Reviewerly/exHarmony huggingface dataset:
JTAG: A Multifaceted Tool for Cyber Security

JTAG is an IEEE standard for testing and debugging electronic circuits. In general, this standard allows serial test instructions and test data to be passed through the input ports. After execution of test instructions, the corresponding serial output can be shifted out. Due to this powerful low level hardware access, JTAG has been explored in literature for performing cyber attacks on devices. On the other hand, its potential has also been realized to defend against attacks at the lowest level. In this paper, we explore JTAG as a tool on both ends of the spectrum.
A Transfer Learning Approach for Emotion Intensity Prediction in Microblog Text

Emotional expressions are an important part of daily communication between people. Emotions are commonly transferred non verbally through facial expressions, eye contact and tone of voice. With the rise in social media usage, textual communication in which emotions are expressed has also witnessed a great increase. In this paper automatic emotion intensity prediction from text is addressed. Different approaches are explored to find out the best model to predict the degree of a specific emotion in text. Experimentation was conducted using the dataset provided by SemEval-2018 Task 1: Affect in Tweets. Experiments were conducted to identify regression systems and parameter settings that perform consistently well for this problem space. The presented research highlights the importance of the Transfer Learning approach in inducing knowledge from state of the art models in sentiment analysis for use in the task of emotion intensity prediction.
Traffic Classification and Application Identification Based on Machine Learning in Large-Scale Supercomputing Center

Internet traffic classification and application identification associated with network traffic is an essential step for network security and traffic engineering. The traditional port-based, payload-based or statistic-based classification methods do not work well in practice. To solve the problem, in this paper, we propose a new classification model which is verified by supervised learning algorithms (e.g., RandomForest, C4.5, KNN). The model includes two main parts: a clustering flow label propagation technique based on equivalent flow-labeled propagation and a synthetic-flow feature generation algorithm based on Bidirectional-flow (BDF). To further improve the accuracy of traffic classifiers and reduce the cost, we also propose a feature selection algorithm. The experiments are done on real-world network traffic from a large-scale Supercomputing center, showing that the proposed model achieves better performance and higher accuracy for network traffic classification.
Fusion of Multiple Representations Extracted from a Single Sensor’s Data for Activity Recognition Using CNNs

With the emerging ubiquitous sensing field, it has become possible to build assistive technologies for persons during their daily life activities to provide personalized feedback and services. For instance, it is possible to detect an individual's behavioral information (e.g. physical activity, location, and mood) by using sensors embedded in smartwatches and smartphones. To detect human's daily life activities, accelerometers have been widely used in wearable devices. In the current research, usually a single data representation is used, i.e., either image or feature vector representations. In this paper, a novel method is proposed to address two key aspects for the future development of robust deep learning methods for Human Activity Recognition (HAR): (1) multiple representations of a single sensor's data and (2) fusion of these multiple representations. The presented method utilizes Deep Convolutional Neural Networks (CNNs) and was evaluated using a publicly available HAR dataset. The proposed method showed promising performance, with the best result reaching an overall accuracy of 0.97, which outperforms current conventional approaches.
Continuous Modeling of Power Plant Performance with Regularized Extreme Learning Machine

Power plant modeling is critically important for power plant operation optimization and cost reduction. The inherently nonstationary characteristics of power plants raise a big challenge to the learning mechanisms and require the learning algorithms to adapt effectively and promptly to the continuously drifting environments. In our previous study, we proposed an online ensemble regression approach, with extreme learning machine (ELM) as the base model, to model power plant performance in a dynamic environment, which can autonomously update models to respond to environmental changes, either gradual or abrupt. However, one drawback we observed for the proposed approach is that the algorithm performance is not stable due to the randomness nature of ELMs. In this paper, we address this issue by applying regularized ELM as the base model within the online ensemble framework. The empirical results on three real power plant data sets demonstrate that the proposed modification can lead to more stable generalization performance of the algorithm. At the same time, the algorithm consistently achieves performance with mean average percentage error less than the required 1% threshold in real field operations.
A Block Cryptographic Algorithm for Wireless Sensor Networks Based on Hybrid Chaotic Map

Nodes in Wireless Sensor Networks (WSNs) have limitations in terms of computing speed, storage space and energy consumption. There are some drawbacks in existing algorithms for WSNs, such as too many complex operations in RC6 and large memory occupation in Rijndael. A chaotic block encryption algorithm is proposed in this paper, which aims to address these problems. The classical Feistel network structure is employed in the proposed algorithm, since it is a widely used block encryption structure with low resource consumption. Moreover, RC5 is used to generate round keys by using a hybrid chaotic map. Experimental results show that this work has a better performance than conventional algorithms in terms of security and efficiency. Thus, the proposed algorithm is feasible for the data security in WSNs.
A Deep Neural Network Model for Predicting User Behavior on Facebook

On Facebook, the social media site, liking, commenting and sharing the posts or status of other users are usually considered to be the key mechanisms for exchanging opinions about different topics. Due to the non-availability of data and security constraints, only few research studies have analyzed such behavior. In this paper, we introduced a novel deep neural network model for user behavior prediction (like and comment). We presented an embedding representation method for the textual content of comments and posts based on the contextual recursive auto-encoders model. The users were represented using a deep joint auto-encoders model to fuse the users' like and comment information, and train the users' combined embedding representation. Then, the user behaviors towards a given post were embedded into the same feature space of users and posts, using the joint auto-encoders model. Thereafter, we used a fully connected layer for behavior prediction. To train and evaluate the effectiveness of the proposed method, we also constructed a large dataset collected from Facebook. The experimental results show that the proposed method could achieve better results than the previous alternative methods.
Automatically Build Corpora for Chinese Spelling Check Based on the Input Method

Chinese Spelling Check (CSC) is very important for Chinese language processing. To utilize supervised learning for CSC, one of the main challenges is that high-quality annotated corpora are not enough in building models. This paper proposes new approaches to automatically build the corpora of CSC based on the input method. We build two corpora: one is used to check the errors in the texts generated by the Pinyin input method, called p-corpus, and the other is used to check the errors in the texts generated by the voice input method, called v-corpus. The p-corpus is constructed using two methods, one is based on the conversion between Chinese characters and the sounds of the characters, and the other is based on Automatic Speech Recognition (ASR). The v-corpus is constructed based on ASR. We use the misspelled sentences in real language situation as the test set. Experimental results demonstrate that our corpora can get a better checking effect than the benchmark corpus.
Behavioral Repertoire via Generative Adversarial Policy Networks

Learning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpected changes in the environment may require using different behaviors to achieve the same effect, for instance to reach and grasp an object in changing clutter. An emerging paradigm addressing this robustness issue is to learn a diverse set of successful behaviors for a given task, from which a robot can select the most suitable policy when faced with a new environment. In this paper, we explore a novel realization of this vision by learning a generative model over policies. Rather than learning a single policy, or a small fixed repertoire, our generative model for policies compactly encodes an unbounded number of policies and allows novel controller variants to be sampled. Leveraging our generative policy network, a robot can sample novel behaviors until it finds one that works for a new environment. We demonstrate this idea with an application of robust ball-throwing in the presence of obstacles. We show that this approach achieves a greater diversity of behaviors than an existing evolutionary approach, while maintaining good efficacy of sampled behaviors, allowing a Baxter robot to hit targets more often when ball throwing in the presence of obstacles.
A Privacy-Preserving Filter for Oblique Face Images Based on Adaptive Hopping Gaussian Mixtures

Photographs taken in public places often contain faces of bystanders thus leading to a perceived or actual violation of privacy. To address this issue, we propose to pseudo-randomly modify the appearance of face regions in the images using a privacy filter that prevents a human or a face recogniser from inferring the identity of people. The filter, which is applied only when the resolution is high enough for a face to be recognisable, adaptively distorts the face appearance as a function of its resolution. Moreover, the proposed filter locally changes the values of its parameters to counter attacks that attempt to estimate them. The filter exploits both global adaptiveness to reduce distortion and local parameter hopping to make their estimation difficult for an attacker. In order to evaluate the efficiency of the proposed approach, we consider an important scenario of oblique face images: photographs taken with low altitude Micro Aerial Vehicles (MAVs). We use a state-of-the-art face recognition algorithm and synthetically generated face data with 3D geometric image transformations that mimic faces captured from an MAV at different heights and pitch angles. Experimental results show that the proposed filter protects privacy while reducing distortion, and is also robust against attacks.
Learning Minority Class prior to Minority Oversampling

The success of minority oversampling in dealing with class-imbalanced dataset is well manifested by existing approaches. But that do not guarantee the true class of the synthetic minority points. We address the given context in this paper, Learning Minority Class prior to Minority Oversampling (LMCMO). To guarantee the class information of synthetic minority points, we estimate the minority spaces before generating the synthetic minority points. The performance efficiency of the LMCMO oversampled dataset is tested on C4.5 decision tree and Linear Support Vector Machine (SVM) classifier. Empirical evaluations on 21 datasets using four diversified metrics indicate substantial improvement in Linear SVM outcomes of the proposed method over four competing methods. A modest but still significant gain is achieved by our method over other methods on classification using C4.5 decision tree.
MDT: A Multi-Description Topic Based Clustering Approach for Composite-Service Discovery

With the rapid development of Service-Oriented Architecture that enables rapid composition and integration of large-scale distributed systems, the number of web accessible services and their compositions is increasing rapidly, which makes it difficult to efficiently discover desired composite-services from a sea of candidates. Recently, topic-based clustering techniques have emerged as a promising way to improve the efficiency and accuracy of service discovery. However, it is a challenge to extract topics from short description texts of composite-services, for short texts do not provide sufficient word occurrences. In view of this problem, a multi-description topic based clustering approach for composite-service discovery is proposed. In this approach, descriptions of the component services of each composite-service are combined for topic extraction and modeling. Then, composite-services are clustered based on their topic similarities. Finally, composite-services are discovered from the cluster that is most topic-similar to the service queries. The effectiveness of the proposed approach was verified by some experiments on the real-world data at the end of this paper.
Tiger

We propose Tiger, an eyewear system for helping users follow the 20-20-20 rule to alleviate the Computer Vision Syndrome symptoms. It monitors user's screen viewing activities and provides real-time feedback to help users follow the rule. For accurate screen viewing detection, we devise a light-weight multi-sensory fusion approach with three sensing modalities, color, IMU, and lidar. We also design the real-time feedback to effectively lead users to follow the rule. Our evaluation shows that Tiger accurately detects screen viewing events, and is robust to the differences in screen types, contents, and ambient light. Our user study shows positive perception of Tiger regarding its usefulness, acceptance, and real-time feedback.
Modeling and simulation of cooperative transport of an object by two mobile manipulators on an uneven terrain using KSOM network

In this paper, a learning-based method for cooperative load transport by two 16 DOF wheeled mobile manipulators (MM) on a rough surface is proposed. Each MM has a 10 DOF mobile platform with a six DOF manipulator on top of it. As the MM system is redundant, the standard pseudo inverse or Jacobian methods for finding the inverse kinematics solution cannot be used in real time. Thus, an advanced Kohonen self-organizing map (KSOM) network is used to learn the inverse kinematic mapping and resolve the redundancy of the MMs. Considering the nonholonomic constraints of the vehicle and manipulability measure of the manipulator, the forward kinematic model of the MM system is used to train the network. A user defined object path governs the end effector paths of the two MMs for carrying the object and the trained network resolves the redundancy of the MMs to follow the respective end effector paths. The advantage of the proposed method is that once a terrain is learned, multiple MMs can be simulated to carry an object together in real time, without complex inverse kinematics. Simulation results show the usefulness of the proposed method.
An Algorithm for Transmitting VR Video Based on Adaptive Modulation

Virtual reality (VR) is making waves around the world recently. However, traditional video streaming is not suitable for VR video because of the huge size and view switch requirements of VR videos. Since the view of each user is limited, it is unnecessary to send the whole 360-degree scene at high quality which can be a heavy burden for the transmission system. Assuming filed-of-view (FoV) of each user can be predicted with high probability, we can divide the video screen into partitions and send those partitions which will appear in FoV at high quality. Hence, we propose an novel strategy for VR video streaming. First, we define a quality-of-experience metric to measure the viewing experience of users and define a channel model to reflect the fluctuation of the wireless channel. Next, we formulate the optimization problem and find its feasible solution by convex optimization. In order to improve bandwidth efficiency, we also add adaptive modulation to this part. Finally, we compare our algorithm with other VR streaming algorithm in the simulation. It turns out that our algorithm outperforms other algorithms.
Approximate Bayesian Neural Network Trained with Ensemble Kalman Filter

Neural networks have achieved significant success in many areas. Nevertheless, conventional neural networks lack uncertainty information, which plays an important role especially in critical-safety applications such as self-driving cars. When uncertainty is characterized using probability the main modeling approach is the construction of Bayesian neural networks. Obtaining the posterior distribution for these models is computationally intensive and analytical solutions are intractable. In this work, we propose a novel algorithm to infer the weights for Bayesian neural networks based on the ensemble Kalman filter. To evaluate the performance of the algorithm, we use ten regression datasets from University of California at Irvine machine learning repository, and a natural language dataset. The results suggest that EnKF can be used as a gradient-free alternative to training deep neural networks to capture prediction uncertainty.
Power-Aware Adaptive Encryption

Recently, there has been a significant increase in number of resource-constrained devices, which are typically battery-powered. Encryption schemes are employed to protect privacy of exchanged data. In such devices, balancing data security and devices resources is a critical design challenge. For resource constrained devices, energy is the most crucial resource. The research motivation is to adapt cipher energy based to predefined power levels using a single cipher with multiple implementation options.In this paper we propose a scheme to adjust power and energy consumption of single lightweight cipher based on current device power level. The scheme adjusts cipher energy by varying design options and operation complexity of the cipher. This includes varying number of implemented rounds in hardware, key scheduling and number of algorithm iterations. While altering number of algorithm iteration reduces security, it is better than inhibiting encryption or shutting down the device. Our proposed scheme allows adjustable encryption for low power level. Results from Field-programmable gate array (FPGA) implementations show energy savings of 35%-39% compared with implementations of existing designs.
Learning Data Space Transformation Matrix from Pruned Imbalanced Datasets for Nearest Neighbor Classification

The nearest neighbor classifier is deemed to be the litmus test for the worst-case scenario and has been often been relied on by data mining researchers to test the robustness of their algorithms. However not all datasets are suited for the distance-based classification, and a prior transformation of the data space generally helps. This paper proposes a novel hybrid sampling with data space transformation that boosts the performance of the nearest neighbor classifier for imbalanced datasets. The SSOMaj-SMOTE-SSOMin three-step pruning and resampling technique, introduced in a recent work by the authors, is used in the first stage of our experiments for achieving a balance between the under-represented minority and over-represented majority class. In the second stage, the transformation matrix learnt from the pruned dataset is used to transform the pruned training distribution and the test sample space. The proposed method thus enforces the spatial arrangement of the sampled training dataset into the test sample space. A variety of popular data space transformation techniques are investigated for the application. The consequences of transforming the original training space, based on the transformation learnt from the pruned training set are also investigated. Experiments on benchmark datasets with comparison to the state-of-the-art prove the supremacy of our learning approach for imbalanced datasets.
Metric Learning based Framework for Streaming Classification with Concept Evolution

A primary challenge in label prediction over a stream of continuously occurring data instances is the emergence of instances belonging to unknown or novel classes. It is imperative to detect such novel-class instances quickly along the stream for a superior prediction performance. Existing techniques that perform novel class detection typically employ a clustering-based mechanism by observing that instances belonging to the same class (intra-class) are closer to each other (cohesion) than inter-class samples (separation). While this is generally true in low dimensional feature spaces, we observe that such a property is not intrinsic among instances in complex real-world high-dimensional feature space such as images and text. In this paper, we focus on addressing this key challenge that negatively affects prediction performance of a data stream classifier. Concretely, we develop a metric learning mechanism that transforms high-dimensional features into a latent feature space to make above property holds true. Unlike existing metric learning method which only focus on classification task, our approach address the novel class detection and stream classification simultaneously. We showcase a framework along the stream to achieve larger prediction performance compared to existing state-of-the-art detection techniques while using the least amount of labeled data during detection. Extensive experimental results on simulated and real-world stream demonstrate the effectiveness of our approach.
Computer vision and image recognition in archaeology

We apply existing image recognition services (Google Vision API and Clarifai Predict API) for an image repository of about 150,000 archaeological images from the Çatalhöyük Archaeological Project. We train a custom machine learning model from labeled images with Google AutoML. We report on our initial explorations and discuss their usefulness for extracting and enriching metadata for discovery and research in the archaeological domain.
Optimum Nutrition Intake from Daily Dietary Recommendation for Indonesian Children using Binary Particle Swarm Optimization Algorithm

Optimum nutrition intake in daily dietary habit has a significant role for children growth. Nevertheless, the mistakenness in the fulfillment of nutrition still concerned. It happens because an individual does not have much knowledge about the energy content of food and food combination to meet the nutrition requirement. The objectives of this research are to facilitate an individual to obtain the optimum nutrition intake from their daily dietary habit. This paper proposes a Binary Particle Swarm Optimization (BPSO) algorithm to find the optimum combination of food portion and food option for an individual daily dietary habit. The food data is obtained from ’Tabel Komposisi Pangan Indonesia’ book which contains more than 1600 kind of Indonesian food. The results show that BPSO provides an optimum nutrition intake accuracy of 99.14%. Moreover, the nutritionist is already validated that this experiment is succeed in recommending a variation of daily dietary habit that meet an optimum nutrition intake for an individual. Based on this result it can be conducted that BPSO can provide the better accuracy of optimum nutrition intake than Genetic Algorithm (GA), while GA can only provide an optimum nutrition intake accuracy of 97.87%.