Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Alzheimer’s Disease from MRI Data Using a Lightweight Deep Convolutional Model

Alzheimer’s disease (AD) is a progressive brain disorder affecting millions of people worldwide. An accurate diagnosis of AD plays a significant role in identifying the progression of the disease at its prodromal stage, i.e., mild cognitive impairment (MCI). In this paper, we propose a lightweight deep model to classify the patients into diagnostic groups, AD vs. normal control (NC) or progressive MCI (pMCI)vs. stable MCI (sMCI), with high accuracy, using MRI data. The proposed model uses separable and attention-based convolution operations. The separable convolution can reduce the complexity of the model by splitting a kernel into two separate kernels that do depth-wise and pointwise convolution operations, respectively. Moreover, integrating an attention-based convolution, which concatenates the convolutional and attentional feature maps, can capture the most relevant features for improved classification with fewer filters. From the experimental results on the Alzheimer’s disease neuroimaging initiative (ADNI) database, compared to the state-of-the-art methods, it is observed that the proposed method shows significant improvement in the classification performance in terms of accuracy, specificity, sensitivity, and AUC. In addition, the proposed method drastically reduces the number of parameters without affecting the performance.
The Realization of Mobile Robot’s Dynamic Obstacle Avoidance with Deep Reinforcement Learning Based on Deterministic Strategy Gradient

When the deep reinforcement learning algorithm based on visual perception is applied to the issue of robot’s dynamic obstacle avoidance, the perception ability of convolutional neural network is combined with the decision control ability of reinforcement learning, and the process from raw visual input to decision action output is realized. But the application scope of deep reinforcement learning algorithm based on Q value is still in low-dimensional and discrete action space. If the continuous action space is discretized, the problem of excessively huge motion space and extremely difficult convergence of network model will be caused. Besides, fine adjustment cannot be realized for the network model, and meanwhile, the division of motion space will also result in information loss. Hence, a deep reinforcement learning algorithm based on deterministic strategy gradient was proposed in this paper, and the strategy was parameterized via convolutional neural network through the integration of reinforcement learning algorithms based on strategy and value.
Deep Recurrent Neural Networks for Building-Level Load Forecasting

Load forecasting plays a crucial role in the day-to-day operations of electric utilities, especially in modern power systems, where a significant share of power generation is attributable to renewable sources. Over the years, several algorithms have been developed to tackle this problem, on time scales ranging from a few hours to several months. Most recent solutions have employed machine learning techniques such as deep learning to increase the granularity of the prediction, down to the single-building level. Here, we employ a framework based on long short-term memory networks to estimate the average power consumption of a single building equipped with solar panels. We show which measurements are more important for an accurate forecast and test several prediction horizons in order to find the best trade-off between training speed and prediction accuracy. Our results reinforce the notion that long short-term memory networks can be successfully used for short-to medium-term load forecasting.
Investigation of Application Layer DDoS Attacks Using Clustering Techniques

The exponential usage of internet attracts cyber criminals to commit crimes and attacks in the network. The forensic investigator investigates the crimes by determining the series of actions performed by an attacker. Digital forensic investigation can be performed by isolating the hard disk, RAM images, log files etc. It is hard to identify the trace of an attack by collecting the evidences from network since the attacker deletes all possible traces. Therefore, the possible way to identify the attack is from the access log traces located in the server. Clustering plays a vital role in identifying attack patterns from the network traffic. In this paper, the performance of clustering techniques such as k-means, GA k-means and Self Organizing Map (SOM) are compared to identify the source of an application layer DDoS attack. These methods are evaluated using web server log files of an apache server and the results demonstrate that the SOM based method achieves high detection rate than k-means and GA k-means with less false positives.
Characterization and Mitigation of IR-Drop in RRAM-based Compute In-Memory

Compute in-memory (CIM) is an exciting circuit innovation that promises to increase effective memory bandwidth and perform computation on the bitlines of memory sub-arrays. Utilizing embedded non-volatile memories (eNVM) such as resistive random access memory (RRAM), various forms of neural networks can be implemented. Unfortunately, CIM faces new challenges traditional CMOS architectures have avoided. In this work, we characterize the impact of IR-drop and device variation (calibrated with measured data on foundry RRAM) and evaluate different approaches to write verify. Using various voltages and pulse widths we program cells to offset IR-drop and demonstrate a $136.4 \times $ reduction in BER during CIM.
Enabling Fog Computing based Dynamic Security Service Function Chaining for 5G IoT

Fog computing is an edge computing strategy which fuels adaptation of Internet of Things (IoT) in many domains. The decentralized and dynamically deployable features of the fog nodes are useful to satisfy the service requisites of IoT nodes. However, security is a prime concern for both fog, and IoT deployments; where their inhibited limited resources are expanding the threat landscape for the malicious adversaries towards resource exhaustive attempts. Despite the available security servicing tools being effective, a singular service is not adequate to address all the intricacies of the contrived threat landscape. Hence, the requirement of multiple security services to operate in a cooperative domain is an obvious fact. However, integrating multiple security services in a resource constrained fog node is a challenge. Thus, in this research, we are leveraging Service Function Chaining (SFC) concept as a method to deploy multiple security services/ tools in a resource constrained edge node (i.e. Raspberry Pi), while evaluating its adaptability in a developed experimental virtual platform. The implemented SFC strategy on the fog node is compared with a resourceful virtual machine to understand the performance issues.
SNNIM: A 10T-SRAM based Spiking-Neural-Network-In-Memory architecture with capacitance computation

Spiking-Neural-Networks (SNN) have natural advantages in high-speed signal processing and big data operation. However, due to the complex implementation of synaptic arrays, SNN based accelerators may face low area utilization and high energy consumption. Computing-In-Memory (CIM) shows great potential in performing intensive and high energy efficient computations. In this work, we proposed a JOT-SRAM based Spiking-Neural-Network-In-Memory architecture (SNNIM) with 28nm CMOS technology node. A compact JOT-SRAM bit-cell was developed to realize signed 5bit synapses arrays and configurable bias arrays (SYBIA). The soma array based standard 8T-SRAM (SMTA) stores the soma membrane voltage and the threshold value. A capacitance computation scheme (CCA) between them was proposed to support various SNN operations. The proposed SNNIM achieved energy efficiency of 25.18 TSyOPSI. And the proposed SNNIM achieved 1.79+× better array efficiency compared with previous works.
Comparative and ensemble visualization of diffusion tensor fields

Scientific visualization of tensor fields is challenging due to the complex and multivariate nature of tensor data. The visualization of multiple tensor fields becomes even more difficult, and still in its infancy. This thesis aims at contributing visual analysis techniques for multiple 3D tensor fields. We focus specifically on the visual analysis of Diffusion Tensor Imaging (DTI) datasets. DTI is a magnetic resonance imaging (MRI) based modality, which is commonly used in neuroscience to investigate brain white matter in vivo. It requires a long scanning time compared to other imaging modalities. Acceleration of MRI acquisitions has the potential to improve the applicability of DTI. Compressed sensing (CS) is a signal reconstruction technique that is used to accelerate MRI acquisitions. The traditional CS method aims at optimizing the global quality of the reconstructed image. However, in practice, the quality of local structures is often of more interest. Therefore, we investigate CS for this purpose and contribute in this direction by adapting the traditional CS reconstruction method to focus on the quality of local structures.
REFLECTIVE TEACHING IN THE ENGLISH TEACHING AND LEARNING PROCESS AT SMA NEGERI 5 KENDARI

The purpose of this study was to investigate reflective teaching in the English Teaching and Learning process at SMA Negeri 5 Kendari. This was a qualitative research with a case study as the method of the study. The data were collected through questionnaire, interview, and observation while the data sources were five English teachers of SMA Negeri 5 Kendari. The result of the study showed that the teachers applied reflective teaching by using some strategies of reflective teaching, namely video recording, observation, action research, students’ feedback, and joined workshop. The next finding showed that the teachers understood how to define reflective teaching and recognize the importance of being reflective in teaching are: the teachers learnt new things while teaching; the teachers learnt from their own experiences as learners and as teachers; and the teachers developed their teaching skills and continuously improved their knowledge. The last finding showed that in doing reflective teaching, the teachers had some difficulties such as lack of time because they had workloads to be done outside of teaching hours in the classroom. The teachers got difficulties in encouraging their colleagues who lacked of readiness and focus of being reflective.
Covert Channel by Abusing X509 Extensions

Malicious actors in the world are using more ingenuity than ever for both data infiltration and exfiltration purposes, also known as command and control communications. In this paper I aim to describe a system that could be used to send or receive data from both a client and a server perspective utilizing research into x509 certificates specifically in areas where you can place arbitrary binary data into the certificate or utilizing them as a covert channel. While lots of attention is given to data infiltration and exfiltration techniques they are commonly done so after they’ve been used in an incident, making this area of cyber security very retroactive in a defensive posture. The aim in presenting this material is to demonstrate that we can take some lessons from the other areas of cyber security research, namely exploitation, and look at potential use cases in how malware authors could utilize technologies outside of their intended purposes to not only accomplish their goals but also end up bypassing common security measures in the process. Doing this sort of research can lead to more advances in defensive security postures by spurring discussions in the community on how a technique either does or doesn’t bypass security measures.
Performance Analysis for Full-Duplex Random Access in Distributed Networks

5G 이동통신의 기술발전에 따라 고속의 데이터통신이 요구되고 대역폭을 효율적으로 사용하기 위한 기술들이 연구되고 있다. 전이중 통신방식은 같은 자원을 이용해서 동시에 송수신이 가능하도록 하는 기술이며 기존의 반이중 통신방식에 비해서 대역폭 효율을 증가시킬 수 있기 때문에 차세대 통신기술로 각광받는 기술이다. 하지만, 전이중 통신 방식은 자기간섭신호에 대한 상쇄기술이 필요하고 많은 단말들이 존재할 경우 새로운 간섭시나리오가 발생하게 된다. 본 논문에서는 전이중 통신방식에 대한 간섭시나리오를 가정하여 반이중 통신에 대한 Random access에서의 성능차이를 시뮬레이션을 통하여 보여주었다. 전이중 통신 방식에서는 양방향의 데이터 전송 이외에 중계기 역할의 전송형태가 나타날 수 있고 각각의 전송형태에 대한 확률을 이용하여 처리량을 계산했고 ON/OFF 시스템 모델로 나타내어 분석하였다. 또한, Stochastic 접근법을 이용하여 실제 환경과 비슷한 대규모 분산 네트워크 시스템에서의 성능을 분석하였고, 일반적인 모델을 가정하였을 때 전이중 통신방식은 반이중 통신방식에 비해 약 3배 정도의 성능이득이 나타나는 것을 확인하였다. ⓒ 2015 DGIST
Real-Time Beamforming Algorithms for the Focal L-Band Array on the Green Bank Telescope

Real-Time Beamforming Algorithms for the Focal L-band Array on the Green Bank Telescope (FLAG) Mark William Ruzindana Department of Electrical and Computer Engineering, BYU Master of Science A phased array feed (PAF) provides a contiguous, electronically synthesized wide field of view for large-dish astronomical observatories. Significant progress has been made in recent years in improving the sensitivity of PAF receivers though optimizing the design of the antenna array, cryogenic cooling of the front end, and implementation of real-time correlation and beamforming in digital signal processing. FLAG is a 19 dual-polarized element phased array with cryogenic LNAs, direct digitization of RF signals at the front end, digital signal transport over fiber, and a real time signal processing back end with up to 150 MHz bandwidth. The digital back end includes multiple processing modes, including real-time beamforming, real-time correlation, and a separate real-time beamformer for commensal radio transient searches. Following a polyphase filterbank operation performed in field programmable gate arrays (FPGAs), beamforming, correlation, and integration are implemented on graphical processing units (GPUs) that perform parallelized operations. Parallelization greatly increases processing speed and allows for real-time signal processing. During a recent test/commissioning of FLAG, Tsys/efficiency of approximately 28 K was measured across the PAF field of view and operating bandwidth, corresponding to a system temperature below 20 K. To demonstrate the astronomical capability of the receiver, a pulsar (PSR B1937+21) was detected with the real-time beamformer. This thesis provides details on the development of the FLAG digital back end, the real-time beamformer, and reports on the commissioning tests of the FLAG PAF receiver developed by the National Radio Astronomy Observatory (NRAO), Green Bank Observatory (GBO), West Virginia University (WVU), and Brigham Young University for the Green Bank Telescope (GBT).
Un hallazgo singular de un grupo de quipus inca en Huaycán De Cieneguilla

Sabemos que el quipu inca era un instrumento sofisticado utilizado en el mantenimiento de los registros del Tawantinsuyu, a cargo de funcionarios enviados desde los centros administrativos estatales hacia los pueblos, con la misión de recolectar información sobre la población de cada comunidad dentro del territorio inca. En el caso de la Costa Central, se menciona que entre 1460 a 1470 habría ocurrido su conquista a manos del general Tupac Yupanqui, durante el gobierno del Inca Pachacutec, siendo anexado el Señorío Ychsma a la creada Provincia Inca de Pachacamac.
Generalized reliability based on distances

The intraclass correlation coefficient (ICC) is a classical index of measurement reliability. With the advent of new and complex types of data for which the ICC is not defined, there is a need for new ways to assess reliability. To meet this need, we propose a new distance‐based ICC (dbICC), defined in terms of arbitrary distances among observations. We introduce a bias correction to improve the coverage of bootstrap confidence intervals for the dbICC, and demonstrate its efficacy via simulation. We illustrate the proposed method by analyzing the test‐retest reliability of brain connectivity matrices derived from a set of repeated functional magnetic resonance imaging scans. The Spearman‐Brown formula, which shows how more intensive measurement increases reliability, is extended to encompass the dbICC.
NHS screening programmes

This is the first article in a series that will focus on a range of NHS screening programmes that are available across the UK. The provision of NHS screening programmes available in each country of the UK differs slightly, as do public health services. This first article provides insight and understanding of the thinking behind population screening, and explains the differences between screening and testing. Various charities and voluntary organisations are working with statutory bodies to encourage screening uptake among various populations; they are calling for better information and easier access to the various screening programmes. The healthcare assistant and assistant practitioner (HCA and AP) are public health practitioners, whose role in improving screening uptake is highlighted in this article.
KAJIAN KARAKTERISTIK PURE KERING UBI JALAR DENGAN PERLAKUAN SUHU DAN LAMA ANNEALING SEBAGAI SEDIAAN PANGAN DARURAT

Klon ubi jalar unggulan Awachy 5 memiliki kandungan pati tinggi dan ketahanan hama yang lebih baik dibandingkan ubi jalar lokal. Namun pengolahannya menjadi pure kering dengan menggunakan suhu tinggi menyebabkan kerusakan granula pati karena kurang stabil terhadap pemanasan dan berdampak pada tekstur produk yang menjadi lengket. Masalah ini dapat diperbaiki melalui proses modifikasi annealing. Tujuan penelitian ini adalah untuk mendapatkan perlakuan suhu dan lama annealing yang menghasilkan pure kering ubi jalar dengan karakteristik yang berbeda. Metode penelitian adalah Rancangan Acak Kelompok (RAK) yang terdiri dari 5 perlakuan dengan 3 ulangan, yaitu kontrol, annealing 40oC selama 4 jam; 40oC, 8 jam; 50oC, 4 jam; dan 50oC, 8 jam. Hasil penelitian menunjukan bahwa pure kering ubi jalar dengan perlakuan annealing 50oC selama 4 jam menghasilkan karakteristik terbaik dengan kadar air 4,88%, suhu awal gelatinisasi 54,30oC, viskositas puncak 850,5 cP, viskositas breakdown 29,37 cP, viskositas setback 395 cP, hardness 108,54 gf, adhesiveness -69,774 gf, kesukaan panelis terhadap warna 3,311, daya rehidrasi 3,216 ml/g, rendemen sebesar 23,11%%, serta memiliki kandungan protein 1,76%, lemak 1,89%, abu 0,76%, dan karbohidrat 90,88%. Karakteristik tersebut menunjukan bahwa pure kering dapat menjadi sediaan pangan darurat dalam bentuk basah dan semi basah seperti sup dan produk siap konsumsi dengan direhidrasi.
ANALISIS PENERAPAN SISTEM INFORMASI PENGELOLAAN NILAI RAPORT MENGGUNAKAN METODE TAM

Era globalisasi sangat berpengaruh terhadap perkembangan teknologi informasi bagi sekolah, salah satunya bagi Madrasah Aliyah Negeri 2 Model (MAN 2 Model) Pekanbaru. MAN 2 Model Pekanbaru, merupakan salah satu sekolah Islam Negeri dan satuan kerja dari Kementrian Agama yang menggunakan sistem informasi, yakni dikenal dengan sistem informasi pengelolaan nilai raport. Sistem ini telah diterapkan selama 2 tahun. Permasalahan yang terjadi selama penerapannya yaitu terjadi error dalam input nilai, format data yang tertukar, dan guru harus mengetahui sendiri batasan dalam penggunaan sistem informasi. Tujuan yang ingin dicapai yaitu untuk menganalisis persepsi penerapan sistem informasi pengelolaan nilai raport berdasarkan masing-masing faktor kebermanfaatan, kemudahan, dan secara bersama-sama terhadap faktor penerimaan teknologi informasi, serta memberikan rekomendasi kepada MAN 2 Model Pekanbaru. Penelitian ini menggunakan metode Technology Acceptance Model (TAM) untuk mengetahui persepsi pengguna terhadap sistem informasi, yang terdiri dari 3 variabel yaitu perceived usefulness (PU), perceived ease of use (PEU), dan accpetance of IT (ACC). Jenis pengolahan data yang digunakan adalah regresi linier berganda, uji T dan uji F untuk hipotesis. Hasil dari regresi linier berganda berdasarkan Unstandardized Coefficent sebesar 4,330, dan seluruh hipotesis berpengaruh positif. Sehingga penelitian ini dapat memberikan rekomendasi bagi MAN 2 Model Pekanbaru.
Weighted hybrid technique for recommender system

Recommender system becomes very popular and has important role in an information system or webpages nowadays. A recommender system tries to make a prediction of which item a user may like based on his activity on the system. There are some familiar techniques to build a recommender system, such as content-based filtering and collaborative filtering. Content-based filtering does not involve opinions from human to make the prediction, while collaborative filtering does, so collaborative filtering can predict more accurately. However, collaborative filtering cannot give prediction to items which have never been rated by any user. In order to cover the drawbacks of each approach with the advantages of other approach, both approaches can be combined with an approach known as hybrid technique. Hybrid technique used in this work is weighted technique in which the prediction score is combination linear of scores gained by techniques that are combined.The purpose of this work is to show how an approach of weighted hybrid technique combining content-based filtering and item-based collaborative filtering can work in a movie recommender system and to show the performance comparison when both approachare combined and when each approach works alone. There are three experiments done in this work, combining both techniques with different parameters. The result shows that the weighted hybrid technique that is done in this work does not really boost the performance up, but it helps to give prediction score for unrated movies that are impossible to be recommended by only using collaborative filtering.
A Virtual Grid Routing Design and Implementation Using Matlab

– Previously, a virtual Grid-based dynamic routes adjustment scheme (Grid Routing) for mobile sink-based wireless sensor networks using NS-2 was introduced. The virtual grid routing design and implementation using Matlab is a basic concern of this paper. The same energy model is used in this paper as used in [12]. Distance and energy of the network communication used in grid routing approach are calculated in matlab.
Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images

Since the resolution of remote sensing infrared images is low, the features of ship targets become unstable. The issue of how to recognize ships with fuzzy features is an open problem. In this paper, we propose a novel ship target recognition algorithm based on Gaussian mixture models (GMMs). In the proposed algorithm, there are mainly two steps. At the first step, the Hu moments of these ship target images are calculated, and the GMMs are trained on the moment features of ships. At the second step, the moment feature of each ship image is assigned to the trained GMMs for recognition. Because of the scale, rotation, translation invariance property of Hu moments and the power feature-space description ability of GMMs, the GMMs-based ship target recognition algorithm can recognize ship reliably. Experimental results of a large simulating image set show that our approach is effective in distinguishing different ship types, and obtains a satisfactory ship recognition performance.
PROFILE REFINEMENT IN ONTOLOGY-BASED RECOMMANDER SYSTEMS FOR ECONOMICAL E-NEWS

This paper is interested in a recommender system of economic news articles. More precisely, it focuses on automatic profile refinement of customers which is an important task over time by taken into account logs of the user concerning especially his/her actions, reading time, and domain specific knowledge. In our approach, ontologies are used to describe and automatically refine these profiles. This work focuses on one particular type of recommender systems which is content-based recommenders. The aim of these recommender systems is to build a user profile and to improve its precision over time. Several improvements that have been made to these recommender systems over the last decade are analyzed. We find that the improvements brought by the use of semantic knowledge are not negligible, therefore semantic web approaches should be more and more used in the future. Nevertheless improvements remain possible in this domain and further research could be interesting.