Reviewerly/exHarmony huggingface dataset:
Single-Image Dehazing via Dark Channel Prior and Adaptive Threshold

Due to the haze weather, the outdoor image quality is degraded, which reduces the image contrast, thereby reducing the efficiency of computer vision systems such as target recognition. There are two aspects of the traditional algorithm based on the principle of dark channel to be improved. First, the restored images obviously contain color distortion in the sky region. Second, the white regions in the scene easily affect the atmospheric light estimated. To solve the above problems, this paper proposes a single-image dehazing and image segmentation method via dark channel prior (DCP) and adaptive threshold. The sky region of hazing image is relatively bright, so sky region does not meet the DCP. The sky part is separated by the adaptive threshold, then the scenery and the sky area are dehazed, respectively. In order to avoid the interference caused by white objects to the estimation of atmospheric light, we estimate the value of atmospheric light using the separated area of the sky. The algorithm in this paper makes up for the shortcoming that the algorithm based on the DCP cannot effectively process the hazing image with sky region, avoiding the effect of white objects on estimating atmospheric light. Experimental results show the feasibility and effectiveness of the improved algorithm.
Study on Supply Chain Management using Blockchain Technology

The rapid increase in use of new technology known as “block chain technologies” in various fields brings massive benefits to the user with having concerns on decentralization, transparency, and security. Information like data, transactions details, time, and money are recorded and stored end to end within the system. This ensures the security and protects data from getting corrupt. Various patterns and algorithm all met to ensure the security level. Block chain technologies are mostly used in automations like E-voting, supply chain etc. This information can be attacked or corrupted by hackers, so they are highly under risk. No ensure transparency inside the system and security outside the system, block chain technologies are used as a major tool. A detailed study has been presented on the various types of techniques and methods that are used in the field of supply chain under block chain technologies.
EP-Bot: Empathetic Chatbot Using Auto-Growing Knowledge Graph

People occasionally interact with each other through conversation.In particular, we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language.Conversational artificial intelligence is an integral part of all the technologies that allow computers to communicate like humans.For a computer to interact like a human being, it must understand the emotions inherent in the conversation and generate the appropriate responses.However, existing dialogue systems focus only on improving the quality of understanding natural language or generating natural language, excluding emotions.We propose a chatbot based on emotion, which is an essential element in conversation.EP-Bot (an Empathetic PolarisX-based chatbot) is an empathetic chatbot that can better understand a person's utterance by utilizing PolarisX, an autogrowing knowledge graph.PolarisX extracts new relationship information and expands the knowledge graph automatically.It is helpful for computers to understand a person's common sense.The proposed EP-Bot extracts knowledge graph embedding using PolarisX and detects emotion and dialog act from the utterance.Then it generates the next utterance using the embeddings.EP-Bot could understand and create a conversation, including the person's common sense, emotion, and intention.We verify the novelty and accuracy of EP-Bot through the experiments.
Non-Euclidean Differentially Private Stochastic Convex Optimization

Differentially private (DP) stochastic convex optimization (SCO) is a fundamental problem, where the goal is to approximately minimize the population risk with respect to a convex loss function, given a dataset of i.i.d. samples from a distribution, while satisfying differential privacy with respect to the dataset. Most of the existing works in the literature of private convex optimization focus on the Euclidean (i.e., $\ell_2$) setting, where the loss is assumed to be Lipschitz (and possibly smooth) w.r.t. the $\ell_2$ norm over a constraint set with bounded $\ell_2$ diameter. Algorithms based on noisy stochastic gradient descent (SGD) are known to attain the optimal excess risk in this setting. In this work, we conduct a systematic study of DP-SCO for $\ell_p$-setups. For $p=1$, under a standard smoothness assumption, we give a new algorithm with nearly optimal excess risk. This result also extends to general polyhedral norms and feasible sets. For $p\in(1, 2)$, we give two new algorithms, whose central building block is a novel privacy mechanism, which generalizes the Gaussian mechanism. Moreover, we establish a lower bound on the excess risk for this range of $p$, showing a necessary dependence on $\sqrt{d}$, where $d$ is the dimension of the space. Our lower bound implies a sudden transition of the excess risk at $p=1$, where the dependence on $d$ changes from logarithmic to polynomial, resolving an open question in prior work [TTZ15] . For $p\in (2, \infty)$, noisy SGD attains optimal excess risk in the low-dimensional regime; in particular, this proves the optimality of noisy SGD for $p=\infty$. Our work draws upon concepts from the geometry of normed spaces, such as the notions of regularity, uniform convexity, and uniform smoothness.
End-to-end deep learning with neuromorphic photonics

Neuromorphic computing has emerged as a highly-promising compute alternative, migrating from von-Neuman architectures towards mimicking the human brain for sustaining computational power increases within a reduced power consumption envelope. Electronic neuromorphic chips like IBM’s TrueNorth, Intel’s Loihi and Mythic’s AI platform reveal a tremendous performance improvement in terms of computational speed and density; at the same time, neuromorphic photonic layouts are constantly gaining ground in exploiting their large component portfolio for enabling GHz-bandwidth and low-energy neurons. Progressing in tight synergy with appropriate training techniques, this evolution has already started to translate into performance improvements in end-to-end applications, highlighting the practical perspectives of the new neural network hardware when effectively synergized with new training frameworks. Herein, we present a complete portfolio of neuromorphic photonic subsystems and architectures, highlighting their utilization in practical application scenario for time series classification and fiber transmission links. Our work extends along feed-forward and recurrent photonic NN models, demonstrating experimental results together with the required training methods for bridging the gap between software-deployed NNs and the photonic hardware. We report on the experimentally validated performance of a 10GHz photonic time series classification engine, presenting also preliminary results on how photonic neurons can replace DSP modules in end-to-end fiber transmission schemes. The perspectives of these layouts to yield energy and area efficiency benefits are discussed through a detailed energy and area breakdown of neuromorphic photonic technologies, highlighting a promising roadmap when plasmo-photonic hardware is adopted.
Improved Design of DES Algorithm Based on Symmetric Encryption Algorithm

To address issues in security & the threat posed to the DES algorithm by brute force cracking, as well as the low efficiency of the 3DES algorithm, I propose extending the length of the DES algorithm's key. First, change the block length of the DES algorithm from 64 bits to 128 bits and expand the key length of the DES algorithm to double length; secondly, iterative encryption is performed according to the new plaintext grouping and the expanded key length. The two groups are iteratively encrypted, and after every single iteration the two sets of R part data obtained from this round of encryption are exchanged, and then the next round of iteration; finally, the improved DES algorithm is compared with the DES algorithm and the 3DES algorithm by experiment. The results showing that the operating efficiency of the improved DES algorithm is better than 3DES algorithm. Therefore, the improved algorithm is proposed here greatly improves the security of the algorithm under the premise of ensuring operating efficiency and has good application value.
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types

Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision models, i.e. pre-training a model for image classification on the ILSVRC dataset, and then fine-tune on any target task. However, previous systematic studies of transfer learning have been limited and the circumstances in which it is expected to work are not fully understood. In this paper we carry out an extensive experimental exploration of transfer learning across vastly different image domains (consumer photos, autonomous driving, aerial imagery, underwater, indoor scenes, synthetic, close-ups) and task types (semantic segmentation, object detection, depth estimation, keypoint detection). Importantly, these are all complex, structured output tasks types relevant to modern computer vision applications. In total we carry out over 1200 transfer experiments, including many where the source and target come from different image domains, task types, or both. We systematically analyze these experiments to understand the impact of image domain, task type, and dataset size on transfer learning performance. Our study leads to several insights and concrete recommendations for practitioners.
Denoising convolutional neural network inspired via multi-layer convolutional sparse coding

Sparse prior to image denoising is a classical research field with a long history in computer vision. We propose an end-to-end supervised neural network, named DnMLCSC-net, which is inspired via multi-layer convolutional sparse coding model embedded with symbiotic analysis–synthesis priors for natural image denoising. Unfolding a multi-layer, learned iterative soft thresholding algorithm (ML-LISTA) and developing into a convolutional recurrent neural network, all parameters in the model are updated adaptively to minimize mixed loss via gradient descent using backpropagation. In addition, a combined ReLU function is taken as the activation function. Inconsistent dilated convolution and batch normalization were empirically introduced into the encoding layers corresponding to the first iteration of ML-LISTA. Experimental results show that our network achieves a competitive denoising effect in comparison with several state-of-the-art denoising methods.
Reliability of GAN Generated Data to Train and Validate Perception Systems for Autonomous Vehicles

Autonomous systems deployed in the real world have to deal with potential problem causing situations that they have never seen during their training phases. Due to the long-tail nature of events, collecting a large amount of data for such corner cases is a difficult task. While simulation is one plausible solution, recent developments in the field of Generative Adversarial Networks (GANs) make them a promising tool to generate and augment realistic data without exhibiting a domain shift from actual real data. In this manuscript, we empirically analyze and propose novel solutions for the trust that we can place on GAN generated data for training and validation of vision-based perception modules like object detection and scenario classification.
Doctor for Machines: A Failure Pattern Analysis Solution for Industry 4.0

The success of IoT depends on our ability to solve challenging problems which were previously infeasible. One of the most critical challenges in IoT space is the preventive maintenance in industrial manufacturing processes to maximize equipment availability and durability. Traditionally, preventive management only follows less cost-effective strategies, say time or usage based management. With large amount sensor data harvested from IoT, we can develop much more intelligent predictive maintenance based on accurate machinery failure prediction. In this paper, we develop a framework named DoM (Doctor for Machines) to produce the best predictive model for several oil and gas industry engagements. Our framework is built in the form of pipeline that allows us to generate multiple models simultaneously with parallel computing. We configure the modeling process by assigned different machine learning tasks as such sampling, feature extraction, modeling, and post-processing into the pipeline. The pipeline forms a machine learning graph workflow. With the automation of execution all the tasks in the workflow, the client can easily choose a best predictive model fitting their failure tolerance. We have successfully applied DoM to six data sets, and identified valuables insights on the best practices of creating predictive model. Our clients have successfully achieved financial benefits by applying DoM to develop predictive maintenance schemes to their expensive equipment.
$i$-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

Contrastive representation learning has shown to be effective to learn representations from unlabeled data. However, much progress has been made in vision domains relying on data augmentations carefully designed using domain knowledge. In this work, we propose i-Mix, a simple yet effective domain-agnostic regularization strategy for improving contrastive representation learning. We cast contrastive learning as training a non-parametric classifier by assigning a unique virtual class to each data in a batch. Then, data instances are mixed in both the input and virtual label spaces, providing more augmented data during training. In experiments, we demonstrate that i-Mix consistently improves the quality of learned representations across domains, including image, speech, and tabular data. Furthermore, we confirm its regularization effect via extensive ablation studies across model and dataset sizes. The code is available at this https URL.
The Practice of Digital Learning (D-Learning) in the Study from Home (SFH) Policy: Teachers' Perceptions

This article investigates the efficacy of Digital Learning implementation in SFH policy from teachers' viewpoints. The outbreak of the COVID-19 pandemic in Indonesia in mid-March 2020 required the Government to take a decisive step in the form of Study from Home (SFH). The policy was aimed at preserving the quality of students' mental and psychological learning while socially and physically distancing. In this descriptive qualitative study, the interviews were conducted online with four teachers in various high schools in Bulukumba Regency, Indonesia. It was also supported by documents. This research found that teachers prefer to use WhatsApp and Google Classroom as the platforms to organize D-Learning. These applications are more comfortable to use, easy to access, and more familiar in their environment. Teachers perceive that D-Learning modifies the learning process so that it becomes more flexible. It also results in more autonomous learners. However, teachers are aware they are not yet ready to provide good technology-based material. They face difficulties in assessing students' progress from home. Students were also found to lack enthusiasm in responding to the learning content. Besides, inadequate facilities and infrastructure limit the students' access to D-Learning. As a result, D-Learning in SFH policy has not been effective for the students' cognitive and affective development and learning goals cannot be maximally achieved. Thus, it is suggested that the government support all efforts to adjust quickly to the D-Learning system's practice, such as by broadcasting more educational content through electronic media and publishing materials of pedagogical specialists for working in D-Learning environments.
Explaining Interpretable Machine Learning: Theory, Methods and Applications

This working paper aims at providing a structured and accessible introduction to the topic of interpretable machine learning. We start with an overview of the research literature and we continue by analyzing selected methods to explain machine learning model outcomes. We apply these methods in two distinct case studies. The theory on machine learning interpretability is discussed together with the concepts of explanation, interpretation, and trust from philosophy and social sciences. We choose counterfactual explanations and Locally Interpretable Model-agnostic Explanations (LIME) as prominent examples of machine learning interpretability methods and we discuss their Python implementations in detail. We apply the chosen methods in two separate case studies; the first uses the Boston Housing dataset to classify census tracts in the Boston metropolitan area. The second case study is focused on the natural language processing and classification of Youtube comments. The results of the first case study show that the existing Python implementation of counterfactual explanations does not allow for controlling sparsity and feasibility of the explanations. Moreover, it does not properly handle datasets with categorical variables. The results of the second case study show that the understandability of LIME explanations depends, among others, on the structure of the text instance to be explained. Therefore, practitioners have to rely on domain knowledge to identify and share only the informative explanations. The above limitations have to be taken care of to ensure applicability of counterfactual explanations and LIME in real-world applications.
On the Untriviality of Trivial Packages: An Empirical Study of npm JavaScript Packages

Nowadays, developing software would be unthinkable without the use of third-party packages. Although such code reuse helps to achieve rapid continuous delivery of software to end-users, blindly reusing code has its pitfalls. For example, prior work has investigated the rationale for using packages that implement simple functionalities, known as trivial packages (i.e., in terms of the code size and complexity). This prior work showed that although these trivial packages were simple, they were popular and prevalent in the npm ecosystem. This popularity and prevalence of trivial packages peaked our interest in questioning the ‘triviality of trivial packages’. To better understand and examine the triviality of trivial packages, we mine a large set of JavaScript projects that use trivial npm packages and evaluate their relative centrality. Specifically, we evaluate the triviality from two complementary points of view: based on project usage and ecosystem usage of these trivial packages. Our result shows that trivial packages are being used in central JavaScript files of a software project. Additionally, by analyzing all external package API calls in these JavaScript files, we found that a high percentage of these API calls are attributed to trivial packages. Therefore, these packages play a significant role in JavaScript files. Furthermore, in the package dependency network, we observed that 16.8 percent packages are trivial and in some cases removing a trivial package can impact approximately 29 percent of the ecosystem. Overall, our finding indicates that although smaller in size and complexity, trivial packages are highly depended on packages by JavaScript projects. Additionally, our study shows that although they might be called trivial, nothing about trivial packages is trivial.
An LSTM&amp;Topic-CNN Model for Classification of Online Chinese Medical Questions

In recent years, people’s interest in health question and answer (Q&A) websites has been growing with the development of the internet technologies. How to seek appropriate professional medical information among the massive data has become the focus of all patients. Therefore, it is vital to obtain reasonable predictions and automatic recommendations on the basis of patients’ keyword descriptions of their health status and question intention. The key to solving this problem is to achieve automatic text classification of health questions. This paper considered a feature fusion model for the classification of Chinese short texts on medical health Q&A websites by combining the text features and topic features. Firstly, we generated the text word vector by word embedding method and obtained the text features under Long Short-Term Memory (LSTM) model. Given the difficulty in determination of topic numbers, we conducted a sub-sample experiment to obtain the few optimal topic numbers under which the classification performances were good. Then we extracted the topic features and used the one-dimensional convolution idea of the Convolutional Neural Network (CNN) model for topic feature filtering. Finally, we combined the two features together subtly for text classification. Two experiments were conducted to illustrate our model in terms of recall rate, precision, and F1 value when the datasets were from different online medical Q&A websites. Results showed that the LSTM&Topic-CNN model could efficiently enhance the classification effect of Chinese medical health question texts.
Technological Opportunity, Technological Leadership Change, and Latecomers’ R&amp;amp;D Resource Allocation between Innovation and Imitation

This study examines when and how latecomers can surpass incumbents in technological capabilities with a focus on the role of technological opportunity. There is disagreement in theoretical prediction and evidence on whether technological opportunity is conducive to technological leadership change. To reconcile this disagreement, we build a computational model of Schumpeterian competition in which incumbents and latecomers compete with innovation and imitation R&D. First, the results suggest that technological opportunity indeed has two opposing effects (i.e., positive and negative) on technological leadership change, and these effects create an inverted-U relationship. When there are few opportunities, leadership change is unlikely to happen because latecomers will rarely achieve a breakthrough. Additionally, abundant opportunities may not be conducive to leadership change either because incumbents move forward faster than latecomers. We further examine why these opposing effects exist by exploring latecomers’ R&D allocation between innovation and imitation. The results highlight that imitation R&D is a necessary condition for latecomers to leverage technological opportunity (i.e., enabling the positive effect of technological opportunity) and overcome their disadvantages in a technologically munificent environment (i.e., mitigating the negative effect of technological opportunity).