Miracle-dz/UniFAR-FTUs huggingface dataset:
SVM-based Connection Classification and Load Balancing Mechanism

The load balancing mechanism of NAPT gateway in campus network directly affects the using experience of clients in campus network. In order to solve the impact of link quality caused by the gateway's load balancing between links of different quality on the connections with different requirements of clients, on the basis of CONNTRACK in Linux, we implemented a prototype of linear classification in traffic attribute system based on Support Vector Machine (SVM) we implemented a prototype of linear classification in traffic attribute system based on SVM with mean shift clustering, sampled and classified the fields of IP, port and flow length in the connections tracked by CONNTRACK, so that the connections with low jitter requirements and large bandwidth requirements are separated by SVM for load balancing, thus improving the using experience of clients. The generalization ability of the simple linear classification model is sufficient to classify the connections of these two attributes. From the comparative data, we can see that this scheme is effective.
Preserving Privacy in Convolutional Neural Network: An ∊-tuple Differential Privacy Approach

Recent breakthrough in neural network has led to the birth of Convolutional neural network (CNN) which has been found to be very efficient especially in the areas of image recognition and classification. This success is traceable to the availability of large datasets and its capability to learn salient and complex data features which subsequently produce a reusable output model $(F_{\theta})$. The $F_{\theta}$ are often made available (e.g. on cloud as-a-service) for others (client) to train their data or do transfer learning, however, an adversary can perpetrate a model inversion attack on the model $F_{\theta}$ to recover training data, hence compromising the sensitivity of the model buildup data. This is possible because CNN as a variant of deep neural network does memorize most of its training data during learning. Consequently, this has pose a privacy concern especially when a medical or financial data are used as model buildup data. Existing researches that proffers privacy preserving approach however suffer from significant accuracy degradation and this has left privacy preserving model on a theoretical desk. In this paper, we proposed an ∊-tuple differential privacy approach that is based on neuron impact factor estimation to preserve privacy of CNN model without significant accuracy degradation. We experiment our approach on two large datasets and the result shows no significant accuracy degradation.
Lebensweise und Alzheimer

Zusammenfassung Alzheimer ist eine Mangelkrankheit, verursacht durch Diskrepanzen zwischen unseren natürlichen Bedürfnissen und Folgen der heutigen Lebensweise: Chronischer Stress, ungesunde Ernährung, Mangel an Mikronährstoffen, Tiefschlaf, Bewegung, sozialen Aktivitäten sowie Verlust des Lebenssinns. Basierend auf einer Allgemeinen Theorie der Alzheimer-Entstehung (Unified Theory of Alzheimer‘s Disease, UTAD) beginnt die Erkrankung mit der gestörten Neubildung von Nervenzellen (Neurogenese) im hippocampalen Zentrum für das autobiografische Erinnern. Die mangelhafte adulte Neurogenese führt zunächst zu einem chronisch erhöhten Level an Stresshormonen, kurz- bis mittelfristig zu Depression und letztendlich zu den für Alzheimer typischen neuropathologischen und kognitiven Veränderungen. Durch gezieltes Beheben der individuellen Mängel (der kausalen Risikofaktoren) im Rahmen einer systembiologischen Intervention, lässt sich Alzheimer jedoch nicht nur vermeiden, im Frühstadium der Erkrankung ist der geistige Abbau sogar noch umkehrbar.
Tagging Part of Speech in Hausa Sentences

Despite the recent exponential growth of Hausa online content on websites like Aminiya.dailytrust. com.ng, Freedomradio.com, Hausa.Leadership. ng, Arewa24.com, and BBC.com/hausa, Sentiment Analysis on such Hausa web content have not been explored in the research community. Thus, Sentiment Analysis of Hausa based web content is a virgin researchable area. However, there are some pre-requisite activities prior to conducting sentiment analysis such as stemming/Lemmatization of words, and Part of Speech (POS) tagging. Unfortunately, for Hausa language, researches towards tools and techniques for these pre-requisite activities are still in the infancy stage, with little work done on stemming of words, but no work so far on Lemmatization and POS tagging. Consequently, this research work proposes a technique/model for POS tagging of Hausa sentences towards the realization of sentiment analysis of Hausa web content. The work is in progress. Preliminary results are encouraging.
No pollution Smart City Sightseeing Based on WSN Monitoring System

The ‘Smart city’ is an emerging concept in the last decade that covers many aspects of progressive city life. Many aspects of the Smart cities are inevitably related to ICT or more precisely to the Internet of Things (IoT). In this paper, we cover a smart environment and smart mobility in cities through a mobile application, sensors and web services that cover these aspects in smart cities. Having this in mind, we propose our IoT/Cloud-based ‘Smart city’ air pollution monitoring system. As the main contribution of this paper, we have designed and implemented a mobile application for air pollution indicators control and corresponding visualization. Additional novelty is the real time recommendations provided by our mobile application on the basis of air pollution parameters value.
An Improved Method for Identification of Pre-miRNA in Drosophila

Identification of microRNAs is important in studies of regulation of gene expression in many biologyical processes. In this study, we developed an improved method for identification of microRNAs in Drosophila. We used the iLearn, PyFeat, and Pse-in-One methods to extract the features and then used Max-Relevance-Max-Distance (MRMD2.0) and t-Distributed Stochastic Neighbour Embedding (t-SNE) to reduce dimension of the features and the random forest classifier in Weka to identify miRNAs. With this method, we found that the discriminative features for identification of pre-miRNAs were, in Drosophila melanogaster, the occurrences of G_GUG and C_AGU when the value of the feature vector was greater than 2, and in Drosophila pseudoobscura, the 4-tuple nucleotide composition and the occurrence of 4-length neighbouring nucleic acids when the value of the feature vector was less than 0.02. These vectors covered all compositional information or the frequency of bases. Classification results showed the classification accuracy was 95.7% and 93.6%, the precision rate was 95.8% and 93.6%, and the recall rate was 95.7% and 93.6% in Drosophila melanogaster and Drosophila pseudoobscura, respectively, which are higher than those reported in previous studies.
Experimental Studies of Tail Shapes for Hummingbird-Like Flapping Wing Micro Air Vehicles

The stability of flying of a hummingbird-like flapping-wing micro air vehicle (MAV) has been challenging. In this paper, experimental studies are reported on the tail shapes of hummingbird-like flapping-wing MAVs, since tails play an important role in-flight stability. Dynamics parameters of hummingbird tails are firstly studied and evaluated. Then man-made tails inspired by the natural hummingbirds are designed, manufactured and optimized for experimental tests. The results show that lift generated by the tail is independent of a fan angle, whereas the pitch moment is related to the fan angle. Further, the tail can be applied to stabilising hovering twin-wing flapping wing MAVs.
Uneven substrates constrain walking speed in ants through modulation of stride frequency more than stride length

Natural terrain is rarely flat. Substrate irregularities challenge walking animals to maintain stability, yet we lack quantitative assessments of walking performance and limb kinematics on naturally uneven ground. We measured how continually uneven 3D-printed substrates influence walking performance of Argentine ants by measuring walking speeds of workers from laboratory colonies and by testing colony-wide substrate preference in field experiments. Tracking limb motion in over 8000 videos, we used statistical models that associate walking speed with limb kinematic parameters to compare movement over flat versus uneven ground of controlled dimensions. We found that uneven substrates reduced preferred and peak walking speeds by up to 42% and that ants actively avoided uneven terrain in the field. Observed speed reductions were modulated primarily by shifts in stride frequency instead of stride length (flat R2: 0.91 versus 0.50), a pattern consistent across flat and uneven substrates. Mixed effect modelling revealed that walking speeds on uneven substrates were accurately predicted based on flat walking data for over 89% of strides. Those strides that were not well modelled primarily involved limb perturbations, including missteps, active foot repositioning and slipping. Together these findings relate kinematic mechanisms underlying walking performance on uneven terrain to ecologically relevant measures under field conditions.
An Approach to Mine SBVR Vocabularies and Rules from Business Documents

Enterprises model the behavior of their business to prepare a communication standard for business analysts and to specify requirements to Information Technology (IT) people. The communication gap between IT group and business analysts, who lie on the opposite end of the business spectrum exists due to the different terminologies used in their respective fields regarding the same context. This gap has led to major software failures which prompted the OMG group has come up with a new standard - Semantic of Business Vocabulary and Business Rules (SBVR). Declarative models are provided by SBVR to represent Business Vocabulary and Business Rules which can be understood by everyone working throughout the business spectrum. Each business is governed by business rules which are constrained by the regulation policy set up by the policy guidelines of the organization and government regulations set up on the organization. Business rules are specified in documents like user guides, requirement documents, terms and conditions, do's and don'ts. Typically a Business Analyst interprets the document and manually extracts rules based on his understanding which leads to potential discrepancies, ambiguities and quality issues in the software system. To minimize such errors, in this paper we present an unsupervised approach to automatically extract SBVR vocabularies and rules from domain-specific business documents. We also present our initial results and comparative study with our earlier approach.
Muscle Synergy Analysis on Upper Limb Movements of Human Arms

Upper limb movements of human arms are natural behaviors which require both the spatial and temporal coordination of multiple muscles. We now investigate how the neural strategy control upper limb movements in the human movement control. These upper limb movements include abduction, adduction, shoulder flexion and shoulder extension. According to our research, we make a hypothesis that a few muscle synergies across different subjects may have similar properties. To validate this hypothesis, we collected and analyzed EMG data in six healthy subjects. Nine muscles were required to reproduce the movement of each subjects. We used a nonnegative factorization approaches to identify muscle synergies during the tasks and to examine the functional significance of such synergies for natural behaviors. Through this finding, the neuromuscular control strategies of upper limb in these movements could be explained clearly, which may also provide significant evidence to support the hypothesis of muscle synergies and a basis for rehabilitation.
Se-DenseNet: Attention-Based Network for Detecting Pathological Images of Metastatic Breast Cancer

Clinically, the doctor judges the degree of tumor infiltration, and identifies whether it is metastasized by observing the histopathological section of the patient, and conducts targeted treatment for patients accordingly. Pathologists must be highly focused on the diagnostic process, but it is still prone to missed diagnosis. This paper presents a complete diagnostic system for the classification of pathological images of metastatic breast cancer, which can automatically identify metastatic breast cancer cells. In this paper, we use convolutional neural networks to extract images intensively and add attention mechanisms to score and re-learn the features to ensure that the effective features of metastatic cancer can be learned quickly and effectively. Furthermore, the generalization performance of the system is improved by using super-resolution to supplement image details. Eventually, the area under the corresponding curve (AUC) for a single model was 0.993, and the auc score on the kaggle test set was 0.001 higher than when the super-resolution treatment was not used. A final score of 0.975 was achieved on the KAGGLE Histopathologic Cancer Detection competition test set. This method can be used to assist doctors in examining pathological sections, and make the diagnosis results relatively objective.
StaticSLAM: A Dynamic Object-free Accurate SLAM System

Localization and mapping in the real scene where movable objects exist is a challenging task, however, most SLAM approaches generally assume that the scene is static. This assumption leads to building a map with movable objects, causing the localization to fail. We propose MODS, a movable object discrimination strategy which generates a knowledge base containing different attributes of an object, classify the object according to various attributes in the knowledge base, and take different measures for different categories. By using MODS, lidar data can be used in a selective manner to avoid data redundancy and reduce the probability of localization failure. We implement a complete SLAM system based on MODS, verify the influence of movable objects on SLAM with KITTI dataset, and also estimate our SLAM system. The qualitative experimental results show that our SLAM system can effectively eliminate the influence of movable objects on mapping and localization. Moreover, the quantitative experiment results show that the time required for localization was reduced by 16.0% on average compared with the state-of-the-art, while the loop-closure can still be correctly detected.
Fabric Defect Detection Using Fully Convolutional Network with Attention Mechanism

Because of the complex and diverse fabric image texture and defects, the traditional fabric defect detection algorithm has poor detection results and low efficiency. Visual saliency model can outstand the defect region from the complex background. However, the previous saliency detection models typically utilize hand-crafted image features to generate the saliency map, and it can only be used for some kinds of fabric type. In this paper, a deep saliency model generated by fully convolutional network with attention mechanism is proposed for fabric defect detection. First, the proposed model extracts multi-level and multi-scale features using Fully Convolutional Networks (FCN), this will improve the characterization ability for fabric texture. Then, the attention mechanism module is incorporated into the backbone network, thus the different feature map is assigned different weight, this further improves the effectiveness of the feature extraction. Finally, multi-level saliency maps are generated after deconvolution, and then fused by a series of short connection structures to better detect the salient region. Experiment results demonstrate that the proposed approach can accurately locate the defect region comparing with the state-of-art methods. Meantime, defect detection ability of the network model can be improved without significantly increasing the amount of calculation and parameters.
A Novel Method for Estimating Respiration Rate based on Ensemble Empirical Mode Decomposition and EKG Slope

The clinical monitor now mostly uses impedance IP (impedance pneumography) to measure respiratory signals. While in breathing, the movement of chest leads to position change of the EKG (Electrocardiogram) electrodes on the skin resulting in a change in impedance which can be used to estimate the respiratory rate. Measuring the EKG's impedance change for estimating the respiratory rate requires some specialized hardware. Other indirect methods for estimating respiratory rate, such as the EDR (EKG Derived Respiration), just simply utilize the EKG signal making use of the inherent variations in respiration wherein the respiratory rate is obtained from the parameter variations within the EKG waveform including RSA (Respiratory Sinus Arrhythmia) and R Peak Amplitude (RPA). This study proposes a new EDR method in which the square of the slope of the EKG waveform is calculated first and then followed by the moving average. The respiratory rate is obtained by the proposed algorithm that employs the modulated time series and compared to the results from RPA and RSA methods. The new method uses EEMD (Ensemble Empirical Mode Decomposition) to remove noise from EKG, reconstructs the respiratory signal by selecting the right IMF (Intrinsic Mode Function) as respiratory signal, and finally compares it with the nasal mouth pressure reference respiratory signal. The new RSS (R-peak Slope Square) method works with adaptive signal processing tool EEMD to obtain the EDR exploring the potential feasibility of clinical application in the future. The results demonstrate that the innovative methods proposed by this study are more accurate than that from RSA in elderly monitoring and nearly same performance as RPA (R-peak Amplitude) as well.
Detection of Pulmonary Nodules based on Reception and Faster R-CNN

Because most lung cancer originates from the canceration of lung nodules, it is very important to accurately locate lung nodules. The difficulty of lung nodule detection lies in the interference of other organs in lung CT images, the similarity of blood vessel cross sections and lung nodule cross sections in lung parenchyma, and the different size and edge shape of lung nodule. To solve these problems, this paper proposes a lung nodule detection algorithm based on the combination of Reception and Faster R-CNN, in which the Reception module is used to extract features, then the Faster R-CNN structure is used to classify and regress suspected lung nodule regions, and the residual network and the Inception structure are combined to form the Reception module. It is because the residual network can effectively alleviate the Vanishing gradient problem caused by the increase of network layers. The Inception structure can connect different convolution layers in parallel to enhance the utilization rate of internal resources. The experimental results show that the accuracy rate of this network is 92.8%, which is better than the existing methods.
OCR-friendly Image Synthesis using Generative Adversarial Networks

With quality being a deciding factor in the accuracy of OCR predictions for a given source image, there comes the need of pre-processing methods to improve the quality of an image before undergoing this process. Towards that, we present a GAN-based method targeted at improving the quality of source image in the fields of image resolution, blur and noise. The model uses an encoder trained to generate latent image representations for a low-quality image, the representations corresponding to blur and noise types present in the image. These representations act as inputs to the constructed conditional GAN. Besides these labels, the generator takes a low-quality image as input and is trained to generate a high-quality image as represented by the target images. The discriminator makes use of a standard OCR score to measure the performance of the generator in order to achieve images that are more OCR-friendly than the given target image. The model uses perceptual losses for training to produce clear images and improve convergence. Improving of image resolution is made by the use of super-resolution by means of sub-pixel convolution. Finally, the paper describes a series of experiments run on the model, as well as the results to show improvements in OCR performance using a single mode; for images suffering a variety of distortions. The results are quantified based on multiple performance measures and the final analysis is presented.
SWOT: Strength, Weaknesses, Opportunities, and Threats for Scaling Agile Methods in Global Software Development

Nowadays, software organizations are deploying agile methods in the geographically distributed environment to get several benefits i.e. significant return on investment it produces, fast project delivery, and managing the dynamic behavior of the project development. The execution of agile practices in Global Software Development (GSD) is more complex than collocated environment (single site development). The objectives of this study is to explore the factors which positively and negatively impact on scaling program of agile development in the GSD context and propose a framework based on the factors for successful scaling of agile programs in GSD context. This study is carried out in two stages. In the first stage, a Systematic Literature Review (SLR) protocol was adopted to investigate the factors. In the second stage, the SWOT analytical framework was followed to classify the factors into four categories: strengths, weaknesses, opportunities, and threats. In the SWOT framework, the factors in strengths and weaknesses categories represent the internal aspects of the organizations while the opportunities and threats categories indicate the external aspects of organization. The findings of this study reported 24 factors. The identified 13 factors positively and 11 factors negatively impact on scaling program of agile development respectively. Moreover, a framework based on SWOT analysis is proposed that provided the classification of factors into four categories. Based on the research outcomes, we can conclude that the identified factors and their classification into SWOT categories provide a robust framework that will assist the agile practitioners for successfully scale agile development in the GSD organizations.