Reviewerly/exHarmony huggingface dataset:
FINANCIAL DEVELOPMENT, TECHNOLOGY AND ECONOMIC DEVELOPMENT: THE ROLE OF INSTITUTIONS IN DEVELOPING COUNTRIES

Financial sector development plays a pivotal role in the process of economic growth and development through mobilization of savings and creating investment opportunities. Financial development also increases the level of technology by providing finance to entrepreneurs for technological innovations which leads to economic growth. Moreover, financial markets develop rapidly in those countries which have strong legal system to enforce property rights, support private contractual arrangement and protect the rights of investors that can support real economic activities. Therefore, the presence of good quality institutions strengthens financial development which leads to technological development and growth. This study investigates the impact of financial development, technology and institutions on economic growth of selected developing countries over the time span of 1996–2015. This study extends the Augmented Solow growth model by incorporating variables such as financial development, technology, institutions and their interaction terms in the model. Due to endogeneity problem, the empirical model used in the study is estimated by System Generalized Method of Moments (System-GMM). Empirical results show that financial development, technology and institutions have very strong effects on the economic growth developing countries. To attain a sustainable economic growth, developing countries should develop their institutions which are necessary for the effective functioning of financial markets that stimulate economic growth by providing finance to entrepreneurs for innovations in technological sectors.
Altered Realities

As the use of both virtual reality (VR) and augmented reality (AR) become more commonplace in everyday life, the importance of including these technologies in schools increases. The focus of this chapter is to explore how these two technologies are being used at in primary, secondary, and tertiary contexts to support student learning. In exploring these technologies, science, technology, engineering, and maths (STEM)-related subjects, with a focus on Science, are examined. In investigating science, an investigation on informal learning is also undertaken. Non-STEM-related subjects including Physical Education, the Creative Arts, and Geography are also reported on. In investigating game-based learning Maths is examined where the concept of location-based learning is discussed. The chapter concludes by exploring how VR and AR can be used to support students with disabilities.
The Selfish Herd Optimizer

In this chapter, a swarm optimization algorithm called Selfish Herd Optimizer (SHO) is presented. The SHO algorithm’s design is based on the emulation of the widely-observed selfish herd behavior, manifested by individuals living in aggregations while exposed to some kind of predation risk. An interesting trait that distinguish the SHO algorithm from other similar approaches is the division of the entire population of search agents in two opposite groups: the members of a selfish herd (the prey), and a pack of starving predators. These two types of search agents interact with each other in ways that allows to emulate the intriguing interaction between prey and predators that arise from the unique behaviors manifested by the members of the so-called selfish herds. This chapter also presents a series of experiments done with the purpose of comparing the performance of the SHO algorithm against other similar swarm optimization approaches, showing remarkable results.
Telling the Story Right

Data visualizations of training performance can provide patients with insight and motivation to engage in future self-training sessions. Still, patients are in practice prone to misinterpret their data in practice due to lack of health numeracy. This paper contributes an ethnographic case study of how an occupational therapist employed storytelling to help 14 sub-acute stroke patients interpret visualizations of their training performance from a cognitive rehabilitation game. The Trail Making Test based game visualized performance through a timeline, heat maps, and aggregate visualizations codesigned with the therapist. The interactive visualizations enabled the therapist to strategically order and emphasize information to support patients' understanding of their health. We provide an analysis of the therapist's storytelling strategy in cognitive training that depended on patient self-awareness. The paper concludes with a discussion of tensions for designing storytelling in visualizations of health data for self-care systems targeted at stroke patients in clinical and home contexts.
Business Process Model Driven Automatic Software Requirements Generation

Requirement engineering is a critical stage in software engineering, it enables requirement engineers extract correct system needs, both functional and non-functional constraints from stakeholders. The majority of the errors found in software functionality are directly linked to the mistakes made during the requirement elicitation phases. Therefore, several approaches have been proposed to enhance existing requirements engineering techniques to both reduce such mistakes and to speed up the requirements engineering process. One type of promising approaches is based on utilizing business process modelling to take benefit from business process models to derive requirements. This paper argues that it is possible to generate requirements from business process models. It proposes an approach to derive system requirements; it employs business process models and then transforms them into requirement models. Evaluation shows the proposed approach was able to generate additional valid use case model features compared to other competing approaches.
A Comparison of Selected Variable Ordering Methods for NFA Induction

In the paper, we study one of the fundamental problems of grammatical inference, namely the induction of nondeterministic finite automata (NFA). We consider the induction of NFA consistent with the given sets of examples and counterexamples. We transform the induction problem into a constraint satisfaction problem and propose two variable ordering methods to solve it. We evaluate experimentally the proposed variable ordering methods and compare them with a state-of-the-art method. Additionally, through the experiments, we assess the impact of sample sets sizes on the performance of the induction algorithm using the respective variable ordering methods.
Observation Tree Approach: Active Learning Relying on Testing

Abstract The correspondence of active learning and testing of finite-state machines (FSMs) has been known for a while; however, it was not utilized in the learning. We propose a new framework called the observation tree approach that allows one to use the testing theory to improve the performance of active learning. The improvement is demonstrated on three novel learning algorithms that implement the observation tree approach. They outperform the standard learning algorithms, such as the L* algorithm, in the setting where a minimally adequate teacher provides counterexamples. Moreover, they can also significantly reduce the dependency on the teacher using the assumption of extra states that is well-established in the testing of FSMs. This is immensely helpful as a teacher does not have to be available if one learns a model of a black box, such as a system only accessible via a network.
Regressive Scale Estimation for Visual Tracking

In visual tracking, the aspect ratio variation of a target frequently appears and accurate scale estimation is challenging. Most tracking methods employ an exhaustive scale search to estimate the target sizes. Due to expensive computation, these methods can only search for finite and discrete scales. They fail if scale varies out of range. We propose a Regressive Scale Estimation (RSE) tracking framework by integrating a positioning model and bounding box regression, for position and scale estimation respectively. We generalize the original single-channel bounding box regression to multi-channel situations, to allow for better employment of multi-channel features. Contrary to most approaches, our method directly searches the target sizes within continuous scale space, which can predict any scale, not limited by a manually specified number of scales. Our scale model regresses the height and width of target simultaneously rather than treating them as one proportional scaling factor, leading to better flexibility. Experiments on Visual-Object-Tracking 2017 (VOT) benchmarks prove that our approach can adapt the target scale and achieve state-of-the-art performance in terms of accuracy and robustness.
Web video classification with visual and contextual semantics

Summary On the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.
Length-adaptive Neural Network for Answer Selection

Answer selection focuses on selecting the correct answer for a question. Most previous work on answer selection achieves good performance by employing an RNN, which processes all question and answer sentences with the same feature extractor regardless of the sentence length. These methods often encounter the problem of long-term dependencies. To address this issue, we propose a Length-adaptive Neural Network (LaNN) for answer selection that can auto-select a neural feature extractor according to the length of the input sentence. In particular, we propose a flexible neural structure that applies a BiLSTM-based feature extractor for short sentences and a Transformer-based feature extractor for long sentences. To the best of our knowledge, LaNN is the first neural network structure that can auto-select the feature extraction mechanism based on the input. We quantify the improvements of LaNN against several competitive baselines on the public WikiQA dataset, showing significant improvements over the state-of-the-art.
Maximal common subsequence algorithms

A common subsequence of two strings is maximal if inserting any character into it no longer yields a common subsequence. The present article proposes a (sub)linearithmic-time, linear-space algorithm for finding a maximal common subsequence of two strings and a linear-time algorithm for determining if a common subsequence of two strings is maximal.
Answer-enhanced Path-aware Relation Detection over Knowledge Base

Knowledge Based Question Answering (KBQA) is one of the most promising approaches to provide suitable answers for the queries posted by users. Relation detection that aims to take full advantage of the substantial knowledge contained in knowledge base (KB) becomes increasingly important. Significant progress has been made in performing relation detection over KB. However, recent deep neural networks that achieve the state of the art on KB-based relation detection task only consider the context information of question sentences rather than the relatedness between question and answer candidates, and exclusively extract the relation from KB triple rather than learn informative relational path. In this paper, we propose a Knowledge-driven Relation Detection network (KRD) to interactively learn answer-enhanced question representations and path-aware relation representations for relation detection. A Siamese LSTM is employed into a similarity matching process between the question representation and relation representation. Experimental results on the SimpleQuestions and WebQSP datasets demonstrate that KRD outperforms the state-of-the-art methods. In addition, a series of ablation test show the robust superiority of the proposed method.
Toward Sustainable Domestication of Smart IoT Mobility Solutions for the Visually Impaired Persons in Africa

According to World Health Organization (WHO) estimates, Africa accounts for 10% of the global visually impaired persons (VIPs). This visual impairment burden is exacerbated by the shortage of specialist medical human resources, orientation and mobility specialists, and high cost of assessing primary eye care services. These render the majority of VIPs to rely heavily on human-assisted guides and ineffective navigation aids for their daily routines and movements. A viable technological solution that can fill this void and meet these mobility needs is the Internet of Things (IoT). This chapter provides an assessment of smart IoT mobility solutions pertinent to the African context. Furthermore, the barriers to the realization of technology domestication as well as growth catalysts are examined. Lastly, this chapter proffers technical recommendations for sustainable domestication of smart IoT mobility solutions for VIPs in Africa.
Rapid Microscopic Analysis Using Natural Neighbor Interpolation

This paper focuses on the use of a method of spatial interpolation to analyze microscopic slides of an Olympus CX21. The method used natural neighbor interpolation (NNI), that entails the analysis of random pre-determined points to interpolate and analyze the slide as a whole [1], [2]. The flood-fill algorithm was used to perform a differential count in conjunction with NNI to analyze samples of cells infected with Malaria. After analyzing selected random points, a summary of the whole slide may be produced. The results of the various tests yielded a percent difference of no more than 20% for the application of NNI.
Towards Embodied StarCraft II Winner Prediction

Realtime strategy games (and especially StarCraft II) are currently becoming the ‘next big thing’ in Game AI, as building human competitive bots for complex games is still not possible. However, the abundance of existing game data makes StarCraft II an ideal testbed for machine learning. We attempt to use this for establishing winner predictors that in strong contrast to existing methods rely on partial information available to one player only. Such predictors can be made available to human players as a supportive AI component, but they can more importantly be used as state evaluations in order to inform strategic planning for a bot. We show that it is actually possible to reach accuracies near to the ones reported for full information with relatively simple techniques. Next to performance, we also look at the interpretability of the models that may be valuable for supporting human players as well as bot creators.
Frame Selection for Producing Recipe with Pictures from an Execution Video of a Recipe

In cooking procedure instruction, text format plays an important role in conveying quantitative information accurately, such as time and quantity. On the other hand, image format can smoothly convey qualitative information (e.g., the target food state of a procedure) at a glance. Our goal is to produce multimedia recipes, which have texts and corresponding pictures, for chefs to better understand the procedures. The system takes a procedural text and its unedited execution video as the input and outputs selected frames for instructions in the text. We assume that a frame suits to an instruction when they share key objects. Under this assumption, we extract the information of key objects using named entity recognizer from the text and object detection from the frame, and we convert them into feature vectors and calculate their cosine similarity. To enhance the measurement, we also calculate the scene importance based on the latest changes in object appearance, and aggregate it to the cosine similarity. Finally we align the instruction sequence and the frame sequence using the Viterbi algorithm referring to this suitability and get the frame selection for each instruction. We implemented our method and tested it on a dataset consisting of text recipes and their execution videos. In the experiments we compared the automatic alignment results with those by human annotators. The precision, recall, and F-measure showed that the proposed approach made a steady improvement in this challenging problem of selecting pictures from an unedited video.
Histopathological Image Analysis on Mouse Testes for Automated Staging of Mouse Seminiferous Tubule

Whole slide image (WSI) of mouse testicular cross-section contains hundreds of seminiferous tubules. Meanwhile, each seminiferous tubule also contains different types of germ cells among different histological regions. These factors make it a challenge to segment distinct germ cells and regions on mouse testicular cross-section. Automated segmentation of different germ cells and regions is the first step to develop a computerized spermatogenesis staging system. In this paper, a set of 28 H&E stained WSIs of mouse testicular cross-section and 209 Stage VI-VIII tubules images were studied to develop an automated multi-task segmentation model. A deep residual network (ResNet) is first presented for seminiferous tubule segmentation from mouse testicular cross-section. According to the types and distribution of germ cells in the tubules, we then present the other deep ResNet for multi-cell (spermatid, spermatocyte, and spermatogonia) segmentation and a fully convolutional network (FCN) for multi-region (elongated spermatid, round spermatid, and spermatogonial & spermatocyte regions) segmentation. To our knowledge, this is the first time to develop a computerized model for analyzing histopathological image of mouse testis. Three segmentation models presented in this paper show good segmentation performance and obtain the pixel accuracy of 94.40%, 91.26%, 93.47% for three segmentation tasks, respectively, which lays a solid foundation for the establishment of mouse spermatogenesis staging system.
Sharing Access to Behavioural and Personal Health Data

Behavioural and personal health data can be collected from smartphones, wearable and sensors. Besides using this data as a tool for self-reflection and self-management of health issues, it can also be beneficial to share it with others. The purpose of sharing can range from getting peer support to communicating better with clinicians or contributing to scientific research. However, sharing access to behavioural data can disclose sensitive details of the individuals' lives, and it may be challenging to make shared-access systems adopted, accepted and continuously used. In order to better inform the development of future systems, a review of the literature and a focus group were conducted. The findings presented here provide pointers to some essential considerations to be taken into account when proposing systems for behavioural data sharing.
Application of Transfer Learning for Fine-Grained Vessel Classification Using a Limited Dataset

The automatic classification of maritime vessel type from low resolution images is a significant challenge and continues to attract increasing interest because of its importance to maritime surveillance. Convolutional neural networks are the method of choice for supervised image classification, but they require a large number of annotated samples, which prevents many superior models being applied to problems with a limited number of training samples. One possible solution is transfer learning where pre-trained models are used on entirely new predictive modeling, transferring knowledge between related source and target domains. Our experimental results demonstrate that a combination of data augmentation and transfer learning leads to a better performance in the presence of small training dataset, even in the a fine-grained classification context.
Why Bitcoin Will Fail to Scale?

Bitcoin falls dramatically short of the scale provided by banks for payments. Its ledger grow by the addition of blocks of ∼ 2000 transaction every 10 minutes. Intuitively, one would expect that increasing the block capacity would solve this scaling problem. However, we show that increasing the block capacity would be futile. We analyze strategic interactions of miners, who are heterogeneous in their power over block addition, and users, who are heterogeneous in the value of their transactions, using a game-theoretic model. We show that a capacity increase can facilitate large miners to tacitly collude artificially reversing back the capacity via strategically adding partially filled blocks in order to extract economic rents. This strategic partial filling crowds out low value payments. Collusion is sustained if the smallest colluding miner has a share of block addition power above a lower bound. We provide empirical evidence of such strategic partial filling of blocksby large miners of Bitcoin.We show that a protocol design intervention can breach the lower bound and eliminate collusion. However, this also makes the system less secure. On the one hand, collusion crowds out low-value payments; on the other hand, if collusion is suppressed, security threatens high-value payments. Thus its untenable to include range of payments with vastly different outside options, willingness to bear security risk and delay onto a single chain. Thus, we show economic limits to the scalability of Bitcoin. Under these economic limits, collusive rent extraction acts an effective mechanism to invest in platform security and build responsiveness to demand shocks. These traits are otherwise hard to attain in dis-intermediated setting owing to the high cost of consensus. We discuss recommendations to public Blockchain designers.