Reviewerly/exHarmony huggingface dataset:
Multi-Hop Question Generation Using Hierarchical Encoding-Decoding and Context Switch Mechanism

Neural auto-regressive sequence-to-sequence models have been dominant in text generation tasks, especially the question generation task. However, neural generation models suffer from the global and local semantic semantic drift problems. Hence, we propose the hierarchical encoding-decoding mechanism that aims at encoding rich structure information of the input passages and reducing the variance in the decoding phase. In the encoder, we hierarchically encode the input passages according to its structure at four granularity-levels: [word, chunk, sentence, document]-level. Second, we progressively select the context vector from the document-level representations to the word-level representations at each decoding time step. At each time-step in the decoding phase, we progressively select the context vector from the document-level representations to word-level. We also propose the context switch mechanism that enables the decoder to use the context vector from the last step when generating the current word at each time-step.It provides a means of improving the stability of the text generation process during the decoding phase when generating a set of consecutive words. Additionally, we inject syntactic parsing knowledge to enrich the word representations. Experimental results show that our proposed model substantially improves the performance and outperforms previous baselines according to both automatic and human evaluation. Besides, we implement a deep and comprehensive analysis of generated questions based on their types.
Monozygotic twin face recognition: An in-depth analysis and plausible improvements

Monozygotic twins, commonly known as identical twins, share extreme facial resemblance that they deceive the majority of the prevailing face recognition systems. Hence, the facial recognition systems, which are turning out to be exploited in almost all real-life biometric authentication-needing applications, require significant upgradation to cope with this great challenge. This paper pays special attention towards categorizing and investigating the existing research on the facial recognition of monozygotic twins in an exhaustive sense. Subsequently, the research gap is analyzed. It is then followed by the description of the possible refinements, like the choice of feature and other methodological advancements, which may aid in augmenting the twin recognition accuracy of the face recognition systems. It is believed that the outcomes of the review can well-support and pave the way for further research in the future. Consequently, the “Double Trouble” posed on the commercial face recognition systems can be effectively evaded.
AUGMENTED REALITY AS A POWERFUL MARKETING TOOL

Today's market is oversaturated with new technologies that are slowly moving through our daily routine. Businesses often have to pursue new strategies to reach new customers and retain existing ones. Popular technologies of today undoubtedly include virtual reality (VR) and augmented reality (AR). The application of augmented reality in the field of trade, marketing, promotion of products and services has its own potential and justification. A questionnaire survey of the current state of AR utilization in companies of Central Slovakia shows that the interest in the use of this technology exists in this area, although in practice it is implemented rather rarely. However, a suitable design solution and the worldwide trend of smartphone use can help to achieve the goal of greater implementation. The design of the methodology for the use of AR in the field of marketing consist of a sequence of several basic steps. Verification of the proposed methodology is carried out on a practical example by creating a real mobile application using augmented reality to promote the selected product. The final mobile application includes several attractive features for the end-user which can be accessed through a smartphone and displayed in augmented reality. Such a mobile application in augmented reality provides the company with a powerful marketing tool, but it also has its limitations.
A New Method of Image Restoration Technology Based on WGAN

With the development of image restoration technology based on deep learning, more complex problems are being solved, especially in image semantic inpainting based on context. Nowadays, image semantic inpainting techniques are becoming more mature. However, due to the limitations of memory, the instability of training, and the lack of sample diversity, the results of image restoration are still encountering difficult problems, such as repairing the content of glitches which cannot be well integrated with the original image. Therefore, we propose an image inpainting network based on Wasserstein generative adversarial network (WGAN) distance. With the corresponding technology having been adjusted and improved, we attempted to use the Adam algorithm to replace the traditional stochastic gradient descent, and another algorithm to optimize the training used in recent years. We evaluated our algorithm on the ImageNet dataset. We obtained high-quality restoration results, indicating that our algorithm improves the clarity and consistency of the image.
Hypergraph Convolutional Network with Hybrid Higher-Order Neighbors

Hypergraph-based methods can learn non-pairwise associations more efficiently in many real-world datasets. However, existing hypergraph-based methods do not consider the relationship of the hybrid neighborhood. To address this issue, we propose a hybrid higher-order neighborhood based hypergraph convolutional network (HybridHGCN). Technically, feature embeddings are generated via k-hop hypergraph convolution layers and mixed by the hybrid message operator. To evaluate the proposed HybridHGCN, we conduct experiments on the citation network datasets and the visual object datasets. The experimental results show that HybridHGCN brings significant improvements over state-of-the-art hypergraph neural network baselines.
Improvements of SpeeD’s Romanian ASR system during ReTeRom project

Automatic speech recognition (ASR) for Romanian language is on an ascending trend of interest for the scientific community. In the last two years several research groups reported valuable results on speech recognition and dialogue tasks for Romanian. In our paper we present the improvements we recently obtained by collecting and using more text and audio data for training the language and acoustic models. We emphasize the automatic methodologies employed to facilitate data collection and annotation. In comparison to our previous work, we report state-of-the-art results for read speech (WER of 1.6%) and significantly better results on spontaneous speech: relative improvement around 40%). In order to facilitate direct comparison with other ASR systems, we release all evaluation datasets, totaling 10 hours of manually annotated speech.
Generating Informative Dialogue Responses with Keywords-Guided Networks

Recently, open-domain dialogue systems have attracted growing attention. Most of them use the sequence-to-sequence (Seq2Seq) architecture to generate dialogue responses. However, traditional Seq2Seq-based open-domain dialogue models tend to generate generic and safe responses, which are less informative, unlike human responses. In this paper, we propose a simple but effective Keywords-guided Sequence-to-sequence model (KW-Seq2Seq) which uses keywords information as guidance to generate open-domain dialogue responses. Specifically, given the dialogue context, KW-Seq2Seq first uses a keywords decoder to predict a sequence of topic keywords, and then generates the final response under the guidance of them. Extensive experiments demonstrate that the keywords information can facilitate the model to produce more informative, coherent, and fluent responses, yielding substantive gain in both automatic and human evaluation metrics.
FILENG: An Automatic English Subtitle Generator from Filipino Video Clips using Hidden Markov Model

The researchers thought of creating a system to promote the Filipino language, especially Tagalog, through a labeled text or subtitle. The researchers have developed a system, FILENG: An Automatic English Subtitle Generator from Tagalog Video Clips, that can automatically generate an English subtitle from Tagalog video clips. The researchers used sphinx4 as the tool for creating a speech to text extraction. The timeframe is also included in the output. Seventy nine percent accuracy of the system was recorded by the proponents. The study utilized results of Precision, Recall and the F-Measure from the tested gathered data. Enhancement of the system must be considered for further study.
Designing Opportunistic Social Matching Systems for Women's Safety During Face-to-Face Social Encounters

This paper presents emerging findings from a participatory design study with women about how future designs of opportunistic social matching systems can foreground user safety. The study is motivated by a relative lack of focus on user safety in social matching systems (Tinder, Bumble, etc.) in light of advances to user discovery mechanisms that enable increasingly rapid face-to-face encounters. Findings highlight ways that social matching systems could support users’ safety after they have left home to pursue a social opportunity in the physical world. Co-designers envisioned a social matching system acting as a “guardian” that genuinely cares for, and monitors, the user's safety even if it cannot flawlessly mitigate harm. Rather than respond only after a definitive harm has occurred, co-designers expected the system to intervene earlier, during ambiguously unsafe or uncomfortable situations. The paper considers directions for future work in response to users’ expectations for how social matching systems should coordinate with trusted contacts to intervene in unsafe situations.
MEGA: Predicting the best classifier combination using meta-learning and a genetic algorithm

Classifier combination through ensemble systems is one of the most effective approaches to improve the accuracy of classification systems. Ensemble systems are generally used to combine classifiers; However, selecting the best combination of individual classifiers is a challenging task. In this paper, we propose an efficient assembling method that employs both meta-learning and a genetic algorithm for the selection of the best classifiers. Our method is called MEGA, standing for using MEta-learning and a Genetic Algorithm for algorithm recommendation. MEGA has three main components: Training, Model Interpretation and Testing. The Training component extracts meta-features of each training dataset and uses a genetic algorithm to discover the best classifier combination. The Model Interpretation component interprets the relationships between meta-features and classifiers using a priori and multi-label decision tree algorithms. Finally, the Testing component uses a weighted k-nearest-neighbors algorithm to predict the best combination of classifiers for unseen datasets. We present extensive experimental results that demonstrate the performance of MEGA. MEGA achieves superior results in a comparison of three other methods and, most importantly, is able to find novel interpretable rules that can be used to select the best combination of classifiers for an unseen dataset.
Functional signatures: new definition and constructions

Functional signatures (FS) enable a master authority to delegate its signing privilege to an assistant. Concretely, the master authority uses its secret key skF to issue a signing key skf for a designated function $$f \in {{\cal F}_{{\rm{FS}}}}$$ and sends both f and skf to the assistant $${\cal E}$$ , which is then able to compute a signature σf with respect to pkF for a message y in the range of f. In this paper, we modify the syntax of FS slightly to support the application scenario where a certificate of authorization is necessary. Compared with the original FS, our definition requires that $${{\cal F}_{{\rm{FS}}}}$$ is an injective function family and for any f0, $${f_1} \in {{\cal F}_{{\rm{FS}}}}$$ there does not exist an intersection between range(f0) and range(f1). Accordingly, we redefine the security of FS and introduce two additional security notions, called unlinkability and accountability. Signatures σf in our definition do not expose the intention of the master authority. We propose two constructions of FS. The first one is a generic construction based on signatures with perfectly re-randomizable keys, non-interactive zero-knowledge proof (NIZK) and traditional digital signatures, and the other is based on RSA (Rivest-Shamir-Adleman) signatures with full domain hash and NIZK. We prove that both schemes are secure under the given security models.
Traceable Identity-Based Ring Signature for Protecting Mobile IoT Devices

In mobile internet of things (MIoTs), it is very important how to protect the identities of terminal devices so as to implement the privacy security. On the one hand, if a malicious MIoT device issues its false data under full privacy protection, then this device may abuse the privacy protection function to arbitrarily issue its false data, and it can not be disclosed. Therefore, in this paper we propose a traceable identity-based ring signature (TIBRS) scheme with constant size for protecting and tracing the identities of MIoT devices. In our proposed scheme, the MIoT devices can anonymously issue their data by traceable identity-based ring signatures, where their identities are protected. On the other hand, if their identities need to be traced, then a trusted third party (private key generator) can trace the identities of these MIoT devices on their generated signatures. Based on the related security model, our proposed scheme is proved to have a security reduction to the computational Diffie-Hellman assumption in the standard model, where our scheme has the anonymity with enough security to protecting the identities of MIoT devices. Compared with other related works, our scheme requires less computational overhead on MIoT devices, which generates constant size signatures.
VidLife: A Dataset for Life Event Extraction from Videos

Filming video blogs, which is shortened to vlog, becomes a popular way for people to record their life experiences in recent years. In this work, we present a novel task that is aimed at extracting life events from videos and constructing personal knowledge bases of individuals. In contrast to most existing researches in the field of computer vision that focus on identifying low-level script-like activities such as moving boxes, our goal is to extract life events where high-level activities like moving into a new house are recorded. The challenges to be tackled include: (1) identifying which objects in a given scene related to the life events of the protagonist we concern, and (2) determining the association between an extracted visual concept and a more high-level description of a video clip. To address the research issues, we construct a video life event extraction dataset VidLife by exploiting videos from the TV series The Big Bang Theory, in which the plot is around the daily lives of several characters. A pilot multitask learning model is proposed to extract life events given video clips and subtitles for storing in the personal knowledge base.
DESYR

The formulation of a claim rests at the core of argument mining. To demarcate between a claim and a non-claim is arduous for both humans and machines, owing to latent linguistic variance between the two and the inadequacy of extensive definition-based formalization. Furthermore, the increase in the usage of online social media has resulted in an explosion of unsolicited information on the web presented as informal text. To account for the aforementioned, in this paper, we propose DESYR. It is a framework that intends on annulling the said issues for informal web-based text by leveraging a combination of hierarchical representation learning (dependency-inspired Poincaré embedding), definition-based alignment, and feature projection. We do away with fine-tuning compute-heavy language models in favor of fabricating a more domain-centric but lighter approach. Experimental results indicate that DESYR builds upon the state-of-the-art system across four benchmark claim datasets, most of which were constructed with informal texts. We see an increase of 3 claim-F1 points on the LESA-Twitter dataset, an increase of 1 claim-F1 point and 9 macro-F1 points on the Online Comments (OC) dataset, an increase of 24 claim-F1 points and 17 macro-F1 points on the Web Discourse (WD) dataset, and an increase of 8 claim-F1 points and 5 macro-F1 points on the Micro Texts (MT) dataset. We also perform an extensive analysis of the results. We make a 100-D pre-trained version of our Poincaré-variant along with the source code.
Development of optimized memory based VLSI architecture with histogram analysis for image contrast enhancement

The natural scenarios show that the images which are taken under low light environments experiences weak luminosity and low-contrast issues frequently. One of the vital units of image contrast enhancement accelerator is a Histogram computation. In this paper, a new VLSI structure is developed for contrast enhancement using optimized memory-based histogram analysis. Initially, a comparator-less parallel rank ordering filter is proposed to remove the noise in the pre-processing stage. Then, the pipelining and parallel process of the memory-based histogram analysis is enhanced by introducing an enhanced transient search optimization (ETSO) approach that selects suitable data to perform the data comparison process in the processing elements (PEs). In addition, a flexible digital comparators (FDC), pipelined structure of bilinear interpolation and reciprocal units are introduced in the proposed contrast enhancement accelerator to save area and power usage. Also, the histogram memory unit is designed by considering dual data flow with self-configurable characteristics for the selection of appropriate data flow in the contrast enhancement application. At last, the stated VLSI structures for both histogram computation and enhancement unit are implemented in FPGA for meeting their speed and resource constraints. The key factors of the proposed design are lowest delay of 5.256 ns and lower power consumption of 0.289 W against existing accelerators.
Ordinal Regression with Explainable Distance Metric Learning Based on Ordered Sequences: Extended Abstract

Ordinal regression addresses the problem of predicting non-numerical ordered classes. It walks a fine line between standard regression and classification, and the problem is often addressed from one of these perspectives. This can lead to suboptimal results as the ordinal information in the data may not be properly exploited. In this work we propose a distance metric learning algorithm to handle ordinal regression. Our model aims at optimizing the number of ordered sequences in local neighborhoods of the data, so that the learned distance can then be used by a distance-based predictor and improve its performance in ordinal regression problems. We evaluate our algorithm on several ordinal regression datasets and show that it outperforms the current distance metric learning for ordinal regression proposals, as well as being competitive with respect to the state-of-the-art of ordinal regression. The current paper is an extended abstract for the work [1].
CAFT: Class Aware Frequency Transform for Reducing Domain Gap

This work explores the usage of Fourier Transform for reducing the domain gap between the Source (e.g. Synthetic Image) and Target domain (e.g. Real Image) towards solving the Domain Adaptation problem. Most of the Unsupervised Domain Adaptation (UDA) algorithms reduce the global domain shift between labelled Source and unlabelled Target domain by matching the marginal distribution. UDA performance deteriorates for the cases where the domain gap between Source and Target is significant. To improve the overall performance of the existing UDA algorithms the proposed method attempts to bring the Source domain closer to the Target domain with the help of pseudo label based class consistent low-frequency swapping. This traditional image processing technique results in computational efficiency, especially compared to the state-of-the-art deep learning methods that use complex adversarial training. The proposed method Class Aware Frequency Transformation (CAFT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ) can easily be plugged into any existing UDA algorithm to improve its performance. We evaluate CAFT on various domain adaptation datasets and algorithms and have achieved performance gains across all the popular benchmarks.
Variational Bayesian Kalman filter using natural gradient

We propose a technique based on the natural gradient method for variational lower bound maximization for a variational Bayesian Kalman filter. The natural gradient approach is applied to the Kullback-Leibler divergence between the parameterized variational distribution and the posterior density of interest. Using a Gaussian assumption for the parametrized variational distribution, we obtain a closed-form iterative procedure for the Kullback-Leibler divergence minimization, producing estimates of the variational hyper-parameters of state estimation and the associated error covariance. Simulation results in both a Doppler radar tracking scenario and a bearing-only tracking scenario are presented, showing that the proposed natural gradient method outperforms existing methods which are based on other linearization techniques in terms of tracking accuracy.
Public Life in Public Space (PLPS): A multi-task, multi-group video dataset for public life research

Human-computer interaction (HCI) is a multidisciplinary field of study focusing on the design of computer technology and, in particular, the interactions between humans and computers. Public space, between the urban buildings, is an open and accessible area to people. Public life, happening in public spaces, is about human activity, human interaction, expression of human feeling in the wild. Affective behavior analysis in the public space is the basic topic of the public life research, which is the key to achieve HCI applications through comprehensively understanding people’s feelings, emotions, social behaviors and their correlations in a ‘human-centered’ and engaging manner. However, it is a challenging task to design a robust HCI system due to the lack of multi-task datasets (including emotion, behavior, social relations, etc), collected under the uncontrolled conditions in real public spaces. In spite that existing separate datasets in computer vision can somehow meet the requirement of public life research, they are neither captured from real public spaces nor for multiple tasks, which cannot comprehensively support the joint research of public life. To tackle this issue, this paper presents a multi-task, multi-group human-oriented video dataset, namely public life in public space (PLPS). Specifically, multi-tasks in terms of activity recognition, emotion recognition and social relation recognition are integrated for each video data. Multi-group and multi-level labels in terms of individuals, groups, video clips are included in the dataset. With PLPS, more sophisticated computer vision model for comprehensive public life research can be facilitated.