Reviewerly/exHarmony huggingface dataset:
Domain Adaptive Network Embedding

Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph-structured data. However, as previous methods usually focus on learning embedding for a single network, they cannot learn representations transferable on multiple networks. Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this article, we propose a Domain Adaptive Network Embedding framework, which applies Graph Convolutional Network to learn transferable embedding. In DANE, nodes from multiple networks are encoded to vectors via a shared and aligned embedding space. The distribution of embedding on different networks are further aligned by Adversarial Learning Regularization. To achieve better performance in scenarios where labels are provided, DANE adopts a cross-entropy error term of the GCN framework and class centroid aligning method. Moreover, DANE's advantages in learning transferable network embedding can be guaranteed theoretically. Extensive experiments reflect that the proposed framework outperforms other well-recognized network embedding baselines in cross-network domain adaptation tasks, and the semi-supervised components improve the performance significantly.
Is There a Need to Address Human Values in Domain Modelling?

Consideration of human values during software development can improve technology acceptance and minimize its negative societal implications. However, important human values often get ignored during requirements analysis and specification. Modelling is a fundamental requirements engineering (RE) activity. As a pilot study, we consider domain modelling, an RE modelling technique which often forms the basis of system design and hence significantly impacts the whole software development process. This position paper presents initial evidence that human values are indeed reflected in domain models. Consequently, paying inadequate attention to human values during domain modelling may result in negative financial or reputational implications for software organizations. We posit that human values-enriched domain modelling guidelines may help in identifying what stakeholders really want to avoid system rejection and negative social implications and call upon the RE community to integrate human values-enriched guidelines into RE modelling techniques.
A Robust Bayesian Approach for Online Filtering in the Presence of Contaminated Observations

This article proposes an online scheme for state estimation of a generic class of nonlinear dynamical systems in the presence of abnormal measurement data from sensors. We thoroughly illustrate why the performance of standard recursive Bayesian inference degrades in the presence of any measurement distortion. After demonstrating how different abnormalities can be accommodated explicitly inside a generic state-space model, we propose a robust mechanism to perform recursive Bayesian inference on the presented model to not only detect but also mitigate the effect of corrupted measurements in the final state estimates. Using simulations and experimentation, we demonstrate the success of the proposed framework in reducing the impact of different types of distortions in measurements. The ability to tackle different kinds of measurement abnormalities during online inference sets the proposed method apart from the existing techniques.
Deep Learning for Multi-Class Antisocial Behavior Identification From Twitter

Social Media has become an integral part of our daily life. Not only it enables collaboration and flow of information but has also become an imperative tool for businesses and governments around the world. All this makes a compelling case for everyone to be on some sort of online social media platform. However, this virtuousness is overshadowed by some of its shortcomings. The manifestation of antisocial behaviour online is a growing concern that hinders participation and cultivates numerous social problems. Antisocial behaviour exists in its various forms such as aggression, disregard for safety, lack of remorse, unlawful behaviour, etc. The paper introduces a deep learning-based approach to detect and classify online antisocial behaviour (ASB). The automatic content classification addresses the issue of scalability, which is imperative when dealing with online platforms. A benchmark dataset was created with multi-class annotation under the supervision of a domain expert. Extensive experiments were conducted with multiple deep learning algorithms and their superior results were validated against the results from the traditional machine learning algorithms. Visually enhanced interpretation of the classification process is presented for model and error analyses. Accuracy of up to 99% in class identification was achieved on the ground truth dataset for empirical validation. The study is an evidence of how the cutting-edge deep learning technology can be utilized to solve a real-world problem of curtailing antisocial behaviour, which is a public health threat and a social problem.
Evaluation Of Feature Selection for Improvement Backpropagation Neural Network in Divorce Predictions

In every domestic life always has its own problems, and every household must have their own conflict. Problems and conflicts that come in the life of the household can actually be part of the process to mature each other's partners, but sometimes the problems and conflicts that trigger divorce. Dataset from the UCI Repository, an evaluation and improvement process was carried out on the Backpropagation Neural Netwok(BPNN) algorithm and also performed a set of parameters are set and then validated, able to predict whether a married couple will divorce or not with sufficient results. high. And also do a process for some feature selection, then the value or rating of the most significant features will be processed on the Backpropagation Neural Network Algorithm and compare the results of the Gain Ratio, Information Gain, Relief and Correlation feature selection. This model underwent several validation processes so as to achieve a fairly high accuracy by using the Relief feature selection that is 99.41%.
Hazard Driven Threat Modelling for Cyber Physical Systems

Adversarial actors have shown their ability to infiltrate enterprise networks deployed around Cyber Physical Systems (CPSs) through social engineering, credential stealing and file-less infections. When inside, they can gain enough privileges to maliciously call legitimate APIs and apply unsafe control actions to degrade the system performance and undermine its safety. Our work lies at the intersection of security and safety, and aims to understand dependencies among security, reliability and safety in CPS/IoT. We present a methodology to perform hazard driven threat modelling and impact assessment in the context of CPSs. The process starts from the analysis of behavioural, functional and architectural models of the CPS. We then apply System Theoretic Process Analysis (STPA) on the functional model to highlight high-level abuse cases. We leverage a mapping between the architectural and the system theoretic(ST) models to enumerate those components whose impairment provides the attacker with enough privileges to tamper with or disrupt the data-flows. This enables us to find a causal connection between the attack surface (in the architectural model) and system level losses. We then link the behavioural and system theoretic representations of the CPS to quantify the impact of the attack. Using our methodology it is possible to compute a comprehensive attack graph of the known attack paths and to perform both a qualitative and quantitative impact assessment of the exploitation of vulnerabilities affecting target nodes. The framework and methodology are illustrated using a small scale example featuring a Communication Based Train Control (CBTC) system. Aspects regarding the scalability of our methodology and its application in real world scenarios are also considered. Finally, we discuss the possibility of using the results obtained to engineer both design time and real time defensive mechanisms.
Sieving for Twin Smooth Integers with Solutions to the Prouhet-Tarry-Escott Problem

We give a sieving algorithm for finding pairs of consecutive smooth numbers that utilizes solutions to the Prouhet-Tarry-Escott (PTE) problem. Any such solution induces two degree-n polynomials, a(x) and b(x), that differ by a constant integer C and completely split into linear factors in $$\mathbb {Z}[x]$$ . It follows that for any $$\ell \in \mathbb {Z}$$ such that $$a(\ell ) \equiv b(\ell ) \equiv 0 \bmod {C}$$ , the two integers $$a(\ell )/C$$ and $$b(\ell )/C$$ differ by 1 and necessarily contain n factors of roughly the same size. For a fixed smoothness bound B, restricting the search to pairs of integers that are parameterized in this way increases the probability that they are B-smooth. Our algorithm combines a simple sieve with parametrizations given by a collection of solutions to the PTE problem. The motivation for finding large twin smooth integers lies in their application to compact isogeny-based post-quantum protocols. The recent key exchange scheme B-SIDH and the recent digital signature scheme SQISign both require large primes that lie between two smooth integers; finding such a prime can be seen as a special case of finding twin smooth integers under the additional stipulation that their sum is a prime p. When searching for cryptographic parameters with $$2^{240} \le p <2^{256}$$ , an implementation of our sieve found primes p where $$p+1$$ and $$p-1$$ are $$2^{15}$$ -smooth; the smoothest prior parameters had a similar sized prime for which $$p-1$$ and $$p+1$$ were $$2^{19}$$ -smooth. In targeting higher security levels, our sieve found a 376-bit prime lying between two $$2^{21}$$ -smooth integers, a 384-bit prime lying between two $$2^{22}$$ -smooth integers, and a 512-bit prime lying between two $$2^{28}$$ -smooth integers. Our analysis shows that using previously known methods to find high-security instances subject to these smoothness bounds is computationally infeasible.
Anomaly Detection on IOT Data for Smart City

Smart Cities are probably on the more complex environment for IOT data collection. IOT data could have different producers, sample rates, periodic and aperiodic, typical trends, structures and stacks, faults, etc. Thus, a strongly flexible and scalable solution is needed to avoid investing huge amount of resources in anomaly detection that has to be done in real time and has to be agnostic to the above-mentioned problems. This paper presents a solution for automatic detection of anomalies. The proposed approach scales seamlessly and integrates in different contexts, featuring different sensor types, protocols, and data formats, and computationally cheap. The research has been developed in the context of Snap4City PCP Select4Cities project and is presently implemented in the Https://www.snap4city.org solution adopted in several cities and regions.
Quantitative assessment of the relative tectonic activity using the analytical hierarchy process in the northwestern margin of the Lut Block, Central Iran

In this study, the analytical hierarchy process (AHP) was used to enhance the accuracy of the results obtained from the geomorphic analysis of tectonic activity. For this purpose, the relative tectonic activity index was computed by applying different and specific weights to each index based on the general geological and structural conditions of the study area. The Kuh-e-Sarhangi Mountain with a NE-SW strike is located in the northern part of the Tabas Block in the Central Iran Plate. The Kuh-e-Sarhangi Fault is considered one of the major faults in Central Iran and shows evidence of Quaternary activity. In this study, in order to evaluate the activity of the faults, six morphotectonic indices were computed and the different conditions which influenced the morphometric factors were considered and analyzed. The relative tectonic activity of Kuh-e-Sarhangi was classified based on the analytical hierarchy process method. These results were confirmed by field studies. The results of this method fitted well with some independently detected Quaternary faults and other reactivated faults in the Kuh-e-Sarhangi Mountain. With a right-lateral movement, some of these faults have caused the displacement of rivers up to 900 m. The dispersal of the high tectonic activity classes based on the AHP method conforms to the post-Neogene activity of the region. The distribution of the classes illustrates that morphotectonic anomalies correspond with the incised alluvial fans and terraces and the bisected and displaced rivers in the study area. Accordingly, the integration of the results of field investigations, satellite images, morphometry, and the AHP method shows a high accordance with the active structures and demonstrates that the study area is tectonically active. This analysis illustrates the recent movements of the Kuh-e-Sarhangi Fault and the activity of the Lut Block due to the convergent movement of the Arabian plate toward Eurasia.
Mixed Reality Spatial Computing in a Remote Learning Classroom

We present a case study on the use of mixed reality (MR) spatial computing in a fully remote classroom. We conducted a 10-week undergraduate class fully online, using a combination of traditional teleconferencing software and MR spatial computing (Magic Leap One headsets) using an avatar-mediated social interaction application (Spatial). The class culminated in a virtual poster session, using Spatial in MR to present project results, and we conducted a preliminary investigation of students experiences via interviews and questionnaires. Students reported that they had a good experience using MR for the poster session and that they thought it provided advantages over 2D video conferencing. Particular advantages cited were a stronger sense that they were in the presence of other students and instructors, an improved ability to tell where others were directing their attention, and a better ability to share 3D project content and collaborate.
Visual Delegates - Enhancing Player Perception by Visually Delegating Player Character Sensation

While a player character in a game can experience all perceptual modalities, player senses are often limited, especially to the visual channel. As a result, many video games apply visual delegates that function as a substitution to enhance player perception and intensify the relation between player and player character. For instance, in first-person actions games, red colored edges on the screen layer visually inform the player of being hit and, to some extent, this visual feedback delegates the character's affect (i.e., pain) to the player's perception. Existing games apply such delegates in various forms. Yet, there is no systematic investigation or classification of these visual delegates resulting in a lack of understanding how to appropriately assign visual delegates to perceptual events in game design. Using a grounded theory-based approach, we systematically analyzed 102 character-centered video games. We collected 67 visual delegates and derived 12 associated groups of five basic perceptions. As a first, our work provides a systematic classification and collection that allows game designers to find appropriate visual delegates for character perceptions they want to trigger in players.
Social Network Recommender System, A Neural Network Approach

Social networks have increased considerably due to the development of networks with specific purposes and represent a high percentage of daily communications between people. Due to the large amount of content in any type of social network, it is necessary to guide users to find the content that best suits their needs. The inclusion of artificial intelligence techniques greatly facilitates the task of finding relevant content. This document presents a recommendation system (RS) for a business and employment-oriented social network. Therefore, job offers are recommended to users, but other users are also encouraged to follow them. The system presented is based on virtual agent organizations, and uses artificial neural networks to determine whether job offers and users should be recommended or not. The system has been evaluated on a real social network and has provided a high acceptance rate of both job offers and user recommendations.
A User-Defined Location-Sharing Scheme with Efficiency and Privacy in Mobile Social Networks

The popularity of the modern smart devices and mobile social networks (MSNs) brings mobile users better experiences and services by taking advantage of location-aware capabilities. Location sharing, as an important function of MSNs, has attracted attention with growing popularity. While the users get great benefits and conveniences from MSNs, they also have high concerns about the privacy of location. However, in the existing solution, the privacy of users can hardly be guaranteed without the assumption of full trust in the service provider (SP), and few previous research studies have discussed the individual requirement of mobile users in MSNs. In this paper, we propose a user-defined location-sharing scheme (ULSS) to achieve enhanced privacy preservation under different contexts. We present a coarse-grained proximity detection method and a lightweight order-preserving encryption- (OPE-) based method to provide the users with flexible privacy preservation at different privacy levels. The proposed scheme preserves user’s location privacy with respect to SP, friends, and other adversaries, getting rid of the introduction of fully trusted party (TTP). Extensive experiments were conducted to verify the effectiveness and efficiency of our proposed scheme.
Supporting user preferences in search-based product line architecture design using Machine Learning

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line. PLA design requires intensive human effort as it involves several conflicting factors. In order to support this task, an interactive search-based approach, automated by a tool named OPLA-Tool, was proposed in a previous work. Through this tool the software architect evaluates the generated solutions during the optimization process. Considering that evaluating PLA is a complex task and search-based algorithms demand a high number of generations, the evaluation of all solutions in all generations cause human fatigue. In this work, we incorporated in OPLA-Tool a Machine Learning (ML) model to represent the architect in some moments during the optimization process aiming to decrease the architect's effort. Through the execution of a quantiqualitative exploratory study it was possible to demonstrate the reduction of the fatigue problem and that the solutions produced at the end of the process, in most cases, met the architect's needs.
Multi-View Hand-Hygiene Recognition for Food Safety

A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene actions, with the goal of improving hand-hygiene effectiveness. Our proposed two-stage system processes untrimmed video from both egocentric and third-person cameras. In the first stage, a low-cost coarse classifier efficiently localizes the hand-hygiene period; in the second stage, more complex refinement classifiers recognize seven specific actions within the hand-hygiene period. We demonstrate that our two-stage system has significantly lower computational requirements without a loss of recognition accuracy. Specifically, the computationally complex refinement classifiers process less than 68% of the untrimmed videos, and we anticipate further computational gains in videos that contain a larger fraction of non-hygiene actions. Our results demonstrate that a carefully designed video action recognition system can play an important role in improving hand hygiene for food safety.
Specializing Coherence, Consistency, and Push/Pull for GPU Graph Analytics

This work explores the interaction of three communication-centric design dimensions for graph workloads on emerging integrated CPU-GPU systems: update propagation with and without fine-grained synchronization (push vs. pull), emerging coherence protocols (GPU vs. DeNovo coherence), and software-centric consistency models (DRF0, DRF1, and DRFrlx). We show that these dimensions are inter-dependent and the best design depends on the graph algorithm and input. We develop a model to predict this best design, motivating flexible and hardware-software co-designed GPU memory systems.
Scaling Correctness-by-Construction

The correctness-by-construction paradigm allows developers to derive formally correct programs from a pair of first-order precondition and postcondition. Although tool support has been proposed recently, and thus correctness-by-construction has left the period of pen-and-paper proofs, it is still applied in an unstructured manner to independent algorithmic problems only. To scale correctness-by-construction to more complex programs and to establish a repository of reusable off-the-shelf components, we present a formal framework and open-source tool support called ArchiCorC. In ArchiCorC, a developer models UML-style software components comprising required and provided interfaces, where methods contained in interfaces are associated to specification contracts and mapped to correct-by-construction implementations. We describe our proposed mathematical model for the horizontal and vertical composition of correct-by-construction components, and identify properties that allow to reuse them across different projects. Finally, we demonstrate feasibility of our approach on a case study and discuss future research directions related to the integration of correct-by-construction components into software engineering practices.
Studying Computational Fluid Dynamics in a New Dimension with Virtual Reality

Communication strategies for complex problems in the field of chemical and process engineering is becoming more and more important. Especially computational fluid dynamics (CFD) results require well-established spatial reasoning skills. In this contribution, we show how we implemented virtual reality (VR) into the Master course "CFD for process engineering" at Clausthal University of Technology. We discuss advantages and disadvantages of applying VR during the course. In addition, we show how VR can enable students to get a thorough understanding of the interplay between reactor design and performance.