Reviewerly/exHarmony huggingface dataset:
Market Orientation in Small and Medium Enterprises: Determinants and Implications for Enterprise Development (Evidence from Addis Ababa, Ethiopia)

Market orientation has attracted considerable attention in academics and from industry practitioners in the last three or so decades and pretty many studies have been conducted in this area.Except few, such studies have been conducted in large firms and in developed countries.Such research is either missing in least developed countries such as Ethiopia and emerging countries and on small and medium firms or little (if any) with limited coverage and significance.The Ethiopian small and medium enterprises sector has not been adequately studied empirically in this regard.These reasons gave the rationale to conduct the current research.Thus, the study aimed at investigating the determinants of and benefits of market orientation in small and medium enterprises in Addis Ababa City in terms of firm performance.Descriptive and cause-effect designs were used to achieve the objectives of the study.Data were collected using standardized instrument and measurements from 200 randomly selected respondents drawn from manufacturing small and medium enterprises.Statistical tools and econometric models were employed to undertake quantitative analysis.Descriptive findings of the study indicated that the sample enterprises were slightly market oriented.Besides, the study revealed that market orientation is affected by operators' characteristics and enterprise features.The study also showed that market orientation and its constructs have strong and significant relationships with the indicators of enterprises' performance.Indeed, market orientation has significant and positive effect on enterprises' sales growth, long term focus and profit emphasis.In conclusion, the study has immense implications in terms of adding insights to the existing body of literature because it tested some hypotheses held in the literature and generated confirmatory results, and for managerial practices of small and medium enterprises.It also has implications for responsible government offices to reorient their business development services and training supports provided to small enterprises so as to make them more market oriented.
Compositionality for Recursive Neural Networks.

Modelling compositionality has been a longstanding area of research in the field of vector space semantics. The categorical approach to compositionality maps grammar onto vector spaces in a principled way, but comes under fire for requiring the formation of very high-dimensional matrices and tensors, and therefore being computationally infeasible. In this paper I show how a linear simplification of recursive neural tensor network models can be mapped directly onto the categorical approach, giving a way of computing the required matrices and tensors. This mapping suggests a number of lines of research for both categorical compositional vector space models of meaning and for recursive neural network models of compositionality.
PrivySharing: A Blockchain-based Framework for Integrity and Privacy-preserving Data Sharing in Smart Cities

The ubiquitous use of Internet of Things (IoT) ranges from industrial control systems to e-Health, e-commerce, smart cities, supply chain management, smart cars, cyber-physical systems and a lot more. However, the data collected and processed by IoT systems especially the ones with centralized control are vulnerable to availability, integrity, and privacy threats. Hence, we present “PrivySharing,” a blockchain-based innovative framework for integrity and privacy-preserving IoT data sharing in a smart city environment. The proposed scheme is distinct from existing technologies on many aspects. The data privacy is preserved by dividing the blockchain network into various channels, where every channel processes a specific type of data such as health, smart car, smart energy or financial data. Moreover, access to user data within a channel is controlled by embedding access control rules in the smart contracts. In addition, users' data within a channel is further isolated and secured by using private data collection. Likewise, the REST API that enables clients to interact with the blockchain network has dual security in the form of an API Key and OAuth 2.0. The proposed solution also conforms to some of the significant requirements outlined in the European Union General Data Protection Regulation. Lastly, we present a system of reward in the form of a digital token “PrivyCoin” for the users for sharing their data with the stakeholders/third parties.
Gaussian Process Model based Reinforcement Learning

Reinforcement learning (RL) has been a promising approach in robotics and control because data-driven learning methods can reduce system reliance on human engineering knowledge. A model-based RL autonomously learns observed dynamics based on a general flexible nonparametric approach. Probabilistic Inference for Learning COntrol (PILCO) is one of the most data-efficient model-based RL frameworks. Since PILCO sets up a Bayesian estimator problem with a Gaussian process regression, it derives a fully deterministic approximate inference for policy evaluation, which makes it computationally efficient. However, PILCO requires a task-specific scenario. If an agent is given a new goal that is different than the original training goal, PILCO should relearn its model from scratch. This paper extends PILCO to tune a linear feedback controller with a quadratic cost function, where the quadratic cost function commonly used in control systems can adjust the trade-off relationship between control input consumption and convergence rate. The suggested method is not only able to maintain the analytic and deterministic approximate inference for policy evaluation, but is also able to interpret the controller design. The suggested RL framework is applied to the control of a small quadrotor unmanned aerial vehicle (UAV) with no given dynamics. The simulation results show the convergence of the learning control performance as a function of the number of RL iterations.
OD-GCN object detection by knowledge graph with GCN.

Classical object detection methods only extract the objects' image featuresvia CNN, lack of utilizing the relationship among objects in the same image. Inthis article, we introduce the graph convolutional networks (GCN) into theobject detection field and propose a new framework called OD-GCN (objectdetection with graph convolutional network). It utilizes the categoryrelationship to improve the detection precision. We set up a knowledge graph toreflect the co-exist relationships among objects. GCN plays the role ofpost-processing to adjust the output of base object detection models, so it isa flexible framework that any pre-trained object detection models can be usedas the base model. In experiments, we try several popular base detectionmodels. OD-GCN always improve mAP by 1-5pp on COCO dataset. In addition,visualized analysis reveals the benchmark improvement is quite reasonable inhuman's opinion.
Research Through Board Game Design

This research presents the design of a board game that explores issues related to privacy, ethics, trust, risk, acceptability, and security within the Internet of Things (IoT). In particular, it aims to assist players in developing mental models of the increasing hybrid digital/physical spaces they inhabit in which notions of public and private are increasingly blurred. The game is based on an Heterotopical Model for Inter-Spatial Interaction, inspired by Michel Foucault’s essay “Of Other Spaces”, which can act as a lens for designing IoT products and services. In the game players: explore the spatial division between physical and virtual; and are rather exposed to its procedural rhetoric which highlights how notions of public and private are in constant flux and must be constantly renegotiated as they add or make connections with any new IoT devices they encounter. As the meaning of any game only emerges through play, it was developed through iterative play-testing in which player experience was evaluated against the intended rhetoric. This led to a number of fundamental re-designs and proved useful for evaluating the model itself. This discussion highlights that while game design research somewhat sits apart from more general design research it aligns closely with research through design.
Empowering Young Job Seekers with Virtual Reality

This paper presents the results of the Virtual Internship project that aims to help young job seekers get insights of different workplaces via immersive and interactive experiences. We designed a concept of ‘Immersive Job Taste’ that provides a rich presentation of occupations with elements of workplace training, targeting a specific group of young job seekers, including high-school students and unemployed. We developed several scenarios and applied different virtual and augmented reality concepts to build prototypes for different types of devices. The intermediary and the final versions of the prototypes were evaluated by several groups of primary users and experts, including over 70 young job seekers and high school students and over 45 various professionals and experts. The data were collected using questionnaires and interviews. The results indicate a generally very positive attitude towards the concept of immersive job taste, although with significant differences between job seekers and experts. The prototype developed for room-scale virtual reality with controllers was generally evaluated better than those including cardboard with 360 videos or with animated 3D graphics and augmented reality glasses. In the paper, we discuss several aspects, such as the potential of immersive technologies for career guidance, fighting youth unemployment by better informing the young job seekers, and various practical and technology considerations.
AES: Automated Evaluation Systems for Computer Programing Course

Evaluation of descriptive type questions automatically is an important problem. In this paper, we have concentrated on programing language course which is a core subject in CS discipline. In this paper, we have given a tool prototype which evaluates the descriptive type of questions in computer programing course using the notion of program equivalence.
k-NN Ensemble DARA Approach to Learning Relational

Due to the growing amount of data generated and stored in relational databases, relational learning has attracted the interest of researchers in recent years. Many approaches have been developed in order to learn relational data. One of the approaches used to learn relational data is Dynamic Aggregation of Relational Attributes (DARA). The DARA algorithm is designed to summarize relational data with one-to-many relations. However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset. A feature selection process can be introduced to overcome this problem. These selected features can then be further optimized to achieve a good classification result when using k Nearest Neighbour (k-NN) classifier. Several clustering runs can be performed for different values of k to yield an ensemble of clustering results. This paper proposes a two-layered genetic algorithm-based feature selection in order to improve the classification performance of learning relational database using a k-NN ensemble classifier. The proposed method involves the task of omitting less relevant features but retaining the diversity of the classifiers so as to improve the performance of the k-NN ensemble. Based on the results obtained, it shows that the proposed k-NN ensemble is able to improve the performance of traditional k-NN classifiers.
On the complexity of bounded-depth circuits and formulas over the basis of fan-in gates

Abstract We obtain estimates for the complexity of the implementation of n -place Boolean functions by circuits and formulas built of unbounded fan-in conjunction and disjunction gates and either negation gates or negations of variables as inputs. Restrictions on the depth of circuits and formulas are imposed. In a number of cases, the estimates obtained in the paper are shown to be asymptotically sharp. In particular, for the complexity of circuits with variables and their negations on inputs, the Shannon function is asymptotically estimated as $2\cdot {{2}^{n/2}};$ this estimate is attained on depth-3 circuits.
Compiling Optimization for Neural Network Accelerators

Nowadays artificial neural networks are one of the most common computational models among all the intelligent methods. To cope with the ever-growing scales of neural networks and the restrictions of system energy consumption, there comes out a bunch of neural network (NN) accelerators. However, owing to their dedicated architecture, programming on NN accelerators is different from general processors. In order to improve performance, it is necessary to use global structure information of NN model to optimize the compilation. In this paper, we introduce a series of layer-based compile optimizations for NN accelerators. From top to bottom, we define a type of computational graph, carrying necessary information such as relationship between layer nodes and data nodes. Then according to the pattern of a NN layer computation process, we apply an intra layer loop unrolling and pipelining, including fine-grained and coarse-grained two levels. Similarly, we apply layer fusion optimization based on our computational graph and abstract pipelining stage. After expanding pipelining stages of layers, we can reduce some redundant IO operations, which we call it layer elimination optimization. The experiment results show that with our proposed optimizations the inference process can achieve up to 1.34x speedup than not using fusion optimization.
Suggesting reviewers of software artifacts using traceability graphs

During the lifecycle of a software project, software artifacts constantly change. A change should be peer-reviewed to ensure the software quality. To maximize the benefit of review, the reviewer(s) should be chosen appropriately. However, choosing the right reviewer(s) might not be trivial especially in large projects. Researchers developed different methods to recommend reviewers. In this study, we introduce a novel approach for reviewer recommendation problem. Our approach utilizes the traceability graph of a software project and assigns a know-about score to each developer, then recommends the developers who have the maximum know-about score for an artifact. We tested our approach on an open source project and achieved top-3 recall of 0.85 with an MRR (mean reciprocal ranking) of 0.73.
A Model-Driven Decision Support System for Aid in a Natural Disaster

This article deals with the architecture of a support system for helping decision makers in optimizing aid distribution during natural disaster situations. In such type of events, one of the critical tasks is the transportation of staff, people, food and medicines. Given the complexity of the operations scheduling, the article proposes a model-driven decision support system with an embedded module for solving a vehicle routing problem.
Using Micro-Collections in Social Media to Generate Seeds for Web Archive Collections

In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events ranging from elections to disease outbreaks. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but this ability comes at a cost: it is time consuming to collect these seeds. Two main strategies adopted by curators for discovering seeds include scraping Web (e.g., Google) Search Engine Result Pages (SERPs) and social media (e.g., Twitter) SERPs. In this work, we studied three social media platforms in order to provide some insight on the characteristics of seeds generated from different sources. First, we developed a simple vocabulary for describing social media posts across different platforms. Second, we introduced a novel source for generating seeds from URIs in the threaded conversations of social media posts created by single or multiple users. Users on social media sites routinely create and share posts about news events consisting of hand-selected URIs of news stories, tweets, videos, etc. In this work, we call these posts micro-collections, whether shared on Reddit or Twitter, and we consider them as an important source for seeds. This is because, the effort taken to create micro-collections is an indication of editorial activity and a demonstration of domain expertise. Third, we generated 23,112 seed collections with text and hashtag queries from 449,347 social media posts from Reddit, Twitter, and Scoop.it. We collected in total 120,444 URIs from the conventional scraped SERP posts and micro-collections. We characterized the resultant seed collections across multiple dimensions including the distribution of URIs, precision, ages, diversity of webpages, etc. We showed that seeds generated by scraping SERPs had a higher median probability (0.63) of producing relevant URIs than micro-collections (0.5). However, micro-collections were more likely to produce seeds with a higher precision than conventional SERP collections for Twitter collections generated with hashtags. Also, micro-collections were more likely to produce older webpages and more non-HTML documents.
Distribution Forest: An Anomaly Detection Method Based on Isolation Forest

Anomaly detection refers to finding patterns in the data that do not meet expectations. Anomaly detection has a variety of application domains and scenarios, such as network intrusion detection, fraud detection and fault detection. This paper proposes a new anomaly detection method Distribution Forest (dForest) inspired by Isolation Forest (iForest). dForest builds an ensemble of special binary trees called distribution tree (dTree). The basic idea of our method is to guide the building of dTree by the distribution of data at each node. And each node of dTree is treated as a subspace of input space. When dForest is built, the anomalies have a shorter path length than the normal instances. dForest has a different explanation from other methods. Compared with iForest, LOF and iNNE, the proposed method achieves competitive results in terms of AUC on different benchmark datasets. Also, dForest performs well in both semi-supervised and unsupervised anomaly detection modes.
Data Normalisation using Differential Evolution and Aggregated Logistic Functions

Can evolutionary algorithms discover transformation functions capable of normalising arbitrary data? Many statistical and machine learning techniques that input data is normally distributed. However, in most realistic situations the data is distinctly non-normal. Therefore predictive performance may be compromised if steps are not taken to address this issue. We propose in this paper a new algorithm in which differential evolution is used to optimise the multiple parameters of a complex data transformation function. In order to optimise the transformation function, differential evolution is guided by a statistic used to measure the normality of the resulting output data. A set of soft constraints for controlling the scale and position of the output distribution are also included. On a set of fifteen datasets which includes a mixture of artificial data drawn from various different non-normal distributions; blood glucose level data from patients with diabetes; and gene expression data; our method is shown to effectively normalise the data in most cases.
Turning a Corner with a Dubins Car

We study the problem of computing shortest collision-free Dubins paths when turning a corner. We present a sufficient condition for a closed-form solution. Specifically, consider S as the set consisting of paths of the form RSRSR, RSRSL, LSRSR and LSRSL that pass through the interior corner, where sub-paths RSR, RSL, and LSR are elementary Dubins paths composed of segments which are either straight (S) or turning left (L) or right (R). We find the closed-form optimal path around a corner when S is nonempty. Our solution can be used in an efficient path planner, for example, when navigating corridors. It can also be used as a subroutine for planners such as RRTs.
Novelty detection in very high resolution urban scenes with Density Forests

Uncertainty in deep learning has recently received a lot of attention. While deep neural networks have shown better accuracy than other competing methods in many benchmarks, it has been shown that they may yield wrong predictions with unreasonably high confidence. This has increased the interest in methods that help providing better confidence estimates in neural networks, some using specifically designed architectures with probabilistic building blocks, and others using a standard architecture with an additional confidence estimation step based on its output. This work proposes a confidence estimation method for Convolutional Neural Networks based on fitting a forest of randomized density estimation decision trees to the network activations before the final classification layer and compares it to other confidence estimation methods based on standard architectures. The methods are compared on a semantic labelling dataset with very high resolution satellite imagery. Our results show that methods based on intermediate network activations lead to better confidence estimates in novelty detection, i.e., in the discovery of classes that are not present in the training set.
PPD: Pyramid Patch Descriptor via Convolutional Neural Network

Local features play an important role in remote sensing image matching, and handcrafted features have been excessively used in this area for a long time. This article proposes a pyramid convolutional neural triplet network that extracts a 128-dimensional deep descriptor that significantly improves the matching performance. The proposed approach first extracts deep descriptors of the anchor patches and corresponding positive patches in a batch using the proposed pyramid convolutional neural network. Following this step, the approaches chooses the closest negative patch for each anchor patch and corresponding positive patch pair to form the triplet sample based on the descriptor distances among all other image patches in the batch. These triplets are used to optimize the parameters of the network using a new loss function. We evaluated the proposed deep descriptors on two benchmark data sets (Brown and HPatches) as well as real image data sets. The results reveal that the proposed descriptor achieves the state-of-the-art performance on the Brown data set and a comparatively very high performance on the HPatches data set. The proposed approach finds more correct matches than the classical handcrafted feature descriptors on aerial image pairs and is observed to be robust to variations in the viewpoint and illumination.
Search-Based Predictive Modelling for Software Engineering: How Far Have We Gone?

In this keynote I introduce the use of Predictive Analytics for Software Engineering (SE) and then focus on the use of search-based heuristics to tackle long-standing SE prediction problems including (but not limited to) software development effort estimation and software defect prediction. I review recent research in Search-Based Predictive Modelling for SE in order to assess the maturity of the field and point out promising research directions. I conclude my keynote by discussing best practices for a rigorous and realistic empirical evaluation of search-based predictive models, a condicio sine qua non to facilitate the adoption of prediction models in software industry practices.