection of industrial product damage using machine learning is a promising research area. At the same time, various machine learning methods based on convolutional neural networks have a very important role in the application of visual automatic detection. Therefore, the machine vision-based automatic detection of high-speed railway rail damage has received widespread attention. This paper proposes an efficient detection method for the damage of high-speed railway rails called SCueU-Net. For the first time, the combination of U-Net graph segmentation network and the saliency cues method of damage location is applied to the task of high-speed railway rail damage detection. The experimental results show that our method has a detection accuracy rate of 99.76%, which is 6.74% higher than the recent method in damage identification accuracy, which improves the detection efficiency of rail damage significantly.
Early Detection of Alzheimer's Disease Using Various Machine Learning Techniques: A Comparative Study

Alzheimer's disease (AD) is an incurable, progressive neurodegenerative disease, which leads to the loss of memory. Various psychological tests used by doctors to diagnose patients, albeit, there is no well-defined procedure to detect AD in its early stage. This paper comprises of exhaustive analysis and accuracy of various machine learning techniques on a combination of different biomarkers associated with the disease. Since no prior research has suggested the use of Voting Classifier Algorithm for early detection of AD, the present study attempts to crystallize the intrusion of the averaging factor through Soft Voting Classifier, which influences the accuracy of the output in a way, that it removes the possibility of inaccuracies in the result by averaging the probable outcomes of the classifiers in the previous stage, giving the best possible result and making it more precise. A performance gain of 86% is obtained using Soft Voting classifier, wherein 437 cases are analyzed in our dataset, eliminating all the ineffective entries.
Facial Expression Recognition Based on Optimized ResNet

Facial expression recognition (FER) is a very challenging task for machines to understand the emotional changes in human beings. Recently, using deep learning methods has greatly improved the accuracy of FER. However, considering the requirements of deploying deep learning algorithms in embedded systems, the size of neural networks needs to be further optimized on the basis of ensuring the high accuracy. In order to reduce the model parameters and maintain the high FER accuracy, inspired by the advances that Residual Network (ResNet) have achieved in image recognition and classification, we propose a simplified and efficient neural network based on ResNet and Squeeze-and-Excitation (SENet). Experiments show that the proposed algorithm obtains recognition accuracy of 74.143% and 95.253% on the FFE2013 and CK+ databases, respectively. Compared with state-of-the-art methods using Visual Geometry Group (VGG) or other deep neural networks, the proposed algorithm not only improves the accuracy, but also reduces the size of the model, which is competitive with existing methods in terms of size of the model parameters and recognition accuracy. Furthermore, this work also shows that only increasing the network layers hardly improves the recognition accuracy on the FFE2013 and CK+ databases.
QoS-Aware Bandwidth Allocation and Concurrent Scheduling for Terahertz Wireless Backhaul Networks

In recent years, with the spectrum resources in low frequency bands becoming increasingly insufficient, the carrier frequency of wireless communication has started its evolution to the Terahertz (THz) frequency band, and meanwhile the THz communication system has become one of the research hot spots. In this paper, we investigate the problem of concurrent transmission scheduling for THz wireless backhaul network. To increase the weighted sum of completed flows with their quality of service (QoS) requirements satisfied in THz wireless backhaul network, we propose an algorithm of QoS-aware bandwidth allocation and concurrent scheduling (IHQB). Considering the priority of the flow, the reciprocal of the priority is taken as the weight of the flow. In this algorithm, QoS awareness and bandwidth allocation are exploited to achieve more successfully scheduled flows and higher network throughput. The concurrent scheduling with the maximum independent set (MIS) is allocated to different frequency bands. And the mentioned MIS is obtained from the conflict graph which has been established under the conditions of half-duplex and interference threshold. Simulation results show that IHQB performs better than other algorithms in terms of the weighted sum of completed flows and system throughput, especially compared with STDMA.
Unsupervised Feature Learning to Improve Transferability of Landslide Susceptibility Representations

A landslide susceptibility map (LSM) is of vital importance for risk recognition and prevention. In the last decade, statistical methods have gradually exerted their impact on mapping the landslide susceptibility to locate the high-risk places of landslide. However, due to the complexity of getting full access to the thematic information in large scenarios, most of these statistical methods generally suffer from overfitting, inadequate representative power, and the inability to transfer the learned representation to other places. To solve these challenges, this study designed an unsupervised representation learning module, which features independence, compactness, robustness, and transferability. Specifically, we first stack restricted Boltzmann machines and denoising autoencoder to unsupervised discover the underlying representations embedded in the thematic maps. Then, we applied the transferring strategy in an adversarial manner to generalize the learned representations to the sample-scarce area. Experimental results and analyses using data in different regions have revealed that the proposed method can be generalized well between different LSM scenarios. In terms of precision, it outperforms other methods by a large margin, e.g., by around 7% compared to multilayer perceptrons with the same configuration, and by 3%–4% to the state of art algorithm random forest. Besides, compared to other methods, the landslide susceptibility map that is predicted by the proposed method featuring smoothness and stableness seems more reliable, and is more according to some prior knowledge that, for example, distance to the drainage, slope, and stratum, should exert dominant effects on the occurrence of a landslide.
Design of Integrated Duplexing and Multi-Band Filtering Slot Antennas

In this paper, a duplexing antenna and two multi-band filtering antennas using resonator loaded slot are presented. The loaded slot can be flexibly designed as a dual-band or a tri-band antenna by changing the number of the loaded resonator. The operating frequencies of the slot antenna can be easily controlled through tuning the lengths and positions of the loaded elements. By integrating the multi-band antenna and the multi-mode resonator, duplexing and filtering antennas can be realized. The duplexing antenna consists of two single-mode resonators, a dual-mode stub-loaded resonator as the junction resonator and a dual-band slot antenna. The dual-band and tri-band filtering antennas each contains a dual-mode resonator or a tri-mode resonator, radiating through resonator loaded slot antenna. Since the slot is the only radiation element, all the operating bands of the three antennas show good bi-directional radiation patterns. For validation, all of the antennas are designed, manufactured and measured.
FlexMesh: Flexibly Chaining Network Functions on Programmable Data Planes at Runtime

Programmable data planes (PDP) enable operators to implement various functions (e.g. routing and access control) on high-performance switches and define the chains of these functions with a switch profile. However, with the number of deployed functions increasing, the switch profile faces growing complexity during development and inflexibility to chain functions at runtime. This paper presents FlexMesh, an integrated platform which aims to introduce flexibility and simplicity to PDP while being compatible with existing programmable devices. FlexMesh designs (1) a set of chaining primitives, so operators can easily describe the function chain for each flow without facing the complexity of customizing the switch profile during development; and (2) a data plane model that can be reconfigured at runtime and can flexibly construct user-desired function chains. We implement FlexMesh based on P4 and evaluate it on various targets. Results indicate that with minor performance overheads, FlexMesh can be an efficient development-assistance tool for operators, as well as an automated platform to chain NFs flexibly while keeping conformance to complex policies.
Simulating RPL Attacks in 6lowpan for Detection Purposes

The Internet of Things (IoT) integrates the Internet and electronic devices belonging to different domains, such as smart home automation, industrial processes, military applications, health, and environmental monitoring. Usually, IoT devices have limited resources and Low Power and Lossy Networks (LLNs) are being used to interconnect such devices. Routing Protocol for Low-Power and Lossy Networks (RPL) is one of the preferred routing protocols for this type of network, since it was specially developed for LLNs, also known as IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). In this paper the most well-known routing attacks against 6LoWPAN networks were studied and implemented through simulation, conducting a behavioral analysis of network components (resources, topology, and data traffic) under attack condition. In order to achieve a better understanding on how attacks in 6LoWPAN work, we first conducted a study on 6LoWPAN networks and RPL protocol functioning. Furthermore, we also studied a series of well-known routing attacks against this type of Wireless Sensor Networks and these attacks were then simulated using Cooja simulator provided by Contiki operating system. The results obtained after the simulations are discussed along with other previous researches. This analysis may be of real interest when it comes to identify indicators of compromise for each type of attack and appropriate countermeasures for prevention and detection of these attacks.
Factors favouring the development and maintenance of outbreaks in an invasive leaf miner Cameraria ohridella (Lepidoptera: Gracillariidae): a life table study

1 Life tables were constructed for seven generations of the invasive horse‐chestnut leaf miner Cameraria ohridella in Switzerland, to assess the factors allowing the moth to build and maintain outbreak densities and to identify ‘gaps’ among their mortality factors that could be targeted by new control methods. The fecundity of females was calculated and the mortality factors affecting all developmental stages determined.
Heart Failure Education Bundle to Improve Self-Care And Reduce Hospital Readmission

Background: The Center for Disease Control and Prevention has identified heart failure has a major health concern in the United States. There are 5.7 million Americans currently diagnosed with HF, with 550,000 cases annually. The total cost to manage HF is predicted to reach $69.5 billion, with 30-day hospital readmission as the greatest financial expenditure. The lack of patient education was noted as weakness for many healthcare organizations leading to an increase in hospital readmission. The urgency to manage HF more effectively has resulted in regulatory policies requiring healthcare providers and organizations to improve self-care HF management. Methods: An education bundle was created for HF patients and presented by a Nurse Practitioner, a Pharmacist, and a Registered Dietician. Patients were invited to attend the special educational sessions from a hospital generated list of HF patients with 1 or more 30-day readmission post hospital discharge. Results: The 3 participants who completed the program showed an increase in the total score in each of the 3 sections of the SCHIF V6.2 questionnaire in self-care behavior (6.3), self-care management (5.3) and self-care confidence (5.7), and were able to avoid hospital readmission at 30, 60 and 90 days. Conclusion: The HF education bundle has been shown to be an effective non-pharmacological intervention in the management of HF disease. HEART FAILURE EDUCATION: IMPROVING SELF-CARE 4 Table of
Analog IGZO Memristor With Extended Capabilities

In this paper an IGZO memristor with multiple states and analog tuning extended capability is reported. The device has a planar structure and is fabricated by magnetron sputtering on glass substrate. The device resistance could be gradually increased and decreased within the range of one order of magnitude. Larger resistance changes are also possible but they are mostly irreversible. The obtained memristor looks promising to be used as electronic synapse in hardware implemented artificial neural networks or for applications in analog computing and cryptography.
PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

This study proposes the prefixSpan based pattern mining using time sliding weight from streaming data. To discover sequential patterns, it applies a time sliding weight to create a structure of projected DB Tree. For the time sliding weight, a time window is applied to the sequential data to calculate the label and support of the window. When a projected DB Tree is designed, the time weight calculated for each pattern is inserted in a table. At this time, the tree is updated by deleting the node whose time weight is less than the reference value. For this reason, whenever data is updated, the tree is sorted again. The reordering process removes the pattern of less influence by applying time weights. Therefore, it is possible to construct a projected DB Tree that can extract influential patterns. The performance of the proposed method is evaluated in three aspects. Firstly, the conventional PrefixSpan algorithm is compared with the proposed PrefixSpan algorithm based on time sliding weight in terms of the pattern generation time according to size of data. Secondly, the fitness of the proposed algorithm is evaluated through cross-validation. Thirdly, GSP, SPADE, and prefixSpan algorithms are compared with the proposed algorithm through F-measure. As a result of the evaluation, the proposed algorithm improves accuracy 75% more than PrefixSpan algorithm and the sequential pattern algorithms of GSP and SPADE. Regarding the comparison of F-measure based on precision and recall, the proposed one improves its performance about 83%.
AUTOGENOUS IMMUNITY TO ENDOGENOUS RNA TUMOR VIRUS

The radioimmune precipitation assay using 3H-labeled AKR leukemia virus was applied to the detection and quantitation of natural serum antibodies directed against endogenous murine leukemia virus (MuLV) envelope antigens B6C3F1 and BALB/c mice, which have low natural incidences of leukemia and lymphoma, and AKR mice, which have a high incidence, were used in this study. Sera from mice of various age groups were assayed. A marked difference in age-associated levels of the autogenous immune response to endogenous murine leukemia virus was detected, and the quantitative differences among these strains were inversely related to the incidence of lymphoma. The radioimmune precipitation test as applied was 500 times more sensitive than virus neutralization. That the reactions we have observed are specific is suggested by several lines of evidence, including the nonreactivity of normal hamster and absorbed rat serum, the positive reaction of absorbed rat anti-AKR serum, the inhibition of precipitation of labeled virus by purified unlabeled virus, and isopycnic gradient analysis of the reactive products.
GGM-Net: Graph Geometric Moments Convolution Neural Network for Point Cloud Shape Classification

Geometric feature acts as an important role in point cloud shape classification tasks. Previous methods have proved that the geometric information of point clouds effectively improves the classification accuracy. Mo-Net firstly introduced geometric moments into point cloud shape classification, which, to fit the form of second order geometric moments, extends the number of input channels from three to nine. Unfortunately, similar to PointNet, Mo-Net cannot capture the local structures. To address this issue, we propose a graph geometric moments convolution neural network (GGM-Net), which learns local geometric features from geometric moments representation of a local point set. The core module of the GGM-Net is to learn features from geometric moments (termed as GGM convolution). Specifically, the GGM convolution learns point features and local features from the first and second order geometric moments of points and its local neighbors, respectively, and then combines these features by using an addition operation. In this way, a geometrical local representation about points is obtained, which leads to much surface geometry awareness and robustness. Equipped with the GGM convolution, GGM-Net, a simple end-to-end architecture, is developed to achieve a competitive accuracy on the benchmark dataset ModelNet40 and perform more efficiently in terms of memory and computational complexity.
Rethinking ACKs at the Transport Layer

Acknowledgements are a core component of transport protocols. They can send control information and be used to receive feedback about transmission progress, to measure responsiveness and capacity of the network path, and numerous other purposes. This paper takes a long look at the way acknowledgements are used across different transports and what ACK information is actually needed in new protocol designs. TCP’s use of ACKs has inspired the design of other Internet transports and was used as a model for QUIC.
Exploiting Text Matching Techniques for Knowledge-Grounded Conversation

Knowledge-grounded conversation models aim at generating informative responses for the given dialogue context, based on external knowledge. To generate an informative and context-coherent response, it is important to conjugate dialogue context and external knowledge in a balanced manner. However, existing studies have paid less attention to finding appropriate knowledge sentences from external knowledge sources than to generating proper sentences with correct dialogue acts. In this paper, we propose two knowledge selection strategies: 1) Reduce-Match and 2) Match-Reduce and explore several neural knowledge-grounded conversation models based on each strategy. Models based on Reduce-Match strategy first distill the whole dialogue context into a single vector with salient features preserved and then compare this context vector with the representation of knowledge sentences to predict a relevant knowledge sentence. Models based on Match-Reduce strategy first match every turn of the context with knowledge sentences to capture fine-grained interactions and aggregate them while minimizing information loss to predict the knowledge sentence. Experimental results show that conversation models using each of our knowledge selection strategies outperform the competitive baselines not only in terms of knowledge selection accuracy but also in response generation performance. Our best model based on Match-Reduce outperforms the baselines in the comparative studies with the Wizard of Wikipedia dataset. Also, our best model based on Reduce-Match outperforms them with the CMU Document Grounded Conversations dataset.
Selenium Source Effect on Broiler Carcass Characteristics, Meat Quality and Drip Loss

Abstract Downs, K.M. , Hess, J.B. and Bilgili, S.F. 2000. Selenium source effect on broiler carcass characteristics, meat quality and drip loss. J. Appl. Anim. Res., 18: 61–72. Two 49 day broiler growout studies were conducted to evaluate the influence of supplemental dietary selenium sources on meat drip loss, yield and quality parameters. Birds were fed a control diet with either no supplemental selenium, sodium selenite or selenium-enriched yeast. A random sample of test birds were processed to evaluate carcass yield, deboning yield, whole carcass and breast fillet drip loss, water holding capacity, expressible moisture and cooking loss. Carcass and deboning yield and meat quality were not significantly affected by the type of selenium added to broiler diets. However, drip loss of breast fillets was greatest when an inorganic source of supplemental selenium was used. When compared with current supplementation strategies using an inorganic form, an orġanic source of selenium (selenium-yeast) added to broiler diets may reduce product drip loss of deboned meat.