Reviewerly/exHarmony huggingface dataset:
Clustering Storage Method for Digital Evidence Storage Using Software Defined Storage

Abstract The increasing of cybercrime means an increase in the number of digital evidences produced in these criminal activities. Also, to pay attention to the security and integrity of digital data, the construction of digital evidence storage must pay attention to the need for convenience in adding storage and the need for investigators to avoid geographical problems in carrying out their duties. An ideal implementation of the problem is the development of network-based digital evidence storage using the Software-Defined Storage to build a clustered storage system. In this study, we build a clustered storage system using Ceph that will be functioned as digital evidence storage. Ceph is a Software-Defined Storage which is designed to provide excellent performance, reliability and scalability. On the other hand, since digital evidence is fragile, then the security of storage system must also be considered so that the stored digital evidence in the storage system can be accepted in court. The security includes aspects of confidentiality, integrity and availability aspects. We were able to build a storage system using Ceph and as a result, our system storage has met the aforementioned scalability, confidentiality, integrity and availability aspects. The storage system is expected to contribute in terms of digital evidence storage and preservations.
An LSTM Auto-Encoder for Single-Channel Speaker Attention System

In this paper, we utilized a set of long short term memory (LSTM) deep neural networks to distinguish a particular speaker from the rest of the speakers in a single channel recorded speech. The structure of this network is modified to provide the suitable result. The proposed architecture models the sequence of spectral data in each frame as the key feature. Each network has two memory cells and accepts an 8 band spectral window as the input. The results of the reconstructions of different bands are merged to rebuild the speaker's utterance. We evaluated the intended speaker's reconstruction performance of the proposed system with PESQ and MSE measures. Using all utterances of each speaker in TIMIT dataset as the training data to build an LSTM based attention auto-encoder model, we achieved 3.66 in PESQ measure to rebuild the intended speaker. In contrast, the PESQ was 1.92 in average for other speakers when we used the mentioned speaker's network. This test was successfully repeated for different utterances of different speakers.
Concurrent Request Multiplexing for Cloud Composite Service Reservation

Combining a series of atomic services into a composite value-added service offers promise in meeting the increasingly diverse needs of users in the cloud market. However, without considering concurrency of composite service requests among multiple users, existing studies seldom focus on the competition relationship among users and thus lack an incentive mechanism to efficiently provide the request strategy of users. In this article, taking into consideration the multi-user competition and interaction with the cloud provider, we design a composite service reservation framework in which users’ requests can be rationally and efficiently multiplexed. Due to selfishness, each user tends to expect to maximize its own utility in terms of revenue, payoff, and performance of composite service. The optimization problem is modeled from the perspective of game theory and defined as a non-cooperative game. The existence of Nash equilibrium solution of the formulated game is proved by an equivalent variational inequality problem. An iterative proximate algorithm ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IPA</i> ) is proposed to find a utility-balanced request strategy, which theoretically leads to a Nash equilibrium solution. A series of simulation experiments are conducted to verify our theoretical analysis. The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IPA</i> converges to a Nash equilibrium in an acceptable number of iterations. The stable request strategies can improve the utilities of users and the resource utilization of the cloud provider, as the proposed framework empowers users to transfer requests from peak periods into non-peak ones.
Detecting Falls with Recurrent Autoencoders and Body Acceleration Data

According to the world population statistics, the elderly proportion is continuously growing. With this growth, the concerns of this population increase. Falls are a common problem within the elderly population causing considerable health and financial impacts. In order to mitigate such impacts, automatic fall detection systems are more and more employed by the elderly. In this work, we propose a fall detection system based on the body acceleration data, where we investigate the use of recurrent-autoencoders to distinguish between normal activities of daily living and falls. We study different training strategies combined with either the use of the autoencoder as feature extractor or employing its reconstruction error for decision. Experiments on the UniMiB SHARE fall data set provides interesting insights about the designed system. .
Activity Recognition from Sensor Fusion on Fireman's Helmet

Recognizing human activities in emergency situations is critical for first responders to ensure their safety and well-being. In many cases, the thick smoke in a burning building impairs computer vision algorithms for activity recognition. Here we present a helmet-based sensor fusion method with IMU and time-of-fly laser distance sensor. We use a Decision Tree to as a classifier and select the most significant features. Our test shows that the method can recognize over seven activities: walking, running, crawling, duck walking, standing, walking upstairs and downstairs, with an accuracy between 81.7% and 93.6%. With limited training data and a lightweight requirement for implementation on the fireman's helmet the Decision Tree provided an accurate and reliable result. The use of the 1-D Lidar, which is not feasible in typical activity recognition application but essential for the helmet, combined with the 10-DOF IMU sensors improved the robustness of the classifier. We found this sensor fusion approach needs much less training data, compared to methods such as Deep Learning. Once implemented on the helmet the activity recognition is executed in real-time at sampling rate at 50 Hz within a 2-second window.
FAQAugmenter: Suggesting Questions for Enterprise FAQ Pages

Lack of comprehensive information on frequently asked questions (FAQ) web pages forces users to pose their questions on community question answering forums or contact businesses over slow media like emails or phone calls. This in turn often results into sub-optimal user experience and opportunity loss for businesses. While previous work focuses on FAQ mining and answering queries from FAQ pages, there is no work on verifying completeness or augmenting FAQ pages. We present a system, called FAQAugmenter, which given an FAQ web page, (1) harnesses signals from query logs and the web corpus to identify missing topics, and (2) suggests ranked list of questions for FAQ web page augmentation. Our experiments with FAQ pages from five enterprises each across three categories (banks, hospitals and airports) show that FAQAugmenter suggests high quality relevant questions. FAQAugmenter will contribute significantly not just in improving quality of FAQ web pages but also in turn improving quality of downstream applications like Microsoft QnA Maker.
Classification of Turkish E-Mails with Doc2Vec

In this study, it was aimed to determine the Turkish e-mails which are spam by developing the application with machine learning techniques for Turkish e-mails. E-mails received from the TurkishEmail data set were used as training and test data. Numerical representation of the e-mails was obtained by using the algorithms of Doc2Vec library and then the feature was extracted. Test data were analyzed by various classification algorithms and the results were discussed.
Radar Emitter Recognition Based on Ambiguity Function Contour Plot

Aiming at the problem that the radar emitter recognition accuracy is low, the recognition algorithm is complex, an algorithm that recognize radar emitter using ambiguity function contour plot is proposed. A model is set to extract ambiguity function contour plot's PHOW (Pyramid Histogram of Words) and Gist joint descriptor based on complementation of local descriptors and global descriptors. Key parameters of joint descriptors are determined by the experiments. Finally, the recognition of the radar emitter is realized by SVM (Support Vector Machine). The experimental results show that the proposed method can enhance the representation of ambiguity function contour plots under small sample conditions and has higher recognition accuracy.
Parallel Coordinates Graph in Bundling Technique

Parallel coordinates are well-known instruments for large and multi-dimensional data set visualization. However, data cluttering and overplotting are significant problems for graphing parallel coordinates and becoming worst in the Big Data age. Thus, by bundling comparable edges or polylines in parallel coordinate graph, the bundling method has overcome the above mentioned issues. This research utilizes one of the bundling clustering techniques applied to a parallel coordinate graph to show and explore the information relationship pattern. A clustering prototype called PCG In Bundling is being developed with the aim of visually interpreting the data by converting the static data form into an interactive form. The photovoltaic dataset used in this project was primarily gathered from the UiTM Shah Alam Green Energy Research Centre. Currently, the dataset is stored in Microsoft Excel, which is static and hardly interpretable. This project therefore seeks to assist users analyze and discover the relationship patterns on the dataset by visualizing the graph of parallel coordinates. Bundling method has therefore been introduced to the parallel coordinate graph of photovoltaic datasets to cluster information and help users discover data relationship patterns. The functionality of this clustering prototype has been evaluated. As anticipated, the cost-based edge-bundling algorithm implemented in this project is efficient and can assist users analyze and interpret data relationship patterns. To conclude, this project is a success and fulfilled all the necessary scopes.
Depthwise Separable Convolutional Neural Network for Image Forensics

General-purpose forensics on small image patches appears to be feasible and important, but in fact poses a challenge due to insufficient statistics. Furthermore, there is a need to develop a forensic approach that can automatically learn effective and robust features related to image forensics with high parameter efficiency. In this paper, we propose a depthwise separable convolutional neural network (CNN) for the simultaneous detection of eleven types of image manipulations in image patches. Different from the previous CNNs based on standard convolution, depthwise separable convolution is introduced in the proposed CNN to adaptively extract forensics-related features from image patches with better parameter efficiency. When compared with four state-of-the-art methods, experiments demonstrate that the proposed CNN architecture can achieve better performance, e.g., the improvement in terms of accuracy in the detection of 32 × 32 images is up to 7.33%. It also achieves significantly better overall performance for different databases and better robustness against JPEG compression.
Recent advances on big data analysis for malaria prediction and various diagnosis methodologies

Malaria, being a prevalent disease caused by the Plasmodium species in urban areas, requires a fast and efficient system to control. To deal with this issue, various techniques such as big data analysis for malaria prediction and diagnosis models (clinical diagnosis, manual microscopic examination, quantitative Buffy coat, rapid diagnostic test, computerized diagnosis) have been developed. In this study, various malaria predictions as well as screening models have been reviewed. It analyzed the importance of disease prediction based on different approaches, and diagnosis systems using various techniques. Prediction model includes big data analysis based on data such as environmental data, clinical data, etc. Diagnosis model requires blood smears preparation and examining the blood components present in the smears. In clinical and manual blood smear examination, diagnosis is done by clinical expert. Practically, manual microscopic blood smear observation remains the gold standard method for malaria screening. However, the visual malaria screening has limited reliability, i.e., time consuming and erroneousness. To overcome these, computerized diagnosis systems have been reported. In the computerized diagnosis system, different techniques, i.e., erythrocyte classification and parasite segmentation, have been evolved for malaria diagnoses. The analysis of the importance of the various features, feature selection techniques and classifiers has also been done for computerized diagnosis techniques.
Metro Futures: Experience-Centred Co-Design at Scale

This paper discusses how characteristics of experience-centred and collaborative design can be translated to larger scales. We describe Metro Futures, a region-wide public consultation on the design of new light rail trains, where we followed an experience-centred co-design approach supported by digital media and tools to develop findings with a core group of 20 ?co-researchers' and ~4000 public participants. The paper discusses how the characteristics of a focus on experience, and collaborative design exploration were achieved with co-researchers and, at scale, through online and face-to-face interactions using various digital media and tools. Whilst not at the depth of smaller scales, there are opportunities to retain characteristics of experience-centred co-design at scale to produce findings that can usefully inform ensuing design work, and avoid the averaging of public contributions often evident in large scale public consultations.
Rotation &amp; Viewpoint Angle Prediction in Capsule Network

Convolutional neural network (CNN) is effective in detecting features and classifying object but less effective in exploring spatial relationships among the features. Capsule network introduces stacking layers called capsules and routing algorithm. Such a capsule structure is proved to handle spatial relationships better than CNN architecture. This paper aims at exploring capsule network ability to adapt with the rotation and viewpoint change in image recognition for MNIST, SmallNORB and CAS-PEAL datasets compared to CNN architecture (VGG-based network). The experimental results show that capsule network performs better than CNN for rotation estimation, whereas CNN architecture performs slightly better than capsule network for viewpoint change. The experiments also show that capsule network may have capability to generalize better in some untrained data for rotation and viewpoint change. Capsule network is quite promising architecture in classification and spatial context.
Deep Learning for Anime Style Transfer

Some artificial systems based on a deep neural network create artistic images of high perceptual quality. However, it is usually suitable for use in abstract styles. The performances of existing style transfer algorithms on anime style are not very satisfactory, because it is either not sufficiently stylized or distorted severely in comic characters' domain. In this paper, we propose a novel anime style transfer algorithm using deep neural network, which treats foreground and background differently. Moreover, our method also could transfer the style for video with a style image. We combine semantic segmentation and spatial control to transfer the specified style to the specified area. By designing the initial image and the loss function. Users could adjust the feature weights of different regions to maintain the artistic conception of the target style, and combine optical flow to ensure frame coherence in a video. Finally, some experimental results demonstrate the effectiveness of our proposed method.
Aspect-Based Sentiment Analysis of Financial Headlines and Microblogs

To improve the performance in e-commerce markets, big giants like Amazon, Myntra and Flipkart are providing consumers with a platform to review their services and also give them an opportunity to provide a useful insight of the service to the future buyers. On the other hand, companies use such reviews to make a significant upgradation in their products (or services) to survive in the competition from others in the market. This shows the importance of studying user views or opinions on a particular product (or service) consumed by users. In Natural Language Processing (NLP), the process of studying such user opinion is termed as opinion mining. It is a task of finding out overall sentiment present in a review. Past research in this area has assumed that a sentence cannot have multiple sentiments associated with it. However, this is not true. For example, “This car looks beautiful, but does not handle very well.” comprises a positive sentiment towards the looks of the car but a negative sentiment towards its handling. To address such issues, aspect-based sentiment analysis (ABSA) was introduced. ABSA aims to detect an aspect (i.e. features) in a given text and then perform sentiment analysis of the text with respect to that aspect. The chapter aims to discuss the concept of ABSA for the problem introduced as a FiQA 2018 challenge subtask 1 ( https://sites.google.com/view/fiqa ) in WWW 2018 shared task. It highlights all the state-of-the-art models in the domain and discusses some new approaches. We propose neural network models combined with hand-engineered features and attention mechanism, to perform ABSA on financial headlines and microblogs. Our proposed model outperformed the existing state-of-the-art results in sentiment part by 50% and in the aspect part by 20%.
Quotienting Impertinent Camera Kinematics for 3D Video Stabilization

With the recent advent of methods that allow for real-time computation, dense 3D flows have become a viable basis for fast camera motion estimation. Most importantly, dense flows are more robust than the sparse feature matching techniques used by existing 3D stabilization methods, able to better handle large camera displacements and occlusions similar to those often found in consumer videos. Here we introduce a framework for 3D video stabilization that relies on dense scene flow alone. The foundation of this approach is a novel camera motion model that allows for real-world camera poses to be recovered directly from 3D motion fields. Moreover, this model can be extended to describe certain types of non-rigid artifacts that are commonly found in videos, such as those resulting from zooms. This framework gives rise to several robust regimes that produce high-quality stabilization of the kind achieved by prior full 3D methods while avoiding the fragility typically present in feature-based approaches. As an added benefit, our framework is fast: the simplicity of our motion model and efficient flow calculations combine to enable stabilization at a high frame rate.
BTS-PD: A Blockchain Based Traceability System for P2P Distribution

The application of peer to peer (P2P) technology has brought convenience to people, however, it has caused some social problems such as copyright infringement and the spread of Trojans and viruses. In order to effectively monitor malicious activities and security violations, it is essential to build a data traceability framework, with which each data object in the P2P environment can be tracked and recorded. Blockchain technology provides a promising mechanism for establishing a file distribution traceability system due to the natures, such as openness, decentralization and anti-tampering. In this article, we propose BTS-PD, a blockchain based traceability system for P2P distribution. The related information of P2P seed file is stored in the blockchain to ensure the credibility of the traceability information. InterPlanetary File System (IPFS) is used to store the P2P seed to expand the storage of the fabric blockchain. Based on BTS-PD, it becomes more credible and convenient for tracking security of P2P distribution. Implemented based on the Hyperledger Fabric, the experiment results show that BTS-PD can work well and support normal P2P distribution.
Real-Time Human-Robot Communication for Manipulation Tasks in Partially Observed Environments

In human teams, visual and auditory cues are often used to communicate information about the task and/or environment that may not otherwise be directly observable. Analogously, robots that primarily rely on visual sensors cannot directly observe some attributes of objects that may be necessary for reference resolution or task execution. The experiments in this paper address natural language interaction in human-robot teams for tasks where multi-modal (e.g. visual, auditory, haptic, etc) observations are necessary for robust execution. We present a probabilistic model, verified through physical experiments, that allows robots to acquire knowledge about the latent aspects of the workspace through language and physical interaction in an efficient manner. The model's effectiveness is demonstrated on a mobile and a stationary manipulator in real-world scenarios by following instructions under partial knowledge of object states in the environment.
Modification of variably modified permutation composition (vmpc) algorithm genetic key algorithm for data security

Abstract Cryptography is the study of mathematical techniques related to information security aspects such as validity, data integrity, and data authentication. In this study, Variably Modified Permutation Composition (VMPC) algorithm is modified by adding complementary methods to the encryption and decryption process. Keys that are generated randomly will be optimized using Genetic Algorithm. The Variably Modified Permutation Composition (VMPC) algorithm is a symmetrical stream cipher algorithm similar to the RC4 cipher designed by Bartosz Zoltak. The Variably Modified Permutation Composition (VMPC) algorithm is an extension of the one-way function VMPC that was developed into a byte-based encryption algorithm. In its use the VMPC is generated by an 8-bit stream of 256 element permutations. The initial state of the permutation is calculated in VMPC Key Scheduling. The Genetic Algorithm produces a unique intermediate key for each algorithm run. The intermediary key is combined with the Ciphertext in the first level which produces the second level Ciphertext, up to the third level Ciphertext. Attackers will not be able to carry out attacks such as brute force, differential attacks or statistical attacks without having knowledge of the key. From the results of the tests carried out, the more characters that are encrypted and decrypted, the longer it will take. On testing the avalanche effect produces an average value of 51.25% for keys without optimization while 53.42% for keys optimized. The effect of key optimization using Genetic Algorithm has increased the value of the avalanche effect and has an effect on the results of changing bits in the ciphertext.