Reviewerly/exHarmony huggingface dataset:
Private Investment and Business Opportunities in Ethiopia: A Case Study of Mettu Town in Ethiopia

Investment has a very crucial role from the point of view of national economy. For any country, investment, as well as business ethics, is necessarily to create a good business environment. The rate of growth of the economy and employment is directly related to the level of investment and business ethics. This study mainly concerns about the contribution of private domestic investment towards multidimensional business opportunities including employment creation in Mettu Town of Ethiopia. Its main objective is to assess the contribution of private domestic investment towards employment creation in Mettu town. The research is conducted by using both primary and secondary data. Primary data are collected through interview and questionnaire and secondary data are gathered from published and unpublished materials. The researcher used purpose and systematic random sample techniques as a major advantage of easy administer relatively convenient and less time-consuming. The researcher collected data from 51 investors from a total of 106 investors in the service, industrial and agricultural sectors to assess the contribution of private domestic investments towards employment creation in Mettu town. The method of analysis is the descriptive analysis which includes statistical tools such as table and percentage with interpretation in order to achieve the objective of the study. The results from the descriptive analysis of private domestic investment towards business opportunities and employment creation are found significant despite different challenges of investment such as high-interest rate, lack of capital, high taxation, poor infrastructure, etc. Thus, to achieve private domestic investment the measures include government motivation, good investment policymakers who take a remarkable decision in order to facilitate the growth of investment which results in increasing the participation of private investors. Keywords: private domestic investment, business opportunities, business ethics employment opportunities, industrial sector, agriculture sector, and government policies.
Weakly non-rigid MR-TRUS prostate registration using fully convolutional and recurrent neural networks

In this study, we propose a new deep learning (DL) framework, a combination of fully convolutional and recurrent neural networks, and integrate them with a weakly supervised method for 3D MRI-transrectal ultrasound (TRUS) image registration. The MR and TRUS images are often highly anisotropic in its dimensions. For instance, in 3D US images the scale of each voxel in depth is often 5~10 times larger than that in each image slice. This high anisotropy makes the common 3D isotropic kernel suffer poor generality, resulting in unsatisfactory registration results in terms of accuracy. The key idea of the paper is to explicitly leverage 3D image anisotropy through the exploitation of the intra-slice context with a fully convolutional network (FCN) and the utilization of the inter-slice context with a recurrent neural network (RNN). After the 3D hierarchical features in MRI and TRUS have been extracted, we generate the dense deformation field by aligning corresponding prostate labels for individual image pairs. Experimental results showed that our proposed FCN-RNN neural network produces a mean target registration error (TRE) of 2.77±1.40 mm, and a mean dice similarity coefficient (DSC) of 0.9.
Hardware/Algorithm Co-optimization for Fully-Parallelized Compact Decision Tree Ensembles on FPGAs

Decision tree ensembles, such as random forests, are well-known classification and regression methods with high accuracy and robustness, especially for categorical data that combines multiple weak learners called decision trees. We propose an architecture/algorithm co-design method for implementing fully parallelized fast decision tree ensembles on FPGAs. The method first produces compact and almost equivalent representations of original input decision trees by threshold compaction. For each input feature, comparisons with similar thresholds are merged into fewer variations, so the number of comparisons is reduced. The decision tree with merged thresholds is perfectly extracted as hard-wired logic for the highest throughput. In this study, we developed a prototype hardware synthesis compiler that generates a Verilog hardware description language (HDL) description from a compressed representation. The experiment successfully demonstrates that the proposed method reduces the sizes of generated hardware without accuracy degradation.
DO-U-Net for Segmentation and Counting

Many image analysis tasks involve the automatic segmentation and counting of objects with specific characteristics. However, we find that current approaches look to either segment objects or count them through bounding boxes, and those methodologies that both segment and count struggle with co-located and overlapping objects. This restricts our capabilities when, for example, we require the area covered by particular objects as well as the number of those objects present, especially when we have a large amount of images to obtain this information for. In this paper, we address this by proposing a Dual-Output U-Net. DO-U-Net is an Encoder-Decoder style, Fully Convolutional Network (FCN) for object segmentation and counting in image processing. Our proposed architecture achieves precision and sensitivity superior to other, similar models by producing two target outputs: a segmentation mask and an edge mask. Two case studies are used to demonstrate the capabilities of DO-U-Net: locating and counting Internally Displaced People (IDP) tents in satellite imagery, and the segmentation and counting of erythrocytes in blood smears. The model was demonstrated to work with a relatively small training dataset, achieving a sensitivity of 98.69% for IDP camps of the fixed resolution, and 94.66% for a scale-invariant IDP model. DO-U-Net achieved a sensitivity of 99.07% on the erythrocytes dataset. DO-U-Net has a reduced memory footprint, allowing for training and deployment on a machine with a lower to mid-range GPU, making it accessible to a wider audience, including non-governmental organisations (NGOs) providing humanitarian aid, as well as health care organisations.
Mobile Based for Basic English Learning Assessment with Augmented Reality

Nowadays Augmented Reality (AR) is very popular in teaching and learning, especially in leading student to observe learning material targets which supported by online learning resources using mobile device and wireless technology.Because English is a foreign language, most students have problems in learning English.Generally, their problems were related to low motivation, bad study habit and attitude toward learning.Significantly, formative assessment is important in improving student learning achievement.furthermore, a formative assessment can help teachers to identified students' progress, modify instruction, provide immediately feedback and achieve learning goals.The aim of this study was to build Mobile Based Assessment with Augmented Reality based on formative assessment mechanism, examine student's learning achievement, student's acceptance and student's motivation in Basic English Learning.A total of 34 elementary students of Grade 4 were selected to participated in this study.The descriptive analysis was used to analyze and described the data obtained.The result indicated that using Mobile Based Assessment with Augmented Reality based on formative assessment mechanism improved not only student's learning achievement but also student's motivation in Basic English Learning.
A Memory-Efficient Tool for Bengali Parts of Speech Tagging

Detecting parts of speech of words in a sentence efficiently is an inseparable task in natural language processing (NLP). The goal of this study is to detect the parts of speech of words in a Bengali sentence with higher accuracy and memory optimization. In this study, we develop such a parts of speech tagging tool specifically for those people who want to learn or research on Bengali grammar. Besides, it has an impact on the quality of education as our tool aims to provide knowledge about Bengali grammar. Although there are different existing studies on Bengali parts of speech tagging such as hybrid model, global linear model, maximum entropy model, hidden Markov model, unsupervised model, Bangla parsing algorithm, rule-based classifier, decision tree algorithm, Baum–Welch algorithm, unsupervised word segmentation algorithm, etc., none of these studies consider memory optimization technique. Therefore, in this study, we propose and endeavor to implement our own rule-based parts of speech tagging mechanism with an intent to achieve higher accuracy by optimizing memory overhead.
Multi-Scale Feature Channel Attention Generative Adversarial Network for Face Sketch Synthesis

Face sketch synthesis for photos is an applied research topic and it is critical for criminal investigation. However, sketch synthesis remains some challenges because of the blur and artifacts in the generated face sketches. To mitigate these problems in face sketch synthesis, we propose a fast Generative Adversarial Network with fast Multi-scale feature channel Attention, namely MAGAN. In the generator network, multi-scale features are extracted by proposed multi-scale feature extraction to produce detailed sketches. Then, a channel attention mechanism is applied to emphasize the significance of important feature channels, further enhancing the synthesized sketches. Besides, the loss of patch-wise high-layer features from the VGG-19 network is applied to supervise the generator to synthesize more realistic sketches. To accelerate the training process, the features from the pooling layers are adopted to calculate the pseudo sketch feature loss. The experimental results demonstrate that our MAGAN can achieve better performance in both visual evaluations and quantitative evaluations (in terms of feature similarity and learned perceptual image patch similarity), compared with the state-of-the-art methods.
Nonvanishing of hyperelliptic zeta functions over finite fields

Fixing $t \in \mathbb{R}$ and a finite field $\mathbb{F}_q$ of odd characteristic, we give an explicit upper bound on the proportion of genus $g$ hyperelliptic curves over $\mathbb{F}_q$ whose zeta function vanishes at $\frac{1}{2} + it$. Our upper bound is independent of $g$ and tends to $0$ as $q$ grows.
Multimodal Fusion Method Based on Self-Attention Mechanism

Multimodal fusion is one of the popular research directions of multimodal research, and it is also an emerging research field of artificial intelligence. Multimodal fusion is aimed at taking advantage of the complementarity of heterogeneous data and providing reliable classification for the model. Multimodal data fusion is to transform data from multiple single-mode representations to a compact multimodal representation. In previous multimodal data fusion studies, most of the research in this field used multimodal representations of tensors. As the input is converted into a tensor, the dimensions and computational complexity increase exponentially. In this paper, we propose a low-rank tensor multimodal fusion method with an attention mechanism, which improves efficiency and reduces computational complexity. We evaluate our model through three multimodal fusion tasks, which are based on a public data set: CMU-MOSI, IEMOCAP, and POM. Our model achieves a good performance while flexibly capturing the global and local connections. Compared with other multimodal fusions represented by tensors, experiments show that our model can achieve better results steadily under a series of attention mechanisms.
Single-shot spatial frequency multiplex fringe pattern for phase unwrapping using deep learning

Fringe projection profilometry (FPP) has been widely applied in three-dimensional (3D) measurement owing to its high measurement accuracy and simple structure. In FPP, how to effectively recover the absolute phase, especially through a single image, has always been a huge challenge and eternal pursuit. The frequency-multiplex methods can maximize the efficiency of phase unwrapping by mixing the multi-frequency information used to eliminate phase ambiguity in the spectrum. However, spectrum aliasing and the resulting phase unwrapping errors are still pressing difficulties. Inspired by the successful application of deep learning in FPP, we propose a single-shot frequency multiplex fringe pattern for phase unwrapping approach using deep learning. Through extensive data learning, the properly trained neural networks can directly learn to obtain spectrum-aliasingfree phase information and robust phase unwrapping from single-frame compound input. Experimental results demonstrate that compared with convenient frequency-multiplex methods, our deep-learning-based approach can achieve more accurate and stable absolute phase retrieval.
The Impact of Controlled Vocabularies on Requirements Engineering Activities: A Systematic Mapping Study

Context: The use of controlled vocabularies (CVs) aims to increase the quality of the specifications of the software requirements, by producing well-written documentation to reduce both ambiguities and complexity. Many studies suggest that defects introduced at the requirements engineering (RE) phase have a negative impact, significantly higher than defects in the later stages of the software development lifecycle. However, the knowledge we have about the impact of using CVs, in specific RE activities, is very scarce. Objective: To identify and classify the type of CVs, and the impact they have on the requirements engineering phase of software development. Method: A systematic mapping study, collecting empirical evidence that is published up to July 2019. Results: This work identified 2348 papers published pertinent to CVs and RE, but only 90 primary published papers were chosen as relevant. The process of data extraction revealed that 79 studies reported the use of ontologies, whereas the remaining 11 were focused on taxonomies. The activities of RE with greater empirical support were those of specification (29 studies) and elicitation (28 studies). Seventeen different impacts of the CVs on the RE activities were classified and ranked, being the two most cited: guidance and understanding (38%), and automation and tool support (22%). Conclusions: The evolution of the last 10 years in the number of published papers shows that interest in the use of CVs remains high. The research community has a broad representation, distributed across the five continents. Most of the research focuses on the application of ontologies and taxonomies, whereas the use of thesauri and folksonomies is less reported. The evidence demonstrates the usefulness of the CVs in all RE activities, especially during elicitation and specification, helping developers understand, facilitating the automation process and identifying defects, conflicts and ambiguities in the requirements. Collaboration in research between academic and industrial contexts is low and should be promoted.
Melting SNOW-V: improved lightweight architectures

Abstract is a stream cipher proposed by Ekdahl et al. at IACR ToSC 2019(3) with an objective to be deployed as the encryption primitive in 5G systems. The stream cipher offers 256-bit security and is ready for deployment in the post-quantum era, in which as a rule of thumb (due to Grover’s algorithm), quantum security will vary as the square root of the classical security parameters. The authors further report good software performance figures in systems supporting the instruction set. However, they only provide a theoretical analysis of the cipher’s hardware efficiency. In this paper, we aim to fill this gap. We look at the three most important metrics of hardware efficiency: area, speed and power/energy, and propose circuits that optimize each of these metrics and validate our results using three different standard cell libraries. The smallest circuit we propose occupies only around 4776 gate equivalents of silicon area. Furthermore, we also report implementations which consume as little as 12.7 pJ per 128 bits of keystream and operate at a throughput rate of more than 1 Tbps.
“It’s Not as Simple as Just Looking at One Chart”: A Qualitative Study Exploring Clinician’s Opinions on Various Visualisation Strategies to Represent Longitudinal Actigraphy Data

&lt;b&gt;&lt;i&gt;Background:&lt;/i&gt;&lt;/b&gt; Data derived from wearable activity trackers may provide important clinical insights into disease progression and response to intervention, but only if clinicians can interpret it in a meaningful manner. Longitudinal activity data can be visually presented in multiple ways, but research has failed to explore how clinicians interact with and interpret these visualisations. In response, this study developed a variety of visualisations to understand whether alternative data presentation strategies can provide clinicians with meaningful insights into patient’s physical activity patterns. &lt;b&gt;&lt;i&gt;Objective:&lt;/i&gt;&lt;/b&gt; To explore clinicians’ opinions on different visualisations of actigraphy data. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; Four visualisations (stacked bar chart, clustered bar chart, linear heatmap and radial heatmap) were created using Matplotlib and Seaborn Python libraries. A focus group was conducted with 14 clinicians across 2 hospitals. Focus groups were audio-recorded, transcribed and analysed using inductive thematic analysis. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; Three major themes were identified: (1) the importance of context, (2) interpreting the visualisations and (3) applying visualisations to clinical practice. Although clinicians saw the potential value in the visualisations, they expressed a need for further contextual information to gain clinical benefits from them. Allied health professionals preferred more granular, temporal information compared to doctors. Specifically, physiotherapists favoured heatmaps, whereas the remaining members of the team favoured stacked bar charts. Overall, heatmaps were considered more difficult to interpret. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; The current lack of contextual data provided by wearables hampers their use in clinical practice. Clinicians favour data presented in a familiar format and yet desire multi-faceted filtering. Future research should implement user-centred design processes to identify ways in which all clinical needs can be met, potentially using an interactive system that caters for multiple levels of granularity. Irrespective of how data is displayed, unless clinicians can apply it in a manner that best supports their role, the potential of this data cannot be fully realised.
Full-Field Displacement Measurements of Helicopter Rotor Blades Using Stereophotogrammetry

This study presents a stereophotogrammetry approach to achieve full-field displacement measurements of helicopter rotor blades. The method is demonstrated in the wind tunnel test of a 2 m diameter rotor, conducted at the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>5.5</mn> <mtext> </mtext> <mtext>m</mtext> <mo>×</mo> <mn>4</mn> <mtext> </mtext> <mtext>m</mtext> </math> Aeroacoustic Wind Tunnel of China Aerodynamics Research and Development Center (CARDC). By arranging the retroreflective targets on the special hat installed directly above the rotor hub, the dynamic motion of the rotor shaft was tracked accurately, and a unified coordinate system was established on the rotor. Therefore, three-dimensional coordinates of instantaneously measured targets attached on the blade could be transformed to the unified rotor coordinate system, thereby providing a basis for consistently calculating the blade displacements at different test conditions. Moreover, location deviations of the blade caused by the vibration of the measuring system or the rotor due to freestream and rotor rotation were also effectively corrected through coordinate transformation. Comparisons of experimental and simulation results for a range of hover and forward flight conditions show good magnitude and trend agreements.
3- AND 5-ISOGENIES OF SUPERSINGULAR EDWARDS CURVES

An analysis is made of the properties and conditions for the existence of 3- and 5-isogenies of complete and quadratic supersingular Edwards curves. For the encapsulation of keys based on the SIDH algorithm, it is proposed to use isogeny of minimal odd degrees 3 and 5, which allows bypassing the problem of singular points of the 2nd and 4th orders, characteristic of 2-isogenies. A review of the main properties of the classes of complete, quadratic, and twisted Edwards curves over a simple field is given. Equations for the isogeny of odd degrees are reduced to a form adapted to curves in the form of Weierstrass. To do this, use the modified law of addition of curve points in the generalized Edwards form, which preserves the horizontal symmetry of the curve return points. Examples of the calculation of 3- and 5-isogenies of complete Edwards supersingular curves over small simple fields are given, and the properties of the isogeny composition for their calculation with large-order kernels are discussed. Equations are obtained for upper complexity estimates for computing isogeny of odd degrees 3 and 5 in the classes of complete and quadratic Edwards curves in projective coordinates; algorithms are constructed for calculating 3- and 5-isogenies of Edwards curves with complexity 6M + 4S and 12M + 5S, respectively. The conditions for the existence of supersingular complete and quadratic Edwards curves of order 4·3m·5n and 8·3m·5n are found. Some parameters of the cryptosystem are determined when implementing the SIDH algorithm at the level of quantum security of 128 bits
Citizen science archaeological finds on the Semantic Web: the FindSampo framework

FindSampo fosters collecting, sharing, publishing and studying archaeological finds discovered by the public. The framework includes the following: a mobile find-reporting system; a semantic portal for researchers, the public and collection managers to use; and a Linked Open Data service for creating custom data analyses and for application developers.
Past is important: Improved image captioning by looking back in time

A major development in the area of image captioning consists of trying to incorporate visual attention in the design of language generative model. However, most previous studies only emphasize its role in enhancing visual composition at the current moment, while neglect its role in global sequence reasoning. This problem appears not only in captioning model, but also in reinforcement learning structure. To tackle this issue, we first propose a Visual Reserved model that enables previous visual context to be considered for the current sequence reasoning. Next, a Attentional-Fluctuation Supervised model is also proposed in reinforcement learning structure. Compared against the traditional strategies that only take non-differentiable Natural Language Processing (NLP) metrics as the incentive standard, the proposed model regards the fluctuation of previous attention matrix as an important indicator to judge the convergence of the captioning model. The proposed methods have been tested on MS-COCO captioning dataset and achieve competitive results evaluated by the evaluation server of MS COCO captioning challenge.