Reviewerly/exHarmony huggingface dataset:
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution

In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and the zooming ratio of the laparoscope, following the surgeon’s instructions. However, moving the laparoscope frequently may lead to unstable and suboptimal views, while the adjustment of zooming ratio may interrupt the workflow of the surgical operation. To this end, we propose a multi-scale Generative Adversarial Network (GAN)-based video super-resolution method to construct a framework for automatic zooming ratio adjustment. It can provide automatic real-time zooming for high-quality visualization of the Region of Interest (ROI) during the surgical operation. In the pipeline of the framework, the Kernel Correlation Filter (KCF) tracker is used for tracking the tips of the surgical tools, while the Semi-Global Block Matching (SGBM)-based depth estimation and Recurrent Neural Network (RNN)-based context-awareness are employed to determine the upscaling ratio for zooming. The framework is validated with the JIGSAW dataset and Hamlyn Centre Laparoscopic/Endoscopic Video Datasets, with results demonstrating its practicability.
Underwater Image Color Correction Using Ensemble Colorization Network

Underwater image color correction has been gaining traction due to its usage in marine biology and surveillance. Color corrected images also help marine archaeologists in locating objects. The underwater image suffers from various degradation with respect to the depth at which the image is taken. In this paper, we propose an alternate path to correct the color of the underwater images. We address the problem of underwater image color correction as a colorization task. For this purpose, we propose a deep learning architecture that comprises of an ensemble encoder and a decoder. The ensemble encoder part uses pre-trained networks to extract multi-level features. These features are then fused together and are used up by the decoder to generate the color corrected output. We evaluate the performance of our model using reference-based as well as no reference-based metrics. The metrics indicate that the produced results are inline with the human perceptual system.
FAMGAN

Micro-expressions (MEs) are significant and effective clues to reveal the true feelings and emotions of human beings, and thus MEs analysis is widely used in different fields such as medical diagnosis, interrogation and security. However, it is extremely difficult to elicit and label MEs, resulting in a lack of sufficient MEs data for MEs analysis. To address this challenge and inspired by the current face generation technology, in this paper we introduce Generative Adversarial Network based on fine-grained Action Units (AUs) modulation to generate MEs sequence (FAMGAN). Specifically, after comprehensively analyzing the factors that lead to inaccurate AU values detection, we performed fine-grained AUs modulation, which includes carefully eliminating the various noises and dealing with the asymmetry of AUs intensity. Additionally, we incorporate super-resolution into our model to enhance the quality of the generated images. Through experiments, we show that the system achieves very competitive results on the Micro-Expression Grand Challenge (MEGC2021).
Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting

Zero-shot cross-domain crowd counting is a challenging task where a crowd counting model is trained on a source domain (i.e., training dataset) and no additional labeled or unlabeled data is available for fine-tuning the model when testing on an unseen target domain (i.e., a different testing dataset). The generalisation performance of existing crowd counting methods is typically limited due to the large gap between source and target domains. Here, we propose a novel Crowd Counting framework built upon an external Momentum Template, termed C2MoT, which enables the encoding of domain specific information via an external template representation. Specifically, the Momentum Template (MoT) is learned in a momentum updating way during offline training, and then is dynamically updated for each test image in online cross-dataset evaluation. Thanks to the dynamically updated MoT, our C2MoT effectively generates dense target correspondences that explicitly accounts for head regions, and then effectively predicts the density map based on the normalized correspondence map. Experiments on large scale datasets show that our proposed C2MoT achieves leading zero-shot cross-domain crowd counting performance without model fine-tuning, while also outperforming domain adaptation methods that use fine-tuning on target domain data. Moreover, C2MoT also obtains state-of-the-art counting performance on the source domain.
Semantic and Geometric Modeling with Neural Message Passing in 3D Scene Graphs for Hierarchical Mechanical Search

Searching for objects in indoor organized environments such as homes or offices is part of our everyday activities. When looking for a desired object, we reason about the rooms and containers the object is likely to be in; the same type of container will have a different probability of containing the target depending on which room it is in. We also combine geometric and semantic information to infer what container is best to search, or what other objects are best to move, if the target object is hidden from view. We use a 3D scene graph representation to capture the hierarchical, semantic, and geometric aspects of this problem. To exploit this representation in a search process, we introduce Hierarchical Mechanical Search (HMS), a method that guides an agent’s actions towards finding a target object specified with a natural language description. HMS is based on a novel neural network architecture that uses neural message passing of vectors with visual, geometric, and linguistic information to allow HMS to process data across layers of the graph while combining semantic and geometric cues. HMS is trained on 1000 3D scene graphs and evaluated on a novel dataset of 500 3D scene graphs with dense placements of semantically related objects in storage locations, and is shown to be significantly better than several baselines at finding objects. It is also close to the oracle policy in terms of the median number of actions required. Additional qualitative results can be found at https://ai.stanford.edu/mech-search/hms
A bibliometric review on development economics research in Vietnam from 2008 to 2020

This study adopts the bibliometric approach to identify the key characteristics in the relationship of demographic factors (age, gender, affiliations, and locations), scientific productivity, and the collaboration among development economics researchers in Vietnam during the period 2008-2020. Overall, the number of publications and authors in development economics are rising steeply with the average annual growth rate of nearly 23% and 26%, respectively. Moreover, the 'quality' of the research appears to be high as 59% of the articles are published in journals in the first and second quartile according Scimago journal ranking. However, the citation counts for these studies indicate their impacts are far more languishing in comparison. In terms of research trends, this study identified three emerging areas of studies that are relatively under-researched, namely natural resources, technology, and urbanization. As for publishing practices, There is also a positive sign for the adoption of open science among Vietnamese researchers in this field. The findings are useful for predicting future research trends. In addition, the study provides several implications for policymakers in Vietnam to enhance research capacity.
Efficient and anonymous multi‐message and multi‐receiver electronic health records sharing scheme without secure channel based on blockchain

Abstract The sharing of electronic health records (EHR) helps the patients increase the accuracy of treatment, while promoting the development of the hospital and the research of medical field compared to traditional paper‐based medical records. However, due to the sensitivity of medical data and the difference of the standard about EHR, it is likely to lead to some security problems such as the leakage of patient's privacy and the difficulty of data sharing. To accomplish the secure storage and effective sharing of EHR, we propose an efficient and anonymous multi‐message and multi‐receiver EHR sharing scheme without secure channel based on blockchain (BEAMMRS). We use blockchain technology and cloud server to store EHR together, which ensures the security and immutability. Moreover, we combine certificateless public key cryptography and elliptic curve cryptography (ECC) to solve the certificate management burden and key escrow problems. Our scheme utilizes multi‐message and multi‐receiver signcryption algorithm to enable medical data providers to send different messages to multiple data requesters by executing one signcryption operation, which satisfies the multi‐message sending requirements of the data providers in the communication environment. Meanwhile, the key generation center (KGC) sends the pseudo partial private key to the data user through the public channel. Only the authorized data user can successfully find out the real partial private key, which avoids the key leakage and economic cost caused by the secure channel. Security analysis and performance evaluation demonstrate that the proposed BEAMMRS scheme meets stronger security and higher computational efficiency than other similar schemes.
Underwater image segmentation in the wild using deep learning

Abstract Image segmentation is an important step in many computer vision and image processing algorithms. It is often adopted in tasks such as object detection, classification, and tracking. The segmentation of underwater images is a challenging problem as the water and particles present in the water scatter and absorb the light rays. These effects make the application of traditional segmentation methods cumbersome. Besides that, to use the state-of-the-art segmentation methods to face this problem, which are based on deep learning, an underwater image segmentation dataset must be proposed. So, in this paper, we develop a dataset of real underwater images, and some other combinations using simulated data, to allow the training of two of the best deep learning segmentation architectures, aiming to deal with segmentation of underwater images in the wild. In addition to models trained in these datasets, fine-tuning and image restoration strategies are explored too. To do a more meaningful evaluation, all the models are compared in the testing set of real underwater images. We show that methods obtain impressive results, mainly when trained with our real dataset, comparing with manually segmented ground truth, even using a relatively small number of labeled underwater training images.
A mapping study on documentation in Continuous Software Development

With an increase in Agile, Lean, and DevOps software methodologies over the last years (collectively referred to as Continuous Software Development (CSD)), we have observed that documentation is often poor. This work aims at collecting studies on documentation challenges, documentation practices, and tools that can support documentation in CSD. A systematic mapping study was conducted to identify and analyze research on documentation in CSD, covering publications between 2001 and 2019. A total of 63 studies were selected. We found 40 studies related to documentation practices and challenges, and 23 studies related to tools used in CSD. The challenges include: informal documentation is hard to understand, documentation is considered as waste, productivity is measured by working software only, documentation is out-of-sync with the software and there is a short-term focus. The practices include: non-written and informal communication, the usage of development artifacts for documentation, and the use of architecture frameworks. We also made an inventory of numerous tools that can be used for documentation purposes in CSD. Overall, we recommend the usage of executable documentation, modern tools and technologies to retrieve information and transform it into documentation, and the practice of minimal documentation upfront combined with detailed design for knowledge transfer afterwards. It is of paramount importance to increase the quantity and quality of documentation in CSD. While this remains challenging, practitioners will benefit from applying the identified practices and tools in order to mitigate the stated challenges.
Forensic Analysis of Tor Browser on Windows 10 and Android 10 Operating Systems

Smartphones and Internet have become prevalent in our society with various applications in businesses, education, healthcare, gaming, and research. One of the major issues with the Internet today is its lack of security since an eavesdropper can potentially intercept the communication. This has contributed towards an increased number of cyber-crime incidents, resulting in an increase in users’ consciousness about the security and privacy of their communication. One example is the shift towards using private browsers such as Tor. Tor is a well-recognized and widely used privacy browser based on The Onion Router network that provisions anonymity over the insecure Internet. This functionality of Tor has been a major hurdle in cybercrime investigations due to the complex nature of its anonymity. This paper investigates artifacts from the Tor privacy browser on the latest Windows 10 and Android 10 devices to determine potential areas where evidence can be found. We examine the registry, storage, and memory of Windows 10 devices and the memory, storage, logs, and Zram of Android 10 devices for three possible scenarios i.e. before, during, and after use of the Tor browser. Our results do not support the claims made by the Tor Project regarding user privacy and anonymity. We find that it is possible to retrieve significant details about a user’s browsing activities while the Tor browser is in use as well as after it is closed (on both operating systems). This paper also provides an investigative methodology for the acquisition and analysis of Tor browser artifacts from different areas of the targeted operating systems. Therefore, it can serve as a base to expand research in the forensic analysis of other privacy browsers and improve the efficiency of cybercrime investigations efficiency.
Clone detection through srcClone: A program slicing based approach

Software clone detection is an often used approach to understand and maintain software systems. One category of clones that is challenging to detect but very useful is semantic clones, which are similar in semantics but differ in syntax significantly. Semantic clone detectors have trouble scaling to larger systems and sometimes struggle with recall and precision. To address this, we developed a slice-based scalable approach that detects both syntactic and semantic code clones, srcClone. srcClone ascertains code segment similarity by assessing the similarity of their corresponding program slices. We employ a lightweight, publicly-available, scalable program slicer within our clone detection approach. Using dependency analysis to detect and assess cloned components, we discover insights about code components that can be affected by a clone pair or set. These elements are critical in impact analysis. It can also be used by program analysts to run on non-compilable and incomplete source code, which serves comprehension and maintenance tasks very well. We first evaluate srcClone by comparing it to six state-of-the-art tools and two additional semantic clone detectors in performance, recall, and precision. We use the BigCloneBench real clones benchmark to facilitate comparison. We use an additional 16 established benchmark scenarios to perform a qualitative comparison between srcClone and 44 clone detection approaches in their capabilities to detect these scenarios. To further measure scalability, we evaluate srcClone on 191 versions of Linux kernel, containing approximately 87 MLOC. In our evaluations, we illustrate our approach is both relatively scalable and accurate. While its precision is slightly less than some other techniques, it makes up for it in higher recall including semantic clones unable to be found by any existing techniques. We contend our approach is an important advancement in software cloning that it is both demonstrably scalable and can detect more types of clones than existing work, thus providing developers greater information into their software.
Actor-critic with familiarity-based trajectory experience replay

This paper aims to solve sample inefficiency in Asynchronous Advantage Actor-Critic (A3C). First, we design a new off-policy actor-critic algorithm, which combines actor-critic with experience replay to improve sample efficiency. Next, we study the sampling method of experience replay for trajectory experience and propose a familiarity-based replay mechanism which uses the number of replay times of experience as the probability weight of sampling. Finally, we use the GAE-V method to correct the bias caused by off-policy learning. We also achieve better performance by adopting a mechanism that combines off-policy learning and on-policy learning to update the network. Our results on Atari and MuJoCo benchmarks show that each of these innovations contributes to improvements in both data efficiency and final performance. Furthermore, our approach keeps a fast coverage speed and the same parallel feature as A3C, and also has better performance on exploration.
Quantum Communication over Atmospheric Channels: A Framework for Optimizing Wavelength and Filtering

The wavelength dependence of the daytime performance of free-space quantum channels is crucial to global-scale quantum networking, but has been relatively unexplored, due to a lack of expertise in atmospheric physics among quantum optics researchers. This study makes progress by simulating quantum key distribution at different wavelengths, under daytime spectral radiance and atmospheric turbulence. Even in the face of multiple unfavorable factors, it is found that shorter wavelengths can nonetheless outperform longer ones. The framework and insights of this study are expected to have strong impact, and to serve as stepping stones for analyzing more complex quantum networking protocols.
An Interpretable Approach to Hateful Meme Detection

Hateful memes are an emerging method of spreading hate on the internet, relying on both images and text to convey a hateful message. We take an interpretable approach to hateful meme detection, using machine learning and simple heuristics to identify the features most important to classifying a meme as hateful. In the process, we build a gradient-boosted decision tree and an LSTM-based model that achieve comparable performance (73.8 validation and 72.7 test auROC) to the gold standard of humans and state-of-the-art transformer models on this challenging task.
Hiring CS Graduates: What We Learned from Employers

Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.
An analysis of the ethical challenges of blockchain-enabled E-healthcare applications in 6G networks

Developments in blockchain technology coupled with rapid developments in network technologies have disrupted traditional business and service models. One such application is in the domain of healthcare. However, the domain's sensitive nature and complexity require blockchain-enabled e-healthcare to ensure utilitarianism while suitably addressing the associated ethical challenges. In this milieu, the paper attempts to identify and evaluate the parameters of ethical challenges associated with blockchain adoption in e-healthcare. This paper contributes to the extant body of knowledge by presenting a critical review of the ethical considerations at the meso level of blockchains in e-healthcare. Based on findings from the literature, the study identified nine parameters of blockchain ethics. Of these, Accuracy and Right to be Forgotten were found to be most critical in terms of ethical dilemmas in healthcare applications. No evidence of ethical dilemma could be found with respect to Accountability and Data Ownership. As these services are deployed over networks, all these challenges are further evaluated in the context of 6G network-based models. This will not only provide the stakeholders with a holistic view of the ethical challenges in various blockchain-enabled healthcare applications but also enable a meticulous transition to the 6G network.
Teaching Students About Conversational AI Using Convo, a Conversational Programming Agent

Smart assistants, like Amazon's Alexa or Apple's Siri, have become commonplace in many people's lives, appearing in their phones and homes. Despite their ubiquity, these conversational AI agents still largely remain a mystery to many, in terms of how they work and what they can do. To lower the barrier to entry to understanding and creating these agents for young students, we expanded on Convo, a conversational programming agent that can respond to both voice and text inputs. The previous version of Convo focused on teaching only programming skills, so we created a simple, intuitive user interface for students to use those programming skills to train and create their own conversational AI agents. We also developed a curriculum to teach students about key concepts in AI and conversational AI in particular. We ran a 3-day workshop with 15 participating middle school students. Through the data collected from the pre- and post-workshop surveys as well as a mid-workshop brainstorming session, we found that after the workshop, students tended to think that conversational AI agents were less intelligent than originally perceived, gained confidence in their abilities to build these agents, and learned some key technical concepts about conversational AI as a whole. Based on these results, we are optimistic about CONVO'S ability to teach and empower students to develop conversational AI agents in an intuitive way.