Reviewerly/exHarmony huggingface dataset:
Kubernetes-based Workload Allocation Optimizer for Minimizing Power Consumption of Computing System with Neural Network

Edge computing has been attracting attention due to the spread of the Internet of Things. For edge computing, containerized applications are deployed on multiple machines, and Kubernetes is an essential platform for container orchestration. In this paper, we introduce a Kubernetes based power consumption centric workload allocation optimizer (WAO), including scheduler and load balancer. By using WAO built with power consumption and response time models for actual edge computing system, 9.9% power consumption was reduced compared to original Kubernetes load balancer. This result indicates that the WAO developed in this study exhibits promising potential for task allocation modules as a micro service platform.
Analysis of Bitcoin Transactions to Detect Illegal Transactions Using Convolutional Neural Networks

The article is devoted to virtual currencies, which is a fast growing and popular market. It was found that for virtual currencies, in particular, for the cryptocurrency Bitcoin, there is a problem of uncontrolled money laundering. This is facilitated by pseudo-anonymization and the presence of illegal exchangers. In this paper, to solve this problem, the method of combining layers in convolutional neural networks is used, which is manifested in the stack layering.In CNN networks, convolutional and erecting layers are usually stacked in a stack, one above the other. The paper proposes a model of Bitcoin transaction analysis to identify anomalies related to money laundering. As such a model, it is proposed to take a combined method, which consists of the method of random forests, enhanced by information from the graph convolutional network, ie, embedded vertices. As a result of the model, we obtained indicators that indicate the presence of possible shadow transactions in the amount of 2-3% of the total market.
Differentially-Private Federated Learning with Long-Term Budget Constraints Using Online Lagrangian Descent

This paper addresses the problem of time-varying data distribution in a fully decentralized federated learning setting with budget constraints. Most existing work cover only fixed data distribution in the centralized setting, which is not applicable when the data becomes time-varying, such as in realtime traffic monitoring. More so, a lot of existing work do not address budget constraint problem common in practical federated learning settings. To address these problems, we propose an online Lagrangian descent algorithm. To provide privacy to the local model updates of the clients, local differential privacy is introduced. We show that our algorithm incurs the best regret bound when compared to other similar algorithms, while satisfying the budget constraints in the long term.
Towards a Self-Adaptive Architecture for Federated Learning of Industrial Automation Systems

Emerging Industry 4.0 architectures deploy data-driven applications and artificial intelligence services across multiple locations under varying ownership, and require specific data protection and privacy considerations to not expose confidential data to third parties. For this reason, federated learning provides a framework for optimizing machine learning models in single manufacturing facilities without requiring access to training data. In this paper, we propose a self-adaptive architecture for federated learning of industrial automation systems. Our approach considers the involved entities on the different levels of abstraction of an industrial ecosystem. To achieve the goal of global model optimization and reduction of communication cycles, each factory internally trains the model in a self-adaptive manner and sends it to the centralized cloud server for global aggregation. We model a multi-assignment optimization problem by dividing the dataset into a number of subsets equal to the number of devices. Each device chooses the right subset to optimize the model at each local iteration. Our initial analysis shows the convergence property of the algorithm on a training dataset with different numbers of factories and devices. Moreover, these results demonstrate higher model accuracy with our self-adaptive architecture than the federated averaging approach for the same number of communication cycles.
Movement Stimulation through Social Media: The Tweeted Perspective and Road Safety Movement in Bangladesh

It is a very common matter that due to the huge popularity of social media, any matter around us, especially any movement, is greatly stimulated by the impact of social media. We can see a parallel contribution on Twitter, Facebook or some other popular social media on every protest. We have noticed the power of hashtags in all recent movements. In this paper we will try to show the impact of social media on protest by including the example data of Bangladesh road safety movement. For Ground protest information we collect national and international newspapers front page coverage as references. Another information source was Twitter. We will use a social media monitoring tool to collect Twitter hashtags information. After that we will made a comparative analysis of the newspaper's coverage as ground protest nature, and Twitter hashtags as social media impact. Finally, we will try to show that how the social media, Twitter influence the ground movement.
Efficient Construction of Functional Representations for Quantum Algorithms

Due to the significant progress made in the implementation of quantum hardware, efficient methods and tools to design corresponding algorithms become increasingly important. Many of these tools rely on functional representations of certain building blocks or even entire quantum algorithms which, however, inherently exhibit an exponential complexity. Although several alternative representations have been proposed to cope with this complexity, the construction of those representations remains a bottleneck. In this work, we propose solutions for efficiently constructing representations of quantum functionality based on the idea of conducting as many operations as possible on as small as possible intermediate representations -- using Decision Diagrams as a representative functional description. Experimental evaluations show that applying these solutions allows to construct the desired representations several factors faster than with state-of-the-art methods. Moreover, if repeating structures (which frequently occur in quantum algorithms) are explicitly exploited, exponential improvements are possible -- allowing to construct the functionality of certain algorithms within seconds, whereas the state of the art fails to construct it in an entire day.
Automatic People Counting System Using Aerial Image Captured by Drone for Event Management

Event management refers to the ability to apply project management skills in order to initiate large scale social or business events. Hence, it requires the use of organizational as well as business management skills to envision, plan, and finally execute any such event. Therefore, to count or estimate the number of people who attend such events is one of important tool in event management. In common, counting number of people in events can be done by counting manually traditional headcount system. Nevertheless, this process or technique consumes much time and is also a difficult task to execute for a considerable number of people or a bigger crowd. Therefore, a modern counting system like automatic people counting system is developed to enhance the process of counting people. Thus, various method of counting has been proposed in the past decades. Consequently, automatic counting people using digital image processing technique is introduced to overcome this problem. Thus, to monitor or to count the number of people can be done by using Unmanned Aerial Vehicle (UAV) or drones. The use of drones can take a broader picture, saving time and becoming more efficient. For this research, the DJI Mavic Pro Drone is used to scout the areas. This paper is focusing on counting the number of people images. Thus, the images are firstly compared between RGB and HSV colour model. Then, the HSV colour model has been chosen for the thresholding process. Here, the images are compared between Otsu thresholding and manual thresholding. Both thresholding method gives a good segmentation result, but Otsu's method is chosen because of its higher accuracy. Moreover, noise removal technique is employed in order to get good smoothing performance and produce better counting results. This paper is fully developed with MATLAB R2013a software. This technique has proven to be a good image processing technique with total accuracy of 91%. The hardware system is also developed to transmit the counting results.
GEM: Glare or Gloom, I Can Still See You – End-to-End Multi-Modal Object Detection

Deep neural networks designed for vision tasks are often prone to failure when they encounter environmental conditions not covered by the training data. Single-modal strategies are insufficient when the sensor fails to acquire information due to malfunction or its design limitations. Multi-sensor configurations are known to provide redundancy, increase reliability, and are crucial in achieving robustness against asymmetric sensor failures. To address the issue of changing lighting conditions and asymmetric sensor degradation in object detection, we develop a multi-modal 2D object detector, and propose deterministic and stochastic sensor-aware feature fusion strategies. The proposed fusion mechanisms are driven by the estimated sensor measurement reliability values/weights. Reliable object detection in harsh lighting conditions is essential for applications such as self-driving vehicles and human-robot interaction. We also propose a new “r-blended” hybrid depth modality for RGB-D sensors. Through extensive experimentation, we show that the proposed strategies outperform the existing state-of-the-art methods on the FLIR-Thermal dataset, and obtain promising results on the SUNRGB-D dataset. We additionally record a new RGB-Infra indoor dataset, namely L515-Indoors, and demonstrate that the proposed object detection methodologies are highly effective for a variety of lighting conditions.
Evaluating the Performance of Various Deep Reinforcement Learning Algorithms for a Conversational Chatbot

Conversational agents are the most popular AI technology in IT trends. Domain specific chatbots are now used by almost every industry in order to upgrade their customer service. The Proposed paper shows the modelling and performance of one such conversational agent created using deep learning. The proposed model utilizes NMT (Neural Machine Translation) from the TensorFlow software libraries. A BiRNN (Bidirectional Recurrent Neural Network) is used in order to process input sentences that contain large number of tokens (20-40 words). In order to understand the context of the input sentence attention model is used along with BiRNN. The conversational models usually have one drawback, that is, they sometimes provide irrelevant answer to the input. This happens quite often in conversational chatbots as the chatbot doesn't realize that it is answering without context. This drawback is solved in the proposed system using Deep Reinforcement Learning technique. Deep reinforcement Learning follows a reward system that enables the bot to differentiate between right and wrong answers. Deep Reinforcement Learning techniques allows the chatbot to understand the sentiment of the query and reply accordingly. The Deep Reinforcement Learning algorithms used in the proposed system is Q-Learning, Deep Q Neural Network (DQN) and Distributional Reinforcement Learning with Quantile Regression (QR-DQN). The performance of each algorithm is evaluated and compared in this paper in order to find the best DRL algorithm. The dataset used in the proposed system is Cornell Movie-dialogs corpus and CoQA (A Conversational Question Answering Challenge). CoQA is a large dataset that contains data collected from 8000+ conversations in the form of questions and answers. The main goal of the proposed work is to increase the relevancy of the chatbot responses and to increase the perplexity of the conversational chatbot.
A+ Indexes: Tunable and Space-Efficient Adjacency Lists in Graph Database Management Systems

Graph database management systems (GDBMSs) are highly optimized to perform fast traversals, i.e., joins of vertices with their neighbours, by indexing the neighbourhoods of vertices in adjacency lists. However, existing GDBMSs have system-specific and fixed adjacency list structures, which makes each system efficient on only a fixed set of workloads. We describe a new tunable indexing subsystem for GDBMSs, we call A+ indexes, with materialized view support. The subsystem consists of two types of indexes: (i) vertex-partitioned indexes that partition 1-hop materialized views into adjacency lists on either the source or destination vertex IDs; and (ii) edge-partitioned indexes that partition 2-hop views into adjacency lists on one of the edge IDs. As in existing GDBMSs, a system by default requires one forward and one backward vertex-partitioned index, which we call the primary A+ index. Users can tune the primary index or secondary indexes by adding nested partitioning and sorting criteria. Our secondary indexes are space-efficient and use a technique we call offset lists. Our indexing subsystem allows a wider range of applications to benefit from GDBMSs' fast join capabilities. We demonstrate the tunability and space efficiency of A+ indexes through extensive experiments on three workloads.
Teaching-Learning Interaction: A New Concept for Interaction Design to Support Reflective User Agency in Intelligent Systems

Intelligent systems in everyday lives learn about their users to tailor services over time. However, these systems are often designed with little consideration of user agency on their learning processes, hindering users from taking full advantage of the systems. In this paper, we propose Teaching-Learning Interaction (TLI) as a new form of interaction that affords user agency by letting users reflectively shepherd an intelligent system's manner of learning. Given such agency, users will be able to better personalize services for themselves. We first draw on Schön's notion of knowing-in-action and reflective practice to theoretically ground our concept. We then present the resulting definition of TLI and three design qualities, which are further concretized with three design examples. We end with discussion on the implications of TLI for HCI design.
Gibbs partitions, Riemann–Liouville fractional operators, Mittag–Leffler functions, and fragmentations derived from stable subordinators

Abstract Pitman (2003), and subsequently Gnedin and Pitman (2006), showed that a large class of random partitions of the integers derived from a stable subordinator of index $\alpha\in(0,1)$ have infinite Gibbs (product) structure as a characterizing feature. The most notable case are random partitions derived from the two-parameter Poisson–Dirichlet distribution, $\textrm{PD}(\alpha,\theta)$ , whose corresponding $\alpha$ -diversity/local time have generalized Mittag–Leffler distributions, denoted by $\textrm{ML}(\alpha,\theta)$ . Our aim in this work is to provide indications on the utility of the wider class of Gibbs partitions as it relates to a study of Riemann–Liouville fractional integrals and size-biased sampling, and in decompositions of special functions, and its potential use in the understanding of various constructions of more exotic processes. We provide characterizations of general laws associated with nested families of $\textrm{PD}(\alpha,\theta)$ mass partitions that are constructed from fragmentation operations described in Dong et al. (2014). These operations are known to be related in distribution to various constructions of discrete random trees/graphs in [ n ], and their scaling limits. A centerpiece of our work is results related to Mittag–Leffler functions, which play a key role in fractional calculus and are otherwise Laplace transforms of the $\textrm{ML}(\alpha,\theta)$ variables. Notably, this leads to an interpretation within the context of $\textrm{PD}(\alpha,\theta)$ laws conditioned on Poisson point process counts over intervals of scaled lengths of the $\alpha$ -diversity.
Hybrid swarm optimization algorithm based on task scheduling in a cloud environment

Summary Cloud computing is the current computing standard, which provides information technology (IT) services over the Internet on demand. In the cloud environment, a task is mapped with an available resource to attain a good result. Task scheduling is the technique that is used to allocate tasks on virtual machines (VMs) of a server based on its capacity of workload. Tasks are scheduled to the server in such a way to minimize traffic and time delay. Particle swarm optimization (PSO) is the best existing algorithm used to schedule a task to an existing resource on the environment of the cloud. By PSO, the task is scheduled for an existing resource to reduce computational cost. In this paper, a hybrid swarm optimization (HSO) algorithm, which is the combination of PSO and salp swarm optimization (SSO), is proposed to resolve task scheduling issues in the cloud environment. The main goal of HSO is to schedule the task to the available resource in such a way to reduce the execution time and computation cost. Multilayer logistic regression (MLR) is an approach used to detect the overloaded VMs, so that a task can be scheduled to a VM according to its capacity of workload. The proposed HSO algorithm with MLR is simulated on the cloudsim toolkit, and the results reveal the efficiency of the proposed algorithm in terms of cost, execution time, and makespan. Compared to the existing algorithms such as the genetic algorithms (GAs), the improved efficiency evolution (IDEA), and the PSO, the proposed algorithm reveals superiority in terms of efficiency, resource utilization, and speed.
From Sketching to Natural Language

Data visualization is the primary means by which data analysts explore patterns, trends, and insights in their data. Unfortunately, existing visual analytics tools offer limited expressiveness and scalability when it comes to searching for visualizations over large datasets, making visual data exploration labor-intensive and timeconsuming. We first discuss our prior work on Zenvisage that helps accelerate exploratory data analysis via an interactive interface and an expressive visualization query language, but offers limited flexibility when the pattern of interest is under-specified and approximate. Motivated from our findings from Zenvisage, we develop ShapeSearch, an efficient and flexible pattern-searching tool that enables the search for desired patterns via multiple mechanisms: sketch, natural-language, and visual regular expressions. ShapeSearch leverages a novel shape querying algebra that can express a large class of shape queries and supports query-aware and perceptually-aware optimizations to execute shape queries within interactive response times. To further improve the usability and performance of both Zenvisage and ShapeSearch, we discuss a number of open research problems.
Social bots of conviction as dialogue facilitators for history education: Promoting historical empathy in teens through dialogue

Within the broad range of the various types of chatbots, “bots of conviction” (BoCs) shift the focus from offering information to provoking reflection. In this paper we present the design of a “social” BoC, i.e. one designed to engage not one user but a group of participants in reflective dialogue, with the bot and each other. Our social BoC is designed as a digital experience to support history education for high school students (ages 14-18) and was evaluated with a total of 15 teenagers split into 5 groups. The goal was to assess the efficacy of our approach as a tool to promote historical empathy through dialogue. Our findings highlight the BoC's role in engaging the students in constructive dialogue with each other; and the ways in which it guided perspective taking and collective reflection about the past, while at the same time foregrounding connections to the present.
SSFA: Subset fault analysis of ASCON-128 authenticated cipher

Present-day IoT systems that capture, process, and transfer real-world data, employ lightweight ciphers in sensor devices for applications with multiple limitations, such as restricted size, power consumption, and processing speed. The largest security threat that such devices incur comprises implementation-based attacks, such as fault attacks, power analysis attacks, etc. Therefore, it is imperative to perform a meticulous security evaluation of lightweight ciphers against such implementation attacks. This paper aims at evaluating the security of ASCON against fault analysis attacks. ASCON is an authenticated cipher, the CAESAR competition winner under lightweight use case portfolio, in February 2019. The use of 128-bit random nonce as part of the input state makes the cipher resistant against classical cryptanalysis techniques such as differential cryptanalysis, linear cryptanalysis, and variants. However, the key whitening operation with the finalization stage's output to produce the tag T (a publicly available value) creates an attack path for an adversary. Based on this vulnerability, we propose a key recovery attack called Preliminary attack, in which we discuss three methods to mount the proposed Preliminary attack. Furthermore, the S-box used in ASCON possesses a component function with zero correlation immunity that renders it vulnerable against subset cryptanalysis. We propose a novel key recovery attack: Subset fault analysis (SSFA) attack that exploits the vulnerable S-box. Both the proposed attacks can be mounted with different granularities and can uniquely determine the key of full-round ASCON. We also discuss some probable countermeasures to throttle the proposed attacks. Particularly, we recommend an S-box mapping that is resistant to the proposed attack. The recommended S-box preserves all other essential cryptographic properties of the original S-box used in ASCON.
K-CSRL: Knowledge Enhanced Conversational Semantic Role Labeling

Semantic role labeling (SRL) is widely used to extract predicate-argument pairs from sentences. Traditional SRL methods can perform well on the single sentence but fail to work in dialogue scenario where ellipsis and anaphora frequently occurs. Some research work has been proposed to solve this problem, i.e. Conversational Semantic Role Labeling (CSRL), but there are still huge room for improvements. The error case study of BERT-based CSRL model has shown that the majority of the errors are observed in boundary matching, especially in entity mention detection. We think the premier cause of this kind of error is the deficiency of external knowledge such that the ill-informed model cannot correctly capture and correlate the entities. To this end, we propose to incorporate external knowledge into BERT using visible masking strategy. We evaluate our proposed model on DuConv dataset. Experimental results show that our model with knowledge enhancement outperforms the benchmarks. Further analysis also demonstrates that dialogue SRL can benefit from external knowledge.