Miracle-dz/UniFAR-FTUs huggingface dataset:
Rancang Bangun Sistem Pakar Diagnosa tingkat Depresi Pada Mahasiswa Tingkat Akhir Menggunakan Metode Fuzzy Tsukamoto(Studi Kasus : Universitas Siliwangi)

Depresi adalah penyakit mental yang umum tapi serius biasanya ditandai dengan perasaan sedih atau cemas. Kebanyakan mahasiswa kadang-kadang merasa sedih atau cemas, tapi emosi ini biasanya berlalu dengan cepat dalam beberapa hari. Depresi yang tidak diobati berlangsung untuk waktu yang lama, mengganggu kegiatan sehari-hari, dan jauh lebih dari sekedar "sedikit murung" atau "merasa sedih". Pada tahun 2011, Asosiasi Kesehatan American College National College Health Assessment (ACHA-NCHA) sebuah survei nasional pada mahasiswa di 2 dan 4 lembaga menemukan bahwa sekitar 30 persen dari mahasiswa melaporkan merasa "begitu tertekan sehingga sulit untuk berfungsi" pada beberapa waktu dalam satu tahun terakhir. Dengan dasar tersebut maka diperlukan sebuah sistem pakaruntuk membantu mahasiswa dalam mendeteksi tingkat depresi. Adapun sistem pakar yang dibuat dalam pembuatan sistem pakar ada tiga tahap utama dalam pengembangan software ini: fuzzifikasi, inferensi dan defuzzifikasi, menggunakan Tsukamoto pada tahap inference. Pada tahap defuzzifikasi, Center Average Deffuzzyfier digunakan untuk mendapatkan aturan outputcrisp.Basis Aturan yang digunakan dalam penelitian ini sebanyak 64 aturan. Adapun sistem pakar yang dibangun menggunakan metode Fuzzy Tsukamoto sebagai metode penalaran untuk menentukan hasil deteksi berdasarkan gejala yang ditunjukkan. Sistem pakar ini dibangun berbasis Desktop agar dapat digunakan oleh mahasiswa dan instansi/lembaga yang membutuhkan. Berdasarkanpengujian yang dilakukan, sistem pakar ini valid dengan tingkatakurasi sebesar 96% dalam memberikan hasil deteksi yang sesuai dengan pakar, dari hasil data sebanyak 25 percobaan. Selain itu sistem dapat beroperasi baik.
Sentiment Analysis of Chinese Version Using SVM & RNN

The analysis methods on Chinese sentiment analysis and English sentiment analysis are different. As there is no clear space between Chinese words, when analysis Chinese version, we first need to segment sentences into words, then to extract features, and finally to train classifier. Generally researcher has use the SVM or RNN model to did the sentiment analysis. They has get many good result but not good enough. Basing on the analysis of specific text, this article trying to use the jieba1 segmentation technology to segment sentences, then using word2vec tool translate the version into vectors, and finally using SVM and RNN to make classifiers. Comparing the accuracy and recall rate under different feature extraction methods. The experiment's results show that the combined classifier is better than the traditional classifier.
The Study of User Model of Personalized Recommendation System Based on Linked Course Data

The massive information on Internet makes users obtain information become in efficient.Similarly,the users in the field of e-learning face with the problems that learning resources are inefficient and learning paths unreasonable,etc . Linked Courses data is a standard of W3C proposed will be organized the courses in the national excellent courses into a linked data database. On the basis the author design a user model to meet the user's learning requirements, which will be the most suitable learning Resources recommended to the user. Finally, the author validate the model with experimental ,the application shows that the model can indicate the user's learning requirements,and rational and efficient learning path is recommended.
The US West Advanced Technologies prototype multimedia communication system

The authors describe a prototype geographically distributed, real-time multimedia communication system currently in everyday use at US West Advanced Technologies. To test it, nine of approximately 40 researchers in the Science and Technology Division have been relocated from the US West Advanced technologies headquarters in Englewood, Colorado, to Boulder, Colorado, a distance of some 40 miles. After an explanation of the technical infrastructure of the system and some of the choices made in building it, the authors discuss some of the problems encountered and some of the lessons learned from this experience. The experience described is grouped into two main areas: (1) the engineering of the system, and (2) the behavioral and social aspects of using and living with the system. <<ETX>>
A Bayesian Approach for Suspicious Financial Activity Reporting

Abstract This paper presents a Bayesian network (BN)-based approach to analyse customers' transactions in a financial institution and then to detect suspicious patterns in them. The approach is developed as part of an anti-money laundering system that requires identification of suspicious transactions so that they are reported to the concerned authorities in a timely manner. The proposed BN is designed on the basis of rules suggested by the State Bank of Pakistan in its 2008 regulations to declare a transaction as suspicious. Using transaction history, the proposed approach assigns a baseline money laundering score to each customer. The score is an indication of the customer's transaction behaviour. During the live operational mode, if there is a significant difference in customer's historical transactional pattern and the current behaviour, an alert is generated which requires the branch manager (or compliance head) to verify the reason for the difference. The approach has been tested on real financial transactions set having more than 8.2 million records of more than hundred thousand customers.
Taxonomy of Web 2.0 Applications with Educational Potential

In order to help teachers in selecting web services that are useful for sharing learning materials, communication between teacher and stu-dents, and collaboration among students, this chapter presents a taxonomy of Web 2.0 applications with educational potential. For each category of the taxonomy the most popular representatives are mentioned, followed by a brief explanation of their implementation in the e-learning environment.
Sophisticated test facility to detect land mines

In the framework of the Dutch government humanitarian demining project 'HOM-2000', an outdoor test facility has been realized to test, improve and develop detection equipment for land mines. This sophisticated facility, allows us to access and compare the performance of the individual and of a combination of different sensor against a variety of threats. The test facility entails six test lanes of 30 square meters each, filled with different types of soil. The groundwater level of the lanes can be regulated separately and the temperature of the soil and of the st miens is monitored. A moveable measurement platform has been realized which is completely free of electrically conducting materials. With this platform the individual or fused detection system can be moved automatically over the whole test site with an accuracy of one centimeter in every direction. Test mines and mine-like objects have been placed in the lanes. The set of test mines contains nine different types of both anti personnel and anti tank mines, low metal content and non-metal mines. To simulate the high explosives, the test mines have been filled with a silicone rubber.
Self-Adjusting Atomic Memory for Dynamic Systems based on Quorums On-The-Fly. Correctness Study

Atomic memory is a fundamental building block for classical distributed applications. The new emergent large scale applications such as e-auctions or e-commerce need similar fundamental abstractions able to cope with the highly dynamicity of p2p environments. In this paper we prove the correctness of a Self-Adjusting Atomic Memory that implements multi-writer/multi-reader atomic operations in dynamic systems. The architecture of this system was introduced in [AGGV05, AGGV05raptech]. The self-healing property guarantees the capability to cope with replica volatility (unavoidable in a p2p environment), while the self-adjusting property helps it to adapt on-the-fly to the dynamicity of client access. \\ Ce papier propose la preuve de correction de SAM - une memoire atomique auto-ajustable basee sur l'utilisation de systemes de quorums. L'architecture de SAM a ete decrite dans [AGGV05, AGGV05raptech]. Nous detaillons egalement un des modules du systeme qui permet de realiser la consultations ``a la volee'' des quorums en lecture ou ecriture.
Regular Expressions and Automata using Haskell

The paper begins with definitions of regular expressions, and how strings are matched to them; this also gives our first Haskell treatment also. After describing the abstract data type of sets we define non-deterministic finite automata, and their implementation in Haskell. We then show how to build an NFA corresponding to each regular expression, and how such a machine can be optimised, first by transforming it into a deterministic machine, and then by minimising the state space of the DFA. We conclude with a discussion of regular definitions, and show how recognisers for strings matching regular definitions can be built.
Overview of ghost correction for HDR video stream generation

Most digital cameras use low dynamic range image sensors, these LDR sensors can capture only a limited luminance dynamic range of the scene[1], to about two orders of magnitude (about 256 to 1024 levels). However, the dynamic range of real-world scenes varies over several orders of magnitude (10.000 levels). To overcome this limitation, several methods exist for creating high dynamic range (HDR) image (expensive method uses dedicated HDR image sensor and low-cost solutions using a conventional LDR image sensor). Large number of low-cost solutions applies a temporal exposure bracketing. The HDR image may be constructed with a HDR standard method (an additional step called tone mapping is required to display the HDR image on conventional system), or by fusing LDR images in different exposures time directly, providing HDR-like[2] images which can be handled directly by LDR image monitors. Temporal exposure bracketing solution is used for static scenes but it cannot be applied directly for dynamic scenes or HDR videos since camera or object motion in bracketed exposures creates artifacts called ghost[3], in HDR image. There are a several technics allowing the detection and removing ghost artifacts (Variance based ghost detection, Entropy based ghost detection, Bitmap based ghost detection, Graph-Cuts based ghost detection …) [4], nevertheless most of these methods are expensive in calculating time and they cannot be considered for real-time implementations. The originality and the final goal of our work are to upgrade our current smart camera allowing HDR video stream generation with a sensor full-resolution (1280x1024) at 60 fps [5]. The HDR stream is performed using exposure bracketing techniques (obtained with conventional LDR image sensor) combined with a tone mapping algorithm. In this paper, we propose an overview of the different methods to correct ghost artifacts which are available in the state of art. The selection of algorithms is done concerning our final goal which is real-time hardware implementation of the ghost detection and removing phases.
Discovering the Hidden Cross-Dataset Links in Data.gov

Data mash-ups unleash users from the limit of accessing one dataset at a time, and enable a broader view based on the integrated data. A key challenge to mashing up Data.gov datasets lies in the fact that cross-dataset links are rarely published explicitly by dataset owners, making it hard for users to find related datasets for building mash-ups. In this paper, we show several types of hidden cross-dataset links found in Data.gov and explain how they can be obtained using semantic technologies in Rensselaer's Linking Open Government Data project.
Sliders for the Smart: Type of Rating Scale on the Web Interacts With Educational Level

Slider scales and radio buttons scales were experimentally compared in horizontal and vertical orientation. Slider scales lead to statistically significantly higher break-off rates (odds ratio = 6.9) and substantially higher response times. Problems with slider scales were especially prevalent in participants with less than average education, suggesting the slider scale format is more challenging in terms of previous knowledge needed or cognitive load. An alternative explanation, technology-dependent sampling (Buchanan & Reips, 2001), cannot fully account for the current results. The authors clearly advise against the use of Java-based slider scales and advocate low-tech solutions for the design of Web-based data collection. Orientation on screen had no observable effect on data quality or usability of rating scales. Implications of item format for Web-based surveys are discussed.
Multidimensional PRMA with prioritized Bayesian broadcast-a MAC strategy for multiservice traffic over UMTS

Multidimensional packet reservation multiple access is proposed as a medium-access control (MAC) strategy for the uplink channel of the UTRA (UMTS terrestrial radio access) time-division/code-division multiple-access (TD/CDMA) mode to benefit from efficient statistical multiplexing on the large common pool of available resources (i.e., slots defined both in time and code domain). A prioritized Bayesian broadcast algorithm is derived to stabilize multidimensional packet reservation multiple access (MD PRMA) and to allow for access delay discrimination of four different access classes. Access delay spread can be selected, and trading voice-dropping ratio against data-access delay is possible. To control multiple-access interference, Bayesian broadcast can be combined with load-based access control. The performance of both frequency-division duplex (FDD) and time-division duplex (TDD) mode is evaluated, the latter particularly relevant for TD/CDMA. For mixed voice, Worldwide Web (WWW) browsing, and e-mail traffic, the UMTS WWW model is used, while the e-mail traffic model is derived here.
Graph theoretical analysis and application of fMRI⁃based brain network in Alzheimer's disease

Alzheimer's disease (AD), a progressive neurodegenerative disease, is clinically characterized by impaired memory and many other cognitive functions. However, the pathophysiological mechanisms underlying the disease are not thoroughly understood. In recent years, using functional magnetic resonance imaging (fMRI) as well as advanced graph theory based network analysis approach, several studies of patients with AD suggested abnormal topological organization in both global and regional properties of functional brain networks, specifically, as demonstrated by a loss of small ⁃ world network characteristics. These studies provide novel insights into the pathophysiological mechanisms of AD and could be helpful in developing imaging biomarkers for disease diagnosis. In this paper we introduce the essential concepts of complex brain networks theory, and review recent advances of the study on human functional brain networks in AD, especially focusing on the graph theoretical analysis of small ⁃ world network based on fMRI. We also propound the existent problems and research orientation. DOI：10.3969/j.issn. 1672⁃6731.2012. 04.023
Evaluation and Choice of Various Branch Predictors for Low-Power Embedded Processor

Power is an important design constraint in embedded computing systems. To meet the power constraint, microarchitecture and hardware designed to achieve high performance need to be revisited, from both performance and power angles. This paper studies one of them:branch predictor. As well known, branch prediction is critical to exploit instruction level parallelism effectively, but may incur additional power consumption due to the hardware resource dedicated for branch prediction and the extra power consumed on mispredicted branches. This paper explores the design space of branch prediction mechanisms and tries to find the most beneficial one to realize low-power embedded processor. The sample processor studied is Godson-like processor, which is a dual-issue, out-of-order processor with deep pipeline, supporting MIPS instruction set.
Packet voice transmission using Java programming language

We investigate the suitability of Java in real time communication over the network. We describe the development of a voice transmission application that allows full-duplex communications over the Ethernet. The Ethernet (using CSMA/CD MAC protocol) is essentially a data network with no special scheme for real time traffic, therefore, it is up to the application layer to provide some strategies to control the traffic congestion usually by trading off the voice quality. There are several strategies that are promising such as lowering the sampling rate or increasing the threshold for silence suppression. Using the results of experiments, it is illustrated that increasing the threshold for silence suppression significantly reduces the packet voice traffic during the critical periods of congestion in the network. The results indicate that significant capacity and performance advantages are gained in the network as a result of the silence suppression scheme.
High power-added efficiency MMIC amplifier for 2.4 GHz wireless communications

This work describes the design and the measured performance of a high-efficiency monolithic microwave integrated circuit (MMIC) amplifier for wireless communications in the 2.4 GHz band. The monolithic technology employed in the circuit integration is based on standard 0.5-/spl mu/m-gate-length MESFET. The design procedure is based on load-cycle graphic optimization of the transistor performance. On-wafer experimental characterization shows output power up to 24 dBm and excellent results of power-added efficiency up to 79% with 19.5 dBm output power at low drain bias voltage. The amplifier performance achieved and the circuit size, which is 1 mm/sup 2/, are suitable for use in the transmitter chains of wireless communication systems in the 2.4 GHz band.
Integrating optimal control with rules using neural networks

A recurrent neural network architecture augmented with rules capable of controlling nonlinear plants are presented. Using a recurrent form of the backpropagation algorithm, control is achieved by optimizing the network weights in the presence of task-adapted subnetworks representing rules. A quadratic cost function of endpoint trajectory values is minimized along with performance constraint penalties. The approach is demonstrated for a control task consisting of an aircraft flight path transition problem. It is shown that the network yields excellent performance while remaining within acceptable system constraints and while observing typical flight rules. <<ETX>>
Local estimation of land cover classification quality using machine learning methods

This paper describes research to obtain local or pixel scale estimates of classification quality from classification of remote sensing data produced with nonparametric machine learning algorithms. The approach utilizes boosting, a method of improving classification accuracy using multiple iterations of a classification algorithm. Boosting has been shown to successfully improve classification accuracy, to be resistant to overfitting, and to be effective with remote sensing data. A recent statistical examination of boosting has shown that this algorithm is a form of additive logistic regression. As such, boosting can provide probabilities of class membership. These probabilities of class membership are evaluated as a measure of local classification quality. This approach is shown to be an effective predictor of classification errors and therefore, provides a means of assessing local classification quality.
Distributed robust H ∞ composite-rotating consensus of second order multi-agent systems

In this paper, the distributed h ∞ composite-rotating consensus problem is concerned for a class of second-order multi-agent systems. Firstly, based on local state feedback and communication feedback, a distributed control algorithm is proposed. Then, sufficient conditions are derived in order to make all agents reach a composite-rotating consensus with the desired h ∞ performance. Finally, the simulations are given to show the effectiveness of the theoretical results.
Access Rates Analysis Of Routing Stack Transformation

The security of data is a very important aspect of every institution. One of the methods of boosting data security is by use of routing stack transformation. This approach successfully hides router inter-networking operating system from potential hackers by giving wrong or inaccurate results about the target operating system (including its manufacturer, model and version). This process normally involves changing the system kernel and modifying the transmission control protocol/Internet protocol (TCP/IP) parameters. In this paper, we investigated the effects of the above changes in data transmission rates (bit rates). This was done by use of experimentations before and after these alterations. A number of packets were sent from source and the time they take to reach the target is measured. This information was then used to determine the access rates in kilobits per second across a given communication link. We found that the network speeds were slightly reduced due to the TCP/IP and kernel changes. This could be attributed to the fact that network devices now took a long time trying to learn the format of the new packets, hence the subsequent delays. Due to this, a number of ways of boosting network speed are suggested. The simulated results tend to agree with the real trend observed in that in both scenarios, network access rates were reduced after routing stack transformation.
An object oriented approach for an iconic kernel system IKS

To provide for the realization of machine-independent image-processing systems which support problem-oriented procedure description and development as well as the generation of reusable software, an iconic kernel system (IKS) was developed. The IKS provides a description foundation for iconic processing and serves as a concrete organizational framework for implementation and application of image-processing tasks. This is achieved using a uniform and comprehensive fundamental operation model for iconic operations and an object-oriented approach for system architecture. <<ETX>>
WeGliss : clavier virtuel réduit pour Wiimote

Text entry is not very used on wii games. Virtual keyboards are not adapted to novice users. However, news activities using text entry are developing such as chat, internet access, e-commerce, … This paper presents a new virtual keyboard called “WeGliss”. First test was done with nine adults. The majority has preferred our new keyboard and has obtained a better speed of text entry or equivalent to a standard virtual keyboard. We have continued the test with three children about ten years old. With lower speed, they had the same progression and the same preference as adults for WeGliss keyboard.
A Guide to Software Configuration Management

Preface. Overview of Software Configuration Management. The Software Development Process. Pitfalls in the Software Development Process. Need and Importance of Software Configuration Management. SCM - Basic Concepts. The Different Phases in Software Configuration Management. Configuration Identification. Configuration Control. Status Accounting. Configuration Audits and Reviews. Software Configuration Management Plans. SCM Organization. Software Configuration Management Tools. SCM Implementation. SCM in Very Large Projects. Trends in SCM - The Future Directions. Appendix: SCM Vendors and Tools. SCM Standards. SCM Resources on the Internet. Bibliography. Glossary and Acronyms.