Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis of Network Comments Based on GCNN

Recently, satisfactory performance has been achieved on sentiment analysis tasks by using recurrent neural network (RNN). As a derived model of RNN, gated recurrent units (GRU) model has a great advantage in dealing with long text sequence problems, while the performance is limited when working with short texts. Therefore, to effectively extract the contextual information of network comments, and take advantage of convolutional neural network (CNN) that can capture the local features, we propose a GCNN (gated convolutional neural network) model based on GRU and CNN. In this paper, we compare the proposed GCNN model with CNN and RNN models on standard datasets. Experiment results show that the GCNN model achieves state-of-the-art performance on the sentiment analysis tasks, with 94.9% and 90.0% F1-score on hotel comments (ChnSentiCorp) corpus and the IMDB corpus respectively.
An Improved K-Means Clustering Algorithm Based on Semantic Model

K-means algorithm is one of the most influential clustering algorithms in the field of data mining. It is widely used in many fields, such as school, daily consumption, transfer, and curriculum arrangement of different student groups. However, the traditional k-means algorithm is relatively sensitive to the initial cluster center, and the clustering result is excessively dependent on the initial center. In order to obtain a more accurate clustering result, we proposes a k-means algorithm based on semantic improvement. In this paper, we calculate the mesh density of the sample, set the density threshold to remove the outliers, and divide the core points, boundary points, noise points, optimized clusters according to the grid density of the data points, effectively reduce noise interference, and build the semantic relationship of the data in the cluster and optimizes the selection of the initial cluster center point. The simulation experiment is carried out by using five common datasets provided by the UCI database. The results show that the search method based on the improved k-means algorithm is reduced in the data iteration time compared with the prior art. Improvements have been made in terms of accuracy.
Clonal Propagation In Vitro of Paphiopedilum Hybrids from Adult Plants

The aim of this study was to develop an efficient protocol for shoot tip culture from adult plants of Paphiopedilum Pfitzer. A considerable seasonal effect on explant collection was observed in the aseptic cultures established from adult plants, including the survival and microbial contamination of explants. The shoot tip explants excised from adult plants in February and May showed higher survival and had less contamination than those explants excised in August and November. Moreover, the season of explant collection also affected the subsequent shoot forming capacity and multiplication of axillary buds. In Paphiopedilum ‘In-Charm Silver Bell’, higher shoot forming capacity was observed in February and May, whereas higher shoot multiplication was observed only in February. In Paphiopedilum ‘Hsinying Maudiae Leopard’, both February and May were optimal timing for shoot forming capacity and multiplication. We also demonstrated the effectiveness of transcinnamic acid (tCA), an antiauxin chemical in diminishing the apical dominance of shoot tip explant and thus improving the axillary bud outgrowth. In P. ‘In-Charm Silver Bell’, the addition of 100 μM tCA plus 13.3 μM 6-benzylaminopurine (BA) for 1 month promoted axillary shoot bud formation from shoot tip explants as compared with the control.
Predicting the learner's personality from educational data using supervised learning

Differences in the learners' personality have an impact on their learning outcomes and achievements. Therefore, there is a need to automatically predict and identify their personalities in an unobtrusive way, and build the learner model accordingly. In this paper, we try to identify the learner's personality dimensions, according to the big five personality model, using educational data features in order to develop an automatic classifier that predicts the learner's personality discreetly based on his/her traces in an online learning system. We applied seven different supervised learning classification algorithms, using personality scores for each dimension (high or low) as target values, and analyzed the results. The findings were encouraging and revealed that most of Big Five personality dimensions can in fact be predicted using mainly educational data features, which could have an added value on unobtrusive dynamic learner modelling.
Raftnet: Extract Task-aware Features for Pedestrian Attribute Recognition

Pedestrian attribute recognition, a multi-task problem, is a popular task in computer vision. Generally, deep learning end-to-end networks to predict attributes are the basic method to solve this problem. To fully use deep neural network, this paper proposes a novel network structure called Raft Block. Raft Block is designed not only to extract task-specific features, but also to share the features of different tasks. Using the Raft Block, we build an end-to-end network Raftnet for pedestrian attribute recognition. We implement experiments on three public datasets, the results prove that the design idea of Raft Block is valid and effective. Specifically, we achieve state-of-art results as 85.64% and 82.79% mean accuracy on Market-1501 and DukeMTMC datasets, and competitive result as 72.53% mAP on PA-100K dataset.
A beautiful image or not: a comparative study on classical machine learning and deep learning

With the development of web services and Apps on the Internet, food images are emerging into our life. Consumers from yelp or the dianping service upload a lot of food pictures every day. The images usually express the users' feelings and are shared among the social network. There have been different researches on the images. However, there is few research on how to evaluate the food image is beautiful or not. Therefore, we came up with an idea to classify food pictures by their appearance, which is meaningful in multiple applications, especially picking beautiful pictures to help businesses attract customers. In order to realize this idea, we collected 1067 food images through web crawling and questionnaires. Each image has a unique label: beautiful or not beautiful. Machine learning methods are used in this paper to model the data. CNN models in deep learning: VGGNet, AlexNet, and ResNet can get good results, e.g., ResNet can achieve the accuracy of 95.83%. However, with a good feature engineering job, the classifiers, which are random forest and support vector machine can reach a better accuracy of 99.63%. The experimental results indicate feature engineering is a vital issue in the food image evaluation problem, which lacks of labeled data.
Image-Text Embedding with Hierarchical Knowledge for Cross-Modal Retrieval

Heterogeneous data embedding is a process of mapping different kinds of data into a common vector space of a certain dimension. Image-text embedding also means mapping image and text data that have completely different characteristics into a common vector space. In this paper, we propose an image-text embedding method using hierarchical knowledge such as coarse and fine labels of text data. The proposed method improves the training efficiency of the embedding model by fixing the coarse label vectors. In addition, the loss function is designed by arbitrarily selecting the negative sample from the fine labels having a hierarchical relationship with the coarse label, so that the difference between the vectors of the fine labels which have same coarse label becomes larger. So, when the images that are visual data is mapped into a common vector space, the semantic of images becomes clear. Experimental results show that embedding with hierarchical knowledge has been successfully performed using the proposed methodology and that cross-modal retrieval can be efficiently performed through embedding model.
On station and on-farm evaluation of two Tanzania chicken ecotypes for body weights at different ages and for egg production

A study was carried out to evaluate genetic potential of two chicken ecotypes of Tanzania viz. Kuchi and Medium managed both under intensive and extensive management systems. Traits studied under intensive conditions included body weights at 8 (Bwt8), 12 (Bwt12), 16 (Bwt16), and 20 (Bwt20) weeks of age, age at sexual maturity (AFE), egg number in the first 90 days after sexual maturity (EN-90), egg weight (EW), eggshell thickness (STH), egg shape index (ESI), while under extensive management it involved only body weights at various ages. There were significant differences between ecotypes with respect to body weights (P 0.05). Bwt8, Bwt12, Bwt16 and Bwt20 under intensive management averaged over both sexes for Kuchi and Mediumecotypes were 490 and 404 g; 954 and 776 g; 1394 and 1183 g and 1647 and 1447 g, respectively. Corresponding body weights under extensive management were 348 and 273 g; 685 and 581 g; 974 and 845 g; 1188 and 1046 g, respectively. Average AFE, EN-90, EW, and ESI under intensive managements forKuchi and Medium ecotypes were 173 and 168 days; 45 and 49 eggs; 45 and 42 g; 75 and 74%, respectively, while average STH in both ecotypes was around 37 µ. From these results, it was concluded that Kuchi was superior to Mediumecotype in terms of body weights and converse was true for most of egg production and related traits, and further that their performance can further be improved by improving both management system and improving their genetic potential through within ecotype selection. Since Kuchi ecotype was superior toMedium ecotype in terms body weights and opposite was true for Mediumecotype in terms of most egg production and related traits. Therefore, Kuchiecotype could be good starting genetic material for further improvement in body weight, and Medium ecotype in egg production traits. Key words: Body-weights, egg-traits, chicken-ecotype.
Effects of different feeding frequencies on growth performance and feed consumption in juvenile turbot (Scophthalmus maximus Linnaeus, 1758)

Bu arastirma farkli yemleme sikliklarinin yavru kalkan ( Scophthalmus maximus Linnaeus, 1758)’da buyume, yem tuketimi ve yemden yararlanma oranina etkilerini belirlemek amaciyla yurutulmustur. Alti muamele, haftada 6 veya 7 gun ikiser ogun (sirasiyla 6:1/1 ve 7:0/2), bir gun ikiser ogun yemleme ve ardindan bir veya iki gun ac birakma (sirasiyla 1:1/2 ve 1:2/2), iki gun cift ogun yemleme bir gun ac birakma (2:1/2) ve haftada alti gun tek ogun (6:1/1) seklinde olmustur. Denemede ortalama baslangic agirligi 110.3±0.5 g olan baliklarla 6 hafta surmus ve baliklar her ogunde doyuncaya kadar yemlenmistir. Her bir muamele uc tekerrurlu olarak test edilmistir. Deneme sonunda 7:0/2 ve 6:2/2 muamelelerindeki baliklar 1:2/2’dekilerden onemli derecede daha yuksek buyume orani gostermistir (p<0.05). Buyume hizinda gozlenen bu farkliliklar, tuketilen yem miktarinin bir yansimasi sonucu olmus; daha sik beslenen baliklar toplamda daha fazla yem tuketmisler ve 7:0/2, 6:2/2 ve 2:1/2’dek i bireyler 1:2/2’dekilere gore onemli derecede daha fazla tuketmislerdir (p<0.05). Buna zit olarak, gunluk yem tuketimi veya ogun basina yem tuketimi yemleme gunu ile zit bir iliski gostermistir (sirasiyla, n= 18, r 2 =0.89 ve n=18, r 2 =0.87). Yemden yararlanma etkinligi muamelelerden etkilenmemistir. Maksimum buyume icin gerekli minimum ogun sayisi kuadratik kirik modeli ile 1.756 olarak tahmin edilmis ve bu da asagi yukari hafta 6 gun cift ogunlu uygulamaya esit bulunmustur.
Expressway Crash Prediction based on Traffic Big Data

With the development of society, the number of vehicles increases rapidly. The vehicle plays an important role in people's life, however the problem of traffic safety caused by vehicles has also become increasingly prominent. In China, the high crash rate and casualty rate on expressways have always troubled traffic management department. So crash prediction on expressway becomes vital. Conventionally, crash prediction is based on traffic flow data. These data do not contain all the necessary factors. In this paper, we propose a method of prediction using real-world data, including historical accident data, road geometry data, vehicle speed data, and weather data. We treat the crash prediction problem as a binary classification problem. For classification, sample imbalanced is a great challenge in practice. Modifying sample weights is applied to handle this challenge. Three machine learning classification techniques, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and Xgboost, are considered to carry out the crash prediction task respectively. The best recall and precision rate of these models are respectively 0.764253 and 0.01062. The proposed method can be integrated into urban traffic control systems toward police dispatch and crash prevention.
Investigation of Genetic Diversity in Afghan Bread Wheat Genotypes Using SSR and AFLP Markers

Genetic diversity assessment is the principle component for conservation and characterization of germplasm. Genetic diversity study of Afghan bread wheat genotypes is a first step to identify and to select high performance genotypes and distribute to wheat breeding programs. The main objective of this study is to investigate of genetic diversity in 35 Afghan bread wheat genotypes by using Simple Sequence Repeat (SSR) and Amplified Fragment Length Polymorphism (AFLP) markers. DNA extraction according to Cetyl Trimethyl Ammonium Bromide (CTAB) method was conducted and the total genomic DNA was isolated from each variety. Sixty-four SSR primer markers were used and eighteen EcoRI+(N)/MseI+(N) primer combinations with their primer sequences were used for selective polymerase chain reaction (PCR) amplification. Every SSR and AFLP fragment was scored as present (1) or absent (0) within all genotypes under study. Marker/ Value ratio of pairwise genetic distance between genotypes according to the SSRs data was from 0.508 to 0.691 with an average distance of 0.599. Relatively different grouping pattern in comparison to AFLP data observed through cluster analysis. Both types of molecular markers (AFLP and SSR) used in this research proved to be suitable for investigating genetic diversity in the genotypes of Afghan bread wheat, however, AFLP markers gave better view of genetically relationships among genotypes than the SSR markers. The grouping generated by AFLP data showed a special agreement with the origin regions of genotypes (Ariana-07 and Mazar-99 originating from the north of Afghanistan, Lalmi-03 and Kabul-02. Large number of DNA bands identified with AFLP markers might provide a better estimation of genetic similarity than those of SSR markers.
Deep Learning Using Risk-Reward Function for Stock Market Prediction

Many recent studies have attempted to apply a deep learning approach to build a model for stock market prediction. Most of these studies have concentrated on using prediction accuracy as a performance metric. Some of them have also performed trading simulations to evaluate financial performance. However, financial performance was not improved significantly because the loss function used in the training process focused primarily on prediction accuracy. In this paper, we propose a new framework to train a deep neural network for stock market prediction. A new loss function was developed by adding a risk-reward function, which is derived by the trading simulation results. A new scoring metric called Sharpe-F1 score, which is a combination of Sharpe ratio and F1 score is used for model selection. We employ the best prediction model from our previous work, which consists of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) architectures and takes event embedding vectors, historical prices and a set of technical indicators as inputs. The robustness of our framework is evaluated on two datasets by varying the key parameters used in the proposed framework. The results show that financial performance can be improved by adding a risk-reward function into the loss function used in the training process.
A particle swarm optimization-based container scheduling algorithm of docker platform

Docker is rapidly changing the rules of operation in the field of cloud computing, and completely subverts the development of cloud technology. Swarm is a Docker container-based cluster management tool. By analyzing and researching the overall architecture and scheduling strategy of Swarm, in this paper, we propose a Particle Swarm Optimization-based container scheduling algorithm of Docker platform, which to have solved the problem of insufficient resource utilization and load balance. We distribute application containers on Docker hosts, balance resource usage, and ultimately improve application performance. Experimental results show that the performance of the PSO is improved 20% and 19% higher than the spread and improved random respectively under the same host configuration.
Hydrogen Sulfide Oxidation: Adaptive Changes in Mitochondria of SW480 Colorectal Cancer Cells upon Exposure to Hypoxia

Hydrogen sulfide (H2S), a known inhibitor of cytochrome c oxidase (CcOX), plays a key signaling role in human (patho)physiology. H2S is synthesized endogenously and mainly metabolized by a mitochondrial sulfide-oxidizing pathway including sulfide:quinone oxidoreductase (SQR), whereby H2S-derived electrons are injected into the respiratory chain stimulating O2 consumption and ATP synthesis. Under hypoxic conditions, H2S has higher stability and is synthesized at higher levels with protective effects for the cell. Herein, working on SW480 colon cancer cells, we evaluated the effect of hypoxia on the ability of cells to metabolize H2S. The sulfide-oxidizing activity was assessed by high-resolution respirometry, measuring the stimulatory effect of sulfide on rotenone-inhibited cell respiration in the absence or presence of antimycin A. Compared to cells grown under normoxic conditions (air O2), cells exposed for 24 h to hypoxia (1% O2) displayed a 1.3-fold reduction in maximal sulfide-oxidizing activity and 2.7-fold lower basal O2 respiration. Based on citrate synthase activity assays, mitochondria of hypoxia-treated cells were 1.8-fold less abundant and displayed 1.4-fold higher maximal sulfide-oxidizing activity and 2.6-fold enrichment in SQR as evaluated by immunoblotting. We speculate that under hypoxic conditions mitochondria undergo these adaptive changes to protect cell respiration from H2S poisoning.
A fast spectrum sensing and revocation testbed based on USRP and GNU radio for cognitive radio networks

Since the use of wireless devices in the already overcrowded licensed and unlicensed bands had been further growing recently. Realization of wireless communications in Cognitive Radio (CR) is one of the most urgent research and development issues to accommodate rapidly increasing wireless access. In order to ensure avoidance of interference from Secondary Users (SUs) to Primary Users (PUs), adoption of White Space Database (WSDB) is one of the important strategies. In this paper, we have implemented the PAWS API and WSDB for spectrum allocation and show that mechanism operations with an experimental DSA network, which enables the SUs to share the White Space (WS) of PUs in a fine geolocation and timescale. In the experimental setup, the DSA network consists of the secondary transmitter node (STN) and secondary receiver node (SRN), a sensing node (SN), and primary transmitter node (PTN). The network is constructed by using the proposed spectrum sensing and revocation testbed. The testbed employs the Universal Software Radio Peripheral (USRP) devices and open source software, named GNU radio, to develop the spectrum sensing and revocation mechanism. We demonstrate the communication between the STN and the SRN, while avoiding interference to PTN which operates in the same spectrum. The experimental shows that the network is capable of detecting PTN in real time and revocation spectrum adaptively to avoid interference to primary receiver node (PRN). The experimental results are presented to show that the performance of proposed mechanism in terms of the expending time of sensing available channels and revocation spectrum.
Virus-induced gene silencing for phenylalanine ammonia-lyase affects pepper adaption to low temperature

Reverse genetics approaches in plants rely on post-transcriptional gene silencing to study the function of genes. In particular, virus-induced gene silencing (VIGS) has been successfully applied to identify gene function in some crops. To date, it is unclear whether phenylalanine ammonia-lyase (PAL) is involved in low temperature tolerance in the pepper. Here, we used an agroinfiltration protocol with tobacco rattle virus (TRV) constructs containing partial sequences from CaPAL for VIGS to test its role in anthocyanin biosynthesis and response to low temperature in the pepper (Capsicum annuum). We found that accumulation of anthocyanins in the leaves of pepper plants transformed with the TRV2:CaPAL vector was significantly reduced compared with peppers transformed with the empty TRV2 vector (TRV2:00). A significant reduction in expression of genes related to anthocyanins synthesis was also detected in peppers transformed with TRV2:CaPAL. When silenced pepper plants were exposed to a low temperature, we found decreased antioxidant system, PAL activity, and photosynthesis in plants transformed with TRV2:CaPAL compared with peppers transformed with TRV2:00. Low transcriptions of cold stress-response genes demonstrated that pepper tolerance to low temperature decreased. Future studies focused on the interaction between CaPAL and other abiotic and biotic stressors will shed further light into the role of CaPAL in stress response.
A Dangerous Goods Detection Approach Based on YOLOv3

In modern life, safety inspection plays an indispensable role at almost every corner of the society. Owing to the rapid growth of urban population and travel demand, people's requirement for security is growing. The measures of safety inspection changes from manual work to auxiliary machinery in the wake of scientific-technical progress. In the recent decades, as X-ray scanners have been widely used in safety inspection, real-time dangerous goods detection in X-ray images is more and more important. However, some dangerous goods are so small that it is difficult to detect them. In this paper, we present a detection method based on YOLOv3 which preprocess the data sets to improve detection accuracy of small objects. We divide the original images into several sub-images that are mainly in size of 416×416 by the contours of luggage. By training YOLOv3 with these sub-images, we reduce the errors of resizing input images. The experiment results show that our method perform better than YOLOv3 in small object detection.