Miracle-dz/UniFAR-FTUs huggingface dataset:
Signal-Layer Security and Trust-Boundary Identification based on Hardware-Software Interface Definition

An important trend in the automotive domain is to adapt established functional safety processes and methods for security engineering. Although functional safety and cyber-security engineering have a considerable overlap, the trend of adapting methods from one domain to the other is often challenged by non-domain experts. Just as safety became a critical part of the development in the late 20th century, modern vehicles are now required to become resilient against cyber-attacks. As vehicle providers gear up for this challenge, they can capitalize on experiences from many other domains, but must also face several unique challenges. Such as, that cyber-security engineering will now join reliability and safety as a cornerstone for success in the automotive industry and approaches need to be integrated into the mainly safety oriented development lifecycle of the domain. The recently released SAE J3061 guidebook for cyber-physical vehicle systems focus on designing cyber-security aware systems in close relation to the automotive safety standard ISO 26262. The key contribution of this paper is to analyse a method to identify attack vectors on complex automotive systems via signal interfaces and propose a security classification scheme and protection mechanisms on signal layer. To that aim, the hardware-software interface (HSI), a central development artefact of the ISO 26262 functional safety development process, is used and extended to support the cyber-security engineering process and provide cyber-security countermeasures on signal layer.
Alone at work: Isolation, competition and co-dependency in flexibilised retail

Previous studies of flexibilisation through employer-controlled flexible scheduling and off-site isolated work have shown how such practices have detrimental effects on workers’ wellbeing. This qualitative study, set in Swedish retail, adds to previous findings by showing how flexible scheduling practices that include irregular variation of work hours and headcount have consequences also for worker interaction in the workplace. Even on-site work can be experienced as isolating if workers are ‘spread too thin’ in efforts to reduce labour costs. Set in two different retail settings, the study demonstrates and discusses how inter-employee competition and co-dependency are created, respectively. The authors also discuss how the flexibilisation described in this study reduced possibilities for face-to-face meetings and communication between co-workers, between workers and managers, and between workers and union representatives. Finally, it is discussed how the kind of flexibilisation described in this study coincides with defeatism and barriers to collective voice as well as action.
elementr: An R package for reducing elemental data from LA‐ICPMS analysis of biological calcified structures

Elemental analysis of biological calcified structures (e.g. fish otoliths, mollusc shelves, coral skeletons or fish and shark bones) provides invaluable information regarding ecological processes for many aquatic species. Despite this importance, the reduction of the raw data obtained through Laser‐ablation Inductively Coupled Plasma Mass Spectrometry (LA‐ICPMS) (i.e. the conversion of the machine raw signal into elemental concentrations) remains a challenge as the tools developed so far for carrying out this task have important limitations for aquatic ecologists. Here, we introduce the elementr R‐package which provides a handy, reliable and transparent way to reduce elemental data acquired from spot or transect LA‐ICPMS analysis of biological calcified structures. This free and open‐source software, implemented based on state‐of‐the‐art literature, handles data from both standards and samples, allowing fast and simultaneous calculations of concentration for any chemical element, correction for potential machine drift, and realignment and averaging for sample replicates when needed. The major attributes of elementr are: (i) its user‐friendly graphical interface which provides widgets to set all the reduction settings (i.e. no programming skills are required to run it), (ii) its reactivity whereby the software continuously observes any setting change made by the user, re‐calculates and displays all updated results, allowing therefore users to visually check the validity of their settings and to tune them if needed and (iii) an object oriented underlying that facilitates subsequent handling of LA‐ICPMS data in R. Despite the elementr design being most suited to the needs of aquatic ecologists, its use could be broadened to other research fields (i.e. geology, material engineering) due to its flexibility. Moreover, the open‐source approach used for programming this software allows its expansion in order to refine calculation procedures or to add new functionalities.
Cyclostationary-based jammer detection for wideband radios using compressed sensing and artificial neural network

Cognitive radio is a promising technology for frequency allocation to improve the spectrum utilization efficiency of licensed bands. However, in recent years, the attention of the researchers is focused on security issues that have to be faced by cognitive radio technology. One of the most important issues consists of radio frequency jamming attacks, where adversaries can use on-the-fly reconfigurability and learning capabilities of cognitive radios in order to devise and deploy advanced jamming tactics. Jamming attacks can noticeably affect the performance of wireless communication systems and can lead to significant overhead in terms of data re-transmission and increased power consumption. In this article, a novel compressed sensing-based jammer detection algorithm is proposed using cyclic spectral analysis and artificial neural networks for wideband cognitive radios. A wideband spectrum is considered that is composed of multiple narrowband signals. Narrowband signals can be legitimate or jamming signals. Compressed sensing is used to reduce the overhead of the analog-to-digital conversion and it allows one to estimate a wideband spectrum with sub-Nyquist rate sampling. After the signal has been estimated, the second-order statistics, namely, spectral correlation function, is computed to extract cyclic features of the wideband signal. Finally, a pre-trained artificial neural network is proposed to classify each narrowband signal as a legitimate or jamming signal. Performances of proposed algorithm are shown with Monte-Carlo simulations under different empirical setups.
Research on IMM-UKF Based Civilian Unmanned Aerial Vehicle Integrated Navigation Algorithm

The main navigation method of Civilian Unmanned Aerial Vehicle (CUAV) is Global Positioning System (GPS) navigation, GPS navigation failure will make CUAV lost control, resulting in heavy losses. How to ensure the reliability and navigation precision of CUAV under the failure of GPS is a hot issue currently studied. In this paper, the self-developed Beidou satellite navigation system (BDS) by China is used to assist the Inertial Navigation System (INS) of CUAV navigation, when the GPS navigation is failure. The IMM-UKF method is used to overcome the uncertainties of statistical characteristics and the dispersion of filtering. The algorithm is verified by MATLAB software. The simulation results show that compared with the traditional EKF algorithm, this IMM-UKF algorithm can improve the navigation accuracy of the integrated navigation system and prevent the filter diverging. It can effectively solve the problem of navigational problems of CUAV under GPS failure.
Future Generation Computer Systems

Term representation methods as computable and semantic tools have been widely applied in social network analysis. This paper provides a new perspective that can incrementally factorize co-occurrence matrixtoquerylatestsemanticvectors.Wedividethestreamingsocialnetworkdataintooldandupdated training tasks respectively, and factorize the training objective function based on stochastic gradient methodstoupdatevectors.Weprovethattheincrementalobjectivefunctionisconvergent. Experimental results demonstrate that our incremental factorizing can save a substantial amount of time by speeding up training convergence. The smaller the updated data is, the faster the update factorizing process can be, even 30 times faster than existing methods in certain cases. To evaluate the correctness of incremental representation, social text similarity/relatedness, linguistic tasks, network event detection, social user multi-label classification and user clustering for social network analysis are employed as benchmarks in
PENGARUH PROGRAM PENGEMBANGAN KARIR TERHADAP KINERJA KARYAWAN

This research is intended to find out how the influence of Career Development on Employee Performance PT Bumi Andalas Permai (BAP) Palembang. In this case the sampling using the Saturated sampling method with the employee population of PT BAP Palembang as many as 78 people. From the results of the questionnaire, processed by using SPSS for Windows version 19 by using simple linear regression method, hypothesis test (t test), and correlation coefficient.Based on the analysis results obtained coefficient (R) of 0.033 which shows the relationship between the two low variables. In the hypothesis test (t test) obtained t count value of -0.285 and t value table 1.991, and significant value 0.777. And when viewed from the probability (sig), career development has a probability value of 0.777. It has also been explained that 0.777> 0.05, then H1 is rejected and H0 is accepted, indicating there is no effect of career development on employee performance. The effect of career development on employee performance can be seen also from the value of regression coefficient (R) 0,001, which shows that the percentage contribution of career development influence to employee performance equal to 0,1%. Can also be interpreted that the variation of career development variables used to explain 0.1% variation of performance variables. The conclusion : career development program does not effect employee performance on PT BAP Palembang.
Detection and attribution methodologies overview (APPENDIX C)

In this appendix, we present a brief overview of the methodologies and methodological issues for detection and attribution of climate change. Attributing an observed change or an event partly to a causal factor (such as anthropogenic climate forcing) normally requires that the change first be detectable (Hegerl et al. 2010). A detectable observed change is one which is determined to be highly unlikely to occur (less than about a 10% chance) due to internal variability alone, without necessarily being ascribed to a causal factor. An attributable change refers to a change in which the relative contribution of causal factors has been evaluated along with an assignment of statistical confidence (e.g., Bindoff et al. 2013; Hegerl et al. 2010). As outlined in Bindoff et al. (2013), the conceptual framework for most detection and attribution studies consists of four elements: 1) relevant observations; 2) the estimated time history of relevant climate forcings (such as greenhouse gas concentrations or volcanic activity); 3) a modeled estimate of the impact of the climate forcings on the climate variables of interest; and 4) an estimate of the internal (unforced) variability of the climate variables of interest—that is, the changes that can occur due to natural unforced variations of the ocean, atmosphere, land, cryosphere, and other elements of the climate system in the absence of external forcings. The four elements above can be used together with a detection and attribution framework to assess possible causes of observed changes. CSSR 5OD: FINAL CLEARANCE Appendix C Subject to Final Copyedit 28 June 2017 652 Appendix C. Detection and Attribution Methodologies Overview 1 C.
Scalable sequential Monte Carlo methods and probabilistic approach to combinatorial problems

In this thesis, we consider two combinatorial problems arising in phylogenetics and computational forestry. We develop sequential Monte Carlo based inference methods to tackle the challenges arising from these applications. In the phylogenetics application, the SMC is placed under stringent restrictions in terms of computer memory to the point where using sufficient number of particles may be prohibitive. To that end, a new method, streaming particle filter (SPF), is proposed which alleviates this problem and study some of the theoretical properties of the algorithm, including L2 convergence of the estimators derived from the SPF simulation. In the case of the computational forestry, the combinatorial problem that is tackled in this thesis is referred to as the knot matching problem. This problem is formulated as a graph matching problem on a K-partite hypergraph. A model for graph matching that admits efficient parameter inference is proposed along with a sequential Monte Carlo sampler for graph matching based on this model. A detailed background on each of the two problems are presented and evaluated on simulated and real data.
Gamifying a Course to Teach Games and Simulations for Learning

This design case includes the challenges, considerations, and decisions associated with the design, development, and delivery of a master’s level educational technology course on teaching and learning with games and simulations. A master’s level course, Digital Games and Simulations in the Classroom, faced redesign in order to add a gamification component to be modeled through 3D GameLab and Shivtr. The design decisions made and resulting issues are detailed in this paper. This paper aims to reveal benefits in two key areas: (a) helping a population of primarily non-gamer educational technology graduate students see games and simulations as viable resources for improving learning and (b) modeling gamification as a means to help instructors use gamification as their own instructional strategy.
Image based book cover recognition and retrieval

In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.
Independent Component Analysis: Applications in ECG signal processing

Our work aims at processing of ECG signals using Independent Component Analysis (ICA). ICA serves in many biomedical applications as a preprocessing or feature extraction technique. These applications are listed in state-of-theart chapter, which describes ICA algorithms in general and also presents basic algorithms and their applications in processing of various biomedical signals. ICA represents a solution of the Blind Source Separation (BSS) problem, which can be stated as extraction of signals based merely on their mixtures. In our work we used JADE algorithm for solving two problems: de-noising of electrocardiographic (ECG) signals and beat detection. The de-noising algorithm, which we have developed, is an automatic method capable to deal with strong uncommon noises, present in nearly every holter ECG recording. The method is based on detection of noisy components and their removal from ECG data. The results show that our method is able to reduce both normal and uncommon noises. The algorithm for beat detection is based on extraction of ECG activity from noisy recording using JADE algorithm. The method is an extension of the well-known Christov’s beat detection algorithm, which detects beats using combined adaptive threshold on transformed ECG signal (complex lead). Our extension adds estimation of independent components of measured signal into the transformation of ECG creating a signal called complex component, which enhances ECG activity and enables beat detection in presence of strong noises. This makes the beat detection algorithm much more robust in cases of unpredictable noise appearances typical for holter ECGs and telemedical applications of ECG. Methods were tested and compared with other state-of-the-art methods using standard databases.
SPAG: Index of spatial agglomeration

The paper develops a spatial agglomeration index (SPAG) of the economic activity for the point geo‐localizations of firms. It includes the effects of location, the distance between firms and the overlapping impact of the firms' size. The SPAG builds a new class of measures of the spatial density of the economic activity inside the region, based on the geometrical representation of firms with circles, without referring to the commonly used Ripley's K function. The SPAG measures the degree of divergence from the benchmark distribution, what detects different spatial distributions as clusters or borderline dispersion. We test SPAG with real point data. El articulo desarrolla un indice de aglomeracion espacial (SPAG, por sus siglas en ingles) de la actividad economica para las geolocalizaciones puntuales de empresas. Este incluye los efectos de la localizacion, la distancia entre empresas y la superposicion del impacto del tamano de las empresas. El SPAG desarrolla una nueva clase de medidas de la densidad espacial de la actividad economica dentro de la region, a partir de la representacion geometrica de empresas mediante circulos, sin referirse a la funcion K de Ripley que se usa comunmente. El SPAG mide el grado de divergencia con respecto a la distribucion de referencia, lo que detecta diferentes distribuciones espaciales como clusters o dispersion de linea fronteriza. Se probo el SPAG con datos puntuales reales. 本稿では、企業のpoint geo‐localizationのための経済活動の空間的集積指標 (SPAG:spatial agglomeration index)を開発する。 これには、立地、企業間の距離、企業の規模が惹起する作用の重複が含まれる。 SPAGにより、よく用いられるRipleyのK関数法を参照することなく、円形を用いて企業を幾何学的に表す方法を基にした、地域内の経済活動の空間的密度の新しい計測法を作成する。 SPAGは、ベンチマークの分布からの発散度を測定し、クラスタまたは境界線分散として異なる空間分布を検出する。 実際の点データを用いてSPAGの検証を行う。
First International Conference on Evacuation Modelling and Management Exit choice, (pre-)movement time and (pre-)evacuation behaviour in hotel fire evacuation - Behavioural analysis and validation of the use of serious gaming in experimental research

Abstract Human behaviour in fires is mainly studied by incident evaluations and real-life experiments, such as unannounced evacuation drills. The possibilities of virtual reality for studying human behaviour in fires are so far hardly adopted by researchers. Nevertheless, the application of a behavioural assessment and rese arch tool (BART) in virtual reality is expected to be a valua ble supplement on the existing research methods. The innovative instrument will be validated by comparing the results of experiments in a virtual environment with results of the same experiments in real life. In this paper some results of case stud ies on evacuation behaviour in a real hotel building, as well as in a virtual hotel building in BART are given. The participants' r oute choice, pre-movement time, pre-evacuation behaviour, movement time and evacuation behaviour are part of the analysis in the paper. © 2010 Published by Elsevier Ltd. Keywords: Human behaviour in fire; evacuation; wayfinding; experimental research; validation of serious game
Degree of leanness and lean maturity: exploring the effects on financial performance

Past literature has investigated the relationship between lean production and financial performance with mixed results. Most studies focused on subsets of lean practices to measure lean production, thus failing to consider it as a managerial system that combines a large number of practices. To overcome this issue, the present research assessed lean production as a function of a firm’s degree of leanness based on the leanness methods research. Moreover, though lean production accrues benefits from a sustainable implementation over time, past literature has scarcely investigated the role of lean maturity. The present research hypothesises that lean maturity positively affects financial performance as well as it moderates the degree of leanness-financial performance relationship. Findings indicate that degree of leanness is not associated with financial performance whereas lean maturity positively influences financial performance. Moreover, lean maturity positively moderates the relationship between degree of leanness and financial performance.
Personal named entity linking based on simple partial tree matching and context free grammar

Personal name disambiguation is the task of linking a personal name to a unique comparable entry in the real world, also known as named entity linking (NEL). Algorithms for NEL consist of three main components: extractor, searcher, and disambiguator. Existing approaches for NEL use exact-matched look-up over the surface form to generate a set of candidate entities in each of the mentioned names. The exact-matched look-up is wholly inadequate to generate a candidate entity due to the fact that the personal names within a web page lack uniform representation. In addition, the performance of a disambiguator in ranking candidate entities is limited by context similarity. Context similarity is an inflexible feature for personal disambiguation because natural language is highly variable. We propose a new approach that can be used to both identify and disambiguate personal names mentioned on a web page. Our NEL algorithm uses: as an extractor: a control flow graph; AlchemyAPI, as a searcher: Personal Name Transformation Modules (PNTM) based on Context Free Grammar and the Jaro-Winkler text similarity metric and as a disambiguator: the entity coherence method: the Occupation Architecture for Personal Name Disambiguation (OAPnDis), personal name concepts and Simple Partial Tree Matching (SPTM). Experimental results, evaluated on real-world data sets, show that the accuracy of our NEL is 92%, which is higher than the accuracy of previously used methods.
A Survey on Anomaly Based Host Intrusion Detection System

An intrusion detection system (IDS) is hardware, software or a combination of two, for monitoring network or system activities to detect malicious signs. In computer security, designing a robust intrusion detection system is one of the most fundamental and important problems. The primary function of system is detecting intrusion and gives alerts when user tries to intrusion on timely manner. In these techniques when IDS find out intrusion it will send alert massage to the system administrator. Anomaly detection is an important problem that has been researched within diverse research areas and application domains. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. From the existing anomaly detection techniques, each technique has relative strengths and weaknesses. The current state of the experiment practice in the field of anomaly-based intrusion detection is reviewed and survey recent studies in this. This survey provides a study of existing anomaly detection techniques, and how the techniques used in one area can be applied in another application domain.