Reviewerly/exHarmony huggingface dataset:
Occupancy Sensing and Activity Recognition with Cameras and Wireless Sensors

We present a system work combining visual cameras and wireless sensors for human occupancy detection and activity recognition. We describe our testbed system, data collected from a human subject study, observations from long-term occupancy experiments, and preliminary analytical results. We apply machine learning algorithms to the human activity recognition data, and identify challenges in applying the state-of-the-art deep learning techniques to wireless sensing of human activity. We find that packet loss due to wireless interference has a significant effect on time series classification. We also find that the convolutional neural networks significantly outperforms the conventional support vector machine method, but further experiments need to be performed to investigate environment-independent classification and the overfitting issue. Finally, we discuss future research topics that can use our testbed of wireless sensors and visual cameras to automate data labeling in deep learning model training.
Investigating Phishing Susceptibility—An Analysis of Neural Measures

Phishing is an attempt to acquire sensitive information from a user by malicious means. The losses due to phishing have exceeded a trillion dollars globally. In investigating phishing susceptibility, literature has largely examined structural and individual characteristics. Very little attention has been paid to neural measures within phishing contexts. In this paper, we explore the role of cognitive responses and correlated brain responses in phishing context. Such research is useful because a deeper understanding of persuasion techniques can inform the design of effective countermeasures for detecting and blocking phishing messages.
On a loss-based prior for the number of components in mixture models

We introduce a prior distribution for the number of components of a mixture model. The prior considers the worth of each possible mixture, measured by a loss function with two components: one measures the loss in information in choosing the wrong mixture and one the loss due to complexity.
"Magic Flowerpot": An AR Game for Learning about Plants

This paper presents an Augmented Reality (AR) pervasive game that aims to promote learning about the plants in our local environment. Players collect plants in their locale, grow them at home as they grow a real plant, and compose an AR garden with them. The game combines physical elements (i.e. a plant pot with sensors, RFID tags containing plant information) with virtual ones (i.e. AR representations of the plants). The final objective of this research is to investigate how the game experience is affected when moving from the virtual to the real world some components of the game, such as its context, its challenge or its reward. We aim to study if educational benefits can still be obtained when the game is only semi-integrated in the real world.
Learning Subjective Attributes of Images from Auxiliary Sources

Recent years have seen unprecedented research on using artificial intelligence to understand the subjective attributes of images and videos. These attributes are not objective properties of the content but are highly dependent on the perception of the viewers. Subjective attributes are extremely valuable in many applications where images are tailored to the needs of a large group, which consists of many individuals with inherently different ideas and preferences. For instance, marketing experts choose images to establish specific associations in the consumers' minds, while psychologists look for pictures with adequate emotions for therapy. Unfortunately, most of the existing frameworks either focus on objective attributes or rely on large scale datasets of annotated images, making them costly and unable to clearly measure multiple interpretations of a single input. Meanwhile, we can see that users or organizations often interact with images in a multitude of real-life applications, such as the sharing of photographs by brands on social media or the re-posting of image microblogs by users. We argue that these aggregated interactions can serve as auxiliary information to infer image interpretations. To this end, we propose a probabilistic learning framework capable of transferring such subjective information to the image-level labels based on a known aggregated distribution. We use our framework to rank images by subjective attributes from the domain knowledge of social media marketing and personality psychology. Extensive studies and visualizations show that using auxiliary information is a viable line of research for the multimedia community to perform subjective attributes prediction.
Performance Enhancement of Cloud Based Storage using Disk Scheduling Technique

Cloud-based high-performance storage techniques are proposed in this article. Primarily, the performance of storage is dependent on seek time of incoming track requests, IOPS(Input/output operations per second), and throughput. The proposed technique is designed to calculate the average seek time of incoming track requests, IOPS and throughput calculation, sorting algorithms and R/W head movement in order to represent the possibility of better performance than existing high performing algorithms.
Evaluation of ASTER TIR Data-Based Lithological Indices in Parts of Madhya Pradesh and Chhattisgarh State, India

The present study was performed in some parts of Madhya Pradesh and Chhattisgarh State, India to compare the different quartz indices, feldspar indices and mafic indices according to Ninomiya (2005) and Guha (2016) using thermal infrared (TIR) bands (band 10, band 11, band 12, band 13, and band 14) of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for detecting quartz, feldspar and mafic minerals. Results showed that these indices are equally useful for delineating quartz, feldspar or mafic minerals. It was noticed from the correlation coefficients that Guha’s mafic index (GMI) and Ninomiya’s mafic index (NMI) presented almost the same result. Guha’s quartz index (GQI) was more powerful than Ninomiya’s quartz index (NQI) in identifying quartz content in alkali granites and this GQI was also comparable with the Rockwall and Hofstra’s quartz index (RHQI) in identifying quartz content in alkali granite.
Cyber Physical IoT Device Management Using a Lightweight Agent

Increasingly IoT-enabled infrastructure such as smart cities, energy delivery systems, communication networks, manufacturing plants and transportation systems are unable to manage devices with various makes, models, configurations and applications. Considering the fragility of these industrial IoT devices and their cyber-physical nature, it is important not to impede on their limited memory and processing power. Existing solutions for IoT device management such as IoT platforms and hardware solutions have considerable downsides. We propose an agent-based mechanism to control and manage diverse IoT devices that has limited impact on device operations. Our agent is tested in the context of delivering security updates to IoT devices and demonstrates the efficiency of the agent-based management architecture.
Analysis of Chinese Comments on Douban Based on Naive Bayes

The naive Bayesian classification is commonly used as a basic machine learning classification. In this paper, we use the sentimental classifier based on naive Bayesian principle for Chinese text sentimental classification. The training set and test set of this article are both from the Chinese film reviews in Douban and crawled by python. Based on naive Bayesian principle, we construct a data sentiment classifier. And analyze the emotional inclination of the test text using the constructed sentimental classifier. And compared with the classification result of the classifier based on TF-IDF and the classification result of the sentimental naive Bayesian classifier which based on the traditional Chinese sentiment dictionary. To test the influence of the naive Bayesian classification and the composition of the training set on the Chinese text sentiment classification. The experimental results show that the naive Bayesian sentiment classifier based on the training set of the same data source can classify Chinese text in the uniform field efficiently and accurately.
Deep convolution network for dense crowd counting

Estimating the total number of people in a crowded situation is a challenging task due to numerous occlusions and perspective changes existing in crowd images. To address this issue, the authors have proposed a new deep learning framework for accurate and efficient crowd counting here. Inspired by multi-column convolutional neural network (MCNN) and contextual pyramid convolutional neural network (CP-CNN), the authors use a combination of a two branches, convolutional neutral network (CNN) and transposed convolutional layers, to generate a high-quality density map. The two-branch CNN for feature extraction generates a density map that is only a quarter of size of the original image Then a set of transposed convolutional layers and convolutional layers are combined with the network to make up for the detail loss of the density map conducted by stacked pooling. Compared with MCNN and CP-CNN, the authors’ approach employs fewer branches and simpler architecture. Experimental result shows that their approach achieves MAE 80.7 and MSE 131.2 in ShanghaiTech PartA dataset, MAE 15.6 and MSE 26.8 in ShanghaiTech PartB dataset, and MAE Average 7.1 in WorldExpo'10 dataset.
UAV Autonomous Landing using Visual Servo Control based on Aerostack

The autonomous landing for unmanned aerial vehicles (UAVs) plays an important role in driving the unmanned systems towards practical applications. However, the traditional position-based landing control method relies heavily on satellite navigation and positioning systems, which cannot be applied in environments with weak satellite signals such as indoors and valleys. In order to solve these problems, this paper proposes a visual servo control method for the UAV autonomous landing based on Aerostack software framework. The method takes the real-time on-board camera image as input and extracts tag features on the ground. In addition, the autonomous landing visual servo law is then constructed to obtain the velocity control signals which are able to guide the drone to land accurately. The experimental results in the Gazebo simulation environment show that the proposed algorithm can achieve robust and accurate autonomous landing without satellite navigation signals. Most specifically, the distance deviation is less than 0.1 meters and the yaw deviation is less than 0.1 radians.
Automated Attendance Systems Using Face Recognition by K-Means Algorithms

Attendance plays a crucial role in educational institutions, several of the other industries and workplaces. Nowadays it's taken by standard methodology i.e. Attendance are taken manually. This method takes a great deal of your time and additionally there might be an error. Face recognition system could be a technology capable of distinguishing or confirming someone from a digital image or from a video supply. In this paper, we tend to build a system that marks the presence of students or employees by recognizing their faces and manufacturing the attending sheet automatically. Face Recognition's accuracy rate is definitely littered with the factors like changes in illumination, posture changes, expression changes, and occlusion. In this paper, a K-means clustering algorithmic rule is employed to research the facial expression. The biometric features of the face unit are extracted and also the K-mean clustering technique is used to cluster the face features. Then, SVM methodology is employed to classify the features of the image. It may accomplish high recognition performance with fewer feature numbers. Finally, a report (attendance sheet) is generated for interpretation.
Authentication Protocol for Real-Time Wearable Medical Sensor Networks Using Biometrics and Continuous Monitoring

The open nature of wireless medical sensor networks in a public untrusted environment makes them vulnerable to various security threats and puts the security and privacy of patient information at risk. This paper introduces a new ECC based lightweight mutual authentication and key agreement protocol to be used in real-time wireless medical sensor networks between doctors/nurses, trusted servers, sensors and patients. Unlike existing schemes, our scheme uses biometrics on both doctor/nurse and patient sides. It allows the doctor/nurse to login to the system using his/her fingerprint and verifies patient identity by means of continuous monitoring of physiological data (e.g., ECG signals) in which verification of the patient identity is carried out automatically and at set intervals to detect physical theft of the sensor which may be hooked on to a different patient. Our scheme also uses dynamic identity to provide user anonymity and mitigate against user traceability. Security analysis shows that our protocol is resistant to the user, sensor and patient impersonation attacks, physical sensor theft, and so on. Performance analysis proved our scheme to be competitive in comparison to existing schemes relative to the added security benefits it provides.
Melody Slot Machine

This paper describes the "Melody Slot Machine," an interactive music system that enables control over virtual performers. Conventional virtual players focus on what kind of output performance is given to the input performance, and the performance output is difficult to control. The Melody Slot Machine enables the user to select the melody to be played next by the virtual player by rotating a dial. Furthermore, the performer is projected on a holographic display, and the user can feel as if a real virtual player is there. To achieve this, the system needs to change the melody and the performance video.
Fast Robustness Prediction for Deep Neural Network

Deep neural networks (DNNs) have achieved impressive performance in many difficult tasks. However, DNN models are essentially uninterpretable to humans, and unfortunately prone to adversarial attacks, which hinders their adoption in security and safety-critical scenarios. The robustness of a DNN model, which measures its stableness against adversarial attacks, becomes an important topic in both the machine learning and the software engineering communities. Analytical evaluation of DNN robustness is difficult due to the high-dimensionality of inputs, the huge amount of parameters, and the nonlinear network structure. In practice, the degree of robustness of DNNs is empirically approximated with adversarial searching, which is computationally expensive and cannot be applied in resource constrained settings such as embedded computing. In this paper, we propose to predict the robustness of a DNN model for each input with another DNN model, which takes the output of neurons of the former model as input. We train a regression model to encode the connections between output of the penultimate layer of a DNN model and its robustness. With this trained model, the robustness for an input can be predicted instantaneously. Experiments with MNIST and CIFAR10 datasets and LeNet, VGG and ResNet DNN models were conducted to evaluate the efficacy of the proposed approach. The results indicated that our approach achieved 0.05-0.21 mean absolute errors and significantly outperformed confidence and surprise adequacy-based approaches.
Nonlinear Causal Link Estimation Under Hidden Confounding with an Application to Time Series Anomaly Detection

Causality analysis represents one of the most important tasks when examining dynamical systems such as ecological time series. We propose to mitigate the problem of inferring nonlinear cause-effect dependencies in the presence of a hidden confounder by using deep learning with domain knowledge integration. Moreover, we suggest a time series anomaly detection approach using causal link intensity increase as an indicator of the anomaly. Our proposed method is based on the Causal Effect Variational Autoencoder (CEVAE) which we extend and apply to anomaly detection in time series. We evaluate our method on synthetic data having properties of ecological time series and compare to the vector autoregressive Granger causality (VAR-GC) baseline.
What Are Real JSON Schemas Like?

Recently, the semantics of the JSON Schema format, a de-facto standard for JSON schema declarations, has been formalized. It turns out that JSON Schema is a surprisingly complex schema language based on an open document semantics. In this paper, we present a first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing.
Chatbot-based assertion generation from natural language specifications

We present an approach to simplify the task of extracting assertions from specifications given in natural language. Our goal is to accept and understand a broad range of linguistic variation, allowing the author of the natural language specifications to express herself freely. To enable this, we leverage the Dialogflow framework from Google. Dialogflow is usually used to build chatbots that understand and respond to conversational statements. We have trained a Dialogflow model to recognize a range of different natural language expressions of properties, and to identify key information inside the expression. The model responses to each statement with a generated SystemVerilog assertion whose semantic meaning is equivalent to that of the English statement.
Nominal classification

Abstract It is often claimed that reference tracking is a key function of nominal classification systems, not least because of the role such systems can play in referent disambiguation. This paper reports on the results of a comparative study of reference in texts from four languages, focusing specifically on the disambiguating function of nominal classification. The results strongly suggest that disambiguation is not a primary function of nominal classification systems. While gender and/or classifiers sometimes contribute to the avoidance of referential conflict, the reality is that the conditions have to be just right – all competing references must be of opposing genders, and those genders must be formally distinct – and this happens with surprisingly low frequency. We are better off viewing disambiguation as a convenient by-product of nominal classification systems that a language can exploit when conditions allow.
Weighted distributed differential privacy ERM: Convex and non-convex

Distributed machine learning is an approach allowing different parties to learn a model over all data sets without disclosing their own data. In this paper, we propose a weighted distributed differential privacy (WD-DP) empirical risk minimization (ERM) method to train a model in distributed setting, considering different weights of different clients. We guarantee differential privacy by gradient perturbation, adding Gaussian noise, and advance the state-of-the-art on gradient perturbation method in distributed setting. By detailed theoretical analysis, we show that in distributed setting, the noise bound and the excess empirical risk bound can be improved by considering different weights held by multiple parties. Moreover, considering that the constraint of convex loss function in ERM is not easy to achieve in some situations, we generalize our method to non-convex loss functions which satisfy Polyak-Lojasiewicz condition. Experiments on real data sets show that our method is more reliable and we improve the performance of distributed differential privacy ERM, especially in the case that data scale on different clients is uneven.
Towards Formal Verification of Plans for Cognition-Enabled Autonomous Robotic Agents

In this paper, we propose the first approach for verifying plans of cognition-enabled autonomous robots that perform everyday manipulation activities in human environments. Our methodology is based on the new Intermediate Plan Verification Language (IPVL) which is used to represent plans, environments, and robot belief states in one joint formal model. We devise a symbolic execution engine for IPVL and show the effectiveness of our overall verification methodology in a case study.
Multi-dimensional Evaluation of Temporal Neural Networks on Solar Irradiance Forecasting

With recent developments and advances in machine learning methods, traditional time series analysis techniques, e.g., ARMA, ARIMA, ARIMAX, SARIMAX, etc. models are being replaced by deep learning models. The application of Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) to the problem of solar irradiance prediction has been shown in the literature to achieve state-of-the-art performance in this domain. For sequence modeling, Temporal Convolutional Networks (TCN) are gaining increasing attention because of the excellent trade-off between performance accuracy and time in training the models. In this paper, an evaluation of these deep learning methods is completed on the application of short-term, one hour ahead solar irradiance forecasting. The results show that TCNs can reduce the resource usage, depending on the data-folding size and model complexity, to require only 5% of the training time with only a loss of 6% RMSE.
HD3

Live streaming applications are becoming increasingly popular recently, and it exposes new technical challenges compared to regular video streaming. High video quality and low latency are two main requirements in live streaming scenarios. A live streaming application needs to make bitrate and target buffer level decisions as well as sets a continuous latency limit value to skip video frames. We formulate the live streaming task as a reinforcement learning problem with discrete-continuous hybrid action spaces, then propose a novel deep reinforcement learning (DRL) algorithm HD3 which can take hybrid actions to solve it. We compare HD3 with several state-of-the-art DRL algorithms on various network environments, and the simulation results show that HD3 can outperform all the other comparison schemes. We emphasize that HD3 generates a single agent which can perform well on different network conditions and video scenes.
A-I-PoCoTo

PoCoTo is known as a web-based interactive tool for the postcorrection of OCR-results on historical texts. In this paper we first introduce A-PoCoTo, a fully automated extension of PoCoTo designed for the use in large-scale digitization projects. Among other features, A-PoCoTo takes into account the recognition results of several OCR-engines on the given input text, and sentence context is used for refining rankings and decisions. Preliminary evaluation results are given. In view of the very high level of accuracy needed for many scholarly applications it is questionable if a fully automated process is always able to fully meet the standards expected by researchers in Digital Humanities. We describe the architecture of A-I-PoCoTo, a postcorrection system (under development) combining automated postcorrection as a first step and interactive postcorrection as an optional second step. In A-I-PoCoTo decisions and correction steps of the automated component are stored in a special protocol. Views offered by the graphical user interface help to efficiently confirm, reject, or improve these decisions as a first step of the manual postcorrection.