Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysing trade-offs in frameworks for the design of smart environments

ABSTRACT Smart Interactive Experiences (SIEs) are usage situations enabled by the Internet of Things that empower users to interact with the surrounding environment. The goal of our research is to define methodologies and software environments to support the design of SIEs; more specifically, we focus on design paradigms suitable for experts of given domains, who however might not be experts in technology. In this context, this paper discusses some trade-offs that we identified between six different dimensions that characterise the quality of software environments for SIE design. The trade-offs emerged from the analysis of data collected in an experimental study that compared three different design paradigms to understand in which measure each paradigm supports the creative process for SIE design. After reporting on the study procedure and the data analyses, the paper illustrates how the resulting trade-offs led us to identify alternatives for SIE design paradigms, and to structure on their basis a modular architecture of a software platform where the strengths of the three paradigms can be exploited flexibly, i.e. depending on the constraints and the requirements characterising specific design situations.
The Influence of Light Intensity and Photoperiod on the Growth of two Fresh Water Green Algae Tetranephris brasiliensis and Scenedesmus sp Isolated from Estuary

The culture of microalgae requires rigorous control of all growth factors: nutrients, pH, temperature, and light. Among these factors, the light that directly influences photosynthesis mechanism is an important factor in defining optimal conditions for the culture. In this study, the effects of various intensities and its photoperiods on the growth and biochemical composition (carbohydrates, proteins) of two freshwater green algae (Tetranephris brasiliensis and Scenedesmus sp) were studied. Three different light intensities (50, 100 μmol m-2 s-1 and daylight) and three photoperiod cycles (24:0, 18:06 light: dark and natural photoperiod) is launched at a constant temperature of 23°C. Microalgal growth was measured using cell count, specific growth rate and OD in three. The results showed that under the light intensity of 100 μmol m-2 s-1 and photoperiod of 18 hour light: 6 hour dark cycle, Tetranephris brasiliensis, Scenedesmus sp. was found to grow favorably with a maximum cell concentration of 2.88 × 107 cells/mL, which corresponds to the growth rate of 0.253 d-1 for Tetranephris brasiliensis and 2.56 × 107 cells/mL which corresponds to the growth rate of 0.190 d-1 for Scenedesmus sp. These results confirm the real effect of photoperiod on the microalgae growth rate and show a good correlation between the absorbance measurements and cell concentrations.
Information Processing and Overload in Group Conversation: A Graph-Based Prediction Model

Based on analyzing verbal and nonverbal features of small group conversations in a task-based scenario, this work focuses on automatic detection of group member perceptions about how well they are making use of available information, and whether they are experiencing information overload. Both the verbal and nonverbal features are derived from graph-based social network representations of the group interaction. For the task of predicting the information use ratings, a predictive model using random forests with verbal and nonverbal features significantly outperforms baselines in which the mean or median values of the training data are predicted, as well as significantly outperforming a linear regression baseline. For the task of predicting information overload ratings, the multimodal random forests model again outperforms all other models, including significant improvement over linear regression and gradient boosting models. However, on that task the best model is not significantly better than the mean and median baselines. For both tasks, we analyze performance using the full multimodal feature set versus using only linguistic features or only turn-taking features. While utilizing the full feature set yields the best performance in terms of mean squared error (MSE), there are no statistically significant differences, and using only linguistic features gives comparable performance. We provide a detailed analysis of the individual features that are most useful for each task. Beyond the immediate prediction tasks, our more general goal is to represent conversational interaction in such a way that yields a small number of features capturing the group interaction in an easily interpretable manner. The proposed approach is relevant to many other group prediction tasks as well, and is distinct from both classical natural language processing (NLP) as well as more current deep learning/artificial neural network approaches.
Energy-Efficient Elevating Transfer Vehicle Routing for Automated Multi-Level Material Handling Systems

We investigate an energy-efficient elevating transfer vehicle routing problem (ETVRP), in which an elevating transfer vehicle (ETV) serves a multi-level freight handling system to transport cargo containers between airside and landside in an air cargo terminal. The problem can be regarded as a special case of stacker crane problem defined on a regular grid graph constructed by uniform rectangular tiles. Even with the special grid network structure, the ETVRP is still NP-hard in general. We manage to identify a subset of the ETVRP instances that are polynomially solvable based on the condition of free-permutation. For general ETVRPs, we propose a new and more efficient exact formulation whose dimensionality does not constantly increase with the number of requests and is bounded by the size of the underlying grid network. To further enhance computational efficiency, we develop two approximation algorithms, one of which is asymptotically optimal and has the time-complexity that grows linearly with the number of requests; the other has a bounded time-complexity and works better for instances with smaller arc lengths. Combining these two algorithms can guarantee an approximation ratio of 5/3. The performances of the proposed formulation and the approximation algorithms are further examined through numerical simulations. Note to Practitioners—Elevating transfer vehicles (ETVs) are widely utilized in automated multi-level material handling systems for transporting, storing, and retrieving units vertically and horizontally. We develop an efficient and effective method to reduce energy consumption of operating ETV, which is critical for improving both financial and environmental sustainability of these systems. The method can be adopted to realize online timely decision making for automatic vehicle routing when facing a huge number of pickup and delivery requests in multi-level material handling systems. A class of popular application scenarios, termed “Free-Permutation,” is identified and mathematically characterized, in which the energy consumption can be exactly minimized in a theoretical sense within tractable computational times. For general application scenarios, an efficient and robust method is designed to guarantee to save the energy consumption to a level lower than 67% above the theoretical minimum level. Preliminary numerical experiments suggest the efficiency and effectiveness of this approach, but it has not yet been incorporated into nor tested in a practical system. In the future research, we will approach the application scenarios with two or more ETVs simultaneously operated in a multi-level material handling system.
Impregnation Protocols on Alumina Beads for Controlling the Preparation of Supported Metal Catalysts

Whereas the synthesis principles of supported metal catalysts are well documented in the open literature, impregnation protocols on shaped bodies represent sensitive industrial know-how and are, therefore, rarely found. We investigated various synthesis parameters for both wetness (WI) and dry (DI) impregnations to prepare Pd/γ–Al2O3 alumina beads. Two kinds of catalysts were achieved: homogeneously dispersed catalysts with no metal gradient across the beads and eggshell catalysts. A combination of optical images, Castaing microprobe analysis, elemental analysis, and TEM made it possible to discriminate between catalysts according to their metal loading, location across the bead diameter, and metal dispersion. Regardless of the macropore structure of the alumina beads, we found that volatile solvents (acetone) were preferred for preparing homogeneous catalysts by WI, whereas the use of a viscous aqueous solution (water/glycerol) in DI resulted in an eggshell-type catalyst. The atomic layer deposition (ALD) method was also investigated as a physical vapor phase deposition method for preparing eggshell catalysts. Representative-shaped catalysts were tested for CO oxidation as a model reaction in order to highlight the differences between catalysts with a homogeneous metal distribution (no metal gradient) and eggshell-type.
Emulsion-templated, polymerization-free, ultralight sponges for oil spills reclamation

We report the fabrication of monolithic, macroporous, hydrophobic polymer sponges from a water-in-oil (w/o) high internal phase emulsion (HIPE) without using polymerization process. They were formed from sulfonated polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene (SSEBS) and tetra-functional poly(amidoamine) (PAMAM) dendrimers via ionic interaction within a W/O HIPE. The sponges obtained possessed typical emulsion-templated interconnected macroporous structures, with void size ranging from 1 to 20 μm and a density of 0.08–0.1 g mL–1. The sponges were hydrophobic, with a water contact angle of around 128.1°, enabling highly selective absorption of oil from oil-water mixture with an absorption capacity from 15.4 to 25.8 g g–1 for organic solvents and oils. The sponges were recyclable and their absorption capacity was preserved at around 85% after being reused for 30 times. The high selectivity, absorption capacity and reusability enabled these sponges to be an excellent candidate in the reclamation of oils from wastewater and oil leakage.
Synthesis of Dipeptide Aldehyde Fragment of Janolusimide

Janolusimide A, and its analogue Janolusimide B, are natural marine tripeptide toxins that act as cholinergic agents and are thus effective antifeedants. Their neurotoxic activity is shown to be antagonized by atropine suggesting an inhibition interaction with acetylcholine receptors and is consequently a potential pesticide. This research project consists of developing conditions for the synthesis of the dipeptide aldehyde fragment in Janolusimide using a Weinreb amide reduction approach. Previous syntheses of Janolusimide required an expensive organoborane catalyst and several synthetic steps. Our process via the Weinreb amide is likely to produce the desired compound in a shorter synthetic process. Progress on this project is still ongoing through optimizing reduction conditions for the Weinreb amide. Future use of the finished product can range from advancement of its enantiomers and their chemical effects to use as a pesticide.
Brain Tumor Segmentation through Region-based, Supervised and Unsupervised Learning Methods: A Literature Survey Brain Tumor Segmentation through Image Processing Methods: A Literature Survey

Image segmentation is one of the most trending fields in the domain of digital image processing. For years, researchers have shown a remarkable progress in the field of Image Segmentation, precisely, for brain tumor extraction from various medical imaging modalities including X-Ray, Computed Tomography and most importantly, Magnetic Resonance Images (MRI). In these medical imaging modalities, accurate and reliable brain tumor segmentation is extremely imperative to perform safe diagnose, healthy treatment planning and consistent treatment outcome evaluation in order to understand and cure the complexities of chronic diseases such as Cancer. This paper presents various image processing techniques that are currently being used for brain tumor extraction from medical images. Though some great work has been done in this domain but none of the techniques has been widely accepted to be brought into practice in real time clinical analysis. The paper concludes with proposing some solutions that would aid in refining the results of the techniques which will lead to clinical acceptance of these computer aided methods.
Latency Hiding based Warp Scheduling Policy for High Performance GPUs

LRR(Loose Round Robin) warp scheduling policy for GPU architecture results in high warp-level parallelism and balanced loads across multiple warps. However, traditional LRR policy makes multiple warps execute long latency operations at the same time. In cases that no more warps to be issued under long latency, the throughput of GPUs may be degraded significantly. In this paper, we propose a new warp scheduling policy which utilizes latency hiding, leading to more utilized memory resources in high performance GPUs. The proposed warp scheduler prioritizes memory instruction based on GTO(Greedy Then Oldest) policy in order to provide reduced memory stalls. When no warps can execute memory instruction any more, the warp scheduler selects a warp for computation instruction by round robin manner. Furthermore, our proposed technique achieves high performance by using additional information about recently committed warps. According to our experimental results, our proposed technique improves GPU performance by 12.7% and 5.6% over LRR and GTO on average, respectively.
Detecting Malicious PDF Files Using Semi-Supervised Learning Method

With the increase in popularity of Portable Document Format (PDF) documents and increasing vulnerability of PDF users, effective detection of malicious PDF documents has become as a more and more significant issue. In this paper, we proposed a way to detect malicious PDF files by using semi-supervise learning method. Compare with previous studies, this method not only improve detection accuracy and generalization ability by combining with three different classifiers, but also effectively utilize the abundant unlabeled PDF files to retrain classifiers and update module by selecting the “useful” files from unlabeled test set.
Dynamic Pricing in an Evolving and Unknown Marketplace

We consider a firm that sells a single type product on multiple local markets over a finite horizon via dynamically adjusted prices. To prevent price discrimination, prices posted on different local markets at the same time are the same. The entire horizon consists of one or multiple change-points. Each local market's demand function linearly evolves over time between any two consecutive change-points. Each change-point is classified as either a zero-order or a first-order change-point in terms of how smooth the demand function changes at this point. At a zero-order change-point, at least one local market's demand function has an abrupt change. At a first-order change-point, all local markets' demand functions continuously evolve over time, but at least one local market's demand evolution speed has an abrupt change. The firm has no information about any parameter that modulates the demand evolution process before the start of the horizon. The firm aims at finding a pricing policy that yields as much revenue as possible. We show that the regret under any pricing policy is lower bounded by CT^{1/2} with C>0, and the lower bound becomes as worse as CT^{2/3} if at least one change-point is a first-order change-point. We propose a Joint Change-Point Detection and Time-adjusted Upper Confidence Bound (CU) algorithm. This algorithm consists of two components: the change-point detection component and the exploration-exploitation component. In the change-point detection component, the firm uniformly samples each price for one time in each batch of the time interval with the same length. She uses sales data collected at the times that she uniformly samples prices to both detect whether a change occurs and judge whether it is a zero-order or a first-order change if it occurs. In the exploration-exploitation component, the firm implements a time-adjusted upper confidence bound (UCB) algorithm between two consecutive detected change-points. Because demand dynamically evolves between two consecutive change-points, we introduce a time factor into the classical UCB algorithm to correct the bias of using historic sales data to estimate demand at present. We show that the CU algorithm achieves the regret lower bounds (up to logarithmic factors).
Page Ranking Algorithms for Web Mining: A Review

It is becoming very difficult for the web search engines to provide relevant information to the users with the growth of the WWW One of the Data Mining technique called Web mining is defined to extract the hidden information from the web documents and services. As per the information that is hidden, web mining can be divided into three different types: web content mining, web structure mining and web usage mining. The main application of web mining can be seen in the case of search engines. In order to rank their search results, they are using various page ranking algorithms that are either based on the content of the web pages or on the link structure of WWW. In this paper, a survey of page ranking algorithms based on both content and link structure of the web page and comparison of some important algorithms in context of performance has been carried out.
User-based collaborative filtering recommendation method combining with privacy concerns intensity in mobile commerce

The existing personalised recommender system gives little consideration to users' privacy concerns in mobile commerce. In order to address this issue and some other shortcomings in item recommendations, the paper proposes a novel user-based collaborative filtering recommendation method combining with privacy concerns intensity and introduces the users' six dimensions privacy concerns factors, such as privacy tendency, internal control point, openness, extroversion, agreeableness, and social group influence. The paper puts forward the metric method of privacy concerns intensity with these privacy concerns influence factors, which are used to obtain the similarity preference of users for collective filtering recommendation. Experiments show that this method has more advantages than other algorithms. More importantly, a combination of subjective privacy concerns and objective recommendation technology can reduce the influence of users' privacy concerns on their acceptance of mobile personalised service.
Innovation Scorecard in an Agile Software Development Working Environment

Background This paper provides a literature analysis of relationships between agile management methods and innovation management control. It considers a potential implementation of an Innovation Scorecard framework within software development organisations. Methodology/methods The theoretical background is based on a methodical system approach: analysis, comparison and synthesis. Analysis is used to acquire new knowledge and its interpretation. When processing secondary data, the secondary analysis method was used. Professional literature, particularly foreign sources, provided a source of secondary data. Synthesis was used designing an Innovation Scorecard solution within a short-term project that was based on a Scrum approach. Scientific aim The scientific aim of the paper is to gain knowledge and analyze the present status of agile management and innovation management control as it pertains to the Czech and foreign professional literature. The objective of the article rests in the summary and presentation of results of a literature analysis of the relationship between those areas. In addition, the paper is also important in terms of innovation management, which is a field of science, and also of related disciplines, specifically strategic management. Findings On the basis of the literature review, the process for the design and implementation of an Innovation Scorecard within an IT organization is proposed. The Innovation Scorecard methodology does not represent a static framework but can be customized according to the specific needs of an individual innovation project. When applied in Agile environemnts measuring gates (check-points) can be removed to fit in with that particular environment. Conclusions The authors present the potential benefits of the Innovation Scorecard implementation. They are aware of limitations which lead to opportunities in any future research. These shortfalls will be recovered in a planned followed-up project known as ‘Innovation Scorecard: Management Control Framework of Innovation Project in IT industry’ no. TL02000007. This is supported through funding from the Program Eta by the Technology Agency of the Czech Republic.
Photocatalytic Oxidative C–H Thiolation: Synthesis of Benzothiazoles and Sulfenylated Indoles

Abstract We report studies on the photocatalytic formation of C–S bonds to form benzothiazoles via an intramolecular cyclization and sulfenylated indoles via an intermolecular reaction. Cyclic voltammetry (CV) and density functional theory studies suggest that benzothiazole formation proceeds via a mechanism that involves an electrophilic sulfur radical, while the indole sulfenylation likely proceeds via a nucleophilic sulfur radical adding into a radical cationic indole. These conditions were successfully extended to several thiobenzamides and indole substrates.
The Acceptability of Decentralized Energy Systems: Identifying Value Conflicts Through Simulations Of Decentralized Energy Systems For City Districts

This research has been performed to help the Dutch government with the decisions and choices it has to make in order to meet the requirements of the Paris Agreement and the Climate Agreement. Decentralized energy systems can be a solution for meeting these requirements, however trade-offs have to be made between different system criteria, which are evaluated as values in this research. The aim of this research is to find out what strategies can reduce and deal with value conflicts that can be identified when designing a decentralized energy system considering the properties of a city district. A specific city district is analyzed, i.e. 'De Vruchtenbuurt' in The Hague, the Netherlands. The research starts with an identification of values, conversion factors, governance models and technical designs which are important when changing to a decentralized energy system in 'De Vruchtenbuurt'. Following the Capability Approach is used to structure the different components and the relations between them. Subsequently, a translation of the components into a simulation model is made, using the values to evaluate the acceptability of the capabilities. By analyzing the values that are conflicting (for different conversion groups) for different input parameter settings, insights are gained about possible value conflicts that can emerge in 'De Vruchtenbuurt' and possibly also in other city districts. Finally, an analysis on strategies is developed that can be effective to limit the emergence of the identified value conflicts and therefore possibly support the change to a decentralized energy system in 'De Vruchtenbuurt' and in other city districts. Policies (financial instruments and supporting innovations) that create more feasible solutions for the heterogeneous households are effective to limit the context dependent value conflicts. It is not realistic to eliminate all value conflicts, since some of the values are inherently in conflict with each other. For these conflicts thresholds can be formulated that should be met for every value. Given that value conflicts exist, focusing on the process of the change to a decentralized energy system is seen as important factor for support and participation.