Reviewerly/exHarmony huggingface dataset:
Data-Driven Model Set Design for Model Averaged Particle Filter

This paper is concerned with sequential state filtering in the presence of nonlinearity, non-Gaussianity and model uncertainty. For this problem, the Bayesian model averaged particle filter (BMAPF) is perhaps one of the most efficient solutions. Major advances of BMAPF have been made, while it still lacks a generic and practical approach to design the model set. This paper fills in this gap by proposing a generic data-driven method for BMAPF model set design. Unlike existent methods, the proposed solution does not require any prior knowledge on the parameter value of the true model; it only assumes that a small number of noisy observations are pre-obtained. The Bayesian optimization (BO) method is adapted to search the model components, each of which is associated with a specific segment of the pre-obtained dataset. The average performance of these model components is guaranteed since each one's parameter is elaborately tuned via BO to maximize the marginal likelihood. The diversity in the model components is also ensured, as different components match the different segments of the pre-obtained dataset, respectively. Computer simulations are used to demonstrate the effectiveness of the proposed method.
Asynchrounous Decentralized Learning of a Neural Network

In this work, we exploit an asynchronous computing framework namely ARock to learn a deep neural network called self-size estimating feedforward neural network (SSFN) in a decentralized scenario. Using this algorithm namely asynchronous decentralized SSFN (dSSFN), we provide the centralized equivalent solution under certain technical assumptions. Asynchronous dSSFN relaxes the communication bottleneck by allowing one node activation and one side communication, which reduces the communication overhead significantly, consequently increasing the learning speed. We compare asynchronous dSSFN with traditional synchronous dSSFN in the experimental results, which shows the competitive performance of asynchronous dSSFN, especially when the communication network is sparse.
Full Reference Video Quality Measures Improvement Using Neural Networks

The accuracy of video quality metrics (VQMs) is an important issue for several applications. In this work, first we observe that the accuracy of several video quality metrics (VQMs) is strongly related to the spatial complexity index (SI) of the source. In particular, our investigation suggests that the VQMs are more likely to inaccurately predict the subjective quality of the processed video sequences derived from sources characterized by low SI. To address such a situation, we propose a machine learning based improvement for each of the VQMs considered in this work and a video quality metric fusion index (VQMFI) that jointly exploits all the VQMs considered in the study as well as spatiotemporal features to produce a better estimation of the subjective quality. Computational results demonstrate the superiority of our proposals on several datasets.
Comparison and correction of element measurements using XRF core scanning and ICP-AES methods in lacustrine sediments

Abstract An integrated analysis has been carried out on core dated by qualitative X-ray fluorescence core scanning combination of quantitative ICP-AES in order to characterize the extent of elements Cd, Cr, Cu, Zn, and Pb in the Lake Toson in the northeastern Tibet Plateau. When combining both the quantitative and qualitative measurements, we observed a significantly positive correlation. Comparing the ratios between element intensities and concentration, we found that the XRF element intensities were related to the amount of water. Using the intensity of Cl as an indicator of water content, the element intensities of Cd, Cr, Cu, Zn, and Pb in the core samples were corrected. The correlation coefficients of each pairs of the two datasets among Cd, Cr, Cu, Zn, and Pb were significantly raised. These data sets with highly correlations were used to produce a transfer function to relate XRF element intensity with element concentration. Calculated element concentrations from the intensities show only minor differences from concentrations obtained by ICP-AES, indicating XRF core scanners are a very useful tool for measuring element concentration in lacustrine sediments.
Bi-orthogonal wavelets for investigating Gibbs effects via oblique extension principle

Abstract Gibbs effect is generally known for Fourier and Wavelets expansions of a function in the neighborhood of its discontinuities points which deals with the nonuniform convergence of its truncated sums of these expansions. We study this phenomenon using bi-orthogonal wavelets (or simply, framelets) using pseudo-spline tight framelets generated using the oblique extension principle. We present some examples to illustrate the results.
The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting

The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.
A Blind Robust QR Code Watermarking Approach Based on DCT

In this paper, an approach to watermarking images by embedding quick response (QR) code in a digital image based on the discrete cosine transform (DCT) is proposed. A block structure for manipulation is formed by partitioning the host image into non-overlapping blocks of 8'8 pixels and applying DCT to each block separately. A single watermark is embedded in different location blocks by quantization index modulation (QIM) to ensure that the watermark can still survive, even if the watermarked image is cropped over a large area. By employing the error correction mechanism of QR code, the information contained within a watermarked image can potentially be decoded. Experiment results indicate that the proposed scheme can withstand a variety of image processing attacks. Compared with the other watermarking scheme, the proposed method exhibits superior robustness and imperceptibility with the same payload capacity.
The Private Governance of Identity on the Silk Road

Long before the creation of blockchain platforms, the rise of personal computing and Internet connectivity brought with it a digital, online dimension of the material world, leading to the socio-technical construct known as ‘digital identity’. After the online discussion boards and emailing lists of the early 90s, individuals started socializing via the Internet more predominantly using social networks. One specific type of platform links this online socializing and transacting to blockchain-based spaces: dark web marketplaces. Identified as second-generation cryptocommunities, dark web marketplaces deployed cryptography for the use of pseudonymous identity, for communication, but also currency. This paper explores two questions in this fascinating space: what was the role of identity on the Silk Road, and what governance lessons can be drawn from this illustration for the purpose of applying them to more recent cybercommunities such as Ethereum? The paper is structured as follows. The first part describes the Silk Road and sketches its essential characteristics. The second part looks at how individuals could become platform users on the Silk Road, by analyzing the contractual relationship between the Silk Road and an individual user based on the rights and obligations enshrined in the Silk Road Terms of Service (ToS). The third part critically reflects on arbitrariness as the main pitfall arising out of the private regulatory framework created by the Silk Road, and contributes to existing narratives surrounding the regulatory nature of code by proposing a code-as-procedure perspective for analyzing this regulatory framework. Part four concludes.
Addressing Accent Mismatch In Mandarin-English Code-Switching Speech Recognition

Automatic speech recognition systems suffer from accuracy degradation when code-switching (multiple languages are spoken in a single utterance) is encountered. This is especially common for non-native speakers where there is a mismatch between speech and acoustic model. In this paper, we experiment on Mandarin-English code-switching audio spoken by native Chinese speakers and evaluate three techniques to improve accuracy-data adaptation, individual senone modeling and lexicon enrichment. Our results show the recognition of accented speech improves up to 12% on various code-switching datasets. We also propose several metrics to measure code-switching recognition quality, not captured in typical word error rate (WER) measurement.
A Hierarchical Model for Dialog Act Recognition Considering Acoustic and Lexical Context Information

Dialog act recognition (DAR) is important to capture speakers' intention in a dialog system. Traditional methods commonly use the lexical information from transcripts, acoustic information from speech, and dialog context information to do DAR. However, in these methods, textual context information may be considered, whereas acoustic context information is ignored, which leads to ambiguity in certain DAs especially in Mandarin. To solve the problem, we propose a hierarchical model for DAR considering context information of both lexical and acoustic prosody. The experimental results on a Mandarin dialog corpus demonstrate that the contextual-acoustic information is helpful for recognizing DAs. The contextually specific prosodies involved in the utterances such as the echo question and open-end question are beneficial to identify the users' intention. We also investigate the effect of the context length on the DAR. The proper context length is approximately equal to the length of the entire subtopics.
Optimizing Data-Driven Models for Summarization as Parallel Tasks

This paper presents tackling of a hard optimization problem of computational linguistics, specifically automatic multi-document text summarization, using grid computing. The main challenge of multi-document summarization is to extract the most relevant and unique information effectively and efficiently from a set of topic-related documents, constrained to a specified length. In the Big Data/Text era, where the information increases exponentially, optimization becomes essential in selection of the most representative sentences for generating the best summaries. Therefore, a data-driven summarization model is proposed and optimized during a run of Differential Evolution (DE). Different DE runs are distributed to a grid in parallel as optimization tasks, seeking high processing throughput despite the demanding complexity of the linguistic model, especially on longer multi-documents where DE improves results given more iterations. Namely, parallelization and the grid enable, running several independent DE runs at same time within fixed real-time budget. Such approach results in improving a Document Understanding Conference (DUC) benchmark recall metric over a previous setting.
Requirements Management Techniques and Tools in Small and Medium Enterprises (SMEs): a Systematic Review

Currently, software is an important element used in different areas, both in the personal or professional. Therefore, it is required that the software be of quality, that is, that it fulfills the purpose for which it was created and satisfies the client. That's why to develop software, you must follow an engineering approach as well as a discipline, being of special importance the Requirements Management (REQM). In such a way that this work considers the REQM as one of the basic areas of project management, it starts with the requirements to define the "what to build" for the product and the project. This paper presents a software engineering systematic review process, the phases followed, the description of each one of them, as well as its implementation applied to the subject of the techniques and tools of the REQM area in software development Small and Medium Enterprises (SME's).
Security Analysis of Improved One-Time Pad Cryptography Using TRNG Key Generator

Cryptography is one of the important aspect of data and information security. The security strength of cryptographic algorithms rely on the secrecy and randomness of keys. In this study, bitwise operations, Fisher-Yates shuffling algorithm, and cipher text mapping are integrated in the proposed TRNG key generator for One-Time Pad cryptography. Frequency monobit, frequency within a block, and runs tests are performed to evaluate the key randomness. The proposed method is also evaluated in terms of avalanche effect and brute force attack. Tests results indicate that the proposed method generates more random keys and has a higher level of security compared with the usual OTP using PRNG and TRNGs that do not undergo a refining phase.
A Underwater Image Enhanced Algorithm combined MSR and Channel Energy

Vision is the most important source of information for Unmanned Underwater Vehicle(UUV), only a clear image can make it possible for a UUV to operate automatically, Due to strong absorption and light scattering for water, the image of underwater imaging has a serious degradation, whether in distance or in quality. Based on classical Retinex theory, this paper proposed an underwater image enhanced algorithm. For different channel of underwater image has apparently different energy, especially green channel has overwhelming power than others, we added a step to energy balance of the RGB three channel in classical Multi-Scale Retinex (MSR). Experiment data show that, compared to the traditional three-channel simple addition, our improvement can get more natural recovered underwater image.
Fuzzy Logic Controller for Autonomous Mobile Robot Navigation

Autonomous mobile robot is a mobile robot that can move without human supervision. The use of autonomous mobile robots has increased in various fields such as in the industrial, agricultural, and military sectors. This paper presents the design and implementation of fuzzy control for nonholonomic unicycle mobile robot. In the experiment presented herewith, the controller was designed with 35 Fuzzy Logic rules and three types of membership functions to allow mobile robot to track different desired trajectories. The proposed controller is designed on Matlab/Simulink environment. Simulations are presented to show the performance of the fuzzy logic controller.
Auxiliary Capsules for Natural Language Understanding

Lately, joint training of Intent detection and Slot filling has become the best-performing approach in the field of Natural Language Understanding (NLU). In this work we extend the newly introduced application of Capsule Networks for NLU to a multi-task learning environment, using relevant auxiliary tasks. Specifically, our models perform joint Intent classification and Slot filling with the aid of Named Entity Recognition (NER) and Part of Speech (POS) tagging tasks. This allows us to exploit the hierarchical relationships between the Intents of the utterances and the different features of input text, not only Slots but also Named Entity mentions, Parts of Speech, quantity indications, etc. The models developed in this work are evaluated on standard benchmarks, achieving state-of-the-art results on the SNIPS dataset while outperforming the best commercial systems on several low-resource datasets.
Spatial and Multicriteria Analysis of Dimension Stones and Crushed Rocks Quarrying in the Context of Sustainable Regional Development: Case Study of Lower Silesia (Poland)

This study aimed to analyze and assess the spatial and temporal trends in distribution of reserves and production of igneous and metamorphic dimension stones and crushed rocks in the Lower Silesia region, which with 90% of total supply is the key source region of these rock raw materials in Poland. The research utilized descriptive statistics to examine temporal variations of production, as well as to determine sufficiency of these resources for four different scenarios and seven main lithological groups of rocks. Spatial statistics in the geographic information system was used to map changes in spatial distribution of production with the density function, as well as to determine areas of highest concentration in the 2010–2018 period. Then, 169 communes in the region were assessed using the multicriteria analytical hierarchy process (AHP) technique to identify local communities prospective for development of this mining sector. Strong, positive correlation (r = 0.645) between year to year production change with annual rate of gross domestic product was determined. Sufficiency of economic reserves was estimated, in four scenarios, as being the lowest for melaphyre and porphyry (25–49 years), and the highest for marble (380–389 years). Reserves of basalt and amphibolite should last for approximately 50 years (32–60 and 36–67 years, respectively), granite for 82 to 110 years, and gabbro and gneiss for over 100 years (78–159 and 76–189 years, respectively). Maps revealed a possible trend of increasing production in quarries located in the central and eastern parts of Lower Silesia, whereas multicriteria analysis allowed for the identification of nine communes with the highest potential for rock raw material quarrying. The practical outcome of this study is a knowledge database for authorities, upon which sustainable management of regional rock raw materials can be based in the context of economic, social, and environmental impacts of their extraction.
A New Polyphase Down-Sampling-Based Multiple Description Image Coding

Multiple description coding (MDC) is an efficient source coding technique for error-prone transmission over multiple channels. In this paper, we focus on the design of a new polyphase down-sampling based MDC (NPDS-MDC) for image signals. The encoding of our proposed NPDS-MDC consists of three steps. First, we perform down-sampling on each N×N image block according to the quincunx down-sampling pattern. Second, we propose a new transform and apply it to the down-sampled pixels to produce the side descriptions. Third, we develop an error compensation algorithm to reduce the compression distortion occurring on the down-sampled pixels. In our scheme, the side decoding is performed posterior to image interpolation with reference to the down-sampled compressed pixels. Moreover, the central decoding is achieved by interlacing the side descriptions. We also propose a compression-constrained central deblocking algorithm to further improve the efficiency of the central decoding. The experimental results indicate that our proposed MDC scheme offers clearly superior performance, especially at high bit rates, as compared to the state-of-the-art methods for various types of images.
Using the Actionable Intelligence Approach for the DPI of Cybercrime Insider Investigation

Cybercrime threats are often originating from trusted, malicious, or negligent insiders, who have excessive access privileges to an organization's network, system, or data. The sophistication of insider threats has led to cybercrime issues. Even when an incident is detected, the follow-up countermeasures are required to analyze the results. The analysis of cybercrime insider investigation presents many opportunities for actionable intelligence on improving the quality and value of digital evidence. There are several advantages of applying Deep Packet Inspection (DPI) methods in cybercrime insider investigation. This study discusses the importance of actionable intelligence to conduct investigations and addresses the countermeasure of a cybercrime insider investigation with DPI to detect anomalies in network packets.
Exploration of the Characteristics of Emotion Distribution in Korean TV Series: Common Pattern and Statistical Complexity

The series is one of the most popular formats of television programs, but the emotional aspects of its content have not attracted much attention from researchers. Furthermore, the cinemetric approach, in which move data are quantitively examined, has not been used to analyze affective content in television series. Therefore, this study focused on emotion distribution in Korean television series. In all, 337 episodes from 20 series were divided into shots, then keyframes were selected from the shots. Facial emotions on the keyframes were measured using an online artificial intelligence service. Emotion distributions were obtained from each episode and then were observed for common patterns. In addition, the statistical complexity of the distributions were calculated and compared by the temporal phase of the episode, channel type, genre, ratings, and season. The results show that the emotion distributions of all episodes in all series had the same form: an asymmetric U-shaped distribution biased toward zero. The statistical complexity of emotion distribution was greater in early episodes than in later episodes of a series, and it was higher in episodes with higher ratings. In addition, the complexity of emotion distribution was higher in hard genres and during the summer season. There was no significant difference between terrestrial television channels and cable/general programming channels.
Combining Product Line Approach and Formal Specification for Product Family Modeling

The lack of systematic approach for product family modeling leads to difficulties in developing a series of high-quality software products simultaneously. This paper tackles this challenge by combining product line approach and formal specification technique. After constructing the feature model of the target family, our approach will start the formalization of the bottom-level features. For each feature, a formal model will be built according to its inner structure and function details. Any problem occurred during this process will cause the evolvement of the feature model under the guidance of a set of pre-defined evolution rules. The final product family model will be established when no problem can be found in both feature model and feature specification. This interleaving of feature model evolvement and feature formalization facilitates consistent and complete product family modeling from both abstract and concrete levels. A case study is given to illustrate the validity of the approach.