Reviewerly/exHarmony huggingface dataset:
Provide a model for establishing a comprehensive knowledge management system in knowledge-based organizations based on success factors

Purpose: This study provides a model for establishing a comprehensive knowledge management system in knowledge-based organizations based on success factors. Research methodology: A researcher-made questionnaire was used to examine these factors and finally to present the model. The statistical population of this study is all managers and employees of knowledge-based organizations. There are 150 of them and 108 people were randomly selected as the research sample. Result: The results of factor analysis showed that all 7 factors under study explain 65.16% of the total variance, which is acceptable. On the other hand, the results of the Friedman test also showed the first to the seventh priority of effective factors of knowledge success in construction projects, including human resource development, knowledge-based orientation, knowledge evaluation and transfer, information systems infrastructure, business culture, modeling. Finally, there was a conflict between people. A model was designed according to the mentioned priority. Limitations: This research only described knowledge-based organizations. Contribution: In this article, using a comprehensive knowledge management system, an attempt is made to provide a mechanism for establishing and implementing a comprehensive knowledge management system in knowledge-based organizations to help it take an important step towards capacity building to create value in processes and exchanges. The knowledge of the experts of the organization should be removed in order to direct the tacit knowledge of the experts, which is the main capital of any organization, towards this important issue.
Fast Training Logistic Regression via Adaptive Sampling

Logistic regression has been widely used in artificial intelligence and machine learning due to its deep theoretical basis and good practical performance. Its training process aims to solve a large-scale optimization problem characterized by a likelihood function, where the gradient descent approach is the most commonly used. However, when the data size is large, it is very time-consuming because it computes the gradient using all the training data in every iteration. Though this difficulty can be solved by random sampling, the appropriate sampled examples size is difficult to be predetermined and the obtained could be not robust. To overcome this deficiency, we propose a novel algorithm for fast training logistic regression via adaptive sampling. The proposed method decomposes the problem of gradient estimation into several subproblems according to its dimension; then, each subproblem is solved independently by adaptive sampling. Each element of the gradient estimation is obtained by successively sampling a fixed volume training example multiple times until it satisfies its stopping criteria. The final estimation is combined with the results of all the subproblems. It is proved that the obtained gradient estimation is a robust estimation, and it could keep the objective function value decreasing in the iterative calculation. Compared with the representative algorithms using random sampling, the experimental results show that this algorithm obtains comparable classification performance with much less training time.
Requirements prioritization aspects quantification for value-based software developments

Requirements prioritization considered as an important activity in requirements engineering, helps in decision making for software development. Requirements prioritization is performed to select the candidate requirements for different software releases. Different prioritization techniques are available in literature to facilitate experts for requirements prioritization in the industry. It is also evident from the literature that different requirements prioritization aspects are considered to support the process of prioritization. Consideration of these aspects is more worth-fuller in the domain of value-based software engineering where the success of the system depends on the success of software. These aspects are reviewed from literature and summarized accordingly to improve the process of requirements prioritization. Moreover, these aspects are classified and quantified as technical aspects and business aspects based on relevance towards prioritization process. Further efforts are made to validate these aspects and their logical grouping from industry experts through survey. Most of the existing techniques in general and value-based software development in specific missing consideration of these aspects. This research contribution is an effort to highlight and summarize identification and quantification of possible aspects to be a part of requirements prioritization process.
Interaction by Hand-Tracking in a Virtual Reality Environment

This article describes the elaboration of a low-cost system that allows user interaction with VR environments modeled in Unity and viewed through Google Cardboard. The proposed system divides the processing in a client-server architecture in which the VR experience is executed on the client, while it sends pictures captured from the mobile device inside the Card Board to the server. This server processes the hand pose information and sends it to the client.
Uncertainty principles in term of supports in Hankel wavelet setting

Uncertainty principles in term of supports
Multilingual Simultaneous Neural Machine Translation

Simultaneous machine translation (SIMT) involves translating source utterances to the target language in real-time before the speaker utterance completes.This paper proposes the multilingual approach to SIMT, where a single model simultaneously translates between multiple language-pairs.This not only results in more efficiency in terms of the number of models and parameters (hence simpler deployment), but may also lead to higher performing models by capturing commonalities among the languages.We further explore simple and effective multilingual architectures based on two strong recently proposed SIMT models.Our results on translating from two Germanic languages (German, Dutch) and three Romance languages (French, Italian, Romanian) into English show (i) the single multilingual model is on-par or better than individual models, and (ii) multilingual SIMT models trained based on language families are on-par or better than the universal model trained for all languages. 1
Hierarchically Structured Network With Attention Convolution and Embedding Propagation for Imbalanced Few-Shot Learning

Generally, a few-shot distribution shift will lead to a poor generalization. Furthermore, while the number of instances of each class in the real world may significantly different, the existing few-shot classification methods are based on the assumption that the number of samples in each class is equal, which causes the trained classifier invalid. Moreover, through ResNet and WRN (Wide Residual Network) have achieved great success in the image processing field, the depth and width of CNNs constrain the conventional convolution layer performance. Thus, to overcome the above problems, the model of this paper proposes a novel few-shot classification model that uses learning balance variables to decide how much to learn from the imbalance dataset, which dynamically generates the convolution kernel based on each input. In our model, to extend the decision boundaries and enhance the class representations, this paper uses embedding propagation as a regularizer for manifold smoothing. Manifold smoothing can effectively solve the above problems of transductive learning. The interpolations between neural network features based on similarity graphs are used by embedding propagation. Experiments show that embedding propagation can produce a better embedding manifold and our model in standard few-shot datasets, such as miniImagenet, tieredImagenet, CUB has state-of-the-art results. It significantly outperforms the existing few-shot approaches, which consistently improves the accuracy of the models by about 11%.
MOALLEMCorpus: A Large-Scale Multimedia Corpus for Children Education of Arabic Vocabularies

The education of children with learning difficulties is a challenging task especially during COVID-19 pandemic. In fact, these children need to go regularly to specialized school, receive focused education, and interact with teachers to learn. Instructors allocate important time to teach them and use different approaches including, attractive stories, tangible photos, physical plays, sites visit, awards and gifts. However, these modes of education become nowadays very hard to achieve as instructors are teaching from homes or offices through the Internet. They cannot have face-to-face meetings with children in classrooms. The major issue is how to find the necessary materials to teach them the new Arabic vocabularies and explain their meanings in an effective manner. Instructors can use textbooks, online libraries and search engines looking for Arabic educational resources while most of them are in English or in other western languages. The process is very long, time consuming and does not fill-in the gap. We propose to build a new educational large-scale multimedia Arabic corpus that provides thousands of vocabularies and chunks associated with best representative images. The instructors can use the bimodal corpus directly during the learning sessions to explain new Arabic words through images. It currently covers the animals' domain and contains thousands of well-structured and interconnected entities. Instructors can collaborate in enhancing the corpus by adding new materials through a web-based platform and build then rich source of learning materials.
Review Paper of Human Activity Recognition using Smartphone

Human recogntion technologies are gaining significant research attention, where the model can be trained to be more precise to recognize the poses performed by objects. Activity identification is a kind of problem, which needs more research consideration and improvement. There is also an increasing need to detect different poses of objects. To tackle the issue, different sensors like Gyroscope and Accelerometer are required to classify the data in the form of images using machine learning algorithms like SVM and CNN. These approaches help us in implementing real-time applicaions such as health monitoring, tracking sports activity and security. The paper also discusses about its benefits, limitations and prominent approach for human activity recognition.
MLE-Guided Parameter Search for Task Loss Minimization in Neural Sequence Modeling

Neural autoregressive sequence models are used to generate sequences in a variety of natural language processing (NLP) tasks, where they are evaluated according to sequence-level task losses. These models are typically trained with maximum likelihood estimation, which ignores the task loss, yet empirically performs well as a surrogate objective. Typical approaches to directly optimizing the task loss such as policy gradient and minimum risk training are based around sampling in the sequence space to obtain candidate update directions that are scored based on the loss of a single sequence. In this paper, we develop an alternative method based on random search in the parameter space that leverages access to the maximum likelihood gradient. We propose maximum likelihood guided parameter search (MGS), which samples from a distribution over update directions that is a mixture of random search around the current parameters and around the maximum likelihood gradient, with each direction weighted by its improvement in the task loss. MGS shifts sampling to the parameter space, and scores candidates using losses that are pooled from multiple sequences. Our experiments show that MGS is capable of optimizing sequence-level losses, with substantial reductions in repetition and non-termination in sequence completion, and similar improvements to those of minimum risk training in machine translation.
A Generative Adversarial Framework for Bounding Confounded Causal Effects

Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounding based on Pearl's structural causal model. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, using an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution, and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make assumption about the type of structural equations and variables. Experiments using both synthetic and real-world datasets are conducted.
CogNet: Bridging Linguistic Knowledge, World Knowledge and Commonsense Knowledge

In this paper, we present CogNet, a knowledge base (KB) dedicated to integrating three types of knowledge: (1) linguistic knowledge from FrameNet, which schematically describes situations, objects and events. (2) world knowledge from YAGO, Freebase, DBpedia and Wikidata, which provides explicit knowledge about specific instances. (3) commonsense knowledge from ConceptNet, which describes implicit general facts. To model these different types of knowledge consistently, we introduce a three-level unified frame-styled representation architecture. To integrate free-form commonsense knowledge with other structured knowledge, we propose a strategy that combines automated labeling and crowdsourced annotation. At present, CogNet integrates 1,000+ semantic frames from linguistic KBs, 20,000,000+ frame instances from world KBs, as well as 90,000+ commonsense assertions from commonsense KBs. All these data can be easily queried and explored on our online platform, and free to download in RDF format for utilization under a CC-BY-SA 4.0 license. The demo and data are available at http://cognet.top/.
Identification of Breast Cancer from Thermal Imaging using SVM and Random Forest Method

In the recent years, the carcinoma disease in women has significantly observed and it has also caused deaths as per WHO 2018 report of carcinoma statistics recorded approximately 2 lakhs registered cases and around 90000 reported deaths. The speed of survival has become very difficult at higher stages of growth and quite 45% of women's in India suffer from stage 3 and 4 of carcinoma. The target of this research is to deliver a report on carcinoma on the basis of the performance of Support Vector Machine [SVM] methodology and random forest using 5 folds, 10 folds, 20 folds with a training set size 50, 60, 70, 80 and 90 respectively. These techniques have achieved an accuracy of 94.5% and 98.40% through the cross validation of Support Vector Machine [SVM] and Random Forest [RF] method.
Beyond Sentence-Level End-to-End Speech Translation: Context Helps

Document-level contextual information has shown benefits to text-based machine translation, but whether and how context helps endto-end (E2E) speech translation (ST) is still under-studied.We fill this gap through extensive experiments using a simple concatenationbased context-aware ST model, paired with adaptive feature selection on speech encodings for computational efficiency.We investigate several decoding approaches, and introduce inmodel ensemble decoding which jointly performs document-and sentence-level translation using the same model.Our results on the MuST-C benchmark with Transformer demonstrate the effectiveness of context to E2E ST.Compared to sentence-level ST, context-aware ST obtains better translation quality (+0.18-2.61BLEU), improves pronoun and homophone translation, shows better robustness to (artificial) audio segmentation errors, and reduces latency and flicker to deliver higher quality for simultaneous translation. 1
Comprehensive Integration of API Usage Patterns

Nowadays, developers often reuse existing APIs to implement their programming tasks. A lot of API usage patterns are mined to help developers learn API usage rules. However, there are still many missing variables to be synthesized when developers integrate the patterns into their programming context. To deal with this issue, we propose a comprehensive approach to integrate API usage patterns in this paper. We first perform an empirical study by analyzing how API usage patterns are integrated in real-world projects. We find the expressions for variable synthesis is often non-trivial and can be divided into 5 syntax types. Based on the observation, we promote an approach to help developers interactively complete API usage patterns. Compared to the existing code completion techniques, our approach can recommend infrequent expressions accompanied with their real-world usage examples according to the user intent. The evaluation shows that our approach could assist users to integrate APIs more efficiently and complete the programming tasks faster than existing works.
Neural-Symbolic Commonsense Reasoner with Relation Predictors

Commonsense reasoning aims to incorporate sets of commonsense facts, retrieved from Commonsense Knowledge Graphs (CKG), to draw conclusion about ordinary situations.The dynamic nature of commonsense knowledge postulates models capable of performing multi-hop reasoning over new situations.This feature also results in having large-scale sparse Knowledge Graphs, where such reasoning process is needed to predict relations between new events.However, existing approaches in this area are limited by considering CKGs as a limited set of facts, thus rendering them unfit for reasoning over new unseen situations and events.In this paper, we present a neural-symbolic reasoner, which is capable of reasoning over large-scale dynamic CKGs.The logic rules for reasoning over CKGs are learned during training by our model.In addition to providing interpretable explanation, the learned logic rules help to generalise prediction to newly introduced events.Experimental results on the task of link prediction on CKGs prove the effectiveness of our model by outperforming the state-of-the-art models.
Robotic Manipulation with Reinforcement Learning, State Representation Learning, and Imitation Learning (Student Abstract)

Humans possess the advanced ability to grab, hold, and manipulate objects with dexterous hands. What about robots? Can they interact with the surrounding world intelligently to achieve certain goals (e.g., grasping, object-relocation)? Actually, robotic manipulation is central to achieving the premise of robotics and represents immense potential to be widely applied in various scenarios like industries, hospitals, and homes. In this work, we aim to address multiple robotic manipulation tasks like grasping, button-pushing, and door-opening with reinforcement learning (RL), state representation learning (SRL), and imitation learning. For diverse missions, we self-built the PyBullet or MuJoCo simulated environments and independently explored three different learning-style methods to successfully solve such tasks: (1) Normal reinforcement learning methods; (2) Combined state representation learning (SRL) and RL approaches; (3) Imitation learning bootstrapped RL algorithms.
Hybrid dual stream blender for wide baseline view synthesis

Free navigation of a scene requires warping some reference views to some desired target viewpoint and blending them to synthesize a virtual view. Convolutional Neural Networks (ConvNets) based methods can learn both the warping and blending tasks jointly. Such methods are often designed for moderate inter-camera baseline distance and larger kernels are required for warping if the baseline distance increases. Algorithmic methods can in principle deal with large baselines, however the synthesized view suffers from artifacts near disoccluded pixels. We present a hybrid approach where first, reference views are algorithmically warped to the target position and then are blended via a ConvNet. Preliminary view warping allows reducing the size of the convolutional kernels and thus the learnable parameters count. We propose a residual encoder–decoder for image blending with a Siamese encoder to further keep the parameters count low. We also contribute a hole inpainting algorithm to fill the disocclusions in the warped views. Our view synthesis experiments on real multiview sequences show better objective image quality than state-of-the-art methods due to fewer artifacts in the synthesized images.
Situational Awareness of COVID Pandemie data using Virtual Reality

Situational awareness provides the decision making capability to identify, process, and comprehend big data. In our approach, situational awareness is achieved by integrating and analyzing multiple aspects of data using stacked bar graphs and geographic representations of the data. We provide a data visualization tool to represent COVID pandemic data on top of the geographical information. The combination of geospatial and temporal data provides the information needed to conduct situational analysis for the COVTD-19 pandemic. By providing interactivity, geographical maps can be viewed from different perspectives and offer insight into the dynamical aspects of the COVTD-19 pandemic for the fifty states in the USA. We have overlaid dynamic information on top of a geographical representation in an intuitive way for decision making. We describe how modeling and simulation of data increase situational awareness, especially when coupled with immersive virtual reality interaction. This paper presents an immersive virtual reality (VR environment and mobile environment for data visualization using Oculus Rift head-mounted display and smartphones. This work combines neural network predictions with human-centric situational awareness and data analytics to provide accurate, timely, and scientific strategies in combatting and mitigating the spread of the coronavirus pandemic. Testing and evaluation of the data visualization tool have been done with realtime feed of COVID pandemic data set for immersive environment, non-immersive environment, and mobile environment.
COSY: COunterfactual SYntax for Cross-Lingual Understanding

Pre-trained multilingual language models, e.g., multilingual-BERT, are widely used in crosslingual tasks, yielding the state-of-the-art performance.However, such models suffer from a large performance gap between source and target languages, especially in the zero-shot setting, where the models are fine-tuned only on English but tested on other languages for the same task.We tackle this issue by incorporating language-agnostic information, specifically, universal syntax such as dependency relations and POS tags, into language models, based on the observation that universal syntax is transferable across different languages.Our approach, named COunterfactual SYntax (COSY), includes the design of SYntaxaware networks as well as a COunterfactual training method to implicitly force the networks to learn not only the semantics but also the syntax.To evaluate COSY, we conduct cross-lingual experiments on natural language inference and question answering using mBERT and XLM-R as network backbones.Our results show that COSY achieves the stateof-the-art performance for both tasks, without using auxiliary dataset. 1 "I love apples ."