Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from coloured depth images. A grasp describes how a robotic end-effector can be arranged to securely grasp an object and successfully lift it without slippage. In this study, a ResNet-50 convolutional neural network (CNN) model is trained on the Cornell grasp dataset. The training was completed within 30 hours using a workstation PC with accelerated GPU support via an NVIDIA Titan X. The trained grasp detection model was further evaluated with a Baxter research robot and a Microsoft Kinect-v2 and a successful grasp detection accuracy of 93.91% was achieved on a diverse set of novel objects. Physical grasping trials were conducted on a set of 8 different objects. The overall system achieves an average grasp success rate of 65.0% while performing the grasp detection in under 25 milliseconds. The results analysis concluded that the objects with reasonably straight edges and moderately pronounced heights above the table are easily detected and grasped by the system. Statement of Originality I hereby declare that the content of this thesis is the original work of the author, and all of the material is presented in accordance with academic rules and ethical conduct. I also certify that, to the best of my knowledge, this thesis does not infringe upon anyones copyright nor violate any proprietary rights, and that any ideas, techniques, quotations, and material from other peoples works included in this thesis are fully cited as defined by the university policy on standard referencing practices.
Loneliness as impetus, isolation as inertia: Reporting cheating peers in business school

Abstract Business education scholarship has explored cheating behavior as a function of demographics, culture, personality, and other factors. Expanding current knowledge on the topic, the authors provide the first known empirical investigation of two negatively valenced psychosocial conditions—social loneliness and social isolation—in relation to students’ decisions to (not) report their peers’ cheating behaviors. Surveying 231 graduate business students from Europe and South Asia, the authors find that social loneliness gives impetus for students to report such behaviors, while social isolation provides inertia to not report them. The results offer fresh insights for faculty and administrators hoping to improve their understanding of academic dishonesty among business students.
Strategies to Maximize the Wood Production in Amazon Forest

BackgroundThis study aimed to develop a procedure to determine which logging diameter would achieve optimal wood production by species, aiming to support sustainable management of the Amazon forest. Two main methodologies of analysis by species were combined: probability density function (PDF) and growth modeling. The growth models were used to derive the volume increment curves at the individual tree level. To detect the points of maximum annual increment in volume at the population tree level we used PDF with adjusted growth equations.ResultsThe population maximum annual volumetric increments occurred in smaller diameters compared to that of the individual-level. When combining shorter cutting cycles with the population biological rotation point considered as the minimum cutting diameter (MCD), we observed higher annual increments in volume than that achieved using the Brazilian law criteria (MCD = 50 cm) or other MCD tested.Conclusion﻿The procedure proposed may be used by forest managers and forest law-makers, aiming to maximize sustainable wood production in the Amazon forest.
A study of model parameters for scaling up word to sentence similarity tasks in distributional semantics

Representation of sentences that captures semantics is an essential part of natural language processing systems, such as information retrieval or machine translation. The representation of a sentence is commonly built by combining the representations of the words that the sentence consists of. Similarity between words is widely used as a proxy to evaluate semantic representations. Word similarity models are well-studied and are shown to positively correlate with human similarity judgements. Current evaluation of models of sentential similarity builds on the results obtained in lexical experiments. The main focus is how the lexical representations are used, rather than what they should be. It is often assumed that the optimal representations for word similarity are also optimal for sentence similarity. This work discards this assumption and systematically looks for lexical representations that are optimal for similarity measurement between sentences. We find that the best representation for word similarity is not always the best for sentence similarity and vice versa. The best models in word similarity tasks perform best with additive composition. However, the best result on compositional tasks is achieved with Kroneckerbased composition. There are representations that are equally good in both tasks when used with multiplicative composition. The systematic study of the parameters of similarity models reveals that the more information lexical representations contain, the more attention should be paid to noise. In particular, the word vectors in models with the feature size at the magnitude of the vocabulary size should be sparse, but if a small number of context features is used then the vectors should be dense. Given the right lexical representations, compositional operators achieve state-of-the-art performance, improving over models that use neural-word embeddings. To avoid overfitting, either several test datasets should be used or parameter selection should be based on parameters’ average behaviours. Submitted for the degree of Doctor of Philosophy Queen Mary University of London March 9, 2018 6
Examining Learning Transformation in Project-Based Learning Process

Purpose The purpose of this paper is to present a learning transformation framework for analyzing how learning evolves during project-based learning (PBL) process. Here, the authors adopt Lewin’s (1951) change theory as an analytical lens to examine the project-based learning curriculum at a University called UNI-X. This is a major contribution to PBL literature, as little is known about the dynamics of learning during PBL process. In fact, the learning transformation framework can serve as the basis for further research in PBL process. For educators, this paper provides them with useful insights on how to break project members’ escalating commitment to previous failing ideas and accept alternative workable ideas. Educators can use the framework in post-mortem analyses of projects to devise useful actions for facilitating learning transformation during PBL process. Design/methodology/approach The strategy used in this paper was to undertake in-depth case research of PBL courses developed and taught in UNI-X. Focused group interviews were conducted with 28 students, 12 faculty and 5 industry project sponsors asking specifically their perceptions of PBL’s course design, delivery and its impact on overall student experience. Findings This paper presents a learning transformation framework for analyzing the change process of how learners experiment new ideas, explore alternative ideas and eventually come to a consensus to accept new ideas during PBL in a collaborative project environment. By drawing upon a case study of UNI-X, the authors argue that unfreezing beliefs of previous ideas is critical if alternative ideas are to be developed. It is clear that the entire process of ‘unfreezing-changing-refreezing’ has occurred in the PBL courses at UNI-X and enacted through unfreezing beliefs in previous ideas, changing previous beliefs and refreezing the new beliefs. Through interviews with students, instructors and project sponsors in the PBL courses, the authors gathered data to examine how project members could give up previous ideas and accept alternative ideas. Originality/value The authors adopt Lewin’s (1951) change theory as an analytical lens to examine the project-based learning curriculum at UNI-X. This is a major contribution to PBL literature, as little is known about the dynamics of learning during PBL process. In fact, the learning transformation framework can serve as the basis for further research in PBL process.
Comprehensive Sample Augmentation by Fully Considering SSS Imaging Mechanism and Environment for Shipwreck Detection Under Zero Real Samples

To solve the shortage of training samples when using deep learning to detect shipwrecks, a comprehensive sample augmentation method is proposed. The method fully considers the imaging mechanism and environment of side-scan sonar (SSS), such as acoustic emission and reception, waterbody, target reflection, and seafloor background, and generates diverse and representative shipwreck samples from five aspects of target diversity, target texture, imaging resolution, equipment and environmental noises, and background through a series of novel sample augmentation methods. Under the condition of zero real SSS samples, a detection model of YOLOv5s was established with these amplified samples and achieved a mean average precision (MAP) better than 96% for real SSS data detection.
Research on Fuzzy Three Stage Tandem Queues

This project intends a general procedure to obtain the membership function of the performance measures in three stage tandem queues when the inter-arrival time and service time are in fuzzy. The basic idea is to diminish the crisp queue into fuzzy by applying the α-cut approach technique. A pair of parametric program is formulated to depict that family of crisp queue in which the membership functions of the system performance are acquired.
Mini-Review: SARS-CoV-2 and COVID-19

On 31 December 2019, the cases of pneumonia caused by unknown etiology had emerged. These cases were reported in Wuhan city, Hubei Province of China. Chinese authorities identified the causative agent and announced to be a novel coronavirus. The tentative name of disease is COVID-19, abbreviating of coronavirus disease-19. The incubation period of the disease ranges from 2 to 14 days, however, 80% of the patients have mild or asymptomatic illness while 15 % and 5% of the patients had exhibited sever and critical cases respectively. The etiology of COVID-19 was known as SARS-CoV-2 and belongs to betacoranviruse as reported by the International Committee on Taxonomy of Viruses (ICTV) especially Coronaviridae Study Group (CSG). In addition, this virus is currently believed to be within bat-coronaviruses besides it possesses a close relationship with SARS-CoV more than MERS-CoV. Although, the majority of the diagnosed patients had symptoms, there were asymptomatic persons who can spread the SARS-CoV-2. Upon the emergence of worldwide distribution of this virus, the WHO had declared it as a global outbreak and pandemic. Unfortunately, at present time, there are neither vaccine and nor an approved COVID-19 specific drug against SARS-CoV-2. One of the remarkable pathogenesis mechanistic step of this virus is taking possession of the affinity to angiotensin-converting enzyme 2 (ACE2). This mini-review summarizes the origin and molecular identification of the virus as well as the host immune responses. SARS-CoV-2 , COVID-19, ACE2, origin
New social consumer? Determining factors of Facebook commerce

ABSTRACT The main purpose of this paper is to analyze the influence of networks on individuals’ purchase decision-making processes within the field of digital marketing, and how its use can favor sales development. The scientific literature review allowed us to justify a behavioral model that explains the intention to use based on a set of endogenous constructs or variables. To carry out this research, we created a survey answered by 205 Spanish users of social networks who had previously watched a video explaining the functioning of this new commercial format. Using the existing relationship between diverse variables used in prior studies on social commerce and selecting a substantial group of them, we discovered the significant relationship between subjective norms, usefulness and perceived value with the intention to purchase via social networks.
Application of machine learning techniques to exploration: an example using self-organising maps for garnet data

The application of machine learning techniques in the earth sciences is relatively rare despite the availability of large, suitable datasets. The chemistry of diamond indicator minerals is one such dataset that has been routinely and consistently collected for the purposes of exploration and research for at least the last 50 years. There are established methodologies for interpreting these data, particularly for garnet (e.g. Gurney, 1984; Grütter et al., 2004), which are in widespread use by diamond explorers today. The existing methodologies are effective at categorising and prioritising garnet chemical data, in most cases by using binary or linear cuts in the data for one or more majorelement variables. The self-organising map (SOM) is a variety of machine learning technique which organises data based on all input variables simultaneously. By applying this technique to garnet compositional dataset, relationships emerge which are not apparent in diagrams traditionally used for garnet indicator data interpretation. These relationships can be used as the basis of an exploration tool which more effectively sub-divides and prioritises garnet data from all parageneses.
ERPOT: A Quad-Criteria Scheduling Heuristic to Optimize Execution Time, Reliability, Power Consumption and Temperature in Multicores

We investigate multi-criteria optimization and Pareto front generation. Given an application modeled as a Directed Acyclic Graph (DAG) of tasks and a multicore architecture, we produce a set of non-dominated (in the Pareto sense) static schedules of this DAG onto this multicore. The criteria we address are the execution time, reliability, power consumption, and peak temperature. These criteria exhibit complex antagonistic relations, which make the problem challenging. For instance, improving the reliability requires adding some redundancy in the schedule, which penalizes the execution time. To produce Pareto fronts in this 4-dimension space, we transform three of the four criteria into constraints (the reliability, the power consumption, and the peak temperature), and we minimize the fourth one (the execution time of the schedule) under these three constraints. By varying the thresholds used for the three constraints, we are able to produce a Pareto front of non-dominated solutions. We propose two algorithms to compute static schedules. The first is a ready list scheduling heuristic called Execution time, Reliability, POwer consumption and Temperature (ERPOT). ERPOT actively replicates the tasks to increase the reliability, uses Dynamic Voltage and Frequency Scaling to decrease the power consumption, and inserts cooling times to control the peak temperature. The second algorithm uses an Integer Linear Programming (ILP) program to compute an optimal schedule. However, because our multi-criteria scheduling problem is NP-complete, the ILP algorithm is limited to very small problem instances. Comparisons showed that the schedules produced by ERPOT are on average only 10 percent worse than the optimal schedules computed by the ILP program, and that ERPOT outperforms the PowerPerf-PET heuristic from the literature on average by 33 percent.
Managerial Competencies and Venture Survival: The Case of Women Entrepreneurs Owner – Managers of Micro-Small Enterprises (MSEs) in Khartoum State – Sudan

The objective of this study is to investigate the management competencies of female entrepreneurs, owner – managers of micro-small enterprises (MSEs) within urban settings in Khartoum State, Sudan. It addresses a knowledge gap in the literature regarding competencies of female entrepreneurs in Sudan. It is an exploratory study that follows an interpretive approach and applies Grounded Theory techniques. Five core competency areas are brought to focus based on previous competency models. The study findings elicit that competency levels possessed by owner – managers of (MSEs) vary according to the competency area, line of activity, type of venture and the attributes of the entrepreneur herself. Female entrepreneurs tend to adhere to a relational management style and a pragmatic approach, with high capacity to innovatively adjust to complex operational conditions, supported by their personal networks. Enhancing some competency areas through education and skill development programs is recommended to further ensure venture survival and success.
Utilising an in silico approach to determine vulnerability to reentrant arrhythmias

Post-myocardial infarction arrhythmias are a leading cause of death in developed countries, motivating research to improve treatment strategies. Ischemic damage occurring due to a myocardial infarction results in the formation of inexcitable infarct scars. Electrical activation waves circumvent these structural barriers and can propagate in perpetual reentrant circuits giving rise to ventricular tachycardia. The success of reentrant propagation depends on the wavelength of the activation wave with respect to the physical path length, determining the extent of wavefront-waveback interactions. A wide variety of animal and computational models are used to perform research however, the optimal species for studying clinical arrhythmias is unknown. One aim of the research in this Thesis was to suggest a species model which most closely replicates clinical arrhythmia dynamics. Computational models were utilised to compare the susceptibility to reentry, by calculating the effective electrical size of the heart, which takes into consideration both the size of the wavelength and the physical size of the heart. Results suggested that species differences in effective size exist between human and animal models. However, the effective size of the rabbit model was most similar to the human. This conclusion was utilised to formulate methodologies for the following studies. Current techniques employed to locate ablation lesion sites during radiofrequency catheter ablation are inaccurate, leading to insufficient procedure success rates. Here, a method to accurately locate optimal ablation lesion targets was investigated utilising computational models. Quantification of wavelength permitted the observation of wavefront-waveback interactions to predict susceptibility to reentry. The clinical application of the methodology was modelled to ensure that ablation of the susceptible tissue could terminate reentry and that the limitations of clinical data acquisition did not invalidate the technique. The method accurately located a region of tissue where reentry could potentially occur and ablation of the region terminated reentry even when clinical protocol was simulated. This research suggests the most suitable species models to research ventricular tachycardia, guiding further in vivo and in silico methodologies. Additionally, a protocol to improve the success of the ablation procedure has been further investigated. Clinical implementation of this technique could vastly improve the treatment of post-myocardial infarction arrhythmias.
Food Processing Value Chains in Zambia: Governance in the Maize Value Chain

The analytical dimension of governance within global value chains has attracted a number of scholars over the years (Gereffi, 1994; Gibbon and Ponte, 2005; Granleese, 2009; Ponte, 2014). However, for Africa, these studies have been limited to high value crops such as cocoa, coffee, fruits and vegetables which enable African countries to be inserted in global value chains (Granleese, 2009, Daviron and Ponte, 2005; Dolan and Humphrey, 2000). As a result, limited evidence exists on how governance plays out on staple food value chains that are usually relegated to domestic and regional markets (Minten, 2015).
The Hogg Eco-Anxiety Scale: Development and Validation of a Multidimensional Scale

Anxiety relating to the ecological crisis, or eco-anxiety, is a subject of growing research significance. We used a multi-study mixed-methods design to explore eco-anxiety in Australia and New Zealand, validating a new eco-anxiety scale. In Study One, we developed and tested a 7-item eco-anxiety scale (n = 334), finding that this captured some, but not all, experiences of eco-anxiety, informing further scale development in Study Two. Exploratory (n = 365) and Confirmatory Factor Analysis (n = 370) supported a final 13-item scale that captured four dimensions of eco-anxiety: affective symptoms, rumination, behavioural symptoms, and anxiety about one’s negative impact on the planet, which were each distinct from stress, anxiety and depression. A further longitudinal sample (n = 189) examined the stability of these factors across time. Findings support eco-anxiety as a legitimate psychological experience, reliably measured using our 13-item eco-anxiety scale, and differentiated from other mental health outcomes.
An Empirical study of Binary Neural Networks' Optimisation

Neural networks with deterministic binary weights using the Straight-Through-Estimator (STE) have been shown to achieve state-of-the-art results, but their training process is not well-founded. This is due to the discrepancy between the evaluated function in the forward path, and the weight updates in the back-propagation, updates which do not correspond to gradients of the forward path. Efﬁcient convergence and accuracy of binary models often rely on careful ﬁne-tuning and various ad-hoc techniques. In this work, we empirically identify and study the effectiveness of the various ad-hoc techniques commonly used in the literature, providing best-practices for efﬁcient training of binary models. We show that adapting learning rates using second-moment methods is crucial for the successful use of the STE, and that other optimisers can easily get stuck in local minima. We also ﬁnd that many of the commonly employed tricks are only effective towards the end of the training, with these methods making early stages of the training considerably slower. Our analysis disambiguates necessary from unnecessary ad-hoc techniques for the training of binary neural networks, paving the way for future development of solid theoretical foundations for these. Our newly-found insights further lead to new procedures which make training of existing binary neural networks notably faster.