Miracle-dz/UniFAR-FTUs huggingface dataset:
Subgroup and Community Analytics

Subgroup discovery and community detection are two approaches hav- ing been studied in dierent research areas like data mining and social network analysis. In this context, these techniques are especially helpful in order to provide for analytical and explorative data mining approaches, and for extracting knowl- edge for humans. We present an organized picture of recent research in subgroup discovery and community detection. Specically, we also include recent advances on mining attributed graphs. That is, we include complex relational graphs that are annotated with additional information, e.g., attribute information on the nodes and/or edges of the graph. Then, descriptive patterns can be extracted using a vari- ety of techniques, ranging from structural approaches to description-based methods. This includes detecting cohesive subgroups, correlated patterns, subspace cluster- ing, and exceptional model mining methods.
International Journal of Recent Trends in Engineering , Vol 2 , No . 3 , November 2009 114 Critical Review on Software Reliability Models

Demand on complex systems has increased more rapidly. The size and complexity of computer systems has grown during the past decades in a very impressive manner. Several software reliability models have been discovered since 1972. Lots of work has been done on software reliability estimation. Some of major models that have appeared in the literature are discussed in this paper. Taxonomy on software reliability models has been presented as a major contribution. Reliability models based on the various dimensions. The models under review reflect either infinite or finite number of failures. All exponential distribution models reflect finite failures. On the other hand logarithmic distribution based model reflect infinite failures.
THE SEVEN-LENS AIR-SURVEY CAMERA

Abstract By kind permission of the Editor of the Royal Engineers Journal, this paper has been adapted from the article by the Author in the issue of June 1937 (Vol. li, pp. 217–223). We are also indebted to the Editor for placing generously at our disposal the blocks used to illustrate the paper.
Splitting the unit delay [FIR/all pass filters design]

A fractional delay filter is a device for bandlimited interpolation between samples. It finds applications in numerous fields of signal processing, including communications, array processing, speech processing, and music technology. We present a comprehensive review of FIR and allpass filter design techniques for bandlimited approximation of a fractional digital delay. Emphasis is on simple and efficient methods that are well suited for fast coefficient update or continuous control of the delay value. Various new approaches are proposed and several examples are provided to illustrate the performance of the methods. We also discuss the implementation complexity of the algorithms. We focus on four applications where fractional delay filters are needed: synchronization of digital modems, incommensurate sampling rate conversion, high-resolution pitch prediction, and sound synthesis of musical instruments.
Semantic Morphable Models

In this thesis we discuss how computers can automatically interpret images of human faces. The applications of face image analysis systems range from image description, face analysis, interpretation, human-computer interaction, forensics to image manipulation. The analysis of faces in unconstrained scenes is a challenging task. Faces appear in images in a high variety of shape and texture and factors influencing the image formation process like illumination, 3D pose and the scene itself. A face is only a component of a scene and can be occluded by glasses or various other objects in front of the face. We propose an attribute-based image description framework for the analysis of unconstrained face images. The core of the framework are copula Morphable Models to jointly model facial shape, color and attributes in a generative statistical way. A set of model parameters for a face image directly holds facial attributes as image description. We estimate the model parameters for a new image in an Analysis-by-Synthesis setting. In this process, we include a semantic segmentation of the target image into semantic regions to be targeted by their associated models. Different models compete to explain the image pixels. We focus on face image analysis and use a face, a beard and a non-face model to explain different parts of input images. This semantic Morphable Model framework leads to better face explanation since only pixels belonging to the face have to be explained by the face model. We include occlusions or beards as semantic regions and model them as separated classes in the implemented application of the proposed framework. A main cornerstone for the Analysis-by-Synthesis process is illumination estimation. Illumination dominates facial appearance and varies strongly in natural images. We explicitly estimate the illumination condition robust to occlusions and outliers. This thesis combines copula Morphable Models, semantic model adaptation, image segmentation and robust illumination estimation which are necessary to build the overall semantic Morphable Model framework.
A pattern skipping method for weighted random pattern testing

Conventional stored pattern testing of very large scan design networks is increasingly burdened by the immense volume of pattern data that must be explicitly stored and manipulated for testing. Weighted random pattern testing uses an effective test data coding scheme to reduce the required explicit storage at the expense of increased test time. A novel method is introduced for efficiently skipping over ineffective WRP tests without adding any substantial storage overhead. Examples indicate that the method in a very practical way combines the short test times of stored pattern methods with the storage efficiency of WRP. <<ETX>>
PHEME : computing veracity : the fourth challenge of big social data

The veracity of information spreading through social media can sometimes be hard to establish and the deliberate or accidental spread of false information, especially during natural disasters or emergencies, is quite common. We coined the term phemes to describe fast spreading memes which are enhanced with truthfulness information. The PHEME project (http://www.pheme.eu) attempts to identify in real-time four kinds of phemes: controversy, speculation, misinformation and disinformation. This brings challenges in modelling the social network spread of and the online conversations around phemes; developing rumour detection methods; and using historical data to model trustworthiness of the information source.
Analysis of Covert Hardware Attacks

Current embedded system, such as cell phones and smart-cards, in corporate security devices or cryptographic processor. These cryptographic devices often store private keys or other sensitive data, so compromise of this data or the underlying hardware may lead to loss of privacy, forged access, or monetary theft. Even if the attackers fail to gain the secret information that is stored in a hardware, they may be able to disrupt the hardware or deny service leading to other kinds of security failures in the system. Therefore hardware attacks targets this security devices. Hardware attacks could be covert or overt based on awareness of the targeted system. This paper reviews proposed Accessibility/Resources/Time (ART) schema that quantifies hardware attacks. We focus in this paper on presenting covert attacks and quantify the attack using the ART schema. Keywords-hardware attack; side-channel attack; ART schema; hardware security; covert attack;
Heterogeneous voice flows-oriented call admission control in IEEE 802.11e WLANs

Considering the circumstance of heterogeneous voice flows, first, by applying Markov chain, this paper proposes an unsaturated analytical model for the IEEE 802.11e EDCA protocol, which considers the condition of non-ideal transmission channel and the character of the occurrence of backoff countdown at the beginning of time slot in EDCA protocol. Furthermore, according to the proposed model, the media access delay and throughput of a flow are analysed, and the flow-oriented call admission control (CAC) scheme is proposed. Finally, the simulation results are shown to confirm that the proposed CAC scheme can guarantee the requirements of throughput and delay of voice flows, and can admit more voice flows to improve the utilisation efficiency of network resources by choosing the appropriate values of the minimum contention window or the appropriate varieties of voice flows.
Combining fuzzy rules and a neural network in an adaptive system

It has been shown that humans can rely on both rules or associations to solve a problem. We present a model in which rules may be applied to a particular sequence learning task, but rather than the rules being applied in an all-or-none fashion, a continuum from fully representing to not representing a rule is required in order to model human task performance.
Compatible HDTV coding for broadband ISDN

A compatible digital HDTV (high-definition television) coding scheme is proposed for broadband ISDN (integrated services digital network) applications. This scheme is capable of compressing HDTV for transmission at the H4 access rate of roughly 135 Mb/s with an embedded signal for delivering EQTV (extended quality TV) over a subchannel of 45 Mb/s. Two major processing components are introduced in this system to facilitate compatible coding; one is decimation in the frequency domain and the other is progressive coding. The decimation, performed in the DCT (discrete cosine transform) domain, results in a low pass filtered and decimated version of HDTV. The progressive coding squeezes the decimated signal into the 45 Mb/s subchannel and properly refines it for 135 MB/s HDTV coding. Simulation results show that high quality HDTV can be achieved at 135 Mb/s and that the quality of the embedded 45 Mb/s signal is also very good. <<ETX>>
Adaptive Load Balancing Scheme For Data Center Networks Using Software Defined Network

A new adaptive load balancing scheme for data center networks is proposed in this paper by utilizing the characteristics of Software Defined Networks. Mininet was utilized for the purpose of emulating and evaluating the proposed design, Miniedit was utilized as a GUI tool. In order to obtain a similar environment to the data center network, Fat-Tree topology was utilized. Different scenarios and traffic distributions were applied in order to cover as much cases of the real traffic as possible. The suggested design showed superiority over the traditional scheme in term of throughput and loss rate for all the evaluated scenarios. Two scenarios were implemented; the proposed algorithm presented a loss-free performance compared to 15% to 31% loss rate in the traditional scheme for the first scenario. The proposed scheme showed up to 81% improvement in the loss rate in the second scenario. In term of throughput, the proposed scheme maintained the same level of throughput in the first scenario compared to an average of 5Mbps reduction in the throughput when using the traditional scheme. While in the second scenario, the new scheme outperformed the traditional scheme by showing an improvement of up to 16.6% in the throughput value.
Application-driven temparature-aware solutions for video coding

This thesis presents application-driven temperature-aware solutions for next generation video coding systems, such as the High Efficiency Video Coding (HEVC). Different from state-ofthe-art works, the proposed solutions raise the abstraction of temperature management to the application-level, where video coding characteristics and video content properties are used to leverage thermal-aware solutions for video coding with low QoS (Quality of Service) degradation. Several video coding and temperature analyses are performed to understand the behavior of temperature when encoding different video sequences. Based on the analyses results, different approaches are proposed to mitigate the temperature effects on video coding systems. Application-driven temperature management for HEVC uses run-time encoder configuration selection to keep temperature under safe operational state while providing good visual quality results. Temperature optimization using approximate computing uses contentdriven approximations to reduce the on-chip temperature of HEVC encoding. Applicationdriven temperature-aware scheduler leverages application-specific knowledge to guide a scheduling technique targeting reducing the spatial temperature gradients that are resulted from the unbalance workload nature of multi-threaded video coding application. The proposed solutions are able to provide up to 10 °C of chip temperature reduction with negligible compression efficiency loss. Besides, when compared with previous works the resulted objective video quality (PSNR) is from 12 dB up to 20 dB higher. Moreover, the proposed scheduler eliminates spatial temperature gradients greater than 5 oC of multi-core architectures. As conclusion, this thesis demonstrates that leveraging application-specific knowledge and video content properties has a significant potential to improve temperature profiles of video coding systems while still keeping good quality results.
A new location updating method for digital cellular systems

A novel location updating technique, the multilayer location updating method (MULTI). is proposed. The location updating signal traffic is distributed over all cells using this method, while, in the conventional method, it is concentrated on the cells located on the location area boundary. Short-term switching instability can be avoided. This method effectively reduces the required control channel number in the ultimate microcell system. The method was proposed as a Japanese digital cellular standard. <<ETX>>
Teramac configurable custom computer

Prototypes are invaluable for studying special purpose parallel architectures and custom computing. We have built a configurable custom computing engine, based on field programmable gate arrays, to enable experiments on an interesting scale. The Teramac configurable hardware system can execute synchronous logic designs of up to one million gates at rates up to one megahertz. Search and retrieval of nontext data from very large databases can be greatly accelerated using special purpose parallel hardware. We are using Teramac to conduct experiments with special purpose processors involving search of nontext databases.
Capturing and sharing war memories

The paper is organized along four sections introducing the relevant aspects related to collective memories and memory technology. The last two sections offer the presentation and a user study of this system for gathering and sharing collective war memories. Collective Memories Although the common approaches to the study of memory-related processes were traditionally focused on the individual, the social character of memory has a long standing acknowledgment in the humanities. In fact, in the last two decades there has been a growing interest in collective memory which spans psychology, history, communication and philosophy [2].
Applications of Machine Learning - A Survey

Machine learning is a powerful artificial intelligence tool that enables us to crunch petabytes of data and make sense of a complicated world. It is transforming a wide variety of industries. It is solving previously unsolved problems. Machine learning is concerned with the development of efficient algorithms and mathematical models for finding patterns and statistical dependencies in large volumes of data.In this paper we have attempted a study covering a few areas where machine learning is applied.
MapReduce for HITS Algorithm with Application to Chinese Word Networks

This paper presents a design and implement of HITS algorithm on Hadoop with application to Chinese words networks analysis. To access the importance of word in the text, we build a directed Chinese word network based on word adjacency information, and similar to the idea of HITS algorithm we define values of authority and hub for each word associated with in-degree and outdegree. In order to solve the problem of large-scale computation requirements, we design an algorithm based on Hadoop platform with map/reduce framework. In this implement, a division unit is a record composed of a word and its adjacency information, and the values of authority and hub of words are calculated in parallel by Map and Reduce. Experiment results shows that the implement is effective.
Extracting and using procedural knowledge in a CAD task

The declarative and procedural knowledge, or cognitive strategies, used by expert and novice CAD operators on a 3D design task are determined in order to understand how training and management of CAD operators could affect this knowledge. Results indicate that novices were variable in performance not because of differences in declarative knowledge (on which they were trained) but because of differences in procedural knowledge (on which no training was given). The design expert could transfer procedural knowledge from other systems to the CAD system tested. The system expert could perform fast, because of the highly developed declarative knowledge, without thinking about the strategies. This research indicates the need for attention by managers of CAD systems to procedural knowledge training for system experts and novice CAD operators; declarative knowledge training can be emphasized for design experts. >
and Parallel Execution of Agent-Based Models

We propose a framework for defining agent-based models (ABMs) and two algorithms for the automatic parallelization of agent-based models, a general version P-ABMG for all ABMs definable in the framework and a more specific variant P-ABMS for “spatial ABMs” targeted at SWARM and ANT-based models, where the additional spatial information can be utilized to obtain performance improvements. Both algorithms can automatically distribute ABMs over multiple CPUs and dynamically adjust the degree of parallelization based on available computational resources throughout the simulation runs. We also describe a first implementation of P-ABMS in our SWAGES environment and report both results from simulations with simple SWARM agents that provide a lower bound for the performance gains achievable by the algorithm and results from simulations with more complex deliberative agents, which need to synchronize their state after each update cycle. Even in the latter case, we show that in some conditions the algorithm is able to achieve close-to-maximum performance gains.
Propagation and antenna measurements using antenna switching and random field measurements

For antenna diversity measurements, it is necessary to calibrate receivers for every sequence of measurement. A calibration-free measurement system using a single receiver and antenna switching is proposed. Experiments showing the antenna sampling interval needed for characterizing diversity antennas are presented. Antenna switching can also be used to compare the relative gain of a test and a reference antenna in identical environments by the random field measurements (RFM) method. We measured the gain reduction caused by the human carrier of portable phone antennas and by the inclination angle of the handheld terminal antenna. The gain was -8.4 dB with 2.8 dB standard deviation for the vertical orientation. >
Massive data processing techniques and model in Internet of things

with the further development of Internet,rapid scales. Technology the expansion of Internet use is growing due to such rapid growth in this industry, the amount of data dealt by Internet of things becomes the feature of massive amount. The key problems that should be considered with, is how to efficiently processing these data, from which can get useful information, and then to provide intelligent decision. In connection with the new inquiry of massive data processing in the era of Internet of things. To understand the massive data processing technique is used in the Internet of things, through the analysis of massive, heterogeneous, Multi-dimension and dynamic network data. To explore from 4 aspects of the network data acquisition, data transmission, data model and storage, data processing applications, based on the data processing model of Internet of things technology system , construct a general network data storage, analysis and presentation model.
Making people play for Lexical Acquisition with the JeuxDeMots prototype

Lexical and semantic information are difficult to collect either automatically or manually, and as being instrumental in many (if not all) NLP applications, there is a serious bottleneck for advances in this research field. In this paper, we propose a novel approach by making people play some king of associative word games. Doing so, we can memorize associations where a pair of players agrees. We present the principles of the game and some insights of the data collected so far.
Noise/music and representation systems

The word ‘noise’ has taken on various meanings throughout the course of twentieth-century music. Technology has had direct influence on the presence of noise, as phenomenon and as concept, both through its newfound ubiquity in modernity and through its use directly in music production – in electroacoustics. The creative use of technologies has lead to new representation systems for music, and noise – considered as that outside of a given representation – was brought into meaning. This paper examines several moments in which a change in representation brought noise into musical consideration – leading to a ‘noise music’ for its time before simply becoming understood as music.
NOVEL METHODS IN INFORMATION MANAGEMENT FOR ADVANCED AUDIO WORKFLOWS

This paper discusses architectural aspects of a software library for unified metadata management in audio processing applications. The data incorporates editorial, production, acoustical and musicological features for a variety of use cases, ranging from adaptive audio effects to alternative metadata based visualisation. Our system is designed to capture information, prescribed by modular ontology schema. This advocates the development of intelligent user interfaces and advanced media workflows in music production environments. In an effort to reach these goals, we argue for the need of modularity and interoperable semantics in representing information. We discuss the advantages of extensible Semantic Web ontologies as opposed to using specialised but disharmonious metadata formats. Concepts and techniques permitting seamless integration with existing audio production software are described in detail.
Fast Network-Based Brute-Force Detection

Today, the Internet is a crucial business factor for most companies. Different traditional business divisions like distance selling or money transfers enhanced or even switched to the Internet, others emerged directly from it and a billion dollar business evolved over the past years. Therefore, the high fiscal values are alluring criminals. Attacks with the aid of the Internet can be executed from a safe distance, different (or even missing) IT laws in different countries are hampering the transboundary criminal execution. For example, brute-force attacks to gain access to systems and servers are still a popular and successful attack type. After gaining access, sensitive data can be copied, spyware can be installed, etc. Current protection mechanisms require extensive administration or can reduce network performance. Therefore, we propose a new architecture for network-based brute-force detection in encrypted environments. The system evaluates the similarity of the network packet payload-sizes of different connections. No information about the encryption in use or the functionality of the authorization process is required. Based on the high similarity of rejected connections, an identification of bruteforce attacks is realized. Keywords-brute force; intrusion detection; network-based; similarity; inherent knowledge.