Reviewerly/exHarmony huggingface dataset:
First-person Video Analysis for Evaluating Skill Level in the Humanitude Tender-Care Technique

In this paper, we describe a wearable first-person video (FPV) analysis system for evaluating the skill levels of caregivers. This is a part of our project that aims to quantize and analyze the tender-care technique known as Humanitude by using wearable sensing and AI technology devices. Using our system, caregivers can evaluate and elevate their care levels by themselves. From the FPVs of care sessions taken by wearable cameras worn by caregivers, we obtained the 3D facial distance, pose and eye-contact states between caregivers and receivers by using facial landmark detection and deep neural network (DNN)-based eye contact detection. We applied statistical analysis to these features and developed algorithms that provide scores for tender-care skill. In experiments, we first evaluated the performance of our DNN-based eye contact detection by using eye contact datasets prepared from YouTube videos and FPVs that assume conversational scenes. We then performed skill evaluations by using Humanitude training scenes involving three novice caregivers, two Humanitude experts and seven middle-level students. The results showed that our eye contact detection outperformed existing methods and that our skill evaluations can estimate the care skill levels.
Blockchain and Initial Coin Offerings (ICOs): A New Way of Crowdfunding

Today, developers, businesses and individuals increasingly are using blockchain and initial coin offerings to raise capital. These new innovations may offer to entrepreneurs and investors interesting theoretical opportunities, as well as pose strategic, regulatory and law enforcement challenges, since they may enable the change of the business models of existing players in entrepreneurial finance and push the new players even further. The objective of this study is to investigate the potential advantages and challenges of blockchain technology and ICOs, and how they are going to affect the entrepreneurial environment. In particular, in addition to provide a first overview on blockchain and ICOs in the context of entrepreneurial finance, this study also aims to provide practical contributions for regulators, entrepreneurs and investors interested in these new financial innovations, by outlining ICOs’ advantages and risks, and provide a research agenda for future research in this topic.
Nano-materials: Novel and Promising Adsorbents for Water Treatment

Recently, nanotechnology has been utilized in more or less all fields, such as physics, chemistry, materials science, biology, medicine, etc. In this relation, nanotechnology is also applied in the removal of various impurities/pollutants including pathogenic bacteria and virus from water and other water bodies. Among the various methods used for wastewater treatment, adsorption is found to be one of the better methods because of its low-cost and simplicity of operation. Recently, nanoadsorbents have been explored for the removal of various pollutants from waste water. In this paper, the utilization of nanomaterials, as an effective adsorbent for the removal of heavy metals, organic compounds, pesticides, pharmaceuticals and other pollutants, is reviewed and discussed. It is to be mentioned that adsorption capacities of the adsorbents vary depending on the characteristics of the adsorbents, the extent of chemical modification and the concentration of adsorbates. There are also few issues and drawbacks on the utilization of nanoadsorbents that have been addressed. To find out the practical utilization of nanomaterials as low-cost adsorbents on the commercial scale, more research should be conducted in this direction. This review article demonstrated that nanomaterials may be effectively utilized for the treatment of wastewater and may be the future novel and promising adsorbents/materials for water treatment.
Differentially Private Kalman Filtering

This chapter is concerned with the design of model-based differentially private filters, when the privacy-sensitive signal to be processed can be modeled as the output of a linear finite-dimensional system with publicly known parameters. Such models can capture for example known physical laws that govern the behavior of the input signal, e.g., a kinematic model linking position and velocity measurements obtained from individual users. In the absence of privacy constraint, Kalman filtering provides a solution to the problem of estimating the state of the system while minimizing the mean square error. We adapt here this filter to accommodate differential privacy constraints, for various scenarios involving either individual or collective signals.
A Simple Joint Model for Improved Contextual Neural Lemmatization

Chaitanya Malaviya, Shijie Wu, Ryan Cotterell. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Virtual Portals for a Smart Fruition of Historical and Archaeological Contexts

The experimentation of new effective visualization techniques provides viable technological solutions for the archaeological heritage fruition and the visitors involvement within cultural places (museums, gallery, archaeological sites). This article discusses the feasibility of a project aimed at extending outdoor the fruition of archaeological sites, by exploiting mixed reality technology. In a previous work we focused on the development of a mobile application that exploits the Augmented Reality technology, limited to an indoor fruition. Therefore we decided to extend the app by introducing a strategy for an on-site outdoor exploration based on “virtual portals”. Virtual portals are the medium that allows the transition from reality (present) to virtuality (past) and vice versa. They are located outdoor at some point of interest, so that user has the perception of passing through different space-temporal dimensions: this is actually what we want to provide as user personal experience. We considered two archaeological areas as case studies, both excavations sites of the University of Salento, as an instance of a wider network we aim to create. In this paper we give some hints about a feasible design for this project.
A Comparative Analysis of Worldwide Trends in the Use of Information and Communications Technology in Engineering Education

This paper forecasts learning technologies that are predicted to impact the practice of engineering education according to the perceptions of engineering education researchers and practitioners. These forecasts were derived from three worldwide surveys carried out in 2013, 2014 and 2015. The responses were analyzed to know the technologies that will have an impact in the short, medium and long term. In turn, differences in forecasts according to area of specialization and geographical region were studied. Moreover, this paper applies social analysis (Google Trends) and bibliometric analysis (Google Scholar, Scopus and Web of Science) to these predictions in order to discover which technologies were successful and really impacted in engineering education, and which ones failed to have the predicted impact and why.
Ordered Memory

Stack-augmented recurrent neural networks (RNNs) have been of interest to the deep learning community for some time. However, the difficulty of training memory models remains a problem obstructing the widespread use of such models. In this paper, we propose the Ordered Memory architecture. Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of the memory. We also introduce a new Gated Recursive Cell to compose lower-level representations into higher-level representation. We demonstrate that our model achieves strong performance on the logical inference task (Bowman et al., 2015) and the ListOps (Nangia and Bowman, 2018) task. We can also interpret the model to retrieve the induced tree structure, and find that these induced structures align with the ground truth. Finally, we evaluate our model on the Stanford Sentiment Treebank tasks (Socher et al., 2013), and find that it performs comparatively with the state-of-the-art methods in the literature.
Radio Environment Maps for Military Cognitive Networks: Deployment of Sensors vs. Map Quality

In this paper we discuss the dependency between deployment of sensors and map quality in the Radio Environment Map (REM) concept. The architecture of REM supporting military communications systems is described. The map construction techniques based on spatial statistics and the problem of REM quality are presented. The results of field tests done for UHF range with different arrangements of sensors are shown. Exemplary REM maps with selected interpolation techniques are presented. Finally, the problem of deployment of sensors versus REM map quality is analyzed.
Efficient digital signatures from RSA without random oracles

Improving efficiency of digital signature scheme is important since digital signature scheme is a core building block for many privacy protocols. There are some proposals regarding efficient digital signatures whose security arguments are guaranteed by the standard assumption such as RSA assumption. Although several RSA-based digital signature schemes achieve a short signature size, many of them essentially rely on random oracle heuristics. In 2009, Hohenberger and Water proposed an excellent approach to the design of a short RSA-based signature scheme without random oracles (CRYPTO 2009). However, their scheme requires signers to execute an expensive prime-number generation several times, and leaves the reduction in signing and verifying costs as important open problems. In this paper, we propose an efficient digital signature scheme from the above category. That is, we propose a short RSA signature scheme in the standard model, which requires less prime-number generations than those in the previous best scheme of Böhl, Hofheinz, Jager, Koch, and Striecks (Journal of Cryptology 2015). More precisely, the BHJKS scheme requires signers to generate O(log λ) prime-numbers for each signature; however, our scheme requires almost a constant time (e.g., log log λ) of prime-number generation in the signing algorithm, where λ is the security parameter.
Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks

To investigate whether multi-view convolutional neural networks can improve a fully automated lymph node detection system for pelvic MR Lymphography (MRL) images of patients with prostate cancer.A fully automated computer-aided detection (CAD) system had been previously developed to detect lymph nodes in MRL studies. The CAD system was extended with three types of 2D multi-view convolutional neural networks (CNN) aiming to reduce false positives (FP). A 2D multi-view CNN is an efficient approximation of a 3D CNN, and three types were evaluated: a 1-view, 3-view, and 9-view 2D CNN. The three deep learning CNN architectures were trained and configured on retrospective data of 240 prostate cancer patients that received MRL images as the standard of care between January 2008 and April 2010. The MRL used ferumoxtran-10 as a contrast agent and comprised at least two imaging sequences: a 3D T1-weighted and a 3D T2*-weighted sequence. A total of 5089 lymph nodes were annotated by two expert readers, reading in consensus. A first experiment compared the performance with and without CNNs and a second experiment compared the individual contribution of the 1-view, 3-view, or 9-view architecture to the performance. The performances were visually compared using free-receiver operating characteristic (FROC) analysis and statistically compared using partial area under the FROC curve analysis. Training and analysis were performed using bootstrapped FROC and 5-fold cross-validation.Adding multi-view CNNs significantly (p < 0.01) reduced false positive detections. The 3-view and 9-view CNN outperformed (p < 0.01) the 1-view CNN, reducing FP from 20.6 to 7.8/image at 80% sensitivity.Multi-view convolutional neural networks significantly reduce false positives in a lymph node detection system for MRL images, and three orthogonal views are sufficient. At the achieved level of performance, CAD for MRL may help speed up finding lymph nodes and assessing them for potential metastatic involvement.
Biomedical Image Security Using Matrix Manipulation and DNA Encryption

Biomedical image analysis is an integral part of the modern healthcare industry and has a huge impact on the modern world. Automated computer-aided systems are highly beneficial for fast, accurate and efficient diagnosis of the biomedical images. Remote healthcare systems allow doctors and patients to perform their jobs from separate geographic locations. Moreover, expert opinion about a patient can be obtained from a doctor who is in a different country or in some distant location within stipulated amount of time. Remote healthcare systems require digital biomedical images to be transferred over the network. But several security threats are associated with the transmission of the biomedical images. Privacy of the patients must be preserved by keeping the images safe from any unauthorized access. Moreover, the contents of the biomedical images must be preserved efficiently so that no one can tamper it. Data tampering can produce drastic results in many cases. In this work, a method for biomedical image security has been proposed. DNA encryption method is one of the emerging methods in the field of cryptography. A secure and lossless encryption method is developed in this work. Various numerical parameters are used to evaluate the performance of the proposed method which proves the effectiveness of the algorithm.
Where2Change: Change Request Localization for App Reviews

Million of mobile apps have been released to the market. Developers need to maintain these apps so that they can continue to benefit end users. Developers usually extract useful information from user reviews to maintain and evolve mobile apps. One of the important activities that developers need to do while reading user reviews is to locate the source code related to requested changes. Unfortunately, this manual work is costly and time consuming since: (1) an app can receive thousands of reviews, and (2) a mobile app can consist of hundreds of source code files. To address this challenge, Palomba <i>et al.</i> recently proposed <monospace>CHANGEADVISOR</monospace> that utilizes user reviews to locate source code to be changed. However, we find that it cannot identify real source code to be changed for part of reviews. In this work, we aim to advance Palomba <i>et al.</i> 's work by proposing a novel approach that can achieve higher accuracy in change localization. Our approach first extracts the informative sentences (i.e., user feedback) from user reviews and identifies user feedback related to various problems and feature requests, and then cluster the corresponding user feedback into groups. Each group reports the similar users’ needs. Next, these groups are mapped to issue reports by using <inline-formula><tex-math notation="LaTeX">$Word2Vec$</tex-math></inline-formula> . The resultant enriched text consisting of user feedback and their corresponding issue reports is used to identify source code classes that should be changed by using our novel <i>weight selection</i> -based cosine similarity metric. We have evaluated the new proposed change request localization approach ( <monospace>Where2Change</monospace> ) on 31,597 user reviews and 3,272 issue reports of 10 open source mobile apps. The experiments demonstrate that <monospace>Where2Change</monospace> can successfully locate more source code classes related to the change requests for more user feedback clusters than <monospace>CHANGEADVISOR</monospace> as demonstrated by higher Top-N and Recall values. The differences reach up to 17 for Top-1, 18.1 for Top-3, 17.9 for Top-5, and 50.08 percent for Recall. In addition, we also compare the performance of <monospace>Where2Change</monospace> and two previous Information Retrieval (IR)-based fault localization technologies: <monospace>BLUiR</monospace> and <monospace>BLIA</monospace> . The results showed that our approach performs better than them. As an important part of our work, we conduct an empirical study to investigate the value of using both user reviews and historical issue reports for change request localization; the results shown that historical issue reports can help to improve the performance of change localization.
Estimating Gradients for Discrete Random Variables by Sampling without Replacement

We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples. We show that our estimator can be derived as the Rao-Blackwellization of three different estimators. Combining our estimator with REINFORCE, we obtain a policy gradient estimator and we reduce its variance using a built-in control variate which is obtained without additional model evaluations. The resulting estimator is closely related to other gradient estimators. Experiments with a toy problem, a categorical Variational Auto-Encoder and a structured prediction problem show that our estimator is the only estimator that is consistently among the best estimators in both high and low entropy settings.
Improving Generalization in Meta Reinforcement Learning using Neural Objectives

Biological evolution has distilled the experiences of many learners into the general learning algorithms of humans. Our novel meta-reinforcement learning algorithm MetaGenRL is inspired by this process. MetaGenRL distills the experiences of many complex agents to meta-learn a low-complexity neural objective function that affects how future individuals will learn. Unlike recent meta-RL algorithms, MetaGenRL can generalize to new environments that are entirely different from those used for meta-training. In some cases, it even outperforms human-engineered RL algorithms. MetaGenRL uses off-policy second-order gradients during meta-training that greatly increase its sample efficiency.
Generating justifications for norm-related agent decisions

We present an approach to generating natural language justifications of decisions derived from norm-based reasoning.Assuming an agent which maximally satisfies a set of rules specified in an object-oriented temporal logic, the user can ask factual questions (about the agent's rules, actions, and the extent to which the agent violated the rules) as well as "why" questions that require the agent comparing actual behavior to counterfactual trajectories with respect to these rules.To produce natural-sounding explanations, we focus on the subproblem of producing natural language clauses from statements in a fragment of temporal logic, and then describe how to embed these clauses into explanatory sentences.We use a human judgment evaluation on a testbed task to compare our approach to variants in terms of intelligibility, mental model and perceived trust.
Needs Analysis Of Infographic Media Using Technology for Learning Physics

In general, students still have problems in learning Physics, especially on the concept of kinematics of rectilinear motions. One way for students to easily understand the concept is with the help of learning media using technology. This study aims to analyze the needs of infographic media for physics learning of the concept kinematics of rectilinear motions, especially for the concept of uniform rectilinear motion and accelerated uniform motion in Senior High School. This research was conducted to teachers, students, and document analysis, namely syllabus and list of instructional media at Senior High School in Sambas district using interview technique, observation, and documentation study. Researcher use triangulation and membercheck to ensure the validity of the findings of this study. The results showed that based on document analysis showed the lack of supporting books and no instructional media for the kinematics of rectilinear motions concept in selected schools and based on interviews to teachers and students that they are very interested in infographic learning media and are in need of infographic as a medium of Physics learning, the concept of kinematics rectilinear motions. The intended infographic media will be well packaged and accessible via facebook social media and backed up using smartphones as a learning tool for learners.
The Real Effects of Environmental Activist Investing

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Blind Spot Detection for Safe Sim-to-Real Transfer

Agents trained in simulation may make errors when performing actions in the real world due to mismatches between training and execution environments. These mistakes can be dangerous and difficult for the agent to discover because the agent is unable to predict them a priori. In this work, we propose the use of oracle feedback to learn a predictive model of these blind spots in order to reduce costly errors in real-world applications. We focus on blind spots in reinforcement learning (RL) that occur due to incomplete state representation: when the agent lacks necessary features to represent the true state of the world, and thus cannot distinguish between numerous states. We formalize the problem of discovering blind spots in RL as a noisy supervised learning problem with class imbalance. Our system learns models for predicting blind spots within unseen regions of the state space by combining techniques for label aggregation, calibration, and supervised learning. These models take into consideration noise emerging from different forms of oracle feedback, including demonstrations and corrections.&#x0D; We evaluate our approach across two domains and demonstrate that it achieves higher predictive performance than baseline methods, and also that the learned model can be used to selectively query an oracle at execution time to prevent errors. We also empirically analyze the biases of various feedback types and how these biases influence the discovery of blind spots. Further, we include analyses of our approach that incorporate relaxed initial optimality assumptions. (Interestingly, relaxing the assumptions of an optimal oracle and an optimal simulator policy helped our models to perform better.) We also propose extensions to our method that are intended to improve performance when using corrections and demonstrations data.
Parallel spatial-temporal convolutional neural networks for anomaly detection and location in crowded scenes

Anomaly detection and location in crowded scenes have attracted a lot of attention in computer vision research community recently due to the increased applications of intelligent surveillance improve security in public. We propose a novel parallel spatial-temporal convolution neural networks model to detect and localize the abnormal behavior in video surveillance. Our approach contains two main steps. Firstly, considering the typical position of camera and the large number of background information, we introduce a novel spatial-temporal cuboid of interest detection method with varied-size cell structure and optical flow algorithm. Then, we use the parallel 3D convolution neural networks to describe the same behavior in different temporal-lengths. That step ensures that the most of behavior information in cuboids could be captured, also insures the reduction of information unrelated to the major behavior. The evaluation results on benchmark datasets show the superiority of our method compared to the state-of-the-art methods.