Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved control strategy of the modular multilevel converter for high power drive applications in low frequency operation

Modular Multilevel Converters (M2C) are considered an attractive solution for high power drives. However, its operation at low rotational speeds can produce undesired voltage fluctuations in the M2C capacitors. In this paper, two methodologies to improve the converter performance in this speed range are analysed and tested. The first strategy proposes the control of the inner converter currents combining a synchronous dq rotating frame and resonant controllers to improve the current tracking and to reduce the voltage fluctuations. The second strategy achieves the reduction of the voltage fluctuations by adjusting the DC Port voltage as a function of the machine frequency. Both methods are validated by simulation and experimental work, where a 18 cell M2C prototype is applied to drive an induction machine.
A Study on Metamodeling Techniques, Ensembles, and Multi-Surrogates in Surrogate-Assisted Memetic Algorithms

Surrogate-Assisted Memetic Algorithm(SAMA) is a hybrid evolutionary algorithm, particularly a memetic algorithm that employs surrogate models in the optimization search. Since most of the objective function evaluations in SAMA are approximated, the search performance of SAMA is likely to be afiected by the characteristics of the models used. In this paper, we study the search performance of using difierent metamodeling techniques, ensembles, and multisurrogates in SAMA. In particular, we consider the SAMATRF, a SAMA model management framework that incorporates a trust region scheme for interleaving use of exact objective function with computationally cheap local metamodels during local searches. Four difierent metamodels, namely Gaussian Process (GP), Radial Basis Function (RBF), Polynomial Regression (PR), and Extreme Learning Machine (ELM) neural network are used in the study. Empirical results obtained show that while some metamodeling techniques perform best on particular benchmark problems, ensemble of metamodels and multi-surrogates yield robust and improved solution quality on the benchmark problems in general, for the same computational budget.
New Jaccard-Distance Based Support Vector Machine Kernel for Handwritten Digit Recognition

This paper proposes a new negative Jaccard distance- based kernel for Support Vector Machines (SVM). The Jaccard distance is based on shape comparison between data, which could have a particular importance for handwritten character recognition where each class has its own shape form. So, it seems more proficient than Euclidian distance that is used with conventional kernels. The performance of negative Jaccard kernel is evaluated comparatively to standard SVM kernels for handwritten digit recognition. Experiments are conducted on both One-Against-All (OAA) and One-Against-One (OAO) multi-class SVM implementations using samples taken from USPS database. The results obtained showed that Jaccard Negative Distance kernel outperforms other kernels in most cases.
Extraction of virtual-source injection velocity in sub-100 nm III–V HFETs

We have experimentally extracted the virtual-source electron injection velocity, v<inf>x0</inf>, of various III–V HFETs at room temperature. This is the carrier velocity that matters for logic applications of these transistors. Sub-100 nm devices with μn ≫ 10,000 cm<sup>2</sup>/V-s exhibit v<inf>x0</inf> in excess of 3 × 10<sup>7</sup> cm/s even at V<inf>DD</inf> = 0.5 V. This is over 2 times that of state-of-the-art Si devices at V<inf>DD</inf> ≫ 1. We have verified our extraction methodology for v<inf>x0</inf> by building a simple charge-based semi-empirical model for the I–V characteristics of III–V HFETs. This model yields an excellent description of the entire I-V characteristics of the devices from subthreshold to inversion and from linear to saturation regimes with fitted electron velocities that are very close to those independently obtained through our proposed extraction methodology.
Simplified Protocol Capture (SIMPCAP)

Standard packet capture architectures have no inherent support for protocol decoding. Client systems are required to handle all decoding and maintenance of protocol handling constructs in a proprietary fashion. Resultant system architectures are often not optimized and difficult to expand upon, particularly for defining and implementing new and unhandled protocols. This paper describes a new protocol decoding system called Simplified Protocol Capture (SIMPCAP). The system, developed for legacy use with LIBPCAP, constitutes an optimized high-level library architecture that automates protocol decoding and maintains protocol definition knowledge constructs globally. The SIMPCAP framework incorporates a high level API (application programming interface) for convenient and flexible access to protocol field state.
OPENHART 2013 EVALUATION : DESCRIPTION OF THE LITIS HANDWRITING RECOGNITION SYSTEM

In this paper, we present the Arabic handwriting recognition system that was submitted to the 2013 NIST Open Handwriting Recognition and Translation Evaluation (OpenHaRT 2013). Our baseline recognition system is based on Hidden Markov Models and we also propose a lattice-based framework to combine the outputs from several different recognition engines. Keywords—Document recognition, Arabic handwriting, OpenHaRT, Hidden Markov Models.
ISBP: Understanding the Security Rule of Users' Information-Sharing Behaviors in Partnership

The rapid growth of social network data has given rise to high security awareness among users, especially when they exchange and share their personal information. However, because users have different feelings about sharing their information, they are often puzzled about who their partners for exchanging information can be and what information they can share. Is it possible to assist users in forming a partnership network in which they can exchange and share information with little worry? We propose a modified information sharing behavior prediction (ISBP) model that can help in understanding the underlying rules by which users share their information with partners in light of three common aspects: what types of items users are likely to share, what characteristics of users make them likely to share information, and what features of users’ sharing behavior are easy to predict. This model is applied with machine learning techniques in WEKA to predict users’ decisions pertaining to information sharing behavior and form them into trustable partnership networks by learning their features. In the experiment section, by using two real-life datasets consisting of citizens’ sharing behavior, we identify the effect of highly sensitive requests on sharing behavior adjacent to individual variables: the younger participants’ partners are more difficult to predict than those of the older participants, whereas the partners of people who are not computer majors are easier to predict than those of people who are computer majors. Based on these findings, we believe that it is necessary and feasible to offer users personalized suggestions on information sharing decisions, and this is pioneering work that could benefit college researchers focusing on user-centric strategies and website owners who want to collect more user information without raising their privacy awareness or losing their trustworthiness.
Inflammation in atherosclerosis: some thoughts about acute coronary syndromes.

The innate or natural immune response is the body’s rapid first line of defense for environmental threats (eg, trauma and infection), responding long before an immune defense is mounted.1,2 This response involves a series of reactions that have evolved to limit damage, isolate pathogens (noxious agents), and initiate repair processes. Cells distinguish between pathogen and self by using signals from pattern-recognition receptors, scavenger receptors (CD-36, SR-A), and Toll-like receptors (TLRs) on macrophage and dendritic cells.2 These receptors recognize pathogen-associated patterns in nucleic acids, proteins, carbohydrates, and lipids. Flow-dependent regulation of TLR2 surface expression in endothelial cells and ligation of TLR1, TLR2, and TLR4 in plaque result in recruitment of adaptor protein myeloid differentiation factor 88, followed by activation of nuclear factor-κB and mitogen-activated protein kinases.1–4 This results in a cascade of proinflammatory molecules such as interleukin (IL)-6 that drive C-reactive protein (CRP) production, chemokines that act as chemoattractants, and serine proteases that drive thrombosis, all of which contribute to inflammation and pathogen clearance. Accumulating evidence supports a central role for inflammation in preclinical atherosclerosis, with acute coronary syndrome (ACS) as a principle clinical expression.4 Indeed, ACS, ischemic brain syndrome (stroke/transient ischemic attack), and peripheral arterial occlusion1–4 result from a chronic inflammatory process, as well as disorders of lipid metabolism, modified by genetic and environmental factors. Arterial wall function and structure are modulated by interactions between injurious agents, blood vessel wall elements and monocytes, T lymphocytes, and platelets. Invading mononuclear cells release enzymes (eg, matrix metalloproteinases [MMPs]) that degrade collagen and elastin, thereby allowing cells to invade by disrupting matrix layers that otherwise stabilize developing plaque (Figures 1 and 2⇓). Clot forming and inflammatory pathways then work in tandem to accelerate local macrophage and T-cell activation, which contributes to plaque erosion or rupture, forming a …
Processo de incubação, características empreendedoras e aprendizagem empreendedora: uma perspectiva interativa

O presente trabalho foi realizado na incubadora tecnologica de Santa Maria (ITSM), com o intuito de analisar a influencia do processo de incubacao sobre as caracteristicas e a aprendizagem empreendedoras, na percepcao de gestores de negocios que vivenciam este processo na ITSM. Para isso, os dados foram coletados por meio da aplicacao do questionario desenvolvido por David McClelland (1972) sobre CCE´s e da realizacao de entrevistas semiestruturadas com oito gestores de negocios incubados. Os questionamentos abordados na entrevista tiveram foco na historia de vida, trajetoria profissional, processo de incubacao e aprendizagem empreendedora, esta ultima obedecendo as premissas propostas por Politis (2005). Para a analise dos dados utilizou-se a abordagem descrita por Sampieri (2006), analisando-se qualitativamente os questionarios aplicados, e tecnica de analise de conteudo das entrevistas, baseada em Bardin (2011). As categorias de analise definidas a priori foram: processo de incubacao, caracteristicas empreendedoras e aprendizagem empreendedora. As categorias de analise definidas nao a priori, com base nos relatos dos entrevistados, foram: demandas percebidas e necessidades de interacao. Destaca-se que o objetivo do estudo foi atendido plenamente, em funcao de que, a partir da perspectiva dos incubados, a ITSM proporcionou oportunidades de desenvolvimento para estes individuos, bem como para seus negocios, estimulando a aprendizagem dos mesmos por meio de um carater experiencial. Como contribuicao, a partir dos resultados obtidos, elaborou-se um plano da acao para a ITSM, com o objetivo de reduzir as demandas percebidas pelos incubados, acerca do processo de incubacao.
A Novel Multi Scale Approach for Detecting High Bandwidth Aggregates in Network Trafﬁc

Today the scale, complexity and intensity of Denial of Service attacks has increased many folds. These attacks have moved from simple flooding based attacks to sophisticated Application based attacks as well as Protocol specific attacks.The challenge is to develop detection algorithms that can distinguish between the attacks like the new pulsating denial of service and legitimate traffic like Flash events. The presence of self-similarity in computer network traffic has introduced a newer dimension in techniques being developed for anomaly detection in aggregated network traffic.We propose use of wavelets to distinguish between legitimate flash events and pulsating distributed denial of service attacks and generating images to show point-of-presence of the attack.The detection methodology has also been tested on KDD Dataset.
Dynamic media buffer control scheme for seamless streaming in wireless local area networks

Multimedia streaming between smart devices has come into general use in Wi-Fi networks, for example, streaming a video content in mobile phone to smart TV. However, drastic and frequent changes of link quality caused by wireless channel characteristics such as multipath fading and Doppler effect leads the network throughput unstable. In order to support seamless play even in unstable wireless link, the buffer control for media playback should be smarter and more robust against radical network variations. To this end, we propose a novel dynamic buffering mechanism for HTTP streaming. This is evaluated by experiments: streaming a multimedia content from a smartphone to a display device when they are connected to the same Wi-Fi Access Point. We show that the proposed mechanism outperforms an existing solution in terms of the number of buffering and the total buffering time. We also verify how fast the proposed method adapts to sudden changes of wireless link condition.
Process Scheduling Evaluations using Multithreaded Software Modules

A fuel supply control method for controlling the quantity of fuel being supplied to an internal combustion engine having a catalytic means for purifying detrimental ingredients in exhaust gases, in a feedback manner responsive to the output of a means for detecting the concentration of an ingredient in the exhaust gases. When the engine is operating in a predetermined high speed operating region wherein the rotational speed of the engine is higher than a predetermined speed, the above feedback control is interrupted and the fuel quantity is increased by a predetermined amount so as to make the air/fuel ratio of a mixture being supplied to the engine richer than a theoretical mixture ratio. Preferably, the above predetermined speed is set at a value which is a maximum value of rpm above which the catalyst bed temperature of the exhaust gas purifying means will exceed a predetermined maximum allowable value, if the air/fuel ratio is controlled to the theoretical mixture ratio or a value close thereto. Further, preferably, the above predetermined fuel amount by which the fuel quantity is increased in the above predetermined high speed operating region is set to larger values as the intake pipe absolute pressure increases.
A Simulation Tool for Evaluating Fiber Tractography Algorithms

Diffusion-Tensor Magnetic Resonance Imaging is a popular imaging modality for reconstructing the nerve fiber structure in the brain. In recent years many fiber tracking algorithms have been proposed, but little research has been done comparing them and evaluating their suitability for pathological neuroanatomy. We present a modeling tool for designing normal and abnormal neuroanatomical features and for synthesizing diffusion tensor imaging data from it in order to compare fiber tractography algorithms. Initial results indicate that our modeling techniques make it easy to simulate DTI data sets for healthy and pathological brain anatomy. We compared three simple fiber tractography algorithms using our synthesized data and we obtained similar results as reported in the literature for more basic artificial DTI data sets.
A Step Towards Context Based Machine Translation of Asian Languages

In this paper we present a new paradigm of machine translation of natural languages particularly Asian languages. Our approach leads to a context based system where semantic modelling is done within the framework of context dependent structures. It is the context that determines the true meaning of a discourse. Thus we translate the context and the meaning associated with that rather than the text itself. It is noticed that the cultural values of Eastern societies are very similar which gives similar cultural contexts in which a more accurate translation of the content is possible. Introduction An automatic machine translator is every NLP researcher's dream and thus it has generated a lot of enthusiasm and excellent research but with out much success. Some of the important works in Asian languages are of particular interest [1, 2, 3]. Most of the outcome of these works is in form of systems mainly capable of providing facilities and support for human translation and the success have been limited to technical manuals of specific nature. The systems provide word-for-word as well as phrase translation with multiple alternatives that can only be decided through human interaction. A lot of work in formalising the semantics and composition of Western languages, particularly English, has been done [4, 5, 6, 7]. Such semantic representations are based on context free grammars and strongly tied with the language syntax [8]. This only allows us to construct systems based on a specific language with out any portability to other languages. Recently a new paradigm of natural language processing is developed that is based on context but free from grammar or composition [9, 10, 11, 12, 13]. This gives a hope of a system capable of interpreting different languages at the same time in a particular context. Since the Asian languages are very symbolic in nature, a context based system is the most suited method of extracting and representing semantics independent of grammatical or lexical structures. In this paper we will explore the possibility of a context based grammar free system for multi-lingual processing of Asian languages which can also provide a bridge between Asian and Western languages. 1 Context Based Systems One of the major obstacles in machine translation of natural languages is that the meaning is dependent on the context in which the script is written or spoken. Here we will use the techniques developed in [10, 13] for interpretation of natural languages to find a specific mapping for the translation of words, phrases or may be entire sentences provided we know the context of such discourse. The basic relations used in formalizing contexts as objects are: ist(c,p) means the proposition p is true in the context c
Concurrent learning of visual codebooks and object categories in open-ended domains

In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline.
An efficient key management scheme in hierarchical wireless sensor networks

In this paper, a secure efficient hierarchical key management scheme (SEHKM) for wireless sensor networks is proposed. It satisfies several requirements of WSN. The design of the scheme is motivated by the constraints of sensor networks and high resources cost in traditional key management techniques. The key management scheme supports the establishment of three types of keys to encrypt messages sent among sensor nodes. They are network key, group key and pairwise key. Network key encrypts broadcast messages and authenticates new nodes. Group key is shared by all nodes in the same cluster. Pairwise key is shared with a specific pair of nodes. In hierarchical WSN, cluster heads are very important due to the structure of the network. In order to improve security strength and reduce resource cost and risk after cluster head compromise, an assistant node is introduced in the scheme. SHEKM includes key establishment, key transportation, mechanisms after node operation and dynamical freshness of keys. The evaluation of SEHKM is presented as well as the comparison with some existing key management schemes. The performance analysis and simulation show that SEHKM is very efficient in computation, communication, and storage while the security strength is high.
Better-than-voltage scaling energy reduction in approximate SRAMs via bit dropping and bit reuse

This paper explores the effectiveness of different knobs to dynamically trade energy consumption with output quality in approximate SRAMs for error-tolerant applications (such as video). Leveraging the different impact of errors on quality at most significant bit (MSB) and least significant bit (LSB) positions, energy savings higher than those provided by simple voltage scaling are enabled. Firstly, a comparison of two techniques, dual-VDD and LSB dropping, is carried out showing that the latter is preferable thanks to its intrinsic simplicity and more pronounced energy savings. Secondly, a selective Error Correction Code (ECC) technique which reuses the LSBs as check bits to protect MSBs is investigated. Measurements on a 28nm CMOS 32kb SRAM show that bit dropping and bit reuse achieve an energy reduction of up to 33% and 28%, compared to simple voltage scaling at iso-quality. When combined together, the two techniques achieve a better energy saving (40%) and a supply voltage reduction of about 100mV at iso-quality. Finally, guidelines to select the energy-optimal combination of the two techniques are provided for a given quality target.
Association Control Based Load Balancing for Tactical Information Communication Networks

In this paper, we propose a distributed load balancing using association control, especially for Tactical Information Communication Networks (TICNs). We develop a scheme for effectively balancing the load at the Base Stations (BSs), where a BS's load represents the amount of resources utilized by a BS, thus minimizing the blocking probability as well as the transmission failure probability. In order to make it more suitable to the military environment, we develop the proposed scheme in a completely distributed manner. Extensive simulations are performed and the proposed scheme is found to outperform the association schemes existing in the literature.
Trolling ourselves to death? Social media and post-truth politics

There are at least two ways of framing the problem of post-truth politics. One is to focus on the media, or journalism. A second is to focus on media, or technologies of communication. Between the two, which can be said to be the driver of our post-truth world? This paper follows the second framework, locating the problem of post-truth politics in social media. It further suggests that trolling has gone mainstream, shaping politics and even legislation. Adding a twist to Neil Postman’s (1985) classic thesis, I argue that we are not so much amusing, as trolling ourselves to death.