Reviewerly/exHarmony huggingface dataset:
Deep Determinantal Q-Learning with Role Aware

Q-DPP is a multi-agent reinforcement learning (MARL) algorithm capable of eliminating the priori structural constraints on the central value function. However, due to the high-dimensional matrix operations with the increasing of the number of agents, Q-DPP shows poor empirical performance in complex environment, such as Starcraft Multi-Agent Challenge (SMAC). To improve the scalability and reduce computational complexity of Q-DPP, we propose a novel Deep Determinantal Q-Learning with Role Aware (RO-DPP). Concretely, we introduce the role concept by learning a role selector and cluster actions based on the impact of agent’s actions on the environment and other agents, and thus break down the joint action spaces into reduced role action spaces, which computes the kernel matrix of Q-DPP in a greatly reduced observation-action space. The experiments on micromanagement benchmark of famous game StarCraft II validate the effectiveness of the proposed RO-DPP.
Path Inference Based on Voronoi Graph

Traditional path planning approach is generally focused on finding a single one shortest path given the temporal or spatial constraints. However, the strategy of single-one optimal path is unable to adapt to various situations in real applications. For example, in the scenarios of vehicle patrolling and search &rescue, the comprehensiveness of coverage is much more important. Rather than finding a single one optimal shortest path as the traditional path planning does, our path inference approach aims at inferring all the possible paths which satisfy the specified temporal and spatial constraints. Path inference is able to provide more valuable insights for internet routing, migration pattern study, urban construction and adversary profiling. In this work, we propose a novel path inference algorithm based on Voronoi graph. We model the 2D geospatial topology with Voronoi cells according to the spatial constraint, represent the adjacency relationships of Voronoi cells with an adjacency matrix and infer all the possible paths in the derived Voronoi graph satisfying the user-specified temporal and spatial constraints. Experimental results indicate that our path inference algorithm based on Voronoi graph is able to perform more delicately and efficiently than the traditional path planning approach in term of both space partition and path inference.
Addressing Biases in the Texts Using an End-to-End Pipeline Approach

The concept of fairness is gaining popularity in academia and industry. Social media is especially vulnerable to media biases and toxic language and comments. We propose a fair ML pipeline that takes a text as input and determines whether it contains biases and toxic content. Then, based on pre-trained word embeddings, it suggests a set of new words by substituting the biased words, the idea is to lessen the effects of those biases by replacing them with alternative words. We compare our approach to existing fairness models to determine its effectiveness. The results show that our proposed pipeline can detect, identify, and mitigate biases in social media data.
A Comprehensive Analysis on Associative Classification in Building Maintenance Datasets

Several works have been conducted to promote a standard for exchanging building data. However, during the operation and maintenance phase of a building, there is still no emerging standard. Existing solutions continue to require manual tasks to match and share data and information among various actors such as owners, service providers, and occupants. This process becomes tedious due to the large volume of exchanged data, hence there is a need to find other ways of matching and identifying association rules between data of different views. In this paper we propose a new associative classification approach that integrates multi-criteria analysis at the association rule ranking level, and some application for data matching and system interoperability. We conducted experiments on both real building maintenance data sets and some UCI Machine Learning data sets, and the results show that our approach achieves good precision/accuracy and produces a less complex classifier that is easily exploitable by business experts.
Role Understanding for Spoken Dialogue System Based on Relevance Ranking

Recognizing different roles in spoken dialogue is a challenging task that significantly impacts the quality of tailored and personalized responses. However, current research fails to address the crucial role of identifying roles while matching questions in dialogue. To tackle this issue, we have proposed a relevance ranking method that effectively distinguishes between different roles in dialogue. Our approach involves creating a set of candidate passages that include different roles, and then comparing their semantic similarity with the dialogue to determine the correct role. Our experimental results on the Multiparty-Dialog-RC dataset demonstrate that our proposed method improves performance by 3.52%.
Improving Recommender System Diversity with Variational Autoencoders

Focusing only on relevance when training recommender systems can lead to suboptimal recommendations. On eCommerce platforms, this not only leads to poor user experience for customers but also limits opportunities for vendors due to popularity bias. Consequently, there is an increasing interest in systems that produce recommendations that are not only relevant but also diverse. In this work, we have shown how diversity can be measured and improved in the context of a large eCommerce recommendation platform. We first evaluate how diversity should be measured feasibly in real-world recommender systems and introduce TILD: a novel diversity metric that measures representational diversity across user sessions. Second, we introduce VAE-based Generation of User Profiles (VAE-GUP), wherein we use the generative nature of VAEs to learn a user profile distribution and sample multiple user profiles. In a simple post-processing step, we combine the recommendations generated by these profiles to generate a final list of more diverse recommendations. Through empirical analysis of benchmark and real-world datasets, we show that VAE-GUP can improve several diversity metrics without significantly compromising relevance. This research has been done in collaboration with Bol.com, the largest e-commerce retailer in the Benelux region.
Matching Intentions for Discourse Parsing in Multi-party Dialogues

Discourse parsing in multi-party dialogues is a fundamental task in dialogue systems, which involves identifying relations between elementary discourse units (EDUs), where an EDU is an utterance in a dialogue. Despite the variety of approaches that have been proposed to enhance context information in utterances, detecting related utterances that share only a few common words remains a challenging task. This paper proposes a novel insight that utilizing intention matching can significantly improve the modeling of utterance relations. Specifically, the intention of a speaker’s utterance represents a high-level abstraction of the utterance semantics, including the dialogue context and speaker context. In our model, a graph neural networks is utilized to encode each utterance’s intention, then the graph contrastive learning is used to study the match between utterances and intentions. Furthermore, we design three layers of intention to capture the meaning of utterances at different granularities. We conduct extensive experiments on two standard benchmark datasets, and the results show that our proposed model achieves state-of-the-art performance compared to current approaches, including the GPT-3.5 model.
Influence of Study Stream on the Students’ Information-Seeking Behaviour

The study was conducted to find out the association between information-seeking behaviour and the stream of study (faculty) of postgraduate students. The study used a structured online questionnaire to collect the data during 2022. From a sample of 378 postgraduate students chosen at random from eight faculties (Arts, Science, Engineering &amp;Technology, Law, Commerce, Education, Fine Arts and Social Science) at the University of Rajasthan, Jaipur. The findings revealed a significant association between the respondents’ stream of study (faculty) and their purpose of seeking information, frequency of using the library, the purposes of use of the library, time spent in the library, purpose-wise usage of the internet, information sources used, browsing of different e-resources, and extent of use of e-resources. All selected aspects were found to be affected by the stream of study of the students. This research can be utilized to better understand the psychology of students seeking knowledge. This research may also aid in improving the university’s information delivery system based on students’ information-seeking behaviour.
Building Hydraulic Villages in Gunungsewu Karst Area of Gunungkidul: The Expectation and Reality

The karst landscape of Gunungkidul of Gunungsewu, Java, portrays a rural area with water scarcity problems.The Indonesian Government has initiated Water Development Programs (WDP) since the late 1990s by building piped water networks that mobilize water from underground rivers.However, today, this problem still occurs.Previous studies have shown that hydrological problems of the nature of the karst cause water scarcity in the karst region.Taking different perspectives by borrowing the "hydraulic city" proposed by Anand, casting it to become "hydraulic village" and the politic of governmentality as theoretical lenses, this paper aims to investigate why the WDP has failed to overcome water scarcity problems.First, the WDP has failed to see the human-water-nature relation as an ecological unit of the karst landscape.Second, at the local level, the PDAM also has failed to increase its capacity to be a good governance institution.
Blockchains with Chinese Characteristics

The Chinese blockchain industry and market have unique, unusual, and idiosyncratic characteristics. Several established companies as well as start-ups in China have developed and deployed innovative solutions involving blockchain. On the other hand, some uses of blockchain such as initial coin offerings for capital-raising have been banned. While blockchain is a disruptive technology with the potential to transform the economy and society, it promotes decentralized governance and thus can turn centralized political structures such as those of China upside down. This chapter offers an analysis of seemingly conflicting actions of the Chinese government on the blockchain front. It analyzes how China is creating a delicate balance between encouraging the use of blockchain for the growth and modernization of the economy and making sure that the technology is not used to threaten the legitimacy of the Chinese Communist Party.
Blockchain in the Mineral and Metal Industry

This chapter examines blockchain’s roles in promoting ethical sourcing in the mineral and metal industry. It gives detailed descriptions of how blockchain-based supply chain networks’ higher density of information flow and a high degree of authenticity of information can increase supply chain participants’ compliance with sustainability standards. It gives special consideration to blockchain systems’ roles in overcoming the deficits in the second-party and third-party trust. It also demonstrates how blockchain-based supply chain networks include outside actors and configures the supply chain networks in a way that enhances the empowerment of marginalized groups.
Grundgestalt

This chapter is dedicated to the principle of Grundgestalt, one of the most important theoretical contributions by Arnold Schoenberg, associated with an organicist conception of musical creation. The Grundgestalt is one of the two pillars that support the idea of variation on time, the focus of the second part of the book (the second pillar is discussed in the next chapter). Some related original concepts, as Grundgestalt-components and variables are properly defined and incorporated to the model.
CISKGG: A Collective Intelligence Strategy for Heterogeneous Knowledge Graph Generation for Criminology as a Domain of Choice

Knowledge Graph (KG) generation is a promising problem which has to be tackled in the era of Web 3.0 as the web is moving towards a more intelligent and organized semantic web. This paper proposes a framework for KG generation for criminology as a prospective domain of choice. The proposed model uses two distinct deep learning classifiers namely the Transformers and Gated Recurrent Units (GRUs) to classify the dataset and the generated metadata respectively. A static upper domain ontology is also made use of as a reference ontology to aggregate and grow the knowledge base. Knowledge and instance aggregation is achieved using Wikidata and Moki as standard prospective knowledge stores along with the Latent Dirichlet allocation (LDA) based topic modelling. This framework also generates the ontologies dynamically using OntoColab and the alignment of static and dynamic ontologies is achieve using the Lin Similarity measure with a specified threshold. The encompassment of entities from a News API knowledge stack, which integrates five different news APIs, helps in further enrichment of entities as well as instances. The Second Order Cooccurrence Pointwise Mutual Information (SOC-PMI) measure with the Goodall Index with variation in thresholds and differential step deviance measures ensures relevant information filtering and the Fish School Search with APMI helps in instance alignment along with the optimization of the feasible solution set to a much more optimal solution set to formulate the finalized highly relevant KG. This proposed CISKGG framework achieves an overall precision of 97.02 with the lowest FDR of 0.03.
Comparision of Varied Embedding and Machine Learning Classifiers for Fine Grained Offensive Content Identification

Online offensive behaviour is becoming a serious threat with the increased access to technology for all. Specifically, social media is being used more to exhibit this derogatory behaviour. Researchers tried to address these hostile practices using numerous techniques, but most previous works have ignored the multi-class categorization of online bullying content. Our work aims to efficiently identify the online offensive content and further classify the recognized bullying content into multi-class categories viz age, gender, religion, ethnicity, and others to understand what qualities or features most online offenders target. Used a balanced dataset and experimented with numerous embedding models to generate the most fitting vectors. These vectors are further given as input to baseline machine-learning algorithms for the appropriate classification of identified offensive content into fine-grained categories. We have compared the results obtained from the various combinations of word embedding techniques and Machine Learning classifiers.
Panoptic Image Segmentation through Unet combined with Melody Search Optimization Algorithm for the Realistic Scene Image Understanding

Realistic Scene understanding is a challenging task by recognizing instances along with the semantic scene. This work, efficient panoptic image segmentation through a deep UNet combined with the melody search optimization algorithm for realistic scene images understanding. The visually appealing things have a variety of constituent pieces that capture the unintended organisational linkages between the crucial components of the carefully considered objects and, as a result, group them altogether according to a perceptual organisation model. The melody search optimization algorithm reduces the Unet parameters, thereby improving the accuracy, UNet combines semantic and instance segmentation predictions to form panoptic predictions and reduces the computational time and memory resources. Hence, the scene understanding is performed without requiring knowledge of the physically inspiring things in advance. Our approach is evaluated on four challenging benchmarks: Mapillary Vistas, Indian Driving Dataset, KITTI, and cityscapes. The results show that our method performs better than the current state-of-the-art methodologies for comprehending scene photos. The performance of the presented method was assessed utilizing panoptic quality metrics and computational time.
CDADITagger: An Approach Towards Content Based Annotations Driven Image Tagging Integrating Hybrid Semantics

Considering the rapid growth of multimedia data, especially images, image tagging is considered the most efficient way to organize or retrieve images. The significance of image tagging is growing extensively but the frameworks employed for tagging these images aren't sophisticated. These images aren't properly tagged because of a lack of resources for tagging or manual tagging is a challenging task considering such voluminous data. Already existing frameworks take both the image data and tag-related textual data but ultimately resulted in mediocre or unpalatable performance as they are dataset centered. To overcome these limitations in existing frameworks we proposed an image tagging mechanism, CDADITagger capable of automatically tagging images efficiently and much more reliable compared to existing frameworks. This framework can tackle real-world applications like tagging a new unknown image as the framework isn't powered by dataset alone but is designed to inculcate images from search engines like Google, Bing, etc. to have comprehensive knowledge of real-time data. These images are classified using CNN and tag-related textual data is classified using decision trees for enhanced performance. While tagging images from the classified tags, are sorted based on the semantic computation values, only the top 50% of the instances classified are selected. The tags which are more correlated to the image are ranked and finalized. The proposed semantically inclined framework CDADITagger outshined the well-established frameworks with an accuracy of 96.60% and a precision of 95.84% making it a more reliable approach.
Mining the Web Data: Intelligent Information Retrieval System for Filtering Spam and Sentiment Analysis

World Wide Web has great impact on all aspects of our day-to-day activities and people retrieve all kinds of information available on the Web. Numerous informal written documents, such as tweets, SMS messages, emails, and reviews, can be found on online shopping platforms. This extremely large collection of text documents has made the text analysis now popular field of study and encompasses a wide range of activities, such as sentiment analysis, opinion mining, spam identification, and harmful link detection. This work identifies three challenging research problems: spam filtering, malicious web page detection and opinion mining services. Three models RePID-OK, ReP-ETD and RePC-SSMSM are proposed to filter spam. Analysis of opinion is important and crucial for both individuals and companies by eliminating malicious reviews for decision making. To handle and help the Web users and consumers in making decisions, two models SentReP and W-LRSVM are proposed for classification of sentiments for medical related drugs and movie review data set. The model DeMalFier and BLRFier are designed for detecting spam from websites to test whether a website is malicious or legitimate.
Penerapan Konsep dan Prinsip Good corporate governance pada PENERAPAN KONSEP DAN PRINSIP GGOD CORPORATE GOVERNANCE PADA PT. KIW (Persero)

Good corporate governance is believed to be one of the main keys of the company in the long term and has an impact on the profits achieved by the company, therefore the company must prepare a statement on the implementation of GCG in its annual report. The implementation of GCG that is not maximized by economic stakeholders both from the government and the private sector will result in an economy that does not run well. The main objective of implementing GCG in SOEs is to maximize the performance of state companies so that they are able to be competitive and have a positive impact on all stakeholders. This research takes a case study at PT. KIW (Persero) as one of the SOEs, where the incident is more implementing GCG. In this study using a quantitative descriptive approach. The results of the analysis show that the implementation of GCG concepts and principles has been carried out well although there needs to be an improvement in information governance technology which is not currently owned by PT, KIW (Persero).
Signal Processing Architecture for Real-time Earthquake Early Warning System

Though significant research has been carried out on software based development of Earthquake Early Warning System (EEWS), relatively less work has been reported so far in literature on VLSI implementation of the same which yields ease in deployment and faster processing. The present research work focuses on the implementation of efficient architectures for seismic signal processing scheme pertaining to onsite EEWS. The framework of the proposed signal processing scheme comprises four sub-modules, namely an enhanced variable step-size (EVSS) adaptive filter for signal denoising, short-time-average/long-time-average (STA/LTA) algorithm for event detection, double numerical integration based drift-free displacement estimation unit, and two-step threshold-based decision making unit for warning generation. Automated detection of the seismic P-wave arrival employing STA/LTA algorithm is prone to suffer from erroneous detection due to high background noise. This research work introduces EVSS Least Mean Square (EVSSLMS) algorithm which outperforms the state-of-the-art adaptive algorithms in terms of convergence speed and steady-state error, and achieves a significant enhancement in detection accuracy (>23%) at low signal-to-noise ratio (SNR) Gaussian noise environment as compared to the similar competing algorithms. However, EVSSLMS is not robust against impulsive noise. Hence, robust EVSS Sign-Sign Algorithm (EVS-SSA) is proposed, which exhibits enhancement in detection accuracy (>22%) under impulsive noise environment over the counterparts. The displacement is estimated by performing double integration. To remove low frequency integration drift, high-pass finite impulse response (FIR) filter is used. A novel matrix grouped common sub-expression elimination (MCSE) algorithm is proposed to minimize the logic operators (LOs) and logic depths (LDs) requirement to design FIR filter. Moreover, a robust two-step threshold based decision making scheme has been incorporated in the EEWS to generate the alarm. In this work, high-speed, low hardware complexity, low power architectures of the aforementioned sub-modules are proposed satisfying the basic necessity of a single sensor based EEWS. The processing time of the proposed system is reasonably small as compared to existing systems. For a single chip solution, the architectures are implemented on FPGA and ASIC platformselectronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract. (Abstract)