Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Theoretical and Applied Information Technology Software Risks and Mitigation in Global Software Development

The Global Software Development is becoming so much complex and challengeable as the scope of the problem domain of the user increases day by day advancement. In today's modern world of engineering development, almost all projects suffer from some unwanted threats called risks which can seriously affect the quality, cost and schedule of the projects and hence could have a large negative impact on a project. Even many projects are cancelled due to catastrophic risks so it is very much important for a good project management to identify, monitor, plan and control the potential risks so that it is ensured that the project completes according to the satisfaction of all stakeholders interested in the project. The risks occur in GSD are much more different than those risks that in Local Software Development. Even sometime a tinny risk becomes a serious catastrophic risk in GSD. This paper provides some necessary information regarding the potential risks that can occur in the GSD, their consequences on the projects, and some useful guidelines of handling these risks.
The Role of Audio and Tags in Music Mood Prediction: A Study Using Semantic Layer Projection

Semantic Layer Projection (SLP) is a method for automatically annotating music tracks according to expressed mood based on audio. We evaluate this method by comparing it to a system that infers the mood of a given track using associated tags only. SLP differs from conventional auto-tagging algorithms in that it maps audio features to a low-dimensional semantic layer congruent with the circumplex model of emotion, rather than training a model for each tag separately. We build the semantic layer using two large-scale data sets – crowd-sourced tags from Last.fm, and editorial annotations from the I Like Music (ILM) production music corpus – and use subsets of these corpora to train SLP for mapping audio features to the semantic layer. The performance of the system is assessed in predicting mood ratings on continuous scales in the two data sets mentioned above. The results show that audio is in general more efficient in predicting perceived mood than tags. Furthermore, we analytically demonstrate the benefit of using a combination of semantic tags and audio features in automatic mood annotation.
ARMin - robot for rehabilitation of the upper extremities

Task-oriented repetitive movements can improve motor recovery in patients with neurological or orthopaedic lesions. The application of robotics can serve to assist, enhance, evaluate, and document neurological and orthopaedic rehabilitation. ARMin is a new robot for arm therapy applicable to the training of activities of daily living in clinics. ARMin has a semiexoskeletal structure with six degrees of freedom, and is equipped with position and force sensors. The mechanical structure, the actuators and the sensors of the robot are optimized for patient-cooperative control strategies based on impedance and admittance architectures. This paper describes the mechanical structure, the control system, the sensors and actuators, safety aspects and results of a first pilot study with hemiplegic and spinal cord injured subjects
Precise eye localization with improved SDM

In this paper, we propose a precise and fast eye center localization method. The method is based on SDM [1] algorithm, which has achieved impressive performance in face alignment area. In this work, we further improve the basic SDM algorithm in two aspects: 1) employ the coarse to fine strategy to enhance the robustness to initialization; 2) utilize multiple nonlinear features to ensure the robustness while improve the accuracy. Experimental results on BioID database shows that our improved SDM achieves preferable localization accuracy against the state-of-the-art methods at a speed as fast as 2ms to locate two eyes.
Economic Nationalism in Mergers and Acquisitions

This paper studies the government reaction to large corporate merger attempts in the European Union during 1997-2006 using hand-collected data. It documents widespread economic nationalism in which the government prefers the target companies remain domestically owned rather than foreign-owned. This preference is stronger at times and places with strong far-right parties, weaker governments, and against countries for which the people in the target country have little affinity. This nationalism has both direct and indirect economic impact on mergers and impedes capital flows. In particular, nationalist government reactions deter foreign companies from bidding for other companies in that country in future.
A Trust Evaluation Mechanism Based on Certified Trust and Fuzzy-Derived Reputation

Introducing the trust evaluation into the access control for the service requester in a distributed network environment could be helpful for enhancing the precision and security of authorization decisions, special to the strange requesters. The existing researches of the trust evaluation could be divided into the objective means and the subjective means. The subjective evaluations could be used to dynamically measure the user's trust degree according to the reputation based on its past actions. But it cannot work when the interaction experience with the user is absence. Compared with other subjective evaluation models using the probability theory, the fuzzy based model could exactly depict the unsure and fuzzy phenomena in process. But the existing fuzzy method based on the fuzzy synthetic evaluation needs to do complex calculations between trust vectors. And the chosen operators are not enough accurate. In this paper we promote a comprehensive trust evaluation framework was a fusion of objective and subjective means. An initial trust view for user is provided based on the certified trust and a reputation is derived by the fuzzy synthetic evaluation method based on user's past actions. The proposed fuzzy method can avoid complex calculations between trust vectors, and adopts a more accurate operator to avert the information wastage.
Guaranteed estimation of the parameters of nonlinear continuous‐time models: Contributions of interval analysis

This paper is about guaranteed parameter estimation in two contexts, namely bounded‐error and optimal estimation. In bounded‐error estimation, one looks for the set of all parameter vectors that are consistent with some prior bounds on the errors deemed acceptable between the model behavior and that of the system. In optimal estimation, one looks for the set of all parameter vectors that minimize some cost function quantifying the discrepancy between the behaviors of the system and its model. In both cases, guaranteed means that proven statements are made about the set of interest. The situation is made much more difficult when the model output is assumed to depend nonlinearly in the parameters to be estimated and when dealing with continuous‐time models, as here. Important tools based on interval analysis (IA) that contribute to allowing guaranteed estimation in these challenging conditions are presented. Some are absolutely classical in the context of IA but not so well known in the community of parameter estimation at large. Others have been developed recently and were mainly presented in conferences. Some, such as the use of sensitivity functions to reduce more quickly the size of outer approximations of the sets of interest, are new. Challenges for future research in the context of guaranteed nonlinear estimation are mentioned. Copyright © 2010 John Wiley & Sons, Ltd.
Weighted Automata and Monadic Second Order Logic

Let S be a commutative semiring. M. Droste and P. Gastin have introduced in 2005 weighted monadic second order logic WMSOL with weights in S. They use a syntactic fragment RMSOL of WMSOL to characterize word functions (power series) recognizable by weighted automata, where the semantics of quantifiers is used both as arithmetical operations and, in the boolean case, as quantification. Already in 2001, B. Courcelle, J.Makowsky and U. Rotics have introduced a formalism for graph parameters definable in Monadic Second order Logic, here called MSOLEVAL with values in a ring R. Their framework can be easily adapted to semirings S. This formalism clearly separates the logical part from the arithmetical part and also applies to word functions. In this paper we give two proofs that RMSOL and MSOLEVAL with values in S have the same expressive power over words. One proof shows directly that MSOLEVAL captures the functions recognizable by weighted automata. The other proof shows how to translate the formalisms from one into the other.
Dynamic Output Feedback for Nonlinear Networked Control System with System Delays and Packet Dropout

Abstract Focusing on networked control systems with state delay and packet dropout, a state feedback control for a class of nonlinear networked control systems with system delays and packet dropout is proposed. Then an observer is designed to estimate the system state. Finally, we achieve stabilization of this class of systems through a dynamic output feedback without augmentation of the state space model. From an appropriate Lyapunov-Krasovskii function, sufficient conditions which guarantee the convergence of the state variables and state estimation errors to the origin are deduced and expressed in terms of simple LMIs. Usability and simplicity are the advantages of this approach. A numerical example is given to illustrate the effectiveness of the proposed approach.
A novel DOA estimation method in the presence of multipath interference

Due to the complexity of the actual space transmission channel, multipath signals will incident to antenna array, which makes the accurate DOA (Direction of Arrival) cannot be obtained by using some typical DOA estimation method. According to the structure characteristic of the array manifold of multipath signals, this paper propose a new method to estimate DOA by using cFastICA (complex Fast Independent Component Analysis) algorithm combined with CS (Compressed Sensing) theory. Test results show that the new method not only has better performance than forward/backward spatial smoothing combined with MUSIC (Multiple Signal Classification) algorithm, but also can be more convenient to judge the corresponding relationship between the direct component and multipath components for each source signal. This study could provide a new idea to solve DOA estimation problem on multipath environments.
Uneven Ground: Appalachia since 1945

Appalachia has played a complex and often contradictory role in the unfolding of American history. Created by urban journalists in the years following the Civil War, the idea of Appalachia provided a counterpoint to emerging definitions of progress. Early-twentieth-century critics of modernity saw the region as a remnant of frontier life, a reflection of simpler times that should be preserved and protected. However, supporters of development and of the growth of material production, consumption, and technology decried what they perceived as the isolation and backwardness of the place and sought to 'uplift' the mountain people through education and industrialization.Ronald D Eller has worked with local leaders, state policymakers, and national planners to translate the lessons of private industrial-development history into public policy affecting the region. In "Uneven Ground", Eller examines the politics of development in Appalachia after World War II with an eye toward exploring the idea of progress as it has evolved in modern America. Appalachia's struggle to overcome poverty, to live in harmony with the land, and to respect the diversity of cultures and the value of community is also an American story. In the end, Eller concludes, 'Appalachia was not different from the rest of America; it was in fact a mirror of what the nation was becoming'.
Pharmacovigilance – A Need for Best Patient Care in Pakistan. A review

The drug is unique commodity as being life saving and improves quality of life when used rationally, but it becomes life threatening when used irrationally/ inappropriately. Efficacy, safety and quality of the drugs are three most important characteristics in any registration criteria anywhere on the globe. However, pre registration research/studies on theses aspects can not be extrapolated to effectiveness of drugs under real user conditions. Drug related morbidity and mortality (DRMM) including adverse drug reaction (ADR) is a universal costly problem. The fast growing discipline Pharmacovigilance (PV) focuses on multiple activities which are specifically aimed to improve patient care, assessment of benefit /rationality/safety/effectiveness/risks notification in relation to drug therapy/ quality of medicines and prevention of ADR or any other drug-related problem (DRP). Pharmacovigilance is now at the top of health care all over globe but unfortunately it is practically non-existent in Pakistan. We desperately needs to design/develop and implement vibrant Pharmacovigilance system through collaborative efforts of academia, health care providers including pharmacist , patient ,manufacturer ,government , media , civil society, Uppsala Monitoring Center (UMC), Sweden operating under World Health Organization (WHO), FDA, ISoP. and other international organization working on drug safety. All concerned in drug delivery system must believe in the notion that disease related mortality is sometimes unavoidable but drug related death is now unacceptable. The costs of any ADR becomes high when trust of patients in the health care system is lost.Pakistan needs to think globally while acting locally while designing and implementing PV-programme for best patient care.Implementing concept of Pharmaceutical Care would give an in built Pharmacovigilance within health care system of Pakistan.Pharmacist should take PV as an opportunity for best patient care in Pakistan.
Multi-receiver modulation classification for satellite communications signals

In order to improve the performance of modulation classification systems, the idea of multi-receiver recognition has been developed recently. In this paper multiple receivers' collaboration at different information levels is investigated for classification of signals used in DVB-S2 standard. Three methods are proposed for receivers' cooperation at each one of signal, feature and decision levels. The proposed methods use cumulants and MLP neural network as signal features and classifier respectively. These methods are evaluated and compared through performance, complexity and equipment. The results show that receivers' cooperation at signal level offers more accurate classification compared to feature and decision levels, in addition to less computational complexity.
Innate immune response to oral bacteria and the immune evasive characteristics of periodontal pathogens

Periodontitis is a chronic inflammation of periodontal tissue caused by subgingival plaque-associated bacteria. Periodontitis has long been understood to be the result of an excessive host response to plaque bacteria. In addition, periodontal pathogens have been regarded as the causative agents that induce a hyperinflammatory response from the host. In this brief review, host-microbe interaction of nonperiodontopathic versus periodontopathic bacteria with innate immune components encountered in the gingival sulcus will be described. In particular, we will describe the susceptibility of these microbes to antimicrobial peptides (AMPs) and phagocytosis by neutrophils, the induction of tissue-destructive mediators from neutrophils, the induction of AMPs and interleukin (IL)-8 from gingival epithelial cells, and the pattern recognition receptors that mediate the regulation of AMPs and IL-8 in gingival epithelial cells. This review indicates that true periodontal pathogens are poor activators/suppressors of a host immune response, and they evade host defense mechanisms.
A novel facial feature extraction method based on ICM network for affective recognition

This paper presents a facial expression recognition approach to recognize the affective states. Feature extraction is a vital step in the recognition of facial expressions. In this work, a novel facial feature extraction method based on Intersecting Cortical Model (ICM) is proposed. The ICM network which is a simplified model of Pulse-Coupled Neural Network (PCNN) model has great potential to perform pixel grouping. In the proposed method the normalized face image is segmented into two regions including mouth, eyes using fuzzy c-means clustering (FCM). Segmented face images are imported into an ICM network with 300 iteration number and pulse image produced by the ICM network is chosen as the face code, then the support vector machine (SVM) is trained for discrimination of different expressions to distinguish the different affective states. In order to evaluate the performance of the proposed algorithm, the face image dataset is constructed and the proposed algorithm is used to classify seven basic expressions including happiness, sadness, fear, anger, surprise and hate The experimental results confirm that ICM network has great potential for facial feature extraction and the proposed method for human affective recognition is promising. Fast feature extraction is the most advantage of this method which can be useful for real world application.
Optimising sybil attacks against P2P-based botnets

Addressing and mitigating modern global-scale botnets is a pressing Internet security issue, particularly, given that these botnets are known to be provide attackers with the large-scale low-cost computing infrastructure required to engage in major spam campaigns, larger-scale phishing attacks, etc. Over time, botnets have evolved toward using decentralized peer-to-peer (P2P) command and control (C&C) infrastructures in order to increase their resilience against defender countermeasures, i.e. as seen in Storm's use of Overnet and more recently in the appearance of HTTP-tunneled P2P botnets, such as Waledac and Conficker. The obvious question is, what are effective countermeasures against these modern botnets? This work focuses on evaluating, via simulation, sybil attack-based countermeasures and how such sybil-based strategies should be tailored to allow them to both be effective and implementable on global-scales. Slower-rate sybil infection strategies with random placement of sybils are shown to be nearly as effective as higher-rate infection strategies with targeted placement. This somewhat counter-intuitive result is important, as the former strategy is easier to implement by a loosely co-ordinated collective of globally scattered defenders.
Bayes in the Brain—On Bayesian Modelling in Neuroscience

According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ([1982]). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms. 1 Introduction 2 Theoretical Neuroscientists meet Bayes 3 Is Perception Bayesian Inference? 4 How Should we Understand the Inference from Bayesian Observers to Bayesian Brains? 5 How Could we Discover that Brains are Bayesian? 6 Conclusion 1 Introduction 2 Theoretical Neuroscientists meet Bayes 3 Is Perception Bayesian Inference? 4 How Should we Understand the Inference from Bayesian Observers to Bayesian Brains? 5 How Could we Discover that Brains are Bayesian? 6 Conclusion
Automatic first- and second-order adjoints for truncated Newton

The analysis and modification of numerical programs in the co ntext of generating and optimizing adjoint code automatically prob ably ranges among the technically and theoretically most challenging source tra nsformation problems known today. A complete compiler for the target language (Fo rtran in our case) is needed to cover the technical side. This amounts to a mathe tically motivated semantic transformation of the source code that involves th reversal of the flow of data through the program. Both the arithmetic complexity and the memory requirement can be substantial for large-scale numerical s imulations. Finding the optimal data-flow reversal schedule turns out to be an NP-com plete problem. The same complexity result applies to other domain-specific pee phol optimizations. In this paper we present a first research prototype of the NAGW are Fortran compiler with the ability to generate adjoint code automatical ly. Moreover, we discuss an approach to generating second-order adjoint code for use in N wton-type algorithms for unconstrained nonlinear optimization. While th focus of this paper is mostly on the compiler issues some information on the mathem atical background will be found helpful for motivational purposes.
Uncooperative localization improves attack performance in Underwater Acoustic Networks

Underwater Acoustic Networks (UANs) have become a focus of interest for emerging scientific research and military applications. Recent work has shown that performance of existing security attacks are sensitive to network topology. In this paper, we utilize the mobility of Autonomous Underwater Vehicles (AUVs) to discover the topology of UANs by monitoring the broadcast patterns of geographic routing protocols. In this way, a mobile attacker can take advantage of the geographic information used in UANs to improve attack performance. We evaluate our approach in Aqua-Sim and results show that attack performance of jamming is significantly improved.
Next Generation MEMS Test Chip

After testing each device on the old demonstration chip, new ways of improving it were explored. The previous test chip had several redundant devices which limited the opportunities to characterize the structural films. As a result, the next generation chip was laid out with new and redesigned devices that allow students to explore parameters and concepts significant to MEMS, i.e. stress, strain, Young’s Modulus, spring constants, line width, stiction, and electro-dynamics. This test chip will be submitted to the Multi-User MEMS Processes(MUMPS) for fabrication.
Area-Speed-Efficient Transpose-Memory Architecture for Signal-Processing Systems

This paper presents the design and analysis of a high-speed implementation of a new transpose memory architecture. The proposed memory structure achieves almost 4X improvement in speed while consuming 46% less area, compared to prior work. For example, an 8X8 transpose memory with 12-bit input/output resolution has been implemented in 140 slices on a Virtex-7 Xilinx FPGA platform, achieving 107.83 Gpbs, clocked at 647MHz. The new transpose memory architecture allows 3.5X speed up in performance for the 2D-DCT algorithm, compared to previous work, while consuming 28% less area, and 2D-IDCT achieves 3X speed up compared to prior work, while consuming 20% less area.