Reviewerly/exHarmony huggingface dataset:
Model Interpretability: Advances in Interpretable Machine Learning

This chapter expands on intrinsic model interpretability discussed in the last chapter to include many modern techniques that are both interpretable and accurate on many real-world problems. The chapter starts with differentiating between interpretable and explainable models and why, in specific domains where high stakes decisions need to be made, interpretable models should be a natural choice. The chapter covers some state-of-the-art interpretable models that are ensemble-based, decision tree-based, rules-based, and scoring system based. We describe each algorithm in sufficient detail and then use the diabetes classification or insurance claims regression dataset to practically demonstrate the output of each, along with interpretations and observations.
Denoising and Dehazing an Image in a Cascaded Pattern for Continuous Casting

Automatic vision systems have been widely used in the continuous casting of the steel industry, which improve efficiency and reduce labor. At present, high temperatures with evaporating fog cause images to be noisy and hazy, impeding the usage of advanced machine learning algorithms in this task. Instead of considering denoising and dehazing separately like previous papers, we established that by taking advantage of deep learning in a modeling complex formulation, our proposed algorithm, called Cascaded Denoising and Dehazing Net (CDDNet) reduces noise and hazy in a cascading pattern. Experimental results on both synthesized images and a pragmatic video from a continuous casting factory demonstrate our method’s superior performance in various metrics. Compared with existing methods, CDDNet achieved a 50% improvement in terms of peak signal-to-noise ratio on the validation dataset, and a nearly 5% improvement on a dataset that has never seen before. Besides, our model generalizes so well that processing a video from an operating continuous casting factory with CDDNet resulted in high visual quality.
Applying Lightweight Elliptic Curve Cryptography ECC to Smart Energy IoT Platforms Based on the CoAP Protocol

Nowadays, Morocco has adhered to a policy aimed at modernizing its cities in terms of smart city and green city to improve the quality of life, protect the environment, and compete with the world’s major cities in this promising field. In this perspective, our current work revolves around the innovation of smart cities via new technologies of interconnection between objects and man in maximum respect that possible of the environment and especially the air quality. The rate of innovation in creating smart cities will make it feasible to bring IoT technology into cities development. In this article, we develop a smart and secure IoT platform to monitor and control the energy consumption of any area in a smart city. From this, we used the CoAP and MQTT application protocols for data transfer from the Smart meters to the webserver. So, to optimize the performance of our platform, we’re going to explore several topologies, including: STAR, TREE, MESH and CLUSTER. Ultimately, to overcome existing limitations related to key management and data transformation security by CoAP and MQTT protocols in each topology, we proposed an effective and secure communication protocol to establish a secure session key between IoT devices and the remote server, using the elliptical curve cryptography (ECC/SHA256) that we compared with another RSA/SHA256 (Rivest–Shamir–Adleman) communication protocol in terms of execution time and memory consumption to see the suitability and performance of the platform and infer the best secure topology in our smart city.
Research on Attitude Control for UAV Based on Improved PSO Algorithm

Aiming at the problem of parameter adjustment in nonlinear system controller, an improved particle swarm optimization (PSO) algorithm is used to optimize parameters. Aiming at the defect that PSO algorithm will fall into local optimum in the early iteration, a nonlinear dynamic inertia weight method based on quadratic exponential is proposed. And the distance between the particle and the global optimum is introduced, which make the inertia weight not only changes with the number of iterations, but also the inertial weight of each particle is related to its distance from the global optimum. In order to comprehensively consider fuel consumption and dynamic performance, an improved design of the fitness function introduced the fuel consumption factor is proposed. The effectiveness of the improved PSO algorithm is verified by simulation analysis.
Research on unsupervised people re-identification based on k-means clustering

People re-identification is a direction in the field of computer vision. With the development of deep learning, pedestrian re-identification has become a popular research direction in recent years. At present, pedestrian re-identification has achieved good results in supervised learning with data annotation, but it still faces challenges in the unsupervised field. We propose a pedestrian re-identification method based on SE-ResNet50 k-means clustering and merging. The experimental results in the two large data setsMarket-1501 and Duke MTMC-reID have achieved better than the current mainstream Method for better results.
Towards interactive coordination of heterogeneous robotic teams – Introduction of a reoptimization framework

The coordination of heterogeneous robotic teams demands suitable planning algorithms based on an appropriate model of the problem instance. While there exists a great variety of automated planning algorithms, the modeling of a problem instance often requires expertise in problem recognition and faculty of abstraction—a task which can be done best by humans. In order to exploit the synergy potential inherent in human-machine cooperative planning, we propose a new reoptimization framework based on a genetic algorithm (GA) for heterogeneous multi-robot task allocation problems including cooperative tasks and precedence constraints. The main idea of the reoptimization framework is to reuse insights from previous solutions of similar problem instances. In particular, a modified problem instance, resulting for example from adding or deleting individual tasks, is solved based on the solution of the unmodified problem instance. To this end, we introduce suitable heuristics for the adaption of the initial solution based on the considered problem modification. The simulative investigation of the proposed approach shows great positive effects compared to the application of a standardized GA that does not make use of the solution of the unmodified problem instance.
Enhancement of Computer Network Performance with VLAN

The computer network service provider must provide a good network service. Service quality is the most important factor and must always be maintained properly. The quality of service can change depending on the number and characteristics of service users. This study optimizes the performance of existing computer networks, namely ordinary LAN networks with the application of VLANs. It is very important because the number of network users is increasing and will continue to increase, while the need for good service quality is very urgent. The results showed that VLANs can improve computer network performance from the system existing, namely LAN network.
Aspect-Based Sentiment Analysis with Multi-aspects Heterogeneous Graph Convolutional Networks

The target of aspect-based sentiment analysis (ABSA) mission is in order to perform emotional polarity judgments of the specified aspect among one data set, that is, a sentence. The current models ignore the interaction of multiple aspects within a sentence, and the representation of aspect and context information is inadequate. To solve these problems, we integrate multi-aspects and contextual information into a graph, then put forward a multi-aspects heterogeneous graph convolutional network (MAHGCN) model to update and represent nodes. It is verified by experiments on four data sets that MAHGCN model achieves significant and consistent improvement as compared to other baselines.
Codevelopment Action Learning during the pandemic – findings from two online co-learning and co-creation events

This article discusses the international events known as Proxima, organized by a group of colleagues, whose slogan is ‘Drawing Closer to Go Further’. Two half-day sessions, held in April and May 2020, consisted of 20 simultaneous Codevelopment Group (CDG) online sessions. Held during the global lockdown, these online events generated creative, collaborative and transformative spaces for participants throughout the French-speaking world. A total of 148 people from nine countries, including 15 experienced facilitators took part in this unique experience. After giving an overview of the Action Learning-based CDG method, this article presents the survey results on participant goals, takeaways and the intent to apply/transfer, and shows that 96% of participants responded positively to questions about these aspects. Qualitative content analyses also shows what was achieved, learned and could be transferred, such as a better understanding of the CDG method and its online applications. Overall, this experience further reinforces the fact that CDGs can effectively stimulate meaningful learning, creativity and collaborative action. Finally, in the past, Action Learning and CDG sessions have tended to be held in-person. However, these events showed that virtual sessions can be effective and generate impact when physical presence is not possible.
The reading contracts in the advertising discourse of health communication campaigns: an analysis of the “I know. Do you know?” campaign

Introduction: In view of the syphilis epidemic that Brazil is experiencing and the need to strengthen the discourse on preventing this sexually transmitted infection (STI), the campaign "Eu sei, você sabe?" (I know. Do you know?) was conducted for dissemination of the pieces on social media. Objective: The of aim of this study was to perform a qualitative descriptive analysis of the posts published on the social network Instagram of the National Campaign to Fight Syphilis 2020-21 aimed at young people. Methods: The analysis performed was based on the method suggested by Eliseo Véron and aimed to establish relationships between the "reading contracts" offered and the processes of focusing (strong direct relevance) and defocusing (interdiscursive relevance) acting in the messages with the results of the communication indicator "Reach/Exposure". Results: The analysis was conducted from October 2020 to April 2021. In all, 31 cards were published. Among them, eight publications presented in their content a discourse directed at young people including individuals in the 20-29 age bracket. In general, the pieces provided a common proposal of visual structuring and aimed to address the deficiencies identified in previous campaigns developed and presented in the perspective of the project itself, as well as the propositions established by control agencies for dealing with syphilis in Brazil. Conclusion: Some hypotheses raised on the basis of the analysis were as follows. There is the possibility that the distinction between grammars of production and recognition produce negotiations or resistance to the meaning of the interpellation contained in the theme "Sífilis. Eu sei. Você sabe?” Even if the characters presented in the cards did not include some specific audiences, such as young transgenders, the language offered can generate empathy in these audiences. The production strategies of cards with a more visual aesthetic and added to a predominantly graphic distribution of important information possibly contributed to a greater reach/impact on the social network audience.
Gated Graph Neural Networks (GG-NNs) for Abstractive Multi-Comment Summarization

Summarization of long sequences into a concise statement is a core problem in natural language processing, which requires a non-trivial understanding of the weakly structured text. Therefore, integrating crowdsourced multiple users' comments into a concise summary is even harder because (1) it requires transferring the weakly structured comments to structured knowledge. Besides, (2) the users comments are informal and noisy. In order to capture the long-distance relationships in staggered long sentences, we propose a neural multi-comment summarization (MCS) system that incorporates the sentence relationships via graph heuristics that utilize relation knowledge graphs, i.e., sentence relation graphs (SRG) and approximate dis-course graphs (ADG). Motivated by the promising results of gated graph neural networks (GG- NNs) on highly structured data, we develop a GG-NNs with sequence encoder that incorporates SRG or ADG in order to capture the sentence relationships. Specifi-cally, we employ the GG- NNs on both relation knowledge graphs, with the sentence embeddings as the input node features and the graph heuristics as the edges' weights. Through multiple layer-wise propagations, the GG- NNs generate the salience for each sentence from high-level hidden sentence features. Consequently, we use a greedy heuristic to extract salient users' comments while avoiding the noise in comments. The experimental results show that the proposed MCS improves the summarization performance both quantitatively and qualitatively.
A Fast Density Peak Clustering Method with Autoselect Cluster Centers

Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.
The Construction of Virtual Simulation Teaching Platform for Mining Engineering based on Connotative Development

In view of the urgent need for high-quality talents in the development of national intelligent mining, the virtual simulation teaching platform of mining engineering in our school is constructed based on virtual reality technology with "connotative development" as the guiding ideology. The virtual simulation teaching system of "one center, one platform, six modules and six levels" is created. A three-level progressive mixed teaching model of "virtual classroom as the prelude, real classroom as the foundation, and virtual classroom as the expansion" is formed. The teaching reform based on virtual simulation teaching platform improves the teaching quality and effect, effectively avoids the disadvantages of traditional teaching, and is conducive to training students' practical and innovative ability, and providing high-quality talents for the development of national intelligent mining.
Workload Characteristics in Cloud Data Centers: A Computational Study from Alibaba Cloud

Cloud computing is trending in today's digital era in every form and scaling to a peak in order to deliver the mutual expectations for Cloud consumers and providers through Cloud Data Center's. One such data center so called Alibaba Cloud's Data Cluster is analysed in this paper. The online services and Batch jobs co-exist in the same data cluster which leads to multiplexing of resource utilization in Cloud Data Center's. Therefore our findings and insights from this data production cluster allows the community and data center operators better understand the various workload patterns to improve the resource utilization and failure recovery design.
A Novel Lip Synchronization Approach for Games and Virtual Environments

This paper presents an algorithm for the offline generation of lip-sync animation. It redefines visemes as sets of constraints on the facial articulators such as lips, jaw, tongue. The algorithm was comparatively evaluated with 30 healthy participants by presenting a set of phrases delivered verbally by a virtual character. Each phrase was presented in two versions: once with the traditional lip-sync method, and once with our method. Results confirm that the suggested solution produces more natural animation than standard keyframe interpolation techniques.
Supervised Machine Learning

Machine learning refers to a set of methodologies that allow computers to “learn” the relationship among numerical representations of data. In this Chapter, we focus on an important branch of machine learning, supervised machine learning, and introduce three widely used supervised learning methods, the Support Vector Machine, Random forest, and Gradient Boosting Machine. Python codes examples are included to show how to use these methods in practice.
Detection and Prioritization of Emergency Vehicles in Intelligent Traffic Management System

Citizens of any country deserves to be served with proper emergency services. If the response time in such service workflow increases unreasonably due to traffic congestion, consequences may be fatal. Hence, efficient detection of an emergency vehicle (EV) is necessary for prioritizing their movements. Present study proposes the method, which would facilitate prioritizing of the EV approaching traffic signal using the Shortest Job First (SJF) scheduling algorithm. Here identifying the Sound of EV Siren using the Goertzel algorithm, the EVs and its type are to be detected from a roadside unit (RSU). The magnitudes of tone calculated in RSUs are compared for predicting EV position from the traffic signal. The information of EV type, its position from the signal junction, and the traffic density are used to prioritize one unit out of similar type EVs in an IoT-based Intelligent Traffic Management System (ITMS).
Reinforced Optimal Estimator

Estimating the state of a stochastic system is a long-lasting issue in the areas of engineering and science. Existing methods either use approximations or yield a high computation burden. In this paper, we propose reinforced optimal estimator (ROE), which is an offline estimator for general nonlinear and non-Gaussian stochastic models. This method solves optimal estimation problems offline, and the learned estimator can be applied online efficiently. Firstly, we demonstrate that minimum variance estimation requires us to solve the estimation problem online, which causes low computation efficiency To overcome this drawback, we propose an infinite horizon optimal estimation problem, called reinforcement estimation problem, to obtain the offline estimator. The time-invariant filter of linear systems is shown as an example to analyze the equivalence between reinforcement estimation problem and minimum variance estimation problem. We show that such equivalence can only be found for linear systems, and the proposed problem formulation actually enables us to find the time-invariant estimator for general nonlinear systems. Then, we propose the ROE algorithm, inspired by reinforcement learning, and develop an actor-critic architecture to find a nearly optimal estimator of the reinforcement estimation problem. The estimator is approximated by recurrent neural networks, which has high online computation efficiency. The convergence is proved using contraction mapping and extended policy improvement theorem. Experiment results on complex nonlinear system estimation problems show that our method achieves higher estimation accuracy and computation efficiency than the unscented Kalman filter and particle filter.
The Role of the Self-Presence for Interactive Large Digital Displays: Gesture Interactivity and Mirrored-Self Image for Advertising Content

The gesture interactivity and self-mirroring effects of large digital displays, also known as digital signage, was investigated using the self-presence theory and engagement. By extending the interactive effects of gesture and self-mirroring from the immersive environment to a large interactive display (LID), this study found the implicit mediating roles of social presence and engagement for advertising effectiveness through a 2 (gesture; non-gesture vs. gesture interactivity) x 2 (self-representation; non-self-mirroring vs. self-mirroring interactivity) between-subject design (N = 81) experiment. The results indicated that gesture interactivity enhances a feeling of self-presence, not self-mirroring, and it positively fosters psychological engagement with advertising content. In turn, the heightened engagement results in better advertising effectiveness, such as brand attitude, purchase intention, and recall.
Effects of Political Instability on Economic Growth in the Republic of Congo

The objective of this paper is to analyze the effects of political instability on economic growth in Republic of the Congo.The Autoregressive Lagged Model (ARDL) was used in the study period from 1986 to 2017.The results of this estimation show that political instability is a brake on economic growth.To this end, implications for strengthening policies to promote political stability were formulated.
On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

Predicting energy consumption has been a substantial topic because of its ability to lessen energy wastage and establish an acceptable overall operational efficiency. Thus, this research aims at creating a meta-heuristic-based method for autonomous simulation of heating and cooling loads of buildings. The developed method is envisioned on two tiers, whereas the first tier encompasses the use of a set of meta-heuristic algorithms to amplify the exploration and exploitation of Elman neural network through both parametric and structural learning. In this regard, 10 meta-heuristic were utilized, namely differential evolution, particle swarm optimization, invasive weed optimization, teaching-learning optimization, ant colony optimization, grey wolf optimization, grasshopper optimization, moth-flame optimization, antlion optimization, and arithmetic optimization. The second tier is designated for evaluating the meta-heuristic-based models through performance evaluation and statistical comparisons. An integrative ranking of the models is achieved using average ranking algorithm.
Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing Video Super-resolution

In per-title encoding, to optimize a bitrate ladder over spatial resolution, each video segment is downscaled to a set of spatial resolutions, and they are all encoded at a given set of bitrates. To find the highest quality resolution for each bitrate, the low-resolution encoded videos are upscaled to the original resolution, and a convex hull is formed based on the scaled qualities. Deep learning-based video super-resolution (VSR) approaches show a significant gain over traditional upscaling approaches, and they are becoming more and more efficient over time. This paper improves the per-title encoding over the upscaling methods by using deep neural network-based VSR algorithms. Utilizing a VSR algorithm by improving the quality of low-resolution encodings can improve the convex hull. As a result, it will lead to an improved bitrate ladder. To avoid bandwidth wastage at perceptually lossless bitrates, a maximum threshold for the quality is set, and encodings beyond it are eliminated from the bitrate ladder. Similarly, a minimum threshold is set to avoid low-quality video delivery. The encodings between the maximum and minimum thresholds are selected based on one Just Noticeable Difference. Our experimental results show that the proposed per-title encoding results in a 24% bitrate reduction and 53% storage reduction compared to the state-of-the-art method.