Miracle-dz/UniFAR-FTUs huggingface dataset:
The Efficacy and Safety of a Herbal Toothpaste in Reducing Gingivitis: A Double-Blind, Randomized, Placebo-Controlled, Parallel Allocation Clinical Trial

Aim To examine the efficacy and safety of the toothpaste containing Rhizoma Chuanxiong and Rhizoma Imperatae extracts in reducing gingivitis. Method A double-blind clinical trial was conducted, in which 120 volunteers were randomly assigned to the test group (N = 60) or the control group (N = 60). Tetramethylpyrazine, senkyunolide A, ferulic acid, and ligustilide are the main effective components of Rhizoma Chuanxiong and Rhizoma Imperatae contains the main components of cylindrin, carotene, 5-hydroxytryptamine, potassium, and calcium. The control group used placebo toothpaste containing neither Rhizoma Chuanxiong extract nor Rhizoma Imperatae extract. Plaque, gingivitis, and bleeding were assessed at the baseline, prior to the supragingival scaling, and at 4, 8, and 12 weeks. Results During the trial, both test and control groups showed a decreasing trend compared to the baseline. At the end of 12 weeks, with respect to Gingival Index (GI), Bleeding Index (BI), and Bleeding on Probing percentage (BOP%) scores, there were significant differences between test and control groups (GI, P<0.001, BI, P<0.001, and BOP%, P<0.001, resp.). After 4 weeks of usage, there were no statistically significant differences in all of GI, BI, and BOP% scores between the two groups. However, the decrease became statistically significant at next two intervals (GI, P<0.001, BI, P<0.001, and BOP%, P<0.001, resp.) in the efficiency of GI, BI, and BOP% which was 8.04%, 11.02%, and 37.16%, respectively. There were no treatment-related adverse events reported. Conclusion The toothpaste containing Rhizoma Chuanxiong and Rhizoma Imperatae extracts was well tolerated and significantly reduced gingivitis and bleeding after usage for 12 weeks. There was better improvement at molars, and the more serious the baseline status was, the better the efficacy was.
Decoder-in-the-Loop: Genetic Optimization- Based LDPC Code Design

LDPC code design tools typically rely on asymptotic code behavior and are affected by an unavoidable performance degradation due to model imperfections in the short length regime. We propose an LDPC code design scheme based on an evolutionary algorithm, the Genetic Algorithm (GenAlg), implementing a “decoder-in-the-loop” concept. It inherently takes into consideration the channel, code length and the number of iterations while optimizing the error-rate of the actual decoder hardware architecture. We construct short length LDPC codes (i.e., the parity-check matrix) with error-rate performance comparable to, or even outperforming that of well-designed standardized short length LDPC codes over both AWGN and Rayleigh fading channels. Our proposed algorithm can be used to design LDPC codes with special graph structures (e.g., accumulator-based codes) to facilitate the encoding step, or to satisfy any other practical requirement. Moreover, GenAlg can be used to design LDPC codes with the aim of reducing decoding latency and complexity, leading to coding gains of up to 0.325 dB and 0.8 dB at BLER of 10−5 for both AWGN and Rayleigh fading channels, respectively, when compared to state-of-the-art short LDPC codes. Also, we analyze what can be learned from the resulting codes and, as such, the GenAlg particularly highlights design paradigms of short length LDPC codes (e.g., codes with degree-1 variable nodes obtain very good results).
C L ] 8 M ar 2 01 9 Context-Aware Crosslingual Mapping

Cross-lingual word vectors are typically obtained by fitting an orthogonal matrix that maps the entries of a bilingual dictionary from a source to a target vector space. Word vectors, however, are most commonly used for sentence or document-level representations that are calculated as the weighted average of word embeddings. In this paper, we propose an alternative to word-level mapping that better reflects sentence-level cross-lingual similarity. We incorporate context in the transformation matrix by directly mapping the averaged embeddings of aligned sentences in a parallel corpus. We also implement cross-lingual mapping of deep contextualized word embeddings using parallel sentences with word alignments. In our experiments, both approaches resulted in cross-lingual sentence embeddings that outperformed context-independent word mapping in sentence translation retrieval. Furthermore, the sentence-level transformation could be used for word-level mapping without loss in word translation quality.
Deductive optimization of relational data storage

Optimizing the physical data storage and retrieval of data are two key database management problems. In this paper, we propose a language that can express both a relational query and the layout of its data. Our language can express a wide range of physical database layouts, going well beyond the row- and column-based methods that are widely used in database management systems. We use deductive program synthesis to turn a high-level relational representation of a database query into a highly optimized low-level implementation which operates on a specialized layout of the dataset. We build an optimizing compiler for this language and conduct experiments using a popular database benchmark, which shows that the performance of our specialized queries is better than a state-of-the-art in memory compiled database system while achieving an order-of-magnitude reduction in memory use.
Complex Valued Gated Auto-encoder for Video Frame Prediction

In recent years, complex valued artificial neural networks have gained increasing interest as they allow neural networks to learn richer representations while potentially incorporating less parameters. Especially in the domain of computer graphics, many traditional operations rely heavily on computations in the complex domain, thus complex valued neural networks apply naturally. In this paper, we perform frame predictions in video sequences using a complex valued gated auto-encoder. First, our method is motivated showing how the Fourier transform can be seen as the basis for translational operations. Then, we present how a complex neural network can learn such transformations and compare its performance and parameter efficiency to a real-valued gated autoencoder. Furthermore, we show how extending both - the real and the complex valued - neural networks by using convolutional units can significantly improve prediction performance and parameter efficiency. The networks are assessed on a moving noise and a bouncing ball dataset.
A Learnable Scatternet: Locally Invariant Convolutional Layers

In this paper, we explore tying together the ideas from Scattering Transforms and Convolutional Neural Networks (CNN) for Image Analysis by proposing a learnable ScatterNet. Previous attempts at tying them together in hybrid networks have tended to keep the two parts separate, with the ScatterNet forming a fixed front end and a CNN forming a learned backend. We instead look at adding learning between scattering orders, as well as adding learned layers before the ScatterNet. We do this by breaking down the scattering orders into single convolutional-like layers we call ‘locally invariant’ layers, and adding a learned mixing term to this layer. Our experiments show that these locally invariant layers can improve accuracy when added to either a CNN or a ScatterNet. We also discover some surprising results in that the ScatterNet may be best positioned after one or more layers of learning rather than at the front of a neural network.
Infogain-Driven Dialogue Modeling with Hashcode Representations

We propose a novel dialogue modeling framework which learns binary hashcodes as compressed text representations, allowing for efficient similarity search; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, or infogain, used as a model-selection criterion favoring representations with better alignment between the utterances of the collaborative dialogue participants, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.
Attribute Acquisition in Ontology based on Representation Learning of Hierarchical Classes and Attributes

Attribute acquisition for classes is a key step in ontology construction, which is often achieved by community members manually. This paper investigates an attention-based automatic paradigm called TransATT for attribute acquisition, by learning the representation of hierarchical classes and attributes in Chinese ontology. The attributes of an entity can be acquired by merely inspecting its classes, because the entity can be regard as the instance of its classes and inherit their attributes. For explicitly describing of the class of an entity unambiguously, we propose class-path to represent the hierarchical classes in ontology, instead of the terminal class word of the hypernym-hyponym relation (i.e., is-a relation) based hierarchy. The high performance of TransATT on attribute acquisition indicates the promising ability of the learned representation of class-paths and attributes. Moreover, we construct a dataset named \textbf{BigCilin11k}. To the best of our knowledge, this is the first Chinese dataset with abundant hierarchical classes and entities with attributes.
On Boosting Semantic Street Scene Segmentation with Weak Supervision

Training convolutional networks for semantic segmentation requires per-pixel ground truth labels, which are very time consuming and hence costly to obtain. Therefore, in this work, we research and develop a hierarchical deep network architecture and the corresponding loss for semantic segmentation that can be trained from weak supervision, such as bounding boxes or image level labels, as well as from strong per-pixel supervision. We demonstrate that the hierarchical structure and the simultaneous training on strong (per-pixel) and weak (bounding boxes) labels, even from separate datasets, consistently increases the performance against per-pixel only training. Moreover, we explore the more challenging case of adding weak image-level labels. We collect street scene images and weak labels from the immense Open Images dataset to generate the OpenScapes dataset, and we use this novel dataset to increase segmentation performance on two established per-pixel labeled datasets, Cityscapes and Vistas. We report performance gains up to +13.2% mIoU on crucial street scene classes, and inference speed of 20 fps on a Titan V GPU for Cityscapes at $512 \times 1024$ resolution. Our network and OpenScapes dataset are shared with the research community.
Auto-Encoding Progressive Generative Adversarial Networks for 3D Multi Object Scenes

3D multi object generative models allow us to synthesize a large range of novel 3D multi object scenes and also identify objects, shapes, layouts and their positions. But multi object scenes are difficult to create because of the dataset being multimodal in nature. The conventional 3D generative adversarial models are not efficient in generating multi object scenes, they usually tend to generate either one object or generate fuzzy results of multiple objects. Auto-encoder models have much scope in feature extraction and representation learning using the unsupervised paradigm in probabilistic spaces. We try to make use of this property in our proposed model. In this paper we propose a novel architecture using 3DConvNets trained with the progressive training paradigm that has been able to generate realistic high resolution 3D scenes of rooms, bedrooms, offices etc. with various pieces of furniture and objects. We make use of the adversarial auto-encoder along with the WGAN-GP loss parameter in our discriminator loss function. Finally this new approach to multi object scene generation has also been able to generate more number of objects per scene.
Unsupervised Medical Image Translation Using Cycle-MedGAN

Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. However, for supervised image translation frameworks, co-registered datasets, paired in a pixel-wise sense, are required. This is often difficult to acquire in realistic medical scenarios. On the other hand, unsupervised translation frameworks often result in blurred translated images with unrealistic details. In this work, we propose a new unsupervised translation framework which is titled Cycle-MedGAN. The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images. Qualitative and quantitative comparisons against other unsupervised translation approaches demonstrate the performance of the proposed framework for PET-CT translation and MR motion correction.
Construction of Comprehensive Dosage-Matching Core Histone Mutant Libraries for Saccharomyces cerevisiae

Saccharomyces cerevisiae contains two genes for each core histone, which are presented as pairs under the control of a divergent promoter, i.e., HHT1-HHF1, HHT2-HHF2, HTA1-HTB1 and HTA2-HTB2. HHT1-HHF1, and HHT2-HHF2 encode histone H3 and H4 with identical amino acid sequences but under the control of differently regulated promoters. Previous mutagenesis studies were carried out by deleting one pair and mutating the other one. Here, we present the design and construction of three additional libraries covering HTA1-HTB1, HTA2-HTB2, and HHT1-HHF1 respectively. Together with the previously described library of HHT2-HHF2 mutants, a systematic and complete collection of mutants for each of the eight core S. cerevisiae histone genes becomes available. Each designed mutant was incorporated into the genome, generating three more corresponding libraries of yeast strains. We demonstrated that, although, under normal growth conditions, strains with single-copy integrated histone genes lacked phenotypes, in some growth conditions, growth deficiencies were observed. Specifically, we showed that addition of a second copy of the mutant histone gene could rescue the lethality in some previously known mutants that cannot survive with a single copy. This resource enables systematic studies of function of each nucleosome residue in plasmid, single-copy, and double-copy integrated formats.
Towards Time-Aware Distant Supervision for Relation Extraction

Distant supervision for relation extraction heavily suffers from the wrong labeling problem. To alleviate this issue in news data with the timestamp, we take a new factor time into consideration and propose a novel time-aware distant supervision framework (Time-DS). Time-DS is composed of a time series instance-popularity and two strategies. Instance-popularity is to encode the strong relevance of time and true relation mention. Therefore, instance-popularity would be an effective clue to reduce the noises generated through distant supervision labeling. The two strategies, i.e., hard filter and curriculum learning are both ways to implement instance-popularity for better relation extraction in the manner of Time-DS. The curriculum learning is a more sophisticated and flexible way to exploit instance-popularity to eliminate the bad effects of noises, thus get better relation extraction performance. Experiments on our collected multi-source news corpus show that Time-DS achieves significant improvements for relation extraction.
Generating automated and online test oracles for Simulink models with continuous and uncertain behaviors

Test automation requires automated oracles to assess test outputs. For cyber physical systems (CPS), oracles, in addition to be automated, should ensure some key objectives: (i) they should check test outputs in an online manner to stop expensive test executions as soon as a failure is detected; (ii) they should handle time- and magnitude-continuous CPS behaviors; (iii) they should provide a quantitative degree of satisfaction or failure measure instead of binary pass/fail outputs; and (iv) they should be able to handle uncertainties due to CPS interactions with the environment. We propose an automated approach to translate CPS requirements specified in a logic-based language into test oracles specified in Simulink - a widely-used development and simulation language for CPS. Our approach achieves the objectives noted above through the identification of a fragment of Signal First Order logic (SFOL) to specify requirements, the definition of a quantitative semantics for this fragment and a sound translation of the fragment into Simulink. The results from applying our approach on 11 industrial case studies show that: (i) our requirements language can express all the 98 requirements of our case studies; (ii) the time and effort required by our approach are acceptable, showing potentials for the adoption of our work in practice, and (iii) for large models, our approach can dramatically reduce the test execution time compared to when test outputs are checked in an offline manner.
Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injection

When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must “guess” this missing information, often leading to incorrect translations in the given context. We propose a black-box approach for injecting the missing information to a pre-trained neural machine translation system, allowing to control the morphological variations in the generated translations without changing the underlying model or training data. We evaluate our method on an English to Hebrew translation task, and show that it is effective in injecting the gender and number information and that supplying the correct information improves the translation accuracy in up to 2.3 BLEU on a female-speaker test set for a state-of-the-art online black-box system. Finally, we perform a fine-grained syntactic analysis of the generated translations that shows the effectiveness of our method.
NEARBY Platform for Automatic Asteroids Detection and EURONEAR Surveys

The survey of the nearby space and continuous monitoring of the Near Earth Objects (NEOs) and especially Near Earth Asteroids (NEAs) are essential for the future of our planet and should represent a priority for our solar system research and nearby space exploration. More computing power and sophisticated digital tracking algorithms are needed to cope with the larger astronomy imaging cameras dedicated for survey telescopes. The paper presents the NEARBY platform that aims to experiment new algorithms for automatic image reduction, detection and validation of moving objects in astronomical surveys, specifically NEAs. The NEARBY platform has been developed and experimented through a collaborative research work between the Technical University of Cluj-Napoca (UTCN) and the University of Craiova, Romania, using observing infrastructure of the Instituto de Astrofisica de Canarias (IAC) and Isaac Newton Group (ING), La Palma, Spain. The NEARBY platform has been developed and deployed on the UTCN's cloud infrastructure and the acquired images are processed remotely by the astronomers who transfer it from ING through the web interface of the NEARBY platform. The paper analyzes and highlights the main aspects of the NEARBY platform development, and the results and conclusions on the EURONEAR surveys.
A Taxonomy for Understanding the Security Technical Debts in Blockchain Based Systems

Blockchain is a disruptive technology intended at implementing secure decentralized distributed systems, in which transactional data can be shared, stored and verified by participants of a system using cryptographic and consensus mechanisms, elevating the need for a central authentication/verification authority. Contrary to the belief, blockchain-based systems are not inherently secure by design; it is crucial for security software engineers to be aware of the various blockchain specific architectural design decisions and choices and their consequences on the dependability of the software system. We argue that sub-optimal and ill-informed design decisions and choices of blockchain components and their configurations including smart contracts, key management, cryptographic and consensus mechanisms, on-chain vs. off chain storage choices can introduce security technical debt into the system. The technical debt metaphor can serve as a powerful tool for early, preventive and transparent evaluation of the security design of blockchain-based systems by making the potential security technical debt visible to security software engineers. We review the core architectural components of blockchain-based systems and we show how the ill-choice or sub-optimal design decisions and configuration of these components can manifest into security technical debt. We contribute to a taxonomy that classifies the blockchain specific design decisions and choices and we describe their connection to potential debts. The taxonomy can help architects of this category of systems avoid potential security risks by visualising the security technical debts and raising its visibility. We use examples from two case studies to discuss the taxonomy and its application.
Holdable Haptic Device for 4-DOF Motion Guidance

Hand-held haptic devices can allow for greater freedom of motion and larger workspaces than traditional grounded haptic devices. They can also provide more compelling haptic sensations to the users’ fingertips than many wearable haptic devices because reaction forces can be distributed over a larger area of skin far away from the stimulation site. This paper presents a hand-held kinesthetic gripper that provides guidance cues in four degrees of freedom (DOF). 2-DOF tangential forces on the thumb and index finger combine to create cues to translate or rotate the hand. We demonstrate the device’s capabilities in a three-part user study. First, users moved their hands in response to haptic cues before receiving instruction or training. Then, they trained on cues in eight directions in a forced-choice task. Finally, they repeated the first part, now knowing what each cue intended to convey. Users were able to discriminate each cue over 90% of the time. Users moved correctly in response to the guidance cues both before and after the training and indicated that the cues were easy to follow. The results show promise for holdable kinesthetic devices for haptic feedback and guidance in applications such as medical training, teleoperation, and virtual reality.
Dyna-AIL : Adversarial Imitation Learning by Planning

Adversarial methods for imitation learning have been shown to perform well on various control tasks. However, they require a large number of environment interactions for convergence. In this paper, we propose an end-to-end differentiable adversarial imitation learning algorithm in a Dyna-like framework for switching between model-based planning and model-free learning from expert data. Our results on both discrete and continuous environments show that our approach of using model-based planning along with model-free learning converges to an optimal policy with fewer number of environment interactions in comparison to the state-of-the-art learning methods.