Reviewerly/exHarmony huggingface dataset:
Hubs for VirtuosoNext: Online verification of real-time coordinators

VirtuosoNextTM is a distributed real-time operating system (RTOS) featuring a generic programming model dubbed Interacting Entities. This paper focuses on these interactions, implemented as so-called Hubs. Hubs act as synchronisation and communication mechanisms between the application tasks and implement the services provided by the kernel. While the kernel provides the most basic services, each carefully designed, tested and optimised, tasks are limited to this handful of basic hubs, leaving the development of more complex mechanisms up to application specific implementations. This work presents a toolset that supports the building of new services compositionally, using notions borrowed from the Reo coordination language, on which the developer can delegate coordination-related duties. This toolset uses a formal compositional semantics for hubs that captures dataflow and time, formalising the behaviour of existing hubs, and allowing the definition of new ones. Furthermore, it enables the analysis and verification of hubs under our automata interpretation, including time-sensitive behaviour via the Uppaal model checker, usable on http://arcatools.org/hubs. We illustrate the proposed tools and methods by verifying key properties on different interaction scenarios between tasks and a composed hub.
Building a Graph Database for Storing Heterogeneous Healthcare Data

Healthdata is unstructured and heterogeneous. Storing and managing healthdata therefore require NoSQL data models, so that they can be efficiently accessed. In this paper, we propose a graph-based data model for presenting patient and doctor relationships when a treatment episode starts. On top of this data model, a doctor recommender system and an online healthcare delivery system have been built which can be used for treating patients remotely.
Adaptive caches as a defense mechanism against cache side-channel attacks

Side-channel attacks exploit architectural features of computing systems and algorithmic properties of applications executing on these systems to steal sensitive information. Cache side-channel attacks are more powerful and practical compared to other classes of side-channel attacks due to several factors, such as the ability to be mounted without physical access to the system. Some secure cache architectures have been proposed to counter side-channel attacks. However, they all incur significant performance overheads. This work explores the viability of using adaptive caches, which are conventionally used as a performance-oriented architectural feature, as a defense mechanism against cache side-channel attacks. We conduct an empirical analysis, starting from establishing a baseline for the attacker’s ability to infer information regarding the memory accesses of the victim process when there is no active defense mechanism in place and the attacker is fully aware of all the cache parameters. Then, we analyze the effectiveness of the attack without complete knowledge of the cache configuration. Finally, based on the insight that the success of the attack is heavily dependent on knowledge of the cache configuration, we formulate a cache monitoring and user-defined events detection methodology, implement a generalized run-time cache reconfiguration technique, and observe their effect on successfully detecting and mitigating attacks on the cache subsystem. We observe that reconfiguring different cache parameters during a side-channel attack reduces the accuracy of the attack in detecting cache sets accessed by the victim by 44% on average, with a maximum of 90% reduction.
A Reinforcement Learning Approach to Optimize Discount and Reputation Tradeoffs in E-commerce Systems

Feedback-based reputation systems are widely deployed in E-commerce systems. Evidence shows that earning a reputable label (for sellers of such systems) may take a substantial amount of time, and this implies a reduction of profit. We propose to enhance sellers’ reputation via price discounts. However, the challenges are as follows: (1) The demands from buyers depend on both the discount and reputation, and (2) the demands are unknown to the seller. To address these challenges, we first formulate a profit maximization problem via a semi-Markov decision process to explore the optimal tradeoffs in selecting price discounts. We prove the monotonicity of the optimal profit and optimal discount. Based on the monotonicity, we design a Q-learning with forward projection (QLFP) algorithm, which infers the optimal discount from historical transaction data. We prove that the QLFP algorithm convergences to the optimal policy. We conduct trace-driven simulations using a dataset from eBay to evaluate the QLFP algorithm. Evaluation results show that QLFP improves the profit by as high as 50% over both Q-learning and Speedy Q-learning. The QLFP algorithm also improves both the reputation and profit by as high as two times over the scheme of not providing any price discount.
Invisible AI-driven HCI Systems – When, Why and How

The InvisibleAI (InvAI'20) workshop aims to systematically discuss a growing class of interactive systems that invisibly remove some decision-making tasks away from humans to machines, based on recent advances in artificial intelligence (AI), data science, and sensor or actuation technology. While the interest in the affordances as well as the risks of hidden pervasive AI are high on the agenda in public debate, discussion on the topic is needed within the human-computer interaction (HCI) community. In particular, we want to gather insights, ideas, and models for approaching the use of barely noticeable AI decision-making in systems design from a human-centered perspective, so as to make the most out of the automated systems and algorithms that support human activity both as designers and users. Concurrently, these systems should safeguard that humans remain in charge when it counts (high stakes decisions, privacy, monitoring lack of explainability and fairness, etc.). What to automate and what not to automate is often a system designer's choice [8]. By taking the established concept of explicit interaction between a system and its user as a point of departure, and inviting authors to provide examples from their own research, we aim to stimulate dynamic discussion while keeping the workshop concrete and system design-focused. The workshop especially directs itself to participants from the interaction design, AI, and HCI communities. The targeted scientific outcome of the workshop is an up-to-date ontology of invisible AI-HCI systems and hybrid human-AI collaboration mechanisms, and approaches. Additionally, we expect that the workgroups and the roundtables will provide starting points shaping continued discussions, new collaborations, and innovative scientific contributions that springboard from the workgroups' findings. The focus of the proposed workshop involves the bridging of two spaces of computational research that impact user experiences and societal domains (HCI and AI). Thus, the proposed workshop topic aligns well with the theme of this year's NordiCHI conference which is Shaping Experiences, Shaping Society.
Solving Multi-Agent Pickup and Delivery Problems Using a Genetic Algorithm

In the Multi-Agent Pickup and Delivery (MAPD) problem, agents must process a sequence of tasks that may appear in the system in different time-steps. Commonly, this problem has two parts: (i) task allocation, where the agent receives the appropriate task, and (ii) path planning, where the best path for the agent to perform its task, without colliding with other agents, is defined. In this work, we propose an integer-encoded genetic algorithm for solving the task allocation part of the MAPD problem combined with two-path planning algorithms already known in the literature: the Prioritized Planning and the Improved Conflict-Based Search (ICBS). Computational experiments were carried out with different numbers of agents and the frequency of tasks. The results show that the proposed approach achieves better results for large instances when compared to another technique from the literature.
Research on simulation technology of Shipborne Satellite Communication System

Abstract In this paper, aiming at the architecture, key technologies and member functions of the simulation of the onboard satellite system, a simulation federate model with good hierarchical structure is designed, and a satellite communication simulation system based on HLA is constructed. The key problems such as time unification, fault simulation and real-time visual display are solved. Through the functional simulation of the operation process of the Shipborne Satellite Communication System in the distributed virtual simulation environment, the results show that the system has good interactivity and scalability, and is an effective means to test the reliability of equipment and reduce the risk of scientific test.
Implementation of LSB-RSA Algorithm for the Authenticity of the JPG File Certificate

In an institution, the issue of a certificate document cannot be separated. Now, the development of technology makes certificate documents not only issued in paper form but can also be published online. The document must have security to prove its authenticity. If the document of the paper certificate there is a serial number or unique code to prove its authenticity, online certificate documents must also have a unique code to prove their authenticity. In this study, the LSB and RSA methods are used to prove the authenticity of the certificate. The secret message on the certificate document will be encrypted using the RSA algorithm. Encrypted messages will be entered into digital images using the LSB method. The results are represented in measurements, MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), and BER (Bit Error Ratio). The combination of algorithms in this study produced very good values, the average PSNR value reached 73,4252 dB and an average value of BER equal to 1.4939.
'Learning to code in a virtual world'

Software programming is increasingly becoming a community-driven effort, with online discussion channels becoming vital resources for learning and knowledge sharing. This study explores differences in the discourse patterns of two popular online programming communities (Stack Overflow and r/Askprogramming) to provide preliminary insights into the type of learning practices these collectives support and scaffold. A three-step content analysis framework that investigates a sample of 8639 and 6126 contributions from Stack Overflow and r/Askprogramming respectively is presented. Preliminary results indicate that differences emerge in the scope of topics and the nature of responses the communities provide. While Stack Overflow is more task-specific, r/Askprogramming supports a greater sense of bonding and camaraderie among community members in addition to task-specific discussions. These results provide insights into the type of practices these communities support, which can be essential in considering how online communities that support learning activities should be designed.
Improving Digital Forensic Readiness in DevOps Context: Lessons Learned from XYZ Company

DevOps is a relatively new methodology and culture in software development to deliver software faster and with higher quality. DevOps changes how an organization works by flattening structures, increasing collaboration, and also promotes automation. However, it might pose serious security problems if outsourcing, intellectual property, and data protection are not put into consideration. XYZ Company is a typical small software company that is transforming to embrace DevOps. Digital forensic is a post-mortem mechanism to analyze incidents to help organizations mitigate and doing lawsuits. Digital forensic readiness (DFR) is assessed using Elyas et al [3] DFR framework. DFR improvement is part of the company’s effort to maintain the security level. The method we took and the issues we faced in this transformation are shared in this report.
Community enhancement network embedding based on edge reweighting preprocessing

Abstract Network embedding has attracted considerable attention in recent years. It represents nodes in a network into a low-dimensional vector space while keeping the properties of the network. Some methods (e.g. ComE, MNMF, and CARE) have been proposed to preserve the community property in network embedding, and they have obtained good results in some downstream network analysis tasks. However, there still exists a significant challenge because nodes may lose important structural information following embedding. To address this problem, we propose a community structure enhancement framework for network embedding, based on edge reweighting. Through edge reweighting, the weight of intra-community edges is increased while the weight of inter-community edges is decreased. Therefore, after embedding, nodes in the same community are closer to each other than nodes in different communities in the embedding space. We apply the edge reweighting as a preprocessing stage in network embedding, and construct an enhanced network by incorporating enhanced community structures into the original network. By doing this, the embedded vectors from the enhanced network can better perform all downstream network analysis tasks. Extensive experiments are conducted on two network analysis tasks (community detection and node classification) with synthetic and real-world datasets. The results show that our method outperforms state-of-the-art network embedding methods.
Investigating Parallelization of MAML

We propose a meta-learning framework to distribute Model-Agnostic Meta-Learning (DMAML), a widely used meta-learning algorithm, over multiple workers running in parallel. DMAML enables us to use multiple servers for learning and might be crucial if we want to tackle more challenging problems that often require more CPU time for simulation. In this work, we apply distributed MAML on supervised regression and image recognition tasks, which are quasi benchmark tasks in the field of meta-learning. We show the impact of parallelization w.r.t. wall clock time. Therefore, we compare distributing MAML over multiple workers and merging the model parameters after parallel learning with parallelizing MAML itself. We also investigate the impact of the hyperparameters on learning and point out further potential improvements.
Red Teams - Pentesters, APTs, or Neither

In cybersecurity, red teams emulate real threats and launch attack operations to assess an organization's security posture and provide experience to it's defenders. Although red team engagements share similarities with penetration testing and vulnerability scanning, they differ significantly in terms of goals and purpose. Moreover, just mimicking real threats does not make red teams APTs. This paper summarizes various academic and industrial views on the topic of cyber red teaming and aims at clarifying the common properties that differentiate it from other types of security assessments and real attackers. Based on evidence from case studies and various expert opinions, we have concluded that red teaming is a distinct type of security assessment, and that penetration tests are often mislabeled as red team engagements.
Assessing uranium and select trace elements associated with breccia pipe uranium deposits in the Colorado River and main tributaries in Grand Canyon, USA

Assessing chemical loading from streams in remote, difficult-to-access watersheds is challenging. The Grand Canyon area in northern Arizona, an international tourist destination and sacred place for many Native Americans, is characterized by broad plateaus divided by canyons as much as two-thousand meters deep and hosts some of the highest-grade uranium deposits in the U.S. From 2015–2018 major surface waters in Grand Canyon were monitored for select elements associated with breccia-pipe uranium deposits in the area, including uranium, arsenic, cadmium, and lead. Dissolved constituents in the Colorado River were monitored upstream (Lees Ferry), in the middle (Phantom Ranch), and downstream (Diamond Creek) of uranium mining areas. Concentrations of uranium, arsenic, cadmium, and lead at these main-stem sites varied little during the study period and were all well below human health and aquatic life benchmark criteria (30, 10, 5, and 15 μg/L maximum contaminant levels and 15, 150, 0.8, and 3.1 μg/L aquatic life criteria, respectively). Additionally, dissolved and sediment-bound constituents were monitored during a wide range of streamflow conditions at Little Colorado River, Kanab Creek, and Havasu Creek tributaries, whose watersheds have experienced different levels of uranium mining activities over time. Samples from the tributary sites contained ≤3.8 μg/L of dissolved cadmium and lead, and ≤17 μg/L of dissolved uranium. Dissolved arsenic also was mostly below human and aquatic life criteria at Little Colorado River and Kanab Creek; however, 63% of water samples from Havasu Creek were above the maximum contaminant level for arsenic. Arsenic in suspended sediment was greater than sediment quality guidelines in 9%, 35%, and 35% of samples from Little Colorado River, Kanab Creek, and Havasu Creek, respectively. At the concentrations observed during this study, tributaries contributed on average only about 0.12 μg/L of arsenic and 0.03 μg/L of uranium to the main-stem river. This study demonstrates how chemical loading from mined watersheds may be reliably assessed across a wide range of flow conditions in challenging locations.
Bayesian Model for Evaluating Real-World Adaptation Progress of a Cyber-Physical System

Cyber-physical systems, also known as CPS, have come to stay. There is no doubt, CPS would one day outnumber humans in industries. How do we evaluate the adaptation progress of these systems considering changing environmental conditions? A failed implementation of a CPS can result to a loss. Since CPSs are designed to automate industrial activities, which are centred on the use of several technologies, collaboration with humans may sometimes be inevitable. CPSs are needed to automate several processes and thus help firms compete favourably within an industry. This chapter focuses on the adaptation of CPS in diverse work environment. Considering the ecosystem of the CPS, the authors present a Bayesian model evaluating the progress of adaptation of a CPS given some known conditions.
Understanding Deep Learning: Expected Spanning Dimension and Controlling the Flexibility of Neural Networks

Neural Networks (NN) provide state-of-the-art performance in many problem domains. They can accommodate a vast number of parameters and still perform well when classic machine learning techniques provided with the same number of parameters would tend to overfit. To further the understanding of such incongruities, we develop a metric called the Expected Spanning Dimension (ESD) which allows one to measure the intrinsic flexibility of a NN. We analyze NNs from the small, in which the ESD can be exactly computed, to large real-world networks with millions of parameters, in which we demonstrate how the ESD can be numerically approximated efficiently. The small NNs we study can be understood in detail, their ESD can be computed analytically, and they provide opportunities for understanding their performance from a theoretical perspective. On the other hand, applying the ESD to large-scale NNs sheds light on their relative generalization performances and provides suggestions as to how such NNs may be improved.
A Different Approach for Clique and Household Analysis in Synthetic Telecom Data Using Propositional Logic

Summary - In this paper we propose an non-machine learning artificial intelligence (AI) based approach for telecom data analysis, with a special focus on clique detection. Clique detection can be used to identify households, which is a major challenge in telecom data analysis and predictive analytics. Our approach does not use any form of machine learning, but another type of algorithm: satisfiability for propositional logic. This is a neglected approach in modern AI, and we aim to demonstrate that for certain tasks, it may be a good alternative to machine learning-based approaches. We have used a simple DPLL satisfiability solver over an artificially generated telecom dataset (due to GDPR regulations), but our approach can be implemented on any telecom data by following the SAT encoding we have developed, and the DPLL solver can be substituted by a more advanced alternative such as CDCL. This paper extends the method presented in [1] for banking logs to data containing caller information, and proposes a more efficient encoding.
An empirical study of multi-threading paradigms Reactive programming vs continuation-passing style

This paper compares two popular multi-threading paradigms: reactive programming and continuation-passing style as today there are many software paradigms which could be replaced by each other, and programmers sometimes have dilemma which one to choose. . To conduct the comparison, the paper proposes a method for evaluating software paradigms in regards to Maintainability, Performance and Testability quality attributes, chosen due to their importance in modern software development. RxJava and Kotlin Coroutines serve as the specific implementations of the two paradigms, since both enjoy popularity in the industry and have proven themselves in real-world projects. As a result, the paper identifies that both frameworks have similar characteristics in regards to performance and testability, however Kotlin Coroutines demonstrates better maintainability. Moreover, the paper establishes a multi-dimensional evaluation method for comparing the frameworks, and identifies what factors should be taken into account when selecting a particular programming paradigm, also these findings could be used in software patterns comparison with some improvements and modifications.
Sparse robust graph-regularized non-negative matrix factorization based on correntropy

Non-negative Matrix Factorization (NMF) is a popular data dimension reduction method in recent years. The traditional NMF method has high sensitivity to data noise. In the paper, we propose a model called Sparse Robust Graph-regularized Non-negative Matrix Factorization based on Correntropy (SGNMFC). The maximized correntropy replaces the traditional minimized Euclidean distance to improve the robustness of the algorithm. Through the kernel function, correntropy can give less weight to outliers and noise in data but give greater weight to meaningful data. Meanwhile, the geometry structure of the high-dimensional data is completely preserved in the low-dimensional manifold through the graph regularization. Feature selection and sample clustering are commonly used methods for analyzing genes. Sparse constraints are applied to the loss function to reduce matrix complexity and analysis difficulty. Comparing the other five similar methods, the effectiveness of the SGNMFC model is proved by selection of differentially expressed genes and sample clustering experiments in three The Cancer Genome Atlas (TCGA) datasets.
Marshall McLuhan and the therapeutic importance of the telephone

A brief essay that discusses the significance of Marshall McLuhan’s (1967) The Medium is the Massage in the context of online psychotherapy currently required in the pandemic of covid-19.