Reviewerly/exHarmony huggingface dataset:
Convolutional neural network with structural input for visual object tracking

Numerous deep learning approaches have been applied to visual object tracking owing to their capabilities to leverage huge training data for performance improvement. Most of these approaches have limitations with regard to learning target specific information rich features and therefore observe reduced accuracy in the presence of different challenges such as occlusion, scale variations, rotation and clutter. We proposed a deep neural network that takes input in the form of two stacked patches and regresses both the similarity and the dis-similarity scores in single evaluation. Image patches are concatenated depth-wise and fed to a six channel input of the network. The proposed network is generic and exploits the structural differences between the two input patches to obtain more accurate similarity and dissimilarity scores. Online learning is enforced via short-term and long-term updates to improve the tracking performance. Extensive experimental evaluations have been performed on OTB2015 and TempleColor128 benchmark datasets. Comparisons with state-of-the-art methods indicate that the proposed framework has achieved better tracking performance. The proposed tracking framework has obtained improved accuracy in different challenges including occlusion, background clutter, in-plane rotation and scale variations.
An iterative oversampling approach for ordinal classification

The machine learning field has grown considerably in the last years. There are, however, some problems still to be solved. The characteristics of the training sets, for instance, are known to affect the classifiers performance. Here, and inspired by medical applications, we are interested in studying datasets that are both ordinal and imbalanced. Ordinal datasets present labels where only the relative ordering between different values is significant. Imbalanced datasets have very different quantity of examples per class.
Is Seeing Believing?

Mediated social touch (MST) provides physical contact over a distance for geographically separated individuals. Despite advances in actuator technologies, it remains difficult to recreate the feel and sensation of natural human touch. Combining touch with morphologically congruent visual feedback may overcome limitations related to the low fidelity of current-day tactile displays. Being able to both feel and see the touch act being initiated on an input device could enhance the perceived realism of the touches. In two studies, we test the effects of such visual feedback on self-reported naturalness of touch, social presence, and emotional experiences.
Multi‐key homomorphic authenticators

Homomorphic authenticators (HAs) enable a client to authenticate a large collection of data elements and outsource them, along with the corresponding authenticators, to an untrusted server. At any later point, the server can generate a short authenticator vouching for the correctness of the output y of a function f computed on the outsourced data, i.e. . The notion of HAs studied so far, however, only supports executions of computations over data authenticated by a single user. Motivated by realistic scenarios in which large datasets include data provided by multiple users, we study the concept of multi-key homomorphic authenticators. In a nutshell, multi-key HAs are like HAs with the extra feature of allowing the holder of public evaluation keys to compute on data authenticated under different secret keys. In this paper, we introduce and formally define multi-key HAs. Secondly, we propose a construction of a multi-key homomorphic signature based on standard lattices and supporting the evaluation of circuits of bounded polynomial depth. Thirdly, we provide a construction of multi-key homomorphic MACs based only on pseudorandom functions and supporting the evaluation of low-degree arithmetic circuits.
Evolution of the Unix System Architecture: An Exploratory Case Study

Unix has evolved for almost five decades, shaping modern operating systems, key software technologies, and development practices.Studying the evolution of this remarkable system from an architectural perspective can provide insights on how to manage the growth of large, complex, and long-lived software systems.Along main Unix releases leading to the FreeBSD lineage we examine core architectural design decisions, the number of features, and code complexity, based on the analysis of source code, reference documentation, and related publications.We report that the growth in size has been uniform, with some notable outliers, while cyclomatic complexity has been religiously safeguarded.A large number of Unix-defining design decisions were implemented right from the very early beginning, with most of them still playing a major role.Unix continues to evolve from an architectural perspective, but the rate of architectural innovation has slowed down over the system's lifetime.Architectural technical debt has accrued in the forms of functionality duplication and unused facilities, but in terms of cyclomatic complexity it is systematically being paid back through what appears to be a self-correcting process.Some unsung architectural forces that shaped Unix are the emphasis on conventions over rigid enforcement, the drive for portability, a sophisticated ecosystem of other operating systems and development organizations, and the emergence of a federated architecture, often through the adoption of third-party subsystems.These findings have led us to form an initial theory on the architecture evolution of large, complex operating system software.
Creating Positive Experiences with Digital Companions

Over the last decade, advances in machine learning have multiplied the possibilities for applications of artificial intelligence. One of these applications are digital companions that assist their users in tasks and activities. In this study, we wanted to evaluate whether digital companions can be designed to create possibilities for positive experiences in work contexts and also be perceived as such using a Wizard-of-Oz prototype of a companion that supports workshop planning. We find that the work is perceived as more positive, more natural, and the content that is presented as more positive after the interaction with a companion that has been designed for positive experience compared to a neutral companion with the same functionality.
Implementation of the FATI hierarchical fuzzy inference system using the immutability decomposition method

Most practical implementations of fuzzy inference systems chiefly encompass rule-based systems (FITA) due to their lower implementation cost. Classic fuzzy relational inference systems (FATI) are rarely used in practice. This paper presents a hierarchical architecture of the FATI system, which allows one to reduce the resources necessary for its software or hardware implementation. The system consists of single-input single-output (SISO) subsystems, which have the same architecture, but differ from each other in the contents of their knowledge in the form of fuzzy subrelations. These subrelations are created using decomposition of a global fuzzy relation of the primary classic multiple-input single-output (MISO) fuzzy inference system. Calculation of the global fuzzy relation, based on the knowledge base of the FITA system, is time-consuming and requires much memory to store it; therefore, decomposition has been moved to the linguistic level. Decomposition is a lossy operation; thus, the fuzzy inference result of the hierarchical system is fuzzier than the classic one. To avoid inference error, we present novel methods based on derivative linguistic values and partitioning of the primary knowledge base with immutability of its contents during the decomposition step, which can be used for any fuzzy inference system. An example of a FATI system proves the suitability of the developed methods in a real practical application.
Cyber security awareness for small, medium and micro enterprises (SMMEs) in South Africa

SMMEs are increasingly depending on IT to manage their business operations effectively. In South Africa, there is an increase of cyberattacks on organisations regardless of size and industry, although SMMEs are most impacted. Yet, SMMEs do not always pay attention to cyber security issues. The primary objective of this paper is to review, discover and update researchers about prior cyber security awareness (CSA) studies conducted in the context of South African SMMEs, which warrant future work. A second objective is to discover and analyse frameworks to identify building blocks for a CSA framework. Therefore, in order to respond to research questions, the literature was reviewed systematically. The evaluation shows the absence of previous studies on the topic. However, it was concluded that none of the identified frameworks can be utilised in an SMME context. In addition, ten common components of CSA were identified, that can be considered in future research.
The Body as Starting Point

Inbodied design is an emerging area in HCI that focuses on using knowledge of the body's internal systems and processes to better inform em-bodied and circum-bodied design spaces. The current challenge in developing an inbodied approach to HCI research/design is domain expertise: accessing sufficient and appropriate information about how the body itself works and how the body's different systems interact dynamically. In this workshop, we review and build on last year's introduction to inbodied foundations, focusing on applying inbodied knowledge to design challenges to explore (1) the foundational pillars of the inbodied design approach, and (2) how inbodied knowledge can affect / alter our understanding of em-bodied and circum-bodied design challenges and better inform design decisions. Our aim with this hands-on and cross-domain workshop is for HCI researchers to create innovative designs taking the body as a starting point
Pseudo-haptic Controls for Mid-air Finger-based Menu Interaction

Virtual Reality (VR) is more accessible than ever these days. While topics like performance, motion sickness and presence are well investigated, basic topics as VR User Interfaces (UIs) for menu control are lagging far behind. A major issue is the absence of haptic feedback and naturalness, especially when considering mid-air finger-based interaction in VR, when "grabbable" controllers are not available. In this work, we present and compare the following two visual approaches to mid-air finger-based menu control in VR environments: a planar UI similar to common 2D desktop UIs, and a pseudo-haptic UI based on physical metaphors. The results show that the pseudo-haptic UI performs better in terms of all tested aspects including workload, user experience, motion sickness and immersion.
A Response to the United Nations CITES Blockchain Challenge: Incremental and Integrative PoA-based Permit Exchange

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) negotiated and administered by the United Nations Environment Programme (UNEP) regulates the international trade of endangered species and derived products through a permit-based system. Currently, the permit process is paper-based and hence highly prone to manipulations and errors. Being aware of blockchains' potential, the CITES Secretariat defined a challenge to determine whether a blockchain-based system can address the aforementioned issues and serve as a secure, efficient, and affordable permit processing system. In this paper, we respond to the CITES Blockchain Challenge. First, we analyze the permit process and discuss how blockchain systems can improve that process in a way traditional systems cannot. Building on these results, we design a blockchain-based system that enables secure, manipulation-resistant permit validation, produces an immutable record of processed permits, and is in compliance with the CITES agreement. To evaluate this design, we developed a proof-of-concept implementation compatible with the paper-based permit process and deployed it to a Proof-of-Authority-based blockchain network. This allows incremental adoption and integration with the existing process, thereby increasing acceptance and addressing affordability. Finally, we describe how a blockchain-based system could disruptively improve the established permit process by enforcing quotas and tracking provenance.
Analysis of architectural variants for auditable blockchain-based private data sharing

Many applications by design depend on costly trusted third-party auditors. One such example is the industrial application case of federated multi-disciplinary optimization (MDO), in which different organizations contribute to a complex engineering design effort. Although blockchain and distributed ledger technology (DLT) has strong potential in reducing the dependence on such intermediaries, the architectural complexity involved in designing a solution is daunting.
Relational database security using digital watermarking and evolutionary techniques

Abstract Advancement in the field of communication facilitates the world for electronic sharing of huge amount of data. Relational databases most widely used and shared through Internet by the communities and organizations. They work in collaborative environment for sharing of knowledge and for applying data mining techniques for finding different market trends and interests for research groups. Proof of ownership and database security are major issues related to online sharing of database. Watermarking facilitates solution for ownership protection and reversible watermarking ensures data recovery in case of any kind of malicious cyberattacks. The major challenge of watermarking are (1) imperceptibility of watermark, (2) robustness of watermark against any kind of data alteration attacks, and (3) data recovery. In this research, a reversible watermarking technique for numerical relational databases by using evolutionary techniques has been proposed that ensures the integrity of underlying data and robustness of watermark. Moreover, maximum Relevance and Minimum Redundancy (mRMR) technique has been used for feature subset selection. Binary Bat Algorithm (BBA) as constraints optimization technique used for watermark creation. Result shows effectiveness of the proposed scheme against data tempering attacks. The proposed BBA provides better results as compare to Genetic Algorithm.
AR-C3D: Action Recognition Accelerator for Human-Computer Interaction on FPGA

In recent years, action recognition has been widely explored and attains significant performance improvement. In this paper, we propose a real-time action recognition specified convolutional 3D (AR-C3D) neural network for human-computer interaction. The CNN structure is optimized to decrease the complexity. Furthermore, Winograd algorithm is adopted to accelerate computation. It achieves 89.9% accuracy in the application which refers to the robot classifies the video captured by itself and would either imitate human's action or give verbal feedback. The Artix-7 FPGA implementation result outperforms previous work in terms of resource utilization and no external storage is consumed. One video can be processed in 6.6ms, and the power consumption is only 2.7W.
Towards a Multi-level Exploration of Human and Computational Re-representation in Unified Cognitive Frameworks

Re-representation is a critical ability to (i) understanding human creative problem solving, and (ii) modeling computational cognitive systems able to support or perform creative problem solving tasks on their own. This paper proposes a unified multi-level cognitive approach to investigating re-representation: the study of sensory-based, concept-based and problem template based possible forms of re-representations in an integrated manner. Descriptions and explanations of each level prepare the ground for further computational modeling. A study is deployed in order to explore the relationship between the various tasks proposed to reflect re-representation. A significant correlation between the investigated tasks is discovered. Two previous studies from the literature are replicated. A new strong and significant relationship between the Pattern Meanings Test and the Alternative Uses Test is observed.
Development and Validation of an Instrument for Measuring Digital Empowerment of Primary School Students

It is essential to enhance young learners' digital competency skills in the hope of empowering them in the digital world. Although past studies have examined measurements of university learners' digital empowerment, the literature has lacked discussion of digital empowerment in the primary school context. This study aims to develop and validate an instrument on digital empowerment of primary school students. The sample consists of 328 local primary school students from grades 4 to 6. Exploratory factor analysis showed a multi-dimensional structure of digital empowerment composed of (1) meaningfulness, (2) impact, (3) creativity belief, and (4) competence belief. Confirmatory factor analysis further confirmed the validity and reliability of digital empowerment as a multi-dimensional construct. This digital empowerment scale can serve as a tool to evaluate young learners' perceived competence when using digital technologies from the technological world in the primary school context.
Remote sensing of surface Hydrothermal Alteration, identification of Minerals and Thermal anomalies at Sveifluháls-Krýsuvík high-temperature Geothermal field, SW Iceland

This study used optical remote sensors to identify surface hydrothermal alteration and thermal anomalies in Krýsuvík geothermal field. Multispectral Landsat and ASTER satellite images were used to identify hydrothermal alteration minerals and thermal anomalies. A hyperspectral image from Hyperion was used for the analysis of absorption features. Spectral analysis from the visible (VIS) to the short wavelength infrared (SWIR) allowed the identification of possible sulfur, iron oxides, and montmorillonite. A time series analysis of thermal anomalies using the nighttime satellite images from 2002 to 2017 detected extinct surface hydrothermal activity southwest of the study area, and a thermal anomaly possibly affected by crustal deformation in the southeast. In Seltún area, thermal infrared (TIR) images acquired by a camera on an unmanned aerial vehicle (UAV) were compared with ground measurements; the aim was assessing the accuracy of the TIR images regarding the distance between the camera and the ground. The TIR image taken at 30 m elevation was used to calculate radiative heat flux; values were in same order of magnitude than the heat flux through the soil estimated by using ground measurements. This study provides insights for monitoring natural or induced changes on the surface geothermal activity of geothermal fields.
Implementation of PV Monitoring System Using Python

In this paper, we provide a method for monitoring solar power plants and analyzing monitoring data. Most monitoring systems collect data from the PV inverter through RTU and transmit it to the remote server.In this paper, We provide a method to collect the information provided by the PV inverter through the Modbus/TCP and analyze it through DNN(Deep Neural Network), one of the techniques of machine learning using python and open source libraries.
Soft Computing Techniques Based Automatic Query Expansion Approach for Improving Document Retrieval

Automatic Query Expansion provides most suitable terms for retrieving relevant documents from datasets. In this paper, a new query expansion approach is proposed and implemented which is based on accelerated particle swarm optimization(PSO). Focus of the proposed technique is to find the most suitable expanded query in place of related expansion terms. The proposed technique uses Fuzzy logic to enhance the accuracy of accelerated particle swarm optimization with changing different parameters. The proposed technique is compared with another state of the art work on different performance parameters such as F-measure and Mean Average Precision. The efficiency of the proposed algorithm technique is also evaluated on two datasets as CACM and CISI. The results indicate that the proposed technique shows improved results compared to existing similar existing technique.
Brick

Multiplayer augmented reality (AR) games allow players to inhabit a shared physical environment populated with interactive digital objects. However, currently available games fall short because of either limited synchronicity or limited opportunities for player movement. Here, we present Brick, a synchronous multiplayer AR game at the room scale. Brick's players collaborate to fill in a pattern of empty slots using digital "bricks" scattered about the room. This paper provides an overview of Brick from a design and technical perspective. It also discusses how Brick extends the current scope of AR games to include collaborative gameplay.
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric

We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning). Recent studies in PU learning have shown superior performance theoretically and empirically. However, most existing algorithms may not be suitable for large-scale datasets because they face repeated computations of a large Gram matrix or require massive hyperparameter optimization. In this paper, we propose a computationally efficient and theoretically grounded PU learning algorithm. The proposed PU learning algorithm produces a closed-form classifier when the hypothesis space is a closed ball in reproducing kernel Hilbert space. In addition, we establish upper bounds of the estimation error and the excess risk. The obtained estimation error bound is sharper than existing results and the derived excess risk bound has an explicit form, which vanishes as sample sizes increase. Finally, we conduct extensive numerical experiments using both synthetic and real datasets, demonstrating improved accuracy, scalability, and robustness of the proposed algorithm.
Aiding the realization of service-oriented distributed systems

Reuse-based software engineering is becoming the main approach for developing business and commercial systems. Service choreographies support the reuse-based service-oriented philosophy in that they represent a powerful and flexible approach to realize systems by (possibly) reusing services and composing them in a fully distributed way. A key enabler for the realization of choreographies is the ability to (i) reuse existing applications and services that can play the role of choreography participants, (ii) aid developers in writing the code of those participants whose roles cannot be covered through reuse, and (iii) automatically synthesize the coordination logic required for their correct interaction. The contribution of this paper is the definition and realization of a novel approach to the synthesis of service choreographies that allows developers to just fill-in-the-blank of automatically generated code templates of single (uncovered) choreography tasks, without the need of considering all the message flows specified by the choreography and the related distributed coordination issues.
Statically analyzing information flows

In the context of systems security, information flows play a central role. Unhandled information flows potentially leave the door open to very dangerous types of attacks, such as code injection or sensitive information leakage. Information flows verification is based on the definition of Non-Interference [8], which is known to be an hyperproperty [7], i.e., a property of sets of executions. The sound verification of hyperproperties is not trivial [3, 16]: It is not easy to adapt classic verification methods, used for trace properties, in order to deal with hyperproperties. In the present work, we design an abstract interpretation-based static analyzer soundly checking Non-Interference. In particular, we define an hyper abstract domain, able to approximate the information flows occurring in the analyzed programs.