Reviewerly/exHarmony huggingface dataset:
Focused Quantization for Sparse CNNs

Deep convolutional neural networks (CNNs) are powerful tools for a wide range of vision tasks, but the enormous amount of memory and compute resources required by CNNs poses a challenge in deploying them on constrained devices. Existing compression techniques, while excelling at reducing model sizes, struggle to be computationally friendly. In this paper, we attend to the statistical properties of sparse CNNs and present focused quantization, a novel quantization strategy based on power-of-two values, which exploits the weight distributions after fine-grained pruning. The proposed method dynamically discovers the most effective numerical representation for weights in layers with varying sparsities, significantly reducing model sizes. Multiplications in quantized CNNs are replaced with much cheaper bit-shift operations for efficient inference. Coupled with lossless encoding, we build a compression pipeline that provides CNNs with high compression ratios (CR), low computation cost and minimal loss in accuracies. In ResNet-50, we achieved a 18.08x CR with only 0.24% loss in top-5 accuracy, outperforming existing compression methods. We fully compress a ResNet-18 and found that it is not only higher in CR and top-5 accuracy, but also more hardware efficient as it requires fewer logic gates to implement when compared to other state-of-the-art quantization methods assuming the same throughput.
Research on Autonomous Navigation Control of Unmanned Ship Based on Unity3D

In order to study the state of autonomous piloting of unmanned ship in different terrains, based on the Unity3D game engine software, the ship piloting simulation of the sea surface and the inland river scene is designed respectively. The autonomous navigation control of unmanned ships is implemented using a deep reinforcement learning algorithm, so as to enable the unmanned ship to improve the adaptability under the complicated and variable real-time simulation conditions, and constantly improve and optimize through machine reinforcement learning. Finally, the simulation results show that the unmanned ship can successfully avoid obstacles and reach the destination in a complex environment on the simulation platform based on Unity3D.
Feature2Vec: Distributional semantic modelling of human property knowledge

Feature norm datasets of human conceptual knowledge, collected in surveys of human volunteers, yield highly interpretable models of word meaning and play an important role in neurolinguistic research on semantic cognition.However, these datasets are limited in size due to practical obstacles associated with exhaustively listing properties for a large number of words.In contrast, the development of distributional modelling techniques and the availability of vast text corpora have allowed researchers to construct effective vector space models of word meaning over large lexicons.However, this comes at the cost of interpretable, human-like information about word meaning.We propose a method for mapping human property knowledge onto a distributional semantic space, which adapts the word2vec architecture to the task of modelling concept features.Our approach gives a measure of concept and feature affinity in a single semantic space, which makes for easy and efficient ranking of candidate human-derived semantic properties for arbitrary words.We compare our model with a previous approach, and show that it performs better on several evaluation tasks.Finally, we discuss how our method could be used to develop efficient sampling techniques to extend existing feature norm datasets in a reliable way.
Negative Focus Detection via Contextual Attention Mechanism

Negation is a universal but complicated linguistic phenomenon, which has received considerable attention from the NLP community over the last decade, since a negated statement often carries both an explicit negative focus and implicit positive meanings.For the sake of understanding a negated statement, it is critical to precisely detect the negative focus in context.However, how to capture contextual information for negative focus detection is still an open challenge.To well address this, we come up with an attention-based neural network to model contextual information.In particular, we introduce a framework which consists of a Bidirectional Long Short-Term Memory (BiLSTM) neural network and a Conditional Random Fields (CRF) layer to effectively encode the order information and the long-range context dependency in a sentence.Moreover, we design two types of attention mechanisms, word-level contextual attention and topic-level contextual attention, to take advantage of contextual information across sentences from both the word perspective and the topic perspective, respectively.Experimental results on the SEM'12 shared task corpus show that our approach achieves the best performance on negative focus detection, yielding an absolute improvement of 2.11% over the state-of-the-art.This demonstrates the great effectiveness of the two types of contextual attention mechanisms.
Korean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model

Korean morphological analysis has been considered as a sequence of morpheme processing and POS tagging.Thus, a pipeline model of the tasks has been adopted widely by previous studies.However, the model has a problem that it cannot utilize interactions among the tasks.This paper formulates Korean morphological analysis as a combination of the tasks and presents a tied sequence-tosequence multi-task model for training the two tasks simultaneously without any explicit regularization.The experiments prove the proposed model achieves the state-of-the-art performance.
Triad Constraints for Learning Causal Structure of Latent Variables

Learning causal structure from observational data has attracted much attention, and it is notoriously challenging to find the underlying structure in the presence of confounders (hidden direct common causes of two variables). In this paper, by properly leveraging the non-Gaussianity of the data, we propose to estimate the structure over latent variables with the so-called Triad constraints: we design a form of pseudo-residual from three variables, and show that when causal relations are linear and noise terms are non-Gaussian, the causal direction between the latent variables for the three observed variables is identifiable by checking a certain kind of independence relationship. In other words, the Triad constraints help us to locate latent confounders and determine the causal direction between them. This goes far beyond the Tetrad constraints and reveals more information about the underlying structure from non-Gaussian data. Finally, based on the Triad constraints, we develop a two-step algorithm to learn the causal structure corresponding to measurement models. Experimental results on both synthetic and real data demonstrate the effectiveness and reliability of our method.
IIT-KGP at MEDIQA 2019: Recognizing Question Entailment using Sci-BERT stacked with a Gradient Boosting Classifier

The number of people turning to the Internet to search for a diverse range of health-related subjects continues to grow and with this multitude of information available, duplicate questions become more frequent and finding the most appropriate answers becomes problematic.This issue is important for questionanswering platforms as it complicates the retrieval of all information relevant to the same topic, particularly when questions similar in essence are expressed differently, and answering a given medical question by retrieving similar questions that are already answered by human experts seems to be a promising solution.In this paper we present our novel approach to detect question entailment by determining the type of question asked rather than focusing on the type of the ailment given.This unique methodology makes the approach robust towards examples which have different ailment names but are synonyms of each other.Also it enables us to check entailment at a much more fine-grained level.
Emotion Detection with Neural Personal Discrimination

There have been a recent line of works to automatically predict the emotions of posts in social media.Existing approaches consider the posts individually and predict their emotions independently.Different from previous researches, we explore the dependence among relevant posts via the authors' backgrounds, since the authors with similar backgrounds, e.g., gender, location, tend to express similar emotions.However, such personal attributes are not easy to obtain in most social media websites, and it is hard to capture attributesaware words to connect similar people.Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions.In particular, we employ adversarial discriminators to determine the personal attributes, with attention mechanisms to aggregate attributes-aware words.In this way, social correlationship among different posts can be better addressed.Experimental results show the usefulness of personal attributes, and the effectiveness of our proposed NPD approach in capturing such personal attributes with significant gains over the state-of-the-art models.
Compute-Extensive Background Subtraction for Efficient Ghost Suppression

Efficient background modelling has always been an active area of research due to its immense importance as a preliminary step in various machine-vision applications. Several techniques have been proposed to date that strive to achieve higher accuracy without compromising on computational and hardware demands. One of such techniques, Visual Background Extractor (Vibe), has set benchmarks due to its fewer memory requirements and good results. However, it suffers from high false positives due to its slower, selective and random update policy. This paper proposes a novel sample-consensus pixel-based technique for efficient foreground segmentation complemented with faster ghost suppression. This is achieved by employing segmentation masks exploiting both static and dynamic properties of pixels depicting likeliness towards absorption into the foregrounds. Dynamic characteristics of the proposed approach handle `object present in the first frame' problem while static characteristics handle improper illumination and shadows in videos in lesser time. It aims not only at suppressing ghosts in the foreground mask but also allows their absorption by updating the background model with such regions. It also proposes a unique spatio-temporal model initialization technique for handling continuous noise. The proposed approach proved to produce outstanding results when compared with 9 traditional and 13 state-of-the-art algorithms.
Multiple-matrix vector multiplication with crossbar phase-change memory

Vector multiplication involving multiple matrices is theoretically realized by a crossbar array of conventional phase-change random-access memories whose device resistance can be continuously adjusted by altering the amorphization/crystallization pulse numbers and amplitudes. The designed circuits comprise input current, memory conductance, and output voltage, to represent the known vector, matrix coefficient, and required solution, respectively. The calculated results from simulations show good agreement with analytical solutions, demonstrating capability for in-memory computation.
A Common Method for Detecting Multiple Steganographies in Low-Bit-Rate Compressed Speech Based on Bayesian Inference

Analysis-by-synthesis linear predictive coding (AbS-LPC) is widely used in a variety of low-bit-rate speech codecs. The existing steganalysis methods for AbS-LPC low-bit-rate compressed speech steganography are specifically designed for one certain category of steganography methods, thus lacking generalization capability. In this paper, a common method for detecting multiple steganographies in low-bit-rate compressed speech based on a code element Bayesian network is proposed. In an AbS-LPC low-bit-rate compressed speech stream, spatiotemporal correlations exist between the code elements, and steganography will eventually change the values of these code elements. Thus, the method presented in this paper is developed from the code element perspective. It consists of constructing a code element Bayesian network based on the strong correlations between code elements, learning the network parameters by utilizing a Dirichlet distribution as the prior distribution, and finally implementing steganalysis based on Bayesian inference. Experimental results demonstrate that the proposed method performs better than the existing steganalysis methods for detecting multiple steganographies in the AbS-LPC low-bit-rate compressed speech.
Organizational justice and its impact on job satisfaction: Evidence from transportation industry

Organizational justice (OJ) has a significant impact on employees' job satisfaction which means the importance of caring and using it in any organization.OJ shows the importance of showing clear interest regarding the procedures related to work such as clearing the rights and duties for employees, providing the employees with opportunities to complain about the decisions, representations of information related to work, decisions and alternatives.Numerous studies are available on this, but Jordan scenario is different due to Arab economic and political conditions.Middle East is recognized due to richness of oil and transportation of oil to other parts of the world.Therefore, the performance of employees directly affects the whole world if the oil industry under performs.The proposed study of this paper is a quantitative survey that empirically verifies whether organizational justice has significant influence on job satisfaction.Data are collected from employees of logistic sector of Jordan city and they are analyzed by multiple regressions through SPSS.The results indicate that organizational justice has a significant impact on employees' job satisfaction.The distributive justice has no impact on job satisfaction which means that there is no effect of delegating authorities and sharing them with employees and no impact of distributing equal opportunities and work schedules on job satisfaction.The procedural justice had an impact on job satisfaction, which means the importance of showing clear interest regarding the procedures related to work.The effect of interactional justice on job satisfaction is found to be relatively high.The study recommends the managers to encourage working by team sole and build good relationships with all the workers, helping the employees to evaluate themselves and seeking to solve the disputes and conflicts fairly.
A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems

For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype’s user interface (UI) features and segmentation methodologies.
Students’ Motivation to Learn English Using Mobile Applications: The Case of Duolingo and Hello English

The development of language learning mobile application soft-wares (apps) for smart mobile devices such as smartphones and tablet computer has risen amazingly in these recent days. These apps were continuously reported to be highly potential to support the students to learn the target language. This study introduced two free English learning apps to the students and investigated how they made use of the apps to support their English learning. More specifically, it examined how their English motivation in learning English is, how they used the apps, and whether their different level of motivation influenced the time they spent in accessing the apps. The study was conducted in a mixed method approach utilizing daily journal and questionnaire as the instruments to collect data. The finding pointed that the students were considerably active in using both of the apps. They could enjoy the practicality and flexibility of learning English by the apps despite some superficial engagement. In addition, although studentsâ€™ English learning motivation was found to have no influence on the amount of time spent in using the apps, their positive response toward using the apps was a potential indication to foster independent English learning outside the classroom. A further detailed finding was discussed along with important issues in the current trends of mobile language learning.
Understanding User Behaviors When Phishing Attacks Occur

To study user security-related behaviors, we conduct an experimental study where participants take part in our experiments in a lab contained environment. We used a set of emails including phishing emails from the real world. We collect data including participants' basic information and time measurement. We check whether or not factors such as intervention, phishing types, and incentive mechanisms play a major role in user behaviors when phishing attacks occur.
Entanglement generation via diffraction

Quantum entanglement is an important resource for next-generation technologies. We show that diffracting systems can supplant beam splitters and, more generally, interferometric networks for entanglement generation---systems as simple as screens with pinholes can create entanglement. We then discuss the necessary and sufficient conditions for entanglement to be generated by states input to any passive linear interferometric network. Entanglement generated in free space can now be harnessed in quantum-optical applications ranging from quantum computation and communication to quantum metrology.
Education Needs in Research Data Management for Science-Based Disciplines

Research data management is a prominent and evolving consideration for the academic community, especially in scientific disciplines. This research study surveyed 131 graduate students and 79 faculty members in the sciences at two public doctoral universities to determine the importance, knowledge, and interest levels around research data management training and education. The authors adapted 12 competencies for measurement in the study. Graduate students and faculty ranked the following areas most important among the 12 competencies: ethics and attribution, data visualization, and quality assurance. Graduate students indicated they were least knowledgeable and skilled in data curation and re-use, metadata and data description, data conversion and interoperability, and data preservation. Their responses generally matched the perceptions of faculty. The study also examined how graduate students learn research data management, and how faculty perceive that their students learn research data management. Results showed that graduate students utilize self-learning most often and that faculty may be less influential in research data management education than they perceive. Responses for graduate students between the two institutions were not statistically different, except in the area of perceived deficiencies in data visualization competency.
Deep Multimodality Model for Multi-task Multi-view Learning

Many real-world problems exhibit the coexistence of multiple types of heterogeneity, such as view heterogeneity (i.e., multi-view property) and task heterogeneity (i.e., multi-task property). For example, in an image classification problem containing multiple poses of the same object, each pose can be considered as one view, and the detection of each type of object can be treated as one task. Furthermore, in some problems, the data type of multiple views might be different. In a web classification problem, for instance, we might be provided an image and text mixed data set, where the web pages are characterized by both images and texts. A common strategy to solve this kind of problem is to leverage the consistency of views and the relatedness of tasks to build the prediction model. In the context of deep neural network, multitask relatedness is usually realized by grouping tasks at each layer, while multi-view consistency is usually enforced by finding the maximal correlation coefficient between views. However, there is no existing deep learning algorithm that jointly models task and view dual heterogeneity, particularly for a data set with multiple modalities (text and image mixed data set or text and video mixed data set, etc.). In this paper, we bridge this gap by proposing a deep multi-task multi-view learning framework that learns a deep representation for such dual-heterogeneity problems. Empirical studies on multiple real-world data sets demonstrate the effectiveness of our proposed Deep-MTMV algorithm.MSC codesMulti-view LearningMulti-task LearningDeep learning
Copula and Case-Stacking Annotations for Korean AMR

This paper concerns the application of Abstract Meaning Representation (AMR) to Korean.In this regard, it focuses on the copula construction and its negation and the case-stacking phenomenon thereof.To illustrate this clearly, we reviewed the :domain annotation scheme from various perspectives.In this process, the existing annotation guidelines were improved to devise annotation schemes for each issue under the principle of pursuing consistency and efficiency of annotation without distorting the characteristics of Korean.
An Improved Red Deer Algorithm to Address a Direct Current Brushless Motor Design Problem

The Red Deer Algorithm (RDA) is one of recent metaheuristic algorithms inspired by the behavior of red deers during a breading season. The RDA revealed its performance for a variety of combinatorial optimization problems in different real-world applications. In this paper, the parameters and operators of RDA using some adaptive strategies have been modified to improve the performance of this optimizer. To prove the efficiency of Improved RDA (IRDA), not only some benchmarked functions are utilized but also a Direct Current (DC) brushless motor design as one of real-world engineering design issues. The results of developed IRDA have been compared with its general idea and existing algorithms from the literature. This comparative study confirms that the offered IRDA outperforms the other algorithms and provide very competitive results.