Miracle-dz/UniFAR-FTUs huggingface dataset:
Concept Drift Handling in Information Systems: Preserving the Validity of Deployed Machine Learning Models

Predictions computed by supervised machine learning models play a crucial role in a variety of innovative applications in business and industry. Typically, value is generated as soon as these models are deployed and continuously used in information systems of an organization. However, machine learning endeavors predominantly focus on conceiving applications for static situations. In this context, the management of the models’ lifecycle to preserve their effectiveness over time in dynamic environments is still in its infancy. Therefore, this thesis starts with systematically analyzing the full lifecycle of machine learning applications from an information systems (IS) perspective—and understanding and documenting all choices that have to be made throughout this cycle. On that basis, we then perform a qualitative study via practitioner interviews to map particular challenges in the deployment phase. In this context, we identify concept drift as a particularly important challenge to overcome: Concept drift refers to changes in the environment over time which affect the behavior of a machine learning model. This can have an impact on the model’s prediction quality and its overall utility. We analyze and categorize concept drift handling approaches covering both the detection of concept drift as well as the appropriate adaptation of the model. We identify two particular research gaps: the handling of concept drift for regression tasks and the handling of concept drift for tasks where additional labels for retraining a model are hard or costly to obtain. For both areas, we develop new methods and demonstrate their effectiveness in technical experiments and real-world use cases. This thesis contributes new methods to handle concept drift, for particular difficult contexts: First, this thesis should raise researchers’ and practitioners’ awareness for the topic of changing input data over time as well as for its impact on deployed machine learning. Second, the developed and tested methods can either be implemented directly or serve as inspiration to conceive appropriate drift handling strategies within information systems. Finally, we expect that advanced concept drift handling not only technically ensures a reliable prediction quality over time, but that it will also increase trust and acceptance of machine learning-based information systems—and, thus, help to boost the impact of machine learning.
Hexane Isomers in Faujasite: Anomalous Diffusion and Kinetic Separation

Adsorption and diffusion properties of the five hexane isomers in zeolite Y have been obtained from molecular dynamic simulations. The self-diffusivities (Ds) of the isomers exhibit an anomalous dependence on their molecular diameter, where the larger-sized doubly branched hexane isomer 2,2- dimethylbutane shows the maximum Ds among all the isomers. This anomalous dependence of Ds as well as the computed activation energies Ea are in excellent agreement with the predictions of the levitation effect. These findings also explain the trends in Ea of hexane isomers in zeolite BEA, which was observed by Barcia et al. in their experimental study. The order of exit of different isomers from a zeolite Y column depends on the order of their Ds at different temperature ranges. n-Hexane shows a tendency to bend by increasing its gauche conformer population in order to reduce the energetic barrier it experiences at the 12-ring window of the zeolite Y cage.
ASE: A Value Set Decision Procedure for Symbolic Execution

A symbolic execution engine regularly queries a Satisfiability Modulo Theory (SMT) solver to determine reachability of code during execution. Unfortunately, the SMT solver is often the bottleneck of symbolic execution. Inspired by abstract interpretation, we propose an abstract symbolic execution (ASE) engine which aims at querying the SMT solver less often by trying to compute reachability faster through an increasingly weaker abstraction. For this purpose, we have designed and implemented a value set decision procedure based on strided value interval (SVI) sets for efficiently determining precise, or under-approximating value sets for variables. Our ASE engine begins reasoning with respect to the SVI abstraction, and then only if needed uses the theory of bit-vectors implemented in SMT solvers. Our ASE engine efficiently detects when the former abstraction becomes incomplete to move on and try the next abstraction.We have designed and implemented a prototype of our engine for a subset of 64-bit RISC-V. Our experimental evaluation shows that our prototype often improves symbolic execution time by significantly reducing the number of SMT queries while, whenever the abstraction does not work, the overhead for trying still remains low.
High-Grade Intraventricular Astroblastoma in a Young Adult: A Rare and Controversial Tumor to Manage

Astroblastoma is a rare primary central nervous system tumor of controversial site of origin. They account for 0.45-2.8% of all primary neuroepithelial central nervous system. It has been reported in paediatric age group with bimodal age distribution affecting more females with male to female ratio being 1:11. Astroblastomas are controversial and challenging tumors in terms of diagnosis and therapeutics. Since it carries an unpredictable disease course it needs a regular follow up even for low grade tumor. Authors have tried various schedules of post op radiotherapy after maximum safe resection. Various chemotherapeutic drugs combination have also been tried without much success. We here report a 35 years old female patient who was diagnosed with high grade astroblastoma referred for post-operative radiotherapy after gross total resection. Since it is extremely rare tumor, its treatment still not well defined and also makes it difficult conduct studies to examine tumor characteristics.
The Currency Exposure and Syariah Compliant Status under Different Time Domains: Insights from Maximal Overlap Discrete Wavelet Transformation (MODWT) Analysis

The rise of the Islamic capital market in the emerging economy of Malaysia over the past few decades motivates us to investigate the impact of the Syariah compliant status on the level of exchange rate exposure. The Syariah compliant status implies that a company should use a minimal level of debt as capital to finance its operations. Given this, it is hypothesized that Syariah compliant firms should exhibit lower levels of exchange rate exposure than their counterparts, supported by the strong theoretical connection between interest rate and exchange rate. In terms of specification, previous efforts in pricing exchange risk failed to capture the true size of exposure due to the use of single time domain in traditional model. To cater the bias in estimation, this study intends to calculate multi-horizon exchange rate exposure based on maximal overlap discrete wavelet transformation to decompose single series into multiple time domains. The financial risk analysis involves 30 listed non-financial individual stocks in Malaysia having different Syariah compliant status from November 2013 until May 2018. As a result, the study finds no significant difference in currency exposure between Syariah compliant and non-Syariah compliant stocks. Secondly, it is found that the extent of currency exposure and the percentage of exposed firms exhibit non-homogenous trend across different time scales where large amount of exposure is concentrated at higher scale. From policy implications, the study suggests that Syariah compliant firms and non-Syariah compliant firms share the same exchange risk profile where exchange risk management routine is expected to be identical for both groups. Besides, the enhanced level of exposure at higher scale requires vigorous financial risk hedging strategies especially within widened investment interval.
Demystifying Heart Failure with Mid-Range Ejection Fraction Using Machine Learning

Treating Heart Failure (HF) patients with mid-range Ejection Fraction (HFmrEF) is a challenging task due to prognostic uncertainty and transitional behaviour of HFmrEF, often referred to as “grey-area”. In this study, we address the uncertainty of HFmrEF through Machine Learning (ML) by classifying it into two well studied phenotypes: HF with preserved Ejection Fraction and HF with reduced Ejection Fraction, using the data from clinical attributes. We propose a semi-supervised Active Learning based model that uses significantly lesser data to tackle the need of supervised label validation and performs on-par with supervised ML models developed for comparison. We believe the use of proposed ML models can enable experts in making informed data-driven decisions leading to the accurate prognosis of HF patients.
The Challenges in Marketing a Tourist Destination, a Case Study of Sierra Leone

This paper attempts to investigate the challenges in marketing a tourist destination, a case study of Sierra Leone. This paper offers a comprehensive understanding of the concept of tourist destination marketing and challenges. It is descriptive as it describes destination marketing concept and opinion of respondents on the challenges of marketing destination.200 respondents from the Ministry of Tourism and Cultural Affairs and National Tourist Board were selected using a simple random sampling technique. A structured questionnaire was administered among the respondents and only 200 was returned filled which form the bases for analysis. The collected data was analyzed by the used of Statistical Packages for the Social Sciences and Garrett ranking. It was found out that Sierra Leone cannot attract tourist due to inadequate fund for marketing plans as a major challenge. It is recommended that the government of Sierra Leone to ensure that adequate funding is provided for the marketing and promotion of the country if it to be a tourist destination.
Sensitivity and Frequency Coupling Indexes of Respiratory Sinus Arrhythmia in Response to Continuously Increasing and Decreasing Tidal Volume

In 40 healthy subjects we compared the effects of chirped respiratory frequency (CRF) from 0.15 to 0.5 Hz combined with continuously increasing and decreasing tidal volume <tex>$(V_{T},\ 1.0-2.5-1.0\ l)\ (CRFV_{T})vs$</tex>. those of the same CRF at fixed <tex>$V_{T}(1.0l)$</tex> on the 45-s time-courses, estimated by a time-frequency distribution, of the central frequency and power of the high frequency components of <tex>$RR\ (_{CF}HF_{RR},_{P}HF_{RR})$</tex> and of respiration <tex>$(_{CF}HF_{RES}, {}_{P}HF_{RES})$</tex>, from which respiratory sinus arrhythmia <tex>$(RS\mathrm{A})$</tex> sensitivity <tex>$(RSA_{S})$</tex> by alpha index, its coherence <tex>$(RSA_{CO})$</tex>, and the <tex>$_{CF}HF_{RR}-_{CF}HF_{RES}$</tex> and <tex>$RSA_{S}-_{CF}HF_{RES}$</tex> relations were computed. Compared to <tex>$CRF,. CRFV_{T}$</tex> caused: smaller <tex>$(p < 0.04)$</tex> slope, intercept and correlation of <tex>$RSA_{S}-_{CF}HF_{RES}$</tex> relation, with accentuation of the progressive reduction of <tex>$RSA_{S}$</tex> dynamics and smaller <tex>$(p < 0.03)$</tex> means at 7.5, 15, 22.5 and 30s;smaller <tex>$(p < 0.03) 15, 22.5$</tex> and 30s means of <tex>$RSA_{CO}$</tex> dynamics; greater <tex>$(p < 0.02)$</tex> intercept and smaller <tex>$(\mathrm{p} < 0.04)$</tex> slope of the <tex>$_{CF}HF_{RR}-_{CF}HF_{RR}$</tex> relation, with greater <tex>$(p < 0.01)$</tex> means at 7.5,15, 22.5 and 30s of <tex>${}_{CF}HF_{RR}$</tex> dynamics. Our findings support that increasing-decreasing <tex>$V_{T}$</tex> provokes an important depression of the <tex>$RSA_{S}-_{CF}HF_{RES}$</tex> relation and a slight elevation on <tex>$_{CF}HF_{RR}-_{CF}HF_{RES}$</tex> relation, suggesting a great reduction of the <tex>$RS\ A$</tex> gain driving mechanism and a slight enhancement of the frequency coupling one.
Examining mycofiltration efficacy in a first order stream

Bacterial contamination from sanitary and combined sewer overflows, leaking sewer infrastructure, and stormwater runoff decreases urban surface water quality. This research investigates a bioremediation technique, mycofiltration, to mitigate episodic bacterial contamination in first-order urban streams, which has previously been demonstrated to work in lab experiments. The objectives are: (1) establish the spatial distribution of E. coli in the Upper South River watershed, and (2) evaluate the potential for Trametes versicolor fungal spawn to decrease E. coli concentrations when accounting for short hydrologic retention and surface water-groundwater interactions inherent in streams via a stream table experiment. The Trametes versicolor mycofilter overall reduced concentrations of E. coli, but no more than was reduced by stream sediments alone. These findings suggest the usefulness of mycofiltration may be limited by decreased contact time or hyporheic flow paths that bypass the mycelium installation. INDEX WORDS: Mycoremediation, Mycofiltration, Urban hydrology, South River, Water quality, Bacterial contamination EXAMINING MYCOFILTRATION EFFICACY IN A FIRST ORDER STREAM
Biochemical characterization of blended mustard oils and their physico-chemical attributes for nutritional enrichment

Blending technology has the potential to put the puissance of two or more edible oil in order to offer balanced fatty acid ratio and natural antioxidants thereby increasing the stability of oil. The objective of the present study was to develop healthier and stable blends using fresh crude mustard oil (MO),crude palm oil (PO) and crude rice bran oil (RBO). Therefore, three mustard oil blends were prepared as blend A, blend B and blend C in the ratio i.e., 40:40:20, 50:30:20 and 60:20:20, respectively. These blends as well as individual oils (mustard oil, palm oil, and rice bran oil) were analyzed for their physico-chemical and biochemical characteristics. Peroxide value and free fatty acid parameters of oils and blends were studied at regular interval i.e., 0, 7,14,21,28 days. All the parameters were evaluated at room temperature. Consequently, mustard oil and blend C (60:20:20) contained highest amount of monounsaturated and polyunsaturated fatty acid i.e., 60.81% and 26.98% and 61.325% and 21.847%, respectively. Saturated fatty acids were found to be highest in palm oil (47.682 %) and blend A (26.354%). Oil stability index indicated that palm oil (4.17) and blend A (1.88) to be highly stable. Minimum peroxide value and free fatty acid increase was observed in mustard oil (3.25 to 6.83 mEq/kg and 1.20 to 5.17%) and blend A(8.04 to 13.06 mEq/kg and 4.07 to 6.77%). Highest DPPH radical scavenging activity and total antioxidant capacity was displayed by mustard oil (9.36 μg/ml and 58.66 mg/g) and blend A (8.44 μg/ml and 60.86 mg/g). The observed results suggested that blend A displayed better nutritional output.
BEACON: Directed Grey-Box Fuzzing with Provable Path Pruning

Unlike coverage-based fuzzing that gives equal attention to every part of a code, directed fuzzing aims to direct a fuzzer to a specific target in the code, e.g., the code with potential vulnerabilities. Despite much progress, we observe that existing directed fuzzers are still not efficient as they often symbolically or concretely execute a lot of program paths that cannot reach the target code. They thus waste a lot of computational resources. This paper presents BEACON, which can effectively direct a grey-box fuzzer in the sea of paths in a provable manner. That is, assisted by a lightweight static analysis that computes abstracted preconditions for reaching the target, we can prune 82.94% of the executing paths at runtime with negligible analysis overhead (<5h) but with the guarantee that the pruned paths must be spurious with respect to the target. We have implemented our approach, BEACON, and compared it to five state-of-the-art (directed) fuzzers in the application scenario of vulnerability reproduction. The evaluation results demonstrate that BEACON is 11.50x faster on average than existing directed grey-box fuzzers and it can also improve the speed of the conventional coverage-guided fuzzers, AFL, AFL++, and Mopt, to reproduce specific bugs with 6.31x, 11.86x, and 10.92x speedup, respectively. More interestingly, when used to test the vulnerability patches, BEACON found 14 incomplete fixes of existing CVE-identified vulnerabilities and 8 new bugs while 10 of them are exploitable with new CVE ids assigned.
Organic Agriculture: Farmers Perception and Adaptation in Northern Thailand

This study aimed to examine factors impacting on farmers’ decision to convert from conventional to organic agriculture in Phayao and Nan Provinces, Thailand. The perceptions in conversion from conventional agriculture to organic agriculture toward the intention to change behavior, attitudes, and decision-making were analyzed. The data were collected by questionnaires administered to 124 farmers. The theory of planned behavior and the impacting factors of farm practices using logit model were used for analysis. The reasons for adopting organic agriculture were separated into three categories: (1) knowledge and understanding; farmers understood healthy farming but had little organic farm management information; (2) farm size impacts the costs of production; and (3) farm economics, including costs of delivery, storage, and markets, were a barrier to organic farming. These influenced attitudes, group norms, and perceived behavior. The study found that 50% of farmers using conventional practices were unwilling to change their practices, 16.1% had not decided, but 25.8% decided to change to organic practices. Organic agriculture in the study area increased to approximately 30.65%. The study suggests that farmers’ long-term benefits were implementation of agricultural policies supporting equipment, financial resources, knowledge, green technologies, training, and extension.
Work design and its Relation to Productivity among Staff Nurses

1 B.Sc. Nursing Science, 2 Assistant Professor of Nursing Administration, 3 Assistant Professor of Nursing Administration, Faculty of Nursing, Menoufia University Nursing productivity refers to effectiveness of nursing care which relates to this quality and appropriateness and efficiency of care. The purpose of this study was to investigate work design and its relation to productivity among staff nurses. Design; a descriptive corelational research design. A convenient sample of 400 staff nurses was selected to carry out this study. Setting; this study was conducted at Menoufia University Hospital at Shebin El-Kom , Menoufia Governorate, Egypt. Two instruments were used to conduct the study for work design questionnaire and productivity questionnaire. Results: there was a positive statistically significant correlation between studied staff nurses' work design and productivity. The majority of studied staff nurses had moderate levels of work design characters; the first ranking with the highest mean score was social characteristics while the last ranking was task character. Moreover, more than two third of studied staff nurses had low levels of productivity, nearly one fifth of them had moderate level of productivity. Otherwise, the minority of them had high levels productivity. Additionally, the first ranking of productivity was work organization and the last ranking with the lowest mean score of productivity was working conditions. Conclusion: there was positive highly statistically significant correlation between work design and staff nurses' productivity. Recommendation; continuing education programs and training courses for nurse manager and their staff about work design character and productivity.
Evaluation of Sludge Properties at an Activated Sludge Plant

Composition of pre-settled water, sludge properties in two different lines of an activated sludge plant, and suspended solids (SS) content in outgoing water were followed three times a week during one year. Sludge characteristics were: settling properties, residual turbidity, floc strength, filtration resistance, amount of extracellular polymers, microscope appearance and total length of filamentous organisms. The outgoing SS could be related to residual turbidity and sludge volume. These factors depended on the floc structure and the filament length. These were determined by sludge load or sludge age, water composition and the hydrodynamic history of the sludge. On basis of these and earlier findings a general model for properties of flocs built by floc-forming organisms in activated sludge is proposed.
Multi-Label Classification on 12, 6, 4, 3 and 2 Lead Electrocardiography Signals Using Convolutional Recurrent Neural Networks

Automatic identification of cardiac abnormalities through the ECG with a reduced lead system (less than the standard 12-lead) can provide a valuable easy to use and lower cost diagnostic alternative to ordinary 12-lead ECG devices. This study investigates the use of Convolutional Recurrent Neural Networks (CRNNs) to identify cardiac abnormalities in 12, 6, 4, 3 and 2 lead ECG data. Multi-label classification with CRNNs relies on effective data pre-processing, model architecture and hyperparameter tuning. ECG signals were first pre-processed and then zero-padded or clipped to have an equal duration of 10 seconds). Additionally, a wavelet-based ECG segmentation algorithm was used to extract the characteristics and locations of the PQRST complexes (features), and both PQRST fiducial points and extracted features were used as inputs to two Convolutional Recurrent Neural Networks (CRNN), respectively, each one consisting of eight layers. The two CRNNs were subsequently concatenated. Final challenge results of the proposed method achieved an official score of −0.35 for the all-lead combination and a rank of 36 (team name: heartMAASters). In the discussion we provide some theoretical considerations on why we would expect the enhanced model to show a better performance.
Domain-Specific Topic Model for Knowledge Discovery in Computational and Data-Intensive Scientific Communities

Shortened time to knowledge discovery and adapting prior domain knowledge is a challenge for computational and data-intensive communities such as e.g., bioinformatics and neuroscience. The challenge for a domain scientist lies in the actions to obtain guidance through query of massive information from diverse text corpus comprising of a wide-ranging set of topics when: investigating new methods, developing new tools, or integrating datasets. In this paper, we propose a novel “domain-specific topic model” (DSTM) to discover latent knowledge patterns about relationships among research topics, tools and datasets from exemplary scientific domains. Our DSTM is a generative model that extends the Latent Dirichlet Allocation (LDA) model and uses the Markov chain Monte Carlo (MCMC) algorithm to infer latent patterns within a specific domain in an unsupervised manner. We apply our DSTM to large collections of data from bioinformatics and neuroscience domains that include more than 25,000 of papers over the last ten years, featuring hundreds of tools and datasets that are commonly used in relevant studies. Evaluation experiments based on generalization and information retrieval metrics show that our model has better performance than the state-of-the-art baseline models for discovering highly-specific latent topics within a domain. Lastly, we demonstrate applications that benefit from our DSTM to discover intra-domain, cross-domain and trend knowledge patterns.