Miracle-dz/UniFAR-FTUs huggingface dataset:
Jena Research Papers in Business and Economics Production Planning of Mixed-Model Assembly Lines : Overview and Extensions

Mixed-model assembly lines are of great practical relevance and are widely used in a range of industries, such as the final assembly of the automotive and electronics industries. Prior research mainly selected and discussed isolated problems rather than considering the whole planning process. In this article mixed-model production planning is decomposed into five steps: initial configuration of the line, master scheduling, reconfiguration planning, sequencing and resequencing. The paper reviews and discusses all relevant planning steps and proposes general planning instruments as well as formalized decision models for those steps, which have not been thoroughly investigated in the literature thus far.
Hiring Discrimination Against Women: Distinguishing Taste Based Discrimination from Statistical Discrimination

We propose a new method which allows for measuring separately taste based discrimination from statistical discrimination in the hiring process. We consider two types of statistical discriminations against women: first, when a recruiter doubts the productivity of the workers; second, when a recruiter anticipates a maternity leave. We have collected experimental data from which we obtain optimal estimates of the discrimination components. The method is applied to three construction jobs: masonry, plumbing and electricity. We find that each job exhibits a different discrimination type. Furthermore we find that the most obvious forms of discrimination may not be the most harmful.
Karakteristik Instrumen Penilaian Hasil Belajar Matematika Ranah Kognitif yang Dikembangkan Mengacu pada Model PISA

Penelitian ini untuk mengetahui karakteristik instrumen penilaian hasil belajar yang dikembangkan berupa validitas, reliabilitas, daya pembeda, tingkat kesukaran, dan nilai guessing . Selanjutnya, data penelitian dianalisis secara kuantitatif menggunakan uji validitas, uji reliabilitas, dan melalui software BILOG-MG untuk mengetahui karakteristik tiap instrumen penilaian hasil belajar yang dikembangkan. Hasil penelitian menunjukkan bahwa 7 butir soal yang dikembangkan dinyatakan valid dengan reliabilitas sedang dan 1 butir soal dinyatakan tidak valid. Sedangkan 7 butir soal yang dinyatakan valid meliputi daya pembeda, tingkat kesukaran, dan nilai guessing termasuk kategori baik, hanya butir soal nomor 1 yang mempunyai kategori tingkat kesukaran tidak baik.
Range-Only Tracking in Multipath Environments - An Algorithm Based on Particle Filtering

In complex propagation environments with multipath reflections determining the time-of-arrival (TOA) of the line-of-sight (LoS) signal, which is required for localization, is challenging. As a result the localization accuracy of TOA-based systems degrade in such environments. We are pursuing a novel approach where the TOA algorithm returns not one but multiple candidate TOA values as this list is more likely to contain the LoS TOA value and the localization algorithm uses all of these to form a more reliable estimate of the node location. In this paper we present a new algorithm for localization and tracking based on multiple candidate TOA values from each anchor. We show that this is similar to the well-known data association problem in target tracking and exploit this similarity to propose a particle filter based algorithm that has linear computational complexity in the number of anchors. The performance of the algorithm is validated using simulated data.
Croatian Speech Recognition

In the chapter we describe procedures for Croatian speech recognition which are used in a limited domain spoken dialog system for Croatian speech. The dialog system would provide information about weather in different regions of Croatia for different time periods (Žibert et al., 2003). The spoken dialog system includes modules for automatic speech recognition (ASR), spoken language understanding and text-to-speech synthesis. In this work ASR module based on data-driven statistical and rule-based knowledge approach is discussed.
Investigating the Management of Diversity in Former Model C Schools in Gauteng

ABSTRACT The purpose of this article is to investigate the management of diversity in the staffroom of former model C schools in South Africa. Use was made of a structured questionnaire to probe the perceptions of teachers regarding the management of diversity. A factor analytic procedure and statistical testing indicated that four underlying factors enhance diversity management. These factors were, aspects enhancing the management of diversity, aspects cultivating a culture of mutual respect, aspects impeding the management of diversity and critical aspects influencing the management of diversity in the staffroom. Based on its findings, this study recommends that further investigation into the management of diversity be conducted in the context of a growing diverse staff and learner growth in the previously Whites-only schools. As schools continue to face the challenges of managing diversity, the need to build capacity among school management teams and teaching staff is imperative.
Web-based visualisation of head pose and facial expressions changes: Monitoring human activity using depth data

Despite significant recent advances in the field of head pose estimation and facial expression recognition, raising the cognitive level when analysing human activity presents serious challenges to current concepts. Motivated by the need of generating comprehensible visual representations from different sets of data, we introduce a system capable of monitoring human activity through head pose and facial expression changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor). An approach build on discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Such mechanism can yield a platform for objective and effortless assessment of human activity within the context of serious gaming and human-computer interaction.
0.935 - Approximation Randomized Algorithm for MAX 2SAT and Its Derandomization

In this paper, we propose 0.935-approximation algorithm for MAX 2SAT. The approximation ratio is better than the previously known result by Zwick, which is equal to 0.93109. The algorithm solves the SDP relaxation problem proposed by Goemans and Williamson for the first time. We do not use the ‘rotation’ technique proposed by Feige and Goemans. We improve the approximation ratio by using hyperplane separation technique with skewed distribution function on the sphere. We introduce a class of skewed distribution functions defined on the 2-dimensional sphere satisfying that for any function in the class, we can design a skewed distribution functions on any dimensional sphere without decreasing the approximation ratio. We also searched and found a good distribution function defined on the 2-dimensional sphere numerically. And we propose the derandomized algorithm for the introduced distribution functions.
The Existing Laws Regarding Punishment for Household Violence against Females in Bangladesh: A Study on the Perception of Educated Class

Household violence against females is becoming a social problem in Bangladesh. It creates disorganization in the family as a unit of society. To stop it, several laws are created by the government, but the existing laws could not stop it . The paper aims at studying the perception of educated class about household violence against females and the laws regarding punishment in Bangladesh. To know the objectives, the study followed analytical research design, social survey method & stratified random sampling. For some variables, the Likert scaling (1-to-5 rating scale) has been applied in the study. By using SPSS, the collected data has been analyzed i.e. frequency and percentage has been done. For hypotheses testing, a ᵡ 2 -test has been used to test the degree to which two or more groups vary or differ in an experiment. The study findings suggest that out of 101 respondents, only 34.65% know details about the existing laws regarding household violence against females, simultaneously 39.6% don’t know and 25.74% are undecided . Out of total, 30.69% respondents have supported the existing punishment provided by the existing laws, 63.36% don’t support it and 5.94% undecided. Again they think that the offender should be given exemplary punishment (18.75%), more hard punishment (32.81%), hurt by giving equal attack (2.97%) and bring change in mentality (43.75%). Out of total, 48.51% respondents opined that it will be possible to stop violence against females by creating consciousness about laws, simultaneously 47.52% by proper implementation and 11.88% by reformation of the existing laws. The result of hypotheses testing indicates that the no. of household violence against females will also be increased after proper implementation & reformation of existing laws, taking shelter of law of a woman and giving punishment the offender with imprisonment for seven years with fine for hurt wife by beating. So, awareness is the only way to stop household violence against females. Therefore, these findings may help the government , NGOs and medias to make awareness among the people of the society about the existing laws and females’ rights resulting the household violence against females may be stopped. Keywords: Household Violence against Females, Laws, Punishment, Perception, Educated Class, Bangladesh
Nonparametric Bayesian Models for Unsupervised Learning

Unsupervised learning is an important topic in machine learning. In particular, clustering is an unsupervised learning problem that arises in a variety of applications for data analysis and mining. Unfortunately, clustering is an ill-posed problem and, as such, a challenging one: no ground-truth that can be used to validate clustering results is available. Two issues arise as a consequence. Various clustering algorithms embed their own bias resulting from different optimization criteria. As a result, each algorithm may discover different patterns in a given dataset. The second issue concerns the setting of parameters. In clustering, parameter setting controls the characterization of individual clusters, and the total number of clusters in the data. Clustering ensembles have been proposed to address the issue of different biases induced by various algorithms. Clustering ensembles combine different clustering results, and can provide solutions that are robust against spurious elements in the data. Although clustering ensembles provide a significant advance, they do not address satisfactorily the model selection and the parameter tuning problem. Bayesian approaches have been applied to clustering to address the parameter tuning and model selection issues. Bayesian methods provide a principled way to address these problems by assuming prior distributions on model parameters. Prior distributions assign low probabilities to parameter values which are unlikely. Therefore they serve as regularizers for modeling parameters, and can help avoid over-fitting. In addition, the marginal likelihood is used by Bayesian approaches as the criterion for model selection. Although Bayesian methods provide a principled way to perform parameter tuning and model selection, the key question "How many clusters? " is still open. This is a fundamental question for model selection. A special kind of Bayesian methods, nonparametric Bayesian approaches, have been proposed to address this important model selection issue. Unlike parametric Bayesian models, for which the number of parameters is finite and fixed, nonparametric Bayesian models allow the number of parameters to grow with the number of observations. After observing the data, nonparametric Bayesian models fit the data with finite dimensional parameters. An additional issue with clustering is high dimensionality. High-dimensional data pose a difficult challenge to the clustering process. A common scenario with high-dimensional data is that clusters may exist in different subspaces comprised of different combinations of features (dimensions). In other words, data points in a cluster may be similar to each other along a subset of dimensions, but not in all dimensions. People have proposed subspace clustering techniques, a.k. a. co-clustering or bi-clustering, to address the dimensionality issue (here, I use the term co-clustering). Like clustering, also co-clustering suffers from the ill-posed nature and the lack of ground-truth to validate the results.
Integration of Chemical and Biochemical Analysis Systems into a Glass Microchip

This review focuses on the integration of chemical and biochemical analysis systems into glass microchips for general use. By combining multiphase laminar flow driven by pressure and micro unit operations, such as mixing, reaction, extraction and separation, continuous-flow chemical processing systems can be realized in the microchip format, while the application of electrophoresis-based chip technology is limited. The performances of several analysis systems were greatly improved by microchip integration because of some characteristics of microspace, i.e. , a large specific interface area, a short molecular diffusion time, a small heat capacity and so on. By applying these concepts, several different analysis systems, i.e. , wet analysis of cobalt ion, multi-ion sensor, immunoassay, and cellular analysis, were successfully integrated on a microchip. These microchip technologies are promising for meeting the future demands of high-throughput chemical processing.
Digital Speech Processing in Noisy Environments-049035 , Spring 2011

Statistical processing of the linear predictive coding (LPC) coefficients of a reverberant speech signal is one of the methods used for the dereverberation of the signal. In this paper, we begin with an analysis of the effects of reverberation on the LPC coefficients obtained from an M -channel array, where we employ mathematical tools from statistical room acoustics (SRA) theory. Algorithm for dereverberation of LPC coefficients is later developed, based on results of this analysis. In our suggested improvement, we show how its complexity can be reduced significantly. In addition, we evaluate the performance of the algorithm in presence of noise.
A Stochastic Cost Function for Stereo Vision

The goal of this paper is to present a novel stochastic cost function for binocular stereo vision that delivers statistics about the most probable disparities on the pixel level. We drive these statistics by many independent stochastic processes so that robustness to outliers can be achieved. Each of these stochastic processes may be understood as an individual who is requested to deliver his opinion about the depth. Finally, the idea is to fuse all these individual measurements into one global disparity map. In this paper, we use random walks for this.
Adaptive multi-cue fusion for visual target tracking based on uncertainly measure

This paper presents a novel adaptive tracking algorithm that fuses multiple cues based on feature uncertainty measurement in the particle filter framework. We first introduce a self-adaptive multi-cue fusion strategy, which overcomes the drawbacks of the traditional product fusion and sum fusion strategies. Furthermore, the proposed strategy effectively sharpens the distribution of the fused posterior as well as makes the tracking results less sensitive to the noise. Then, based on the fact that tracking failure often happens in the cases of low discriminative abilities of the observed features, we define a new feature uncertainty measurement. The proposed uncertainty measurement is thereafter used to adaptively adjust the relative contributions of different cues to tracking. An extensive number of comparative experiments show that the proposed tracking algorithm is more stable and robust than the single feature, product fusion, and sum fusion tracking algorithms.
A proposed architecture for context-aware mobility management

Mobility management is one area where context-awareness can be of great use. However, most context-management systems have been developed for smart spaces and are not designed for highly mobile devices. We propose an architecture for context-aware mobility management that is both distributed and hierarchical. The architecture consists of a global context server, a number of local proxy and adaptation servers, and the mobile nodes. Our architecture is unique in that both the local servers and the mobile nodes make mobility decisions, instead of having all the intelligence reside on network side components or central servers only, as is the case in most context management systems.
A Bayesian performance bound for time-delay of arrival based acoustic source tracking in a reverberant environment

Acoustic source tracking in a room environment based on a number of distributed microphone pairs has been widely studied in the past. Based on the received microphone pair signals, the time-delay of arrival (TDOA) measurement is easily accessible. Bayesian tracking approaches such as extended Kalman filter (EKF) and particle filtering (PF) are subsequently applied to estimate the source position. In this paper, the Bayesian performance bound, namely posterior Cramér-Rao bound (PCRB) is derived for such a tracking scheme. Since the position estimation is indirectly related to the received signal, a two-stage approach is developed to formulate the Fisher information matrix (FIM). First, the Cramér-Rao bound (CRB) of the TDOA measurement in the noisy and reverberant environment is calculated. The CRB is then regarded as the variance of the TDOAs in the measurement function to obtain the PCRB. Also, two different TDOA measurement models are considered: 1) single TDOA corresponding to the largest peak of the generalized cross-correlation (GCC) function; and 2) multiple TDOAs from several peaks in GCC function. The later measurement model implies a higher probability of detection and heavier false alarms. The PCRB for both measurement models are derived. Simulations under different noisy and reverberant environments are organized to validate the proposed PCRB.
An efficient Soliton-like network coding protocol for the resource-constrained Y-network

Originally designed for point-to-point transmissions, fountain codes are capacity-achieving codes over a binary erasure channel. On the other hand, network coding is an optimal multihop data dissemination protocol whose high decoding complexity makes it too expensive for resource-constrained applications. Soliton-like rateless coding (SLRC) has previously combined network and fountain coding paradigms such that a less complex decoder can be applied. Specifically, the coding done at an intermediate relay node allows a belief propagation decoder to be efficiently applied. We extend the SLRC protocol and propose the Improved Soliton-like Rateless Coding (ISLRC) protocol. In ISLRC, the relay applies intelligent coding and shapes the degree distribution such that performance is better than SLRC. In addition, ISLRCs performance gains are achieved using fewer resources than SLRC while maintaining all the key properties of SLRC. Simulation results show that even under the worst-case scenario of ISLRC, better performance can be achieved compared to SLRC and other existing schemes.
Design of an application-specific PLD architecture

This paper presents a new application-specific PLD architecture which adopts a bit-level super-systolic array for application-specific arithmetic operation such as MAC. The proposed design offers a significant alternative view on programmable logic device. The bit-level super-systolic array whose cells contain another systolic array is ideal for newly proposed PLD architecture in terms of area efficiency and clock speed as it limits the routing requirement in a PLD to local interconnections between logic units and to global interconnections between logic modules. The maximum clock cycle is limited only by one AND gate and one full adder.
Voids in Real Space and in Redshift Space

Using two-dimensional numerical simulations of gravitational clustering, with initial power spectra of a power-law form with index n, we compare the properties of voids in real space to their properties in redshift space. Both the void probability function (VPF) and the underdense probability function (UPF) are enhanced in redshift space. The enhancement is greatest in the limit n=-2. The VPF and UPF treat voids statistically and assume that two-dimensional voids are circular; we present an algorithm which detects individual voids. Voids found by this algorithm are ellipses whose enclosed density of galaxies falls below a threshold density. When voids are identified using this algorithm, the mean void size and the maximum void size both increase in going from real space to redshift space. The increase is greatest in the limit n=-2. In redshift space, the principal axes of the largest voids in the n=2 and n=0 simulations show a statistically significant tendency (at the 95% confidence level) to be distributed anisotropically, relative to the line of sight from the origin to the void center.
Partition Scheduling on an IMA Platform with Strict Periodicity and Communication Delays

Integrated Modular Avionics (IMA) architectures employ a high-integrity, partitioned environment that hosts multiple avionics functions of different criticalities on a shared computing platform. IMA provides benefits of flexibility and scalability and allow for weight savings. However the complexity of the integration process is increased due to the sharing of resources. One of the main problem encountered during the integration process is to find a proper scheme for scheduling avionics functions onto offered processing units while ensuring at the same time execution, resource and safety demands. In this paper, we present a MILP formulation for the scheduling of avionics functions on the resources of an IMA architecture. This mixed integer linear programming formulation takes into account resource constraints as well as temporal constraints related to ensuring a feasible schedule for the strictly periodic avionics functions. The objective function is to maximize the evolution margin for the functions, so that spare resources can be allocated to meet the resource demand growth of the hosted functions.