Miracle-dz/UniFAR-FTUs huggingface dataset:
State-space transition for unforced nonlinear systems

To unforced nonlinear systems, based on the state equation of control systems, the definition of state space transition is given. First of all, the state space transition of the system is obtained by utilizing the solution of related linear homogeneous equation; then based on the differential equation in series forms, the state space transition of the state equation of the unforced nonlinear system is given by heuristic method. The method in this paper can be used to the design of nonlinear control systems.
A spectrum smoothing method for speaker verification

In speech processing, speech signal is usually processed frame by frame due to the non-stationary characteristic of speech. In this paper, a frequency-domain averaging based frame smoothing method is proposed. Besides the conventional frame shift, we introduce a short time shift to create several frames around current frame. Then we take the average of power spectrum for these frames. The average will be treated as a new frame instead of current frame. The new frame is considered to retain more integrated phonetic information than conventional frames. An experiment on speaker verification task showed that this method could improve the performance of speaker verification. The evaluation tasks performed on the NIST SRE 2008 database showed that our proposed method could achieve a better verification performance when compared with the conventional framing methods.
PENGGUNAAN STRATEGI PEMBELAJARAN INKUIRI UNTUK MENINGKATKAN METAKOGNISI SISWA SMA

Penelitian ini bertujuan untuk mengetahui (1) peningkatkan metakognisi siswa melalui strategi pembelajaran inkuiri materi kelarutan dan hasil kali kelarutan di suatu SMA di Donorojo, (2) perbedaan antara penggunaan strategi pembelajaran inkuiri dan pembelajaran langsung terhadap peningkatan metakognisi siswa. Metode eksperimen dengan desain pretest-posttest group design digunakan dalam penelitian ini. Objek penelitian ini adalah siswa kelas XI IPA. Teknik pengambilan sampel adalah cluster random sampling karena populasi berdistribusi normal dan homogen. Hasil penelitian menunjukkan bahwa rata-rata nilai posttes kelas eksperimen 75,15 dan kelas kontrol 72,42. Berdasarkan hasil analisis N-gain pada kelas eksperimen menunjukkan peningkatan metakognisi sebesar 0,68 antara sebelum dan sesudah penggunaan strategi pembelajaran inkuiri. Sedangkan hasil analisis N-gain pada kelas kontrol menunjukkan peningkatan metakognisi sebesar 0,62 antara sebelum dan sesudah penggunaan strategi pembelajaran langsung. Pada uji hipotesis diperoleh t hitung sebesar 1,65 kurang dari t 1,66 yang berarti rata-rata hasil belajar kognitif kelas eksperimen tidak berbeda dari kelas kontrol. Kesimpulan dari penelitian ini adalah (1) strategi pembelajaran inkuiri dapat meningkatkan metakognisi siswa, (2) tidak ada perbedaan yang signifikan antara penggunaan strategi pembelajaran inkuiri dan pembelajaran langsung terhadap peningkatan metakognisi siswa. The objective of this study is to find out (1) the improvement of the students’ metacognition by using the inquiry learning method on the water-soluble substance and the result of its product in a high school in Donorojo, (2) the significant difference between the use of inquiry learning method and the direct instruction method to the improvement of students' metacognition. This study is an experimental research and the design was pretest-posttest group design. Object of this study is eleventh grade students of science program. The selection of the sample was by using cluster random sampling because the populations of this study were normal and homogenous. The result of this study showed that the mean score of post-test in the experiment group (75.15) was higher than that of the control group (72.42). The results of the analysis of N-gain in experiment group showed an increase of students’ metacognition 0.68 between before and after the use of inquiry learning method. While the results of the analysis of N-gain in the control group shows an improvement of students’ metacognition 0.62 between before and after the use of direct instruction method. In the hypothesis result, t (1.65) was lower than that of the t (1.66). The result indicated that the mean score of cognitive aspect of the experiment group was the same as the control one. Based on the result of this study, it can be concluded that (1) the inquiry learning can improve the students’ metacognition, (2) there was no significant difference between the use of inquiry learning method and direct instruction method to the improvement of students' metacognition.
Feature Selection and Extraction for Malware Classification

The explosive amount of malware continues their threats in network and operating systems. Signature-based method is widely used for detecting malware. Unfortunately, it is unable to determine variant malware on-the-fly. On the hand, behavior-based method can effectively characterize the behaviors of malware. However, it is time-consuming to train and predict for each specific family of malware. We propose a generic and efficient algorithm to classify malware. Our method combines the selection and the extraction of features, which significantly reduces the dimensionality of features for training and classification. Based on malware behaviors collected from a sandbox environment, our method proceeds in five steps: (a) extracting n-gram feature space data from behavior logs; (b) building a support vector machine (SVM) classifier for malware classification; (c) selecting a subset of features; (d) transforming high-dimensional feature vectors into low-dimensional feature vectors; and (e) selecting models. Experiments were conducted on a real-world data set with 4,288 samples from 9 families, which demonstrated the effectiveness and the efficiency of our approach.
The Effect of Statistical Multiplexing on the Long-Range Dependence of Internet Packet Traffic

As the number of active connections (NAC) on an Internet link increases, the long-range dependence of packet traffic changes due to increased statistical multiplexing of packets from different connections. Four packet traffic variables are studied as time series — inter-arrival times, sizes, packet counts in 100 ms intervals, and byte counts in 100 ms intervals. Results are based on the following: (1) the mathematical theory of marked point processes; (2) empirical study of 2526 packet traces, 5 min or 90 sec in duration, from 6 Internet monitors measuring 15 interfaces ranging from 100 mbps to 622 mbps; (3) simple statistical models for the traffic variables; and (4) network simulation with NS. All variables have components of long-range dependence at all levels of the NAC. But the variances of the long-range dependent components of the sizes and of the inter-arrivals decrease to zero as the NAC increases; the sizes tend toward independent, and the inter-arrivals tend toward independent or very short range dependent. These changes in the sizes and inter-arrivals are not arrested by the increased upstream queueing that eventually occurs as the NAC increases. The long-range dependence of the count variables does not change with the NAC, but their standard deviations relative to the means decrease like one over the square root of the NAC, making the counts smooth relative to the mean. Theory suggests that once the NAC is large enough, increased upstream queueing should alter these properties of the counts, but in the empirical study and in the simulation study the NAC was not large enough to observe an alteration for 100 ms counts. The change in the long-range dependence of the sizes and inter-arrivals does not contradict the constancy of the long-range dependence of the counts because the summation operations that produce counts from the arrivals and sizes act on an increasing number of packets as the NAC increases.
The mobile Internet and digital citizenship in African-American and Latino communities

ABSTRACT Smartphone use is transforming the meaning of being online, especially for African-Americans and Latinos. To what extent has this enabled these populations to become digital citizens, able to participate in society online? Internet use is increasingly important for the exercise of the political, economic and social rights that have often been associated with citizenship [Mossberger, K. , Tolbert, C. J., & McNeal, R. S. (2008). Digital citizenship: The Internet, society, and participation. Cambridge, MA: MIT Press], and can be measured by the political and economic activities that individuals engage in online. Using unique survey data from a diverse city, we use multilevel analysis and interactions to examine relationships between forms of access and activities online in 2013, controlling for neighborhood context as well as individual characteristics. In contrast with prior work, we find that while broadband access is most strongly associated with political and economic activities online, that mobile is as well. The effects are strongest for African-Americans and Latinos, especially for Latinos who live in heavily Latino neighborhoods – who have lagged behind furthest in Internet use.
The uses of equality in an elite school in India: enterprise and merit

Abstract This article examines the different uses of equality in one elite international school in India. We focus on how conceptions of equality can be enrolled into particular scripts of benevolence and gifting through which elite distinctions are constituted and enacted. Our analysis of interviews with students, teachers and parents in the school highlights three dominant and interrelated uses of equality that are examined with respect to entrepreneurial social ideals in contemporary neoliberal India: equality for profit; equality as deferred; and equality as a differential inclusion of the poor. We call this concept ‘enterprise equality’ and consider its performative effects, namely in reconstituting and justifying normative inequality. Enterprise equality does two related things which have significant global consequences: it can function as a process of ‘decontestation’ – a way to ‘block hearing’ about alternative political/economic arrangements of equality; and it can reaffirm the cultural rhetoric of the elite as meritorious in their privilege.
Forum based P2P network design using stable nodes

Internet forums provide a varied and graceful stage for those who have hobbies or interests in common and want to share their contents of interest with each other. Traditionally they use Client-Server (CS) model. A distinct characteristic of a Internet forum is that some registered nodes1 periodically visit the forum. It implies that those nodes are regular visitors and can be considered relatively stable for some duration of time. The efficient use of these relatively stable nodes is very useful in a P2P network design, especially when the forum server is with limited bandwidth. In this paper, we propose a hybrid P2P network structure which is targeted for Internet forums. In the proposed system, the forum server maintains a list of registered nodes and classifies some nodes that are regularly visiting the forum with high probability as stable nodes. The P2P network created by these stable nodes is triggered when the forum server is highly demanded due to a special event. To resolve the bandwidth shortage problem, the forum server selects stable nodes in advance, according to each node's activity history. For the stable P2P node selection, we consider two types of mechanisms; a time period mechanism and a valid date window (VDW) mechanism, according to the considered time period. Then, we confirm that our proposed P2P model outperforms the traditional CS model through simulations.
An IMU-based system identification technique for quadrotors

Accurate system identification has an important role in designing high-performance controllers for robotic platforms. Most advanced controllers are designed based on the plants' dynamic models and their identified parameters. However, some models and their parameters change because of payload or condition variations. Online identification of these changes leads to develop high performance adaptive controllers. In this research, a rigorous amount of study is carried out to identify the parametric variations of a multi-rotor using a sensory system. The proposed method applies an inertial measurement unit (IMU) to identify the exact center of mass and its displacements. This method, which is based on the genetic algorithm (GA), is independent of the IMU location. The proposed algorithm is theoretically investigated while experimental results are presented to validate the performance of the algorithm.
AMTCP: an adaptive multi-path transmission control protocol

Enabling multiple paths in datacenter networks is a common practice to improve the performance and robustness. Multi-path TCP (MPTCP) explores multiple paths by splitting a single flow into multiple subflows. The number of the subflows in MPTCP is determined before a connection is established, and it usually remains unchanged during the lifetime of that connection. While MPTCP improves both bandwidth efficiency and network reliability, more subflows incur additional overhead, especially for small (so-called mice) subflows. Additionally, it is difficult to choose the appropriate number of the subflows for each TCP connection to achieve good performance without incurring significant overhead. To address this problem, we propose an adaptive multi-path transmission control protocol, namely the AMTCP, which dynamically adjusts the number of the subflows according to application workloads. Specifically, AMTCP divides the time into small intervals and measures the throughput of each subflow over the latest interval, then adjusts the number of the subflows dynamically with the goal of reducing resource and scheduling overheads for mice flows and achieving a higher throughput for elephant flows. Our evaluations show that AMTCP increases the throughput by over 30% compared to conventional TCP. Meanwhile, AMTCP decreases the average number of the subflows by more than 37.5% while achieving a similar throughput compared to MPTCP.
Regularization of functional determinants using boundary perturbations

The formalism which has been developed to give general expressions for the determinants of differential operators is extended to the physically interesting situation where these operators have a zero mode which has been extracted. In the approach adopted here, this mode is removed by a novel regularization procedure, which allows remarkably simple expressions for these determinants to be derived.
Design and development of a visual environment for writing DyLOG programs

In this article we present a visual development envi- ronment for writing DyLOG programs, explaining the motivations to this work and the main design choices. We will also analyze the main components of the system and the features offered to the user. The visual environment encompasses a fully new implementation of the DyLOG language, where Java is used instead of Sicstus Prolog, and an OWL ontology that will allow the use of the language in Semantic Web applications. I. I NTRODUCTION
Delving into deep representations for remote sensing image retrieval

Deep features extracted from Convolutional Neural Networks (CNNs) have proved to have strong ability to transfer for various visual recognition tasks, such as image classification, object detection, fine-grained recognition, and image instance-level retrieval. In this paper, we focus on the issue of high-resolution remote sensing (HRRS) image retrieval and take advantage of CNN representations to solve this significant problem. In order to explore how domain-specific fine-tuning would impact the property of CNNs, we retrain several representative off-the-shelf CNN models with a public HRRS dataset RSSCN7, which contains limited annotated satellite images, and perform retrieval on other two standard HRRS datasets, RS19 and UCM. Then we introduce multi-scale concatenation and multi-patch pooling methods for further performance improvement. Our experimental results indicate that fine-tuning is effective to make progress on CNN transferability and provide remarkable accuracy that outperforms previous state-of-the-art methods.
Multi-level comparison of data deduplication in a backup scenario

Data deduplication systems detect redundancies between data blocks to either reduce storage needs or to reduce network traffic. A class of deduplication systems splits the data stream into data blocks (chunks) and then finds exact duplicates of these blocks. This paper compares the influence of different chunking approaches on multiple levels. On a macroscopic level, we compare the chunking approaches based on real-life user data in a weekly full backup scenario, both at a single point in time as well as over several weeks. In addition, we analyze how small changes affect the deduplication ratio for different file types on a microscopic level for chunking approaches and delta encoding. An intuitive assumption is that small semantic changes on documents cause only small modifications in the binary representation of files, which would imply a high ratio of deduplication. We will show that this assumption is not valid for many important file types and that application-specific chunking can help to further decrease storage capacity demands.
A virtual reality brainwave entrainment method for human augmentation applications

In order to investigate the brain activity under rhythmic stimulation we developed a novel architecture for Augmented Cognition by integrating latest wearable Virtual Reality (VR) immersive HeadMounted-Displays with wireless, mobile neuro-biofeedback systems for Real-time, Autonomous Cognitive State Detection, Manipulation and Augmentation. We integrated in our open-source VR framework both latest experimental Oculus Rift HMDs (DK1 and DK2) together with the Emotiv EPOC mobile EEG headset in an innovative, open architecture for user position, orientation and brainwave frequency tracking. We carried out a novel experiment with five groups tracking the brain frequency states of people while relaxing or performing certain sensorimotor tasks in an interactive, immersive 3D environment. The primary aim of this novel research is human cognition augmentation and improvement of task performance via a unique VR-induced entrainment methodology. In most of our experimental groups, we witnessed the transition of the beta-wave to the alpha-wave state. The brainwave entrainment at alpha frequency (10Hz) and relaxation via modern VR, using our open s/w framework, allowed us to verify for the first time enhanced task performance in a fast-learning setup. These first results empower us to plan future studies concerning clinical treatments in VR entrainment using modern wearable devices. This work was supported by the FORTH Institute of Computer Science. A virtual reality brainwave entrainment method for human augmentation applications
Towards the Internet of Things: Integration of Web Services and Field Level Devices

Handling the development and management of networked embedded systems becomes more and more complex with the increasing number and diversity of connected components. The challenges encountered during application development can be mastered by applying a model driven development approach and the concepts of service oriented architectures (SOA). Both development strategies are well established in their domains and, in combination, provide an excellent base for the development of networked embedded systems. However embedded networks nowadays seldom operate in isolation. The increasing interaction between embedded devices and Web based services, as envisioned by the Internet of Things, requires interfaces that provide Internet based access from and to services in the embedded network. We propose a development platform for embedded networks that eases the creation and maintenance of embedded networks and supports a seamless interaction between services from the embedded world and Web services through a bridge component. Despite creating a mere interface between the embedded world and Web services, this paper discusses how the embedded system can also be integrated semantically.
Differentiating Your Friends for Scaling Online Social Networks

Online social networks (OSN) have been increasingly popular and attracted hundreds of millions of users, and they are usually deployed on cluster systems. A crucial problem for scaling online social networks is allocation and replication of user data records in cluster nodes with the objective of reducing access time and minimizing the costs of storage and intra-cluster communication. In these applications, users not only access their own data but also data of their friends. It is preferable that all data required by a user be placed in the same node so that access time can be reduced and communication overhead is small. The inherently complex social interactions between users, however, pose great challenges to the mechanism of data allocation and replication. Analyzing a large real dataset from OSNs, we observe that for over 90% of users, all their interactions are contributed by only 22.03% of their fiends, a Pareto distribution property. Thus, the majority of the interactions of a user are attributed to a small subset of the user's friends. Inspired by this observation, we first build a dynamic weighted social graph which differentiates the importance of social interactions between a user and the user's friends. Using this graph, we design WEPAR, an online partitioning and replication algorithm taking into account both read and write operations. WEPAR tries to place master data copies of users with frequent interactions in the same cluster node, and generates slave data copies for the users who tend to receive relatively more reading requests from a cluster-node. Extensive evaluations based on real datasets show that our approach significantly reduces storage cost and improves write response time with read response time comparable to that of existing algorithms.
Comparing diagnosability in continuous and discrete-events systems

This paper is concerned with diagnosability analysis, which proves a requisite for several tasks during the system 's life cycle. The Model-Based Diagnosis (MBD) community has developed specific approaches for Continuous Systems (CS) and for Discrete Event Systems (DES) in two distinct and parallel tracks. In this paper, the correspondences between the concepts used in CS and DES approaches are clarified and it is shown that the diagnosability problem can be brought back to the same formulation using the concept of signatures. These results bridges CS and DES diagnosability and open perspectives for hybrid model based diagnosis.