Reviewerly/exHarmony huggingface dataset:
A state-of-the-art survey on recommendation system and prospective extensions

With the new era of the Internet, we have a large amount of data available in the form of ratings, reviews, graphs, images, etc. However, still, people face difficulty in finding useful information or knowledge from those data. To address these challenges, recommendation systems come into the picture by providing useful content to the user based on users’ history and similarity among users. Content-based and collaborative filtering are two major building blocks of recommendation systems. Recommendation systems have been applied into numbers of a domain such as recommending movies, music, course, literature, items, people, links, location, healthcare, agriculture. In the agriculture domain, appropriate crops to cultivate and selecting applicable pesticides based on land quality and types of crops are interesting factors to consider for a country like India. Initially, we review different types of recommendation systems along with its application area. Subsequently, we explore various parameters to evaluate recommendation systems followed by open issues and research challenges. We further study the work carried out by existing researchers in the said domain. As part of our contribution through this research, we have selected the Agriculture domain and proposed our algorithm for recommending crops based on various parameters. As an outcome of our contribution, a crop is recommended to farmers based on his land. Also, the system recommends a list of lands for a given crop. Using statistical analysis, we achieve accuracy from 93% to 97%.
Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods

As Photovoltaic (PV) energy is impacted by various weather variables such as solar radiation and temperature, one of the key challenges facing solar energy forecasting is choosing the right inputs to achieve the most accurate prediction. Weather datasets, past power data sets, or both sets can be utilized to build different forecasting models. However, operators of grid-connected PV farms do not always have full sets of data available to them especially over an extended period of time as required by key techniques such as multiple regression (MR) or artificial neural network (ANN). Therefore, the research reported here considered these two main approaches of building prediction models and compared their performance when utilizing structural, time-series, and hybrid methods for data input. Three years of PV power generation data (of an actual farm) as well as historical weather data (of the same location) with several key variables were collected and utilized to build and test six prediction models. Models were built and designed to forecast the PV power for a 24-hour ahead horizon with 15 min resolutions. Results of comparative performance analysis show that different models have different prediction accuracy depending on the input method used to build the model: ANN models perform better than the MR regardless of the input method used. The hybrid input method results in better prediction accuracy for both MR and ANN techniques, while using the time-series method results in the least accurate forecasting models. Furthermore, sensitivity analysis shows that poor data quality does impact forecasting accuracy negatively especially for the structural approach. • Analysed ANN and MR PV power forecasting performance over structural, time-series, and hybrid input data methods. • To ensure realistic results, weather and production data was collected over 3 years for a 546 kWp grid-connected PV farm. • The six models were built and tested under regulatory conditions of 24-hour PV power prediction with 15 mins resolution. • ANN models perform better regardless of input method, while hybrid input method is better in accuracy for both MR and ANN. • Results help PV farm operators to achieve better forecasting accuracy depending on the data available to them.
Towards a Notion of Agent Coordination Context

On the one hand, recent studies on the history of human societies suggest that the role of the environment has to be taken explicitly into account in order to understand evolution of individuals and groups in any non-trivial setting. On the other hand, the notion of context is well-known and relevant to several research areas such as natural language, philosophy, logic, and artificial intelligence. In these areas, contexts are typically used to model the effect of the environment (in its most general sense, including the spatial and temporal interpretation of the term) on the communication occurring amongst active entities, such as humans or artificial agents. Generalising upon the recently introduced notion of context-dependent coordination, in this seminal paper we propose the notion of agent coordination context as a means to model and shape the space of agent interaction and communication. From a theoretical perspective, agent coordination contexts can serve the purposes of (i) enabling agents to model the environment where they interact and communicate (the subjective viewpoint), and (ii) providing a framework to express how the environment affects interpretation of agent communication acts (the objective viewpoint). From an engineering perspective, the notion of agent coordination context enables in principle agents to perceive the space where they act and interact, reason about the effect of their actions and communications, and possibly affect their environment to achieve their goals. Also, agent coordination contexts allow engineers to encapsulate rules for governing applications built as agent systems, mediate the interactions amongst agents and the environment, and possibly affect them so as to change global application behaviour incrementally and dynamically.
A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks

Empirical studies show that gradient-based methods can learn deep neural networks (DNNs) with very good generalization performance in the over-parameterization regime, where DNNs can easily fit a random labeling of the training data. Very recently, a line of work explains in theory that with over-parameterization and proper random initialization, gradient-based methods can find the global minima of the training loss for DNNs. However, existing generalization error bounds are unable to explain the good generalization performance of over-parameterized DNNs. The major limitation of most existing generalization bounds is that they are based on uniform convergence and are independent of the training algorithm. In this work, we derive an algorithm-dependent generalization error bound for deep ReLU networks, and show that under certain assumptions on the data distribution, gradient descent (GD) with proper random initialization is able to train a sufficiently over-parameterized DNN to achieve arbitrarily small generalization error. Our work sheds light on explaining the good generalization performance of over-parameterized deep neural networks.
A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices

Deep learning (DL) is a hot topic in current pattern recognition and machine learning. DL has unprecedented potential to solve many complex machine learning problems and is clearly attractive in the framework of mobile devices. The availability of powerful pattern recognition tools creates tremendous opportunities for next-generation smart applications. A convolutional neural network (CNN) enables data-driven learning and extraction of highly representative, hierarchical image features from appropriate training data. However, for some data sets, the CNN classification method needs adjustments in its structure and parameters. Mobile computing has certain requirements for running time and network weight of the neural network. In this paper, we first design an image processing module for a mobile device based on the characteristics of a CNN. Then, we describe how to use the mobile to collect data, process the data, and construct the data set. Finally, considering the computing environment and data characteristics of mobile devices, we propose a lightweight network structure for optical character recognition (OCR) on specific data sets. The proposed method using a CNN has been validated by comparison with the results of existing methods, used for optical character recognition.
Young Children's Reading and Learning with Conversational Agents

Young children increasingly interact with voice-driven interfaces, such as conversational agents (CAs). The social nature of CAs makes them good learning partners for children. We have designed a storytelling CA to engage children in book reading activities. This case study presents the design of this CA and investigates children's interactions with and perception of the CA. Through observation, we found that children actively responded to the CA's prompts, reacted to the CA's feedback with great affect, and quickly learned the schema of interacting with a digital interlocutor. We also discovered that the availability of scaffolding appeared to facilitate child-CA conversation and learning. A brief post-reading interview suggested that children enjoyed their interaction with the CA. Design implications for dialogic systems for young children's informal learning are discussed.
Online Convex Optimization in Adversarial Markov Decision Processes

We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes, and the transition function is not known to the learner. We show $\tilde{O}(L|X|\sqrt{|A|T})$ regret bound, where $T$ is the number of episodes, $X$ is the state space, $A$ is the action space, and $L$ is the length of each episode. Our online algorithm is implemented using entropic regularization methodology, which allows to extend the original adversarial MDP model to handle convex performance criteria (different ways to aggregate the losses of a single episode) , as well as improve previous regret bounds.
A wireless IOT system towards gait detection technique using FSR sensor and wearable IOT devices

Purpose The recent advancement in gait analysis combines internet of things that provides better observations of person living behavior. The biomechanical model used for elderly and physically challenged persons is related to gait-related parameters, and the accuracy of the existing systems significantly varies according to different person abilities and their challenges. The paper aims to discuss these issues. Design/methodology/approach Deployment of wearable sensors in gait analysis provides a better solution while tracking the changes of the personal style, and this proposed model uses an electronics system using force sensing resistor and body sensors. Findings Experimental results provide an average gait recognition of 95 percent compared to the existing neural network-based gait analysis model based on the walking speeds and threshold values. Originality/value The sensors are used to monitor and update the predicted values of a person for analysis. Using IoT a communication process is performed in the research work by identifying a physically challenged person even in crowded areas.
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence

Regularization is typically understood as improving generalization by altering the landscape of local extrema to which the model eventually converges. Deep neural networks (DNNs), however, challenge this view: We show that removing regularization after an initial transient period has little effect on generalization, even if the final loss landscape is the same as if there had been no regularization. In some cases, generalization even improves after interrupting regularization. Conversely, if regularization is applied only after the initial transient, it has no effect on the final solution, whose generalization gap is as bad as if regularization never happened. This suggests that what matters for training deep networks is not just whether or how, but when to regularize. The phenomena we observe are manifest in different datasets (CIFAR-10, CIFAR-100, SVHN, ImageNet), different architectures (ResNet-18, All-CNN), different regularization methods (weight decay, data augmentation, mixup), different learning rate schedules (exponential, piece-wise constant). They collectively suggest that there is a critical period'' for regularizing deep networks that is decisive of the final performance. More analysis should, therefore, focus on the transient rather than asymptotic behavior of learning.
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

Inspired by recent work in attention models for image captioning and question answering, we present a soft attention model for the reinforcement learning domain. This model bottlenecks the view of an agent by a soft, top-down attention mechanism, forcing the agent to focus on task-relevant information by sequentially querying its view of the environment. The output of the attention mechanism allows direct observation of the information used by the agent to select its actions, enabling easier interpretation of this model than of traditional models. We analyze the different strategies the agents learn and show that a handful of strategies arise repeatedly across different games. We also show that the model learns to query separately about space and content (``where'' vs. ``what''). We demonstrate that an agent using this mechanism can achieve performance competitive with state-of-the-art models on ATARI tasks while still being interpretable.
Volumetric Correspondence Networks for Optical Flow

Many classic tasks in vision -- such as the estimation of optical flow or stereo disparities -- can be cast as dense correspondence matching. Well-known techniques for doing so make use of a cost volume, typically a 4D tensor of match costs between all pixels in a 2D image and their potential matches in a 2D search window. State-of-the-art (SOTA) deep networks for flow/stereo make use of such volumetric representations as internal layers. However, such layers require significant amounts of memory and compute, making them cumbersome to use in practice. As a result, SOTA networks also employ various heuristics designed to limit volumetric processing, leading to limited accuracy and overfitting. Instead, we introduce several simple modifications that dramatically simplify the use of volumetric layers - (1) volumetric encoder-decoder architectures that efficiently capture large receptive fields, (2) multi-channel cost volumes that capture multi-dimensional notions of pixel similarities, and finally, (3) separable volumetric filtering that significantly reduces computation and parameters while preserving accuracy. Our innovations dramatically improve accuracy over SOTA on standard benchmarks while being significantly easier to work with - training converges in 10X fewer iterations, and most importantly, our networks generalize across correspondence tasks. On-the-fly adaptation of search windows allows us to repurpose optical flow networks for stereo (and vice versa), and can also be used to implement adaptive networks that increase search window sizes on-demand.
End-to-End Text-to-Speech for Low-Resource Languages by Cross-Lingual Transfer Learning

End-to-end text-to-speech (TTS) has shown great success on large quantities of paired text plus speech data. However, laborious data collection remains difficult for at least 95% of the languages over the world, which hinders the development of TTS in different languages. In this paper, we aim to build TTS systems for such low-resource (target) languages where only very limited paired data are available. We show such TTS can be effectively constructed by transferring knowledge from a high-resource (source) language. Since the model trained on source language cannot be directly applied to target language due to input space mismatch, we propose a method to learn a mapping between source and target linguistic symbols. Benefiting from this learned mapping, pronunciation information can be preserved throughout the transferring procedure. Preliminary experiments show that we only need around 15 minutes of paired data to obtain a relatively good TTS system. Furthermore, analytic studies demonstrated that the automatically discovered mapping correlate well with the phonetic expertise.
Monotonic Multihead Attention

Simultaneous machine translation models start generating a target sequence before they have encoded or read the source sequence. Recent approach for this task either apply a fixed policy on transformer, or a learnable monotonic attention on a weaker recurrent neural network based structure. In this paper, we propose a new attention mechanism, Monotonic Multihead Attention (MMA), which introduced the monotonic attention mechanism to multihead attention. We also introduced two novel interpretable approaches for latency control that are specifically designed for multiple attentions. We apply MMA to the simultaneous machine translation task and demonstrate better latency-quality tradeoffs compared to MILk, the previous state-of-the-art approach. Code will be released upon publication.
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks

As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a variety of fields, there is an increasing interest in understanding the complex internal mechanisms of DNNs. In this paper, we propose Relative Attributing Propagation (RAP), which decomposes the output predictions of DNNs with a new perspective of separating the relevant (positive) and irrelevant (negative) attributions according to the relative influence between the layers. The relevance of each neuron is identified with respect to its degree of contribution, separated into positive and negative, while preserving the conservation rule. Considering the relevance assigned to neurons in terms of relative priority, RAP allows each neuron to be assigned with a bi-polar importance score concerning the output: from highly relevant to highly irrelevant. Therefore, our method makes it possible to interpret DNNs with much clearer and attentive visualizations of the separated attributions than the conventional explaining methods. To verify that the attributions propagated by RAP correctly account for each meaning, we utilize the evaluation metrics: (i) Outside-inside relevance ratio, (ii) Segmentation mIOU and (iii) Region perturbation. In all experiments and metrics, we present a sizable gap in comparison to the existing literature.
Comparison of Viola-Jones Haar Cascade Classifier and Histogram of Oriented Gradients (HOG) for face detection

Abstract Human face recognition is one of the most challenging topics in the areas of image processing, computer vision, and pattern recognition. Before recognizing the human face, it is necessary to detect a face then extract the face features. Many methods have been created and developed in order to perform face detection and two of the most popular methods are Viola-Jones Haar Cascade Classifier (V-J) and Histogram of Oriented Gradients (HOG). This paper proposed a comparison between VJ and HOG for detecting the face. V-J method calculate Integral Image through Haar-like feature with AdaBoost process to make a robust cascade classifier, HOG compute the classifier for each image in and scale of the image, applied the sliding windows, extracted HOG descriptor at each window and applied the classifier, if the classifier detected an object with enough probability that resembles a face, the classifier recording the bounding box of the window and applied non-maximum suppression to make the accuracy increased. The experimental results show that the system successfully detected face based on the determined algorithm. That is mean the application using computer vision can detect face and compare the results.
Blockchain-Based Transcripts for Mobile Higher-Education

Blockchain has emerged as a transformative technology, from its beginning as the basis of cryptocurrencies to wider applications in areas such as property registration and insurance due to its characteristic as a distributed ledger which can remove the need for a trusted third party to facilitate transaction.This spread of the technology to new application areas has been driven by the development of smart contractsblockchain-based protocols which can automatically enforce a contract.One area where the types of problems being considered for blockchain exists is higher education.Students in higher education are increasingly mobile, and in an ever more agile world, the friction and delays caused by multiple levels of administration in higher education can cause many anxieties and hardships for students.Distance learning as a primary platform for higher education promises to open up higher education to a wider range of learners than ever before.In this paper, we review the use of blockchain in higher education and experimental implementations of a blockchain-based university transcript system in order to empower students and better fit today's ever more agile society and then describe some experimental results in using blockchain for higher education transcripts.
Digital Juries: A Civics-Oriented Approach to Platform Governance

As concerns have grown regarding harmful content spread on social media, platform mechanisms for content moderation have become increasingly significant. However, many existing platform governance structures lack formal processes for democratic participation by users of the platform. Drawing inspiration from constitutional jury trials in many legal systems, this paper proposes digital juries as a civics-oriented approach for adjudicating content moderation cases. Building on existing theoretical models of jury decision-making, we outline a 5-stage model characterizing the space of design considerations in a digital jury process. We implement two examples of jury designs involving blind-voting and deliberation. From users who participate in our jury implementations, we gather informed judgments of the democratic legitimacy of a jury process for content moderation. We find that digital juries are perceived as more procedurally just than existing common platform moderation practices, but also find disagreement over whether jury decisions should be enforced or used as recommendations.
The diamond mesh, a phase-error- and loss-tolerant field-programmable MZI-based optical processor for optical neural networks

This paper presents the performance analysis of a phase error- and loss-tolerant multiport field-programmable MZI-based structure for optical neural networks (ONNs). Compared to the triangular (Reck) mesh, our proposed diamond mesh makes use of a larger number of MZIs, leading to a symmetric topology and adding additional degrees of freedom for the weight matrix optimization in the backpropagation process. Furthermore, the additional MZIs enable the diamond mesh to optimally eliminate the excess light intensity that degrades the performance of the ONNs through the tapered out waveguides. Our results show that the diamond topology is more robust to the inevitable imperfections in practice, i.e., insertion loss of the constituent MZIs and the phase errors. This robustness allows for better classification accuracy in the presence of experimental imperfections. The practical performance and the scalability of the two structures implementing different sizes of optical neural networks are analytically compared. The obtained results confirm that the diamond mesh is more error- and loss-tolerant in classifying the data samples in different sizes of ONNs.