Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Microarchitectural Cache Attacks

This paper summarizes the basic concepts and development process of cache side-channel attack, analyses three basic methods (Evict and Time, Prime and Probe, Flush and Reload) from four aspects: Attack conditions, realization process, applicability, and characteristics, then I expound how to apply side-channel attack methods on CPU vulnerability
A Robust Synchronization-Based Chaotic Secure Communication Scheme With Double-Layered and Multiple Hybrid Networks

In this paper, based on the exponential synchronization of double-layered networks composed of multiple subnets, we bring forward a chaotic secure communication scheme, which is robust to different network sizes, time-varying delays, and stochastic noise. Cluster analysis is used to process the nodes with different dynamics, such that the subnets in the transmitter and receiver are one-to-one correspondence and constitute subnet pairs, while the node size of each subnet can be inconsistent. Each subnet pair is only accountable for encrypting/decrypting a part of information. There are many encryption/decryption units and they are operating in parallel, which can not only indicate the complex characteristic of different nodes but also speed up the information encryption/decryption. The proposed scheme applies the chaotic signals yielded by many chaotic systems as key sequences, such that the key space can grow with node sizes in the transmitter. This scheme is not subject to the constraint on the amplitude of the original message and chaotic signal, the influence of time-delay and stochastic noise, and it shows that only the nodes with large degrees need to apply controllers in restoring the original information. Both theoretical analysis and numerical simulation manifest the feasibility and validity of the given scheme.
ADOPTION FACTORS OF HEALTH MONITORINGSYSTEMS FOR SMART HEALTHCARE: A SYSTEMATICREVIEW

Although there have been several studies that focus on technical aspects of health monitoring systems, adoption of these systems remains largely unknown. A limited number of researches has explored the factors influencing users’ adoption of these systems that provide successful system implementation. In this study, we reviewed articles addresses the adoption of health monitoring systems. We extract criteria that influence the adoption decision that has been made at some stage to use health monitoring systems. Then group these criteria into nine categories in terms of cost, ease of use, environment, knowledge, management, security, social influence, subjective norm, technical, usefulness, and enjoyment. By using these criteria, we develop an adoption model for health monitoring systems.
Performance Analysis of Future Video Coding (FVC) Standard Technology

The Future Video Coding (FVC) is a new state of the art video compression standard that is going to standardize, as the next generation of High Efficiency Video Coding (HEVC) standard. The FVC standard applies newly designed block structure, which is called quadtree plus binary tree (QTBT) to improve the coding efficiency. Also, intra and inter prediction parts were changed to improve the coding performance when comparing to the previous coding standard such as HEVC and H.264/AVC. Experimental results shows that we are able to achieve the average BD-rate reduction of 25.46%, 38.00% and 35.78% for Y, U and V, respectively. In terms of complexity, the FVC takes about 14 times longer than the consumed time of HEVC encoder.
Multiple-Input Single-Output Synthetic Aperture Radar and Space-Time Adaptive Processing

This thesis investigates the plausibility of implementing a multiple-input singleoutput (MISO) synthetic aperture radar (SAR) system for space-time adaptive processing (STAP) with a limited data rate requirement of a single receiver. A MISOSAR system could provide processing flexibility to radar systems such as the Gotcha radar system developed at the Air Force Research Laboratory. Gotcha is an airborne wide-beam multi-mode radar system used to cover a large area for surveillance. In order to apply multiple algorithms to a large amount of data in real time, the data is downlinked to a supercomputer on the ground. STAP is an adaptive filtering technique which can be used for improved detection of slow moving targets in the presence of clutter. However, STAP is typically implemented using an array of receiving elements, which significantly increases the data rate for downlinking to the ground. While MISO systems are common in communications applications, it is not a common radar system design approach. The MISO system requires additional waveform design considerations in order to obtain orthogonal transmit waveforms. However, the MISO system provides the additional degrees of freedom needed to apply STAP while maintaining a single receiver data rate.
Identification of The Tuberculosis (TB) Disease Based on XRay Images Using Probabilistic Neural Network (PNN)

Tuberculosis is an airborne contagious disease caused by the Bacterium Mycobacterium Tuberculosis which means major threat to global health. If Tuberculosis is not treated promptly and quickly, it will cause the disease to get worse. For that, the treatment should not be done carelessly. Therefore, a system is needed to identify the Tuberculosis diseases to help the doctors as an early stage in diagnosing the disease. In this research, Probabilistic Neural Network (PNN) method was used to identify the presence of Tuberculosis by system to help the doctor to make the decision. The stages before doing an identification such as the image acquisition, pre-processing, and feature extractions using Invariant Moment. This study was conducted using train data of 105 normal X-Ray images and 105 images of pulmonary Tuberculosis X-Rays. For the testing data used of 50 X-Ray images. The results showed that the proposed method was able to identify the Tuberculosis disease with an accuracy of 96%.
Morphable DRAM Cache Design for Hybrid Memory Systems

DRAM caches have emerged as an efficient new layer in the memory hierarchy to address the increasing diversity of memory components. When a small amount of fast memory is combined with slow but large memory, the cache-based organization of the fast memory can provide a SW-transparent solution for the hybrid memory systems. In such DRAM cache designs, their effectiveness is affected by the bandwidth and latency of both fast and slow memory. To quantitatively assess the effect of memory configurations and application patterns on the DRAM cache designs, this article first investigates how three prior approaches perform with six hybrid memory scenarios. From the investigation, we observe no single DRAM cache organization always outperforms the other organizations across the diverse hybrid memory configurations and memory access patterns. Based on this observation, this article proposes a reconfigurable DRAM cache design that can adapt to different hybrid memory combinations and workload patterns. Unlike the fixed tag and data arrays of conventional on-chip SRAM caches, this study advocates to exploit the flexibility of DRAM caches, which can store tags and data to DRAM in any arbitrary way. Using a sample-based mechanism, the proposed DRAM cache controller dynamically finds the best organization from three candidates and applies the best one by reconfiguring the tags and data layout in the DRAM cache. Our evaluation shows that the proposed morphable DRAM cache can outperform the fixed DRAM configurations across six hybrid memory configurations.
SISTEM PENCARIAN LOKASI BENGKEL MOBIL RESMI MENGGUNAKAN TEKNIK PENGOLAHAN SUARA DAN PEMROSESAN BAHASA ALAMI

Banyaknya jumlah bengkel resmi mobil yang tersebar di wilayah Bandar Lampung, terkadang masih membuat bingung pengguna dalam menemukan bengkel mobil mereka. Hal ini dikarenakan lokasi-lokasi bengkel mobil yang tercantum pada buku panduan hanya mencantumkan nama bengkel dan alamatnya saja. Permasalahannya adalah tidak semua orang mengetahui nama bengkel resmi dan alamat yang tercantum pada buku panduan. Salah satu cara untuk memberikan kemudahan pengguna mencari lokasi bengkel mobil resmi adalah dengan menggunakan teknik visualisasi peta, pengolahan suara dan pemrosesan bahasa alami menggunakan smartphone Android. Penelitian ini membahas bagaimana menerapkan teknik visualisasi peta dengan Google Maps, pengolahan suara dengan Speech Recognition dan Text to Speech serta pemrosesan bahasa alami yang akan dikombinasikan pada aplikasi. Hasil dari penelitian ini diharapkan dapat menjadi aplikasi yang dapat membantu para pengguna dalam mencari rute terdekat dalam mencari bengkel resmi mobil dengan bantuan Speech Recognition dan Text to Speech berupa aplikasi yang dapat mengolah keluaran suara dari pengguna menjadi bentuk teks. Berdasarkan hasil pengujian aplikasi ini memiliki tingkat akurasi sebesar 70% dalam menemukan lokasi sesuai dengan yang diucapkan oleh pengguna.
Caching Resource Sharing for Network Slicing in 5G Core Network: A Game Theoretic Approach

Network slicing has been considered a promising technology in next generation mobile networks (5G), which can create virtual networks and provide customized service on demand. Most existing works on network slicing mainly focus on virtualization technology, and have not considered in-network caching well. However, in-network caching, as the one of the key technologies for information-centric networking (ICN), has been considered as a significant approach in 5G network to cope with the traffic explosion and network challenges. In this article, the authors jointly consider in-network caching combining with network slicing. They propose an efficient caching resource sharing scheme for network slicing in 5G core network, aiming at solving the problem of how to efficiently share the limited physical caching resource of Infrastructure Provider (InP) among multiple network slices. In addition, from the perspective of network slicing, the authors formulate caching resource sharing problem as a non-cooperative game, and propose an iteration algorithm based on caching resource updating to obtain the Nash Equilibrium solution. Simulation results show that the proposed algorithm has good convergence performance, and illustrate the effectiveness of the proposed scheme.
Penerapan Metode Profile Matching Pada Sistem Pendukung Keputusan Pemilihan Ketua Program Studi (STUDI Kasus : Program Studi Teknik Informatika STMIK Musi Rawas)

Abstract-- In the organizational structure of a study program, always led by a chairman of the study program. In STMIK Musi Rawas, the process of selecting the head of the study program is usually appointed directly by the chairman of the foundation. The shortcomings of the direct appointment process is not able to see the capabilities possessed by the candidate chairman of the study program be it social skills, planning, management of learning, management of Human Resources, Finance and so forth. Decision support system is a system that can be used to help make decisions based on existing criteria. In this research the method used is Profile Matching method. The concept of Profile Matching method is to compare individual competence into job competence so that it can know the difference of competence (also called gap), the smaller the gap that is generated, the greater the value the greater the greater the chance for someone to occupy the position. The end result of this research is obtained information about the highest value of each candidate chairman of the Study Program based on the calculation of Profile Matching
Emotional Analysis of Jingdong Commodity Review based on Deep Learning

Jingdong commodity sentiment analysis aims to find out the e-commerce users' attitudes towards the products and the emotional orientation of the evaluation, helping other users to make correct decisions and effectively shape the quality portrait of the products. This paper proposes a subject-oriented AT-LSTM model method for traditional cyclic neural networks. The model is characterized by combining the Attention mechanism on the traditional LSTM model, highlighting the influence of the key input on the output through the Attention layer, and identifying the subject of the input phrase through the subject recognition algorithm, and then combine the phrase subject and the LSTM hidden layer result through the Attention mechanism. The experimental results show that the proposed method can obtain higher classification accuracy and save the workload of manual labeling.
On information-optimal scripting of actions

Animals and humans encounter many tasks which permit ritualized behaviours, essentially fixed action sequences or “scripts”, similar to options known from Reinforcement Learning, but proceeding without intermediate decisions. While running a script, they proceed in an open-loop fashion. However even when these are already known, an agent needs to decide whether to perform a basic action or to trigger a script regarding the particular task. Here we study if including such scripts (i.e. behaviour rituals) is advantageous from the point of view of the relevant information required to take the decision to start such a script depending on the tasks. To achieve this, we modify the relevant information framework including sequences of basic actions to the possible actions.
Uplink Pilot Allocation in Massive MIMO over Gauss-Markov Fading Channels

In a time-division duplex (TDD) massive multiple-input multiple-output (MIMO) system, the number of available orthogonal pilot sequences in each cell is limited. In this paper, by assuming Gauss-Markov fading channels, we consider a massive MIMO system where there are less available orthogonal pilots than the number of users. In order to optimize the long-term performance in estimating the uplink (UL) channels, the base station (BS) judiciously decides the allocation of the available pilots to different users in each training phase. The pilot allocation problem is a partially observable Markov decision process with an exploitation-exploration tradeoff that is difficult to analyze. We thus investigate the problem in the framework of restless multi-armed bandits (RMAB) problems and carry out an indexability analysis for the problem. Furthermore, we solve this problem by using the Whittle's index policy with a low complexity. Numerical results demonstrate the superiority of the Whittle's index policy.
Mobile crowd-sensing as a resource for contextualized urban public policies: a study using three use cases on noise and soundscape monitoring

ABSTRACT Environmental noise is a major pollutant in contemporary cities and calls for the active monitoring of noise levels to spot the locations where it most affects the people’s health and well-being. However, due to the complex relationship between environmental noise and its perception by the citizens, it is not sufficient to quantitatively measure environmental noise. We need to collect and aggregate contextualized – both quantitative and qualitative – data about the urban environmental noise so as to be able to study the objective and subjective relationships between sound and living beings. This complex knowledge is a prerequisite for making efficient territorial public policies for soundscapes that are inclined towards living beings welfare. In this paper, we investigate how Mobile Phone Sensing – aka crowd-sensing – enables the gathering of such knowledge, provided the implementation of sensing protocols that are customized according to the context of use and the intended exploitation of the data. Through three case studies that we carried out in France and Finland, we show that MPS is not solely a tool that contributes to sensitizing citizens and decision-makers about noise pollution; it also contributes to increasing our knowledge about the impact of the environmental noise on people’s health and well-being in relation to its physical and subjective perception.
Human Activity Recognition for Production and Logistics - A Systematic Literature Review

This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner’s perspective.
Tool condition monitoring method in milling process using wavelet transform and long short-term memory

Industrial automation is a promising move to fulfill today's competitive manufacturing industry demands by lowering operation costs, increasing productivity and quality. Monitoring the production process is one of the important steps toward total autonomy of manufacturing plants, which reduces routine checks, enables proactive maintenance and reduces repair costs. This research investigates tool wear as one of the most common faults in milling process during cutting of the D2 high speed steel as a hard to cut material using Carbide Walter End Mill Protostar tool. Vibration signal is chosen to represent the system status due to its applicability in industry. Signals are transformed into time-frequency domain using Wavelet Transform method to reveal both time domain and frequency domain features of the signal simultaneously. In order to model the complex and non-linear relations between tool wear and vibration signals under varying cutting parameters, a deep learning based algorithm, Long Short-Term Memory (LSTM) Artificial neural networks (ANNs) is employed. Deep learning algorithms are getting lots of attention recently within the diagnosis and prognosis community because of their exceptional performance in exploiting information in big data to solve complex problems. LSTM network is a type of recurrent ANNs that have some internal cells that act as long-term or short-term memory units, which is most suitable for sequential data and time series like vibration signals in our analysis. After designing the system, performance of the monitoring method is validated using experimentally acquired data with K2X10 Huron high speed CNC machine in LIPPS and Dynamo labs of ETS.
Analysis of Modified Least Significant Bit Polynomial Function Algorithm For Securing Digital Image

Nowadays, there are many types of information, among which is the digital image. Some information must be secured, so it has to be made confidential. One of the methods to secure data is by steganography, wherein information is hidden inside another media called cover. Least Significant Bit (LSB) is the smallest bit in the binary array from the value of a byte. The polynomial function is the total of the power product in one or more variables with coefficients. The digital image can be used as a cover in steganography. The pixel selection for containing the message may be determined by the polynomial function so that the polynomial function can be used as the embedding and extraction key. The larger the ratio between the dimensions of the cover image to the image message, the better values of PSNR and MSE obtained. It can be concluded that the combination of the LSB method and polynomial function may serve as a modification to the LSB method in steganography.
Rotating machine diagnosis using acoustic imaging and artificial intelligence

Mass production of quality equipment in the automotive industry requires controls throughout the production line. These controls are done through monitoring and validation tools for both production and finished products. The use of signal processing methods, applied to acous tic and vibratory recordings collected during the operating cycle, aims to ensure that they are in good working order, to maintain them and to guarantee the quality of the service provided by a manufacturer to its customers. However, sometimes the techniqu es used do not reach the expected performance, which of course depends on the defect to be recognized but also on the conditions under which the measurements were made. This paper introduces a preliminary study on machine diagnosis by combining both signal processing methods and artificial intelligence. Th is w ork is dedicated to develop a system which allows us to measure several high frequency channels and to transmit them to a computer via USB interface.
Power Allocation Over Broad Spectra Optical Wireless Scattering Communication Based on Link Gain Correlation

Power allocation for optical wireless scattering communication over broad spectra is studied based on link gain correlation. We formulate two types of power allocation problems that aim to maximize the transmission rate and to minimize the linear minimum mean square error of detection. Two scenarios of power allocation problems are considered where the parallel channels are aggregated for single-user transmission and separated for different users. We solve the power optimization problems and show that the power allocation remains fixed if the link gain ratios are uniform over the broad spectra. Furthermore investigations show that for single-user transmission optimization, under imperfectly uniform link gain ratios, the optimal power allocation for one link gain realization performs close to optimality for other link gain realizations. Numerical results show that for the single-user transmission with parallel channels, fixed power allocation incurs negligible losses in terms of transmission rate, and signal distortion. Such a finding can significantly reduce the amount of channel feedback for single-user transmission optimization. However, for the multi-user transmission, these performance losses are noticeable.
Mining microblogs for culture-awareness in web adaptation

Prior studies in sociology and human-computer interaction indicate that persons from different countries and cultural origins tend to have their preferences in real-life communication and the usage of web and social media applications. With Twitter data, statistical and machine learning tools, this study advances our understanding of microblogging in respect of cultural differences and demonstrates possible solutions of inferring and exploiting cultural origins for building adaptive web applications. Our findings reveal statistically significant differences in Twitter feature usage in respect of geographic locations of users. These differences in microblogger behaviour and user language defined in user profiles enabled us to infer user country origins with an accuracy of more than 90%. Other user origin predictive solutions we proposed do not require other data sources and human involvement for training the models, enabling the high accuracy of user country inference when exploiting information extracted from a user followers’ network, or with data derived from Twitter profiles. With origin predictive models, we analysed communication and privacy preferences and built a culture-aware recommender system. Our analysis of friend responses shows that Twitter users tend to communicate mostly within their cultural regions. Usage of privacy settings showed that privacy perceptions differ across cultures. Finally, we created and evaluated movie recommendation strategies considering user cultural groups, and addressed a cold-start scenario with a new user. We believe that the findings discussed give insights into the sociological and web research, in particular on cultural differences in online communication.