Reviewerly/exHarmony huggingface dataset:
Investigating Context Features Hidden in End-to-end TTS

Recent studies have introduced end-to-end TTS, which integrates the production of context and acoustic features in statistical parametric speech synthesis. As a result, a single neural network replaced laborious feature engineering with automated feature learning. However, little is known about what types of context information end-to-end TTS extracts from text input before synthesizing speech, and the previous knowledge about context features is barely utilized. In this work, we first point out the model similarity between end-to-end TTS and parametric TTS. Based on the similarity, we evaluate the quality of encoder outputs from an end-to-end TTS system against eight criteria that are derived from a standard set of context information used in parametric TTS. We conduct experiments using an evaluation procedure that has been newly developed in the machine learning literature for quantitative analysis of neural representations, while adapting it to the TTS domain. Experimental results show that the encoder outputs reflect both linguistic and phonetic contexts, such as vowel reduction at phoneme level, lexical stress at syllable level, and part-of-speech at word level, possibly due to the joint optimization of context and acoustic features.
Evaluation of the quality of an image encrytion scheme

Encryption systems have been developed for image viewing applications using the Hill Cipher algorithm. This study aims to evaluate the image encryption quality of the Hill Cipher algorithm. Several traditional metrics are used to evaluate the quality of the encryption scheme. Three of such metrics have been selected for this study. These include, the Colour Histogram, the Maximum Deviation (comparing the original image) and the Entropy Analysis of the encrypted image. Encryption quality results from all three schemes using a variety of images show that a plain Hill Cipher approach gives a good result for all kinds of images but is more suited for colour dense images.
Designing Preferences, Beliefs, and Identities for Artificial Intelligence

Research in artificial intelligence, as well as in economics and other related fields, generally proceeds from the premise that each agent has a well-defined identity, well-defined preferences over outcomes, and well-defined beliefs about the world. However, as we design AI systems, we in fact need to specify where the boundaries between one agent and another in the system lie, what objective functions these agents aim to maximize, and to some extent even what belief formation processes they use.&#x0D; The premise of this paper is that as AI is being broadly deployed in the world, we need well-founded theories of, and methodologies and algorithms for, how to design preferences, identities, and beliefs. This paper lays out an approach to address these problems from a rigorous foundation in decision theory, game theory, social choice theory, and the algorithmic and computational aspects of these fields.
Robust Multi-View Hashing for Cross-Modal Retrieval

Existing hashing methods barely explore the information loss problem during learning the common semantic subspace, thus retrieval performance may be degraded. Besides, these methods mainly rely on the inter-modality or intra-modality correlations separately and fail to exploit the full structure reflected by these correlations. To address these problems, we present a novel cross-modal hashing method, namely Robust Multi-View Hashing (RMVH). To learn a robust latent semantic subspace, we enforce the learnt representations to well reconstruct original features such that more important information can be retained. To comprehensively exploit the relationship between representations of multiple modalities, we utilize Multi-View Learning to construct an affinity matrix to guide the learning of common latent semantic subspace, which can preserve both inter-modality and intra-modality similarities. Instead of relaxing the binary constraints, we leverage the label information to learn hash codes discretely which can avoid the large quantization error and preserve the semantic similarity. Experimental results on three benchmark datasets show that the proposed RMVH achieves superior performance compared with other state-of-the-art methods.
Flat-field calibration method for hyperspectral frame cameras

Abstract This paper presents a method for characterising spatial responsivity of hyperspectral cameras. Knowing the responsivity of the camera as a function of pixel coordinates allows applying a flat-field correction on image data. The method is based on scanning the field of view of the camera with a broadband radiance source, based on an integrating sphere, and combining the captured frames to synthesise a uniform radiance source filling the whole field of view of the camera at the focus distance. The method was compared with a traditional approach where the aperture of an integrating sphere is imaged from a close distance, filling the entire field of view of the camera. The measurement setup was tested with a hyperspectral camera, based on a tunable Fabry–Pérot interferometer. Without the flat-field correction, the average standard deviation of the pixel responsivities across all the spectral channels of the camera was 3.78%. After the correction, the average standard deviation was reduced to 0.40% and 3.25% for the aperture-scanning method and the close-distance method, respectively. The expanded uncertainty ( k = 2) for the flat-field correction obtained using the scanning method was 0.68%–0.78%, depending on the spectral channel of the camera.
Uniform parsing for hyperedge replacement grammars

It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even under seemingly harsh restrictions. This means that the parsing problem is difficult even in the non-uniform setting, in which the grammar is considered to be fixed rather than being part of the input. Little is known about restrictions under which truly uniform polynomial parsing is possible. In this paper we propose a low-degree polynomial-time algorithm that solves the uniform parsing problem for a restricted type of hyperedge-replacement grammars which we expect to be of interest for practical applications.
Cold-Start Aware Deep Memory Network for Multi-Entity Aspect-Based Sentiment Analysis

Various types of target information have been considered in aspect-based sentiment analysis, such as entities and aspects. Existing research has realized the importance of targets and developed methods with the goal of precisely modeling their contexts via generating target-specific representations. However, all these methods ignore that these representations cannot be learned well due to the lack of sufficient human-annotated target-related reviews, which leads to the data sparsity challenge, a.k.a. cold-start problem here. In this paper, we focus on a more general multiple entity aspect-based sentiment analysis (ME-ABSA) task which aims at identifying the sentiment polarity of different aspects of multiple entities in their context. Faced with severe cold-start scenario, we develop a novel and extensible deep memory network framework with cold-start aware computational layers which use frequency-guided attention mechanism to accentuate on the most related targets, and then compose their representations into a complementary vector for enhancing the representations of cold-start entities and aspects. To verify the effectiveness of the framework, we instantiate it with a concrete context encoding method and then apply the model to the ME-ABSA task. Experimental results conducted on two public datasets demonstrate that the proposed approach outperforms state-of-the-art baselines on ME-ABSA task.
Ask the Assistant: Using Google Assistant in Classroom Reading Comprehension Activities

Language education is ever evolving to accommodate changes brought about by time and the development and breakthroughs in the education sector in general, and to rectify or improve non-effective approaches to suit unique language learning needs that may arise.This research was carried out to investigate the use of Google Assistant to help a group of rural primary learners in Baram, Sarawak, Malaysia who struggled with reading comprehension activities.There are two main objectives outlined for this study.First, the research sought to explore the effectiveness of using new emerging technologies such as AI-enabled virtual assistants such as Google Assistant, and to determine its usefulness in language teaching and learning under the technology-assisted language learning (TALL) paradigm.The research also sought to investigate the positive impacts brought by the usage of Google Assistant in classroom practices, namely in reading comprehension activities, and to an extent other language learning process as well.It was found that Google Assistant is an effective language learning facilitator in reading comprehension activities and brought notable positive impacts in reading comprehension activities.
Another Dimension: Towards Multi-subnet Neural Network for Image Sentiment Analysis

Image sentiment analysis has been studied for many years, and most of algorithms take the image sentiment as independent and discrete labels to predict by machine learning. Actually, as a product of multiple hormone combinations, emotions are generated by mutual suppression signal in brain. Inspired by neural microcircuit in amygdala, we propose a novel Multi-Subnet Neural Network (MSNN) that simulates the human brain mechanism for image sentiment classification. Different from traditional neural network, MSNN extends a new domain channel to imitate the way that images stimulate the brain through different neural circuits and produce sentimental semantic information by multi-subnet and signal reforming network. Experiments show that MSNN is well adapted to multi-class image sentiment classification task, and outperforms other multi-class sentiment classification models.
Chasing Sets: How to Use Existential Rules for Expressive Reasoning

We propose that modern existential rule reasoners can enable fully declarative implementations of rule-based inference methods in knowledge representation, in the sense that a particular calculus is captured by a fixed set of rules that can be evaluated on varying inputs (encoded as facts). We introduce Datalog(S) -- Datalog with support for sets -- as a surface language for such translations, and show that it can be captured in a decidable fragment of existential rules. We then implement several known inference methods in Datalog(S), and empirically show that an existing existential rule reasoner can thus be used to solve practical reasoning problems.
Creative Content Marketing In Scientific Publication Management In Industrial Era 4.0

Industry 4.0 is a development trend of intelligent industries where all use of sophisticated equipment and are influenced by technological factors that are developing at this time. The emergence of industry 4.0 affects all aspects of the field including the field of marketing. Marketing is a marketing technique or introducing a product to the public to attract a lot of people. The role of Creative Content is needed in the publication of scientific papers in Industry 4.0. Creative Content can facilitate writers to publish scientific work. Social media is a medium for media creative content in scientific publications. However, the management of online scientific publications or e-journals is still of little use. To achieve Creative Content in scientific publications the need to conduct research or research to find out and obtain information about the role of creative content in scientific publications. The method used in the research is literature study, design, and mind map. Doing research / research is not just going to a place but looking for data in a journal is also needed. This study aims to determine the importance of digital content to improve the dissemination of information on publication management or e-journal in its application within the scope of APTISI Transactions on Management (ATM) journals that have been published online. It can be concluded that the role of creative content in scientific publications is very important, especially in the face of the industrial era 4.0.&#x0D; &#x0D; Keywords: Industry 4.0, ATM, Marketing.
Sonification With Music for Cybersecurity Situational Awareness

Cyber defenders work in stressful, information-rich, and high-stakes environments. While other researchers have considered soniﬁcation for security operations centers (SOCs), the mappings of network events to sound parameters have produced aesthetically unpleasing results. This paper proposes a novel soniﬁcation pro-cess for transforming data about computer network trafﬁc into music. The musical cues relate to notable network events in such a way as to minimize the amount of training time a human listener would need in order to make sense of the cues. We demonstrate our technique on a dataset of 708 million authentication events over nine continuous months from an enterprise network. We il-lustrate a volume-centric approach in relation to the amplitude of the input data, and also a volumetric approach mapping the input data signal into the number of notes played. The resulting music prioritizes aesthetics over bandwidth to balance performance with adoption.
WSD-GAN: Word Sense Disambiguation Using Generative Adversarial Networks

Word Sense Disambiguation (WSD), as a tough task in Natural Language Processing (NLP), aims to identify the correct sense of an ambiguous word in a given context. There are two mainstreams in WSD. Supervised methods mainly utilize labeled context to train a classifier which generates the right probability distribution of word senses. Meanwhile knowledge-based (unsupervised) methods which focus on glosses (word sense definitions) always calculate the similarity of context-gloss pair as score to find out the right word sense. In this paper, we propose a generative adversarial framework WSD-GAN which combines two mainstream methods in WSD. The generative model, based on supervised methods, tries to generate a probability distribution over the word senses. Meanwhile the discriminative model, based on knowledge-based methods, focuses on predicting the relevancy of the context-gloss pairs and identifies the correct pairs over the others. Furthermore, in order to optimize both two models, we leverage policy gradient to enhance the performances of the two models mutually. Our experimental results show that WSD-GAN achieves competitive results on several English all-words WSD datasets.
Identifying Cache-Based Side Channels through Secret-Augmented Abstract Interpretation

Cache-based side channels enable a dedicated attacker to reveal program secrets by measuring the cache access patterns. Practical attacks have been shown against real-world crypto algorithm implementations such as RSA, AES, and ElGamal. By far, identifying information leaks due to cache-based side channels, either in a static or dynamic manner, remains a challenge: the existing approaches fail to offer high precision, full coverage, and good scalability simultaneously, thus impeding their practical use in real-world scenarios. In this paper, we propose a novel static analysis method on binaries to detect cache-based side channels. We use abstract interpretation to reason on program states with respect to abstract values at each program point. To make such abstract interpretation scalable to real-world cryptosystems while offering high precision and full coverage, we propose a novel abstract domain called the Secret-Augmented Symbolic domain (SAS). SAS tracks program secrets and dependencies on them for precision, while it tracks only coarse-grained public information for scalability. We have implemented the proposed technique into a practical tool named CacheS and evaluated it on the implementations of widely-used cryptographic algorithms in real-world crypto libraries, including Libgcrypt, OpenSSL, and mbedTLS. CacheS successfully confirmed a total of 154 information leaks reported by previous research and 54 leaks that were previously unknown. We have reported our findings to the developers. And they confirmed that many of those unknown information leaks do lead to potential side channels.
Application of Educational Games for Formation and Development of ICT competence of Teachers

The process of developing ICT competence of teacher is not only related to fundamental theoretical knowledge, but also to a wide range of practical professional skills. One of the important means of developing ICT competence of teachers are educational games within the course of their professional development. Educational game is a reproduction of real professional situations. It allows participants to experiment, to develop various actions, to make and to correct mistakes which are unacceptable in real life. The experience of educational games within the course of ICT competence development showed that there is a more intensive exchange of ideas and information, which encourages participants to the creative process.
A*+IDA*: A Simple Hybrid Search Algorithm

We present a simple combination of A* and IDA*, which we call A*+IDA*. It runs A* until memory is almost exhausted, then runs IDA* below each frontier node without duplicate checking. It is widely believed that this algorithm is called MREC, but MREC is just IDA* with a transposition table. A*+IDA* is the first algorithm to run significantly faster than IDA* on the 24-Puzzle, by a factor of almost 5. A complex algorithm called dual search was reported to significantly outperform IDA* on the 24-Puzzle, but the original version does not. We made improvements to dual search and our version combined with A*+IDA* outperforms IDA* by a factor of 6.7 on the 24-Puzzle. Our disk-based A*+IDA* shows further improvement on several hard 24-Puzzle instances. We also found optimal solutions to a subset of random 27 and 29-Puzzle problems. A*+IDA* does not outperform IDA* on Rubik’s Cube, for reasons we explain.
Curriculum Learning for Cumulative Return Maximization

Curriculum learning has been successfully used in reinforcement learning to accelerate the learning process, through knowledge transfer between tasks of increasing complexity. Critical tasks, in which suboptimal exploratory actions must be minimized, can benefit from curriculum learning, and its ability to shape exploration through transfer. We propose a task sequencing algorithm maximizing the cumulative return, that is, the return obtained by the agent across all the learning episodes. By maximizing the cumulative return, the agent not only aims at achieving high rewards as fast as possible, but also at doing so while limiting suboptimal actions. We experimentally compare our task sequencing algorithm to several popular metaheuristic algorithms for combinatorial optimization, and show that it achieves significantly better performance on the problem of cumulative return maximization. Furthermore, we validate our algorithm on a critical task, optimizing a home controller for a micro energy grid.
Oblivious and Semi-Oblivious Boundedness for Existential Rules

We study the notion of boundedness in the context positive existential rules, that is, wether there exists an upper bound to the depth of the chase procedure, that is independent from the initial instance. By focussing our attention on the oblivious and the semi-oblivious chase variants, we give a characterization of boundedness in terms of FO-rewritability and chase termination. We show that it is decidable to recognize if a set of rules is bounded for several classes of rules and outline the complexity of the problem.
Principal Component Analysis in the Local Differential Privacy Model

In this paper, we study the Principal Component Analysis (PCA) problem under the (distributed) non-interactive local differential privacy model. For the low dimensional case, we show the optimal rate for the private minimax risk of the k-dimensional PCA using the squared subspace distance as the measurement. For the high dimensional row sparse case, we first give a lower bound on the private minimax risk, . Then we provide an efficient algorithm to achieve a near optimal upper bound. Experiments on both synthetic and real world datasets confirm the theoretical guarantees of our algorithms.
Learning and Inference for Structured Prediction: A Unifying Perspective

In a structured prediction problem, one needs to learn a predictor that, given a structured input, produces a structured object, such as a sequence, tree, or clustering output. Prototypical structured prediction tasks include part-of-speech tagging (predicting POS tag sequence for an input sentence) and semantic segmentation of images (predicting semantic labels for pixels of an input image). Unlike simple classification problems, here there is a need to assign values to multiple output variables accounting for the dependencies between them. Consequently, the prediction step itself (aka ``inference" or ``decoding") is computationally-expensive, and so is the learning process, that typically requires making predictions as part of it. The key learning and inference challenge is due to the exponential size of the structured output space and depend on its complexity. In this paper, we present a unifying perspective of the different frameworks that address structured prediction problems and compare them in terms of their strengths and weaknesses. We also discuss important research directions including integration of deep learning advances into structured prediction, and learning from weakly supervised signals and active querying to overcome the challenges of building structured predictors from small amount of labeled data.
Improving NLTK for Processing Portuguese

Python has a growing community of users, especially in the AI and ML fields. Yet, Computational Processing of Portuguese in this programming language is limited, in both available tools and results. This paper describes NLPyPort, a NLP pipeline in Python, primarily based on NLTK, and focused on Portuguese. It is mostly assembled from pre-existent resources or their adaptations, but improves over the performance of existing alternatives in Python, namely in the tasks of tokenization, PoS tagging, lemmatization and NER.
Harmonizing Different Lemmatization Strategies for Building a Knowledge Base of Linguistic Resources for Latin

The interoperability between lemmatized corpora of Latin and other resources that use the lemma as indexing key is hampered by the multiple lemmatization strategies that different projects adopt.In this paper we discuss how we tackle the challenges raised by harmonizing different lemmatization criteria in a project that aims to connect linguistic resources for Latin using the Linked Data paradigm.The paper introduces the architecture supporting an open-ended, lemma-based Knowledge Base, built to make textual and lexical resources for Latin interoperable.Particularly, the paper describes the inclusion into the Knowledge Base of its lexical basis, of a word formation lexicon and of a lemmatized and syntactically annotated corpus.
Deep Multi-Task Learning with Adversarial-and-Cooperative Nets

In this paper, we propose a deep multi-Task learning model based on Adversarial-and-COoperative nets (TACO). The goal is to use an adversarial-and-cooperative strategy to decouple the task-common and task-specific knowledge, facilitating the fine-grained knowledge sharing among tasks. TACO accommodates multiple game players, i.e., feature extractors, domain discriminator, and tri-classifiers. They play the MinMax games adversarially and cooperatively to distill the task-common and task-specific features, while respecting their discriminative structures. Moreover, it adopts a divide-and-combine strategy to leverage the decoupled multi-view information to further improve the generalization performance of the model. The experimental results show that our proposed method significantly outperforms the state-of-the-art algorithms on the benchmark datasets in both multi-task learning and semi-supervised domain adaptation scenarios.