Miracle-dz/UniFAR-FTUs huggingface dataset:
Combination of krill herd algorithm with chaos theory in global optimization problems

Krill herd optimization algorithm is a biological optimized algorithm which simulates Krill group social behavior in order to solve global optimization problems. The position of each krill in time period is dependent on induced movement of other krills, foraging activity and physical diffusion. In this paper, chaos theory is combined with KHA to improve global optimization problems, which acts on random specifications of this algorithm and logistic chaotic mapping is used in physical diffusion. The proposed method is being evaluated in some problems. Result indicate that krill chaotic algorithm has better performance than standard krill algorithm.
Optimal Subtask Allocation for Human and Robot Collaboration Within Hybrid Assembly System

In human and robot collaborative hybrid assembly cell as we proposed, it is important to develop automatic subtask allocation strategy for human and robot in usage of their advantages. We introduce a folk-joint task model that describes the sequential and parallel features and logic restriction of human and robot collaboration appropriately. To preserve a cost-effectiveness level of task allocation, we develop a logic mathematic method to quantitatively describe this discrete-event system by considering the system tradeoff between the assembly time cost and payment cost. A genetic based revolutionary algorithm is developed for real-time and reliable subtask allocation to meet the required cost-effectiveness. This task allocation strategy is built for a human worker and collaborates with various robot co-workers to meet the small production situation in future. The performance of proposed algorithm is experimentally studied, and the cost-effectiveness is analyzed comparatively on an electronic assembly case.
Photon position operators and localized bases

We extend a procedure for construction of the photon position operators with transverse eigenvectors and commuting components [Phys. Rev. A 59, 954 (1999)] to body rotations described by three Euler angles. The axial angle can be made a function of the two polar angles, and different choices of the functional dependence are analogous to different gauges of a magnetic field. Symmetries broken by a choice of gauge are re-established by transformations within the gauge group. The approach allows several previous proposals to be related. Because of the coupling of the photon momentum and spin, our position operator, like that proposed by Pryce, is a matrix that does not commute with the spin operator. Unlike the Pryce operator, however, our operator has commuting components, but the commutators of these components with the total angular momentum require an extra term to rotate the matrices for each vector component around the momentum direction. Several proofs of the nonexistence of a photon position operator with commuting components are based on overly restrictive premises that do not apply here.
Youth Responses to the Surveillance School: The Bifurcation of Antagonism and Confidence in Surveillance among Teenaged Students

The recent rise of so-called ‘surveillance schools’ is often justified given the need to engender a safe and secure educational environment for students—a fusion of pedagogical and security motives. This article contributes knowledge regarding the attitudes and lived experiences of teenagers in response to school-based surveillance. Focus groups centre discussions on two areas: the effectiveness of policies regarding technology in the classroom as well as school-wide restrictions on Wi-Fi access and the effectiveness of surveillance technologies geared to actively monitor student online activities. We explore a bifurcation of attitudes revealing both resistance to surveillance school practices as well as strong support for monitoring technologies perceived to be effective in addressing cyber-risks like cyberbullying. Our findings point to the need for empirically assessing the contexts where support or antagonism towards surveillance occurs, suggesting neither isomorphic resistance nor wholescale acquiescence.
An automatic evaluation of machine translation and Slavic languages

Evaluation plays a crucial role in the field of machine translation. The aim of our research was to assess the quality - reliability and validity - of automatic evaluation of machine translation, and in our case an on-line statistical machine translation system was used. The results of the reliability analysis showed that the chosen metrics of automatic evaluation are reliable and valid, whereby the validity and reliability were verified for the following language pairs: Slovak-English and Slovak-German.
Mixing characteristics of an inhomogeneous scalar in isotropic and homogeneous sheared turbulence

Turbulent mixing of an inhomogeneous passive scalar field is studied in the context of a nonpremixed reacting flow. Direct numerical simulations of an initial steplike scalar field subjected to homogeneous sheared turbulence have been performed and the results compared with those of the case of decaying isotropic turbulence. For both flow conditions, the gradient of the conserved scalar tends to align itself with the axis of the most compressive strain rate and orthogonal to the local vorticity. The magnitude of the scalar gradient is directly influenced by the local strain rate while its orientation is controlled by the local vorticity. Because of the directional features of sheared turbulence, the orientation of the scalar gradient is more ordered than in isotropic turbulence. In addition, the magnitude of vorticity indirectly affects that of the scalar gradient through strain‐rate amplification by vortex stretching. In both flows, regions of high scalar‐gradient magnitude or scalar dissipation (and the.
Characterization and Identification of Matrix Fraction Descriptions for LTI Systems

This paper shows that the matrix fraction description, given by a pair $\{A(z)~B(z)\}$ of matrix polynomials of $z$, for a linear time-invariant system may not be unique even if $A(z)$ is monic, $A(z)$ and $B(z)$ have no common left factor, and the matrix coefficients corresponding to the highest-order terms of $A(z)$ and $B(z)$ are full row rank. The orders of all possible matrix fraction descriptions (MFDs) of a given system are completely characterized. Testing criteria for determining whether a matrix pair is an MFD of the system are derived, which involve rank tests of certain Toeplitz matrices derived from either the impulse response or output correlation functions of the system. A decision procedure is devised that generates sequentially all MFDs for a given system. Identification algorithms are introduced that estimate all MFDs of a given system from its input-output data or output data only. The results are then extended to cover ARMAX systems.
Design and implementation of Field Service Security Data Warehouse

Field Service Security (FSS) is a systematic project which requires coordination of various departments to ensure the flight safe. At present data of FSS can't provide effective support for OLAP (Online Analytical Process), data mining or decision support because of its inconsistence and dispersion, so bring Data Warehouse technology into FSS is of great of importance. This paper presents how the FSS Data Warehouse is designed and implemented by an improved method-normalized dimensional modeling.
Malware Detection in Cloud Computing Infrastructures

Cloud services are prominent within the private, public and commercial domains. Many of these services are expected to be always on and have a critical nature; therefore, security and resilience are increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection components of our cloud resilience architecture. More specifically, we exhibit the applicability of novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of features gathered at the system and network levels of a cloud node. We demonstrate that our scheme can reach a high detection accuracy of over <inline-formula><tex-math notation="LaTeX"> $90$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="watson-ieq1-2457918.gif"/></alternatives></inline-formula> percent whilst detecting various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only system-level data, but also network-level data depending on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is particularly applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware strains with no prior knowledge of their functionality or their underlying instructions.
First-principles study of boron doping-induced band gap narrowing in 3C-SiC

Based on density functional theory (DFT), the effect of boron (B) doping concentration on band gap of 3C-SiC is investigated. The analysis of density of states (DOS) and electron distribution indicates that the band gap tends to narrow with the increase of B concentration. The top of valence band, is contributed from B 2p level, and the bottom of conduction band, from B 2s in B-doped 3C-SiC. Both of them shift towards lower energy direction. With B concentration increases, the displacement of the bottom of conduction band is larger than that of the top of valence band, resulting in the narrowing of band gap. This result is useful for controlling band gap of doped 3C-SiC, and should be helpful for enhancing reliability and broadening the application ranges of SiC devices.
Weighted Modiied First Order Regression Procedures for Estimation in Linear Models with Missing X-observations

This paper considers the estimation of coeecients in a linear regression model with missing observations in the independent variables and introduces a modiication of the standard rst order regression method for imputation of missing values. The modiication provides stochastic values for imputation and, as an extension, makes use of the principle of weighted mixed regression. The proposed procedures are compared with two popular procedures|one which utilizes only the complete observations and the other which employs the standard rst order regression imputation method for missing values. A simulation experiment to evaluate the gain in eeciency and to examine interesting issues like the impact of varying degree of multicollinearity in explanatory variables is proceeded. Some work on the case of discrete regressor variables is in progress and will be reported in a future article to follow.
The Effectiveness of Attachment-based Parent Training Method in Anxiety Syndrome of Preschool Children

Introduction: The purpose of this study was to investigate the effectiveness of attachment-based parent training on anxiety syndrome in 2- to 6-year-old children. Method: A quasi-experimental design was conducted in this research. 278 mothers completed The Spence Preschool Anxiety Scales (SPAS). 74 mothers that their children’s anxiety scores were one standard deviation above the population’s mean were selected. They completed The Conner’s Behavioral Rating Scale, investigating the absence of any other psychological problems in those children. Finally, 61 mothers were selected and assigned to the experimental and control group and received the training package for eight sessions, 2-hour-long each. Three months after finishing finishing the trainin g sessions for the experimental group and 3 months later, The SPAS were completed by mothers in both groups. Repeated mesures ANOVA was used to analyze the data. Result: The results showed that Parent Training Package diccreased generalized, social and separation anxiety subscales and total anxiety score in the experimental group more than the control group (p<0.0001). This reduction was sustained during the three months of follow-up, but no significant reduction in problems related to obsessive compulsive disoder and physical injury phobia was found after the intervention. Conclusion: It can be concluded that attachment-based parent training can decrease anxiety in preschool children. These results suggest that child therapists can use this method in order to prevent and treat children’s anxiety.
Robust Object Tracking Enhanced by Correction Dictionary

Dictionary learning method based on sparse coding has been widely used in visual tracking, since it has good performance in terms of encoding target appearance. Currently, most of the visual tracking algorithms based on dictionary learning update the dictionary with tracking results in tracking process. Consequently, the total error accumulated over time, and even cause the failure of tracking. In this paper we propose a visual tracking method enhanced by correction dictionary. We use the latest tracking results to learn the correction dictionary, which keeps the most accurate appearance information of the target at current time. In this way, our method can avoid the error accumulation. Besides, we propose a framework to compute a confidence value for correction dictionary. Experiments show the robustness of our proposed visual tracking method based on correction dictionary, especially when the target deformation occurs in the tracking process.
Trm5 and TrmD: Two Enzymes from Distinct Origins Catalyze the Identical tRNA Modification, m1G37

The N1-atom of guanosine at position 37 in transfer RNA (tRNA) is methylated by tRNA methyltransferase 5 (Trm5) in eukaryotes and archaea, and by tRNA methyltransferase D (TrmD) in bacteria. The resultant modified nucleotide m1G37 positively regulates the aminoacylation of the tRNA, and simultaneously functions to prevent the +1 frameshift on the ribosome. Interestingly, Trm5 and TrmD have completely distinct origins, and therefore bear different tertiary folds. In this review, we describe the different strategies utilized by Trm5 and TrmD to recognize their substrate tRNAs, mainly based on their crystal structures complexed with substrate tRNAs.
Best Approximation with Laplacian p-Norms to Predict the Labeling of a Graph

The aim is to predict the labeling of the vertices of a graph. The graph is given. A trial sequence of (vertex,label) pairs is then incrementally revealed to the learner. On each trial a vertex is given and the learner predicts a label and then the true label is returned. The learner’s goal is to minimize mistaken predictions. We propose to solve the problem by the method of best approximation. To this end we propose a (graph Laplacian) p-semi-norm on the space of graph labelings. Thus on every trial we predict with the labeling which minimizes the p-semi-norm and also is consistent with the revealed (vertex,label) pairs. When p = 1 this is the min-cut method of [2] and if p = 2 this is the harmonic energy minimization procedure of [6] also called (Laplacian) interpolated regularization in [1]. We prove mistake bounds for this methodology. We say an edge is cut with respect to a labeling if the connected vertices have disagreeing labels. We find that performing best approximation with p = 1 + where → 0 as the graph diameter D → ∞ gives a bound of O(k logD) versus a bound of O(kD) for best approximation with p = 2 where k is the number of cut edges.
The Predictive Context Tree: Predicting Contexts and Interactions

With a large proportion of people carrying location-aware smartphones, we have an unprecedented platform from which to understand individuals and predict their future actions. This work builds upon the Context Tree data structure that summarises the historical contexts of individuals from augmented geospatial trajectories, and constructs a predictive model for their likely future contexts. The Predictive Context Tree (PCT) is constructed as a hierarchical classifier, capable of predicting both the future locations that a user will visit and the contexts that a user will be immersed within. The PCT is evaluated over real-world geospatial trajectories, and compared against existing location extraction and prediction techniques, as well as a proposed hybrid approach that uses identified land usage elements in combination with machine learning to predict future interactions. Our results demonstrate that higher predictive accuracies can be achieved using this hybrid approach over traditional extracted location datasets, and the PCT itself matches the performance of the hybrid approach at predicting future interactions, while adding utility in the form of context predictions. Such a prediction system is capable of understanding not only where a user will visit, but also their context, in terms of what they are likely to be doing.
Knowledge based genetic algorithm for dynamic route selection

Addresses the problem of selecting a route to a given destination on a road map under a dynamic environment. The proposed solution uses a genetic algorithm adopting viral infection. The method is to use the viruses as domain-specific knowledge. A part of an arterial road is regarded as a virus. We generate a population of viruses in addition to a population of routes. Crossover and infection determine the optimal combination of viruses. When traffic congestion changes during driving, an alternative route can be generated using the viruses and other routes in the population in the shortest time. Experiments using actual road maps show that the infection method is effective for this problem.
A new metric for detecting conflict in functional software requirements

Typically, the development of software system starts with a goal. The goal is implemented by following a methodology consisting of phases. Initially, the goal is formulated as functional requirements when stakeholders of the software system meet and discuss what the software system should do in order fulfill satisfy needs. The functional software requirement document is then converted to software design document either through conceptual model, software code or both. After, the system is tested rigorously before it is implemented. Since the development of a software system consists of phases with each phase depending on prior stages, an inconsistency made in the initial phase of development such as in the requirement specification phase, may be propagated into other phases. A methodology for detecting conflict in functional software requirements through level of Potential Structural Inconsistency (PSI) is presented in this research. This is accomplished, by representing functional software requirements stated in natural language as structural model (i.e. conceptual model) and similarities between these models are obtained as a level of potential structural inconsistency. Sample functional software requirements are analyzed using this methodology and the inconsistency is compared with a particular type of conflict. In conclusion, various inferences are made based on the new methodology and recommendations are given for further improvements and future research.
Sequential randomized algorithms for sampled convex optimization

Motivated by the complexity of solving convex scenario problems in one-shot, two new algorithms for the sequential solution of sampled convex optimization problems are presented, for full constraint satisfaction and partial constraint satisfaction, respectively. A rigorous analysis of the theoretical properties of the algorithms is provided, and the related sample complexity is derived. Extensive numerical simulations for a non-trivial example testify the goodness of the proposed solution.
Emotions Embodied

In one of the most frequently quoted passages in the history of emotion research, William James (1884: 189f) announces that emotions occur when the perception of an exciting fact causes a collection of bodily changes, and “our feeling of the same changes as they occur IS the emotion. ” The same idea occurred to Carl Lange (1984) around the same time. These authors were not the first to draw a link between the emotions and the body. Indeed, this had been a central theme of Descartes’ exquisite opus, The Passions of the Soul. But James and Lange wanted to push things farther than most, suggesting that emotions are exhausted by bodily changes or perceptions thereof. Other kinds of mental episodes might co-occur when we have an emotion state. For James, an emotion follows an exciting perception. But the exciting perception is not a part of the emotion it excited (Ellsworth, 1994, reads James differently, but see Reisenzein et al.’s 1995 convincing response). The majority of contemporary emotion researchers, especially those in philosophy, find this suggestion completely untenable. Surely, emotions involve something more. At their core, emotions are more like judgments or thoughts, than perceptions. They evaluate, assess, or appraise. Emotions are amendable to rational assessment; they report, correctly or incorrectly, on how we are faring in the world. Within this general consensus, there is a further debate about whether the body should figure into a theory of emotions at all. Perhaps James and Lange offer a theory that is not merely incomplete, but entirely off base. Where they view judgments as contingent and non-constitutive concomitants of emotions, it is actually bodily perceptions that deserve this demotion. Perhaps emotions can be, and often are, disembodied in some fundamental sense. I propose to defend James and Lange, though not completely. They should be criticized for their failure to reckon with what can broadly be regarded as the rationality of emotions. That failure, however, has a remedy that does not depart from the spirit of the James-Lange approach. Emotions are somatic, but they are also fundamentally semantic: meaningful commodities in our mental economies. I will not be especially concerned with presenting the somatic theory exactly as it appears in James and Lange. I will not assume that emotions are always consciously felt, as James sometimes implies, nor that the relevant bodily changes must have the origins in the vasomotor system, as suggested by Lange. The core idea that I will defend is that emotions are perceptions (conscious or unconscious) of patterned changes in the body (construed inclusively). I begin by briefly presenting some of the positive evidence for this core idea. Then I discuss six stubborn objections. I argue that the objects can be answered without abandoning the core idea but forward by James and Lange, but they do demand an important amendment.
Stuck in the past with push-pins on paper maps: Challenges of transition from manual to computerized crime mapping and analysis in Kenya

Although crimes and the modus operandi of perpetrators across the world change constantly, some policing tools, especially in developing countries, remain the same. This study sought to assess Kenyan police officers’ satisfaction with the use of paper maps and push-pins (manual tools) in crime analysis, and identify challenges to the adoption of computerized tools such as geographical information systems (GIS) at a time when most operations are driven by technology. A descriptive survey targeting 120 Kenyan police officers was carried out at the National Police Service (NPS) headquarters in Nairobi, but only 94 (78.3%) officers responded. The study reveals that 42.3% of respondents found manual tools to be useful in crime mapping and spatial analysis, whereas 67.8% felt that they were easy to use. The study indicates that 55.0% of respondents were satisfied with manual crime mapping and spatial analysis, mainly because it was easy to use. The study also identifies several challenges to the adoption of GIS in crime analysis. The study concludes that majority of respondents found manual crime-mapping tools to be easy to use, but less useful although still usable in the absence of alternatives. The NPS should consider implementing pilot projects of computerized crime mapping and spatial analysis to allow stakeholders to assess police operations against multiple platforms.