Reviewerly/exHarmony huggingface dataset:
TrustZone support in the memory system

This chapter explains how the TrustZone security extension is supported by a number of memory system features. It covers the classification of memory types from security point of view, how the memory space is partitioned into Secure and Non-secure ranges, and how software can handle pointer checks using the memory system features available in the Armv8-M architecture.
Split-Based Algorithm for Weighted Context-Free Grammar Induction

The split-based method in a weighted context-free grammar (WCFG) induction was formalised and verified on a comprehensive set of context-free languages. WCFG is learned using a novel grammatical inference method. The proposed method learns WCFG from both positive and negative samples, whereas the weights of rules are estimated using a novel Inside–Outside Contrastive Estimation algorithm. The results showed that our approach outperforms in terms of F1 scores of other state-of-the-art methods.
A Family of Unification-Oblivious Program Transformations and Their Applications

We describe a family of program transformations that compile a Horn Clause program into equivalent programs of a simpler and more regular structure. Our transformations, seen as morphisms between term algebras, commute with unification, clause unfoldings and Prolog’s LD-resolution, thus preserving the operational semantics of the Horn Clause programs. As applications, the resulting programs have simpler execution algorithms and can be compiled to minimalist instruction sets.
Performance of an internet data center refrigeration system using an evaporative cooler

The traditional refrigeration method of internet data center (IDC) is mostly air refrigeration, which has undesired cooling effect and high power consumption. This study addresses this problem and proposes an evaporative air cooler (EAC) suitable for IDC. Given the high specific heat capacity of water, the evaporative condensing coil and spray device are added to the evaporative cooler to enhance the heat transfer effect. Heat and mass transfer mathematical models are established to analyze the heat transfer performance. The mathematical model is used to simulate the profile of the heat and mass transfer coefficient of the EAC with the amount of spray water and air flow. The results show that when the air flow changes from 10 to 20 kg/s, the air equivalent heat transfer coefficient increases by about 41%. When the air flow rate is 20 kg/s and the spray water volume is 0.00124 kg/(mꞏs), the total heat transfer coefficient is increased by about 308% compared with the case without spray water.
Evaluation of Adversarial Attacks Based on DL in Communication Networks

Deep Neural Networks (DNN) have strong capabilities of memories, feature identifications and automatic analyses, solving various complex problems. However, DNN classifiers have obvious fragility that adding several unnoticeable perturbations to the original examples will lead to the errors in the classifier identification. In the field of communications, the adversarial examples will greatly reduce the accuracy of the signal identification, causing great information security risks. Considering the adversarial examples pose a serious threat to the security of the DNN models, studying their generation mechanisms and testing their attack effects are critical to ensuring the information security of the communication networks. This paper will study the generation of the adversarial examples and the influences of the adversarial examples on the accuracy of the DNN-based communication signal identification. Meanwhile, this paper will study the influences of the adversarial examples under the white-box models and black-box models, and explore the adversarial attack influences of the factors such as perturbation levels and iterative steps. The insights of this study would be helpful for ensuring the security of information networks and designing robust DNN communication networks.
Currency Substitution under Transaction Costs

A major selling point and feature of cryptocurrencies is that they allow anonymous payments around the globe without a third party watching. For payments of certain goods, this censorship resistance feature makes cryptocurrencies more suitable or less costly a medium of exchange than traditional fiat monies such as Dollars or Euros. On the other hand, there exist goods which are easier to acquire using traditional means of payments. The costs of employing cryptocurrencies may involve fees to miners, while traditional money might be subject to taxation. In this paper, we explore how asymmetry in transaction fees across goods drives currency substitution. We assume a continuum of differentiated goods which can be strictly ordered according to the costs agents occur when purchasing these goods with Bitcoins as opposed to Dollars. In a market equilibrium, agents endogenously decide which goods to acquire using Dollars and which goods to purchase using Bitcoins. The marginal good at which agents are indifferent between purchasing with Bitcoins or Dollars depends on and varies in the size of the value-added-tax and transaction fees to miners.
Sensing Human Respiration with a Conductive Silicone Band

A low-cost sensor for documenting human respiration is described. The sensor is based on a conductive elastomer band that changes electrical resistance when stretched.  When the conductive cord is looped around a volunteer’s chest to a tight fit, the sensing of respiratory cycles can easily be achieved thru expansion / contraction of the silicone band.  As the silicone band can be purchased at a current retail cost of less than $5 USD per meter, the sensor offers significant promise for use within massively parallel, IoT style human health monitoring networks.  This work describes the observed resistance change when mechanical stress is placed on the band, the temporal response of the resistance change which occurs, and documents proof-of-concept measurements that illustrate that a simple doped elastomer cord can serve as a low-cost transducer to report diagnostics of relevance to human health.      Keywords- low-cost sensing; respiration; healthcare; IoT; Covid research; apnea; smart sensor; soft matter; internet of things
Decidable variables for constructive logics

Abstract Ishihara's problem of decidable variables asks which class of decidable propositional variables is sufficient to warrant classical theorems in intuitionistic logic. We present several refinements to the class proposed by Ishii for this problem, which also allows the class to cover Glivenko's logic. We also treat the extension of the problem to minimal logic, suggesting a couple of new classes.
The Stability of Demand for Money in the Proposed Southern African Monetary Union

This study investigates the stability of demand for money in the proposed Southern African Monetary Union (SAMU). The study uses annual data for the period 1981 to 2015 from ten countries making-up the Southern African Development Community (SADC). A standard function of demand for money is designed and estimated using a bounds testing approach to co-integration and error-correction modeling. The findings show divergence across countries in the stability of money. This divergence is articulated in terms of differences in cointegration, CUSUM (cumulative sum) and CUSUMSQ (CUSUM squared) tests, short run and long-term determinants and error correction in event of a shock. Policy implications are discussed in the light of the convergence needed for the feasibility of the proposed SAMU. This study extends the debate in scholarly and policy circles on the feasibility of proposed African monetary unions.
Graph-Based Hybrid Recommendation Model to Alleviate Cold-Start and Sparsity Issue

The main aim of this research work is to find a resolution of cold-start and sparsity issues using graph data for the development of a novel hybrid personalized recommendation model. This research work demonstrates the implementation of a graph model intended to design for recommendation model that assists in resolving the challenges of a volatile market which is very commonly found in the real world. The presented unique hybrid model here combines content-based, knowledge-based and preference-based recommendation techniques for computation tasks of recommendation. This research work puts one step forward toward the challenges of data modeling of structure, semi-structured and unstructured data. The results of the experiment show the proposed model is efficient, flexible and ready to work well in an online Web environment. More than that, the proposed model allows the integration of multiple entities together due to the flexible structure of the graph data model. This research effort works as functional guidelines for everyone who desires to implement a recommendation system using graph data as an integrated component of a real-time Web environment for their computational challenges.
Monthly Averaged All Sky Solar Irradiance Prediction Using Artificial Neural Networks for Chandigarh Region

Solar radiation forecasting is fundamental for carrying out various research works in renewable energy sources (RESs). This paper gives solar radiation intensity prediction approach based on artificial neural networks (ANN). There are numerous geographical and climatic parameters that have significant impact on solar irradiance prediction. The input data is composed of geographical attributes likewise latitude, longitude, altitude, monthly averaged weather parameters over 2 years such as temperature, minimum temperature, maximum temperature, relative humidity, wind speed, sunshine hours and calendar variable—month of the year. ANN is employed on 25 Indian regions with divergent climatic situations, and structure learning is conducted to determine the most suitable topology. For structure learning, comparative analysis is executed on multi-layer feed forward networks and layer recurrent networks. The behaviour of ANN topologies is evaluated based on the mean square error (MSE), training time and correlation coefficient (R-value). The architecture having least MSE is chosen for simulation to predict solar radiation intensity over one year for Hamirpur (H.P.). Outcomes are compared with the readings of pyranometer, installed at Centre of Energy and Environmental Engineering, NIT Hamirpur. Results obtained are found to be good in accuracy, and the chosen architecture is employed to predict monthly averaged solar radiation intensity for Chandigarh.
Application of the Algorithm for the Recognition of Pedestrian Disturbance Patterns by Lucas-Kanade Method in Real Time

The use of security cameras allows obtaining relevant information that through video processing can provide a solution to the problem that every country faces, crime. In the following work an algorithm is developed for the detection of criminal acts, taking into account the disturbance pattern, with the use of the Python 3 programming language. For the identification of people, the area occupied by the body was taken into account, which allowed to discard any other object seen in the video. On the other hand, the monitoring of people and the analysis of changes in speed through distance was the product of tracking a corner of the body identified in a set of superimposed images, this was done with the help of the OpenCV library. The results show that the proposed algorithm can be improved with the additional use of patterns to increase its precision.
Using Google Trends to analyze trends in user queries in ecology

In 2007, Google introduced its Google Trends service to assess statistically users queries. However, despite some success in using Google Trends data for forecasting, most researchers think that without them they can obtain approximately similar results based on other models. Nevertheless, other researcher have been still exploring forecast functionality of the service. The authors analyze Google Trends’ estimates of queries in ecology. Their goal is reveal general trends and contradictions and to analyze RNPLS&amp;T resources’ meeting global trends in ecology and sustainable development information. Google Trends data is about similar to the current user activity idicators for the Library’s e-catalog and do not allow forecasting rise of or decrease in user demands. Google Trends enables to make some, rather approximate estimations of thematic information flow. Therefore it may be used by librarians as a source to evaluate interest users are expected to take in ecological information when the Library is planning for events or collection development. Further research is needed for advanced service application in user demand forecasting. The paper is prepared within the framework of the State Order № 075- 01300-20-00 “Development and improvement of the system of Open Archive of integrated information and library resources of Russian National Public Library for Science and Technology as modern knowledge management system in digital environment: On the way to Open Science” for 2020–2022.
High-quality First-person Rendering Mixed Reality Gaming System for In Home Setting

With the advent of low-cost RGB-D cameras, mixed reality serious games using `live' 3D human avatars have become popular. Here, RGB-D cameras are used for capturing and transferring user' motion and texture onto the 3D human avatar in virtual environments. A system with a single camera is more suitable for such mixed reality games deployed in homes, considering the ease of setting up the system. In these mixed reality games, users can have either a third-person perspective or a first-person perspective of the virtual environments used in the games. Since first-person perspective provides a better Sense of Embodiment (SoE), in this paper, we explore the problem of providing a first-person perspective for mixed reality serious games played in homes. We propose a real time textured humanoid-avatar framework to provide a first-person perspective and address the challenges involved in setting up such a gaming system in homes. Our approach comprises: (a) SMPL humanoid model optimization for capturing user' movements continuously; (b) a real-time texture transferring and merging OpenGL pipeline to build a global texture atlas across multiple video frames. We target the proposed approach towards a serious game for amputees, called Mr.MAPP (Mixed Reality-based framework for Managing Phantom Pain), where amputee' intact limb is mirrored in real-time in the virtual environment. For this purpose, our framework also introduces a mirroring method to generate a textured phantom limb in the virtual environment. We carried out a series of visual and metrics-based studies to evaluate the effectiveness of the proposed approaches for skeletal pose fitting and texture transfer to SMPL humanoid models, as well as the mirroring and texturing missing limb (for future amputee based studies).
CSSM: A Context-Based Semantic Similarity Measure

Semantic similarity is very critical in applications that manipulate data across heterogeneous, autonomous and distributed data sources. It helps connect these data sources. Nevertheless, current semantic similarity approaches were proven to achieve a very moderate accuracy. Furthermore, they are merely applicable when we wish to determine the semantic similarity between words. In this paper, we present CSSM, a Context-based Semantic Similarity Measure. Experimental results on Miller & Charles benchmark dataset show that CSSM performs semantic similarity comparisons properly, and obtains high accuracy.
Blockchain’s Future: Can the Decentralised Blockchain Community Succeed in Creating Standards?

Nakamoto proposed a new solution to transact value via the internet. And since 2009 blockchain technology has expanded and diversified. It has, however, proven to be inefficient in the way it achieves its outcomes, especially through the proof of work protocol. Other developers are promoting alternative methods but, as yet, none has superseded proof of work. The competing protocols illuminate a key feature of the blockchain community, namely, its inability to create consensus in a decentralised community. Because of this lack of consensus the formation of standards is particularly difficult to achieve. We examine three areas where some form of agreement over standards will be essential if blockchain is to evolve successfully. These three areas are blockchain governance, smart contracts, and interoperability of blockchains. We argue that because standards formation is such contested process the blockchain community will persist in creating difficulties for itself until it is able to overcome internal divisions.
Robust Single-object Visual Tracking Framework via Fully Convolutional Siamese Network with Correlation Filter

Recently, there are two kinds of methods in the research field of visual tracking, one is correlation filtering, the other is siamese network. In order to fully utilize the advantages of these two framework, this paper proposed a robust single-object visual tracking framework based on fully convolutional siamese network and correlation filter. Two branches are constructed in this network, namely training template branch and searching template branch, which are share the convolutional layer parameters. Each branch is divided into two sub-branches. In the sub-branches of the training template, two correlation filtering layers are embedded to replace the traditional convolutional layer. Finally, the location of object to be tracked is calculated by constructing response graph. Extensive experiments on challenging benchmark show that the tracking framework in this paper can further improve the accuracy.
Non-Parametric Method for Enhancement of Darker Portion in an Image

Images caught in darker area builds complexities in handling and removing essential data. Improvement of such pictures encourages us to recover significant information. ANN based error back propagation (BP) algorithm is used for enhancing shadow region of an image. Dataset used in this paper is a shadow image with its enhanced output (log transformed), so that model will be able to learn to enhance the shadow region of any given image. Darker locale in an image are successfully reduced in the results obtained. Still there is a scope of improvement through adjustments and variations into various parameters of proposed non-parametric approach.
Distributed Computing with Heterogeneous Servers

Distributed computing is known for its high efficiency of processing large amounts of data in parallel, at the expense of communication load between different servers. Coding was introduced to minimize the communication load by exploiting the repetitive computing, thus drawing great attention within the academia. Most existing works assume that all servers are identical in computational capability, which is inconsistent with practical scenarios. In this paper, we investigate a distributed computing system that consists of two types of servers, i.e., fast servers and slow servers. Due to the heterogeneous computational capabilities within the system, the overall computation time will be delayed by the slow servers, which is called the straggling effect. To this end, we develop a novel framework of coding-based distributed computing to alleviate the straggling effect. Specifically, for a given number of fast servers and slow servers with their corresponding computational capabilities, we aim to minimize the overall computation time by assigning different amounts of workloads to different servers. Further, we derive the information-theoretic lower bound of the communication load of the system, which is shown to be within a constant multiplicative gap to the achievable communication load by our scheme.
On Fuzzy Clustering Algorithms for Nominal Data

This paper presents three fuzzy clustering algorithms for nominal data. The first algorithm is similar to a conventional algorithm for vectorial data developed by introducing variables for controlling the cluster size. The second algorithm is similar to a conventional algorithm for vectorial data developed by regularizing another conventional algorithm for vectorial data with Kullback-Leibler divergence. The third algorithm is developed by regularizing the first algorithm mentioned above with q-divergence. Finally, some numerical experiments are conducted to investigate the features of the proposed algorithms.
Improving Supervised Cross-modal Retrieval with Semantic Graph Embedding

This paper focuses on the use of embedding with global semantic relations to improve the cross modal retrieval. Our method smoothly bridges the heterogeneity gap by graph embedding and then obtains discriminative representation by supervised learning. First, we construct a semantic correlation graph based on the intra-modal similarity and the semantic propagation of pairwise information. Then, embeddings are learnt from the graph semantic structure which enables all the cross-modal data to be mapped into the same space. Second, based on the previous embeddings, we adopt a simple one-branch neural network to enhance the discrimination of the representation by minimizing the discrimination loss and reconstruction loss. Experimental results on three widely-used benchmark datasets clearly demonstrate the improvement of the proposed approach over the state-of-the-art cross-modal retrieval methods.
Foreign Aid Volatility and Lifelong Learning

This paper has put a demand-side empirical structure to the hypothesis that foreign aid volatility adversely affects choices to lifelong learning in recipient countries. Lifelong learning is measured as the combined knowledge acquired during primary, secondary and tertiary educational enrolments. Three types of aggregate foreign aid volatilities are computed in a twofold manner: baseline standard deviations and standard errors (standard deviations of residuals after first-order autoregressive processes). An endogeneity robust system GMM empirical strategy is employed. The findings broadly show that foreign aid volatility does not adversely affect the demand-side choices of lifelong learning in Africa. As a policy implication, when faced with aid uncertainty, the demand for education would increase. This may be explained by the need for more self-reliance in order to mitigate income risks or/and the use of education as means of coping with uncertainty. More policy implications are discussed.
Optimization of Production Profits Using The Firefly Algorithm

Production optimization can be done by determining the optimal amount of product produced to achieve maximum profit according to the limit on the amount of raw material that has been provided for the production process. This study applies the Firefly algorithm for production optimization problems. The dataset used in this study was processed by Linear Programming to obtain the objective function equation and the constraint function. Meanwhile, the fitness value's objective function uses Multi-Objective optimization with the Weight Sum Method mathematical model. The test results with the dataset found the ideal parameter values to obtain the optimal number of products are alpha 0.8, beta 0.25, and gamma 0.0001, with an increase in industrial profit by 0.24%.
Case study: good health and wellbeing (SDG3)

In response to the developing global health crisis arising from the diminishing efficacy of antibiotics, a design-led collaboration between academics from the UK and Ghana explored hygiene practices across different home environments in Ghana. In doing so, we developed our understanding of the home as a source of infection stemming from bacteria, some of which are disease-causing pathogens carried by dust that could carry antimicrobial resistance. These insights were then used by householders to co-design context-specific cleaning practices that sought to mitigate the impacts of pathogen exposure and antimicrobial resistance. This case study outlines the Dust Bunny project and draws on reflections and interviews with the wider research team to offer insights into the challenges and opportunities for design research exemplified in this project, which may be applicable to other Global South-Global North project collaborations.