Reviewerly/exHarmony huggingface dataset:
Flexible FOND Planning with Explicit Fairness Assumptions

We consider the problem of reaching a propositional goal condition in fully-observable non-deterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A new planner is implemented by reducing FOND+ planning to answer set programs, and the performance of the planner is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools.
Clustering and Unclustering: Understanding dialogic potential in design for Ongoingness

We describe three design works from a research through design project seeking to develop new ways to curate and create digital media to support ongoingness - a form of continuing bonds in bereavement that prioritises the present and an ongoing, positive relationship with the deceased. We use the three pieces in this paper as lenses to view how dialogic potential is at play within each piece - drawing on aspects of dialogism, a Bakhtinian theory, to do so. Our aim is that by sharing the work and blending the particular theoretical perspective of dialogism with experiential and design aspects of the pieces we offer up a rich design space and new ways to conceive of design and digital technologies in the context of bereavement and death.
Adaptive and Personalized Persuasive Technologies

The 2021 Adaptive and Personalized Persuasive Technology (ADAPPT 2021) workshop is the second edition of the ADAPPT series, which commenced in 2019. It is organized in conjunction with the 29th Association for Computer Machinery (ACM) Conference on User Modeling, Adaptation and Personalization (UMAP). We summarize the main ideas, topics and results of the 10 papers accepted for publication in the adjunct proceedings of the UMAP conference and for video/oral presentation at the virtual workshop.
Towards exploratory landscape analysis for large-scale optimization

Although exploratory landscape analysis (ELA) has shown its effectiveness in various applications, most previous studies focused only on low- and moderate-dimensional problems. Thus, little is known about the scalability of the ELA approach for large-scale optimization. In this context, first, this paper analyzes the computational cost of features in the flacco package. Our results reveal that two important feature classes (ela_level and ela_meta) cannot be applied to large-scale optimization due to their high computational cost. To improve the scalability of the ELA approach, this paper proposes a dimensionality reduction framework that computes features in a reduced lower-dimensional space than the original solution space. We demonstrate that the proposed framework can drastically reduce the computation time of ela_level and ela_meta for large dimensions. In addition, the proposed framework can make the cell-mapping feature classes scalable for large-scale optimization. Our results also show that features computed by the proposed framework are beneficial for predicting the high-level properties of the 24 large-scale BBOB functions.
Gbk360: A Game Among Tiles In 360 Video Based On Kalman Filter

The tile-based method provides a feasible way for 360 video transmission. Viewpoint prediction and bitrate allocation are important modules in the tile-based method. In this paper, we propose GBK360 (a game based on Kalman filter), a transmission scheme of 360 videos, to improve the user’s quality of experience (QoE). Firstly, we achieve a viewpoint prediction method based on the Kalman filter, which can predict the viewpoint and the error distribution. Then, we regard the allocation of bandwidth to different tiles as a cooperative game and use the idea of game theory to improve the bandwidth utilization efficiency. Different categories of videos are used for experiments. The results showed that the proposed method based on the Kalman filter could increase the hit rate by 50.97% compared with the linear regression method. And the bitrate allocation method based on game theory can improve the bandwidth utilization efficiency and the user’s QoE compared with the other three schemes.
A Nested Named Entity Recognition Method for Traditional Chinese Medicine Records

Recently, with the development of deep neural networks, the named entity recognition (NER) task has been well studied in many domains. Among these domains, the information extraction and structuring of Traditional Chinese medicine (TCM) literature is a popular application of NER approach. TCM records are the summary of TCM knowledge and experience, but there are some obstacles imposed by using common machine learning methods, TCM corpus contains a large number of nested entities. And due to entity boundary problems caused by the difference between words and characters in TCM corpus, many methods that have great performance on the English datasets are not suitable for the NER task in TCM field. In order to solve such problems, we propose a nested NER model for TCM records. First, we use word-character-level embedding to enable the model to achieve more accurate extraction results on TCM records corpus. Then, referring to the main entity categories that need to be recognized from the TCM records, we designed a two-layer labelling strategy. This allows our nested NER model to extract more fine-grained results, and has better support for follow-up work such as knowledge base construction. Finally, we conduct some experiments to verify that our model can effectively achieve the NER task for TCM records.
Defending Deep Learning Based Anomaly Detection Systems Against White-Box Adversarial Examples and Backdoor Attacks

Deep Neural Network (DNN) has witnessed rapid progress and significant successes in the recent years. Wide range of applications depends on the high performance of deep learning to solve real-life challenges. Deep learning is being applied in many safety-critical environments. However, deep neural networks have been recently found vulnerable to adversarial examples and backdoor attacks. Stealthy adversarial examples and backdoor attacks can easily fool deep neural networks to generate the wrong results. The risk of adversarial examples attacks that target deep learning models impedes the wide deployment of deep neural networks in safety-critical environments. In this work we propose a defensive technique for deep learning by combining activation function and neurons pruning to reduce the effects of adversarial examples and backdoor attacks. We evaluate the efficacy of the method on an anomaly detection application using Deep Belief Network (DBN) and Coupled Generative Adversarial Network (CoGAN). The method reduces the loss of accuracy from the attacks from an average 10% to 2% using DBN and from an average 14% to 2% using CoGAN. We evaluate the method using two benchmark datasets: NSL-KDD and ransomware.
Global Tourism and Waves of Terror: Perspectives from Military Expenditure

This study complements existing literature by investigating how military expenditure can modulate the effect of terrorism externalities on tourism. The geographical and temporal scopes are 163 countries and the period 2010-2015. The empirical evidence is based on negative binomial regressions. Terrorism externalities are measured in terms of terror-related incidents, injuries, fatalities and damaged properties. We find that military expenditure significantly lessens the destructive impact of these terror-related incidents in order to induce positive net effects on tourism. This finding is robust to all measurements of terrorism. Homicides and violent demonstrations reduce tourists’ arrivals whereas the rate of incarceration of convicted offenders has the opposite effect. The analysis is extended to income levels and regions in order to provide more opportunities for policy implications. Justifications for differences in these comparative tendencies are discussed.
Work in Progress: Augmented Reality as a strategy to improve learning in engineering

In recent years, the speed of technological advances has forced us to innovate with greater agility. Augmented Reality (AR) has notched an important place in learning due to its increased accessibility to the public and the actors in the educational process. Innovation in education requires using this technology, so academic institutions have shown interest in developing technological applications that support learning processes. There are disciplines in which content development using AR is still an area of opportunity. This work has two purposes: 1) Present an overview of AR's progress and benefits, understood from the professors' and students' perceptions and needs, and 2) present a research proposal in the Operations Research (OR) area for Industrial Engineering students using an educational application of AR. This proposal's research question was: What is the degree of satisfaction of Industrial Engineering students with the Augmented Reality application used in decision-tree practices? This study's findings present a) the students' perception and feedback on Augmented Reality, and b) the need for an interdisciplinary teaching approach.
Investigating User Perceptions of Persuasive Design Elements that Influence Perceived Credibility

Given the prevalent use of technology for human activities, the determinants of user perceptions of the credibility of technological systems must be understood. This study investigated how user perceptions of persuasive elements that are incorporated in Academic Social Networking Sites (ASNSs) influence the Perceived Credibility of these sites. Specifically, the effects of Personalization, Reduction, Tailoring, Self-Monitoring, Rehearsal, Simulation and Tunneling on Perceived Credibility are examined. An online survey was adopted to examine the research model. Partial Least Square techniques were used to assess the significance of the determinants on Perceived Credibility. Results indicate that Tailoring positively influences Perceived Credibility whereas Personalization has a negative significant effect. Implications for theoretical insights into Perceived Credibility and its determinants and design implication are discussed.
A High Efficiency and Accuracy Method for x86 Undocumented Instruction Detection and Classification

Processors are important parts of a computer and have been believed to be secure for a long time. In recent years researches show that processors are, on the contrary, full of undocumented instructions which are caused by design flaws or intentionally hidden by manufacturers. Undocumented instructions can be detected using fuzzing technology. But currently existing methods have low efficiency and accuracy. Furthermore, to analyze the function of detected instructions, a classification method is necessary to reduce the large amount of detected undocumented instructions. Therefore, this paper introduces a high-efficiency instruction searching and classification method by applying instruction format analysis. Results show that our method can successfully find 10 times of the amount of detected undocumented instructions using existing tool with just 30% of executing time. Also, after classifying the large quantity of the detection results, the amount can be reduced to less than 10000 instructions which is a reasonable amount for further research.
Automated Tuning of End-to-end Neural Flight Controllers for Autonomous Nano-drones

Convolutional neural networks (CNNs) are fueling the advancement of autonomous palm-sized drones, i.e., nano-drones, despite their limited power envelope and onboard processing capabilities. Computationally lighter than traditional geometrical approaches, CNNs are the ideal candidates to predict end-to-end signals directly from the sensor inputs to feed to the onboard flight controller. However, these sophisticated CNNs require significant complexity reduction and fine-grained tuning to be successfully deployed aboard a flying nano-drone. To date, these optimizations are mostly hand-crafted and require error-prone, labor-intensive iterative development flows. This work discusses methodologies and software tools to streamline and automate all the deployment stages on a low-power commercial multicore System-on-Chip. We investigate both an industrial closed-source and an academic open-source tool-set with a field-proofed state-of-the-art CNN for autonomous driving. Our results show a $2 \times$ reduction of the memory footprint and a speedup of $1.6 \times$ in the inference time, compared to the original hand-crafted CNN, with the same prediction accuracy.
Mobile application model for solid waste collection management

The quantity and generation rate of solid waste will continuously increase tremendously as long as human activities continue to exist. The consequence caused by badly management of solid waste, it is very dangerous to the world and human health this calls for the need to salvage the situation before it gets out of hand. That conducts us to use best practices to properly manage solid waste collection following the new trend of mobile technology, this research developed a solid waste collection management model with the aim to design a Mobile Application model for solid waste collection management which will help to improve the quality of management in Solid Waste collection and impacting positively the environment by reducing the quantity of Solid waste. This model put the solid waste generators on the central of the solid waste collection system because they are the principal actors producing solid waste, helping Solid waste companies during their solid waste collection process and provides an efficient tool to the municipality council to have an overview to the all activities concerning solid waste collection and oriented their decisions. In this research we provided all necessary and recent mobile technology and a model can be applying to achieve efficient solid waste collection system for household.
Holistic indoor scene understanding by context-supported instance segmentation

We propose a new method flow that utilizes pixel-level labeling information for instance-level object detection in indoor scenes from RGB-D data. Semantic labeling and instance segmentation are two different paradigms for indoor scene understanding that are usually accomplished separately and independently. We are interested in integrating the two tasks in a synergistic way in order to take advantage of their complementary nature for comprehensive understanding. Our work can capitalize on any deep learning networks used for semantic labeling by treating the intermediate layer as the category-wise local detection output, from which instance segmentation is optimized by jointly considering both the spatial fitness and the relational context encoded by three graphical models, namely, the vertical placement model (VPM), horizontal placement model (HPM) and non-placement model (NPM). VPM, HPM and NPM represent three common but distinct indoor object placement configurations: vertical, horizontal and hanging relationships, respectively. Experimental results on two standard RGB-D datasets show that our method can significantly improve small object segmentation with promising overall performance that is competitive with the state-of-the-art methods.
Snake Species Recognition using Tensor Flow Machine Learning Algorithm &amp;amp; Effective Convey System

Snake plays an important role in the ecosystem. Recognition of the snake species is necessary for planning treatment, but it is not always feasible. Snakebite is may be fatal, local information about snake may not be enough to identify the snake species. Attempt to capture an offending snake or kill the snake is not recommended also dangerous. Proposed system is introduced to recognize the snake species accurately in less time. In addition to this effective convey system is integrated to convey snake species to nearest snake catcher and emergency centre.
An efficient sentiment analysis using topic model based optimized recurrent neural network

Abstract In recent years, topic modeling and deep neural network-based methods have attracted much attention in sentiment analysis of online reviews. This paper presents a hybrid topic model-based approach for aspect extraction and sentiment classification of textual reviews. Latent Dirichlet allocation applied for aspect extraction and two-layer bi-directional long short-term memory (LSTM) for sentiment classification. This work also proposes a hill climbing-based approach for tunning model hyperparameters. The proposed model evaluated on three different datasets. Compared to the single-layer Bi-LSTM model, the proposed model gives 95, 95, and 86% accuracy for the movie, mobile, and hotel domain, respectively.
On Logics and Homomorphism Closure

Predicate logic is the premier choice for specifying classes of relational structures. Homomorphisms are key to describing correspondences between relational structures. Questions concerning the interdependencies between these two means of characterizing (classes of) structures are of fundamental interest and can be highly non-trivial to answer. We investigate several problems regarding the homomorphism closure (homclosure) of the class of all (finite or arbitrary) models of logical sentences: membership of structures in a sentence's homclosure; sentence homclosedness; homclosure characterizability in a logic; normal forms for homclosed sentences in certain logics. For a wide variety of fragments of first- and second-order predicate logic, we clarify these problems' computational properties.
Automatic Hyperparameter Optimization for Arbitrary Neural Networks in Serverless AWS Cloud

Deep Neural Networks are the most efficient method to solve many challenging problems. The importance of the subject can be demonstrated by the fact that the 2019 Turing Award was given to the godfathers of AI (and Neural Networks) Yoshua Bengio, Geoffrey Hinton, and Yann LeCun. In spite of the numerous advancements in the field, most of the models are being tuned manually. Accurate models became especially important during the novel coronavirus pandemic.Many day-to-day decisions depend on the model predictions affecting billions of people. We implemented a flexible automatic real-time hyperparameter tuning approach for arbitrary DNN models written in Python and Keras without manual steps. All of the existing tuning libraries require manual steps (like hyperopt, Scikit-Optimize or SageMaker). We provide an innovative methodology to automate hyper-parameter tuning for an arbitrary Neural Network model source code, utilizing Serverless Cloud and implementing revolutionary microservices, security, interoperability and orchestration. Our methodology can be used in numerous applications, including Information and Communication Systems.
Building Alumni Ontology to bridge Industry-Institute Gap using Protégé 5.5

Recently, alumni involvement play a vital role to enhance an education system. They are a stakeholder of an academic advisory board and non-academic advisory board in various organizations. The past and present alumni give their time to develop an education system by conducting various technical activities like expert lectures, workshops, and conferences as per their experiences. In this paper, we present alumni ontology to bridge Industry-Institute Gap using Protégé 5.5. Further, we explain alumni ontology development phases and DL query.
Refining the Blunt Instruments of Cybersecurity: A Framework to Coordinate Prevention and Preservation of Behaviours

Background. Cybersecurity controls are deployed to manage risks posed by malicious behaviours or systems. What is not often considered or articulated is how cybersecurity controls may impact legitimate users (often those whose use of a managed system needs to be protected, and preserved). This oversight characterises the ‘blunt’ nature of many cybersecurity controls. Aim. Here we present a framework produced from a synthesis of methods from cybercrime opportunity reduction and behaviour change, and a consideration of existing risk management guidelines. Method. We illustrate the framework and its principles with a range of examples and a potential application focusing on online abuse and social media controls, relating in turn to issues inherent in cyberbullying and tech-abuse. Results. The framework describes a capacity to improve the precision of cybersecurity controls by examining shared determinants of negative and positive behaviours in a system. This identifies opportunities for risk owners to better protect legitimate users while simultaneously acting to prevent malicious activity in a managed system. Conclusions. We describe capabilities for a novel approach to managing sociotechnical cyber-risk which can be integrated into typical risk management processes. This includes consideration of user activities as a system asset to protect, and a consideration of how to engage with other stakeholders to identify behaviours to preserve in a system.
Design of a Light-weight Natural Scene Text Detection System

Scene Text Detection is one of the vast tasks of computer vision that have received immense progress in the field of deep learning (DL). Several DL models have been made with large scale neural network structures and heavy weight files with a huge number of parameters to increase the natural scene text detection performance. Although these models improve the performance to a high range, but they reduces the speed of the model and it also becomes difficult to use them in platforms with limited resources. Therefore, the goal of our proposed work lies in making models smaller, faster and more efficient. Our proposed model is made in the form of encoder-decoder architecture where the light-weight model, MobileNetV2 is used as the encoder. The decoder is made by concatenating certain feature maps from the encoder with the transposed convolutional layers. After training, the proposed model generates a weight file of 4 MB with 1.1 M parameters. The proposed model can be trained in Google Colaboratory with 12 GB RAM and without using GPU in few hours which shows the speed of the model and which gives the advantage of making the proposed work at free of cost. The performance of the proposed model on Total-Text and ICDAR 2013 datasets shows that the proposed work achieves a desirable performance in detecting texts from natural scene images.