Reviewerly/exHarmony huggingface dataset:
A Deep One-class Model for Network Anomaly Detection

Abstract For traditional network anomaly detection system, the detection performance is related to the selected features and training dataset. But traditional methods adopt handcraft feature selection, which requires heavy human labour and relies on the experts’ knowledge and experience. Besides, the collected dataset for training is not balanced, which makes the prediction of the trained model tends to be biased to the majority class. In this paper, a one-class network anomaly detection model based on the stacked autoencoders was proposed. We use the stacked autoencoders to select the prominent features from the raw collected data, then apply the one-class classification algorithm support vector data description to train a classifier to identify the network traffic into normal data and anomalous data. The experimental results demonstrate the promising results of our approach for network anomaly detection.
Realistically Fingerprinting Social Media Webpages in HTTPS Traffic

In webpage fingerprinting (WPF), an adversary attempts to identify webpages in encrypted network traffic. Identifying social media webpages however is a challenging task, due to the similarity and dynamic nature of such pages. Existing webpage fingerprinting attacks often have unrealistic assumptions regarding the capability of government agencies or knowledge of the criminal's environment, which renders these attacks ineffective when applied to social media platforms. In this paper, we unravel the current concerns in state of the art WPF attacks in a social network context for forensic analysis. To resolve the issues presented, we propose an enhanced version of the WPF attack 'IUPTIS' and introduce an intelligent observer that significantly improves upon previous works. Furthermore, our improvements are compared to related WPF attacks by conducting extensive experiments on two social platforms: Twitter and Instagram. Our examination shows that the improved IUPTIS attack defeats previous works in terms of realistic obstacles such as HTTP/2, caching and performance costs, thus making it feasible to identify social media webpages with minimal resources.
Privacy-Enhancing Fall Detection from Remote Sensor Data Using Multi-Party Computation

Motion-based fall detection systems are concerned with detecting falls from vulnerable users, which is typically performed by classifying measurements from a body-worn inertial measurement unit (IMU) using machine learning. Such systems, however, necessitate the collection of high-resolution measurements that may violate users' privacy, such as revealing their gait, activities of daily living (ADLs), and relative position using dead reckoning. In this paper, we investigate the application of multi-party computation (MPC) to IMU-based fall detection for protecting device measurement confidentiality. Our system is evaluated in a cloud-based setting that precludes parties from learning the underlying data using multiple, disparate cloud instances deployed in three geographical configurations. Using a publicly-available dataset, we demonstrate that MPC-based fall detection from IMU measurements is practical while achieving state-of-the-art error rates. In the best case, our system executes in 365.2 milliseconds, which falls well within the required time window for on-device data acquisition (750ms).
Automatic identification of firing pin impressions based on the Congruent Matching Cell (CMC) method

Firing pin impression is a kind of important physical evidence for forensic investigations. However, due to the small effective area and complex topography, the identifications are still rest on manual evaluations, and the studies on automatic recognition and identification are rare. In this paper, an automatic identification solution for three-dimensional (3D) firing pin impressions is proposed using a newly developed isotropic areal spline filter to extract the individual characteristics from the high order form of firing pin impressions, and an improved algorithm of the Congruent Matching Cells (CMC) method based on the valid data based normalized cross-correlation (VDNCC) to perform the identifications. This method is tested using 4,005 correlations from 90 cartridge cases. The results indicate that the proposed method is effective for identifications of firing pin impressions. The validation tests also show that the characteristics of the ammunitions may seriously affect the identifications and should be tested before performing test fires.
Towards a Dialect Classification in German Speech Samples

The automatic classification of a speaker’s dialect can enrich many applications, e.g. in the human-machine interaction (HMI) or natural language processing (NLP) but also in specific areas such as pronunciation tutoring, forensic analysis or personalization of call-center talks. Although a lot of HMI/NLP-related research has been dedicated to different tasks in affective computing, emotion recognition, semantic understanding and other advanced topics, there seems to be a lack of methods for an automated dialect analysis that is not based on transcriptions, in particular for some languages like German. For other languages such as English, Mandarin or Arabic, a multitude of feature combinations and classification methods has been tried already, which provides a starting point for our study. We describe selected experiments to train suitable classifiers on German dialect varieties in the corpus “Regional Variants of German 1” (RVG1). Our article starts with a systematic choice of appropriate spectral features. In a second step, these features are post-processed with different methods and used to train one Gaussian Mixture Model (GMM) per feature combination as a Universal Background Model (UBM). The resulting UBMs are then adapted to a varied selection of dialects by maximum-a-posteriori (MAP) adaptation. Our preliminary results on German show, that a dialect discrimination and classification is possible. The unweighted recognition accuracy ranges from 32.4 to 54.9% in a 3-dialects test and from 19.6 to 31.4% in a classification of 9-dialects. Some dialects are easier distinguishable, purely using spectral features, while others require a different feature set or more sophisticated classification methods, which we will explore in future experiments.
Privacy of outsourced two‐party <i>k</i>‐means clustering

Summary Many schemes for privacy‐preserving machine learning have been proposed over the past decade. Often, the entities want to keep the privacy of their data while performing machine learning tasks collaboratively, and institutions or end‐users are with limited computing and storage resources. To overcome these issues and to take benefits of cloud computing, it is possible to outsource the execution of a machine learning task to a computing service while retaining confidentiality of the participant's data. Clustering is one of the commonly used tasks in various machine learning and data mining applications. In this paper, we demonstrate that, by using homomorphic encryption, it is possible to outsource the execution of a two‐party k ‐means clustering algorithm to a single cloud server while retaining confidentiality of the test data. To the best of our knowledge, ours is the first reasonable scheme to discuss the two‐party k ‐means clustering algorithm to a single cloud server.
Evaluation on a Wheelchair Simulator Using Limited-Motion Patterns and Vection-Inducing Movies

Existing virtual reality (VR) based wheelchair simulators have difficulty providing both visual and motion feedback at low cost. To address this issue, we propose a VR-based wheelchair simulator using a combination of motions attainable by an electric-powered wheelchair and vection-inducing movies displayed on a head-mounted display. This approach enables the user to have a richer simulation experience, because the scenes of the movie change as if the wheelchair performs motions that are not actually performable. We developed a proof of concept using only consumer products and conducted evaluation tasks, confirming that our approach can provide a richer experience for barrier simulations.
Tackling knowledge needs during software evolution

Developers use a large amount of their time to understand the system they work on, an activity referred to as program comprehension. Especially software evolution and forgetting over time lead to developers becoming unfamiliar with a system. To support them during program comprehension, we can employ knowledge recovery to reverse engineer implicit information from the system and the platform (e.g., GitHub) it is hosted on. However, to recover useful knowledge and to provide it in a useful way, we first need to understand what knowledge developers forget to what extent, what sources are reliable to recover knowledge, and how to trace knowledge to the features in a system. We tackle these three issues, aiming to provide empirical insights and tooling to support developers during software evolution and maintenance. The results help practitioners, as we support the analysis and understanding of systems, as well as researchers, showing opportunities to automate, for example, reverse-engineering techniques.
Restoration of Mammograms by Using Deep Convolutional Denoising Auto-Encoders

To obtain betterGhosh, Swarup Kr image visibility of noisy mammograms, a deep unsupervised learning method for degrading mammogram restoration scheme has been adopted in this paper. A deep convolutional denoising autoencoderBiswas, Biswajit method based on total variational multi-norm lossGhosh, Anupam function minimization approach has been introduced for the restoration of mammograms. The suggested model can extract relevant features and can reduce the dimensionality of the image data while preserving the key features that have been applied to restore image data in feature space. Later, an unsupervised learning is performed based on the relation between key features and the weighted parameters of the network model. Experimental results authenticate that the suggested method outplays a number of state-of-the-art methods. The proposed scheme is not just prone to decrease noise but also restores acceptable structural details of mammograms. After fine-tuning the network, its execution speed for target noisy images increase as compared to other algorithms.
Short-Term Solar Power Forecasting Using SVR on Hybrid PV Power Plant in Indonesia

Considering the environmental issues, the use of renewable energy sources is a far more sustainable solution to meeting the energy demand than fossil fuels. However, the limited availability of renewable energy is a growing problem to be solved. Solar energy has become a popular renewable energy source in several countries such as Indonesia because of their equatorial locations. In this study, limited meteorological measurement has been applied with the aim of forecasting solar power generation for planning photovoltaic (PV) power plants, especially in rural areas, which have limited access to fossil energy. We used limited measurements such as temperature, humidity, and solar radiation. The use of support vector regression (SVR) was applied to improve denoising capabilities and simplify computation. SVR has been evaluated using statistical metrics such as mean absolute percentage error (MAPE), relative root means square error (NRMSE), and coefficient of determination (R2). The results showed the MAPE value obtained 18.56% from the RBF_SVR. NRMSE value performed excellently with 8.02% from the SW-SVR method. R2 also indicated good forecasting with 0.99. The results showed that promising short-term solar power generation forecasting can be applied to estimate the availability of solar power, plan for an extension, and assess the performance of hybrid power plants in Indonesia.
Data Anonymization Based on Natural Equivalent Class

Data anonymization is widely used to preserve the utility of published datasets without compromising privacy. The state-of-the-art data anonymization approaches are mainly single-record-based algorithms. They group similar records together one by one, then form equivalence classes through generalization. However, these algorithms didn't utilize equivalence classes which exist in the raw dataset. In this paper, we propose a new concept named natural equivalent class. It refers to the record set with the same quasi-identifier values naturally existing in the raw dataset. We theoretically prove that the natural equivalent class can effectively reduce the computational complexity of clustering algorithms as well as information loss. Then, we propose a novel clustering-based anonymization algorithm, which tries to cluster records without separating any natural equivalent class. Extensive experiments on real world datasets show that our approach outperforms the previous clustering-based anonymization algorithms in terms of efficiency and data utility.
A Game Model for Gomoku Based on Deep Learning and Monte Carlo Tree Search

Alpha Zero has made remarkable achievements in Go, Chess and Japanese Chess without human knowledge. Generally, the hardware resources have much influence on the effect of model training significantly. It is important to study game model that do not rely excessively on high-performance computing capabilities. In view of this, by referring to the methods used in AlphaGo Zero, this paper studies the model applying deep learning (DL) and monte carlo tree search (MCTS) with a simple deep neural network (DNN) structure on the Game of Gomoku Model, without considering human expert knowledge. Additionally, an improved method to accelerate MCTS search is proposed on the base of the characteristics of Gomoku. Experiments show that this model can improve the chess power in a short training time with limited hardware resources.
Context-Aware Instance Matching Through Graph Embedding in Lexical Semantic Space

Instance matching is one of the processes that facilitate the integration of independently designed knowledge bases. It aims to link co-referent instances with an owl:sameAs connection to allow knowledge bases to complement each other. In this work, we present VDLS, an approach for automatic alignment of instances in RDF knowledge base graphs. VDLS generates for each instance a virtual document from its local description (i.e., data-type properties) and instances related to it through object-type properties (i.e., neighbors). We transform the instance matching problem into a document matching problem and solve it by a vector space embedding technique. We consider the pre-trained word embeddings to assess words similarities at both the lexical and semantic levels. We evaluate our approach on multiple knowledge bases from the instance track of OAEI. The experiments show that VDLS gets prominent results compared to several state-of-the-art existing approaches.
Parallel Methods for Finding k-Mismatch Shortest Unique Substrings Using GPU

k-mismatch shortest unique substring (SUS) queries have been proposed and studied very recently due to its useful applications in the subfield of computational biology. The k-mismatch SUS query over one given position of a string asks for a shortest substring that covers the given position and does not have a duplicate (within a Hamming distance of k) elsewhere in the string. The challenge in SUS query is to collectively find the SUS for every position of a massively long string in a both time- and space-efficient manner. All known efforts and results have been focused on improving and optimizing the time and space efficiency of SUS computation in the sequential CPU model. In this work, we propose the first parallel approach for k-mismatch SUS queries, particularly leveraging on the massive multi-threading architecture of the graphic processing unit (GPU) technology. Experimental study performed on a mid-end GPU using real-world biological data shows that our proposal is consistently faster than the fastest CPU solution by a factor of at least 6 for exact SUS queries ( k=0) and at least 23 for approximate SUS queries over DNA sequences ( ), while maintaining nearly the same peak memory usage as the most memory-efficient sequential CPU proposal. Our work provides practitioners a faster tool for SUS finding on massively long strings, and indeed provides the first practical tool for approximate SUS computation, because the any-case quadratical time cost of the state-of-the-art sequential CPU method for approximate SUS queries does not scale well even to modestly long strings.
Hyper-Parameter Optimization for Improving the Performance of Grammatical Evolution

State-of-the-art Grammatical Evolution systems such as PonyGE2 have a number of hyper-parameters that control the behavior of the internal evolutionary algorithm for evolving the representations of programs. In this paper, a variant of the efficient global optimization (EGO) algorithm is applied for optimizing these hyper-parameters of the PonyGE2-system. This approach is tested on four test problems used in the Grammatical Evolution community: StringMatch, symbolic regression (the `Vladislavleva-4' problem), bank note classification and the so-called Pymax task. The experimental results show that the average performance of the GE system is improved significantly (between 25% and 168%) on all of the test problems. In addition, the resulting overall best hyper-parameter settings are substantially different from the defaults used in PonyGE2.
Gaussian Bare-Bones Brain Storm Optimization Algorithm

Brain Storm Optimization (BSO) is a population-based algorithm developed based on the humans brainstorming process. It has been successfully applied to many applications in the domain of non-linear continuous optimization. The performance of BSO has been enhanced in the literature through many works attempting to improve its different stages. In this work, we propose a Gaussian Bare-Bones version of the Global-best BSO algorithm (BBGBSO). The idea of bare-bones implementations in general is inspired from the convergence characteristics of Particle Swarm Optimization (PSO) where particles converge to the weighted average of the personal-best of the particle and the global-best of the swarm. A number of previous Bare-bones implementations have been proposed in the literature for different algorithms resulting in noticeable performance improvements. Experimental results extracted from many benchmark functions across different problem sizes confirms the promising performance of BBGBSO.
A Wearable Platform to Identify Workers Unsafety Situations

IoT technologies allow the development of innovative solutions to monitoring human activities and, among others, ones connected to workers in order to improve their safety. However, this kind of solutions need to guarantee workers privacy preventing an usage aimed at people's control. Indeed, a similar employment is forbidden in several legislations and, more in general, solutions that have mechanisms that can hypothetical be used to monitoring the workers' activities, usually aren't accepted by workers. In this paper we illustrate the solution developed inside the SmartBench project based on wearable sensors to monitor the workers' status. Specifically, we describe in detail a waist mounted platform able to detect several situations ranging from standing, running, falling, laying down, etc. The platform is also equipped with environmental sensors able to monitor the presence of carbon monoxide, humidity, temperature, etc. Moreover, the platform can share data using an architecture-free communication schema, i.e. BLE protocol, with environmental sensors dispersed in the work area. On the base of such data, the system is able to detect anomalous situations and provide information about the status directly and exclusively to the worker. Only in case of an emergency the information is shared with the supervisor and other workers in order to better manage the crisis. Precisely, in this paper we illustrate the algorithms developed to identify the workers' status on the base of data extracted from the wearable sensor.
Investigating Visualization Techniques for Observing a Group of Virtual Reality Users Using Augmented Reality

As an emerging technology, virtual reality (VR) has been used increasingly as a learning tool to explore “outside the classroom experiences” inside the classroom. While VR provides an immersive experience to the students, it is difficult for the instructor to monitor the students' activities in the VR. Thus, it hinders interactions between the instructor and students. To solve this challenge, we investigated a technique that allows the instructor to observe VR users at scale using Augmented Reality. Augmented Reality techniques are used to visualize the gazes of the VR users in the virtual environment, and improve the instructor's awareness.
Offering smarter learning support through the use of biometrics

The Internet of Things (IoT) has evolved into a mainstream keyword describing the use of interconnected device for data transfer. The authors present their current research study that aims at collecting biometric data from learners and using them for providing innovative feedback for a number of learning tasks. The paper discusses how measuring Galvanic Skin Response heartbeat rate and voice patterns can help to provide an alternative type of learner support. The discussion also covers how biometrics data are filtered by applying a number of profiling techniques to classify learners in different groupings. The paper also briefly touches on hardware aspects of the work carried out, as well as analysis of data sets from a student cohort.