Reviewerly/exHarmony huggingface dataset:
Agent ontology alignment repair through dynamic epistemic logic

Ontology alignments enable agents to communicate while preserving heterogeneity in their information. Alignments may not be provided as input and should be able to evolve when communication fails or when new information contradicting the alignment is acquired. In the Alignment Repair Game (ARG) this evolution is achieved via adaptation operators. ARG was evaluated experimentally and the experiments showed that agents converge towards successful communication and improve their alignments. However, whether the adaptation operators are formally correct, complete or redundant is still an open question. In this paper, we introduce a formal framework based on Dynamic Epistemic Logic that allows us to answer this question. This framework allows us (1) to express the ontologies and alignments used, (2) to model the ARG adaptation operators through announcements and conservative upgrades and (3) to formally establish the correctness, partial redundancy and incompleteness of the adaptation operators in ARG.
Studying the Impact of the Content Selection Method on the Video QoE on Mobile Devices

When conducting video QoE studies participants are usually asked to rate the QoE of prepared test videos. However, participants are given no choice to select content, which they like or in which they are interested. This may cause annoyance or frustration when conducting the QoE study, which eventually might affect the QoE results of the study. The consequent question is whether the content liking has a direct impact on the submitted ratings by the participants and whether the freedom of choosing the video content in QoE studies results in better ratings. To investigate this research question, CroQoE, an existing framework for crowdsourced video testing, is extended and used in a pilot field study. In this work, the results of a QoE study with individual and dynamic content selection are compared to a QoE study with pre-selected contents. Moreover, this work includes a comparison to a previous QoE study for validation. As the previous study was conducted on desktop PCs, the CroQoE study further allows to identify differences in the stalling perception between studies on desktop PCs and mobile devices.
Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation

We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems.
Segregating and Recognizing Human Actions from Video Footages Using LRCN Technique

Computer vision is a vast area of research that includes extracting useful information from images or sequence of images. Human activity recognition is one such field undergoing lots of research. The practical application for this model is vast in various kinds of researches as well as actual practice. This paper proposes a two-model approach using a combination of a convolutional neural network using transfer learning and a long short-term memory model. CNN network is applied to gather the feature vectors for each video, and the LSTM network is used to classify the video activity. Standard activities contain benchpress, horse riding, basketball dunk, etc. A high accuracy level of 94.2% was achieved by the proposed algorithm.
Interactive Scene Generation via Scene Graphs with Attributes

We introduce a simple yet expressive image generation method. On the one hand, it does not require the user to paint the masks or define a bounding box of the various objects, since the model does it by itself. On the other hand, it supports defining a coarse location and size of each object. Based on this, we offer a simple, interactive GUI, that allows a layman user to generate diverse images effortlessly.From a technical perspective, we introduce a dual embedding of layout and appearance. In this scheme, the location, size, and appearance of an object can change independently of each other. This way, the model is able to generate innumerable images per scene graph, to better express the intention of the user.In comparison to previous work, we also offer better quality and higher resolution outputs. This is due to a superior architecture, which is based on a novel set of discriminators. Those discriminators better constrain the shape of the generated mask, as well as capturing the appearance encoding in a counterfactual way.Our code is publicly available at https://www.github.com/ashual/scene_generation.
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization

Searching for accurate Machine and Deep Learning models is a computationally expensive and awfully energivorous process. A strategy which has been gaining recently importance to drastically reduce computational time and energy consumed is to exploit the availability of different information sources, with different computational costs and different fidelity, typically smaller portions of a large dataset. The multi-source optimization strategy fits into the scheme of Gaussian Process based Bayesian Optimization. An Augmented Gaussian Process method exploiting multiple information sources (namely, AGP-MISO) is proposed. The Augmented Gaussian Process is trained using only reliable information among available sources. A novel acquisition function is defined according to the Augmented Gaussian Process. Computational results are reported related to the optimization of the hyperparameters of a Support Vector Machine (SVM) classifier using two sources: a large dataset - the most expensive one - and a smaller portion of it. A comparison with a traditional Bayesian Optimization approach to optimize the hyperparameters of the SVM classifier on the large dataset only is reported.
Improving Costs and Robustness of Machine Learning Classifiers Against Adversarial Attacks via Self Play of Repeated Bayesian Games

We consider the problem of adversarial machine learning where an adversary performs evasion attacks on a classifier-based learner by sending queries with adversarial data of different attack strengths to it. The learner is unaware whether a query sent to it is clean versus adversarial. The objective of the learner is to mitigate the adversary's attacks by reducing its classification errors of adversarial data. To address this problem, we propose a technique where the learner maintains multiple classifiers that are trained with clean as well as adversarial data of different attack strengths. We then describe a game theoretic framework based on a 2-player repeated Bayesian game called Repeated Bayesian Sequential Game with self play, that enables the learner to determine an appropriate classifier to deploy so that the likelihood of correctly classifying the query and preventing the evasion attack is not deteriorated, while reducing the costs to deploy the classifiers. Experimental results of our proposed approach with adversarial text data shows that our RBSG with self play-based technique maintains classifier accuracies comparable with that of an individual, powerful and costly classifier, while strategically using multiple, lower cost but less powerful classifiers to reduce the overall classification costs.
Knowledge-Based Word Sense Disambiguation with Distributional Semantic Expansion

In this paper, we presented a WSD system that uses LDA topics for semantic expansion of document words. Our system also uses sense frequency information from SemCor to give higher priority to the senses which are more probable to happen.
IoT with Cloud-Based End to End Secured Disease Diagnosis Model Using Light Weight Cryptography and Gradient Boosting Tree

Background: With the evolution of the Internet of Things (IoT) technology and connected devices employed in the medicinal domain, the different characteristics of the online healthcare applications become advantageous. Aim: The objective of this paper is to present an IoT and cloud-based secured disease diagnosis model. At present, various e-healthcare applications with the use of the Internet of Things (IoT) offers diverse dimensions and services online. Method: In this paper, an efficient IoT and cloud-based secured classification model are proposed for disease diagnosis. It is used to avail efficient and secured services to the people globally over online healthcare applications. The presented model includes an effective gradient boosting tree (GBT) based data classification and lightweight cryptographic technique named rectangle. The presented GBT–R model offers a better diagnosis in a secure way. Results: It is validated using the Pima Indians diabetes data, and extensive simulation takes place to verify the consistent performance of the employed GBT-R model. Conclusion: The experimental outcome strongly suggested that the presented model shows maximum performance with an accuracy of 94.92.
Can Verifiable Delay Functions Be Based on Random Oracles

Boneh, Bonneau, Bunz, and Fisch (CRYPTO 2018) recently introduced the notion of a verifiable delay function (VDF). VDFs are functions that take a long sequential time T to compute, but whose outputs y := Eval(x) can be efficiently verified (possibly given a proof π) in time t ≪ T (e.g., t = poly(λ, log T) where λ is the security parameter). The first security requirement on a VDF, called uniqueness, is that no polynomial-time algorithm can find a convincing proof π' that verifies for an input x and a different output y' ≠ y. The second security requirement, called sequentiality, is that no polynomial-time algorithm running in time σ < T for some parameter σ (e.g., σ = T^{1/10}) can compute y, even with poly(T,λ) many parallel processors. Starting from the work of Boneh et al., there are now multiple constructions of VDFs from various algebraic assumptions.In this work, we study whether VDFs can be constructed from ideal hash functions in a black-box way, as modeled in the random oracle model (ROM). In the ROM, we measure the running time by the number of oracle queries and the sequentiality by the number of rounds of oracle queries. We rule out two classes of constructions of VDFs in the ROM: - We show that VDFs satisfying perfect uniqueness (i.e., VDFs where no different convincing solution y' ≠ y exists) cannot be constructed in the ROM. More formally, we give an attacker that finds the solution y in ≈ t rounds of queries, asking only poly(T) queries in total. - We also rule out tight verifiable delay functions in the ROM. Tight verifiable delay functions, recently studied by Dottling, Garg, Malavolta, and Vasudevan (ePrint Report 2019), require sequentiality for σ ≈ T-T^ρ for some constant 0 T-(T)/(2t) for a concrete verification time t.
Word Embeddings as Tuples of Feature Probabilities

In this paper, we provide an alternate perspective on word representations, by reinterpreting the dimensions of the vector space of a word embedding as a collection of features. In this reinterpretation, every component of the word vector is normalized against all the word vectors in the vocabulary. This idea now allows us to view each vector as an n-tuple (akin to a fuzzy set), where n is the dimensionality of the word representation and each element represents the probability of the word possessing a feature. Indeed, this representation enables the use fuzzy set theoretic operations, such as union, intersection and difference. Unlike previous attempts, we show that this representation of words provides a notion of similarity which is inherently asymmetric and hence closer to human similarity judgements. We compare the performance of this representation with various benchmarks, and explore some of the unique properties including function word detection, detection of polysemous words, and some insight into the interpretability provided by set theoretic operations.
A Method for Generating Mazes with Length Constraint using Genetic Programming

We examine a method to automatically generate mazes with length constraint. Assuming that the user creates a text including branches, our prototype arranges the input text on a maze space. In this study, we employ genetic programming and define commands to structure our maze generation program. We implemented our program, and as a result, we were able to generate the desired maze for simple input text.
Computing Plan-Length Bounds Using Lengths of Longest Paths.

We devise a method to exactly compute the length of the longest simple path in factored state spaces, like state spaces encountered in classical planning. Although the complexity of this problem is NEXP-Hard, we show that our method can be used to compute practically useful upper-bounds on lengths of plans. We show that the computed upper-bounds are significantly (in many cases, orders of magnitude) better than bounds produced by previous bounding techniques and that they can be used to improve the SAT-based planning.
Implementasi Personal Assistant Library Service (PALS) dalam meningkatkan layanan referensi di perpustakaan perguruan tinggi

Introduction. The academic library in the Pancoran area have user-oriented reference services by creating services called Personal Assistant Library Service (PALS). This paper discusses the reference services at Pancoran University which aims to describe the implementation of PALS at Universitas Pancoran as an effort to improve the academic library. &#x0D; Method. The method uses case study with qualitative approach. Techniques of collecting data through interviews conducted on librarians who work in the library. &#x0D; Results and Discussions. The findings of the strategy undertaken in an effort to improve reference services at the Pancoran University library can be done in a personal way by forming PALS services. In the case of Pancoran University, these efforts are reflected in the collection support provided for new services, reassessing the competencies needed for services and seeing the challenges that exist in its implementation.&#x0D; Conclusion. The improvement of college library services through PALS is an alternative that can be used by other university libraries even though the application at Pancoran University has not been maximized. PALS focuses on the needs of college library users with high ability needs and different subjects with personal handling.
Natural Language Information Extraction Through Non-Factoid Question and Answering System (NLIEQA Non-Factoid)

Over the years, the retrieval of information from unstructured data has increased significantly. The availability of unstructured data, or to be precise, data in the form of natural language is available in abundance. The main role of a Question and Answering System is to process the natural language query and generate a concise answer to it. There are many works done in recent times, which have given a Question and Answering System that helps in answering factoid or list-type queries asked in natural language, but most of them have made use of structured data. The proposed Natural Language Information Extraction through Question and Answering for Non-Factoid cases (NLIEQA Non-Factoid) accepts data and the query fired by the user in the form of natural language text, processes them, and produces the desirable answer. It avoids training the system and the use of Structured Query Language (SQL) for storage and processing. Another advantage of the model is that it can handle complex queries. The model has a strong use of Named Entity Recognition (NER) for classification and extraction of the answers. It also makes use of Stanford’s Natural Language Tool Kit (NLTK) for tokenizing, tagging, and chunking of the text.
Computationally Grounded Quantitative Trust with Time

Although plenty of qualitative logical frameworks have been proposed to evaluate and model trust in multi-agent sittings [2,6,9,11], these approaches generally ignore reasoning about quantitative aspects such as degrees of trust. In this paper, we address this limitation from the modelling and verification perspectives. We construct TCTLG, a logical language to represent the quantitative aspect of trust. Moreover, we develop and implement a new symbolic model checking algorithm for quantifying the relationships among the interacting agents. Finally, we evaluate the tool and report experimental results using a health-care scenario.
Attention Based Transformer for Student Answers Assessment.

Assessing the correctness of student answers in a dialog-based intelligent tutoring system (ITS) is a well-defined Natural Language Processing (NLP) task that has attracted the attention of many researchers in the field. Inspired by Vaswani’s transformer, we propose in this paper an attention-based transformer neural network with a multi-head attention mechanism for the task of student answer assessment. Results show the competitiveness of our proposed model. A highest accuracy of 71.5% was achieved when using ELMo embeddings, 10 heads of attention, and 2 layers. This is very competitive and rivals the highest accuracy achieved by a previously proposed BI-GRU-Capsnet deep network (72.5%) on the same dataset. The main advantages of using transformers over BI-GRU-Capsnet is reducing the training time and giving more space for parallelization.
Few-Shot Class-Incremental Learning via Feature Space Composition.

As a challenging problem in machine learning, few-shot class-incremental learning asynchronously learns a sequence of tasks, acquiring the new knowledge from new tasks (with limited new samples) while keeping the learned knowledge from previous tasks (with old samples discarded). In general, existing approaches resort to one unified feature space for balancing old-knowledge preserving and new-knowledge adaptation. With a limited embedding capacity of feature representation, the unified feature space often makes the learner suffer from semantic drift or overfitting as the number of tasks increases. With this motivation, we propose a novel few-shot class-incremental learning pipeline based on a composite representation space, which makes old-knowledge preserving and new-knowledge adaptation mutually compatible by feature space composition (enlarging the embedding capacity). The composite representation space is generated by integrating two space components (i.e. stable base knowledge space and dynamic lifelong-learning knowledge space) in terms of distance metric construction. With the composite feature space, our method performs remarkably well on the CUB200 and CIFAR100 datasets, outperforming the state-of-the-art algorithms by 10.58% and 14.65% respectively.
A Framework for Collaborative and Interactive Agent-oriented Developer Operations

Considering the increasing prevalence of autonomous systems in today's society, one could expect that agent-oriented programming (AOP) is gaining traction among mainstream software engineering practitioners. However, the tools and frameworks that are used and developed in the academic multi-agent systems engineering community struggle to keep up with recent developments in the software industry in regards to how complex information systems are developed and maintained. An important aspect of recent changes in software engineering practices is the application of technologies that supports the increasingly fast iteration of a programming-testing-deployment cycle. Such approaches require intense collaboration that crosses boundaries between traditionally separated roles like software development, quality assurance, and operations; these approaches are often referred to as DevOps. Researchers need to explore what additional value AOP has to offer in the context of new paradigms and practices. In this paper, we work towards the integration of DevOps and AOP by introducing an extension of jacamo-web, an Integrated Development Environment (IDE) that supports the collaborative, web-based development and real-time continuous integration of autonomous agents and Multi-Agent Systems (MAS).
Accessibility and Characteristics of Web Citations in Journal of Computer-Mediated Communication during 2008-2017

The present paper explores the accessibility and characteristics of web citations in the Journal of Computer Mediated Communication during the years 2008-2017.  A total of 337 articles were downloaded and 17946 references were extracted.  Out of 4548 web citations, DOIs were found in 2360 references, 2178 references contained URLs and 10 references contained arXiv, WOS article identifier, etc.  It was found that 3861 web citations were accessible and the remaining 687 web citations were missing.  The study also investigated the characteristic features of display and destination URLs like the path depth, URL length, file format, and top-level domain.
ATTITUDINAL MEANING IN MARTIN LUTHER KING JR SPEECH: A FUNCTIONAL GRAMMAR APPROACH

Racial injustice becomes a primary issue in Martin Luther King Jr’s entitled I have a dream in facing United States social condition. The speech reflects evaluative values to share perspectives toward the issue that can be analyzed by the appraisal system. The use of language certainly constructs different aims in evaluative language, and subjective presence is the target of evaluation in the appraisal system. The subjectivity of Martin Luther King Jr toward racial issues contains various types that can indicate a strategy to persuade people. The objectives of this study are to investigate the evaluative language of Martin Luther King Jr and to infer the meaning of evaluative language use. The strategy of delivering motivational messages is also discussed after acquiring the types of attitudes that will be related to the social issue. The research method employed in this study is descriptive qualitative. Systemic functional linguistic theory pioneered by Halliday and his colleagues becomes the framework in this study, and the tool is an appraisal system pioneered by J.R. Martin to analyze data. There are 90 clauses that consist of 33 judgement, 32 appreciation, and 26 affect. Judgement as the most dominant type reflects the reality of racial injustice to persuade many people to reject the condition. The strategy of critic conveyed in different types of attitudes can share deep messages to many people who listen. The result is hoped to be additional references in revealing speech strategy by using an appraisal system. The study of the appraisal system is useful to observe the subjectivity involvement in every form of language use.
Physics on ice

When I watch films, TV shows or sports I often find myself thinking about the physics of the situation. Here in North America, we’re approaching the end of ice hockey season.
A Survey Report on Recent Progresses in Nearest Neighbor Realization of Quantum Circuits

The thrive of attending very fast computation has bred the ideas for new computing paradigms like optical computing, quantum computing, and in the last few years, quantum computing has left an impressive footprint before the design industry. Even companies like IBM, Microsoft are claiming the initiation of physical implementation of quantum circuits in on-chip units. But the successful implementation of this circuit needs to satisfy several design constraints and one such constraint is Nearest Neighbor (NN) enforcement. To introduce the reader with the progress made in NN-based implementation of quantum circuits, here, in this survey paper, we have tried to include some of the peer-reviewed works in our content. While describing the works, we have added necessary examples so that it becomes easy to follow for a reader. To this extent, we also have introduced the NN logic and its related cost functions in our survey report.