Reviewerly/exHarmony huggingface dataset:
Grammatical Fireworks Algorithm Method for Breast Lesion Segmentation in DCE MR Images

For cancer detection and tissue characterization, DCE-MRI segmentation and lesion detection is a critical image analysis task. To segment breast MR images for lesion detection, a hard-clustering technique with Grammatical Fireworks algorithm (GFWA) is proposed in this paper. GFWA is a Swarm Programming (SP) system for automatically generating computer programs in any language. GFWA is used to create the cluster core for clustering the breast MR images in this article. The presence of noise and intensity inhomogeneities in MR images complicates the segmentation process. As a result, the MR images are denoised at the start, and strength inhomogeneities are corrected in the preprocessing stage. The proposed GFWA-based clustering technique is used to segment the preprocessed MR images. Finally, from the segmented images, the lesions are removed. The proposed approach is tested on 5 patients’ 25 DCE-MRI slices. The proposed method’s experimental findings are compared to those of the Grammatical Swarm (GS)-based clustering technique and the K-means algorithm. The proposed method outperforms other approaches in terms of both quantitative and qualitative results.
Motion synthesis and editing for the generation of new sign language content

Existing work on the animation of signing avatars often relies on pure procedural techniques or on the playback of Motion Capture (MoCap) data. While the first solution results in robotic and unnatural motions, the second one is very limited in the number of signs that it can produce. In this paper, we propose to implement data-driven motion synthesis techniques to increase the variety of Sign Language (SL) motions that can be made from a limited database. In order to generate new signs and inflection mechanisms based on an annotated French Sign Language MoCap corpus, we rely on phonological recombination, i.e. on the motion retrieval and modular reconstruction of SL content at a phonological level with a particular focus on three phonological components of SL: hand placement, hand configuration and hand movement. We propose to modify the values taken by those components in different signs to create their inflected version or completely new signs by (i) applying motion retrieval at a phonological level to exchange the value of one component without any modification, (ii) editing the retrieved data with different operators, or, (iii) using conventional motion generation techniques such as interpolation or inverse kinematics, which are parameterized to comply to the kinematic properties of real motion observed in the data set. The quality of the synthesized motions is perceptually assessed through two distinct evaluations that involved 75 and 53 participants respectively.
FeSHI: Feature Map-Based Stealthy Hardware Intrinsic Attack

Convolutional Neural Networks (CNN) have shown impressive performance in computer vision, natural language processing, and many other applications, but they exhibit high computations and substantial memory requirements. To address these limitations, especially in resource-constrained devices, the use of cloud computing for CNNs is becoming more popular. This comes with privacy and latency concerns that have motivated the designers to develop embedded hardware accelerators for CNNs. However, designing a specialized accelerator increases the time-to-market and cost of production. Therefore, to reduce the time-to-market and access to state-of-the-art techniques, CNN hardware mapping and deployment on embedded accelerators are often outsourced to untrusted third parties, which is going to be more prevalent in futuristic artificial intelligence of things (AIoT) systems. These AIoT systems anticipates horizontal collaboration among different resource constrained AIoT node devices, where CNN layers are partitioned and these devices collaboratively compute complex CNN tasks. This horizontal collaboration opens another attack surface to the CNN-based application, like inserting the hardware Trojans (HT) into the embedded accelerators designed for the CNN. Therefore, there is a dire need to explore this attack surface for designing the secure embedded hardware accelerators for CNNs. Towards this goal, in this paper, we exploited this attack surface to propose an HT-based attack called FeSHI. Since in horizontal collaboration of RC AIoT devices different sections of CNN architectures are outsourced to different untrusted third parties, the attacker may not know the input image, but it has access to the layer-by-layer output feature maps information for the assigned sections of the CNN architecture. This attack exploits the statistical distribution, i.e., Gaussian distribution, of the layer-by-layer feature maps of the CNN to design two triggers for stealthy HT with a very low probability of triggering. Also three different novel, stealthy and effective trigger designs are proposed. To illustrate the effectiveness of the proposed attack, we deployed the LeNet and LeNet-3D on PYNQ to classify the MNIST and CIFAR-10 datasets, respectively, and tested FeSHI. The experimental results show that FeSHI utilizes up to 2% extra LUTs, and the overall resource overhead is less than 1% compared to the original designs. It is also demonstrated on the PYNQ board that FeSHI triggers the attack vary randomly making it extremely difficult to detect.
Entanglement-enhanced sensing using a chain of qubits with always-on nearest-neighbor interactions

Quantum metrology is the use of genuinely quantum properties such as entanglement as a resource to outperform classical sensing strategies. Typically, entanglement is created by implementing gate operations or inducing many-body interactions. However, existing sensing schemes with these approaches require accurate control of the probe system such as switching on and off the interaction among qubits, which can be challenging for practical applications. Here, we propose an entanglement-enhanced sensing scheme with an always-on nearest-neighbor interaction between qubits. We adopt the transverse field Ising chain as the probe system, making use of the so-called quantum domino dynamics for the generation of the entangled states. In addition to the advantage that our scheme can be implemented without controlling the interactions, it only requires initialization of the system, projective measurements on a single qubit, and control of the uniform magnetic fields. We can achieve an improved sensitivity beyond the standard quantum limit even under the effect of realistic decoherence.
Unanswerable Question Correction in Question Answering over Personal Knowledge Base

People often encounter situations where they need to recall past experiences from their daily life. In this paper, we aim to construct a question answering system that enables human to query their past experiences over personal knowledge base. Previous works on knowledge base question answering focus on finding answers for answerable questions. In the real world applications, however, people often muddle up facts and ask those questions that cannot be answered with knowledge base. This work presents a novel system consisting of question answering model and question generation model. It not only answers answerable questions, but also corrects unanswerable questions if necessary. Our question answering model recognizes the question that is inconsistent with the state of the personal knowledge base and suggests facts that can form a feasible question. Then, the facts are converted to an answerable question by the question generation model. For refining question, we propose a question generation model based on the reinforcement learning (RL) with question editing mechanism. Experimental results show that our proposed system is effective for correcting unanswerable questions in personal knowledge base question answering.
Self-Supervised Self-Supervision by Combining Deep Learning and Probabilistic Logic

Labeling training examples at scale is a perennial challenge in machine learning. Self-supervision methods compensate for the lack of direct supervision by leveraging prior knowledge to automatically generate noisy labeled examples. Deep probabilistic logic (DPL) is a unifying framework for self-supervised learning that represents unknown labels as latent variables and incorporates diverse self-supervision using probabilistic logic to train a deep neural network end-to-end using variational EM. While DPL is successful at combining pre-specified self-supervision, manually crafting self-supervision to attain high accuracy may still be tedious and challenging. In this paper, we propose Self-Supervised Self-Supervision (S4), which adds to DPL the capability to learn new self-supervision automatically. Starting from an initial "seed," S4 iteratively uses the deep neural network to propose new self-supervision. These are either added directly (a form of structured self-training) or verified by a human expert (as in feature-based active learning). Experiments show that S4 is able to automatically propose accurate self-supervision and can often nearly match the accuracy of supervised methods with a tiny fraction of the human effort.
Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physical robots. In this paper, we compare several reinforcement learning (Q-Learning, SARSA) and deep reinforcement learning (Deep Q-Network, Deep Sarsa) methods for a task aimed at achieving a specific goal using robotics arm UR3. The main optimization problem of this experiment is to find the best solution for each RL/DRL scenario and minimize the Euclidean distance accuracy error and smooth the resulting path by the Bézier spline method. The simulation and real word applications are controlled by the Robot Operating System (ROS). The learning environment is implemented using the OpenAI Gym library which uses the RVIZ simulation tool and the Gazebo 3D modeling tool for dynamics and kinematics.
Bibliometric Study of Scholarly Productivity of Library and Information Science Research in Bangladesh from 1971 2020

This paper aims to measure the extent to which Bangladesh-based Library and Information Science (LIS) researchers have published in leading LIS journals indexed by Web of Science (WoS) and SCOPUS. To answer this question, bibliographic information from LIS publications published in leading bibliographic databases from 1971 to 2020 were examined. Data from 266 LIS publication were collected, compiled and cleaned. VOSviewer software was used to carry out the science mapping of bibliometric networks. From the data it was evidenced that joint authorship and international collaboration have been increasing during this time frame. Authors from 20 countries have published in collaboration with Bangladeshi LIS researchers. Faculty members and the Department of Information Science and Library Management (ISLM) of Dhaka University became the most prolific authors and LIS Department in Bangladesh. The data compiled and findings will benefit current Bangladeshi LIS researchers and practitioners to assess areas of focus by highlighting, what they have, what they lack, and how they could grow to get direction for future research.
Grammatical Error Correction as GAN-like Sequence Labeling

In Grammatical Error Correction (GEC), sequence labeling models enjoy fast inference compared to sequence-to-sequence models; however, inference in sequence labeling GEC models is an iterative process, as sentences are passed to the model for multiple rounds of correction, which exposes the model to sentences with progressively fewer errors at each round.Traditional GEC models learn from sentences with fixed error rates.Coupling this with the iterative correction process causes a mismatch between training and inference that affects final performance.In order to address this mismatch, we propose a GAN-like sequence labeling model, which consists of a grammatical error detector as a discriminator and a grammatical error labeler with Gumbel-Softmax sampling as a generator.By sampling from real error distributions, our errors are more genuine compared to traditional synthesized GEC errors, thus alleviating the aforementioned mismatch and allowing for better training.Our results on several evaluation benchmarks demonstrate that our proposed approach is effective and improves the previous state-of-the-art baseline.
Testing the Role of Financial Development and Urbanization in the Conventional EKC: Evidence from China and India

Increase in the greenhouse gases (GHGs) such as carbon dioxide (CO2) are responsible for environmental problems like global warming and climate change.This study applies the Stochastic Impact by Regression on Population, Affluence and Technology (STIRPAT) framework to analyse the impact of population, economic growth, economic development, urbanization, and energy use on per capita carbon emissions for India and China.Environmental Kuznets Curve (EKC) hypothesis is also tested within the STIRPAT framework.A time series analysis is done from 1980 to 2019 for China and India.Through application of Auto Regressive Distributed Lag (ARDL) Model, we found the existence of EKC for China and India.Results further show that population and energy use is positively related to CO2 discharges.In case of India, urbanization positively affects CO2 discharges whereas for China our findings show that urbanization helps in reducing CO2 discharges.However, domestic loan to private sector has resulted in environmental degradation in case of both countries.
Multilingual Agreement for Multilingual Neural Machine Translation

Although multilingual neural machine translation (MNMT) enables multiple language translations, the training process is based on independent multilingual objectives.Most multilingual models can not explicitly exploit different language pairs to assist each other, ignoring the relationships among them.In this work, we propose a novel agreement-based method to encourage multilingual agreement among different translation directions, which minimizes the differences among them.We combine the multilingual training objectives with the agreement term by randomly substituting some fragments of the source language with their counterpart translations of auxiliary languages.To examine the effectiveness of our method, we conduct experiments on the multilingual translation task of 10 language pairs.Experimental results show that our method achieves significant improvements over the previous multilingual baselines.
On the Interplay Between Fine-tuning and Composition in Transformers

Pre-trained transformer language models have shown remarkable performance on a variety of NLP tasks.However, recent research has suggested that phrase-level representations in these models reflect heavy influences of lexical content, but lack evidence of sophisticated, compositional phrase information (Yu and Ettinger, 2020).Here we investigate the impact of fine-tuning on the capacity of contextualized embeddings to capture phrase meaning information beyond lexical content.Specifically, we fine-tune models on an adversarial paraphrase classification task with high lexical overlap, and on a sentiment classification task.After fine-tuning, we analyze phrasal representations in controlled settings following prior work.We find that fine-tuning largely fails to benefit compositionality in these representations, though training on sentiment yields a small, localized benefit for certain models.In follow-up analyses, we identify confounding cues in the paraphrase dataset that may explain the lack of composition benefits from that task, and we discuss potential factors underlying the localized benefits from sentiment training.
USE OF DIGITAL TOOLS IN POSTGRADUATE PEDAGOGICAL EDUCATION

The article is devoted to the problem of using digital tools and the formation of information and digital competence in postgraduate education of teachers. The urgency of the problem is due to the need of society to develop information literacy and information and digital competence as a key competence of modern teachers, as well as the need to organize a form of blended learning in general secondary education, which provides digital literacy of teachers. The aim of the article is to substantiate the feasibility of using digital tools in postgraduate pedagogical education. Research methods are theoretical (analysis of scientific sources, educational online platforms, sites, synthesis, and generalization of results); empirical (presentation and generalization of pedagogical experience). The article analyzes the ways of organizational and methodological assistance from the Ministry of Education and Science of Ukraine, public organizations, for teachers on the organization of blended learning in general secondary education: sites, educational platforms, manuals, recommendations, guidelines. It was found that using modern digital tools in postgraduate pedagogical education, the emphasis should be on organizing the process of interaction in such a way as to guide teachers in the implementation of these digital tools in their professional activities. Was revealed the content of teacher training in the context of the use of information, digital, multimedia technologies in the educational process. Was substantiated the expediency of studying and using digital tools (Padlet, Classroom, Kahoot, Trello, Mentimeter, Zoom, Meet, Canva, Pinterest, Classtime, GoFormative, Wiser, Live Worksheets, Techer Made, Presentation Tube, Zoom, Telegram, We Chat, Google Forms, Viber, Messenger, Moodle, Flipgrid) in postgraduate pedagogical education is substantiated. Were outlined the prospects for further research.
A Unified Multi-Task Learning Framework for Joint Extraction of Entities and Relations

Joint extraction of entities and relations focuses on detecting entity pairs and their relations simultaneously with a unified model. Based on the extraction order, previous works mainly solve this task through relation-last, relation-first and relation-middle manner. However, these methods still suffer from the template-dependency, non-entity detection and non-predefined relation prediction problem. To overcome these challenges, in this paper, we propose a unified multi-task learning framework to divide the task into three interacted sub-tasks. Specifically, we first introduce the type-attentional method for subject extraction to provide prior type information explicitly. Then, the subject-aware relation prediction is presented to select useful relations based on the combination of global and local semantics. Third, we propose a question generation based QA method for object extraction to obtain diverse queries automatically. Notably, our method detects subjects or objects without relying on NER models and thus it is capable of dealing with the non-entity scenario. Finally, three sub-tasks are integrated into a unified model through parameter sharing. Extensive experiments demonstrate that the proposed framework outperforms all the baseline methods on two benchmark datasets, and further achieve excellent performance for non-predefined relations.
Soft-Clipping Swish: A Novel Activation Function for Deep Learning

This study aims to contribute to the improvement of the network's performance through developing a novel activation function. Over time, many activation functions have been proposed in order to solve the issues of the previous functions. We note here more than 50 activation functions that have been proposed, some of them being very popular such as sigmoid, Rectified Linear Unit (ReLU), Swish, Mish but not only. The main idea of this study that stays behind our proposal is a simple one, based on a very popular function called Swish, which is a composition function, having in its componence sigmoid function and ReLU function. Starting from this activation function we decided to ignore the negative region in the way the Rectified Linear Unit does but being different than that one mentioned through a nonlinear curve assured by the Swish positive region. The idea has been come up from a current function called Soft Clipping. We tested this proposal on more datasets in Computer Vision on classification tasks showing its high potential, here we mention MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100 using two popular architectures: LeNet-5 and ResNet20 version 1.
Stratified Negation in Datalog with Metric Temporal Operators

We extend DatalogMTL—Datalog with operators from metric temporal logic—by adding stratified negation as failure. The new language provides additional expressive power for representing and reasoning about temporal data and knowledge in a wide range of applications. We consider models over the rational timeline, study their properties, and establish the computational complexity of reasoning. We show that, as in negation-free DatalogMTL, fact entailment in our language is PSPACE-complete in data and EXPSPACE-complete in combined complexity. Thus, the extension with stratified negation does not lead to higher complexity.
Embracing Ambiguity: Shifting the Training Target of NLI Models

Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels.While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, such as UNLI and ChaosNLI.In this paper, we explore the option of training directly on the estimated label distribution of the annotators in the NLI task, using a learning loss based on this ambiguity distribution instead of the goldlabels.We prepare AmbiNLI, a trial dataset obtained from readily available sources, and show it is possible to reduce ChaosNLI divergence scores when finetuning on this data, a promising first step towards learning how to capture linguistic ambiguity.Additionally, we show that training on the same amount of data but targeting the ambiguity distribution instead of gold-labels can result in models that achieve higher performance and learn better representations for downstream tasks.
Contrastive Fine-tuning Improves Robustness for Neural Rankers

The performance of state-of-the-art neural rankers can deteriorate substantially when exposed to noisy inputs or applied to a new domain.In this paper, we present a novel method for fine-tuning neural rankers that can significantly improve their robustness to out-of-domain data and query perturbations.Specifically, a contrastive loss that compares data points in the representation space is combined with the standard ranking loss during fine-tuning.We use relevance labels to denote similar/dissimilar pairs, which allows the model to learn the underlying matching semantics across different query-document pairs and leads to improved robustness.In experiments with four passage ranking datasets, the proposed contrastive fine-tuning method obtains improvements on robustness to query reformulations, noise perturbations, and zeroshot transfer for both BERT and BART-based rankers.Additionally, our experiments show that contrastive fine-tuning outperforms data augmentation for robustifying neural rankers.