Reviewerly/exHarmony huggingface dataset:
Automating security infrastructures: Practices, imaginaries, politics

This article contributes to emergent debates in critical security studies that consider the processes and effects that arise where new forms of automated technology begin to guide security practices. It does so through research into public Wi-Fi infrastructure that has started to appear across the globe and its mobilization as a device for warning the public about emergencies. I focus specifically on an iteration of this infrastructure developing in New York called LinkNYC. According to the infrastructure’s operators, the processes that underpin emergency communication have gradually become ‘automated’ to accelerate LinkNYC’s deployment during crises. The article pursues three lines of inquiry to explore the automation of security infrastructure, in turn making three correspondent original contributions to wider debates. First, it unpacks the real-time analytics and platform-based data-sharing techniques cultivated to automate emergency communication. Here, I expand understanding of the new forms of automation now integrated into technologies harnessed for security and their practical effects. These forms of automation, I demonstrate secondly, are situated by those governing into wider imaginaries concerning the transformative promise automation bears. I argue that the proliferation of these imaginaries play a crucial role in justifying and dictating the enrolment of new devices into security. Third, it explores how automation affords private companies the opportunity to exercise discretionary decisionmaking that changes how and when infrastructure should operate during emergencies. Developing this argument, I add new dimensions to debates regarding the political ramifications associated with automation by claiming that automation redistributes authority across the public and private organizations that increasingly coordinate in bringing new technologies to bear in the security domain.
Automatic Pose Generation for Robotic 3-D Scanning of Mechanical Parts

In this article, we present an automatic pose generation approach for robotic 3-D scanning systems designed for the reverse engineering of mechanical parts. 3-D scanning of an object is usually a highly complicated process that involves aligning multiple measurements from different viewpoints, fusing the merged data, and finally reconstructing the underlying surface. The reconstruction is often performed in isolation to the acquisition step. In contrast, the measured data often exhibit holes owing to limited viewpoints or noises in measurements, which can be observed after the postprocessing. Previous approaches either use the predefined scan paths without knowledge of the actual surface, or calculate the scan path based on the approximated smooth surface enclosing the incomplete scanned measurement. The need for an accurate 3-D scanning of mechanical parts often arises for reverse engineering purposes, as their shape often exhibits sharp corners or continuous shape of primitives (planes, cylinders, etc.), which the existing pipelines do not consider. We suggest a 3-D scan pipeline specifically designed to generate optimal scan paths of mechanical parts from incomplete data with limited fields of view. Our pipeline detects primitives from the scanned partial data, and utilizes the detection to approximate the underlying shape of the ground truth object; thus, it suggests the next-best-view of the scanner from the scarce measurements. In this regard, the suggested method is the first working pipeline specifically designed for scanning mechanical parts; furthermore, it outperforms previous approaches in terms of an efficient scan path generation.
ML Lifecycle Canvas: Designing Machine Learning-Empowered UX with Material Lifecycle Thinking

As a particular type of artificial intelligence technology, machine learning (ML) is widely used to empower user experience (UX). However, designers, especially the novice designers, struggle to integrate ML into familiar design activities because of its ever-changing and growable nature. This paper proposes a design method called Material Lifecycle Thinking (MLT) that considers ML as a design material with its own lifecycle. MLT encourages designers to regard ML, users, and scenarios as three co-creators who cooperate in creating ML-empowered UX. We have developed ML Lifecycle Canvas (Canvas), a conceptual design tool that incorporates visual representations of the co-creators and ML lifecycle. Canvas guides designers to organize essential information for the application of MLT. By involving design students in the "research through design" process, the development of Canvas was iterated through its application to design projects. MLT and Canvas have been evaluated in design workshops, with completed proposals and evaluation results demonstrating that our work is a solid step forward in bridging the gap between UX and ML.
Resource-Restricted Cryptography: Revisiting MPC Bounds in the Proof-of-Work Era

Traditional bounds on synchronous Byzantine agreement (BA) and secure multi-party computation (MPC) establish that in absence of a private correlated-randomness setup, such as a PKI, protocols can tolerate up to $$t<n/3$$ of the parties being malicious. The introduction of "Nakamoto style" consensus, based on Proof-of-Work (PoW) blockchains, put forth a somewhat different flavor of BA, showing that even a majority of corrupted parties can be tolerated as long as the majority of the computation resources remain at honest hands. This assumption on honest majority of some resource was also extended to other resources such as stake, space, etc., upon which blockchains achieving Nakamoto-style consensus were built that violated the $$t<n/3$$ bound in terms of number of party corruptions. The above state of affairs begs the question of whether the seeming mismatch is due to different goals and models, or whether the resource-restricting paradigm can be generically used to circumvent the n/3 lower bound. In this work we study this question and formally demonstrate how the above paradigm changes the rules of the game in cryptographic definitions. First, we abstract the core properties that the resource-restricting paradigm offers by means of a functionality wrapper, in the UC framework, which when applied to a standard point-to-point network restricts the ability (of the adversary) to send new messages. We show that such a wrapped network can be implemented using the resource-restricting paradigm—concretely, using PoWs and honest majority of computing power—and that the traditional $$t<n/3$$ impossibility results fail when the parties have access to such a network. Our construction is in the fresh Common Reference String (CRS) model—i.e., it assumes a CRS which becomes available to the parties at the same time as to the adversary. We then present constructions for BA and MPC, which given access to such a network tolerate $$t<n/2$$ corruptions without assuming a private correlated randomness setup. We also show how to remove the freshness assumption from the CRS by leveraging the power of a random oracle. Our MPC protocol achieves the standard notion of MPC security, where parties might have dedicated roles, as is for example the case in Oblivious Transfer protocols. This is in contrast to existing solutions basing MPC on PoWs, which associate roles to pseudonyms but do not link these pseudonyms with the actual parties.
A blockchain architecture for industrial applications

Blockchain and the programs running on it, called smart contracts, are increasingly applied in all fields where trust and strong certifications are required. Our work focuses on industrial applications of blockchains and not on cryptocurrencies or tokens. We use frameworks to compare public and permissioned blockchains specifically suited for industrial applications. We also propose a complete solution based on Ethereum to implement a decentralized application, putting together in an original way, components and patterns already used and proven. This solution is characterized by a set of validator nodes running the blockchain using Proof-of-Authority or similar efficient consensus algorithms, by the use of an explorer enabling users to check the blockchain state, and the source code of the smart contracts running on it. From time to time, the hash digest of the last mined block is written into a public blockchain to guarantee immutability. The right to send transactions is granted by validator nodes to users by endowing them with the Ethers mined locally. Overall, the proposed approach has the same transparency and immutability as a public blockchain, largely reducing its drawbacks.
Automatic Identification of Decisions from the Hibernate Developer Mailing List

Decisions run through the whole software development and maintenance processes. Explicitly documenting these decisions helps to organize development knowledge and to reduce its vaporization, thereby controlling the development process and maintenance costs. It can also support the knowledge acquisition process for stakeholders of the project. Meanwhile, developers (e.g., architects) and managers will be able to rely on the decisions made in the past to solve the problems encountered in their current projects. However, identifying decisions from massive textual artifacts, which involves considerable human effort, time, and cost, is usually unaffordable due to limited resources. To address this problem, we conducted an experiment to automatically identify decisions from textual artifacts using machine learning techniques. We created a dataset of 1,300 sentences labelled from the Hibernate developer mailing list, containing 650 decision sentences and non-decision sentences respectively, and trained machine learning models using 160 configurations regarding text preprocessing, feature extraction, and classification algorithms. The results show that (1) the text preprocessing method with Including Stop Words, No Stemming and Lemmatization, and No Filtering Out Sentences performs best when preprocessing posts to identify decisions; (2) the simple Bag-of-Words (BoW) model works best when extracting features to identify decisions; (3) the Support Vector Machine (SVM) algorithm gets the best result when training classifiers to identify decisions; and (4) the SVM algorithm with Including Stop Words (ISW), No Stemming and Lemmatization (NSaL), Filtering Out Sentences by Length (FOSbL), and BoW achieves the best performance (with a precision of 0.640, a recall of 0.932, and an F1-score of 0.759), compared with other configurations when identifying decisions from the mailing list.
Efficient parallel and fast convergence chaotic Jaya algorithms

The Jaya algorithm is a recent heuristic approach for solving optimisation problems. It involves a random search for the global optimum, based on the generation of new individuals using both the best and the worst individuals in the population, thus moving solutions towards the optimum while avoiding the worst current solution. In addition to its performance in terms of optimisation, a lack of control parameters is another significant advantage of this algorithm. However, the number of iterations needed to reach the optimal solution, or close to it, may be very high, and the computational cost can hamper compliance with time requirements. In this work, a chaotic two-dimensional (2D) map is used to accelerate convergence, and parallel algorithms are developed to alleviate the computational cost. Coarse- and fine-grained parallel algorithms are developed, the former based on multi-populations and the latter at the individual level, and in both cases these are accelerated by an improved (computational) use of the chaos map.
Active cyber defence strategies and techniques for banks and financial institutions

Purpose With the growing penetration of financial technology, financial firms and banking institutions have seen a rise in the volume of cyber-attacks in recent years. Cyber criminals are using more sophisticated techniques to beat traditional passive defences. The purpose of this paper is to explore, analyse and recommend various active cyber defence strategies and techniques that can be implemented by organizations in financial sector to secure and safeguard their assets and cyber space. Design/methodology/approach This paper adopts a secondary research approach on the various techniques that can be used effectively to adopt active cyber defence strategy. Findings Based on the existing strategies and techniques available and those being currently developed, this paper proposes a holistic approach that can be adopted by banks and financial institutions to secure their cyber space. This involves a combination of active and passive cyber defence techniques and effective threat intelligence. Originality/value The following paper has been checked for plagiarism and is within the acceptable standards for publishing in this journal. Appropriate references have been duly cited, and the formulation of the final recommendation is the original work of the authors.
Relationships between spectral features, iron oxides and colours of surface soils in northern Jordan

Abstract Ground data on spectral characteristics of Jordan's soils remain sparse, which makes the interpretation of remote sensing datasets challenging. These are, however, very useful for predicting soil properties and agricultural suitability. Previous studies have shown that soil colours correlate well with degrees of weathering intensity, as indicated by magnetic parameters and dithionite‐extractable iron (Fe d ) contents along a climosequence in northern Jordan. This study enlarges the database by the results of 160 bulk samples that were collected systematically from the soil surface at 40 locations. In addition to soil colour and contents of Fe d , we explore mean soil reflectance spectra (MSRS) measured by analytical spectral devices (ASD) and analyse the morphological conditions of the spectra referring to the effects of iron oxides on spectral behaviours. Results show a high correlation of spectral behaviours and colour variations with Fe oxides, and no correlation with soil organic carbon C org . The influence of the Fe oxide contents is clearly apparent in the visible range (VIS). The presence of CaCO 3 increases the reflectance in all spectral ranges. Six soil groups (Gr. I ‐ Gr. VI) were discerned qualitatively and quantitatively in the study area, which largely mirror the intensity of red colour expressed by redness indices. Highlights In northwestern Jordan there is an evident connection between the spectral properties, chemistry and soil colour. This study established a preliminary spectral library of soils in NW Jordan to facilitate the use of remote sensing in soil studies. The morphological properties and statistical analysis of the spectral data show that spectra of soils in NW Jordan are dominated by iron oxides. Spectral properties can be used to characterize the soil colours and types of Fe oxides in soils of Jordan.
Modern piano teaching technologies: Accessibility, effectiveness, the need for pedagogues

The article deals with certain approaches and methods using the latest technology to improve the results of piano teaching. The aim of the study is to analyze and compare the most popular and latest technologies of learning to play the piano in terms of their availability, effectiveness and the need for a teacher. In the work, methods of synthesis and analysis, comparisons were used, with the help of which various technologies of learning to play the piano were studied. As a result, two approaches for introducing modern technologies have become apparent in piano teaching: making group lessons more interactive and increase the scope of individual learning along with the gradual withdrawal of the teacher. However, some new technical solutions are far from being ready for use by educational institutions and individuals; therefore, it currently seems relevant to combine modern technology with conventional music teaching practices.
Realizing the Potential of Blockchain Technology in Smart Grid Applications

Blockchain - a Distributed Ledger Technology (DLT) - has emerged as one of the leading technologies during the past several years. The objective of this paper is to discuss potential use of blockchain technology to support different smart grid applications.
FPGA Implementation of CNN for Handwritten Digit Recognition

Convolutional neural networks (CNN) have been used very successfully in the field of handwritten digit recognition. CNN is difficult to deploy on the embedded platform because of its large computation, complex structure and frequent memory access. In this paper, a manual hardware-level design (i.e., RTL) CNN reconfigurable IP core method is proposed to construct the FPGA basic unit of the CNN structure. Reconfigurable convolution, pooling and fully connected modules in the CNN structure are designed. By setting the parameters of the reconfigurable modules and connecting these modules end-to-end, CNNs of different structure sizes can be quickly deployed. In the full connection module, using double cache technology can reduce the memory access operation of weight parameters and improve the calculation efficiency. In addition, this paper presents an FPGA implementation of CNN for handwritten digit recognition system. The system was built end-to-end using a reconfigurable IP core and was finally implemented on the lntel Cyclone10 FPGA hardware platform. Under the 150MHz clock, CNN only needs 0.0176 ms to recognize a handwritten digital picture, and the accuracy rate is 97.57%.
Attentive multi-stage convolutional neural network for crowd counting

Crowd counting is an important problem in computer vision, whose application can be found in a wide range of tasks. Although this problem has been well studied, how to effectively deal with scale variations and perspective distortions is still a challenge. High-quality crowd density map depends heavily on how well these problems are solved. In this paper, we propose a novel network architecture called Attentive Multi-stage CNN for Crowd Counting (AMCNN). The AMCNN contains two subnetworks, i.e., hierarchical density estimator(HDE) and auxiliary count classifier (AUCC). The HDE adopts a hierarchical strategy to mine semantic features in a coarse-to-fine manner to tackle the problem of scale changes and perspective distortions. And the obtained composite features are used to generate the final density map. In addition, to further improve the density map quality, a soft attention mechanism is integrated into the AMCNN to distinct the foreground and the background. Furthermore, the AUCC is employed to achieve the count classification task, which is complementary to the task of density estimation. We evaluate our model on three public datasets: ShanghaiTech, UCF_CC_50 and Mall. Extensive experiments demonstrate that our counting model is on par with some state-of-the-art methods. Source code will be released at:https://github.com/wxq-ahu/crowd-count-amcnn.
A Cost-Effective and Transferable Methodology for Rooftop PV Potential Assessment in Developing Countries

The efficient uptake of decentralized solar rooftop photovoltaics (PV) is in some cases hindered by ineffective energy and political framework conditions. These may be based on inaccurate and uncertain potential assessments in the early development stage of the solar market. This paper develops a more accurate, cost-effective, and robust potential assessment for emerging and developing economies. Adjusting the module efficiency corresponding to regional and household conditions improves the output accuracy. The rooftop PV market changes are simulated regarding different input changes and policy designs, including changing the Feed-In Tariff (FIT), grid tariff, and technology development. In the case study, the market potential in Vietnam is estimated at 260–280 TWh/a and is clustered into six groups in priority order, in which Hanoi and Ho Chi Minh need the most policy focus. Changing the FIT from 8.83 to 9 Euro cent/kWh and using different regional FITs can activate an additional 16% of the market and lead to a possible 28 million Euro benefit. Increasing the grid tariff to 8.7 cents/kWh could activate the self-consumption model, and the self-sufficient market can be guaranteed in the case of CAPEX and OPEX being lower than 650 Euro/kWp. Future developments of the method should focus on combining this top-down method with detailed bottom-up approaches.
Trends and Challenges for Software Engineering in the Mobile Domain

Mobile computing is becoming a key aspect of our lives. This article builds on conversations held during the 2018 IEEE/ACM International Conference on Mobile Software Engineering and Systems, highlighting mobile computing's impact on software engineering practices, the transition from research to industry, and related areas.
genozip: a fast and efficient compression tool for VCF files

Abstract Motivation genozip is a new lossless compression tool for Variant Call Format (VCF) files. By applying field-specific algorithms and fully utilizing the available computational hardware, genozip achieves the highest compression ratios amongst existing lossless compression tools known to the authors, at speeds comparable with the fastest multi-threaded compressors. Availability and implementation genozip is freely available to non-commercial users. It can be installed via conda-forge, Docker Hub, or downloaded from github.com/divonlan/genozip. Supplementary information Supplementary data are available at Bioinformatics online.
Towards a Unified Framework for Visual Compatibility Prediction

Visual compatibility prediction refers to the task of determining if a set of items go well together. Existing techniques for compatibility prediction prioritize sensitivity to type or context in item representations and evaluate using a fill-in-the-blank (FITB) task. We scale the FITB task to stresstest existing methods which highlights the need for a compatibility prediction framework that is sensitive to multiple modalities of item relationships. In this work, we introduce a unified framework for compatibility learning that is jointly conditioned on the type, context, and style. The framework is composed of TC-GAE, a graph-based network that models type & context; SAE, an autoencoder that models style; and a reinforcement-learning based search technique that incorporates these modalities to learn a unified compatibility measure. We conduct experiments on two standard datasets and significantly outperform existing state-of-the- art methods. We also present qualitative analysis and discussions to study the impact of components of the proposed framework.
Learning blur invariant binary descriptor for face recognition

Binary representations have demonstrated remarkable performance in face recognition for its robustness to local changes and computation efficiency. However, the performance of face recognition based on most binary descriptors are not satisfactory when dealing with blurred face images. To solve this problem, we propose a novel blur invariant binary descriptor for face recognition. Particularly, we maximize the correlation between the binary codes of sharp face images and blurred face images of positive image pairs for learning the projection matrix. After that, we use the learned projection matrix to obtain blur-robust binary codes by quantizing projected pixel difference vectors (PDVs) in the testing stage. Experiment results on FERET and CMU-PIE show that our method achieves better recognition performance than representative binary descriptors LBP and CBFD.