Reviewerly/exHarmony huggingface dataset:
In-Device Feedback in Immersive Head-Mounted Displays for Distance Perception During Teleoperation of Unmanned Ground Vehicles

In recent years, Virtual Reality (VR) Head-Mounted Displays (HMD) have been used to provide an immersive, first-person view in real-time for the remote-control of Unmanned Ground Vehicles (UGV). One critical issue is that it is challenging to perceive the distance of obstacles surrounding the vehicle from 2D views in the HMD, which deteriorates the control of UGV. Conventional distance indicators used in HMD take up screen space which leads clutter on the display and can further reduce situation awareness of the physical environment. To address the issue, in this paper we propose off-screen in-device feedback using vibro-tactile and/or light-visual cues to provide real-time distance information for the remote control of UGV. Results from a study show a significantly better performance with either feedback type, reduced workload and improved usability in a driving task that requires continuous perception of the distance between the UGV and its environmental objects or obstacles. Our findings show a solid case for in-device vibro-tactile and/or light-visual feedback to support remote operation of UGVs that highly relies on distance perception of objects.
Cost-sensitive matrixized classification learning with information entropy

Classifier design is one of the most significant fields in pattern recognition. Most classifiers are measured by classification accuracy, which assumes that all the misclassification cost are the same. In the real world, different misclassifications usually bring different losses. Based on this fact, cost-sensitive learning is becoming a hot research area in pattern recognition. However, in cost-sensitive learning, examples costs are often difficult to achieve and usually decided by the authors experience. Hence, combining the cost-sensitive learning and matrixized learning thoughts, we propose a two-class cost-sensitive matrixized classification model based on information entropy called CsMatMHKS in this paper. The proposed CsMatMHKS introduces information entropy which can reveal the uncertainty of one sample into matrixized learning framework to decrease the total misclassification cost. The experimental results on the UCI datasets and image datasets indicate that the CsMatMHKS not only reduces the sum of classification costs but also has comparable classification accuracy.
Addressing the issue of undeclared work – Part I: Applying associative classification per the CRISP-DM methodology

Addressing undeclared work is a high priority in the labor field for government policymakers since it adversely affects all involved parties and results in significant losses in tax and social security contribution revenues. In the last years, the wide use of ICT in labor inspectorates and the considerable progress in data exchange have resulted in numerous databases dispersed in various units, yet these are not effectively used to increase their functions productivity. This study presents a detailed analysis of a data mining project per the CRISP-DM methodology aiming to assist the labor inspectorates in dealing with undeclared work and other labor law violations. It uses real past inspections data merged with companies characteristics and their employment details and examines the application of two Associative Classification algorithms, the CBA and CBA2, in combination with two types of datasets, a binary and a four-class. The produced models are assessed per the data mining goals and per the initial business objectives, and the research concludes proposing an innovative inspections recommendation tool proved to offer two major benefits: a mechanism for planning targeted inspections of improved efficiency and a knowledge repository for enhancing the inspectors understanding of those features linked with labor law violations.
Group-sparse mode decomposition: A signal decomposition algorithm based on group-sparsity in the frequency domain

A new signal decomposition algorithm is developed in this paper. The goal of signal decomposition is to decompose a complex and nonstationary signal into a set of basic functions, usually called intrinsic mode functions (IMFs). In the proposed algorithm, it is assumed that each IMF has a limited bandwidth, and the frequency bands of various IMFs are disjoint. Based on this assumption, it is suggested to estimate the IMFs using a set of ideal filters designed in the frequency domain. The ideal filter bank is estimated in the proposed algorithm using penalized least-squares, where the weighted ℓ0-norm is utilized as the penalty term. In addition, the weighting parameters (used with the weighted ℓ0-norm) and the regularization parameter are calculated using techniques based on energy detection over short windows. It was found that the optimum value of the regularization parameter depends on the noise variance and the size of the windows over which the energy is calculated. To maintain the adaptivity of the proposed algorithm, two techniques are suggested for estimating the values of these two parameters from the analyzed signals. The numerical results on simulated and real data show that the proposed algorithm is fast, robust against noise, and outperforms some state-of-the-art signal decomposition algorithms.
Microchemical signatures of placer gold grains from the Gamba district, northern Cameroon: Implications for possible bedrock sources

The Central African Fold Belt (CAFB) in Cameroon hosts numerous alluvial gold deposits derived from known deposit types in the eastern and southern part of the country. In contrast, in northern Cameroon, primary source of gold remains enigmatic. Based on the morphological and geochemical signature of alluvial gold grains from northern Cameroon, this study discusses the distance-to-source and the style of gold mineralization of the Gamba district. Three hundred and fifty gold grains collected from the four locations were primarily analyzed by scanning electron microscopy (SEM) to investigate their morphological characteristics, and subsequently the composition was examined through the SEM and electron microprobe analysis (EMPA). The gold grains from the four locations have irregular to rounded morphology, with a microtextures indicating a proximal source at less than 5 km. Most of the gold grains display evidence for mechanical hammering and abrasion. EMPA results show mainly Au-Ag alloys, dominated by high Ag values in the cores from different location. Thus, Djaba is characterized by a range of 1.1–22.1 wt% Ag with 77.2–99.3 wt% Au; Bigbal includes a range of 10.3–32.9 wt% Ag with 66.1–88.7 wt% Au; Bougma is distinguished by a range of 7.3–18.3 wt% Ag with 80.8–91.9 wt% Au; and Mayo Salah is characterized by a range of 2.8–22.8 wt% Ag with 77.3–96.8 wt% Au. These range in compositions suggest multiple deposits of the same type in different locations. Gold grains from the four locations show the Te (0.1–0.3 wt%) content, Bi (0.1–0.7 wt%) content, and Cu (0–0.05 wt%) content, suggesting a source of orogenic gold mineralization. Most of the gold grains are Ag-depleted from both the cores and rims and show lack of Au-rich rims indicate no evidence of supergene precipitation, which suggests that the grains were transferred directly from hypogene source to surficial and fluvial system. EMPA data on the gold grains, plotted in the ternary diagram of Au-Ag-Cu, suggest that gold from the Gamba district is hydrothermal-orogenic in origin. Mineral inclusions include silicates (zircon, titanite, quartz, muscovites, biotite, actinolite, titanite, albite and almandine), oxides (magnetite, ilmenite and hematite), and phosphates (apatites), which suggest the magmatic and metamorphic conditions of formation for the hypogene source. This indicates the granitoids and metasediment of schists which bear multiple quartz veins as the potential bedrock sources. The microchemical signature of placer gold used in this study is a useful tool to explore the hypogene environment of an unknown primary source that can greatly help in mineral exploration in northern Cameroon.
Forecasting and Analysis of Solar Power Output from Integrated Solar Energy and IoT System

Solar-powered irrigation systems has attracted enormous attention considering because it is a green energy source and cost-effective green energy and power supply source for plantations and farms, especially those located in rural areas. Solar power generation systems may experience either insufficient voltage or overvoltage of solar power generation usually occurs especially for the based on the specific country that has specific climate of the installation spot. East coast states of Malaysia face northeast monsoon every year and during this season, the outputs from solar power generation systems will fluctuate greatly that the solar power distribution throughout the year was never reported elsewhere. Thus, in this study, auto-tracking solar panel was installed in a mini farm equipped with Internet-of- Thing (IoT) system for 24/7 data monitoring. From the results, the highest amount of energy generated was found from between 12 pm until 2 pm with approximately 45.9&#x0025; efficiency. Then, ARIMA (11, 2, 4) model was applied using Python tool to forecast the energy generation data obtained. This forecast found the Mean Absolute Percentage Error (MAPE) of around 32.0&#x0025;, which of Mean Absolute Percentage Error (MAPE) implies that the prediction was is about 68.0&#x0025; validity with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MASE) figures of recorded with 1.70 and 0.32, respectively. Forecasting the output is important to ensure the availability of existing and back-up of electricity supply, besides to avoid over and underutilization of electricity.
Isolation Forest Based Anomaly Detection: A Systematic Literature Review

Anomaly detection using machine learning algorithms is rising lately, especially with increased data volume and velocity. One of the most recent anomaly detection algorithms is Isolation Forest (IF). Despite its simplicity, it excels at dealing with high-dimensional data and excels at speed. However, IF is not without weaknesses, and several researchers have found its weaknesses and at the same time provide solutions. Therefore, to help understanding researches related to IF, this paper will discuss 17 studies related to IF improvement by conducting a systematic literature review that comprehensively discusses IF weaknesses, types of data, and causes of occurrence, as well as dissecting the solutions offered and the fields of research that use IF. From the review, it is known that the main cause of the weakness of IF is the random selections of variables in the data split and the solutions proposed by the researchers are divided into three types: pre-IF, post-IF and method improvement. To our knowledge, there is no literature review related to IF improvement, and we expect this paper to help other researchers in developing anomaly detection based on IF.
Information value-based geochemical anomaly modeling: A statistical index to generate enhanced geochemical signatures for mineral exploration targeting

Converting geochemical exploration data into information relevant to descriptive statistics of indicator elements facilitates gaining the knowledge of spatial and genetic relationships between mineralization and elemental dispersion patterns. This allows obtaining insights into alternatives to exploration strategies aiming at vectoring toward undiscovered mineral deposit sites. In this regard, shaping geochemical data for better understanding of their underlying patterns and extraction of information about ore deposition-related anomalies are challenging issues. To better extraction of the information from the geochemical data, we adapted the “information value” concept to quantify the significance of geochemical anomaly classes as spatial proxies of mineral deposits. To illustrate and evaluate the procedure proposed, a dataset of porphyry-Cu exploration was used to delineate exploration targets in the Baft district, Kerman province, Iran. Through this, geochemical models of information values were created for ore deposition-related elements. Then, the geochemical models were combined to produce an information value-based multi-element geochemical signature aiming at utilizing the prediction ability of every indicator element in a single model. Through the combination, spatial patterns of geochemical signals around mineral deposits are revealed that makes it easier to explore undiscovered deposit sites. We also applied fuzzy operators, as common and alternative practices, to integrate values of the same indicator elements for comparison purpose. The results demonstrated that the model of information value-based multi-element geochemical signature is an efficient geochemical proxy, over those geochemical models generated by the application of fuzzy operators, for exploration targeting.
Understanding students’ software development projects: Effort, performance, satisfaction, skills and their relation to the adequacy of outcomes developed

Given the inclusion of (often team-based) course projects in tertiary software engineering education, it is necessary to investigate software engineering curricula and students’ experiences while undergoing their software engineering training. Previous research efforts have not sufficiently explored students perceptions around the commitment and adequacy of effort spent on software engineering projects, their project performance and skills that are developed during course projects. This gap in skills awareness includes those that are necessary, anticipated and learned, and the challenges to student project success, which may predict project performance. Such insights could inform curricula design, theory and practice, in terms of improving post-study software development success. We conducted a survey involving undergraduate across four universities in New Zealand and Cyprus to explore these issues, where extensive deductive and inductive analyses were performed. Among our findings we observe that students’ commitment of effort on software engineering project seems appropriate. Students are more satisfied with their team’s collaboration performance than technical contributions, but we found that junior students seemed to struggle with teamwork. Further, we observe that the software students developed were of higher quality if they had worked in project teams previously, had stronger technical skills and were involved in timely meetings. This study singles out mechanisms for informing good estimation of effort, mentoring technical competencies and suitable coaching for enhancing project success and student learning.
Interweave features of Deep Convolutional Neural Networks for semantic segmentation

Although semantic segmentation based on Deep Convolutional Neural Networks (DCNN) has made great signs of progress, the issue that features generated by deep models are of low-resolution and will negatively affect the final semantic segmentation performances is not fully addressed yet. In this paper, we propose to adaptively combine high-level and low-level features of DCNN to improve the quality of the features used for semantic segmentation. To this end, we design a feature interweaving neural network module to fuse features from different layers of DCNN to effectively take advantage of their complementary properties. And, in order to enhance complementarity and diminish contradiction of the features for better feature fusion, we propose a feature modulation neural network module to modulate the features before interweaving. Furthermore, global information of images is summarized and used to augment the features for providing guidance for feature interweaving. The proposed method is extensively evaluated and compared to state-of-the-art methods based on two benchmark semantic segmentation datasets Cityscapes and PASCAL VOC 2012 in the experiments. Obtained results demonstrate the effectiveness of the proposed method.
Whether Digital Financial Inclusion Can Improve Capital Misallocation or Not: A Study Based on the Moderating Effect of Economic Policy Uncertainty

This paper discusses the impact of digital financial inclusion on regional capital’s turn from the fictitious to the substantial economy. The continuous decline of the capital efficiency of the real economy is an important reason for the misallocation of financial capital, such as the financialization of real enterprises. Development of the digital financial inclusion helps to relieve small and micro businesses from financing constraints, encourage civilian consumption, and improve services concerning issues of agriculture, rural areas, and farmers. Yet, its financial features also indicate potential systematic risks, manifested as the capital’s departure from its intended purpose of serving the substantial economy, given some beneficiaries’ investment in the fictitious economy. Based on the provincial panel data between 2011 and 2019, this paper constructs an index describing capital’s diversion from the fictitious to the substantial economy. This paper then analyzes the impact of digital financial inclusion on such a diversion of the regional capital, investigating the regulatory effects caused by the uncertainty in economic policies. Empirical study reveals that digital financial inclusion has an evident positive effect on regional capital’s diversion from the fictitious to the substantial economy but without any spatial spillover effect. Among the three subdimensions of digital financial inclusion-scope of coverage, depth of usage, and level of digitalization, the scope of coverage has the strongest positive effect, and digitization level, the weakest. The positive correlation between digital financial inclusion and capital diversion from the fictitious to the substantial economy is under negative regulation due to economic policy uncertainty. In other words, increasing uncertainty in the economic policy would weaken digital financial inclusion’s support of the substantial economy.
An Analysis of the Performance and Configuration Features of MySQL Document Store and Elasticsearch as an Alternative Backend in a Data Replication Solution

In recent years, with the increase in the volume and complexity of data, choosing a suitable database for storing huge amounts of data is not easy, because it must consider aspects such as manageability, scalability, and extensibility. Nowadays, the NoSQL databases have gained immense popularity for their efficiency in managing such datasets compared to relational databases. However, relational databases also exhibit some advantages in certain circumstances, therefore many applications use a combined approach: relational and non-relational. This paper performs a comparative evaluation of two popular open-source DBMSs: MySQL Document Store and Elasticsearch as non-relational DBMSs; this comparison is based on a detailed analysis of CRUD operations for different amounts of data showing how the databases could be modeled and used in an application. A case-study application was developed for this purpose in Java programming language and Spring framework using for data storage both relational MySQL and non-relational Elasticsearch and MySQL Document Store. To model the real situation encountered in several developed applications that use both relational and non-relational databases, a data replication solution that imports data from the primary relational MySQL database into Elasticsearch and MySQL Document Store as possible alternatives for more efficient data search was proposed and implemented.
Intelligent all-fiber device: storage and logic computing

The typical functions of the optical fiber are communication and sensing. However, the fiber functions need to extend to meet the requirements of the development of artificial intelligence. This paper achieves an all-fiber device with storage and logic computing functions using a single-mode fiber and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>Ge</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msub> <mml:msub> <mml:mrow> <mml:mi>Sb</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msub> <mml:msub> <mml:mrow> <mml:mi>Te</mml:mi> </mml:mrow> <mml:mn>5</mml:mn> </mml:msub> </mml:mrow> </mml:math> (GST) material. We use the pulse amplitude modulation (the switching energy is about 50 nJ) to switch the GST state for performing the eight-level data storage (3-bit). The all-fiber memory device has the advantages of high optical contrast (about 38%), good reversibility, and high repeatability. We implement the all-optical logic operations (“AND” and “OR”) by using two memory cells in series and parallel. For the first time, we use the single-mode optical fiber to realize storage and computing functions, and this intelligent fiber has tremendous application potential in intelligent optical fiber communication and portends a new paradigm for brain-like computing.
“Pyramid Deep dehazing”: An unsupervised single image dehazing method using deep image prior

Single image dehazing remove haze from degraded images and recover clean scenes. Prior based Methods can achieve great results on some haze images, but their performance is limited by the handcraft prior itself. Recently, many learning-based approaches has been proposed. Most of these modules rely on matching clean and haze images for training. However, such kind of real world data can be hard to get. Also the domain shift between training and testing data may affect the results. Some unsupervised methods have been proved to work on haze scenes but they still rely on handcraft priors to guide the training. In this paper, we proposed an Unsupervised Single Image Dehazing method using internal learning based on the optical model of haze and other haze-like degradation images. A Pyramid Deep Image strategy is used to gradually generate clean background. The entire training doesn't need any extra data or handcraft prior, and only needs the testing image itself. The proposed method is able to deal with different kinds of haze images including other haze-like degradation (like nighttime images and underwater images).
Multi-target regression via non-linear output structure learning

The problem of simultaneously predicting multiple real-valued outputs using a shared set of input variables is known as multi-target regression and has attracted considerable interest in the past couple of years. The dominant approach in the literature for multi-target regression is to capture the dependencies between the outputs through a linear model and express it as an output mixing matrix. This modelling formalism, however, is too simplistic in real-world problems where the output variables are related to one another in a more complex and non-linear fashion. To address this problem, in this study, we propose a structural modelling approach where the correlations between output variables are modelled using a non-linear approach. In particular, we pose the multi-target regression problem as one of vector-valued composition function learning in the reproducing kernel Hilbert space and propose a non-linear structure learning approach to capture the relationship between the outputs via an output kernel. By virtue of using a non-linear output kernel function, the proposed approach can better discover non-linear dependencies among targets for improved prediction performance. An extensive evaluation conducted on different databases reveals the benefits of the proposed multi-target regression technique against the baseline and the state-of-the-art methods.