Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding user requirements in context: A case study of developing a visualisation tool to map skills in an engineering organisation

We provide a case study of understanding the environment and work context of a visualisation tool used in a collaborative engineering organisation, in order to inform the design, development and evaluation of a software network tool that uses a novel algorithm to search knowledge topics in a document corpus. We utilise focus group and qualitative interview data to understand the dynamics of existing knowledge searches and the visual analytics process of collaborative working environments in an engineering domain. We discuss the enablers and functionality needed in the network tool, and the envisaged challenges in its implementation. These challenges and enablers of a knowledge management visualisation software are then discussed in relation to evaluating the tool in a way that is grounded in the contextual working environment in which it will be used.
Reporting and Non-Reporting Incentives in Leasing

This study sheds light on the extent to which the use of operating leases depends on reporting incentives, such as understating liabilities, and non-reporting incentives that partly arise from the overlap between accounting, bankruptcy, and tax laws, such as increasing financing capacity and flexibility. We provide evidence that expanding financing capacity, accommodating volatile operations, and maximizing the present value of tax deductions are all important drivers of leasing decisions. Our findings suggest that capital markets and contracting-based reporting incentives have little influence on operating lease use. In particular, we find weak evidence that firms increase operating leases in advance of issuing equity, and no evidence that firms use operating leases to window-dress in advance of issuing debt, to avoid debt covenant violations, for compensation purposes, or to paint a better picture on an ongoing basis. These findings are consistent with reporting incentives playing a second-order role in leasing decisions. JEL Classifications: G32; G33; K34; M41.
Nutrition professionals' perception of the ‘healthiness’ of individual foods

Abstract Objectives This paper describes the development of an online questionnaire for testing nutrition professionals' perceptions of the ‘healthiness’ of individual foods and the results of administering that questionnaire. The questionnaire was designed to produce a standard ranking of foods that can be used as a tool for testing nutrient profile models. Design The questionnaire asked respondents to categorise 40 foods (from a master list of 120) in one of six positions, ranging from less to more healthy. The 120 foods were selected to be representative of the British diet. The questionnaire was sent via email to nutrition professionals from the British Dietetic Association and the (British) Nutrition Society. Results Eight hundred and fifty responses were received. These responses were used to rank the 120 foods by the average score which they received from the nutrition professionals. A regression analysis was also carried out to examine the relationship between the scores awarded by the nutrition professionals and various features of the foods: their nutritional content, their average serving size, their frequency of consumption, whether they were drinks or foods, etc. Nearly 50% of the variance in the average scores was explained by the nutritional content of the foods. When other variables were included in the analysis the percentage of variance that was explained increased to 64%. Conclusions The average scores of the foods produce a standard ranking, which can be used as a tool for validating and comparing nutrient profile models. The regression analysis provides some information about how nutrition professionals rank the ‘healthiness’ of individual foods.
Robust H∞ Computed torque Control for Manipulators

This paper aims at addressing the synthesis application of robust control technique in a manipulator based on LMIs (Linear Matrix Inequalities) in order to minimize the H_∞ norm by taking into account the uncertain parameters of the manipulator. The control strategy is based on the compute torque control scheme that is composed of two control loops. The inner loop aims at linearizing the nonlinear robot dynamics using feedback linearization. The outer loop tracks the desired trajectory based on a PD controller, which is robustified against uncertain parameters and neglecting dynamics. The robust control technique is applied in a two degrees of freedom planar manipulator and the numerical results show the benefits of the robustified control strategy in the dynamical performance in terms of tracking accuracy, disturbance rejection and uncertain parameters.
Adaptive Super-Resolution for Remote Sensing Images Based on Sparse Representation With Global Joint Dictionary Model

Sparse representation has been widely used in the field of remote sensing image super-resolution (SR) to restore a high-quality image from a low-resolution (LR) image, e.g., from the blurred and downsampled version of an LR image’s high-resolution (HR) counterpart. It is well known that each image patch can be represented by a linear combination of the atoms of an overcomplete dictionary, and we can obtain an expression of sparse coefficients by $l_{1}$ norm regularization. Owing to the lack of an inner relationship between image patches and an image’s global information, the traditional methods of jointly training two overcomplete dictionaries cannot obtain good SR results. Therefore, we propose an effective approach for remote sensing image SR based on sparse representation. More specifically, a novel global joint dictionary model (GJDM) is used to explore the prior knowledge of images, including local and global characteristics. First, we train two dictionaries for detail image patches and HR patches. Second, in order to enhance the inner relationship between image patches, we introduce a global self-compatibility model for global regularization. Finally, the sparse representation and the local and nonlocal constraints are integrated to improve the performance of the model, and the fast adaptive shrinkage-thresholding algorithm is employed to solve the convex optimization problem in the GJDM. Compared with other methods, the results of the proposed method show good SR performance in preserving details and texture information and significant improvement in a peak signal-to-noise ratio.
Systematic and Empirical Study of the Dependence of Polyphenol Recovery from Apricot Pomace on Temperature and Solvent Concentration Levels

This work aims to study the impact of solvent mixture (between 0 and 50% ethanol/water mixture) and temperature (between 25°C and 75°C) levels on the solid-liquid extraction of phenolic compounds (quantity and bioactivity) from apricot pomace. Results show that the mean augmentation of 1% ethanol in the range [0–12%] enhances by three times the extraction of polyphenols compared to the same augmentation in the range [0–50%]. Similarly, the mean augmentation of 1°Celcius in the range [0–25°Celcius] enhances by two times the extraction of polyphenols compared to the same augmentation in the range [0–75°Celcius]. Moreover, 1% of ethanol exhibited a greater impact on the phenolic compound extraction than 1°Celsius. The response surface methodology showed that the optimal extraction condition was reached with 50% ethanol/water at 75°C giving a total phenolic content (TPC) of 9.8 mg GAE/g DM, a flavonoids content (FC) of 8.9 mg CE/g DM, a tannin content (TC) of 4.72 mg/L, and an antiradical activity (AA) of 44%. High-performance liquid chromatography (HPLC) analysis showed that polyphenols were influenced by the selectivity of the solvent as well as the properties of each phenolic compound. Apricot pomace extracts could therefore be used as natural bioactive molecules for many industrial applications.
Decreased brain sigma-1 receptor contributes to the relationship between heart failure and depression.

AIMS Depression often coexists with cardiovascular disease, such as hypertension and heart failure, in which sympathetic hyperactivation is critically involved. Reduction in the brain sigma-1 receptor (S1R) functions in depression pathogenesis via neuronal activity modulation. We hypothesized that reduced brain S1R exacerbates heart failure, especially with pressure overload via sympathetic hyperactivation and worsening depression. METHODS AND RESULTS Male Institute of Cancer Research mice were treated with aortic banding and, 4 weeks thereafter, fed a high-salt diet for an additional 4 weeks to accelerate cardiac dysfunction (AB-H). Compared with sham-operated controls (Sham), AB-H showed augmented sympathetic activity, decreased per cent fractional shortening, increased left ventricular dimensions, and significantly lower brain S1R expression. Intracerebroventricular (ICV) infusion of S1R agonist PRE084 increased brain S1R expression, lowered sympathetic activity, and improved cardiac function in AB-H. ICV infusion of S1R antagonist BD1063 increased sympathetic activity and decreased cardiac function in Sham. Tail suspension test was used to evaluate the index of depression-like behaviour, with immobility time and strain amplitude recorded as markers of struggle activity using a force transducer. Immobility time increased and strain amplitude decreased in AB-H compared with Sham, and these changes were attenuated by ICV infusion of PRE084. CONCLUSION These results indicate that decreased brain S1R contributes to the relationship between heart failure and depression in a mouse model of pressure overload.
Fast Pedestrian Detection with Multi-scale Classifiers

Computing image feature pyramid has been a common approach in pedestrian detection for improving detection accuracy. However, building feature pyramid is a time consuming task. In this paper we propose a new multi-scale classifier based method. We approximate the nearby scale classifier instead of extracting features multiple times form the resizing images. These approximated classifiers can be applied to achieve object detection without image resizing. In addition, we introduce a new feature, BPG (Binary Pattern of Gradient), to further accelerate the feature extraction speed. The experimental result demonstrates that the new feature is efficient in pedestrian detection. It is also proved that the proposed method not only reduces the detection speed, but also has performance comparable to some state-of-the-art pedestrian detection approaches.
Synthesizing Web Element Locators

To programmatically interact with the user interface of a web application, element locators are used to select and retrieve elements from the Document Object Model (DOM). Element locators are used in JavaScript code, Cascading stylesheets, and test cases to interact with the runtime DOM of the webpage. Constructing these element locators is, however, challenging due to the dynamic nature of the DOM. We find that locators written by web developers can be quite complex, and involve selecting multiple DOM elements. We present an automated technique for synthesizing DOM element locators using examples provided interactively by the developer. The main insight in our approach is that the problem of synthesizing complex multielement locators can be expressed as a constraint solving problem over the domain of valid DOM states in a web application. We implemented our synthesis technique in a tool called LED, which provides an interactive drag and drop support inside the browser for selecting positive and negative examples. We find that LED supports at least 86% of the locators used in the JavaScript code of deployed web applications, and that the locators synthesized by LED have a recall of 98% and a precision of 63%. LED is fast, taking only 0.23 seconds on average to synthesize a locator.
Eye movement simulation and detector creation to reduce laborious parameter adjustments

Eye movements hold information about human perception, intention and cognitive state. Various algorithms have been proposed to identify and distinguish eye movements, particularly fixations, saccades, and smooth pursuits. A major drawback of existing algorithms is that they rely on accurate and constant sampling rates, impeding straightforward adaptation to new movements such as micro saccades. We propose a novel eye movement simulator that i) probabilistically simulates saccade movements as gamma distributions considering different peak velocities and ii) models smooth pursuit onsets with the sigmoid function. This simulator is combined with a machine learning approach to create detectors for general and specific velocity profiles. Additionally, our approach is capable of using any sampling rate, even with fluctuations. The machine learning approach consists of different binary patterns combined using conditional distributions. The simulation is evaluated against publicly available real data using a squared error, and the detectors are evaluated against state-of-the-art algorithms.
Impact Based Piezoelectric Energy Harvesting: Effect of Single Step’s Force and Velocity

This paper reports an impact driven energy harvesting via employing a piezoelectric ceramic disc, in which a usable alternating electrical energy has been harvested via the mechanical impact of the human weight on the surface of a piezoelectric plate transducer. A prototype of a single human step piezoelectric plate impact driven harvester consisting of a piezoelectric transducer disc was tested on a hydraulic pressing machine with variable forces and impact velocities. In this experiment, a piezoelectric ceramic disc with a size of pallet 44mm in diameter and 10mm in thickness was able to transform the mechanical impact into an average output power of up to 14. 5μW across a resistive load of 500kΩ, when a force of 0.75 kN with a velocity of 600mm/min is applied on it
LTE-U and Wi-Fi Coexistence Algorithm Based on Q-Learning in Multi-Channel

Due to the lack of resources in the low spectrum, long term evolution (LTE) in unlicensed spectrum (LTE-U) technology has been proposed to extend LTE to unlicensed spectrum. LTE-U undertakes the task of streaming data traffic for licensed spectrum, which can greatly enhance the capacity of the system. However, the introduction of LTE-U technology also gives rise to the problem of coexistence with Wi-Fi systems. In this paper, an LTE-U and Wi-Fi coexistence algorithm is proposed in multi-channel scenarios based on Q-learning. By taking the idea of alternately transferring data in LTE-U and Wi-Fi, the algorithm takes into account both the fairness and the performance of the system and optimizes the duty cycle. The simulation results show that the proposed algorithm can effectively improve the throughput of the system in the premise of ensuring fairness.
360{\deg} Stance Detection

The proliferation of fake news and filter bubbles makes it increasingly difficult to form an unbiased, balanced opinion towards a topic. To ameliorate this, we propose 360{\deg} Stance Detection, a tool that aggregates news with multiple perspectives on a topic. It presents them on a spectrum ranging from support to opposition, enabling the user to base their opinion on multiple pieces of diverse evidence.
Incremental Evaluation of Sliding-Window Queries over Data Streams

Two research efforts have been conducted to realize sliding-window queries in data stream management systems, namely, query revaluation and incremental evaluation. In the query reevaluation method, two consecutive windows are processed independently of each other. On the other hand, in the incremental evaluation method, the query answer for a window is obtained incrementally from the answer of the preceding window. In this paper, we focus on the incremental evaluation method. Two approaches have been adopted for the incremental evaluation of sliding-window queries, namely, the input-triggered approach and the negative tuples approach. In the input-triggered approach, only the newly inserted tuples flow in the query pipeline and tuple expiration is based on the timestamps of the newly inserted tuples. On the other hand, in the negative tuples approach, tuple expiration is separated from tuple insertion where a tuple flows in the pipeline for every inserted or expired tuple. The negative tuples approach avoids the unpredictable output delays that result from the input-triggered approach. However, negative tuples double the number of tuples through the query pipeline, thus reducing the pipeline bandwidth. Based on a detailed study of the incremental evaluation pipeline, we classify the incremental query operators into two classes according to whether an operator can avoid the processing of negative tuples or not. Based on this classification, we present several optimization techniques over the negative tuples approach that aim to reduce the overhead of processing negative tuples while avoiding the output delay of the query answer. A detailed experimental study, based on a prototype system implementation, shows the performance gains over the input-triggered approach of the negative tuples approach when accompanied with the proposed optimizations
Scalable high resolution panorama composition on data wall system

Image stitching algorithms construct a single composite high resolution image with a larger field of view using multiple low resolution images. Data wall systems combine the power of multiple graphics cards to provide large number of pixels. In this research work, we propose and implement scalable high resolution panorama composition on the data wall system using distributed rendering. We make use of a cross platform OpenGL library for real-time panorama composition. Our algorithm works for image sources with fixed relative positions. Initially, we employ an OpenCV image processing library for panorama calibration. Subsequently, these calibration results are utilized to generate a seamless panoramic image. We construct a high resolution 3×3 data wall system using commodity projectors. We achieve a seamless display with the help of geometric alignment of projector regions and edge blending in overlapping regions. Conventional display devices with their limited image resolution lose details provided by high resolution panorama because of data compression. We integrate panorama composition and the data wall system using distributed rendering. We distribute panorama composition to rendering workstations, which compute their respective screen space bounding regions and operate within it. It provides high resolution panorama with greater details on the data wall system.
From Focused Thought to Reveries: A Memory System for a Conscious Robot

We introduce a memory model for robots that can account for many aspects of an inner world, ranging from object permanence, episodic memory, and planning to imagination and reveries. It is modeled after neurophysiological data and includes parts of the cerebral cortex together with models of arousal systems that are relevant for consciousness. The three central components are an identification network, a localization network, and a working memory network. Attention serves as the interface between the inner and the external world. It directs the flow of information from sensory organs to memory, as well as controlling top-down influences on perception. It also compares external sensations to internal top-down expectations. The model is tested in a number of computer simulations that illustrate how it can operate as a component in various cognitive tasks including perception, the A-not-B test, delayed matching to sample, episodic recall, and vicarious trial and error.
Engineering Context Aware Socio-Technical Systems through Capabilities

In the approach traditionally followed by engineering methods, systems are a product of a development process that starts with a requirements gathering and analysis process, and as such, the final product is expected to be in accordance with agreed requirements. However, reality is often different and more complex, involving sometimes the adoption and integration of off-the-shelf solutions into complex systems, or even the reutilization of existing solutions in different settings than those for which they were conceived initially. Reusing of-the-shelf components motivated by only previously identified requirements they are perceived to support, while not considering the full potential of these components, might result in a dumb down usage of the existing technology, which might work as a blindfold to innovation. The concept of capability is a powerful concept adopted by many areas, as it can be used to provide a holistic view of a product or system, while offering new ways of dealing with complexity. This work proposes the development of a method for capability-based engineering of systems that also allows the combination of systems in order to deliver new types of capabilities, along with the identification of the appropriate conditions for its application. Techniques to define viewpoints that make possible the description of systems and systems’ components by the means of their engineered capabilities will also be a target.
Local Maximum Entropy Shape Functions Based FE-EFGM Coupling

In this paper, a new method for coupling the finite element method (FEM)and the element-free Galerkin method (EFGM) is proposed for linear elastic and geometrically nonlinear problems using local maximum entropy shape functions in theEFG zone of the problem domain. These shape functions possess a weak Kroneckerdelta property at the boundaries which provides a natural way to couple the EFGand the FE regions as compared to the use of moving least square basis functions.In this new approach, there is no need for interface/transition elements between theEFG and the FE regions or any other special treatment for shape function continuity across the FE-EFG interface. One- and two-dimensional linear elastic and two-dimensional geometrically nonlinear benchmark numerical examples are solved by the new approach to demonstrate the implementation and performance of the current approach.
Cumulative Mass and NIOSH Variable Lifting Index Method for Risk Assessment: Possible Relations

Objective The aim of this study was to explore whether the Variable Lifting Index (VLI) can be corrected for cumulative mass and thus test its efficacy in predicting the risk of low-back pain (LBP). Background A validation study of the VLI method was published in this journal reporting promising results. Although several studies highlighted a positive correlation between cumulative load and LBP, cumulative mass has never been considered in any of the studies investigating the relationship between manual material handling and LBP. Method Both VLI and cumulative mass were calculated for 2,374 exposed subjects using a systematic approach. Due to high variability of cumulative mass values, a stratification within VLI categories was employed. Dummy variables (1–4) were assigned to each class and used as a multiplier factor for the VLI, resulting in a new index (VLI_CMM). Data on LBP were collected by occupational physicians at the study sites. Logistic regression was used to estimate the risk of acute LBP within levels of risk exposure when compared with a control group formed by 1,028 unexposed subjects. Results Data showed greatly variable values of cumulative mass across all VLI classes. The potential effect of cumulative mass on damage emerged as not significant (p value = .6526). Conclusion When comparing VLI_CMM with raw VLI, the former failed to prove itself as a better predictor of LBP risk. Application To recognize cumulative mass as a modifier, especially for lumbar degenerative spine diseases, authors of future studies should investigate potential association between the VLI and other damage variables.