Reviewerly/exHarmony huggingface dataset:
Doing business and inclusive human development in Sub-Saharan Africa

Purpose The purpose of this paper is to examine how doing business affects inclusive human development in 48 Sub-Saharan Africa for the period 2000–2012. Design/methodology/approach The measurement of inclusive human development encompasses both absolute pro-poor and relative pro-poor concepts of inclusive development. Three doing business variables are used, namely: the number of start-up procedures required to register a business, time required to start a business, and time to prepare and pay taxes. The empirical evidence is based on fixed effects and generalised method of moments regressions. Findings The findings show that increasing constraints to the doing of business have a negative effect on inclusive human development. Originality/value The study is timely and very relevant to the post-2015 Sustainable Development Agenda for two fundamental reasons: first, exclusive development is a critical policy syndrome in Africa because about 50 per cent of countries in the continent did not attain the Millennium Development Goal extreme poverty target despite enjoying more than two decades of growth resurgence. Second, growth in Africa is primarily driven by large extractive industries and with the population of the continent expected to double in about 30 years, scholarship on entrepreneurship for inclusive development is very welcome. This is essentially because studies have shown that the increase in unemployment (resulting from the underlying demographic change) would be accommodated by the private sector, not the public sector.
An empirical study of tactical vulnerabilities

Architectural security tactics (e.g., authorization, authentication) are used to achieve stakeholders’ security requirements. Security tactics allow the system to react, resist, detect and recover from attacks. Flaws in the adoption of these tactics into the system’s architecture, an incorrect implementation of security tactics, or deterioration of tactic implementations over time can introduce severe vulnerabilities that are exploitable by attackers. Therefore, in this work, we present the Common Architectural Weakness Enumeration (CAWE), a catalog of known weaknesses rooted in the design or implementation of security tactics which can result in tactical vulnerabilities. We categorized all known software weaknesses as tactic-related and non-tactic related. This way, our CAWE catalog enumerates common weaknesses in a security architecture that can lead to tactical vulnerabilities. From our CAWE catalog, we found 223 different types of tactical vulnerabilities. In this work, we also used this catalog to study tactical vulnerabilities in three large-scale open source projects: Chromium, PHP, and Thunderbird. In a detailed analysis, we identified the most occurring vulnerability types on these projects. From this study we observed that (i) Improper Input Validation and Improper Access Control were the most occurring vulnerability types in Chromium, PHP and Thunderbird and (ii) “Validate Inputs” and “Authorize Actors” were the security tactics mostly affected by these tactical vulnerabilities. Moreover, in a qualitative analysis of 632 tactical vulnerabilities and their fixes in these systems, we characterized their root causes and investigated the way the original developers of each system fixed these vulnerabilities. From this qualitative analysis, we found 44 distinct root causes that lead to these tactical vulnerabilities. The results of this study not only show how architectural weaknesses in systems have created severe vulnerabilities, but also provide recommendations driven by empirical data for addressing such security problems.
Multi-level Sequence GAN for Group Activity Recognition

We propose a novel semi supervised, Multi Level Sequential Generative Adversarial Network (MLS-GAN) architecture for group activity recognition. In contrast to previous works which utilise manually annotated individual human action predictions, we allow the models to learn it’s own internal representations to discover pertinent sub-activities that aid the final group activity recognition task. The generator is fed with person-level and scene-level features that are mapped temporally through LSTM networks. Action-based feature fusion is performed through novel gated fusion units that are able to consider long-term dependancies, exploring the relationships among all individual actions, to learn an intermediate representation or ‘action code’ for the current group activity. The network achieves it’s semi-supervised behaviour by allowing it to perform group action classification together with the adversarial real/fake validation. We perform extensive evaluations on different architectural variants to demonstrate the importance of the proposed architecture. Furthermore, we show that utilising both person-level and scene-level features facilitates the group activity prediction better than using only person-level features. Our proposed architecture outperforms current state-of-the-art results for sports and pedestrian based classification tasks on Volleyball and Collective Activity datasets, showing it’s flexible nature for effective learning of group activities (This research was supported by the Australian Research Council’s Linkage Project LP140100282 “Improving Productivity and Efficiency of Australian Airports”).
PocketATM

Visually impaired people (VIPs) face significant usability and privacy challenges using digital finance technologies. In this paper, we focus on investigating these challenges in the context of Automated Teller Machines (ATMs) in India. To find out the accessibility status of ATMs across India, we first reach out to public banks, and then conduct in-person field surveys and an online crowd sourcing survey of 107 ATM machines across 4 cities in India. We find that less than 18% of surveyed machines are accessible, and follow up with 22 interviews with VIPs regarding challenges using ATMs. Based on insights, we design PocketATM: a system that enables VIPs to use ATMs using their own smartphones - a user can pre-authorize a cash withdrawal using a phone application, then go to any nearby ATM to receive the pre-authorized amount. Our usability evaluation with 19 VIPs demonstrates that PocketATM is usable, practical, and could be embraced by VIPs in India.
Aerial GANeration: Towards Realistic Data Augmentation Using Conditional GANs

Environmental perception for autonomous aerial vehicles is a rising field. Recent years have shown a strong increase of performance in terms of accuracy and efficiency with the aid of convolutional neural networks. Thus, the community has established data sets for benchmarking several kinds of algorithms. However, public data is rare for multi-sensor approaches or either not large enough to train very accurate algorithms. For this reason, we propose a method to generate multi-sensor data sets using realistic data augmentation based on conditional generative adversarial networks (cGAN). cGANs have shown impressive results for image to image translation. We use this principle for sensor simulation. Hence, there is no need for expensive and complex 3D engines. Our method encodes ground truth data, e.g. semantics or object boxes that could be drawn randomly, in the conditional image to generate realistic consistent sensor data. Our method is proven for aerial object detection and semantic segmentation on visual data, such as 3D Lidar reconstruction using the ISPRS and DOTA data set. We demonstrate qualitative accuracy improvements for state-of-the-art object detection (YOLO) using our augmentation technique.
Automatic Search for a Variant of Division Property Using Three Subsets

The division property proposed at Eurocrypt’15 is a novel technique to find integral distinguishers, which has been applied to most kinds of symmetric ciphers such as block ciphers, stream ciphers, and authenticated encryption, etc. The original division property is word-oriented, and later the bit-based one was proposed at FSE’16 to get better integral property, which is composed of conventional bit-based division property (two-subset division property) and bit-based division property using three subsets (three-subset division property). Three-subset division property has more potential to achieve better integral distinguishers compared with the two-subset division property. The bit-based division property could not be to apply to ciphers with large block sizes due to its unpractical complexity. At Asiacrypt’16, the two-subset division property was modeled using Mixed Integral Linear Programming (MILP) technique, and the limits of block sizes were eliminated. However, there is still no efficient method searching for three-subset division property. The propagation rule of the XOR operation for $$\mathbb {L}$$ (The definition of $$\mathbb {L}$$ and $$\mathbb {K}$$ is introduced in Sect. 2.), which is a set used in the three-subset division property but not in two-subset one, requires to remove some specific vectors, and new vectors generated from $$\mathbb {L}$$ should be appended to $$\mathbb {K}$$ when Key-XOR operation is applied, both of which are difficult for common automatic tools such as MILP, SMT or CP. In this paper, we overcome one of the two challenges, concretely, we address the problem to add new vectors into $$\mathbb {K}$$ from $$\mathbb {L}$$ in an automatic search model. Moreover, we present a new model automatically searching for a variant three-subset division property (VTDP) with STP solver. The variant is weaker than the original three-subset division property (OTDP) but it is still powerful in some ciphers. Most importantly, this model has no constraints on the block size of target ciphers, which can also be applied to ARX and S-box based ciphers. As illustrations, some improved integral distinguishers have been achieved for SIMON32, SIMON32/48/64(102), SPECK32 and KATAN/KTANTAN32/48/64 according to the number of rounds or number of even/odd-parity bits.
Theoretical and experimental investigation of photovoltaic cell performance, with optimum tilted angle: Basra city case study

This work includes a theoretical and experimental study to evaluate the effect of a photovoltaic cell tilted angle variation on the generated power of a photovoltaic cell, located in Basra city. In this study, to find an optimum tilted angle, without using the tracking system, a mathematical model is used that is programmed by Mat lab R218a. This model was checked using an experimental test rig and the tilted angle varied across a range from 0° to 90°. The results show that this model is more accurate for tilted angle finding and indicates that the optimum yearly tilted angle of a photovoltaic cell located in Basra city (latitude 30° 30”) is equal to 28°.
Safe Convex Learning under Uncertain Constraints

We address the problem of minimizing a convex smooth function f(x) over a compact polyhedral set D given a stochastic zeroth-order constraint feedback model. This problem arises in safety-critical machine learning applications, such as personalized medicine and robotics. In such cases, one needs to ensure constraints are satisfied while exploring the decision space to find optimum of the loss function. We propose a new variant of the Frank-Wolfe algorithm, which applies to the case of uncertain linear constraints. Using robust optimization, we provide the convergence rate of the algorithm while guaranteeing feasibility of all iterates, with high probability.
Measuring and analyzing code authorship in 1 + 118 open source projects

Code authorship is a key information about large-scale software projects. Among others, it reveals the division of work, key collaborators, and developers' profiles. Seeking to better understand authorship in large and successful open source communities, we take the Linux kernel as our first case study. In total, we analyze authorship across 66 stable releases. Our analysis is centered around the Degree-of-Authorship (DOA) metric, which accounts for first authorship events (file creation), as well as further code changes. Authorship along the Linux kernel evolution reveals that (a) only a small portion of developers (26%) makes significant contributions to the code base; this ratio is almost constant during the Linux kernel evolution; (b) the number of files per author is highly skewed—a small group of top-authors (2%) is responsible for hundreds of files, while most authors (75%) are responsible for at most 10 files; (c) most authors in Linux (76%) are specialists and the relation between specialists and generalists tends to be constant; (d) authors with a high number of co-authorship connections tend to work with authors with fewer connections. Furthermore, we replicate the study in an extended dataset, composed of 118 well-known GitHub projects. We identify that most of the authorship patterns observed in the Linux kernel are also common to other open source projects.
Free-Head Appearance-Based Eye Gaze Estimation on Mobile Devices

Eye gaze tracking plays an important role in human-computer interaction applications.In recent years, many research have been performed to explore gaze estimation methods to handle free-head movement, most of which focused on gaze direction estimation.Gaze point estimation on the screen is another important application.In this paper, we proposed a two-step training network, called GazeEstimator, to improve the estimation accuracy of gaze location on mobile devices.The first step is to train an eye landmarks localization network on 300W-LP dataset [1], and the second step is to train a gaze estimation network on GazeCapture dataset [2].Some processing operations are performed between the two networks for data cleaning.The first network is able to localize eye precisely on the image, while the gaze estimation network use only eye images and eye grids as inputs, and it is robust to facial expressions and occlusion.Compared with state-of-the-art gaze estimation method, iTracker, our proposed deep network achieves higher accuracy and is able to estimate gaze location even in the condition that the full face cannot be detected.
DeepCenterline: A Multi-task Fully Convolutional Network for Centerline Extraction

A novel centerline extraction framework is reported which combines an end-to-end trainable multi-task fully convolutional network (FCN) with a minimal path extractor. The FCN simultaneously computes centerline distance maps and detects branch endpoints. The method generates single-pixel-wide centerlines with no spurious branches. It handles arbitrary tree-structured object with no prior assumption regarding depth of the tree or its bifurcation pattern. It is also robust to substantial scale changes across different parts of the target object and minor imperfections of the object’s segmentation mask. To the best of our knowledge, this is the first deep-learning based centerline extraction method that guarantees single-pixel-wide centerline for a complex tree-structured object. The proposed method is validated in coronary artery centerline extraction on a dataset of 620 patients (400 of which used as test set). This application is challenging due to the large number of coronary branches, branch tortuosity, and large variations in length, thickness, shape, etc. The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask. Compared to a state-of-the-art traditional minimal path approach, our method improves patient-level success rate of centerline extraction from 54.3% to 88.8% according to independent human expert review.
Element mobility and spatial zonation associated with the Archean Hamlet orogenic Au deposit, Western Australia: Implications for fluid pathways in shear zones

Mass balance equations can be used to understand metasomatism. Here we use 2726 multi-element chemical analyses to quantify the effect of metasomatism associated with Au mineralisation at the Hamlet Gold Mine (Hamlet), Yilgarn Craton, Western Australia. Principal components analysis is first used to identify covarying elements and six groups of elements are identified: (1) relatively immobile elements, with Zr and Hf providing an appropriate reference frame for mass balance calculations; (2) elements associated with mafic rocks, such as Ca, Co, Fe, Ge, Mg, Mn, and Zn; (3) elements related to potassic alteration, Ba, Cs, K, Rb, and Tl; (4) elements related to sodic alteration, Be, Na, and Sr; (5) chalcophile elements, including Bi, weakly associated with Au; and (6) Au, Te, and S representing the main Au-mineralising event. A statistically-rigorous approach is then used to quantify element addition and depletion and provides 95% confidence limits for changes in mass. To achieve this, the basaltic host rocks are divided into four subunits based on Cr content. Sample subsets are then created to select altered rocks and least-altered protolith rocks, using discrimination diagrams. Pairings of least altered-altered samples from each basalt subunit are used to construct matrices via bootstrapping. Mass balance results from these matrices indicate similar element mobility associated with Au mineralisation across the four basalt subunits, with up to 24% mass loss. Key potassic and sodic group elements are enriched in the Au mineralised zones at the expense of elements with a mafic-association. Spatial patterns of mass change are linear and repetitive within the steeply-dipping Hamlet Shear Zone, interpreted to represent a mesh-like geometry of metasomatism within the plane of the shear zone. Localised enrichment of K, Na, and Bi are related to Au distribution while Ca depletion zones reflect the movement of reactive fluid along the shear plane.
Influence of climatological data records on design of a standalone hybrid PV-hydroelectric power system

Climatological data records have a critical role in the design of renewable power systems. Various data sampling approaches were employed by literature. However, to the authors’ knowledge, there is no work comparing them. This paper compares the results of four commonly used data records sampling scenarios (scenario 1:“15th July”, scenario 2: “a single yearly-averaged day”, scenario 3: “12 monthly-averaged days”, and scenario 4: “full yearly data”) for the multi-objective optimal design of a standalone hybrid PV-hydroelectric system by using NSGA-II. Investment cost and Loss of Power Supply Probability (LPSP) constitute the objective functions of the problem. The results will reveal that scenario 4 is the only scenario that fully satisfies the demanded power profile over the year, whereas scenarios 1 to 3 are not able to satisfy more than 95.3%, 72.3%, and 92.4% of the yearly demanded power profile, respectively. The currently employed optimization procedure, which combines NSGAII with a simple operational strategy, allows for the implementation of all the mentioned scenarios with tractable computation time (35–4150 min on an Intel (R) Core (TM) i7 4 GHz processor). This study can help designers to select a suitable data records through making a trade-off among data availability, computation time, and power supply probability.
Critical Challenges for Requirement Implementation in Global Software Development: A Systematic Literature Review Protocol with Preliminary Results

Context: Successful requirement engineering (RE) leads to successful delivery of software.There exist a lot of challenges during RE process especially in Global Software Development (GSD).The aim of this study is to find challenges during RE in context of GSD.Objective: The objective is to find and analyze the challenges in RE process.Method: Systematic Literature Review (SLR) is used as research method to achieve the goals.Result: SLR protocol is developed.Expected output of this study is to list out all challenges which the stakeholders are facing in implementing RE in the context of GSD through SLR.As a result of SLR protocol, 71 papers are identified.
Particle filter for aircraft mass estimation and uncertainty modeling

This article investigates the estimation of aircraft mass and thrust settings of departing aircraft using a recursive Bayesian method called particle filtering. The method is based on a nonlinear state-space system derived from aircraft point-mass performance models. Using only aircraft surveillance data, flight states such as position, velocity, wind speed, and air temperature are collected and used for the estimations. With the regularized Sample Importance Re-sampling particle filter, we are able to estimate the aircraft mass within 30 seconds once an aircraft is airborne. Using this short flight segment allows the assumption of constant mass and thrust setting. The segment at the start of the climb also represents the time when maximum thrust setting is most likely to occur. This study emphasizes an important aspect of the estimation problem, the observation noise modeling. Four observation noise models are proposed, which are all based on the native navigation accuracy parameters that have been obtained automatically from the surveillance data. Simulations and experiments are conducted to test the theoretical model. The results show that the particle filter is able to quantify uncertainties, as well as determine the noise limit for an accurate estimation. The method of this study is tested with a data-set consisting of 50 Cessna Citation II flights where true masses were recorded.
Investigating Effects of Visual Anchors on Decision‐Making about Misinformation

Abstract Cognitive biases are systematic errors in judgment due to an over‐reliance on rule‐of‐thumb heuristics. Recent research suggests that cognitive biases, like numerical anchoring, transfers to visual analytics in the form of visual anchoring. However, it is unclear how visualization users can be visually anchored and how the anchors affect decision‐making. To investigate, we performed a between‐subjects laboratory experiment with 94 participants to analyze the effects of visual anchors and strategy cues using a visual analytics system. The decision‐making task was to identify misinformation from Twitter news accounts. Participants were randomly assigned to conditions that modified the scenario video (visual anchor) and/or strategy cues provided. Our findings suggest that such interventions affect user activity, speed, confidence, and, under certain circumstances, accuracy. We discuss implications of our results on the forking paths problem and raise concerns on how visualization researchers train users to avoid unintentionally anchoring users and affecting the end result.
CBAM-GAN: Generative Adversarial Networks Based on Convolutional Block Attention Module

Generating images using generative adversarial networks (GAN) is one of the research hotspots. The traditional convolutional GAN treats spatial and channel-wise features equally, which causes the lack of flexibility in extracting features from the discriminator and the generator. To address the issue, we propose generative adversarial networks based on convolutional block attention module (CBAM-GAN) in this paper. CBAM-GAN adds the convolutional block attention module after some convolution operators to adaptively rescale spatial and channel-wise features, which can enhance salient regions and extract more detail features. We apply the network framework of CBAM-GAN to popular GAN models and do an empirical study on MNIST and CIFAR-10 datasets. Experiments show that our model can significantly improve the quality of generated images compared with the traditional convolutional GAN.