Reviewerly/exHarmony huggingface dataset:
XAOM: A method for automatic alignment and orientation of radiographs for computer-aided medical diagnosis

Computer-aided diagnosis relies on machine learning algorithms that require filtered and preprocessed data as the input. Aligning the image in the desired direction is an additional manual step in post-processing, commonly overlooked due to workload issues. Several state-of-the-art approaches for fracture detection and disease-struck region segmentation benefit from correctly oriented images, thus requiring such preprocessing of X-ray images. Furthermore, it is desirable to have archived studies in a standardized format. Radiograph hanging protocols also differ from case to case, which means that images are not always aligned and oriented correctly. As a solution, the paper proposes XAOM, an X-ray Alignment and Orientation Method for images from 21 different body regions. Typically, other methods are crafted for this purpose to suit a specific body region and form of usage. In contrast, the method proposed in this paper is comprehensive and easily tuned to align and orient X-ray images of any body region. XAOM consists of two stages. For the first stage of the method, aligning X-ray images, we experimented with the following approaches: Hough transform, Fast line detection algorithm, and Principal Component Analysis method. For the second stage, we have experimented with the adaptations of several well known convolutional neural network topologies for correctly predicting image orientation: LeNet5, AlexNet, VGG16, VGG19, and ResNet50. In the first stage, the PCA-based approach performed best. The average difference between the angle detected by the algorithm and the angle marked by the experts on the test set containing 200 pediatric X-ray images was 1.65∘, while the median value was 0.11∘. In the second stage, the VGG16-based network topology achieved the best accuracy of 0.993 on a test set containing 4,221 images. XAOM is highly accurate at aligning and orienting pediatric X-ray images of 21 common body regions according to a set standard. The proposed method is also robust and can be easily adjusted to the different alignment and rotation criteria. The Python source code of the best performing implementation of XAOM is publicly available at https://github.com/fhrzic/XAOM.
Smart Recommender System using Deep Learning

Deep neural system has been succeeded in solving recent complex problems in AI, image processing, and natural language processing. In recommendation system innovation, deep learning is an enormous thing. Deep learning is applicable in various systems like music recommendation, speech recognition, book suggestion, and video on demand. Deep learning solves complex relations so many researchers use the deep neural network in their task. Most of the time task requires complex computation. Two models are proposed in the system. The movie lens dataset is used for the recommendation system. With the help of user and item, authors find a relation between them. Current and past historical data collaborative filtering is done. Two deep learning models are used. These models are a complement to each other. Models learn from training the data. The predicted rating is more accurate than the existing method.
A Qualitative Study on Non-Functional Requirements in Agile Software Development

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</b> Widespread use of agile software development (ASD) methods can be observed nowadays. Non-functional requirements (NFRs) are often reported to be a problematic issue for agile projects, since ASD methods tend to neglect NFRs while focusing on incremental delivery of functional features. Despite that, only very few studies have explored the requirements engineering practices used in ASD and dedicated particularly to NFRs. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</b> We aimed to identify and investigate the practices used in industrial ASD projects to identify, elicit and document NFRs. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</b> We conducted a systematic literature review (SLR) and used its findings to prepare an interview guide. We then conducted a series of semi-structured interviews with 10 experienced practitioners. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</b> The SLR revealed a number of strategies related to the timing of NFR identification as well as 13 elicitation practices and 20 documentation techniques. At least some of these findings show discrepancies between ASD theory and practice. The interviews provided a more in-depth understanding of the practices used, and their context. The main findings from the interviews include: practitioners’ attempts to start identifying NFRs early in the project, a lack of elicitation techniques aimed at NFRs only, and different choices about documentation techniques, including the additional techniques introduced to cope specifically with NFRs. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions:</b> It was not confirmed that requirements engineering activities related to NFRs are perceived by ASD practitioners as a problem, as they developed effective practices to deal with this issue. Moreover, our findings show that different approaches to NFRs can be used and give satisfactory results.
Energy, performance and cost efficient cloud datacentres: A survey

In major Information Technology (IT) companies such as Google, Rackspace and Amazon Web Services (AWS), virtualization and containerization technologies are usually used to execute customers’ workloads and applications — as part of their cloud computing services offering. The computational resources are provided through large-scale datacentres, which consume substantial amount of energy and, consequently, affect our environment with global warming. Cloud datacentres have become a backbone for today’s business and economy, which are the fastest-growing electricity consumers, globally. Numerous studies suggest that ∼30% of the US datacentres are comatose and the others are grossly less-utilized, which make it possible to save energy through technologies like virtualization and containerization. These technologies provide support for allocation and consolidation of workloads on appropriate resources. However, consolidation comprises migrations of virtual machines (VMs), containers and/or applications, depending on the underlying virtualization method; that are expensive in terms of energy consumption, performance degradation, and therefore, costs which is mostly not accounted for in many existing models, and, possibly, it could be more energy and performance efficient not to consolidate. This paper describes energy consumption and performance, therefore, cost issues of large-scale datacentres. Besides, we cover various methods for energy and performance efficient distributed systems, clouds and datacentres. We elaborate energy efficiency methods at three different levels: hardware; resource management; and applications. Besides these, different performance management techniques are mapped onto taxonomies and described in details. In last, energy, performance and cost management techniques, at geographically distributed and multi-access edge computing platforms, are described along with critical discussion.
Quantum Computing: Towards Industry Reference Problems

The complexity is increasing rapidly in many areas of the automotive industry. The design of an automobile involves many different engineering disciplines, e. g., mechanical, electrical, and software engineering. The software of a vehicle comprises millions of lines of code. Further, the manufacturing, logistics, distribution, and sales of a vehicle are highly complex. There is an immense need for solving simulation problems, e. g., in battery chemistry, an essential enabler for technological advancements for electric vehicles. In all these domains, myriads of optimization, simulation, and machine learning problems arise. Quantum computing-based approaches promise to overcome some of the inherent scalability limitations of classical approaches. This article investigates quantum computing applications across the automotive value chain and identifies several high-value problems that will benefit from quantum-enhanced solutions.
Heterogeneous graph reasoning for knowledge-grounded medical dialogue system

Beyond the common difficulties faced in task-oriented dialogue system, medical dialogue has recently attracted increasing attention due to its huge application potential while posing more challenges in reasoning over medical domain knowledge and logic. Existing works resort to neural language models for dialogue embedding and neglect the explicit logical reasoning, leading to poor explainable and generalization ability. In this work, we propose an explainable Heterogeneous Graph Reasoning (HGR) model to unify the relational dialogue context understanding and entity-correlation reasoning into a heterogeneous graph structure. HGR encodes entity context according to the corresponding utterance and deduces next response after fusing the underlying medical knowledge with entity context by attentional graph propagation. To push forward the future research on expert-sensitive task-oriented dialogue system, we first release a large-scale Medical Dialogue Consultant benchmark (MDG-C) with 16 Gastrointestinal diseases for evaluating consultant capability and a Medical Dialogue Diagnosis benchmark (MDG-D) with 6 diseases for measuring diagnosis capability of models, respectively. Extensive experiments on both MDG-C and MDG-D benchmarks demonstrate the superiority of our HGR over state-of-the-art knowledge grounded approaches in general fields of medical dialogue system.
Comprehensive analysis of aspect term extraction methods using various text embeddings

• A wide comparison with ablation analysis of Aspect Term Extraction methods. • A guideline to choose the best Aspect Term Extraction models for particular user needs. • We analyzed 88 method combinations, model customizations (LSTM vs. BiLSTM, CRF vs. no CRF) and pre-trained word embeddings. • We also evaluated the results of three contextual text representations (BERT, Flair, ELMo) using the BiLSTM-CRF model. • We analyzed the influence on the performance of extending the word vectorization step with character-based word embeddings. • The experimental results on SemEval datasets revealed that the BiLSTM could be used as a very good predictor. • Language model-based model are not always the best and obvious choice for vector representation layer in NLP tasks. Recently, a variety of model designs and methods have blossomed in the context of the sentiment analysis domain. However, there is still a lack of comprehensive studies of Aspect-based Sentiment Analysis. We want to fill this gap and propose a comparison with ablation analysis of Aspect Term Extraction using various text embeddings methods. We particularly focused on simple architectures based on long short-term memory (LSTM) with optional conditional random field (CRF) enhancement using different pre-trained word embeddings. Moreover, we analyzed the influence on the performance of extending the word vectorization step with character-based word embeddings. The experimental results on SemEval datasets revealed that bi-directional long short-term memory (BiLSTM) could be used as a very good predictor, even comparing to very sophisticated and complex models using huge word embeddings or language models. We presented a comprehensive analysis of various customizations of LSTM-based architecture and word/character embeddings that could be used as a guideline to choose the best model version for particular user needs.
Towards Green Computing Oriented Security: A Lightweight Postquantum Signature for IoE

Postquantum cryptography for elevating security against attacks by quantum computers in the Internet of Everything (IoE) is still in its infancy. Most postquantum based cryptosystems have longer keys and signature sizes and require more computations that span several orders of magnitude in energy consumption and computation time, hence the sizes of the keys and signature are considered as another aspect of security by green design. To address these issues, the security solutions should migrate to the advanced and potent methods for protection against quantum attacks and offer energy efficient and faster cryptocomputations. In this context, a novel security framework Lightweight Postquantum ID-based Signature (LPQS) for secure communication in the IoE environment is presented. The proposed LPQS framework incorporates a supersingular isogeny curve to present a digital signature with small key sizes which is quantum-resistant. To reduce the size of the keys, compressed curves are used and the validation of the signature depends on the commutative property of the curves. The unforgeability of LPQS under an adaptively chosen message attack is proved. Security analysis and the experimental validation of LPQS are performed under a realistic software simulation environment to assess its lightweight performance considering embedded nodes. It is evident that the size of keys and the signature of LPQS is smaller than that of existing signature-based postquantum security techniques for IoE. It is robust in the postquantum environment and efficient in terms of energy and computations.
MC-LSTM: Real-Time 3D Human Action Detection System for Intelligent Healthcare Applications

Due to the movement expressiveness and privacy assurance of human skeleton data, 3D skeleton-based action inference is becoming popular in healthcare applications. These scenarios call for more advanced performance in application-specific algorithms and efficient hardware support. Warnings on health emergencies sensitive to response speed require low latency output and action early detection capabilities. Medical monitoring that works in an always-on edge platform needs the system processor to have extreme energy efficiency. Therefore, in this paper, we propose the MC-LSTM, a functional and versatile 3D skeleton-based action detection system, for the above demands. Our system achieves state-of-the-art accuracy on trimmed and untrimmed cases of general-purpose and medical-specific datasets with early-detection features. Further, the MC-LSTM accelerator supports parallel inference on up to 64 input channels. The implementation on Xilinx ZCU104 reaches a throughput of 18 658 Frames-Per-Second (FPS) and an inference latency of 3.5 ms with the batch size of 64. Accordingly, the power consumption is 3.6 W for the whole FPGA+ARM system, which is 37.8x and 10.4x more energy-efficient than the high-end Titan X GPU and i7-9700 CPU, respectively. Meanwhile, our accelerator also keeps a 4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 5x energy efficiency advantage against the low-power high-performance Firefly-RK3399 board carrying an ARM Cortex-A72+A53 CPU. We further synthesize an 8-bit quantized version on the same hardware, providing a 48.8% increase in energy efficiency under the same throughput.
Review of machine learning and deep learning application in mine microseismic event classification

Purpose. To put forward the concept of machine learning and deep learning approach in Mining Engineering in order to get high accuracy in separating mine microseismic (MS) event from non-useful events such as noise events blasting events and others. Methods. Traditionally applied methods are described and their low impact on classifying MS events is discussed. General historical description of machine learning and deep learning methods is shortly elaborated and different approaches conducted using these methods for classifying MS events are analysed. Findings. Acquired MS data from rock fracturing process recorded by sensors are inaccurate due to complex mining environment. They always need preprocessing in order to classify actual seismic events. Traditional detecting and classifying methods do not always yield precise results, which is especially disappointing when different events have a similar nature. The breakthrough of machine learning and deep learning methods made it possible to classify various MS events with higher precision compared to the traditional one. This paper introduces a state-of-the-art review of the application of machine learning and deep learning in identifying mine MS events. Originality.Previously adopted methods are discussed in short, and a brief historical outline of Machine learning and deep learning development is presented. The recent advancement in discriminating MS events from other events is discussed in the context of these mechanisms, and finally conclusions and suggestions related to the relevant field are drawn. Practical implications. By means of machin learning and deep learning technology mine microseismic events can be identified accurately which allows to determine the source location so as to prevent rock burst. Keywords: rock burst, MS event, blasting event, noise event, machine learning, deep learning
Social Inclusion, Innovation and Food Security in West Africa

To achieve the United Nations Sustainable Goals (SDGs) by 2030, especially goal 2 (SDG-2) which is to “end hunger, achieve sustainable food security, improved nutrition and promote agriculture” this study examines how innovation and social inclusion affect food security in West Africa. The study applies the system Generalised Method of Moments (GMM) on a panel data of 15 West African countries for the period 2005–2018. The result from system GMM shows that innovation and social inclusion are drivers of food security. The implication of this is that increased level of social inclusion and innovation in West African may increase the level of food security by about 41.5% and 13.6% respectively. Therefore, the study concludes that to feed the growing African population, social inclusion should be improved to mitigate risk, vulnerability and socioeconomic shocks faced by farming households. In addition, innovation in agricultural should be enhanced to drive productivity, thereby leading to a sustainable food security.
Complex Surface Reconstruction Based on Fusion of Surface Normals and Sparse Depth Measurement

Precision measurement and reconstruction of detailed surfaces topography is a challenging task for non-diffuse complex parts. Although coordinate measurement machines (CMM) with the touch-trigger probe are widely used in current industry, the measurement efficiency limits their application in the measurement of complex surfaces. This article proposes a multisensor data fusion strategy by integrating the technical merits of CMM and photometric stereo (PS) to achieve multiscale reconstruction of a complex surface with high efficiency. Considering the complementary measurement characteristics of the two approaches, the sparse points from CMM are used to provide global shape information, and the high-resolution surface normal map from PS is used to provide local detailed structure. A multistage neural network is then proposed to fuse these two kinds of modality information such that the global features from the sparse points and the local features from the surface normal map are fused in a coarse-to-fine multistage process so as to make the training process more stable and the reconstruction more accurate. To enhance the generality of the fusion neural network, a synthetic training data set is also designed to include a large variety of multiscale features enriched surfaces. Experiments are conducted to verify the effectiveness of the proposed multisensor fusion strategy in accurate reconstruction of complex surfaces with high efficiency.
The Forchheim Image Database for Camera Identification in the Wild

Image provenance can represent crucial knowledge in criminal investigation and journalistic fact checking. In the last two decades, numerous algorithms have been proposed for obtaining information on the source camera and distribution history of an image. For a fair ranking of these techniques, it is important to rigorously assess their performance on practically relevant test cases. To this end, a number of datasets have been proposed. However, we argue that there is a gap in existing databases: to our knowledge, there is currently no dataset that simultaneously satisfies two goals, namely a) to cleanly separate scene content and forensic traces, and b) to support realistic post-processing like social media recompression.
“This is not how we imagined it”: Technological affordances, economic drivers, and the Internet architecture imaginary

The Internet architecture is widely perceived as engine for innovation by providing the equal opportunity to deploy new protocols and applications. This view reflects an imaginary that guides the co-production of policy and technology that can be traced back to the early days of the Internet, which is still prominent among the engineers in one of the main governance bodies of the Internet, the Internet Engineering Taskforce (IETF). After the privatization of the Internet architecture in the 1990s, the interplay between the architectural principles of end-to-end, permissionless innovation, and openness subverted equality among Internet users and hampered their ability to redesign the Internet. I draw on media studies, science and technology studies and international political economy, and use a combination of qualitative and quantitative methods to show how the Internet architecture’s affordance structure got reconfigured, and how this facilitated the prioritization of corporate interests over the interests of end users.
Learning discrete class-specific prototypes for deep semantic hashing

Deep supervised hashing methods have become popular for large-scale image retrieval tasks. Recently, some deep supervised hashing methods have utilized the semantic clustering of hash codes to improve their semantic discriminative ability and polymerization. However, there exists a semantic gap between the hash codes learned from the visual features and the semantic labels, which weakens the generalization ability of these methods. In addition, the manifold structure of the hash codes in the Hamming space is ignored. In this paper, we propose a novel deep semantic hashing method by learning discrete class-specific prototypes (DCPH). Specifically, we utilize the label information to learn discrete class-specific prototypes as the intermediate semantic representations of the semantic labels, which can reduce the semantic gap between the semantic labels and the hash codes and improve the correlation between the class-specific prototypes and the hash codes. Subsequently, we construct a bipartite graph to build coarse semantic neighborhood relationship between the hash codes and the class-specific prototypes, which can preserve the manifold structural information. Moreover, we utilize the pairwise supervised information to construct a fine semantic neighborhood relationship between the hash codes. Finally, we propose a novel hashing loss based on multitask learning framework to incorporate them into an end-to-end one-stream deep neural network architecture. Experimental results on several large-scale datasets demonstrate that the proposed method can outperform state-of-the-art hashing methods.