Reviewerly/exHarmony huggingface dataset:
Analysis of Evolutionary Algorithms on Fitness Function With Time-Linkage Property

In real-world applications, many optimization problems have the time-linkage property, that is, the objective function value relies on the current solution as well as the historical solutions. Although the rigorous theoretical analysis on evolutionary algorithms has rapidly developed in recent two decades, it remains an open problem to theoretically understand the behaviors of evolutionary algorithms on time-linkage problems. This paper takes the first step to rigorously analyze evolutionary algorithms for time-linkage functions. Based on the basic OneMax function, we propose a time-linkage function where the first bit value of the last time step is integrated but has a different preference from the current first bit. We prove that with probability $1-o(1)$, randomized local search and $(1+1)$ EA cannot find the optimum, and with probability $1-o(1)$, $(\mu+1)$ EA is able to reach the optimum.
Knowledge Graph Extraction from Videos

Nearly all existing techniques for automated video annotation (or captioning) describe videos using natural language sentences. However, this has several shortcomings: (i) it is very hard to then further use the generated natural language annotations in automated data processing, (ii) generating natural language annotations requires solving the hard subtask of generating semantically precise and syntactically correct natural language sentences, which is actually unrelated to the task of video annotation, (iii) it is difficult to quantitatively measure performance, as standard metrics (e.g., accuracy and F1-score) are inapplicable, and (iv) annotations are language-specific. In this paper, we propose the new task of knowledge graph extraction from videos, i.e., producing a description in the form of a knowledge graph of the contents of a given video. Since no datasets exist for this task, we also include a method to automatically generate them, starting from datasets where videos are annotated with natural language. We then describe an initial deep-learning model for knowledge graph extraction from videos, and report results on MSVD* and MSR-VTT*, two datasets obtained from MSVD and MSR-VTT using our method.
On Exploiting Transaction Concurrency To Speed Up Blockchains

Consensus protocols are currently the bottlenecks that prevent blockchain systems from scaling. However, we argue that transaction execution is also important to the performance and security of blockchains. In other words, there are ample opportunities to speed up and further secure blockchains by reducing the cost of transaction execution. Our goal is to understand how much we can speed up blockchains by exploiting transaction concurrency available in blockchain workloads. To this end, we first analyze historical data of seven major public blockchains, namely Bitcoin, Bitcoin Cash, Litecoin, Dogecoin, Ethereum, Ethereum Classic, and Zilliqa. We consider two metrics for concurrency, namely the single-transaction conflict rate per block, and the group conflict rate per block. We find that there is more concurrency in UTXO-based blockchains than in account-based ones, although the amount of concurrency in the former is lower than expected. Another interesting finding is that some blockchains with larger blocks have more concurrency than blockchains with smaller blocks. Next, we propose an analytical model for estimating the transaction execution speed-up given an amount of concurrency. Using results from our empirical analysis, the model estimates that 6× speed-ups in Ethereum can be achieved if all available concurrency is exploited.
Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph

The complexity of multiagent reinforcement learning (MARL) in multiagent systems increases exponentially with respect to the agent number. This scalability issue prevents MARL from being applied in large-scale multiagent systems. However, one critical feature in MARL that is often neglected is that the interactions between agents are quite sparse. Without exploiting this sparsity structure, existing works aggregate information from all of the agents and thus have a high sample complexity. To address this issue, we propose an adaptive sparse attention mechanism by generalizing a sparsity-inducing activation function. Then a sparse communication graph in MARL is learned by graph neural networks based on this new attention mechanism. Through this sparsity structure, the agents can communicate in an effective as well as efficient way via only selectively attending to agents that matter the most and thus the scale of the MARL problem is reduced with little optimality compromised. Comparative results show that our algorithm can learn an interpretable sparse structure and outperforms previous works by a significant margin on applications involving a large-scale multiagent system.
Research on the Cascade Vehicle Detection Method Based on CNN

This paper introduces an adaptive method for detecting front vehicles under complex weather conditions. In the field of vehicle detection from images extracted by cameras installed in vehicles, backgrounds with complicated weather, such as rainy and snowy days, increase the difficulty of target detection. In order to improve the accuracy and robustness of vehicle detection in front of driverless cars, a cascade vehicle detection method combining multifeature fusion and convolutional neural network (CNN) is proposed in this paper. Firstly, local binary patterns, Haar-like and orientation gradient histogram features from the front vehicle are extracted, then principal-component-analysis dimension reduction and serial-fusion processing are performed on the input image. Furthermore, a preliminary screening is conducted as the input of a support vector machine classifier based on the acquired fusion features, and the CNN model is employed to validate cascade detection of the filtered results. Finally, an integrated data set extracted from BDD, Udacity, and other data sets is utilized to test the method proposed. The recall rate is 98.69%, which is better than the traditional feature algorithm, and the recall rate of 97.32% in a complex driving environment indicates that the algorithm possesses good robustness.
Automatic Liver Tumor Segmentation based on Multi-level Deep Convolutional Networks and Fractal Residual Network

The liver is a very important and complex organ in our body. Efficient liver segmentation is a very important interesting and challenging task in the field of medical image processing. The abdomen consists of many organs among which the liver consists of very important parts, such as right lobule, left lobule and hepatic duct. In this research work, for computed tomography (CT) images we designed a feasible upstream approach to segment a liver region from abdomen using multi-level deep convolutional network and Fractal Residual Network (FRN). Our convolutional neural network is designed in such a way that it learns to allot different probabilities for each super pixel based on different regions of the liver by these indirectly different classes are created based on different intensities of super pixels. Further FRN is used to identify the tumor region and finally refinement of tumor segmentation is done with active contour model method. The proposed model (MDCN + FRN) is evaluated on CT images 125 patients from TCI dataset and achieved a dice similarity in an average of 0.89 in training and dice similarity in an average of 0.86 in testing. Compared to other segmentation methods, the experiment conducted shows better performance from proposed method.
Bring Intelligence among Edges: A Blockchain-Assisted Edge Intelligence Approach

The revolutions of computing and communication have opened up demands for the high quality of service (QoS), such as high data transmission, high reliability, and low latency. These new opportunities have spawned numerous studies on edge computing and artificial intelligence (AI), even the cooperation between them, referred to as edge intelligence. However, there are a number of handicaps that prevent edge intelligence from being used as a generic platform. The most intractable one is the heterogeneity and un-credibility among edges, hindering the way of sharing the learning results reliably, flexibly, and efficiently. In this paper, we propose a blockchain-assisted edge intelligence (B-EI) approach to solve the problem. The edge learning nodes train their local intelligence, followed by the improved blockchain to share the local intelligence, constructing edge intelligence among the heterogeneous and uncredible edges. Specifically, the improved blockchain employs a novel learning-measured consensus protocol, named Proof of Learning. The edges, also acted as the blockchain nodes, compete to have more superior local intelligence, instead of solving a hashed result. The superior local intelligence is then shared and distributed with other edges. It is not only beneficial to achieve edge intelligence, but also efficient to employ the computation resource, by replacing the hashing as the intelligence training. In order to show the potential benefits, we then use the proposed B-EI approach to solve a joint resource assignment problem. Simulation results show that our scheme outperforms the other state-of-art solutions, in terms of training episodes, and resource utility.
The challenge of privacy and security when using technology to track people in times of COVID-19 pandemic

Since the start of the Coronavirus disease 2019 (COVID-19) governments and health authorities across the world have find it very difficult in controlling infections. Digital technologies such as artificial intelligence (AI), big data, cloud computing, blockchain and 5G have effectively improved the efficiency of efforts in epidemic monitoring, virus tracking, prevention, control and treatment. Surveillance to halt COVID-19 has raised privacy concerns, as many governments are willing to overlook privacy implications to save lives. The purpose of this paper is to conduct a focused Systematic Literature Review (SLR), to explore the potential benefits and implications of using digital technologies such as AI, big data and cloud to track COVID-19 amongst people in different societies. The aim is to highlight the risks of security and privacy to personal data when using technology to track COVID-19 in societies and identify ways to govern these risks. The paper uses the SLR approach to examine 40 articles published during 2020, ultimately down selecting to the most relevant 24 studies. In this SLR approach we adopted the following steps; formulated the problem, searched the literature, gathered information from studies, evaluated the quality of studies, analysed and integrated the outcomes of studies while concluding by interpreting the evidence and presenting the results. Papers were classified into different categories such as technology use, impact on society and governance. The study highlighted the challenge for government to balance the need of what is good for public health versus individual privacy and freedoms. The findings revealed that although the use of technology help governments and health agencies reduce the spread of the COVID-19 virus, government surveillance to halt has sparked privacy concerns. We suggest some requirements for government policy to be ethical and capable of commanding the trust of the public and present some research questions for future research.
FourierCam: a camera for video spectrum acquisition in a single shot

The novel camera architecture facilitates the development of machine vision. Instead of capturing frame sequences in the temporal domain as traditional video cameras, FourierCam directly measures the pixel-wise temporal spectrum of the video in a single shot through optical coding. Compared to the classic video cameras and time-frequency transformation pipeline, this programmable frequency-domain sampling strategy has an attractive combination of characteristics for low detection bandwidth, low computational burden, and low data volume. Based on the various temporal filter kernel designed by FourierCam, we demonstrated a series of exciting machine vision functions, such as video compression, background subtraction, object extraction, and trajectory tracking.
An Exploratory Study of a Masking Strategy of Cyberdeception Using CyberVAN

During the network reconnaissance process, attackers scan the network to gather information before launching an attack. This is a good chance for defenders to use deception and disrupt the attacker’s learning process. In this paper, we present an exploratory experiment to test the effectiveness of a masking strategy (compared to a random masking strategy) to reduce the utility of attackers. A total of 30 human participants (in the role of attackers) are randomly assigned to one of the two experimental conditions: Optimal or Random (15 in each condition). Attackers appeared to be more successful in launching attacks in the optimal condition compared to the random condition but the total score of attackers was not different from the random masking strategy. Most importantly, we found a generalized tendency to act according to the certainty bias (or risk aversion). These observations will help to improve the current state-of-the-art masking algorithms of cyberdefense.
Recommender System for Digital Storytelling: A Novel Approach to Enhance Cultural Heritage

Italy is characterized by a significant artistic and cultural heritage. In this scenario, it is possible and necessary to implement strategies for cultural heritage enhancement. This vision has become increasingly feasible due to the introduction of systems based on new technologies, particularly those based on intensive use of Information and Communication Technologies (ICT). This approach has allowed the development of applications to build adaptive cultural paths usable by different types of users. In addition to the technological dimension, a fundamental role is played by methodologies that convey information content innovatively and effectively.
YOU BETTA WERK: Using Wearable Technology Performance Driven Inclusive Transdisciplinary Collaboration to Facilitate Authentic Learning

Working or WERKing on a wearable technology project in a transdisciplinary group can be an effective way of learning new skills and collaboration techniques. This paper describes a case study of running a wearable technology group project within an undergraduate course entitled Wearable Technology and Society. The computational media students in the class collaborated with outside performance artists (drag queens and a street dancer) to create interactive performance garments. Design methods such as the use of boundary objects aided in communication of ideas and cooperation across disciplines and cultural barriers. The requirement that the interactive garment function appropriately in a real performance lent urgency and gravity to the experience, motivating cohesive and expedited problem solving in the transdisciplinary group. The use of these methods on a project with real world outcomes and consequences facilitated an authentic learning experience for the students involved.
Machine Learning Basics

chapter Share on Machine Learning Basics Authors: Konstantinos Chatzilygeroudis Search about this author , Ioannis Hatzilygeroudis Search about this author , Isidoros Perikos Search about this author Authors Info & Claims Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 143–193https://doi.org/10.1145/3447404.3447414Online:23 February 2021Publication History 1citation29DownloadsMetricsTotal Citations1Total Downloads29Last 12 Months28Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Service-oriented architecture design for small and medium enterprises with infrastructure and cost optimization.

This article presents the design, administration and implementation of SOA under an infrastructure and cost optimization approach for medium, small, and micro companies. The architecture is defined from a conceptual model that abstracts the business model into three central services: Chat Service, Business Service and Company Service and the physical model that was deployed to carry out its implementation. In turn, the modifications to the traditional SOA are exposed, which were: withdrawal of service contract layer, low impact in web services and decentralization of data storage. As a result, improvements in business agility were obtained with optimization of infrastructure resources, and costs of a software development company.
Effects of Avatar’s Symbolic Gesture in Virtual Reality Brainstorming

Avatar-mediated communication in virtual reality (VR) is challenging when compared to face-to-face communication, particularly, for expressing users' intentions to others. This is due, in part, to the technological medium's restriction of nonverbal communication cues, including gesture and facial expression. In platforms for social VR, there are interfaces that allow users to express their intentions through their avatars by selecting several given symbolic gestures, e.g., "good" and "surprise". However, little is known about the effects of the symbolic gestures on communication. We investigate the effects of symbolic gestures on communication in VR and conducted user experiment with brainstorming tasks in three different conditions: VR with symbolic gestures, VR without symbolic gestures, and face-to-face. Our results showed significant differences in several items of the participants' subjective questionnaires, i.e., participants reported that symbolic gestures helped them to express their intentions and feelings more easily than the other conditions. It means symbolic gestures improve social presence on communication in VR.
Explainable and Adaptable Augmentation in Knowledge Attention Network for Multi-Agent Deep Reinforcement Learning Systems

The scale of modem Artificial Intelligence systems has been growing and entering more research territories by incorporating Deep Learning (DL) and Deep Reinforcement Learning (DRL) methods. More specifically, multi-agent DRL methods have been widely applied to address the problems of high-dimensional computation, which interpret the conditions that real-world systems mainly encounter and the issues that require resolving. However, the current approaches of DL and DRL are often challenged for their untransparent and time-consuming modeling processes in their attempt to achieve a practical and applicable inference based on human-level perspective and acceptance. This paper presents an explainable and adaptable augmented knowledge attention network for multi-agent DRL systems, which uses game theory simulation to tackle the problem of non-stationarity at the beginning, while improving the learning exploration built upon the strategic ontology to achieve the learning convergence more efficiently for autonomous agents. We anticipate that our approach will facilitate future research studies and potential research inspections of emerging multi-agent DRL systems for increasingly complex and autonomous environments.
Robust and label efficient bi-filtering graph convolutional networks for node classification

Due to their success at node classification, Graph Convolutional Networks (GCN) have raised a research upsurge of deep learning on graph-structured data. For the semi-supervised classification, graph convolution essentially acts as a low-pass filter on graph spectral domain. According to Graph Signal Processing theory, the low-pass filter in GCN is a finite impulse response (FIR) graph filter. However, compared with FIR graph filters, infinite impulse response (IIR) graph filters exhibit more powerful representation ability and flexibility. Intuitively, it is feasible to replace FIR filter in GCN with IIR graph filter to improve GCN. Therefore, inspired by the direct implementation of IIR graph filters, we propose a Bi-filtering Graph Convolutional Network (BGCN) which can be realized by simply cascading two sub filtering modules. Experimental results demonstrate that BGCN works well in node classification task and achieves comparable performance to GCN and its variants. The improvement of BGCN, however, is at the expense of a time-complexity increase. To simplify the proposed BGCN, we construct a Simple Bi-filtering Graph Convolution framework (SBGC) from the perspective of Graph Signal Processing. Furthermore, for the implementations of BGCN and SBGC, we design a novel low-pass graph filter to capture the low-frequency components that are beneficial to data representation for the task of node classification. Extensive experiments show that SBGC not only outperforms other baseline methods in performance, but also keeps a high level in computational efficiency. Moreover, it is particularly worth noting that both BGCN and SBGC are robust to feature noise and exhibit high label efficiency.
Personalized Models in Human Activity Recognition using Deep Learning

Current sensor-based human activity recognition techniques that rely on a user-independent model struggle to generalize to new users and on to changes that a person may make over time to his or her way of carrying out activities. Incremental learning is a technique that allows to obtain personalized models which may improve the performance on the classifiers thanks to a continuous learning based on user data. Finally, deep learning techniques have been proven to be more effective with respect to traditional ones in the generation of user-independent models. The aim of our work is therefore to put together deep learning techniques with incremental learning in order to obtain personalized models that perform better with respect to user-independent model and personalized model obtained using traditional machine learning techniques. The experimentation was done by comparing the results obtained by a technique in the state of the art with those obtained by two neural networks (ResNet and a simplified CNN) on three datasets. The experimentation showed that neural networks adapt faster to a new user than the baseline.
Challenges and recommendations to publishing and using credible evidence in software engineering

Context: An evidence-based scientific discipline should produce, consume and disseminate credible evidence.Unfortunately, mistakes are sometimes made, resulting in the production, consumption and dissemination of invalid or otherwise questionable evidence.In the worst cases, such questionable evidence achieves the status of accepted knowledge.There is, therefore, the need to ensure that producers and consumers seek to identify and rectify such situations.Objectives: To raise awareness of the negative impact of misinterpreting evidence and of propagating that misinterpreted evidence, and to provide guidance on how to improve on the type of issues identified.Methods: We use a case-based approach to present and analyse the production, consumption and dissemination of evidence.The cases are based on the literature and our professional experience.These cases illustrate a range of challenges confronting evidence-based researchers as well as the consequences to research when invalid evidence is not corrected in a timely way.Results: We use the cases and the challenges to formulate a framework and a set of recommendations to help the community in producing and consuming credible evidence.Conclusions: We encourage the community to collectively remain alert to the emergence and dissemination of invalid, or otherwise questionable, evidence, and to proactively seek to identify and rectify it.