Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing the designer's toolkit with software comprehension models

Cognitive models of software comprehension are potential sources of theoretical knowledge for tool designers. Although their use in the analysis of existing tools is fairly well established, the literature has shown only limited use of such models for directly developing design ideas. This paper suggests a way of utilizing existing cognitive models of software comprehension to generate design goals and suggest design strategies early in the development cycle. A crucial part of our method is a scheme for explaining the value of tool features by describing the mechanisms that are presumed to underly the expected improvements in task performance.
Unsupervised Text Segmentation using LDA and MCMC

In this paper, we propose a data driven approach to text segmentation, while most of the existing unsupervised methods determine segmentation boundaries by empirically exploring similarity measurement between adjacent units (e.g. sentences). Firstly, we train a latent Dirichlet allocation (LDA) model with the large scale Wikipedia Corpus to avoid the problem of vocabulary mismatch, which makes our approach domain-independent. Secondly, each segment unit is represented with a distribution of the topics, instead of a set of word tokens. Finally, a text input is modeled as a sequence of segment units and Markov Chain Monte Carlo technique is employed to decide the appropriate boundaries. The major advantage of using MCMC is its ability to detect both strong and weak boundaries. Experimental results demonstrate that our proposed approach achieve promising results on a widely used benchmark dataset when compared with the state-of-the-art methods.
The Contribution of Vocational High School Studies to Educational Achievement and Success in the Labor Market

The research was funded by the Maurice Falk Institu te for Economic Research in Israel and was implemented in the Research Room of the Central Bur eau of Statistics (CBS). We wish to thank Orly Furman, David Gordon, Anat Ka tz, Sigalit Shemueli and Hadas Yafe of the CBS for constructing the database and for providing technical support and to Natalia Volchkina of the Hebrew University Social Science Data Center for pr epa ing the Seker examination files. In addition, we wish to thank Haggay Etkes, participants in a Re search Department seminar at the Bank of Israel and participants in an evening seminar on Economic Growth in Israel and Oded Mcdossi and Yossi Shavit for their helpful comments.
The Relationship between Developers and Customers in Agile Methodology

Agile methods such as XP (extreme programming) emphasize the adaptation to changes, the collaboration with customers and the role of individuals. The project manager, developer and customer should have a tactic to handle the uncertain factors and the management conflicts in software development process. XP's on-site customer practice makes the customer play a important role in development process, and facilitate the broad collaboration between developer and customer. Based on the feature of XP project life cycle and practices, this paper explores respective activities and responsibilities between development team and customer team, and the issues in their collaboration. Some practices in which customer participates is addressed, the activities of both developer and customer are described, some probable problems when customer and programmer work together are presented, and a formal example is given.
Open ended intelligence: the individuation of intelligent agents

Artificial general intelligence is a field of research aiming to distil the principles of intelligence that operate independently of a specific problem domain and utilise these principles in order to synthesise systems capable of performing any intellectual task a human being is capable of and beyond. While “narrow” artificial intelligence which focuses on solving specific problems such as speech recognition, text comprehension, visual pattern recognition and robotic motion has shown impressive breakthroughs lately, understanding general intelligence remains elusive. We propose a paradigm shift from intelligence perceived as a competence of individual agents defined in relation to an a priori given problem domain or a goal, to intelligence perceived as a formative process of self-organisation. We call this process open-ended intelligence. Starting with a brief introduction of the current conceptual approach, we expose a number of serious limitations that are traced back to the ontological roots of the concept of intelligence. Open-ended intelligence is then developed as an abstraction of the process of human cognitive development, so its application can be extended to general agents and systems. We introduce and discuss three facets of the idea: the philosophical concept of individuation, sense-making and the individuation of general cognitive agents. We further show how open-ended intelligence can be framed in terms of a distributed, self-organising network of interacting elements and how such process is scalable. The framework highlights an important relation between coordination and intelligence and a new understanding of values.
Ex vivo culture of circulating breast tumor cells for individualized testing of drug susceptibility

Staying one step ahead of tumors Cancer treatments require continual adjustment. A drug that works initially will lose its potency as the tumor acquires new mutations that allow it to bypass the drug's lethal effects. To stay ahead of the tumor, oncologists need a noninvasive way to collect tumor cells from patients over the course of their treatment. Analyzing the mutations in these samples may help them choose the right drugs as the tumors change. In a small study of breast cancer patients, Yu et al. show that rare tumor cells circulating in the blood can be captured in viable form and used for this purpose. Science, this issue p. 216 Mutational analysis of tumor cells isolated from the blood of cancer patients may help optimize treatment selection. Circulating tumor cells (CTCs) are present at low concentrations in the peripheral blood of patients with solid tumors. It has been proposed that the isolation, ex vivo culture, and characterization of CTCs may provide an opportunity to noninvasively monitor the changing patterns of drug susceptibility in individual patients as their tumors acquire new mutations. In a proof-of-concept study, we established CTC cultures from six patients with estrogen receptor–positive breast cancer. Three of five CTC lines tested were tumorigenic in mice. Genome sequencing of the CTC lines revealed preexisting mutations in the PIK3CA gene and newly acquired mutations in the estrogen receptor gene (ESR1), PIK3CA gene, and fibroblast growth factor receptor gene (FGFR2), among others. Drug sensitivity testing of CTC lines with multiple mutations revealed potential new therapeutic targets. With optimization of CTC culture conditions, this strategy may help identify the best therapies for individual cancer patients over the course of their disease.
Initial Algebra Semantics for Cyclic Sharing Tree Structures

Terms are a concise representation of tree structures. Since they can be naturally defined by an inductive type, they offer data structures in functional programming and mechanised reasoning with useful principles such as structural induction and structural recursion. However, for graphs or "tree-like" structures - trees involving cycles and sharing - it remains unclear what kind of inductive structures exists and how we can faithfully assign a term representation of them. In this paper we propose a simple term syntax for cyclic sharing structures that admits structural induction and recursion principles. We show that the obtained syntax is directly usable in the functional language Haskell and the proof assistant Agda, as well as ordinary data structures such as lists and trees. To achieve this goal, we use a categorical approach to initial algebra semantics in a presheaf category. That approach follows the line of Fiore, Plotkin and Turi's models of abstract syntax with variable binding.
Purification and characterization of two larval glycoproteins from the cattle tick, Boophilus annulatus

The present study was conducted to identify new target immunogenic molecules from the larval stage of the cattle tick, Boophilus annulatus (Acari: Ixodidae). Two specific larval glycoproteins (GLPs) were isolated by two-step affinity chromatography. The larval immunogens were first purified with CNBr-Sepharose coupled to rabbit anti-larval immunoglobulins, and the glycoproteins were then purified with Con-A Sepharose. These glycoproteins have molecular weights of approximately 32 and 15 kDa with isoelectric points between 6.8 and 7.2. Antibodies against the two GLPs, labeled I and II, were detected in the anti-whole tick, -whole larval, and -gut antigens through immunoblot analysis. These results suggest that these GLPs are good immunogens and can be useful in the vaccination of cattle against tick infestation.
Body weight dissatisfaction in the Icelandic adult population: a normative discontent?

BACKGROUND Body weight dissatisfaction is a common problem in many modern societies and it appears to have grown over time. This study examined the prevalence and predictors of body weight dissatisfaction among 18- to 79-year-old Icelanders. The distribution of body weight dissatisfaction according to the WHO body mass index criteria for underweight, normal weight, overweight and obesity was also assessed. METHODS The study was based on 5832 participants in the cross-sectional survey 'Health and Well-Being of Icelanders', administered by the Public Health Institute in Iceland in the fall of 2007. RESULTS Around 43% of adult Icelanders are dissatisfied with their own body weight (50% of females; 35% of males) and 72% believe they need to lose weight (80% of females; 63% of males). Despite being in normal range according to the WHO-based BMI criteria, 64% of females in that category believe they need to lose weight. Multivariate results show that indicators of socio-economic status (SES) are not significantly associated with body weight dissatisfaction among females, while among males, those with university education are more often dissatisfied than those with secondary education (OR = 1.5, P < 0.05; for self-perceived need to lose weight, estimates are OR = 1.4, P < 0.05 for females, and OR = 2.5, P < 0.001 for males). CONCLUSION The prevalence of body dissatisfaction is so prominent among Icelandic female adults that it can rightfully be labelled a normative discontent in this population.
Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles

This paper presents an efficient method for estimating capacity-fade uncertainty in lithium-ion batteries (LIBs) in order to integrate them into the battery-management system (BMS) of electric vehicles, which requires simple and inexpensive computation for successful application. The study uses the pseudo-two-dimensional (P2D) electrochemical model, which simulates the battery state by solving a system of coupled nonlinear partial differential equations (PDEs). The model parameters that are responsible for electrode degradation are identified and estimated, based on battery data obtained from the charge cycles. The Bayesian approach, with parameters estimated by probability distributions, is employed to account for uncertainties arising in the model and battery data. The Markov Chain Monte Carlo (MCMC) technique is used to draw samples from the distributions. The complex computations that solve a PDE system for each sample are avoided by employing a polynomial-based metamodel. As a result, the computational cost is reduced from 5.5 h to a few seconds, enabling the integration of the method into the vehicle BMS. Using this approach, the conservative bound of capacity fade can be determined for the vehicle in service, which represents the safety margin reflecting the uncertainty.
Word confusability - measuring hidden Markov model similarity

We address the problem of word confusability in speech recognition by measuring the similarity between Hidden Markov Models (HMMs) using a number of recently developed techniques. The focus is on defining a word confusability that is accurate, in the sense of predicting artificial speech recognition errors, and computationally efficient when applied to speech recognition applications. It is shown by using the edit distance framework for HMMs that we can use statistical information measures of distances between probability distribution functions to define similarity or distance measures between HMMs. We use correlation between errors in a real speech recognizer and the HMM similarities to measure how well each technique works. We demonstrate significant improvements relative to traditional phone confusion weighted edit distance measures by use of a Bhattacharyya divergence-based edit distance. Index Terms: Bayes Error, Bhattacharyya divergence, variational methods, gaussian mixture models, unscented transformation, Kullback‐Leibler distance rate.
Spatially Adaptive Image Denoising Based on Complex Wavelet Coefficients

We propose a computationally feasible adaptive noise smoothing algorithm that considers the local dependency characteristics of images under the complex dual-tree discrete wavelet transform (DWT). The wavelet coefficients with complex dual-tree discrete wavelet are assumed to be zero-mean Gaussian random variables. The variances of local statistics of each coefficient are estimated with a centered square-shaped window for every pixel. Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm
A Challenging Big Dataset for Benchmarking Trademark Retrieval anonymous CONFIDENTIAL

Trademark retrieval (TR) is the problem of retrieving similar trademarks (logos) for a query, and the main aim is to detect copyright infringements in trademarks. Since there are millions of companies worldwide, automatically retrieving similar trademarks has become an important problem, and currently, checking trademark infringements is mostly performed manually by humans. However, although there have been many attempts for automated TR, as also acknowledged in the community, the problem is largely unsolved. One of the main reasons for that is the unavailability of a publicly available comprehensive dataset that includes the various challenges of the TR problem. In this article, we propose and introduce a large dataset composed of more than 930,000 trademarks, and evaluate the existing approaches in the literature on this dataset. We show that the existing methods are far from being useful in such a challenging dataset, and we hope that the dataset can facilitate the development of better methods to make progress in the performance of trademark retrieval systems.
High frequency modeling for cable and induction motor overvoltage studies in long cable drives

High frequency simulation models for power cables and motors are the key tools that aid a better understanding of the overvoltage problem in PWM drives with long feeders. In this paper, the frequency responses of the cable and the motor windings are obtained experimentally and suitable models are developed to match the experimental results. Several lumped segments incorporating a lossy representation of the line is used to model the cable. The cable and induction motor models may be implemented using a computational tool such as Matlab, thereby providing a convenient method to analyze the overvoltage phenomena. Simulation and experimental results are presented for a typical 3 hp induction motor, showing the suitability of the developed simulation models. The most promising dv/dt filter networks are also investigated through simulation analysis, and a design approach based on a trade off between filter losses and motor peak voltage is proposed. Experimental results of an RC filter at the motor terminals demonstrate the validity of the simulation models.
A PHY-layer Authentication Approach for Transmitter Identification in Cognitive Radio Networks

Cognitive radio (CR) was proposed as the key technologies to achieve the secondary usage of the spectrum. The security problems of CR networks have not been intensively studied, such as the primary user emulation (PUE) attacks. In this paper, we study the non-interactive security issues for wireless networks and propose a physical layer authentication approach to prevent PUE attacks in CR networks. We extract transmitter location fingerprints from the wireless medium in the multipath propagation environment. Wavelet transform is used to extract the characteristics of these fingerprints. Simulation and experiment results show that our approach can identify the PUE attackers and the legitimate primary users effectively.
Weighted Performance Based Handover Parameter Optimization in LTE

Handover parameter optimization is a selfoptimizing network (SON) use case that promises significant performance improvement of the radio network. The basic idea is to adapt the handover control parameters, hysteresis and time-to-trigger, to the individual cell situation, in terms of e.g. the building density, cell environment and degree of user mobility. The aim is to reduce the number of handover failures, ping-pong handovers and radio link failures. We propose an handover parameter optimization algorithm that tunes the hysteresis and time-to-trigger in iterative steps and show the system performance improvement with it in both a realistic and a hexagonal simulation scenario.
Multicenter Harmonization of 89Zr PET/CT Performance

This study investigated the feasibility of quantitative accuracy and harmonized image quality in 89Zr-PET/CT multicenter studies. Methods: Five PET/CT scanners from 3 vendors were included. 89Zr activity was measured in a central dose calibrator before delivery. Local activity assays were based on volume as well as on the local dose calibrator. Accuracy and image noise were determined from a cross calibration experiment. Image quality was assessed from recovery coefficients derived from different volume-of-interest (VOI) methods (VOIA50%, based on a 3-dimensional isocontour at 50% of the maximum voxel value with local background correction; VOIMax, based on the voxel with the highest uptake; and VOI3Dpeak, based on a spheric VOI of 1.2-cm diameter positioned so as to maximize the enclosed average). PET images were analyzed before and after postreconstruction smoothing, applied to match image noise. Results: PET/CT accuracy and image noise ranged from −3% to 10% and from 13% to 22%, respectively. VOI3Dpeak produced the most reproducible recovery coefficients. After calibration of the local dose calibrator to the central dose calibrator, differences between the local activity assays were within 6%. Conclusion: This study showed that quantitative accuracy and harmonized image quality can be reached in 89Zr PET/CT multicenter studies.
Towards Believable Crowds : A Generic Multi-Level Framework for Agent Navigation

Path planning and crowd simulation are important computational tasks in computer games and applications of high social relevance, such as crowd management and safety training. Virtual characters (agents) need to autonomously find a path from their current position to a designated goal position. This is usually solved by running the A* algorithm on a grid or a navigation mesh. However, in many modern applications, strictly traversing the resulting path is not sufficient. Agents need to be able to deviate from these paths, e.g. to avoid each other or react to dynamic changes in the environment. Multiple levels of planning are necessary to efficiently simulate realistic behavior, and the underlying data structures and algorithms should support those levels. Many existing crowd simulation frameworks do not have this flexibility. In this paper, we propose a five-level hierarchy for agent navigation in virtual environments. The five levels are high-level planning, global route planning, route following, local movement, and animation. The three center levels concern geometric planning and require a navigation mesh that represents the navigable space of the environment. We describe an efficient and flexible navigation mesh for 2D and multi-layered 3D environments. We also present our crowd simulation software that uses this mesh; we outline its architecture and show that the framework is easily extendible. Finally, we show that our software can simulate large autonomous crowds in real-time.
Gender-related differences in nonlinear indices of heart rate variability

The effect of day-night variations was evaluated in both a female and a male population of healthy subjects. A total of 239 subjects were included in this study, of which 119 male and 120 female. 24 hour Holter recordings were used to analyze heart rate variability. Power spectral indices (total power, low and high frequency power) were calculated together with the nonlinear indices fractal dimension, 1/f slope, the largest Lyapunov exponent and approximate entropy. The female population presented overall lower heart rate variability compared to the male population as expressed in the spectral components of heart rate variability. There was a significant difference between values in the male and female population for approximate entropy and the largest Lyapunov exponent, both during the day and during the night. Fractal dimension showed only a gender difference during sleeping hours. All frequency domain indices and nonlinear measures presented a daynight difference, with exception of low frequency power. Fractal dimension and approximate entropy were highly correlated with high frequency power. The 1/f slope was correlated with low frequency power. Our data indicate an increase in heart rate dynamic complexity during the night. Also women seem to have a more complex regulatory mechanism of heart rate as expressed in differences of nonlinear indices.
An edit operation-based approach to approximate string matching in large DNA databases

In DNA related research, due to various environment conditions, mutations occur very often, where a mutation is defined as a heritable change in the DNA sequence. Therefore, approximate string matching is applied to answer those queries which find mutations. The problem of approximate string matching is that given a user specified parameter, k, we want to find where the substrings, which could have k errors at most as compared to the query sequence, occur in the database sequences. In this paper, we make use of a new index structure to support the proposed method for approximate string matching. In the proposed index structure, EII, we map each overlapping q-gram of the database sequence into an index key, and record occurring positions of the q-gram in the corresponding index entry. In the proposed method, EOB, we first generate all possible mutations for each gram in the query sequence. Then, by utilizing information recorded in the EII structure, we check both local order (i.e., the order of characters in a gram) and global order (i.e., the order of grams in an interval) of these mutations. The final answers could be determined directly without applying dynamic programming which is used in traditional filter methods for approximate string matching. From the experiment results, we show that our method could outperform the (k + s) q- samples filter, a well-known method for approximate string matching, in terms of the processing time with various conditions for short query sequences.