Reviewerly/exHarmony huggingface dataset:
Ontology-Driven Cross-Domain Transfer Learning

The aim of transfer learning is to reuse learnt knowledge across different contexts. In the particular case of cross-domain transfer (also known as domain adaptation), reuse happens across different but related knowledge domains. While there have been promising first results in combining learning with symbolic knowledge to improve cross-domain transfer results, the singular ability of ontologies for providing classificatory knowledge has not been fully exploited so far by the machine learning community. We show that ontologies, if properly designed, are able to support transfer learning by improving generalization and discrimination across classes. We propose an architecture based on direct attribute prediction for combining ontologies with a transfer learning framework, as well as an ontology-based solution for cross-domain generalization based on the integration of top-level and domain ontologies. We validate the solution on an experiment over an image classification task, demonstrating the system’s improved classification performance.
Irony Detection using Emotion Cues

This work is centered on the data made available for the IroSvA challenge, consisting of three variants of Spanish language from three different countries. We propose a simple model for identifying irony, based on tweet embeddings, refraining from using of additional NLP techniques. We aim to find cuest hat are able to generalize the knowledge obtained from a language variant, and evaluate the ability to detectirony in different combinations of variants, from different countries and topics. For this purpose, we propose using six features based on the degree of emotion present in each tweet. These automatically tagged features include 5 levels of strength, ranging from none to very high, of six emotions: love, joy, surprise, sadness, anger, and fear. Experiments were carried out with different combinations of language variants. Obtained results show that exclusively using the information of the emotion levels (discarding the embeddings) could improve the irony detection in a language variant different from that used for training.
The methodology of training an underwater robot control system for operator actions

Abstract The article discusses the approach to building an underwater robot control system based on the “Programming by Demonstration” approach. The basic elements of the implementation of the approach are considered. The participants in the learning process are indicated. Input variables are defined, and a training model is selected. As a result, a training methodology for the underwater robot control system is formulated for the system to perform specified manipulations.
Reentrancy? Yes. Reentrancy Bug? No.

In this paper, we address the reentrancy vulnerability in smart contracts using two program logics. We first propose a coarse-grained logic that extends the standard Hoare logic by a proof rule for reentry trigger commands. We prove this logic sound and complete. We propose another fine-grained logic that considers reentry triggers as entry and exit points of functions. For verifying coarse-grained specifications, these two logics have the same expressiveness, and we can derive coarse-grained judgments from fine-grained ones. In comparison, the fine-grained logic is more useful for fine-grained specifications. We use a toy language to demonstrate our results and formalize all definitions and proofs in Coq ( https://github.com/BruceZoom/Reentry ).
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference

Hybrid Privacy-Preserving Neural Network (HPPNN) implementing linear layers by Homomorphic Encryption (HE) and nonlinear layers by Garbled Circuit (GC) is one of the most promising secure solutions to emerging Machine Learning as a Service (MLaaS). Unfortunately, a HPPNN suffers from long inference latency, e.g., $\sim100$ seconds per image, which makes MLaaS unsatisfactory. Because HE-based linear layers of a HPPNN cost $93\%$ inference latency, it is critical to select a set of HE parameters to minimize computational overhead of linear layers. Prior HPPNNs over-pessimistically select huge HE parameters to maintain large noise budgets, since they use the same set of HE parameters for an entire network and ignore the error tolerance capability of a network. In this paper, for fast and accurate secure neural network inference, we propose an automated layer-wise parameter selector, AutoPrivacy, that leverages deep reinforcement learning to automatically determine a set of HE parameters for each linear layer in a HPPNN. The learning-based HE parameter selection policy outperforms conventional rule-based HE parameter selection policy. Compared to prior HPPNNs, AutoPrivacy-optimized HPPNNs reduce inference latency by $53\%\sim70\%$ with negligible loss of accuracy.
SIGTYP 2020 Shared Task: Prediction of Typological Features

Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages.They have been shown to be useful for downstream applications, including cross-lingual transfer learning and linguistic probing.A major drawback hampering broader adoption of typological KBs is that they are sparsely populated, in the sense that most languages only have annotations for some features, and skewed, in that few features have wide coverage.As typological features often correlate with one another, it is possible to predict them and thus automatically populate typological KBs, which is also the focus of this shared task.Overall, the task attracted 8 submissions from 5 teams, out of which the most successful methods make use of such feature correlations.However, our error analysis reveals that even the strongest submitted systems struggle with predicting feature values for languages where few features are known.
Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning

The most prominent form of human communication and interaction is speech. It plays an indispensable role for expressing emotions, motivating, guiding, and cheering. An ill-intentioned speech can mislead people, societies, and even a nation. A misguided speech can trigger social controversy and can result in violent activities. Every day, there are a lot of speeches being delivered around the world, which are quite impractical to inspect manually. In order to prevent any vicious action resulting from any misguided speech, the development of an automatic system that can efficiently detect suspicious speech has become imperative. In this study, we have presented a framework for acquisition of speech along with the location of the speaker, converting the speeches into texts and, finally, we have proposed a system based on long short-term memory (LSTM) which is a variant of recurrent neural network (RNN) to classify speeches into suspicious and nonsuspicious. We have considered speeches of Bangla language and developed our own dataset that contains about 5000 suspicious and nonsuspicious samples for training and validating our model. A comparative analysis of accuracy among other machine learning algorithms such as logistic regression, SVM, KNN, Naive Bayes, and decision tree is performed in order to evaluate the effectiveness of the system. The experimental results show that our proposed deep learning-based model provides the highest accuracy compared to other algorithms.
Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues

Code-switching is a ubiquitous phenomenon in multilingual communities. Natural language technologies that wish to communicate like humans must therefore adaptively incorporate code-switching techniques when they are deployed in multilingual settings. To this end, we propose a Hindi-English human-machine dialogue system that elicits code-switching conversations in a controlled setting. It uses different code-switching agent strategies to understand how users respond and accommodate to the agent’s language choice. Through this system, we collect and release a new dataset CommonDost, comprising of 439 human-machine multilingual conversations. We adapt pre-defined metrics to discover linguistic accommodation from users to agents. Finally, we compare these dialogues with Spanish-English dialogues collected in a similar setting, and analyze the impact of linguistic and socio-cultural factors on code-switching patterns across the two language pairs.
Learning About Objects by Learning to Interact with Them

Much of the remarkable progress in computer vision has been focused around fully supervised learning mechanisms relying on highly curated datasets for a variety of tasks. In contrast, humans often learn about their world with little to no external supervision. Taking inspiration from infants learning from their environment through play and interaction, we present a computational framework to discover objects and learn their physical properties along this paradigm of Learning from Interaction. Our agent, when placed within the near photo-realistic and physics-enabled AI2-THOR environment, interacts with its world and learns about objects, their geometric extents and relative masses, without any external guidance. Our experiments reveal that this agent learns efficiently and effectively; not just for objects it has interacted with before, but also for novel instances from seen categories as well as novel object categories.
Adaptive Ensemble Variants of Random Vector Functional Link Networks

In this paper, we propose a novel adaptive ensemble variant of random vector functional link (RVFL) networks. Adaptive ensemble RVFL networks assign different weights to the sub-classifiers according to prediction performance of single RVFL network. Generic Adaptive Ensemble RVFL is composed of a series of unrelated, independent weak classifiers. We also employ our adaptive ensemble method to the deep random vector functional link (dRVFL). Each layer in dRVFL can be regarded as a sub-classifier. However, instead of training several models independently, the sub-classifiers of dRVFL can be obtained by training a single network once.
The impact of indirect machine translation on sentiment classification

Sentiment classification has been crucial for many natural language processing (NLP) applications, such as the analysis of movie reviews, tweets, or customer feedback. A sufficiently large amount of data is required to build a robust sentiment classification system. However, such resources are not always available for all domains or for all languages.In this work, we propose employing a machine translation (MT) system to translate customer feedback into another language to investigate in which cases translated sentences can have a positive or negative impact on an automatic sentiment classifier. Furthermore, as performing a direct translation is not always possible, we explore the performance of automatic classifiers on sentences that have been translated using a pivot MT system.We conduct several experiments using the above approaches to analyse the performance of our proposed sentiment classification system and discuss the advantages and drawbacks of classifying translated sentences.
TRESC: Towards redesigning existing symmetric ciphers

Recently, the security of existing symmetric cryptographic algorithms and protocols has been threatened by new performance challenges and vulnerabilities. In this paper, we propose a dynamic key-dependent approach, ”TRESC”, to make existing symmetric ciphers more efficient and robust. This can be done by using dynamic substitution and permutation primitives to reduce the number of rounds while providing better resistance against cryptanalysis and implementation attacks. In this paper, the Key Setup Algorithm (KSA) of Rivest Cipher 4 (RC4) and its modified variants are applied for the construction of these dynamic key-dependent substitution and permutation primitives. The selection of the RC4-KSA is due to its lightweight implementation since it requires simple permutation operation with minimal overhead. The proposed dynamic cryptographic solution can be integrated in any existing symmetric cipher such as Advanced Encryption Standard (AES), SIMON and SPECK. The security and performance analysis show the robustness and effectiveness of the proposed solution, which strikes a good balance between the required security level and system performance.
Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems

In order to improve the sample-efficiency of deep reinforcement learning (DRL), we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS).Although I2A achieves a higher success rate than baselines by augmenting predicted future into a policy network, its complicated architecture introduces unwanted instability.In this work, we propose actor-double-critic (ADC) to improve the stability and overall performance of I2A.ADC simplifies the architecture of I2A to reduce excessive parameters and hyperparameters.More importantly, a separate model-based critic shares parameters between actions and makes back-propagation explicit.In our experiments on Cambridge Restaurant Booking task, ADC enhances success rates considerably and shows robustness to imperfect environment models.In addition, ADC exhibits the stability and sample-efficiency as significantly reducing the baseline standard deviation of success rates and reaching the 80% success rate with half training data.
Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles

The ability to accurately track what happens during a conversation is essential for the performance of a dialogue system.Current stateof-the-art multi-domain dialogue state trackers achieve just over 55% accuracy on the current go-to benchmark, which means that in almost every second dialogue turn they place full confidence in an incorrect dialogue state.Belief trackers, on the other hand, maintain a distribution over possible dialogue states.However, they lack in performance compared to dialogue state trackers, and do not produce well calibrated distributions.In this work we present state-of-the-art performance in calibration for multi-domain dialogue belief trackers using a calibrated ensemble of models.Our resulting dialogue belief tracker also outperforms previous dialogue belief tracking models in terms of accuracy.
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting

Policy gradient methods have shown success in learning control policies for high-dimensional dynamical systems. Their biggest downside is the amount of exploration they require before yielding high-performing policies. In a lifelong learning setting, in which an agent is faced with multiple consecutive tasks over its lifetime, reusing information from previously seen tasks can substantially accelerate the learning of new tasks. We provide a novel method for lifelong policy gradient learning that trains lifelong function approximators directly via policy gradients, allowing the agent to benefit from accumulated knowledge throughout the entire training process. We show empirically that our algorithm learns faster and converges to better policies than single-task and lifelong learning baselines, and completely avoids catastrophic forgetting on a variety of challenging domains.
Augmented reality based gesture detection &amp; object creation system using XCode &amp; ARKit

In this modern era of mobile applications, Augmented Reality (AR) is becoming one of the emerging areas of implementation for researchers around the globe.While live gesture detection and 3D model creation still need more attention from researchers.In this paper, we present an interactive AR character that directly interacts with real objects.The interactive AR character automatically determines how to behave and to control real objects.This current research presents three studies that test the social psychological effects of Augmented Reality.In this research, we are using Apple's IDE for native iOS development on swift, XCode for UI design and architectural functionalities of our application.We used the ARKIT library to import all the necessary functions and classes to manipulate and use according to our needs.Finally, we successfully deployed an IOS application which can detect live gestures of our hand movements and then create 3D models with the help of our hand gestures.
GAN-Based Image Privacy Preservation: Balancing Privacy and Utility

While we enjoy high-quality social network services, image privacy is also under great privacy threats. With the development of deep learning-driven face recognition technologies, traditional protection methods are facing challenges. How to balancing the privacy and the utility of images has been an urgent problem to solve. In this paper, we propose a novel image privacy preservation model using Generative Adversarial Networks(GANs). By generating an fake image that highly matches the key face attributes of the original image, we balance the privacy protection and utility.
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning

Unlabeled data learning has attracted considerable attention recently. However, it is still elusive to extract the expected high-level semantic feature with mere unsupervised learning. In the meantime, semi-supervised learning (SSL) demonstrates a promising future in leveraging few samples. In this paper, we combine both to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL, which strives to improve the classification performance with few labeled data and then reduce the cost in data annotating. Specifically, unsupervised semantic aggregation based on Triplet Mutual Information (T-MI) loss is explored to generate semantic labels for unlabeled data. Then the semantic labels are aligned to the actual class by the supervision of labeled data. Furthermore, a feature pool that stores the labeled samples is dynamically updated to assign proxy labels for unlabeled data, which are used as targets for cross-entropy minimization. Extensive experiments and analysis across four standard semi-supervised learning benchmarks validate that USADTM achieves top performance (e.g., 90.46$\%$ accuracy on CIFAR-10 with 40 labels and 95.20$\%$ accuracy with 250 labels). The code is released at this https URL.
Prediction of concrete compressive strength through artificial neural networks

Prediction of concrete compressive strength through artificial neural networksConcrete properties, including its compressive strength, are in general highly nonlinear functions of its components.Concrete mix design methods are basically simulations that require costly and time consuming adjustments in laboratory.A useful support tool based on artificial neural networks, using a multilayer perceptron network, is proposed in this paper as a means to predict compressive strength of concrete mixes.The developed models are useful for reducing the quantity of laboratory tests required for concrete mix design adjustments.
Exact results on high-dimensional linear regression via statistical physics

It is clear that conventional statistical inference protocols need to be revised to deal correctly with the high-dimensional data that are now common. Most recent studies aimed at achieving this revision rely on powerful approximation techniques that call for rigorous results against which they can be tested. In this context, the simplest case of high-dimensional linear regression has acquired significant new relevance and attention. In this paper we use the statistical physics perspective on inference to derive several exact results for linear regression in the high-dimensional regime.
Sport training action correction by using convolutional neural network

This paper proposes a framework for correcting the actions during sport training. We estimate the positions of bone joint points and the postures by using a convolutional neural network. The positions of bone joint points and the postures are the premise to correct the actions during sport training. In convolutional neural network, it first predicts the positions of bone joint points via confidential map of different parts; then utilizes affinity domain, which is a 2‐dimensional vector set, to predict the directions and positions of four limbs; lastly repeats the above steps and analyzes the bone joint points via greedy analytical reasoning algorithm to construct the human skeleton. The personal action is represented as the constructed skeleton which is used to correct the incorrect or non‐standard actions during sports. The experimental results evaluate the effective of the proposed sport training action correction method.
Laban Movement Analysis applied to Human-Computer Interaction

This paper presents a state-of-the-art review in Hu- man Computer Interaction (HCI), where the Laban Movement Analysis (LMA) method is used to analyze and describe the qualitative aspects of humen gestures and movements. Composed by Shape, Body, Space and Effort, the later is fundamental in research regarding body movements. From the 48 selected papers analyzed, we conclude that the characteristics found in LMA components can be used to parameterize body movements and relate them to emotions and intentions.
Blockchain-Based Framework for Managing Customer Consent in Open Banking

Customer consent is the most important aspect in open banking. How it can be made transparent and effective, lots of areas have already been explored and also explorations are in progress. Bringing transparency to customers and to the whole process is not very easy to achieve especially when multiple parties are involved and they are competitors to some extent in their areas of operations and service offerings to customers. Blockchain technology can be an enabler to bring in the transparency in the process and can help to get the trust of customers which is absolutely essential to make the whole idea of open banking effective. Consortium Blockchain will be best fitted to have maximum efficiency. Regulators need to bring all the interested parties under one framework which will track each and every attribute of customer data and provide the required transparency to customers. The framework provides details to the customer about the rating of banks or any other Third-Party providers on a real-time basis which helps customer to take an instant informed decision about providing or withdrawing consent.
Rancang Bangun Sistem Informasi Siswa Berbasis Web di Sekolah Minggu Buddha Vihara Dharmaloka Pekanbaru

The development of information systems so rapidly that it is possible to do data processing that saves time and money while still producing information that is very accurate and useful. This can support the success of a school or organization in achieving its goals. One of the data processing that needs to use a computerized system is the design of a web-based information system for the Buddhist Sunday School in the Dharma Loka Pekanbaru Temple. The design of the web-based information system of the Buddhist Sunday School in Vihara Dharma Loka has not used a lot of computer-based information systems. This school still uses a manual system, data entry still uses Microsoft Excel such as new student registration, inputting student data, teacher data, student assessment, student attendance, minutes, announcements to students and teachers, and report data. Design a web-based information system for the Buddhist Sunday School at Vihara Dharma Loka using the PHP programming language with a MySQL database. The application design is expected to make it easier to access the data of the Buddhist Sunday School at Vihara Dharma Loka and present the information quickly and more effectively and efficiently