Reviewerly/exHarmony huggingface dataset:
From Deep Image Decomposition to Single Depth Image Super-Resolution

Although many computer vision tasks such as autonomous driving and robot autonomous navigation as well as object recognition and grasping need to use accurate depth information to improve performance, the captured depth images from practical scene are always troubled by low-resolution and contamination. To resolve this problem, we propose a deep single depth image super-resolution method, which includes three parts: depth dual decomposition block, depth image initialization block and depth image rebuilding block. First, we propose a deep dual decomposition network to separate single low-resolution depth image into two high-resolution parts: fine-detail and coarse-structure images with high quality. Second, weighted fusion mechanism is proposed in depth image rebuilding block for feature integration. Finally, these fused features are fed into residual learning-based reconstruction block in depth image rebuilding block to produce high-quality depth image. Experimental results demonstrate that the proposed method can outperform several state-of-the-art depth map super-resolution methods in term of root mean squared error.
Real Time 3D Capturing System for Quality Analysis

A real time 3D capture system for an object is designed and developed in this paper. In a fast-moving world where whatever that is seen around is three dimensional, most of the techniques which are devised to study an object gives a two-dimensional output. In other words, a clear idea about such spatial objects is obtained rarely. The existing techniques which are available, moreover gives an output which is not real-time, that is when an object is subjected for study, the intended results cannot be obtained in a parallel manner. In real time 3D capture, the focus is on studying three- dimensional objects in real-time. This proposal solely accentuates human perception to a different perspective. The primary goal of the technique that is inculcated in this project is to recreate the purpose of the human eye through a capturing system. This is served by the technique of stereovision. This involves distance calculation to an object from point of observation. As a result, the depth is calculated in 3D space. The system consists of two camera modules synchronized together which are further connected to a processor(computer) and output is fed to a display unit. This system brings off the task of quality analysis. The locus point in quality analysis is to find the physical deformities. Predominantly, this system is devised to equip and encourage the small scale production units or factories to aid them in an inclusive way.
Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques

The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96% rating yield using the proposed methodology with the Fast Tree algorithm.
Few-Shot Crowd Counting via Self-supervised Learning

Crowd counting has been developed significantly, attributing to the booming of deep learning. However, deep learning based methods are extreme data consuming, and labeling dataset for crowd counting is a sophisticated task. Both the number and the density of people in an image can be very large while the resolution is too low. Meanwhile, collecting crowd pictures without label is much easier. In this paper, we propose a few-shot crowd counting method based on self-supervised learning to leverage these unlabeled data. We firstly collect and clean an unlabeled dataset consisting of crowd images. And two self-supervised assistant tasks are designed with the purpose of training a backbone model to extract the crowd-related features from those unlabeled images. The parameters of the backbone model are then utilized as an initialization for the crowd counting model, which is further fine-tuned with only very few annotated images. We conduct experiments with different amounts of annotated data. Results have demonstrated the effectiveness of the proposed approach.
Virtual Education: University Students’ Experiences in the Time of Covid-19

The purpose of this study is to unveil the significant experiences about e-learning that students at a private university in Lima had. The information was collected through a virtual forum with the participation of 43 students from the first cycle of university education and according to the inclusion and exclusion criteria, 10 students were selected as the unit of analysis. An interpretative hermeneutic analysis was also carried out using Atlasti.8 as a tool. The main findings refer to making teaching processes more dynamic and flexible, incorporating collaborative virtual learning strategies, creating new synchronous and asynchronous working conditions, in accordance with the existing situation through distance classes via Covid-19.
Low-light-level image pixel super-resolution reconstruction method

Wide field-of-view (FOV) and high-resolution (HR) imaging systems have become indispensable information acquisition equipment in many applications, such as video surveillance, target detection and remotely sensed imagery. However, due to the constraints of spatial sampling and detector processing level, the ability of remote sensing to obtain high spatial resolution is limited, especially in the wide FOV imaging. To solve these problems, we propose a multi-scale feature extraction (MSFE) network to realize super-resolution imaging in a low-light- level (LLL) environment. In order to perform data fusion and information extraction for low resolution (LR) images, the network extracts high-frequency detail information from different dimensions by combining the channel attention mechanism module and skip connection module. In this way, redundant low-frequency signals can pass through the network tail-ends, furthermore, the more important high-frequency components calculation can be focused. The qualitative and quantitative analysis results show that the proposed method achieves the most advanced performance compared with other state-of-the-art methods, which shows the superiority of the design framework and the effectiveness of presenting modules.
Operation Diagnosis on Procedure Graph

Users usually consult the manufacturers or the internet when they encounter operation questions with an electronics product. In this paper, we explore to represent an operation question as a procedure graph and formulate the problem of operation diagnosis as two sub-tasks, namely error node detection, and correction, on top of the graph. We construct the first benchmark for this task and propose a transformer-based model to integrate external knowledge and context information to enhance the performance. Experimental results show the effectiveness of our proposed model.
Relation-Guided Actor Attention for Group Activity Recognition

Group activity recognition has received significant interest due to its widely practical applications in sports analysis, intelligent surveillance and abnormal behavior detection. In a complex multi-person scenario, only a few key actors participate in the overall group activity and others may bring irrelevant information for recognition. However, most previous approaches model all the actors' actions in the scene equivalently. To this end, we propose a relation-guided actor attention (RGAA) module to learn reinforced feature representations for effective group activity recognition. First, a location-aware relation module (LARM) is designed to explore the relation among pairwise actors' features in which appearance and position information are both considered. We propose to stack all the pairwise relation features and the features themselves of an actor to learn actor attention which determines the importance degree from local and global information. Extensive experiments on two publicly benchmarks demonstrate the effectiveness of our method and the state-of-the-art performance is achieved.
Strong Influence of Responses in Training Dialogue Response Generator

The sequence-to-sequence model is a widely used model for dialogue response generators, but it tends to generate safe responses for most input queries. Since safe responses are unattractive and boring, a number of efforts have been made to make the generator produce diverse responses, but generating diverse responses is yet an open problem. As a solution to this problem, this paper proposes a novel response generator, Response Generator with Response Weight (RGRW). The proposed response generator is a transformer-based sequence-to-sequence model of which the encoder is a pre-trained Bidirectional Encoder Representations from Transformers (BERT) and the decoder is a variant of Generative Pre-Training of a language model-2 (GPT-2). Since the attention on the response is not reflected enough at the transformer-based sequence-to-sequence model, the proposed generator enhances the influence of a response by the response weight, which determines the importance of each token in a query with respect to the response. Then, the decoder of the generator processes the response weight as well as a query encoding to generate a diverse response. The effectiveness of RGRW is proven by showing that it generates more diverse and informative responses than the baseline response generator by focusing more on the tokens that are important for generating the response. Additionally, the proposed model overwhelms the Commonsense Knowledge-Aware Dialogue generation model (ConKADI), which is a state-of-the-art model.
Meta-Learning a Solution to the Hanabi Ad-Hoc Challenge

In this work we demonstrate that the First Order Model Agnostic Meta Learning (FOMAML) algorithm trained on the Hanabi Open Agent Dataset (HOAD) results in a model that is able to outplay both a naive MLP baseline, as well as a randomly selected partner in the Hanabi Ad-Hoc Challenge, in both low-shot and zero-shot setups. We first show that HOAD is well suited for the meta-learning task because its agents are high quality and utilize diverse strategies, thereby confirming that MAML is generalizing, and not memorizing agent strategies. We then detail our application of FOMAML to the cooperative decision making problem Hanabi entails, and we also provide evidence supporting recent results that the task update of MAML gives little to no test time performance boost. The pretrained models and game data are made available online at https://github.com/aronsar/hoad.
SAUCE

Recent advances in text representation have shown that training on large amounts of text is crucial for natural language understanding. However, models trained without predefined notions of topical interest typically require careful fine-tuning when transferred to specialized domains. When a sufficient amount of within-domain text may not be available, expanding a seed corpus of relevant documents from large-scale web data poses several challenges. First, corpus expansion requires scoring and ranking each document in the collection, an operation that can quickly become computationally expensive as the web corpora size grows. Relying on dense vector spaces and pairwise similarity adds to the computational expense. Secondly, as the domain concept becomes more nuanced, capturing the long tail of domain-specific rare terms becomes non-trivial, especially under limited seed corpora scenarios.
7.5nJ/inference CMOS Echo State Network for Coronary Heart Disease prediction

This work presents the first on-chip, mixed-signal echo state network (ESN) for early prediction of heart disease. The ESN comprises an input layer, a non-linear projection (NP) layer, and an output layer. Only the output layer of the ESN requires training. The input layer weights are time-invariant and drawn from a static binary random distribution. Thus, the proposed ESN has significantly lower trainable parameters compared to other non-linear neural networks used for similar prediction tasks. A 65nm prototype is validated with the Cleveland Heart Disease (CHD) dataset. The ESN achieves a mean accuracy of 84.6% over 5 test chips while consuming 7.5nJ energy/inference.
Crypto-Currency: Is the Future Dark or Bright

Purpose: This study reviews the research work that has been carried out in the field of cryptocurrency and financial risk during past five years (2013-2018). This paper tracks and reviews the research work publish in leading academic journal in above mentioned journal. It then reviews from pragmatic and methodological approach. Design methodology and approach: this study analyses the selected contribution from the perspective of “Financial risk in cryptocurrency.” And further compares various studies on the article type and methodologies and research designs used in them. Using relational analysis approach recurrent themes are identified and research clusters are formed. Findings: The analysis leads to the identification of the major parameters namely: Financial risk, market risk, liquidity risk and operational risk and these rea the parameters that are impacting the values of cryptocurrency. We have also analysed the issues and challenges in the cryptocurrency market of India. Implications: We have tried to propose a model and collected the information from various reports and papers. We have also find out that various risk which are associated with CC market are controllable in nature on the other hand some of them are uncontrollable in nature. Originality value: Most reviews rely on bibliometric analysis or simple thematic analysis for literature review. This study provides greater insights by using a research paradigm framework to categorize and summarize the literature. This approach provides a comprehensive yet concise picture of the literature to future researchers.
Comparative study of two target detection algorithms in UAV aerial photography detection

In this paper, the Faster R-CNN algorithm and YOLOv3 algorithm are researched and practiced based on the remote sensing image data sets. Using the same data sets and hardware environment, it mainly evaluates the average accuracy and the time-consuming for detection of the target objects in the data sets. These algorithm evaluation indicators evaluate the relative applicability of the two algorithms in practical applications. The reasons are also analyzed for the deficiencies of the two algorithms in the target detection process. It is concluded that the Faster R-CNN algorithm is more suitable for practical applications that require higher target detection accuracy, and the YOLOv3 is more suitable for practical applications that require less time-consuming.
Fuzzy Baselines to Stabilize Policy Gradient Reinforcement Learning

Policy gradient methods are amongst the most efficient for on-policy, model-free reinforcement learning. However, they suffer from high variance in gradient updates, making them unstable during training. Subtracting a baseline from the rewards is an effective strategy to reduce variance, such as in actor-critic models. This work presents a variation of the actor-critic model that uses a fuzzy system instead of a neural network to estimate the state value function. The fuzzy value approximation is inspired by previous value-based methods such as fuzzy Q-learning. Experiments with the cart-pole benchmark show that fuzzy value approximation outperforms several reinforcement learning algorithms in terms of sample-efficiency.
DASP: Distributed and Autonomic Scientific Publisher Proposal for Editorial Process Management on Permissioned Blockchain

This paper presents an open-source Distributed Autonomic Scientific Publisher, which works on a permissioned blockchain network, automatically organized through a smart contract that organizes the whole business logic of the tool, allowing the auditing of the whole process, ensuring the copyrights for the students involved in the publication of each article, besides reducing the implementation costs and the complexity of the editorial mechanisms. The proof of concept was developed as an editorial management tool with the basic processes of submission, review, and evaluation.
A Hybrid Approach for Offline A/B Evaluation for Item Ranking Algorithms in Recommendation Systems

A recommendation system generally outputs a ranked list of items which is presented to the user. Based on the consumption signals from the user (like click, play) in an production environment, various performance metrics like Click Through Rate (CTR), Play Through Rate (PTR), Average Consumption Time etc. are calculated. These metrics are used to objectively evaluate performance of various underlying algorithms (policies) using Online A/B tests. However, if there are many such policies in the innovation pipeline, evaluating them using Online A/B puts significant overhead on production systems and can cause user dissatisfaction if a poor policy is exposed to them. Therefore, pre-production testing in an offline environment has become a highly researched area of significant practical value. During "offline A/B" testing, we are interested in comparing multiple policies based on their potential improvement towards the performance metrics which are most correlated with business KPIs. On the other hand, user's satisfaction is also closely related to the ranked occurrence of items, i.e., the relevant items should occur towards the top of the list. Current well known methods based on counterfactual estimation such as importance sampling and its variants capped importance sampling, normalized capped importance sampling consider only a single performance metric for evaluation and do not consider the user-satisfaction metrics, which could lead to sub-optimal user experience. Further the position bias due to limited real-estate on mobile devices is not considered. To solve these issues, we extend the importance sampling based methods by combining both performance and user satisfaction metrics along with accounting for position bias. We also demonstrate that using such a hybrid metric during offline testing leads to improved correlation with desired business metrics, thus enabling better offline comparison of ranking algorithms.
Automatic Question-Answer Generation from Video Lecture using Neural Machine Translation

Video lectures perform very crucial role in learning nowadays, from video or online lectures a learner can retrieve content or gain knowledge anytime and anywhere. The paper presents Question and Answer Generation from video or online lectures automatically, from such videos the learner can be able to self-assist himself. The main goal of the presented project is to convert video speech into text, and that text is applied for Question-and-Answer Generation Automatically after selecting only highly instructive sentences from the text. Conventional approaches have mainly used heuristic rules that convert a sentence into questions. The proposed Encoder-decoder neural network model which generates significant as well as different questions from the sentences. The encoder is used to read the input text with the position of answer. The output of encoder is fed to the decoder to generates question with answers using SQuAD dataset.
A class of dependent Dirichlet processes via latent multinomial processes

We describe a procedure to introduce general dependence structures on a set of Dirichlet processes. Dependence can be in one direction to define a time series or in two directions to define spatial dependencies. More directions can also be considered. Dependence is induced via a set of latent processes and exploit the conjugacy property between the Dirichlet and the multinomial processes to ensure that the marginal law for each element of the set is a Dirichlet process. Dependence is characterized through the correlation between any two elements. Posterior distributions are obtained when we use the set of Dirichlet processes as prior distributions in a Bayesian nonparametric context. Posterior predictive distributions induce partially exchangeable sequences defined by generalized Pólya urns. A numerical example to illustrate is also included.
Are 20% of Classes Responsible for 80% of Refactorings?

The 80-20 rule is well-known in the real-world. When applied to bugs, it suggests that 80% of bugs arise in just 20% of classes. One research question that has yet to be explored is whether the same rule applies to refactoring activity. In other words, do 20% of classes account for 80% of refactorings applied to a system? In this short paper, we explore this question using data from seven open-source systems drawn from two previous studies. In each case, we explore whether the 80-20 rule applies and suggest why. Results showed limited evidence of an 80-20 rule; in the two systems where it was evident, the refactoring profile implied firstly, a large-scale movement of class fields and methods and, secondly, the deliberate aim of collapsing the class hierarchy using inheritance-based refactorings.
THE TRANSLATION OF INFORMATION PATTERN FOR I CARE A LOT MOVIE SUBTITLE FROM ENGLISH TO INDONESIAN

Movie subtitle translation is the transfer of meaning from source text into target text in the form of text under the screen with limited time and characters. One of the problems in translating movie subtitle is the information pattern. Information patterns are how the information is organized. This information arrangement includes information status and information urgency. This research uses descriptive and comparative methods. The results of the study indicate that (i) there are parallels in information pattern urgency, namely foreground position tends to be in the beginning of the speech; (ii) the misalignment of information status occurs in sentences translation with it subject and that impersonal and in interrogative and imperative sentences translation, as well as in sentences translation with the non-doer subject.
An Interview Transcriber Using Natural Language Processing

During the challenging times of COVID 19, major shifts in the learning and interaction process have been observed especially in the field of education and platforms of wider interactions like seminars. Online medium has been emerging as the order of the day. In this context, this paper looks at a new challenge that is being faced by various institutions while carrying out the interview process or interactions online or on a telephone. The recruiter might miss certain points when evaluating a candidate or sometimes the finer details of an interview are lost in communication, the interviewer might also not be able to recall the details of a particular interviewee at a later point in time. These are some of the challenges that would ultimately reduce the effectiveness of an interview process. We are developing a platform that extracts all the essential information delivered by the person who is being interviewed from the data received by the candidate during the process. The paper makes use of Natural Language Processing (NLP) to extract the key information from an interaction by extracting certain features or important key points by making suitable algorithms with the help of language dependencies. The paper also dealt with the outcome of the interview in the sense that, whether the candidate got selected or not based on her/his answers and how similar they are with the data provided by the company which clearly puts forward what they are looking for in an employee.