Reviewerly/exHarmony huggingface dataset:
Multi-goal multi-agent learning for task-oriented dialogue with bidirectional teacher–student learning

In this work, we propose a multi-goal multi-agent learning (MGMA) framework for task-oriented dialogue generation, which aims to retrieve accurate entities from knowledge base (KB) and generate human-like responses simultaneously. Specifically, MGMA consists of a KB-oriented teacher agent for inquiring KB, a context-oriented teacher agent for extracting dialogue patterns, and a student agent that tries to not only retrieve accurate entities from KB but also generate human-like responses. A “two-to-one” teacher–student learning method is proposed to coordinate these three networks, training the student network to integrate the expert knowledge from the two teacher networks and achieve comprehensive performance in task-oriented dialogue generation. In addition, we also update the two teachers based on the output of the student network, since the space of possible responses is large and the teachers should adapt to the conversation style of the student. In this way, we can obtain more empathetic teachers with better performance. Moreover, in order to build each task-oriented dialogue system effectively, we employ a dialogue memory network to dynamically filter the irrelevant dialogue history and memorize important newly coming information. Another KB memory network, which shares the structural KB tuples throughout the whole conversation, is adopted to dynamically extract KB information with a memory pointer at each utterance. Extensive experiments on three benchmark datasets (i.e., CamRest, In-Car Assistant and Multi-WOZ 2.1) demonstrate that MGMA significantly outperforms baseline methods in terms of both automatic and human evaluation.
Perceus: garbage free reference counting with reuse

We introduce Perceus, an algorithm for precise reference counting with reuse and specialization. Starting from a functional core language with explicit control-flow, Perceus emits precise reference counting instructions such that (cycle-free) programs are _garbage free_, where only live references are retained. This enables further optimizations, like reuse analysis that allows for guaranteed in-place updates at runtime. This in turn enables a novel programming paradigm that we call _functional but in-place_ (FBIP). Much like tail-call optimization enables writing loops with regular function calls, reuse analysis enables writing in-place mutating algorithms in a purely functional way. We give a novel formalization of reference counting in a linear resource calculus, and prove that Perceus is sound and garbage free. We show evidence that Perceus, as implemented in Koka, has good performance and is competitive with other state-of-the-art memory collectors.
BlockRobot: Increasing Privacy in Human Robot Interaction by Using Blockchain

Social robots can cause privacy concerns for humans, both because they collect personal data while moving around globally, and because humans can perceive them as social actors. Nowadays, robots can record and transmit (private) data in a human-readable format, which is identified as a problem in human-robot interaction (HRI). Privacy is an individual's right to have control over its data. With this problem in mind, we present BlockRobot - a new privacy-by-design conceptual model for access control of personal data based on Blockchain (BC) technology. With this solution, we ensure data privacy rights by giving each user control over its data in a decentralized manner without the need for an intermediate data controller. This BC solution grants confidentiality, integrity, and non-repudiation of data transparently and fairly to every user. As proof of concept, we demonstrate the initial implementation of a Decentralized Application (DAP) based on EOS Blockchain integrated with robotic events that contain private data. This paper details the experiments conducted with a Social Robot (SR) in a non-lab environment, and the collected data is analyzed using process mining techniques.
Variational multimodal machine translation with underlying semantic alignment

Capturing the underlying semantic relationships of sentences is helpful for machine translation. Variational neural machine translation approaches provide an effective way to model the uncertain underlying semantics in languages by introducing latent variables. Multitask learning is applied in multimodal machine translation to integrate multimodal data. However, these approaches usually lack a strong interpretation in utilizing out-of-text information in machine translation tasks. In this paper, we propose a novel architecture-free multimodal translation model, called variational multimodal machine translation (VMMT), under the variational framework which can model the uncertainty in languages caused by ambiguity through utilizing visual and textual information. In addition, the proposed model can eliminate the discrepancy between training and prediction in the existing variational translation models by constructing encoders only relying on source data. More importantly, the proposed multimodal translation model is designed as multitask learning in which the shared semantic representation for different modes is learned and the gap among semantic representation from various modes is reduced by incorporating additional constraints. Moreover, the information bottleneck theory is adopted in our variational encoder–decoder model, which helps the encoder to filter redundancy and the decoder to concentrate on useful information. Experiments on multimodal machine translation demonstrate that the proposed model is competitive.
Swarm Contracts: Smart Contracts in Robotic Swarms with Varying Agent Behavior

Multi-agent robotic systems are becoming pervasive in many real-world applications from search and rescue missions to future household robotic appliances that might need to work together to achieve specific tasks. We propose and implement a collaborative environment for secure communication of robotic agents in a prototype agent system that mimics the interactions between agents of varying behaviors using special-purpose smart contracts titled "Swarm Contracts." This paper describes how Swarm Contracts and blockchain technologies increase the interaction efficacy between agents by providing a more trusted information exchange to reach consensus under trustless conditions, assess agent productivity, allocate plans and tasks to deploy distributed solutions, and carry out joint missions. All these features are encapsulated in Swarm Contracts, making the decentralized applications that use them a viable alternative to centralized command and control applications that are pervasive in multi-agent robotics applications of today. We have evaluated the utility of the developed Swarm Contracts in adversarial settings and report the results that are very promising for future applications of such decentralized heterogeneous robotic agent interactions.
User profiling via application usage pattern on digital devices for digital forensics

In digital forensics, user profiling aims to predict characteristics of the user from digital evidence extracted from digital devices (e.g. smartphone, laptop, tablet). Previous researches showed promising results, but there are limitations to apply practical investigations. The researches so far have focused only on specific applications, devices, or operating systems by analyzing the order of execution or volatile data such as network traffic and online content. This paper introduces a user profiling method, named Entity Profiling with Binary Predicates (EPBP) model, which analyzes non-volatile data remained on digital devices. The proposed model defines that a user has two properties: tendency and impact, which indicate patterns of application usage. Based on the attributes, the EPBP model generates users’ profiles and performs similarity analysis to differentiate between the users. We also present methods for clustering and anomaly detection through real case studies.
Aligning social concerns with information system security: A fundamental ontology for social engineering

Along with the rapid development of socio-technical systems, people are playing an increasingly important role in information system and have actually become an essential system component. However, unlike technology-based attacks that have been investigated for decades, social engineering attacks have not been efficiently addressed. In particular, due to the interdisciplinary nature of social engineering, there is a lack of consensus on its definition, hindering the further development of this research field. In this paper, we propose a comprehensive and fundamental ontology of social engineering based on a systematic review of existing social engineering taxonomies and ontologies in order to provide a theoretical foundation for social engineering analysis. The essential contributions of this paper include: (1) propose a comprehensive ontology of social engineering and precisely specify ontological definitions of its essential concepts based on Situation Calculus; (2) enumerate and summarize a set of social engineering techniques and present their fine-grained classification based on the proposed ontology; (3) incorporate psychology and sociology knowledge into social engineering analysis, encapsulating such knowledge in terms of a formalized ontology. We have evaluated our ontology based on a set of real social engineering attacks, the results of which show the usefulness of our proposal.
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Let q be a power of 2. Recently, Tu and Zeng considered trinomials of the form f(X)=X+aX(1∕4)q2(q−1)+bX(3∕4)q2(q−1), where a,b∈Fq2∗. They proved that f is a permutation polynomial of Fq2 if b=a2−q and X3+X+a−1−q has no root in Fq. In this paper, we show that the above sufficient condition is also necessary.
Design and Assessment of Virtual Learning Environments to Support STEM Learning for Autistic Students

This paper discusses the design of Virtual Learning Environments (VLEs) in helping students with autism learn Science and Engineering concepts. The VLEs were created for middle and high school students; subsequently assessment activities were conducted to gain a better understanding of the impact of such VLEs. The learning environments were created using various interfaces and immersion levels; these included haptic based interfaces and fully immersive 3D environments. These VLEs introduced students to concepts in Robotics and Manufacturing. Initial assessment findings have demonstrated the positive impact of such cyber learning techniques and environments.
Mining of top-k high utility itemsets with negative utility

High utility itemset mining (HUIM) with negative utility is an emerging data mining task. However, the setting of the minimum utility threshold is always a challenge when mining high utility itemsets (HUIs) with negative items. Although the top-k HUIM method is very common, this method can only mine itemsets with positive items, and the problem of missing itemsets occurs when mining itemsets with negative items. To solve this problem, we first propose an effective algorithm called THN (Top-k High Utility Itemset Mining with Negative Utility). It proposes a strategy for automatically increasing the minimum utility threshold. In order to solve the problem of multiple scans of the database, it uses transaction merging and dataset projection technology. It uses a redefined sub-tree utility value and a redefined local utility value to prune the search space. Experimental results on real datasets show that THN is efficient in terms of runtime and memory usage, and has excellent scalability. Moreover, experiments show that THN performs particularly well on dense datasets.
Cost-Effective Data Feeds to Blockchains via Workload-Adaptive Data Replication

Feeding external data to a blockchain, a.k.a. data feed, is an essential task to enable blockchain interoperability and support emerging cross-domain applications. Given the data-intensive nature of real-life feeds (e.g., high-frequency price updates) and the high cost of using blockchain, namely Gas, it is imperative to reduce the Gas cost of data feeds. Motivated by the constant-changing workloads in financial applications, this work aims at designing a dynamic, workload-aware approach for Gas cost optimization. This design space is understudied in existing blockchain research which has so far focused on static data placement.
SearchBC: A Blockchain-Based PEKS Framework for IoT Services

Internet of Things (IoT) makes great development and gains popularity with a mature combination with cloud computing. Plaintext data can be encrypted when data owners try to secure the confidentiality, while the encrypted data retrieval can be achieved by a cryptographic primitive named searchable encryption. Public-key encryption with keyword search (PEKS) is built on the asymmetric setting, however, the “honest-but-curious” assumption in PEKS creates challenges on the search reliability when malicious behaviors happen. It is nontrivial to enable the reliable search while preserving keyword privacy, as users who have paid need to receive either correct-and-wanted results or compensations. In this work, we apply blockchain to resolve this problem and design SearchBC, a blockchain-based PEKS framework supporting private, reliable, and fair encrypted search over the asymmetric setting. SearchBC is built on top of the blockchain and a keyword server to allow fairness in transactions and keyword preprocessing. We present a SearchBC instantiation and formally prove its security under the newly defined security model. SearchBC guarantees that search operations are fair and reliable and that the used keyword keeps privacy. The implementation results show that SearchBC provides a feasible means with reasonable communication and computation costs.
Enhancing dull images using discrete wavelet families and fuzzy

Abstract Enhancement of an image which is dull is used in important application like restoring an image. Especially when enhancing a gloomy image it becomes quite difficult. Hence a simpler but effective method for enhancing the image is proposed here. Here a novel method based on graphical user interface which allows the user to enhance the image based on their requirement is proposed. The algorithm used for this is the discrete wavelet transforms families which will decompose the dull image. The four approximation coefficient obtained after decomposition are further manipulated using fuzzy logic in order to enhance the image. And then inverse discrete wavelet transform is used to reconstruct the image. Experimental results show that the proposed method is robust in enhancing the quality of images.
Practical Fine-Grained Binary Code Randomization†

Despite its effectiveness against code reuse attacks, fine-grained code randomization has not been deployed widely due to compatibility as well as performance concerns. Previous techniques often needed source code access to achieve good performance, but this breaks compatibility with today’s binary-based software distribution and update mechanisms. Moreover, previous techniques break C++ exceptions and stack tracing, which are crucial for practical deployment. In this paper, we first propose a new, tunable randomization technique called LLR(k) that is compatible with these features. Since the metadata needed to support exceptions/stack-tracing can reveal considerable information about code layout, we propose a new entropy metric that accounts for leaks of this metadata. We then present a novel metadata reduction technique to significantly increase entropy without degrading exception handling. This enables LLR(k) to achieve strong entropy with a low overhead of 2.26%.
Classifying and diacritizing Arabic poems using deep recurrent neural networks

Poetry has a prominent history in Arabic literature. The classical Arabic poetry has 16 m that vary in rhythm and target purpose. Chanting a poem eloquently requires knowing the poem's meter and obtaining a diacritized version of its verses (letters inscribed with their short vowels); diacritics are often not inscribed in Arabic texts. This work proposes solutions to classify input Arabic text into the 16 poetry meters and prose. It also investigates the automatic diacritization of Arabic poetry. We adopt machine learning approach using a large dataset of 1657 k verses of poems and prose to develop neural networks to classify and diacritize Arabic poetry. We propose deep and narrow recurrent neural networks with bidirectional long short-term memory cells for solving these problems. The proposed model classifies the input text with an average accuracy of 97.27%, which is significantly higher than previous work. We also propose a solution that achieves an accuracy that approaches 100% when multiple verses of the same poem are available through predicting the class from the aggregate probabilities of the multiple verses. Diacritizing poetry is much harder than diacritizing prose due to the poet's meticulous selection of phrases and relaxation of some diacritization rules.
Predicting tweet impact using a novel evidential reasoning prediction method

This study presents a novel evidential reasoning (ER) prediction model called MAKER-RIMER to examine how different features embedded in Twitter posts (tweets) can predict the number of retweets achieved during an electoral campaign. The tweets posted by the two most voted candidates during the official campaign for the 2017 Ecuadorian Presidential election were used for this research. For each tweet, five features including type of tweet, emotion, URL, hashtag, and date are identified and coded to predict if tweets are of either high or low impact. The main contributions of the new proposed model include its suitability to analyse tweet datasets based on likelihood analysis of data. The model is interpretable, and the prediction process relies only on the use of available data. The experimental results show that MAKER-RIMER performed better, in terms of misclassification error, when compared against other predictive machine learning approaches. In addition, the model allows observing which features of the candidates' tweets are linked to high and low impact. Tweets containing allusions to the contender candidate, either with positive or negative connotations, without hashtags, and written towards the end of the campaign, were persistently those with the highest impact. URLs, on the other hand, is the only variable that performs differently for the two candidates in terms of achieving high impact. MAKER-RIMER can provide campaigners of political parties or candidates with a tool to measure how features of tweets are predictors of their impact, which can be useful to tailor Twitter content during electoral campaigns.
Keyword weight optimization using gradient strategies in event focused web crawling

At present, a need for an integrated event focused crawling system for obtaining web data regarding key events is felt. At the time of a disaster or any other important event, several users attempt to find updated information regarding the event. The work has proposed a new and efficient method for such keyword set enhancement. Today, information has been growing rapidly, and it can be very challenging for any search engine to retrieve the necessary information properly. A web crawler is a primary unit of such search engines, and for this, their optimization could have been a major aspect of improving the efficiency of search. The large size and active nature of web information and continuous documentation and data updates are known as the web-based retrieval system. This focused crawling method concentrates on the automatic webpage classification which was used for determining the web page. Though various classifiers are used for determining the webpages, the identification of keywords plays an important role in improving the event focused web crawling. The proposed work has a novel and efficient method for such keyword set enhancement. Metaheuristic based optimized keyword weights are found to be efficient. The Term Frequency (TF) based feature extraction and a keyword weight optimization using the Stochastic Gradient Descent (SGD) algorithm is employed in an event focused web crawling. Gradient descent is a popular algorithm to achieve optimization, and the stochastic algorithm has the advantage of sub-differentiable and differentiable smoothness in the fitness function and is well suited for large data optimization. The algorithm is focused on making the keyword set optimal, and in case the keyword set is found to be better, the result documents returned can be even more relevant to users' queries. For this, Support Vector Machine (SVM) classifiers are employed. The experimental outcomes proved that the suggested technique outperformed the others, including the Particle Swarm Optimization (PSO) based weight-optimized solution. The proposed SGD weight optimization is better by 5.8% compared to PSO, showing its ability to examine high volumes of data.
Data Augmentation of 3D Brain Environment Using Deep Convolutional Refined Auto-Encoding Alpha GAN

Learning-based methods represent the state of the art in path planning problems.Their performance, however, depend on the number of medical images available for the training.Generative Adversarial Networks (GANs) are unsupervised neural networks that can be exploited to synthesize realistic images avoiding the dependency from the original data.In this paper, we propose an innovative type of GAN, Deep Convolutional Refined Auto-Encoding Alpha GAN, able to successfully generate 3D brain Magnetic Resonance Imaging (MRI) data from random vectors by learning the data distribution.We combined a Variational Auto-Encoder GAN with a Code Discriminator to solve the common mode collapse problem and reduce the image blurriness.Finally, we inserted a Refiner in series with the Generator Network in order to smooth the shapes of the images and generate more realistic samples.A qualitative comparison between the generated images and the real ones has been used to test our model's quality.With the use of three indexes, namely the Multi-Scale Structural Similarity Metric, the Maximum Mean Discrepancy and the Intersection over Union, we also performed a quantitative analysis.The final results suggest that our model can be a suitable solution to overcome the shortage of medical images needed for learning-based methods.