Reviewerly/exHarmony huggingface dataset:
Efektivitas media pembelajaran video tutorial terhadap hasil belajar mahasiswa pada Matakuliah IPA Sekolah

This study aimed to describe; (1) The learning outcomes of science department students of Makassar State University on school science courses using video tutorial based learning media; (2) The effectiveness of using tutorial video-based learning media on learning school science courses. This research is a one-shot case study experimental study. The instruments used in the study were observation sheets, questionnaires, and test results. Data analysis techniques were carried out by descriptive statistics and inferential statistical analysis. The results of the study concluded that; (1) The learning outcomes of science department students of Makassar State University towards school science courses after using a practical video tutorial based learning media are also said to be quite good, this is evidenced by the average students values of 75.4; (2) The use of tutorial video-based learning media on School Science course learning will be effective if it is applied in the school's science class. This is evidenced by the H 0 value which stated that the highest learning outcomes is 70% rejected, because the student learning outcomes exceed 70% which is 76.95%.
Linguistic strategies prompting interactions in recipes from Mandarin Chinese food blogs

Abstract This study investigates linguistic strategies used in recipes from Mandarin Chinese food blogs that prompt interactions between writers and readers. Using analytical concepts such as contextualization cues and frames to analyze 122 recipes collected from five popular food blogs in Taiwan, this study explicates two research questions. First, what linguistic strategies do writers frequently employ as contextualization cues to prompt interactions from readers? Second, how do these contextualization cues help readers choose frames when responding to writers? The findings show that writers of popular food blogs often adopt three linguistic strategies to engage readers’ discussions on recipes: narrative orientations, speech acts, and direct reported speech from family members. Three implications arise from interactions in the context of food blogs. First, writers usually adopt manifold contextualization cues to establish the frames of recipes intended by them. Second, the ways writers and readers use language to discuss food and create coherent discourses on food construct food blogs in Taiwan as an online community. Finally, recipes may reflect social phenomena, i.e. in Taiwan, more and more people, especially female caregivers in their families who are concerned about health, started to cook after serious breaches of food safety.
A Novel Predictive Resource Allocation Framework for Cloud Computing

Cloud computing is a new paradigm in which computing is offered as services rather than physical products. Computing services are provided by third-party service providers which offer consumers affordable and flexible computing services via shared resources. Cloud providers offer different layers/levels of services to consumers over many types of application domains. Server consolidation is a technique for reducing operating costs of computer resources in virtualization. These expenses can lead to increasement of financial costs of servers, power consumption of servers, data-center cooling systems and labour costs. It is inefficient if there are under-utilized servers that have more space and consume more resources. The proposed method meets the requirements for effective resource allocation for cloud computing. The effectiveness of the proposed method is evaluated and reveals higher performance in cloud computing.
Bayesian Optimization for Recommender System

Many web services have a Recommender System to help the users in their choices such as movies to watch or products to buy. The aim is to make accurate predictions on the user preferences depending on his/her past choices. Matrix-factorization is one of the most widely adopted method to build a Recommender System. Like many Machine Learning algorithms, matrix-factorization has a set of hyper-parameters to tune, leading to a complex expensive black-box optimization problem. The objective function maps any possible hyper-parameter configuration to a numeric score quantifying the quality of predictions. In this work, we show how Bayesian Optimization can efficiently optimize three hyper-parameters of a Recommender System: number of latent factors, regularization term and learning rate. A widely adopted acquisition function, namely Expected Improvement, is compared with a variant of Thompson Sampling. Numerical for both a benchmark 2-dimensional test function and a Recommender System evaluated on a benchmark dataset proved that Bayesian Optimization is an efficient tool for improving the predictions of a Recommendation System, but a clear choice between the two acquisition function is not evident.
LQR based Fuzzy Logic Rudder Control System using DC Servo Motor

The D.C servo motor is a stable system. In a real-time, stable system is easy to control and it fit for any controllers. This D.C servo motor is used for aircraft rudder position control. Fuzzy logic based LQR controller is proposed to control the position of DC servo motor. This process is useful to control the rudder position of the aircraft system. The D.C servo motor consists of a weight disk that mounted on the rotor. If there is a change in the weight of the disk produces an error value. The error value and rate of change error value are given as the two inputs to the fuzzy logic. The output of fuzzy logic gives the Q value by which plant control action is done effectively. The weight disk acts as a disturbance to the servo motor, it moves stables system to an unstable system. The fuzzy logic implemented with LQR controller helps to reduce the error and it automatically controls the servo motor. From this fuzzy output, Q value makes changes in the LQR controller it helpful to obtain optimal gain value. Fuzzy-based LQR controller produces better steady-state position output than the PID controller steady-state position output.
A Simple and Effective Initialization of CNN for Forensics of Image Processing Operations

In this paper we present a simple yet effective initialization method for convolutional neural networks (CNNs). The proposed method extends the well-known Xavier initialization and can cope well with CNNs used for forensic detection of image processing operations. Our initialization inherits the simplicity and advantages of the Xavier initialization, and the difference is that our method generates a set of high-pass filters for the initialization of CNN's first layer. This allows us to better identify forensic traces which usually lie towards the high-frequency part of the image. We test the proposed method with two CNNs for two forensic problems, i.e., a multiclass classification problem of a group of image processing operations and a median filtering forensic problem with JPEG post-processing. Experimental results show the utility of our initialization.
External Validation of a “Black-Box” Clinical Predictive Model in Nephrology: Can Interpretability Methods Help Illuminate Performance Differences?

The number of machine learning clinical prediction models being published is rising, especially as new fields of application are being explored in medicine. Notwithstanding these advances, only few of such models are actually deployed in clinical contexts for a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients when applied to an external cohort of a German research hospital. To help account for the performance differences observed, we utilized interpretability methods which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. We argue that such methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies.
Screening of Effectiveness Evaluation Index and Construction of Network Index System of Command and Control System

Based on the comprehensive evaluation index of the force system, this paper chooses effectiveness evaluation indicators of command and control system extensively which include 5 first-level indicators, 11 second-level indicators and 86 bottom-level indicators. Firstly, it uses “Rcluster + coefficient of variation analysis” method to quantitatively screen the 86 bottom-level indicator, 58 indicators were retained. Secondly, improve the interpretation of the structural model method is used to construct the rule network index system for the first-level and second-level indicators. Finally, constructed a network index system for the effectiveness evaluation of the command and control system. The study result reveals that the final indicators system reflects 96.6% of original information by 67.4% of initial indicator and representing the network relationship between indicators.
A method for real-time error detection in low-cost environmental sensors data

Purpose The purpose of this paper is to propose a simple, fast, and effective method for detecting measurement errors in data collected with low-cost environmental sensors typically used in building monitoring, evaluation, and automation applications. Design/methodology/approach The method combines two unsupervised learning techniques: a distance-based anomaly detection algorithm analyzing temporal patterns in data, and a density-based algorithm comparing data across different spatially related sensors. Findings Results of tests using 60,000 observations of temperature and humidity collected from 20 sensors during three weeks show that the method effectively identified measurement errors and was not affected by valid unusual events. Precision, recall, and accuracy were 0.999 or higher for all cases tested. Originality/value The method is simple to implement, computationally inexpensive, and fast enough to be used in real-time with modest open-source microprocessors and a wide variety of environmental sensors. It is a robust and convenient approach for overcoming the hardware constraints of low-cost sensors, allowing users to improve the quality of collected data at almost no additional cost and effort.
Bringing Context into Emoji Recommendations (poster)

We present Reeboc that combines machine learning and k-means clustering to analyze the conversation of a chat, extract different emotions or topics of the conversation, and recommend emojis that represent various contexts to the user. Instead of simply analyzing a single input sentence, we consider recent sentences exchanged in a conversation. we performed a user study with 17 participants in 8 groups in a realistic mobile chat environment. Participants spent the least amount of time in identifying and selecting the emojis of their choice with Reeboc (38% faster than without emoji recommendation).
An Efficient Revocable Attribute-Based Signcryption Scheme with Outsourced Designcryption in Cloud Computing

Sensitive data sharing through cloud storage environments has brought varies and flexible secure demands. Attribute-based signcryption (ABSC) is suitable for cloud storage because it provides combined data confidentiality and authentication, and fine-grained data access control. While, the existed ABSC schemes hardly support efficient attribute revocation. In addition, the heavy computational overhead of ABSC limits the applying resource-constrained device in cloud storage environments. In this paper, to tackle the above problems, we propose an efficiently revocable attribute-based signcryption scheme with decryption outsourcing. The proposed scheme achieves the efficient attribute revocation through delegating the cloud server to update ciphertext without decrypting it. During the decryption phase, it outsource massive decryption operations to the proxy server so that computation cost on user’s devices is small and constant. The security analysis proves the correctness, confidentiality, collusion resistance, unforgeability and forward secrecy of our scheme. Furthermore, performance analysis shows that our scheme is efficient in terms of the ciphertext, key size and computation cost while realizing desired functions.
A Technical Survey on Methods for Detecting Rogue Access Points

These days, wireless networks have become an indispensable environment in our life. Wireless broadcasting APs (Access Points) are deployed all around and a user can easily connect to the public network anytime and anywhere. In this situation, an attacker can steal data from the connected user via wireless connection. Rogue AP (Rogue Access Point) is one of deodorization methods of attackers that solicitates users to connect to the rogue AP and intercept user data by setting up an AP with the same configuration of legitimate APs to make users not to distinguish it from the others installed in public places. In this way, if a user is connected to the Rogue AP, an attacker can cause critical problems by taking the user’s data and exploiting it for payment, authentication, and etc. Several researchers have studied ways to detect and prevent rogue APs based on dependent information including SSID (Service Set Identifier) and MAC (Media Access Control) addresses. This paper surveys the trials and methods to mitigate the rogue APs problems. In the survey, the components that can be used are listed and analyzed, enabling them to serve as a cornerstone for further work and present a vision for future research.
Multiple-Instance Discriminant Analysis for Weakly Supervised Segment Annotation

In this paper, we propose a multiple-instance discriminant analysis algorithm for weakly supervised segment annotation. We introduce a selection parameter for each image/video with weak labels and expect that it can sift out object regions from the background clutter to train a better transformation vector. The selection parameter and the transformation parameter are incorporated into a single objective function and optimized in an alternate way. The optimization is an iteration between the eigenvalue decomposition and a set of quadratic programming. We also integrate a regularization term into the objective function to formulate the spatial constraint of segments, which is ignored in ordinary multiple-instance learning methods. The algorithm is able to overcome the limitations that arise when applying ordinary multiple-instance methods to the task. The experimental results validate the effectiveness of our method.
Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues

DensePose supersedes traditional landmark detectors by densely mapping image pixels to body surface coordinates. This power, however, comes at a greatly increased annotation time, as supervising the model requires to manually label hundreds of points per pose instance. In this work, we thus seek methods to significantly slim down the DensePose annotations, proposing more efficient data collection strategies. In particular, we demonstrate that if annotations are collected in video frames, their efficacy can be multiplied for free by using motion cues. To explore this idea, we introduce DensePose-Track, a dataset of videos where selected frames are annotated in the traditional DensePose manner. Then, building on geometric properties of the DensePose mapping, we use the video dynamic to propagate ground-truth annotations in time as well as to learn from Siamese equivariance constraints. Having performed exhaustive empirical evaluation of various data annotation and learning strategies, we demonstrate that doing so can deliver significantly improved pose estimation results over strong baselines. However, despite what is suggested by some recent works, we show that merely synthesizing motion patterns by applying geometric transformations to isolated frames is significantly less effective, and that motion cues help much more when they are extracted from videos.
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees

Deep Reinforcement Learning (DRL) has led to many recent breakthroughs on complex control tasks, such as defeating the best human player in the game of Go. However, decisions made by the DRL agent are not explainable, hindering its applicability in safety-critical settings. Viper, a recently proposed technique, constructs a decision tree policy by mimicking the DRL agent. Decision trees are interpretable as each action made can be traced back to the decision rule path that lead to it. However, one global decision tree approximating the DRL policy has significant limitations with respect to the geometry of decision boundaries. We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions. We propose a training procedure to support non-differentiable decision tree experts and integrate it into imitation learning procedure of Viper. We evaluate our algorithm on four OpenAI gym environments, and show that the policy constructed in such a way is more performant and better mimics the DRL agent by lowering mispredictions and increasing the reward. We also show that MoET policies are amenable for verification using off-the-shelf automated theorem provers such as Z3.
Collaborating CNN and SVM for Automatic Image Annotation

To learn a well-performed image annotation model, a large number of labeled samples are usually required. In this paper, we propose a novel semi-supervised approach based on adaptive weighted fusion for automatic image annotation, which can utilize the labeled data and unlabeled data simultaneously. Firstly, two different classifiers, namely the CNN (convolutional neural network) and the LDA-SVM, are constructed by all the labeled data. These two classifiers are independently represented as different feature views. Then, the most confident data with relevant pseudo-labels are chosen and amalgamated with the whole labeled dataset. After that, the two classifiers are retrained with the new labeled dataset until a stop condition is reached. In each iteration process, the unlabeled samples are labeled by high confidence pseudo-labels that are estimated by an adaptive weighted fusion strategy. Finally, we conduct experiments on two datasets, namely IAPR TC12 and NUS-WIDE, and measure the performance of the model with standard criteria, including precision, recall, F-measure, N+ and mAP. The experimental results show that our approach outperforms many state-of-the-art automatic image annotation approaches.
Two-Layer Feature Selection Algorithm for Recognizing Human Emotions from 3D Motion Analysis

Research on automatic recognition of human emotion from motion is gaining momentum, especially in the areas of virtual reality, robotics, behavior modeling, and biometric identity recognition. One of the challenges is to identify emotion-specific features from a vast number of expressive descriptors of human motion. In this paper, we have developed a novel framework for emotion classification using motion features. We combined a filter-based feature selection algorithm and a genetic algorithm to recognize four basic emotions: happiness, sadness, fear, and anger. The validity of the proposed framework was confirmed on a dataset containing 30 subjects performing expressive walking sequences. Our proposed framework achieved a very high recognition rate outperforming existing state-of-the-art methods in the literature.
Context-Aware Continuous Authentication and Dynamic Device Pairing for Enterprise IoT

Enterprise IoT is an advanced extension of Internet of Things (IoT) that enables intelligent enterprise systems. It focuses on connecting the enterprise assets and their devices with backend application services and frontend user interactions. While it is more complicated and more impactful than smart home, it faces several challenges such as a large number of IoT devices deployed at various locations and also multiple users to be granted with different permissions. To solve these challenges, we propose a multi-domain enterprise IoT system in consideration of both user movement and IoT device relocation. We propose a context-aware continuous authentication method to authenticate mobile users to the IoT domains based on the context inferred from various sensors on the smartphones and in the IoT domains. We also propose a dynamic device pairing method to support the mobility of users and IoT devices based on data (e.g., contexts, access history, etc.) sharing among the IoT domain, the enterprise server, and the mobile users.
Side-Channel Analysis of AES Based on Deep Learning

Side-channel attacks avoid complex analysis of cryptographic algorithms, instead they use side-channel signals captured from a software or a hardware implementation of the algorithm to recover its ...
ADD-FAR

For ensuring safety and for handling emergency situations, most autonomous vehicles use remote human operators by communicating the road condition captured using LiDAR (Light Detection and Ranging) and stereo RGB cameras [10, 11]. Recent research [2, 3] has identified several possible scenarios of forgery attacks during such data communication between autonomous vehicles and human operators. Hence, there is a distinct requirement for forensics research on the multi-modal LiDAR and RGB camera data generated from autonomous vehicles. In this paper, we present a new dataset, ADD-FAR (Attacked Driving Dataset for Forensics Analysis and Research) that contains forged driving scenarios based on KITTI Vision Benchmark Suite [4, 5]. This dataset is created by identifying objects of interest using automated 3D object detection and carrying out the attacks with different levels of risk as defined in [3]. As part of the dataset, we are also providing the scripts used for automatically generating the different types of attacks. These scripts can easily be modified to work with other driving datasets apart from KITTI as well as for creating new forms of attacks.
Keyword-Driven Depressive Tendency Model for Social Media Posts

People are increasingly sharing posts on social media (e.g., Facebook, Twitter, Instagram) that include references to their moods/feelings pertaining to their daily lives. In this study, we used sentiment analysis to explore social media messages for hidden indicators of depression. In cooperation with domain experts, we defined a tendency towards depression as evidenced in social media messages based on DSM-5, a standard classification of mental disorders widely used in the U.S. We also developed three data engineering procedures for the extraction of keywords from posts presenting a depressive tendency. Finally, we created a keyword-driven depressive tendency model by which to detect indications of depression in posts on a major social media platform in Taiwan (PTT). The performance of the proposed model was evaluated using three keyword extraction procedures. The DSM-5-based procedure with manual filtering resulted in the highest accuracy (0.74).
Holographic Projection of 3D Realistic Avatar That Mimics Human Body Motion

Non-realistic avatar and environment representations are widely used since it’s faster and more economical to produce while requiring less computational. With the absence of important behavioral cues, it resulted in low level of interaction that is acquired by the user and the viewer/s. This paper focuses on creating a 3D realistic avatar that mimics real-time human body motion. Mimicking the human body motion of the 3D Avatar is possible through the use of Kinect V2 where a 3D model can follow the movement of the person standing in front of the Kinect sensor. The system is also equipped with a projector, two mobile devices, speaker and a pyramid hologram that should provide a 3D hologram display.