Miracle-dz/UniFAR-FTUs huggingface dataset:
Security requirement with a UML 2.0 profile

Business processes are important for companies because they allow us to obtain an advanced marketplace position, and then, these enterprises can optimize and assure the quality of their products and services. Moreover, business processes are important for software developers, because they can capture from them the necessary requirements for software design and creation. At the same time, organizations have been opened and this implies more vulnerability. In spite of all these facts, security is an aspect that has been scarcely dealt with in the business process modeling. In this paper, we summarize our UML 2.0 profile for secure business process modeling through activity diagrams, and we apply this approach to a typical health-care business process.
A Greedy Algorithm to Cluster Specialists

Several recent deep neural networks experiments leverage the generalist-specialist paradigm for classification. However, no formal study compared the performance of different clustering algorithms for class assignment. In this paper we perform such a study, suggest slight modifications to the clustering procedures, and propose a novel algorithm designed to optimize the performance of of the specialist-generalist classification system. Our experiments on the CIFAR-10 and CIFAR-100 datasets allow us to investigate situations for varying number of classes on similar data. We find that our \emph{greedy pairs} clustering algorithm consistently outperforms other alternatives, while the choice of the confusion matrix has little impact on the final performance.
Parameterized biorthogonal wavelet lifting for lightweight JPEG 2000 transparent encryption

Lightweight encryption offers a cogent alternative to full encryption of visual content in application settings with clients of low processing power, e.g. mobile applications, as it counterbalances security demands and computational demands. We present a lightweight transparent encryption scheme for JPEG 2000 that is based on and integrated into the wavelet lifting scheme. Keys are constructed from parameterized biorthogonal filters. The proposed method comes at extremely low computational cost and provides natural support for transparent encryption. We discuss the advantages and disadvantages of this encryption scheme with respect to keyspace, computational demands, compression performance, and security.
Bit-Interleaved Coded Modulation with Mismatched Decoding Metrics

Bit-interleaved coded modulation (BICM) has become the de facto coding standard for communication systems. Recently, BICM has been cast as a mismatched decoding scheme due to the assumption of independent bit metrics. In addition to this inherent mismatch, practical demodulators may produce mismatched decoding metrics because of implementation constraints, such as clipping and metric approximation to reduce computational complexity. In this paper, we investigate BICM with such metrics. In line with recent works on this topic, we adopt the generalized mutual information (GMI) as the pertinent performance measure. First, we show that level-dependent scaling of logarithmic bit metrics can improve the BICM GMI. Second, we propose a uniform metric scaling which can lead to an improved performance of mismatched sum-product symbol-by-symbol decoding, even if the GMI is not changed. Third, we investigate general metric-mismatch correction methods and analyze their effects in terms of the GMI. By means of three application examples, we illustrate that metric-mismatch correction, including metric scaling, can significantly increase BICM rates.
Cross-project build co-change prediction

Build systems orchestrate how human-readable source code is translated into executable programs. In a software project, source code changes can induce changes in the build system (aka. build co-changes). It is difficult for developers to identify when build co-changes are necessary due to the complexity of build systems. Prediction of build co-changes works well if there is a sufficient amount of training data to build a model. However, in practice, for new projects, there exists a limited number of changes. Using training data from other projects to predict the build co-changes in a new project can help improve the performance of the build co-change prediction. We refer to this problem as cross-project build co-change prediction. In this paper, we propose CroBuild, a novel cross-project build co-change prediction approach that iteratively learns new classifiers. CroBuild constructs an ensemble of classifiers by iteratively building classifiers and assigning them weights according to its prediction error rate. Given that only a small proportion of code changes are build co-changing, we also propose an imbalance-aware approach that learns a threshold boundary between those code changes that are build co-changing and those that are not in order to construct classifiers in each iteration. To examine the benefits of CroBuild, we perform experiments on 4 large datasets including Mozilla, Eclipse-core, Lucene, and Jazz, comprising a total of 50,884 changes. On average, across the 4 datasets, CroBuild achieves a F1-score of up to 0.408. We also compare CroBuild with other approaches such as a basic model, AdaBoost proposed by Freund et al., and TrAdaBoost proposed by Dai et al. On average, across the 4 datasets, the CroBuild approach yields an improvement in F1-scores of 41.54%, 36.63%, and 36.97% over the basic model, AdaBoost, and TrAdaBoost, respectively.
Inductive Logic Programming for Structure-Activity Relationship Studies on Large Scale Data

Inductive Logic Programming (ILP) is a combination of inductive learning and first-order logic aiming to learn first-order hypotheses from training examples. ILP has a serious bottleneck in an intractably enormous hypothesis search space. Thismakes existing approaches perform poorly on large-scale real-world datasets. In this research, we propose a technique to make the system handle an enormous search space efficiently by deriving qualitative information into search heuristics. Currently, heuristic functions used in ILP systems are based only on quantitative information, e.g. number of examples covered and length of candidates. We focus on a kind of data consisting of several parts. The approach aims to find hypotheses describing each class by using both individual and relational features of parts. The data can be found in denoting chemical compound structure for Structure-Activity Relationship studies (SAR). We apply the proposed method to extract rules describing chemical activity from their structures. The experiments are conducted on a real-world dataset. The results are compared to existing ILP methods using ten-fold cross validation.
Who suffers the most from abusive supervision? A moderated mediation model

We examined the interactive effects of calling and competitive climate on the relationship between abusive supervision and turnover intention via organizational cynicism. We hypothesized that high levels of calling would strengthen, and high levels of competitive climate would mitigate, the negative indirect effects of abusive supervision on turnover intention via organizational cynicism. We conducted a survey using a sample of U.S. employees (N = 236) to test five hypotheses. As predicted, results demonstrate that under a high level of abusive supervision, individuals were more likely to become cynical about the organization, which in turn heightened turnover intention. Moderated mediation analyses found that calling exacerbated, but competitive climate attenuated, the mediating effect of organizational cynicism on the relationship between abusive supervision and turnover intention. Underlying mechanisms and implications, as well as avenues for future research, are discussed.
Impact of Early Onset Obesity and Hypertension on the Unfolded Protein Response in Renal Tissues of Juvenile Sheep

Childhood obesity has reached epidemic proportions. Obesity is an independent risk factor for the development of end-stage renal disease. Endoplasmic reticulum stress and subsequent activation of the unfolded protein response (UPR) are implicated in the development of adipose tissue dysregulation and type 2 diabetes mellitus in obesity. The present study explored the impact of adolescent-onset obesity on the UPR after obesity-related hypertension and nephropathy, using an ovine model in which obesity was induced by increased food intake and reduced activity. Obese young adults had a higher mean arterial pressure (lean, 89.6±1.7 mm Hg versus obese, 101±3.0 mm Hg; P<0.01) and greater sensitivity to low physiological doses of angiotensin II. Obesity increased the glomerular area and was associated with activation of the UPR in renal cells with a greater abundance of glucose-regulated protein 78, C/EBP homologous protein, Bax, phosphorylated c-Jun amino-terminal kinase, and activating transcription factor 6 (all P<0.05). In addition, there was a marked upregulation of proinflammatory genes, most notably those involved in macrophage signaling. Reactive oxygen species production and handling were also perturbed in obese adults. Renal endoplasmic reticulum stress was positively correlated with macrophage content (r=0.83; P<0.001), phosphorylated c-Jun amino-terminal kinase (r=0.73; P<0.01), and adiposity (r=0.71; P<0.01). In conclusion, adolescent-onset, obesity-related renal endoplasmic reticulum stress was associated with activation of the UPR, apoptosis, and inflammation, potentially increasing the associated renal damage observed in young adults. The UPR may prove to be a useful therapeutic target for the treatment and prevention of obesity-related nephropathy and associated hypertension, thereby reducing the burden of end-stage renal disease.
Asymmetries and visual field summaries as predictors of glaucoma in the ocular hypertension treatment study.

PURPOSE To evaluate whether baseline visual field data and asymmetries between eyes predict the onset of primary open-angle glaucoma (POAG) in Ocular Hypertension Treatment Study (OHTS) participants. METHODS A new index, mean prognosis (MP), was designed for optimal combination of visual field thresholds, to discriminate between eyes that developed POAG from eyes that did not. Baseline intraocular pressure (IOP) in fellow eyes was used to construct measures of IOP asymmetry. Age-adjusted baseline thresholds were used to develop indicators of visual field asymmetry and summary measures of visual field defects. Marginal multivariate failure time models were constructed that relate the new index MP, IOP asymmetry, and visual field asymmetry to POAG onset for OHTS participants. RESULTS The marginal multivariate failure time analysis showed that the MP index is significantly related to POAG onset (P < 0.0001) and appears to be a more highly significant predictor of POAG onset than either mean deviation (MD; P = 0.17) or pattern standard deviation (PSD; P = 0.046). A 1-mm Hg increase in IOP asymmetry between fellow eyes is associated with a 17% increase in risk for development of POAG. When threshold asymmetry between eyes existed, the eye with lower thresholds was at a 37% greater risk of development of POAG, and this feature was more predictive of POAG onset than the visual field index MD, though not as strong a predictor as PSD. CONCLUSIONS The MP index, IOP asymmetry, and binocular test point asymmetry can assist in clinical evaluation of eyes at risk of development of POAG.
The application of double-gate MOSFET mixer in digital pressure sensor

A digital pressure sensor using a double-gate MOSFET mixer is presented. The sensitive unit of this pressure sensor is comprised of MOS ring oscillators. The syntonic frequency of the ring oscillators will change according to the stress caused by pressure due to the MOSFET piezoresistive effect. The double-gate MOSFET mixer is used as an internal signal processor in order to improve the characteristics of the output signal. The pressure sensor has many good characteristics such as high sensitivity, low temperature coefficient and simple fabrication process. The device was fabricated by a standard IC process mixed with a MEMS process. The sensitivity of fabricated devices is 1.52 kHz/kPa. The temperature shift of zero output is -0.5% FS.
Chitosan Nanoparticles as Carriers for the Delivery of ΦKAZ14 Bacteriophage for Oral Biological Control of Colibacillosis in Chickens

The use of chitosan as a delivery carrier has attracted much attention in recent years. In this study, chitosan nanoparticles (CS-NP) and chitosan-ΦKAZ14 bacteriophage-loaded nanoparticles (C-ΦKAZ14 NP) were prepared by a simple coercavation method and characterized. The objective was to achieve an effective protection of bacteriophage from gastric acids and enzymes in the chicken gastrointestinal tract. The average particle sizes for CS-NP and C-ΦKAZ14 NP were 188 ± 7.4 and 176 ± 3.2 nm, respectively. The zeta potentials for CS-NP and C-ΦKAZ14 NP were 50 and 60 mV, respectively. Differential scanning calorimetry (DSC) of C-ΦKAZ14 NP gave an onset temperature of −17.17 °C with a peak at 17.32 °C and final end set of 17.41 °C, while blank chitosan NP had an onset of −20.00 °C with a peak at −19.78 °C and final end set at −20.47. FT-IR spectroscopy data of both CS-NP and C-ΦKAZ14 NP were the same. Chitosan nanoparticles showed considerable protection of ΦKAZ14 bacteriophage against degradation by enzymes as evidenced in gel electrophoresis, whereby ΦKAZ14 bacteriophage encapsulated in chitosan nanoparticles were protected whereas the naked ΦKAZ14 bacteriophage were degraded. C-ΦKAZ14 NP was non-toxic as shown by a chorioallantoic membrane (CAM) toxicity assay. It was concluded that chitosan nanoparticles could be a potent carrier of ΦKAZ14 bacteriophage for oral therapy against colibacillosis in poultry.
Scaling Up Echo-State Networks With Multiple Light Scattering

Echo-State Networks and Reservoir Computing have been studied for more than a decade. They provide a simpler yet powerful alternative to Recurrent Neural Networks, every internal weight is fixed and only the last linear layer is trained. They involve many multiplications by dense random matrices. Very large networks are difficult to obtain, as the complexity scales quadratically both in time and memory. Here, we present a novel optical implementation of Echo-State Networks using light-scattering media and a Digital Micromirror Device. As a proof of concept, binary networks have been successfully trained to predict the chaotic Mackey-Glass time series. This new method is fast, power efficient and easily scalable to very large networks.
A Novel Approach for Assisting Teachers in Assessment of Student Reading Ability in web-based Learning Environment

Reading exercises are critical for developing strong reading comprehension. However, due to resource constraints and a lack of accurate evaluation methods, English instructors can hardly assess student reading ability effectively. In past decades, learning by reading is known to be challenging for both teachers and students involved, especially for students learning English as a Foreign Language (EFL). To cope with this problem, in this paper, we proposed a Tag-based assessment approach to both elicit reading behaviors from EFL learners and assist the teachers in tracing and evaluating the student reading ability effectively. The experimental results showed that the novel approach can not only find out the relations between learners' tags and their comprehension, but also help teachers to evaluate students' reading ability.
Principles of the Complete Voronoi Diagram Localization

This paper explores the rationale behind the Complete Voronoi Diagram (CVD) Localization, which is a computational geometry approach to the wireless network localization. Our work consists mainly of three parts. The first part focuses on the analysis of CVD's mathematical properties. We characterize CVD's central tendency as the mirror-image distribution and provide mathematical formula for its probability density function. We also provide a closed formula for the relationship between CVD's vertices, chords, and faces, the average chord length, and the average edge number of a CVD polygon. And, the expressions for the average overall and local positioning error are also provided. Based upon these findings, we show that the convergence speed for a CVD based localization scheme is quadratic, and the optimal time and space complexities are Θ(n2) and Θ(n), respectively. The second part proposes a novel approach, called Polling, which utilizes the concept of the Error Region, to further improve the accuracy. Polling, in theory, enables us to make use of the topology information with the quantity up to O(n4) provided by CVD for localization, while a conventional CVD scheme can use only O(1) such information. The third part, through simulations, shows how to use the quasi Analog-to-Digital Conversion (qADC) strategy to handle signal errors. Combined with Polling and qADC, a CVD scheme can provide a simple, robust, and powerful solution to the wireless network localization. Some of our findings and methods may also contribute to the field of computational geometry its own.
Hidden Markov Model ( HMM ) based Speech Synthesis for Urdu Language

This paper describes the development of HMM based speech synthesizer for Urdu language using the HTStoolkit. It describes the modifications needed to original HTS-Demo-scripts to port them, for Urdu language, which are currently available for English, Japanese and Portuguese. That includes the generation of the fullcontext style labels and the creation of the Question file for Urdu phone set. For that the development and structure of utilities are discussed. Plus a list of 200 high frequency Urdu words are selected using the greedy search algorithm. Finally the evaluation of these synthesized words is conducted using naturalness and intelligibility scores. Keywords— Speech Synthesis, Hidden Markov Models (HMMs), Urdu Language, Perceptual Testing
Understanding distributed collaboration in emergency animal disease response

There is an increasing interest in CSCW systems for supporting emergency and crisis management. In this paper we explore work practices in emergency animal disease management focusing on the high-level analysis and decision making of the Australian Consultative Committee for Emergency Animal Disease (CCEAD) - a geographically distributed committee established to recommend action plans during animal disease outbreak. Our findings explore the ways in which they currently share and analyse information together, focusing in particular on their teleconferencing mediated meetings. Our findings highlight factors relating to the time pressure of the task, diverse configuration of the group and asymmetrical settings and how these influence the groups information sharing and communication. We use the findings to discuss implications for collaboration technologies that could support the group and broader implications for similarly structured work groups.
Design of Simple and High Speed Scheme to Protect Mass Storages

The computer industry has entered a stage of unprecedented improvement in CPU performance. However, the speed of file system management of huge information is commonly considered as the main factor that affects the computer performance; for example, the I/O bandwidth is limited by magnetic disks. The capacity and cost of magnetic disks per megabyte have been continually improved, but the rotation speed and seek time are improved very slowly. Recently, many computers have become I/O bound in the applications of video, audio, commercial database, etc. If such an I/O crisis can be resolved, the computer system performance will be improved. In 1988, Patterson et al. proposed the redundant array of independent disks (RAID) system which allows the data to be separated into several disks (Patterson et al., 1988). We can access the data in parallel so that the throughput of I/O systems will be improved. On the other hand, more disks in RAID system have a higher risk of losing data because of high component failure rates. As a result, the safety and reliability have become the major issues in the RAID system. When designing a highly available and reliable RAID system, the method of bit wise parity checking is mostly used to correct errors and to enhance reliability of the RAID system. However, the parity checking method is limited so that only single disk failure can be tolerated. In 1995, Blaum et al. proposed a method called even-odd code, which tolerates up to two disk failures in the RAID system (Blaum et al., 1995). Even-odd code is the first known scheme for tolerating single or double disk failures, providing an optimal solution with regard to both storage and performance. However, the major problem concerning the even-odd code is a variety of modes of operations when solving erasures or up to 2 disk failures. In practical, it is not easy to be integrated into a VSLI. On the other hand, a small write problem is difficult to be solved with the even-odd code (Liao & Jing, 2002). In 1997, Plank proposed a tutorial by using the Reed-Solomon (RS) code to provide error correction in the RAID system (Plank, 1997). In 2000, Jing et al. also proposed a simple algorithm, called RS-RAID system, to combine the RS codes with the RAID system (Jing et al. 2000). In this chapter, we aim to improve RS codes codec to design a fast error and erasure correction for RS-RAID system, and to solve the small write problem in RS codes. In a RS decoder, there are various algorithms to solve the error locator polynomial, which affect the 6
4-[(1-Benzyl-1H-1,2,3-triazol-4-yl)methoxy]benzene-1,2-dicarbonitrile: crystal structure, Hirshfeld surface analysis and energy-minimization calculations

The terminal rings in the title compound have an anti disposition in contrast to a syn conformation calculated in the energy-minimized structure. Supramolecular layers in the ab plane and sustained by methylene-C—H⋯N(triazolyl) and carbonitrile-N⋯π(benzene) interactions feature in the molecular packing.
Multi-authority scheme based CP-ABE with attribute revocation for cloud data storage

Security of the outsourced data has become the core problem of cloud computing. Many security models have been proposed, among which, CP-ABE has gained popularity in this research area. The Ciphertext-Policy Attribute Based Encryption (CP-ABE) is considered as a propitious tool for fine grained data access in the cloud storage system. The existing CP-ABE scheme with only one authority does not solve the trust issues as well as attribute revocation problem. Although many multi-authority CP-ABE schemes have been proposed and solved the trust problem to some extent but failed to provide efficient attribute revocation and solution for system failure. This paper proposes a multi-authority scheme which can solve the trust issues, attribute revocation problem, and system failure.
Abstract: Virtual Machine Packing Algorithms for Lower Power Consumption

VM (Virtual Machine)-based flexible capacity man- agement is an effective scheme to reduce total power consumption in the data center. However, there have been the following issues, tradeoff of power-saving and user experience, decision of VM packing in feasible calculation time and collision avoidance of VM migration processes. In order to resolve these issues, we propose a matching-based and a greedy-type VM packing algorithm, which enables to decide a suitable VM packing plan in polynomial time. The experiments evaluate not only a basic performance, but also a feasibility of the algorithms by comparing with optimization solvers. The feasibility experiment uses a super computer trace data prepared by Center for Computational Sciences of Univer- sity of Tsukuba. The basic performance experiment shows that the algorithms reduce total power consumption by between 18% and 50%.