Reviewerly/exHarmony huggingface dataset:
A Mutual Information Maximization Approach for the Spurious Solution Problem in Weakly Supervised Question Answering

Weakly supervised question answering usually has only the final answers as supervision signals while the correct solutions to derive the answers are not provided.This setting gives rise to the spurious solution problem: there may exist many spurious solutions that coincidentally derive the correct answer, but training on such solutions can hurt model performance (e.g., producing wrong solutions or answers).For example, for discrete reasoning tasks as on DROP, there may exist many equations to derive a numeric answer, and typically only one of them is correct.Previous learning methods mostly filter out spurious solutions with heuristics or using model confidence, but do not explicitly exploit the semantic correlations between a question and its solution.In this paper, to alleviate the spurious solution problem, we propose to explicitly exploit such semantic correlations by maximizing the mutual information between question-answer pairs and predicted solutions.Extensive experiments on four question answering datasets show that our method significantly outperforms previous learning methods in terms of task performance and is more effective in training models to produce correct solutions.
Towards Fully Automated Manga Translation

We tackle the problem of machine translation of manga, Japanese comics. Manga translation involves two important problems in machine translation: context-aware and multimodal translation. Since text and images are mixed up in an unstructured fashion in Manga, obtaining context from the image is essential for manga translation. However, it is still an open problem how to extract context from image and integrate into MT models. In addition, corpus and benchmarks to train and evaluate such model is currently unavailable. In this paper, we make the following four contributions that establishes the foundation of manga translation research. First, we propose multimodal context-aware translation framework. We are the first to incorporate context information obtained from manga image. It enables us to translate texts in speech bubbles that cannot be translated without using context information (e.g., texts in other speech bubbles, gender of speakers, etc.). Second, for training the model, we propose the approach to automatic corpus construction from pairs of original manga and their translations, by which large parallel corpus can be constructed without any manual labeling. Third, we created a new benchmark to evaluate manga translation. Finally, on top of our proposed methods, we devised a first compleheisive system for fully automated manga translation.
Deep Probabilistic Canonical Correlation Analysis

We propose a deep generative framework for multi-view learning based on a probabilistic interpretation of canonical correlation analysis (CCA). The model combines a linear multi-view layer in the latent space with deep generative networks as observation models, to decompose the variability in multiple views into a shared latent representation that describes the common underlying sources of variation and a set of viewspecific components. To approximate the posterior distribution of the latent multi-view layer, an efficient variational inference procedure is developed based on the solution of probabilistic CCA. The model is then generalized to an arbitrary number of views. An empirical analysis confirms that the proposed deep multi-view model can discover subtle relationships between multiple views and recover rich representations.
Sentiment Analysis on Social Media for Albanian Language

The recent advances in technology and particularly, the rising prominence of social media platforms have made it possible to express our emotions through electronic means, which have led to the creation of large collections of unstructured textual documents.These collections can be saved and potentially studied with many modern technologies like Text Mining, Machine Learning and Natural Language Processing to obtain new knowledge from them.Sentiment Analysis is a field of Natural Language Processing that focuses on extracting sentiment from text.Moreover, as a Text Mining technique expresses the ability to track the subjective opinion of a text produced by an entity.The purpose of this paper is to test and review different approaches in Sentiment Analysis for messages in the Albanian language found on Twitter.Additionally, we compare the results among different methods and note the challenges that arise while finally we suggest future directions for further research.This paper's research was conducted as follows: the data was pre-processed, before being converted from text to vector representation using a range of feature extraction techniques such as Bag-of-Words, TF-IDF, Word2Vec, and Glove.We study the performance of sentiment classification techniques from three main approaches: traditional machine learning, lexicon-based and deep learning approach.For model evaluation, since they were trained in unbalanced data, we used not only classical evaluation criteria such as Accuracy, Specificity, Precision, and Recall but more appropriate criteria such as F-measure, Balanced Accuracy, and Matthews Correlation Coefficient (MCC).According to all these criteria, our experiments revealed that LSTM based RNN with Glove as a feature extraction technique provides the best results with F-score = 87.8%,followed by Logistic Regression.
Nested Two-Layer RGB Based Reversible Image Steganography Method

In this study, a new reversible image steganography method based on Red-Green-Blue (RGB) which hides thecolored image into the colored images in two layers nested is proposed. The proposed method hides the 24-bitimage to be hidden by hiding two layers of data firstly in the resized version of the cover image with the LSBmethod, and then hides the resized cover image to the original cover image with the 4-bit method. The proposedmethod offers a secure communication environment as it hides the hidden image in two layers. When thirdparties extract data by using the LSB method, they only access the resized version of the cover image. The 4-bitmethod divides the image to be hidden into 8-bit segments. While the first 4 bits, which are the most importantbits of 8-bit data, are hidden directly, 4 bits that can be neglected with less significance are completed by roundingat approximate value through the method function. In this way, since the 8-bit data is reduced to 4-bits, themethod performs lossy hiding, but doubles the hiding capacity. Peak signal to noise ratio (PSNR), structuralsimilarity quality criterion (SSIM) and chi-square steganalysis method, which are frequently used in the literature,are used to measure the immunity level of the proposed method. When it is concealed at the same ratewith the LSB method and the proposed method, a higher measurement value is obtained in the PSNR imagecriterion, which is 1.2 dB, SSIM 0.0025, BER 0.0129 and NCC image criterion 0.00027. In additional, it wasshown that the proposed method achieved more successful results in chi-square steganalysis and histogramtests compared to the traditional LSB method.
Robust Knowledge Transfer via Hybrid Forward on the Teacher-Student Model

When adopting deep neural networks for a new vision task, a common practice is to start with fine-tuning some off-the-shelf well-trained network models from the community. Since a new task may require training a different network architecture with new domain data, taking advantage of off-the-shelf models is not trivial and generally requires considerable try-and-error and parameter tuning. In this paper, we denote a well-trained model as a teacher network and a model for the new task as a student network. We aim to ease the efforts of transferring knowledge from the teacher to the student network, robust to the gaps between their network architectures, domain data, and task definitions. Specifically, we propose a hybrid forward scheme in training the teacher-student models, alternately updating layer weights of the student model. The key merit of our hybrid forward scheme is on the dynamical balance between the knowledge transfer loss and task specific loss in training. We demonstrate the effectiveness of our method on a variety of tasks, e.g., model compression, segmentation, and detection, under a variety of knowledge transfer settings.
Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate

Accurate detection and classification of online hate is a difficult task.Implicit hate is particularly challenging as such content tends to have unusual syntax, polysemic words, and fewer markers of prejudice (e.g., slurs).This problem is heightened with multimodal content, such as memes (combinations of text and images), as they are often harder to decipher than unimodal content (e.g., text alone).This paper evaluates the role of semantic and multimodal context for detecting implicit and explicit hate.We show that both text-and visual-enrichment improves model performance, with the multimodal model (0.771) outperforming other models' F1 scores (0.544, 0.737, and 0.754).While the unimodal-text context-aware (transformer) model was the most accurate on the subtask of implicit hate detection, the multimodal model outperformed it overall because of a lower propensity towards false positives.We find that all models perform better on content with full annotator agreement and that multimodal models are best at classifying the content where annotators disagree.To conduct these investigations, we undertook highquality annotation of a sample of 5,000 multimodal entries.Tweets were annotated for primary category, modality, and strategy.We make this corpus, along with the codebook, code, and final model, freely available.
Improving Formality Style Transfer with Context-Aware Rule Injection

Models pre-trained on large-scale regular text corpora often do not work well for usergenerated data where the language styles differ significantly from the mainstream text.Here we present Context-Aware Rule Injection (CARI), an innovative method for formality style transfer (FST).CARI injects multiple rules into an end-to-end BERT-based encoder and decoder model.It learns to select optimal rules based on context.The intrinsic evaluation showed that CARI achieved the new highest performance on the FST benchmark dataset.Our extrinsic evaluation showed that CARI can greatly improve the regular pretrained models' performance on several tweet sentiment analysis tasks.
Translating Totally Ordered HTN Planning Problems to Classical Planning Problems Using Regular Approximation of Context-Free Languages

There have been several approaches to use techniques from classical planning in HTN planning. While a direct translation is in general not possible due to the different expressiveness, there have been translations of bounded HTN problems and approaches to use classical heuristics in HTN search procedures. In this paper, we introduce a different approach. We exploit methods from the field of Computational Linguistics introduced to approximate Context-Free Languages by Finite Automata. We use them to approximate the decomposition structure of Totally Ordered (TO) HTN planning problems by classical problems. The resulting problem can then be solved using standard classical planning systems. A subset of TOHTN problems can be translated exactly, i.e., without changing the set of solutions. For problems where an approximation is necessary, we use an overapproximation, i.e., the set of solutions to the classical problem is a superset of that of the HTN problem. We then use plan verification to check whether a solution is valid and thus obtain a sound and complete overall approach. The resulting system outperforms the state of the art on the IPC 2020 benchmark set in terms of coverage.
Path Planning Optimization for Agricultural Spraying Robots Using Hybrid Dragonfly – Cuckoo Search Algorithm

Finding collision-free paths and optimized path coverage over an agricultural landscape has been a critical research problem among scientists and researchers over the years. Key precision farming strategies such as seeding, spraying fertilizers, and harvesting require special path planning techniques for efficient operations and will directly influence reducing the running cost of the farm. The main objective of this research work is to generate an optimized sequential route in an agricultural landscape with the nominal distance. In this proposed work, a novel Hybrid Dragonfly – Cuckoo Search algorithm is proposed and implemented to generate the sequential route for achieving spraying applications in greenhouse environments. Here the agricultural routing problem is expressed as a Travelling Salesman Problem, and the simulations are performed to find the effectiveness of the proposed algorithm. The proposed algorithm has generated better results when compared with other computational techniques such as PSO in terms of both solution quality and computational efficiency.
DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis

Recent work has shown that Aspect-Term Sentiment Analysis (ATSA) can be performed by Gradual Machine Learning (GML), which begins with some automatically labeled easy instances, and then gradually labels more challenging instances by iterative factor graph inference without manual intervention.As a non-i.i.d learning paradigm, GML leverages shared features between labeled and unlabeled instances for knowledge conveyance.However, the existing GML solution extracts sentiment features based on pre-specified lexicons, which are usually inaccurate and incomplete and thus lead to inadequate knowledge conveyance.In this paper, we propose a Deep Neural Network (DNN) driven GML approach for ATSA, which exploits the power of DNN in feature representation for gradual learning.It first uses an unsupervised neural network to cluster the automatically extracted features by their sentiment orientation.Then, it models the clustered features as factors to enable implicit knowledge conveyance for gradual inference in a factor graph.To leverage labeled training data, we also present a hybrid solution that fulfills gradual learning by fusing the influence of supervised DNN predictions and implicit knowledge conveyance in a unified factor graph.Finally, we empirically evaluate the performance of the proposed approach on real benchmark data.Our extensive experiments have shown that the proposed approach consistently achieves the state-of-the-art performance across all the test datasets in both unsupervised and supervised settings and the improvement margins are considerable.
Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models

Mingyue Han, Yinglin Wang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2021.
Automated Symbolic Law Discovery: A Computer Vision Approach

One of the most exciting applications of modern artificial intelligence is to automatically discover scientific laws from experimental data. This is not a trivial problem as it involves searching for a complex mathematical relationship over a large set of explanatory variables and operators that can be combined in an infinite number of ways. Inspired by the incredible success of deep learning in computer vision, we tackle this problem by adapting various successful network architectures into the symbolic law discovery pipeline. The novelty of our approach is in (1) encoding the input data as an image with super-resolution, (2) developing an appropriate deep network pipeline, and (3) predicting the importance of each mathematical operator from the relationship image. This allows us to prior the exponentially large search with the predicted importance of the symbolic operators, which can significantly accelerate the discovery process. We apply our model to a variety of plausible relationships---both simulated and from physics and mathematics domains---involving different dimensions and constituents. We show that our model is able to identify the underlying operators from data, achieving a high accuracy and AUC (91% and 0.96 on average resp.) for systems with as many as ten independent variables. Our method significantly outperforms the current state of the art in terms of data fitting (R^2), discovery rate (recovering the true relationship), and succinctness (output formula complexity). The discovered equations can be seen as first drafts of scientific laws that can be helpful to the scientists for (1) hypothesis building, and (2) understanding the complex underlying structure of the studied phenomena. Our approach holds a real promise to help speed up the rate of scientific discovery.
Knowledge-Grounded Dialogue Generation with Term-level De-noising

Dialogue generation has been improved through injecting knowledge into generative models.However, addition of knowledge through simple selection of sentences or paragraphs is likely to introduce noise and diminish the effectiveness of the generative models.In this paper, we present a novel Knowledge Term Weighting Model (KTWM) that incorporates term-level de-noising of the selected knowledge.KTWM includes a module for generating Simulated Response Vectors (SRVs) and uses SRVs attention distributions with the knowledge embeddings to determine knowledge term weights.Our experiments demonstrate that KTWM, combined with various knowledge selection algorithms, consistently achieves statistically significant improvements over methods without term weighting when applied to two publicly available datasets Wizard of Wikipedia (Wiz) and Holl-E.The results are particularly improved for the Wiz test data with unseen topics, demonstrating the robustness of the KTWM noise-reduction approach.
An Optimal Sanitization Algorithm Based Secure Migration of Virtual Machines in Cloud Datacenters

Cloud is a dominant player in information technology in recent years.In order to meet the increasing demand of computing and storage resources, infrastructure cloud providers are deploying planet scale data centers across the world.These data centers consists stupendous number of servers.These data centers incur very high investments in operational cost, maintenance cost and networking facilities.Because of the huge energy usage, such data centers leave large amount of carbon footprints and thus have adverse effects on the environment.As a result maximum resource utilization and energy consumption are becoming crucial issues to make cloud computing more successful.Intelligent virtual machine migration and consolidation are the primary means to address these issues.However during migration the system integrity and confidentiality of data may be affected.In order to overcome the security issues, this paper proposes a security aware virtual machine migration method.An optimal cryptography algorithm is proposed.This is a combination of sanitization algorithm and orthogonal learning particle swarm optimization (OLPSO) algorithm.This algorithm is used to generate the key and to increase the confidentiality of the migrated data.The performance of the proposed methodology is analyzed and compared in terms of different evaluation metrics.
Penilaian Ranah Afektif dalam Bentuk Penilaian Keterampilan Menulis dengan Model Pembelajaran Think Talk Write (TTW) Menggunakan Media Audiovisual pada Siswa Sekolah Dasar

Permendikbud No. 66/2013 concerning assessment standards explains that the assessment techniques and instruments used in the 2013 Curriculum include competency assessment of attitudes, knowledge, and skills. This means that learning and assessment must develop students' competencies related to the affective, cognitive, and psychomotor domains. Assessment of the affective domain is important because assessing the affective domain must be carried out in learning activities. The education unit needs to design and develop an appropriate affective domain assessment to achieve optimal learning objectives. In this study, the affective domain rating scale uses skills assessment using a Likert scale. Think Talk Write is a learning model that can improve activity and communication among students. Ngalimun argues that the Think Talk Write learning model is a type of learning model that starts with thinking through the reading material, communicated with presentations, discussions, and then reports on the presentation results.
Boosted Performances of NTRUencrypt Post-Quantum Cryptosystem

The bottleneck of all cryptosystems is the difficulty of the computational complexity of the polynomials multiplication, vectors multiplication, etc. Thus most of them use some algorithms to reduce the complexity of the multiplication like NTT, Montgomery, CRT, and Karatsuba algorithms, etc. We contribute by creating a new release of NTRUencrypt1024 with great improvement, by using our own polynomials multiplication algorithm operate in the ring of the form Rq=Zq[X]/(XN+1), combined to Montgomery algorithm rather than using the NTT algorithm as used by the original version. We obtained a good result, our implementation outperforms the original one by speed-up of a factor up to (X10) for encryption and a factor up to (X11) for decryption functions. We note that our improved implementation used the latest hash function standard SHA-3, and reduce the size of the public key, private key, and cipher-text from 4097 bytes to 2049 bytes with the same security level.
An Intelligent Code Search Approach Using Hybrid Encoders

The intelligent code search with natural language queries has become an important researching area in software engineering. In this paper, we propose a novel deep learning framework At-CodeSM for source code search. The powerful code encoder in At-CodeSM, which is implemented with an abstract syntax tree parsing algorithm (Tree-LSTM) and token-level encoders, maintains both the lexical and structural features of source code in the process of code vectorizing. Both the representative and discriminative models are implemented with deep neural networks. Our experiments on the CodeSearchNet dataset show that At-CodeSM yields better performance in the task of intelligent code searching than previous approaches.
Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning

Relational fact extraction aims to extract semantic triplets from unstructured text.In this work, we show that all of the relational fact extraction models can be organized according to a graph-oriented analytical perspective.An efficient model, aDjacency lIst oRiented rElational faCT (DIRECT), is proposed based on this analytical framework.To alleviate challenges of error propagation and sub-task loss equilibrium, DIRECT employs a novel adaptive multi-task learning strategy with dynamic sub-task loss balancing.Extensive experiments are conducted on two benchmark datasets, and results prove that the proposed model outperforms a series of state-of-the-art (SoTA) models for relational triplet extraction.