Miracle-dz/UniFAR-FTUs huggingface dataset:
AKSI UNIASIAH (Blood Transfusion Unit) Based on Android With Waterfall

Teknologi informasi merupakan salah satu teknologi yang sedang berkembang dengan pesat pada saat ini, sehingga informasi yang tersedia dapat berlangsung dengan cepat, efisien serta akurat. Pada Unit Transfusi Darah PMI ini sangat minim pelayanan informasi mengenai ketersediaan darah melalui mobile device sehingga banyak masyarakat yang kurang informasi mengenai persedian darah. Maka perlu ditemukan cara memecahkan masalah yang ada dengan menyediakan sebuah aplikasi berbasis android yang dapat diakses oleh masyarakat. Metode yang digunakan untuk membangun sistem ini menggunakan metode waterfall dan divisualisasikan dengan use case diagram . Hasil dari penelitian ini adalah aplikasi donor darah yang mampu menyediakan data stok darah, jadwal kegiatan donor dan lokasi, serta informasi donor darah.
Segmentation And Spatial Depth Ridge Detection Of Unorganized Point Cloud Data

Visual 3D data are of interest to a number of fields: medical professionals, game designers, graphic designers, and (in the interest of this paper) ichthyologists interested in the taxonomy of fish. Since the release of the Kinect for the Microsoft XBox, game designers have been interested in using the 3D data returned by the device to understand human movement and translate that movement into an interface with which to interact with game systems. In the medical field, researchers must use computer vision tools to navigate through the data found in CT scans and MRI scans. These tools must segment images into the parts that are relevant to researchers and account for noise related to the scanning process all while ignoring other types of noise such as foreign elements in the body that might indicate signs of illness. 3D point cloud data represents some unique challenges. Consider an object scanned with a laser scanner. The scanner returns the surface points of the object, but nothing more. Using the tool Qhull, a researcher can quickly compute the convex hull of an object (which is an interesting challenge in itself), but the convex hull (obviously) leaves out any description of an object’s concave features [4]. Several algorithms have been proposed to illustrate an object’s complete features based on unorganized 3D point cloud data as accurately as possible, most notably Boissonnat’s tetrahedral culling algorithm and The Power Crust algorithm [6] [1]. We introduce a new approach to the area partitioning problem that takes into consideration these algorithms’ strengths and weaknesses. In this paper we propose a methodology for approximating a shape’s solid geometry using the unorganized 3D point cloud data of that shape primarily by utilizing localized principal component analysis information. Our model accounts for three common issues that arise in the scanning of 3D objects: noise in surface points, poorly sampled surface area,
Geometric Camera Pose Refinement with Learned Depth Maps

We present a new method for image-only camera relocalisation composed of a fast image indexing retrieval step followed by pose refinement based on ICP (Iterative Closest Point). The first step aims to find an initial pose for the query by evaluating images similarity with low dimensional global deep descriptors. Subsequently, we predict with a fully convolutional deep encoder-decoder neural network a dense depth map from the image query. We use this depth map to create a local point cloud and refine the initial query pose using an ICP algorithm.We demonstrate the effectiveness of our new approach on various indoor scenes. Compared to learned pose regression methods, our proposal can be used on multiple scenes without the need of a specific weights-setup for each scene, while showing equivalent results.
Improving the performance of Virtualized Network Services based on NFV and SDN

Network Functions Virtualisation (NFV) proposes to move all the traditional network appliances, which require dedicated physical machine, onto virtualised environment (e.g,. Virtual Machine). In this way, many of the current physical devices present in the infrastructure are replaced with standard high volume servers, which could be located in Datacenters, at the edge of the network and in the end user premises. This enables a reduction of the required physical resources thanks to the use of virtualization technologies, already used in cloud computing, and allows services to be more dynamic and scalable. However, differently from traditional cloud applications which are rather demanding in terms of CPU power, network applications are mostly I/O bound, hence the virtualization technologies in use (either standard VM-based or lightweight ones) need to be improved to maximize the network performance. A series of Virtual Network Functions (VNFs) can be connected to each other thanks to Software-Defined Networks (SDN) technologies (e.g., OpenFlow) to create a Network Function Forwarding Graph (NF-FG) that processes the network traffic in the configured order of the graph. Using NF-FGs it is possible to create arbitrary chains of services, and transparently configure different virtualized network services, which can be dynamically instantiated and rearranges depending on the requested service and its requirements. However, the above virtualized technologies are rather demanding in terms of hardware resources (mainly CPU and memory), which may have a non-negligible impact on the cost of providing the services according to this paradigm. This thesis will investigate this problem, proposing a set of solutions that enable the novel NFV paradigm to be efficiently used, hence being able to guarantee both flexibility and efficiency in future network services.
A fuzzy decision scheme for relay selection in cooperative wireless sensor network

In energy‐constrained military wireless sensor networks, minimizing the bit error rate (BER) with little compromise on network lifetime is one of the most challenging issues. This paper presents a new relay selection based on fuzzy logic (RSFL) scheme which provides balance between these parameters. The proposed scheme considers node's residual energy and path loss of the relay‐destination link as the input parameters for the selection of the relay node. The relay node selection by fuzzy logic is based on prioritizing higher residual energy and minimum path loss. To evaluate the performance on wireless sensor network, we compare the proposed scheme with the three existing relay selection strategies, ie, random, maximum residual energy based relay selection (MaxRes), and minimum energy consumption based relay selection (MinEnCon). The simulation results of the proposed scheme in terms of network lifetime, BER, Network Survivability Index (NSI), and average energy of network nodes have been presented and compared with different relay selection schemes. The simulation results show that the proposed RSFL scheme has the lowest BER, moderate network lifetime, average energy, and NSI.
A Persistent Publish/Subscribe System for Mobile Edge Computing

In recent times, we have seen an incredible growth of users adopting mobile devices and wearables, and while the hardware capabilities of these devices have greatly increased year after year, mobile communications still remain a bottleneck for most applications. This is partially caused by the companies’ cloud infrastructure, which effectively represents a large scale communication hub where all kinds of platforms compete with each other for the servers’ processing power and channel throughput. Additionally, wireless technologies used in mobile environments are unreliable, slow and congestion-prone by nature when compared to the wired medium counterpart. To fix the back-and-forth mobile communication overhead, the “Edge” paradigm has been recently introduced with the aim to bring cloud services closer to the customers, by providing an intermediate layer between the end devices and the actual cloud infrastructure, resulting in faster response times. Publish/Subscribe systems, such as Thyme, have also been proposed and proven effective for data dissemination at edge networks, due to the interactions’ loosely coupled nature and scalability. Nonetheless, solely relying on P2P interactions is not feasible in every scenario due to wireless protocols’ range limitations. In this thesis we propose and develop Thyme-Infrastructure, an extension to the Thyme framework, that utilizes available stationary nodes within the edge infrastructure to not only improve the performance of mobile clients within a BSS, by offloading a portion of the requests to be processed by the infrastructure, but also to connect multiple clusters of users within the same venue, with the goal of creating a persistent and global end-to-end storage network. Our experimental results, both in simulated and real-world scenarios, show adequate response times for interactive usage, and low energy consumption, allowing the application to be used in a variety of events without excessive battery drainage. In fact, when compared to the previous version of Thyme, our framework was generally able to improve on all of these metrics. On top of that, we evaluated our system’s latencies against a full-fledged cloud solution and verified that our proposal yielded a considerable speedup across the board.
Recommendation algorithm for mobile E-commerce based on cone depth learning

ABSTRACT Aiming at the real user – commodity behavior data of Alibaba mobile e-commerce platform, the big data development of mobile e-commerce is analyzed in this paper, and a recommendation algorithm based on the mobile e-commerce deep learning is put forward. The study of the recommendation algorithms applies to the information age with the rapid growth of data. The results have verified that the recommendation algorithm proposed in this paper can extract useful information features from extensive information and recommend to the users who need such information to meet the information needs of different users, save the time required for users to search for the information, and improve the utilization of the information. These results indicate that the mobile e-commerce recommendation algorithm based on the cone depth learning has good practicality. It is concluded in this paper that the recommendation algorithm has a relatively high recommendation effect in the mobile e-commerce scenario and that its ideas and results have essential value for the study of mobile e-commerce.
Towards Extracting Protein-Compound Interactions from BioChemical Patents

Author(s): Pister, Kaiser Stefan | Advisor(s): Bergen, Leon | Abstract: We present in this work a protein entity tagging and normalization process focused on data extraction from biochemical patents. The project acts a single stage in the pipeline of general chemical interaction extraction. Novel to this work is the character embedded approach to mention identification and normalization. Additionally, this is the first work to use a siamese network and a prototypical network to augment protein database normalization. Our results show that character embeddings provide a reasonable approach to protein entity extraction achieving up to 6\% better results than previous work, and that normalization tasks can be improved significantly with a learned embedded space.
Developing epidemic forecasting models to assist disease surveillance for influenza with electronic health records

ABSTRACT Seasonal influenza is an important infectious disease monitored by the Centers for Disease Control. Monitoring, early detection, and prediction of influenza outbreaks can accelerate life-saving public health responses. However, ensuring completeness and timeliness of all surveillance systems can be difficult. Current influenza surveillance systems can be supported through the estimation of influenza activity, which we aim to achieve by defining influenza cases through electronic medical records (EMRs) from Chang Gung Memorial Hospital and constructing predictive models for predicting the epidemic trend for influenza. In total, 84,820 influenza cases were matching the case definition in 2010–2015. Using the exponential smoothing model, autoregressive integrated moving average (ARIMA) model, and Prophet model, we trained the models with influenza cases from 2010 to 2014; we then renewed the models at different intervals and compared their capacities. The results show that the ARIMA model performs best in both Linkou and Kaohsiung branches. In conclusion, we successfully constructed an influenza epidemic predictive model based on EMRs, which may be used as a decision support tool for improving influenza surveillance, controlling infection, and managing resources.
Design of RFID Tag Antennas at UHF Band with Different Materials

RFID is an automatic identification technology, that uses RF signal to identify the tagged objects and collect the relevant data in a non-contact manner. It has mainly two parts, reader and chip.The reader can activate if it receives sufficient power from chip. Chapter1describes a comprehensive literature survey on designing of RFID tag antenna, performance of RFID tag in terms of power reflection coefficient,effect due to materials and size, gain of the antenna and field of applications. Chapter 2 presents the narrow band tag antenna designing starting from the selection of basic shape of the radiator and how the structure is resonated at desired frequency by using slot, pin techniques etc. These models are fabricated to test the performance. Further, the designing is extended towards commercial chip.The performance of the antenna in terms of resonating frequency, and read range when antenna is placed on different materials will be observed in chapter3.Bandwidth enhancement techniques are described in chapter4. The gain enhancement techniques are described in chapter5.In Conclusions, remarks and future scope are discussed Chapter6. References and author publications are listed
Feedback Control for Online Training of Neural Networks

Convolutional neural networks (CNNs) are commonly used for image classification tasks, raising the challenge of their application on data flows. During their training, adaptation is often performed by tuning the learning rate. Usual learning rate strategies are time-based i.e. monotonously decreasing. In this paper, we advocate switching to a performance-based adaptation, in order to improve the learning efficiency. We present E (Exponential)/PI (Proportional Integral)-Control, a conditional learning rate strategy that combines a feedback PI controller based on the CNN loss function, with an exponential control signal to smartly boost the learning and adapt the PI parameters. Stability proof is provided as well as an experimental evaluation using two state of the art image datasets (CIFAR-10 and Fashion-MNIST). Results show better performances than the related works (faster network accuracy growth reaching higher levels) and robustness of the E/PI-Control regarding its parametrization.
Applications of Machine Learning in Cyber Security

With the exponential rise in technological awareness in the recent decades, technology has taken over our lives for good, but with the application of computer-aided technological systems in various domains of our day-to-day lives, the potential risks and threats have also come to the fore, aiming at the various security features that include confidentiality, integrity, authentication, authorization, and so on. Computer scientists the world over have tried to come up, time and again, with solutions to these impending problems. With time, attackers have played out complicated attacks on systems that are hard to comprehend and even harder to mitigate. The very fact that a huge amount of data is processed each second in organizations gave birth to the concept of Big Data, thereby making the systems more adept and intelligent in dealing with unprecedented attacks on a real-time basis. This chapter presents a study about applications of machine learning algorithms in cyber security.
One-Bit Compressed Sensing by Convex Relaxation of the Hamming Distance

—In this short note we provide new theoretical analysis of a tractable recovery algorithm for one-bit compressed sensing which is based on minimizing averages of Rectiﬁed Linear Units (ReLU) forming a convex relaxation of the Hamming distance. We provide recovery guarantees, which match theoretical state-of-the-art results, as well as comparison to existing methods in numerical simulations.
A “learning by design” application for modeling, implementing, and evaluating hardware architectures for artificial neural networks at undergraduate level

Artificial intelligence is currently playing an important role in engineering education. Artificial neural networks (ANNs), for example, are one of the most important topics in computer science and computer engineering curricula. The teaching of ANNs, however, is challenging and current efforts are mainly focused on providing computer systems and/or software simulators to enhance results. In the present study, we introduce a field programmable gate array‐based educational system focused on the modeling, implementation, and evaluation of hardware architectures of ANNs type Multilayer Perceptron. The system was designed by taking into account the learning by design approach to creating a modular proposal, which is composed of a set of predefined modules that describe the behavior of each element of an ANN. This feature allows various neural architectures to be easily defined and to be applied to different specific problems. The system helps the teaching‐learning process by providing a design environment where undergraduate students can apply the knowledge acquired in lecture‐based courses related to areas, such as pattern recognition and neural networks. The experimental results present the design and implementation of an ANN hardware architecture that solves the iris‐plant problem.
A novel smartphone application for indoor positioning of users based on machine learning

Smartphones are linked with individuals and are valuable and yet easily available sources for characterizing users' behavior and activities. User's location is among the characteristics of each individual that can be utilized in the provision of location-based services (LBs) in numerous scenarios such as remote health-care and interactive museums. Mobile phone tracking and positioning techniques approximate the position of a mobile phone and thereby its user, by disclosing the actual coordinate of a mobile phone. Considering the advances in positioning techniques, indoor positioning is still a challenging issue, because the coverage of satellite signals is limited in indoor environments. One of the promising solutions for indoor positioning is fingerprinting in which the signals of some known transmitters are measured in several reference points (RPs). This measured data, which is called dataset is stored and used to train a mathematical model that relates the received signal from the transmitters (model input) and the location of that user (the output of the model). Considering all the improvements in indoor positioning, there is still a gap between practical solutions and the optimal solution that provides near theoretical accuracy for positioning. This accuracy directly impacts the level of usability and reliability in corresponding LBSs. In this paper, we develop a smartphone app with the ability to be trained and detect users' location, accurately. We use Gaussian Process Regression (GPR) as a probabilistic method to find the parameters of a non-linear and non-convex indoor positioning model. We collect a dataset of received signals' strength (RSS) in several RPs by using a software which is prepared and installed on an Android smartphone. We also find the accurate 2σ confidence interval in the presented GPR method and evaluate the performance of the proposed method by measured data in a realistic scenario. The measurements confirm that our proposed method outperforms some conventional methods including KNN, SVR and PCA-SVR in terms of accuracy.
Approaches to Describe and Quantify Uncertainty in Bio-physical Agricultural Computer Simulation Models

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Biclustering and coclustering: concepts, algorithms and viability for text mining

Biclustering and coclustering are data mining tasks capable of extracting relevant information from data by applying similarity criteria simultaneously to rows and columns of data matrices. Algorithms used to accomplish these tasks simultaneously cluster objects and attributes, enabling the discovery of biclusters or coclusters. Although similar, the natures and aims of these tasks are different, and coclustering can be seen as a generalization of biclustering. An accurate study on algorithms related to biclustering and coclustering is essential to achieve effectiveness when solving real-world problems. Determining the values appropriate for the parameters of these algorithms is even more difficult when complex real-world data are analyzed. For example, when biclustering or coclustering is applied to textual data (i.e., in text mining), a representation through a vector space model is required. Such representation usually generates vector spaces with a high number of dimensions and high sparsity, which influences the performance of many algorithms. This tutorial aims to didactically present concepts related to the biclustering and coclustering tasks and how two basic algorithms address these concepts. In addition, experiments are presented in data contexts with a high number of dimensions and high sparsity, represented by both a synthetic dataset and a corpus of real-world news. In general and comparative terms, the results obtained show the algorithm used for coclustering (i.e., NBVD) as the most appropriate for the experiments’ context. Although the biclustering algorithm (i.e., Cheng and Church) was responsible for producing less relevant results in textual data than NBVD, its application in data with a high number of dimensions and high sparsity provided a suitable study environment to understand its operation.