Reviewerly/exHarmony huggingface dataset:
Design and Implementation Of A Data Management Server Based on AETA

This paper describes a data management server (the AETA server) that provides a solution to the problem of large-area, high-density deployment and uninterrupted operation faced by the ATEA system (Acoustic and Electromagnetic Testing All in one system). The AETA server helps to receive, process and store the massive monitoring data efficiently and reliably, as well as to manage and maintain the unattended stations remotely. In this sense, The AETA server is designed from the communication layer, aggregation layer, and storage layer, and is deployed with load balancing cluster technology. In addition, the AETA server can remotely monitor the stations and deliver tasks to the stations. The experiment and practice show that the AETA server realized in this paper has excellent performance and can meet the requirements of most of seismic monitoring systems.
The Bitcoin as a Virtual Commodity: Empirical Evidence and Implications

The present work investigates the impact on financial intermediation of distributed ledger technology (DLT), which is usually associated with the blockchain technology and is at the base of the cryptocurrencies' development. "Bitcoin" is the expression of its main application since it was the first new currency that gained popularity some years after its release date and it is still the major cryptocurrency in the market. For this reason, the present analysis is focused on studying its price determination, which seems to be still almost unpredictable. We carry out an empirical analysis based on a cost of production model, trying to detect whether the Bitcoin price could be justified by and connected to the profits and costs associated with the mining effort. We construct a sample model, composed of the hardware devices employed in the mining process. After collecting the technical information required and computing a cost and a profit function for each period, an implied price for the Bitcoin value is derived. The interconnection between this price and the historical one is analyzed adopting a Vector Autoregression (VAR) Model. Our main results put on evidence that there aren't ultimate drivers for Bitcoin price; probably many factors should be expressed and studied at the same time, taking into account their variability and different relevance over time. It seems that the historical price fluctuated around the model (or implied) price until 2017, when the Bitcoin price significantly increased. During the lasts months of 2018 the prices seem to converge again, following a common path. In detail, we focus on the time window in which Bitcoin experienced its higher price volatility; the results suggest that it is disconnected from the one predicted by the model. These findings may depend on the particular features of the new cryptocurrencies, which have not been completely understood yet. In our opinion there is not enough knowledge on cryptocurrencies to assert that Bitcoin price is (or is not) based on the profit and cost derived by the mining process, but these intrinsic characteristics must be considered, including other possible Bitcoin price drivers.
HiLAM-state discriminative multi-task deep neural network in dynamic time warping framework for text-dependent speaker verification

This paper builds on a multi-task Deep Neural Network (DNN), which provides an utterance-level feature representation called j-vector, to implement a Text-dependent Speaker Verification (TDSV) system. This technique exploits the speaker idiosyncrasies associated with individual pass-phrases. However, speaker information is known to be characteristic of more specific speech units and, thus, it is likely that important speaker identity traits might get averaged out if it is considered as a coarse entity spread uniformly across the whole pass-phrase. This work attempts to overcome this limitation and devises a technique to leverage the finer speaker traits. It proposes to align the training data for Multi-task DNN using Hierarchical Multi-Layer Acoustic Model (HiLAM). HiLAM is an HMM-based text-dependent model that defines refined segments of a pass-phrase using Gaussian Mixture Model (GMM) states. This helps to exploit the speaker idiosyncrasies associated with finer and more specific segments of speech. Also, as HiLAM is built using the particular text in question, this alignment technique automatically takes care of the exact context of the speech units in the concerned pass-phrase. The proposed technique has been found to improve the performance of the system significantly. Integrating Dynamic Time Warping (DTW) with this technique leads to further improvement in the performance of the system. Experiments have been validated on Part 1 of RSR2015, RedDots, and NITS-TD databases. The best-performing proposed system achieves a relative Equal Error Rate (EER) reduction of up to 50.98% with respect to the baseline j-vector-based system for the overall test condition in case of RSR2015 database.
Suspect Identification using Local Facial Attributed by Fusing Facial Landmarks on the Forensic Sketch

Faces are the most common biometric used for the identification of a person. Every person has different facial visual attributes that discriminate them from others. Law enforcement agencies use the face as a key evidence to identify the suspect, involved in unlawful activities. To identify the suspect sketches are used to apprehend the suspect. The sketch is the painting of the visual description given by the onlooker. Hand drawn sketch drawn by the sketch artist is uncertain. It depends on the description observation and memory of the eyewitness. Uncertainty of the face visual attributes is generally ignored by the existing methods. Face shape and texture are different in the sketch then the visual features of the facial photo. In this paper, we have proposed a method to retrieve the facial photos of the potential suspect using the sketch as a query. We have used the local facial visual attributes to identify facial features. Firstly, face key points are identified using 81 facial landmarks detector. After that, local facial attributes are extracted and features are calculated. We have utilized the Bayesian classification to retrieve the mugshot images. We have experimented the proposed method on the forensic dataset and compared with state-of-the-art methods. It is evident from the experimental result that the proposed model performs better with the state-of-the-art method methods on forensic sketch datasets.
Context-Aware User and Item Representations Based on Unsupervised Context Extraction From Reviews

User reviews often supply valuable information to alleviate the rating sparsity problem that can occur in recommender systems. Recent work has employed deep learning techniques to learn user and item representations from reviews, which are then used to predict ratings. Such representations are usually learned by considering every word in previous reviews, including words that are irrelevant to user preferences or item features. Some approaches try to identify and extract significant words from reviews based on a predefined list of contexts, where contexts such as the season or weather could have strong influences on user decisions about items, and which are more relevant to their preferences or sought-after features. Specifying optimal values for contexts, however, is not a trivial task and the values are mostly restricted to a single word format. To overcome these limitations, we propose a novel unsupervised method for extracting contexts from reviews, which are then utilized to construct user and item representations. To this end, we adopt a region embedding technique to automatically extract a context as any word in a text region that influences patterns of rating distributions in reviews. Instead of considering every word in all previous reviews, our user and item representations are dynamically constructed based on different relevance levels among the extracted contexts from a particular review by applying our interaction and attention modules. Experiments demonstrated that utilizing our representations for rating prediction could outperform existing state-of-the-art context-aware and review-based recommendation techniques.
Hecatonquiros: Open-source hardware for aerial manipulation applications

This article presents Hecatonquiros, a complete open-source ecosystem for low cost and lightweight robotic manipulators. It has been released to focus on aerial manipulation applications but can be used in any other robotic application that requires the use of manipulators. The proposed framework provides the control system, a simulation environment, and a set of back ends to allow reusing the algorithms with different hardware setups. Additionally, it is released with a set of tools to ease its usage and various examples to teach the users. Several manipulators models and end-effectors are available for the users to adapt to their different requirements. All the hardware is designed to be three-dimensional printed and its components are low cost and available in common robotic stores, so anyone can reproduce and use them. The software is available in the GitHub repository https://github.com/Bardo91/hecatonquiros .
Modern Cloud Services: Key Trends, Models and Tools for Interactive Education

The paper is concerned with the capabilities of modern cloud services in an educational setting. General characterization of the existing clouds includes the analysis of worldwide IT trends, description of different cloud computing models and methods of deployment, such as private, community, public and hybrid clouds, as well as preferred trends for using a particular cloud type. To that end, the authors give data and statistics on the global IT market in the segment of cloud technologies. It is emphasized that SaaS solutions and hybrid clouds with different providers are recognized by users to be the most promising. In this context, a new trend is emerging in the evolution of the most popular clouds: all of them have embarked on a multi-cloud course. Interest to such technological solutions in higher education is also growing. Analyzing the prospects of using hybrid clouds in an academic setting, the authors define the most popular clouds, discuss modern cloud services and tools that can be used for co-working both in the classroom and outside it. In conclusion, the authors present their own learning model based on cloud services that make it truly flexible and interactive, approaching in their intrinsic features to agile education methodology.
IKW: Inter-Kernel Weights for Power Efficient Edge Computing

Deep Convolutional Neural Networks (CNN) have achieved state-of-the-art recognition accuracy in a wide range of computer vision applications like image classification, object detection, semantic segmentation etc. Applications based on CNN require millions of multiply-accumulate (MAC) operations to be performed between input pixels and kernel weights during inference. This work investigates a technique, which can be used to eliminate redundant multiplications for a subset of kernel weights in a CNN layer by utilizing identical and/or similar inter-kernel weights (IKW) across kernels. In this work, IKW technique is used to identify identical and/or similar inter-kernel weights in trained, unpruned/pruned, quantized CNN kernels before inference phase. After identification of identical and/or similar inter-kernel weights, a subset of kernel weights termed non-pivot kernel weights are made zero, the other subset called pivot kernel weights are left unchanged. The multiplication corresponding to non-pivot kernel weights are eliminated, thus reducing computations. The products corresponding to non-pivot kernel weights are supplied by multiplication operation of pivot kernel weights, and hence causing no degradation in inference accuracy. Through experiments on state-of-the-art CNNs, we demonstrate that application of IKW technique enhances kernel sparsity by 9-37% for 8-bit precision kernel weight and 18-43% for 4-bit precision kernel weight without degrading the recognition accuracy of the CNN model. Enhanced kernel sparsity can be used to save power by clock gating the compute unit, or increase execution performance by skipping computations pertaining to zero valued non-pivot kernel weights. In addition, power savings are achieved by eliminating redundant power expensive fixed-point multiplication operations. The practical utility of the IKW technique is demonstrated by mapping it to well-known state-of-the-art CNN accelerator architectures. Mapping of the IKW technique on existing CNN accelerator architectures shows reduction in power by at least 12% for 8-bit precision and 19% for 4-bit precision kernel weight. Improvement in execution performance by at least 2% for 8-bit precision and 13% for 4-bit precision kernel weight is observed.
Bengali Question Answering System for Factoid Questions: A statistical approach

Question answering system in recent days is one of the most trending and interesting topics of research in computational linguistics. Bengali being among the most spoken languages in the world has yet faced difficulties in computational linguistics. This paper demonstrates an attempt to develop a closed domain factoid question answering system for Bengali language. Our proposed system combining multiple sources for answer extraction extracts the answer having the accuracy 66.2% and 56.8% with and without mentioning the object name respectively. The system also hits around 72% documents from which the answer can be extracted. Besides the sub-parts of our system, the question and document classifier provides 90.6% and 75.3% accuracy respectively over five coarse-grained categories.
Tuning the Solar Power Generation Curve by Optimal Design of Solar Tree Orientations

The paper presents a new design of a solar tree where solar panels are appropriately positioned like the leaves of a tree. Compared to fixed orientation solar panels, the main advantage of a solar tree is the ability to optimize the orientation of individual solar leaves in order to tune the power generation curves as required, for example, increasing the energy production during the winter months when solar insolation is low. Since orientation of solar panels is the key to achieve the maximum productivity of solar photovoltaic (PV) plants, data-driven and location-specific approach is employed to determine optimal orientation of five solar panels for solar tree structure for seven locations covering a large latitude range. Compared to the commonly employed latitude tilt orientation for solar PV modules, optimal solar tree design shows the feasibility of tuning the power generation curves to increase the power production in winter months or any other desired months. Scope for such tuning is higher for locations having high direct normal irradiance (DNI) and high standard deviation in the solar insolation curve. Also, higher number of solar panels in a solar tree provides higher degrees of freedom and hence larger flexibility to tune the power generation curve.
Privacy-Aware Human Activity Recognition From a Wearable Camera: Highlights From the IEEE Video And Image Processing Cup 2019 Student Competition [SP Competitions]

Presents highlights from the IEEE Video and Image Processing Cup 2019 Student Competition.
Modulus Calculations on Prime Number Algorithm for Information Hiding With High Comprehensive Performance

Information hiding is an important technique for information security, which is wildly studied by researchers. Recently embedding methods are proposed in spatial, frequency and other domains. After investigating previous literatures, we find that there is still room for embedding performance improvement. Inspired by some literatures, we propose a new method (Modulus Calculations on Prime Number Algorithm, MOPNA) for embedding secret data into cover-images. The main idea of MOPNA is hiding confidential data in paired cover-pixels with modulus calculation based on weight parameters consisting of prime numbers. MOPNA improves the embedding capacity while maintaining good stego-image quality. The correctness of MOPNA method is proved by a combination of mathematical and programming proof. The experimental results prove that the proposed method has high embedding capacity and achieves better comprehensive performance than existing methods.
IoT in Agriculture Industry

Indonesia is known as an agricultural country that has an agricultural area of 7.1 million hectares in 2018. The agricultural sector is one sector that has great potential to get many benefits by implementing IoT technology. IoT implementation has several obstacles such as hardware, software, communication technology, and security. This paper aims to describe IoT-related studies in the agricultural sector that have been carried out since 2010 in the hope of providing evidence that transforming the traditional agricultural sector into a modern agricultural sector is very possible in Indonesia.
A Comparative Study of Track-Before-Detect Algorithms in Radar Sea Clutter

The detection and tracking of targets in the maritime environment can be a challenging problem. One approach to improve the tracking ability of weak targets is to use track before detect (TBD). However, different TBD algorithms have different underlying assumptions about the background clutter which are not always valid. In this paper we consider three TBD algorithms: the histogram probabilistic multi-hypothesis tracker (H-PMHT), the Bernoulli filter and the multi-Bernoulli filter. The H-PMHT inherently assumes the clutter is modelled with a distribution derived from the continuous limit of the Poisson distribution, while the two Bernoulli filters both assume Rayleigh distributed clutter. To quantify performance, we use a Monte Carlo simulation with the sea clutter generated using both Rayleigh and K-distributions. The performance is then determined using the optimal sub-pattern assignment (OSPA) distance.
Using Quantifier Elimination to Enhance the Safety Assurance of Deep Neural Networks

Advances in the field of Machine Learning and Deep Neural Networks (DNNs) has enabled rapid development of sophisticated and autonomous systems. However, the inherent complexity to rigorously assure the safe operation of such systems hinders their real-world adoption in safety-critical domains such as aerospace and medical devices. Hence, there is a surge in interest to explore the use of advanced mathematical techniques such as formal methods to address this challenge. In fact, the initial results of such efforts are promising. Along these lines, we propose the use of quantifier elimination (QE) - a formal method technique, as a complimentary technique to the state-of-the-art static analysis and verification procedures. Using an airborne collision avoidance DNN as a case example, we illustrate the use of QE to formulate the precise range forward propagation through a network as well as analyze its robustness. We discuss the initial results of this ongoing work and explore the future possibilities of extending this approach and/or integrating it with other approaches to perform advanced safety assurance of DNNs.
Human video database for facial feature detection under spectacles with varying alertness levels: a baseline study

The pressing demand for workload along with social media interaction leads to diminished alertness during work hours. Researchers attempted to measure alertness level from various cues like EEG, EOG, video-based eye movement analysis, etc. Among these, video-based eyelid and iris motion tracking gained much attention in recent years. However, most of these implementations are tested on video data of subjects without spectacles. These videos do not pose a challenge for eye detection and tracking. In this work, the authors have designed an experiment to yield a video database of 58 human subjects wearing spectacles and are at different levels of alertness. Along with spectacles, they introduced variation in session, recording frame rate (fps), illumination, and time of the experiment. They carried out an analysis to detect the reliableness of facial and ocular features like yawning and eye-blinks in the context of alertness level detection capability. Also, they observe the influence of spectacles on ocular feature detection performance under spectacles and propose a simple preprocessing step to alleviate the specular reflection problem. Extensive experiments on real-world images demonstrate that the authors’ approach achieves desirable reflection suppression results within minimum execution time compared to the state-of-the-art.
Collaborative Deanonymization

Privacy-seeking cryptocurrency users rely on anonymization techniques like CoinJoin and ring transactions. By using such technologies benign users potentially provide anonymity to bad actors. We propose overlay protocols to resolve the tension between anonymity and accountability in a peer-to-peer manner. Cryptocurrencies can adopt this approach to enable prosecution of publicly recognized crimes. We illustrate how the protocols could apply to Monero rings and CoinJoin transactions in Bitcoin.
Fast matching via ergodic markov chain for super-large graphs

In theory, graph matching is a combinatorial problem. One state-of-the-art technique in graph matching, called spectral matching, relaxes the matching problem for consistent correspondence into spectral decomposition of the affinity matrix of graphs, but the most variations of spectral based algorithms suffer from their O(n4) memory requirement. In this paper we propose a probabilistic spectral matching approach, in which the graph matching problem is formulated as an ergodic Markov chain, and the process of matching is addressed to reach the steady-state of the Markov chain. The approach decomposes the probability transition matrix, and solves the matching problem in O(n2) space complexity using limited computing resource and RAM. This property makes the approach suitable for super-large graphs matching (for example, graphs with the number of points over 1000). We evaluate our algorithm on both the synthetic and the real datasets, and demonstrate that the proposed approach is significantly faster, and consumes smaller memory than SM, RRWM and FaSM with no loss of accuracy.
Towards Efficient and Secure Global Software Development using Blockchain

The economic and strategic gain attracts the software industry to adopt the global software development (GSD) phenomenon. Besides the significant advantages, the GSD firms also face certain critical challenges mainly related to security and management perspective. Blockchain is the new revolutionary technology that offers over the globe secure storage via distributed databases. The objective of this study is to investigate how the adoption of blockchain is beneficial for the GSD project. We have conducted interviews with academic researchers and case studies with industry practitioners to identify the impact of blockchain on GSD projects. We have identified ten key critical areas of GSD the effectively addressed by using the blockchain technology in GSD context. A comparative analysis is conducted with the finding of academic researcher and industry practitioners, and the results ((W=0.86, p=0.005) shows that there is a strong agreement between the understanding of academic researchers and industry practitioners. Besides, we have developed a hypothetical model based on the findings of both studies that shows the positive relationship of blockchain implementation in the GSD domain. We believe that the findings of this study will encourage the practitioners and researchers to develop the new plan and strategies for the adoption of blockchain in GSD.