Reviewerly/exHarmony huggingface dataset:
Significance of Feature Selection and Pruning Algorithms in Machine Learning Classification of E-Mails

Email classification using Machine Learning (ML) algorithm is an application of Artificial Intelligence (AI) where the system learns by itself to provide predictions between ham and spam mails. These algorithms predict a class based on the probability estimated from the features of the particular class label. The classifier performance gets degraded if there are large number of features with complex distributions and limited data instances. This paper illustrates the necessity of a suitable pre-processing stage to eliminate the irrelevant and redundant features before the classifier for enhancing its performance, facilitate the data visualization, reduce the storage requirements and time for modelling. The proposed methodology investigates and evaluates several feature selection and ranking algorithms in email classification process. The approaches used here are classifier-based feature selection methods, schema dependent and independent pruning methods, ranking based fast feature selection methods, cost sensitive classifier and cost-sensitive learner. A quantitative analysis was conducted using different feature selection and pruning algorithms. The important effects we observed here are feature space reduction and manageability of the learning algorithm’s computation criteria. The results of the experimentation show an enhanced accuracy of minimum 10% with better precision values. This paper concludes with significant outcomes like validation of various ranker evaluators, advantages of scheme independent methods in terms of computational factors, the effects of cost-sensitive classifier and cost-sensitive learner. Our experiments also illustrated the importance of classifier dependency in determining the sensitivity of feature selection algorithms and the peril of over-fitting the model in the email domain.
Problems and urban sustainable development in wetlands based on the thermal conditions

Abstract Indonesia’s landscape surrounded by oceans causes weather anomalies from the Indian and Pacific oceans. In the dry season, drought causes frequent forest and land fires. Forest and land fire is a problem for the government and society. The causes of forest and land fires in 2019, according to BMKG, are El Nino and La Nina (ENSO), Indian Ocean Dipole (IOD), Sea Surface Temperature (SST), and the influence of wind movement (Monsoon). Forest and land fires are closely related to climate and weather conditions. WWF Indonesia said that fires in wetlands, especially peatlands, are alarming. In Sumatra and Kalimantan, peatland fires reached an average of 32.1% and 25.1%. Peat contains fuel (plant residues) below the surface, as a result, fires in peatlands spread below the surface of the soil slowly and are difficult to detect and cause thick smoke. The phenomenon of drought in wetlands especially peatlands has been proven to cause forest and land fires. Based on this condition, it is necessary to conduct research related to the thermal conditions of the peat environment, especially the surface thermal conditions in the swamp. This research is important to know the thermal characteristics of swamps so that urban problems due to thick smoke can be identified. This research is quantitative research based on measurement data collected from the field study. This research was conducted in 2 provinces and 3 cities in Kalimantan: Banjarmasin, Kapuas, and Pulang Pisau. This location was chosen because the city is located in wetlands and is often affected by forest and land fires. The measurement results show high thermal conditions during the day when the intensity of solar radiation is also high around 1400 watts / m2.
Research on Building Automation Platform with Computer Data Mining Technology

Abstract Data mining, in general, uses a series of technical means to complete the discovery and display of the potential value of data. Now that the goal is clear, the means to achieve the goal is not the only choice. In today’s real-world data work, the data we need to work with is often diverse [1]. For example, imagine such a scenario: if we need to conduct some analysis on a product, the data may come from user comments and click-through rates on social media, or from transaction data obtained from sales channels, or historical data, or product information captured from product websites. This shows how data mining technology will play a role in the future. Based on this, we design the automatic control platform of data mining, and explore the construction of automatic platform of data mining technology
Identification of Precipitation Clouds Based on Faster-RCNN Method

Precipitation clouds are visible aggregates of hydrometeor in the air that floating in the atmosphere after condensation, which can be divided into stratiform cloud and convective cloud. Different precipitation clouds often accompany different precipitation processes. Accurate identification of precipitation clouds is significant for the prediction of severe precipitation processes. Traditional identification methods mostly depend on the differences of radar reflectivity distribution morphology between stratiform and convective precipitation clouds in three-dimensional space. This paper proposes a new method for precipitation clouds identification based on deep learning algorithm. It mainly includes two parts, which are constant altitude plan position indicator data (CAPPI) inversion for radar reflectivity, and the precipitation clouds identification based on Faster-RCNN. The testing result shows that the method proposed in this paper performs better than typical existing algorithms in terms of accuracy rate. Moreover, this method boasts great advantages in running time and adaptive ability.
Bilattice CADIAG-II: Theory and Experimental Results

CADIAG-II is a functioning experimental fuzzy expert system for computer-assisted differential diagnosis in internal medicine. To overcome the current limitations of the system, we propose an extension based on bilattices. The proposed changes were implemented and reviewed in a retrospective evaluation of 3,131 patients with extended information about patient’s medical history, physical examination, laboratory test results, clinical investigations and—last but not least—clinically confirmed discharge diagnoses.
Fizeau interferometer with double interference cavity for suppressing errors from vibration and air turbulence

In this paper, a Fizeau interferometer with double interference cavity is proposed to solve the influence of the environmental factors in high precision phase measurement. The proposed method adds a reference mirror, combined with a low-coherence source, to construct two interference cavities, which have the synchronous phase change. One of them has an adjustable spatial carrier frequency, which is used to calculate the relative deformation phase during the test, while the other has the same null fringe with the standard Fizeau interferometer. The measured phase can be retrieved by using the least square method with the calculated deformation phase and null fringe patterns. In addition, the visibility of the fringe of the two interference cavities are analyzed. Simulation and experiment demonstrate that the proposed method can realize the dynamic measurement of the mirror under the influence of the time-varying environment, and has reliable measurement accuracy.
Enhancing Viewing Experience of Generated Visual Storylines for Promotional Videos

Visual storyline generation is the problem of selecting and sequencing a set of visual materials (i.e. images and video clips) to produce a video to elicit certain cognitive or emotional responses from viewers. In this paper, we enhance the viewing experience of generated visual storylines with the Shot Composition, Selection and Plotting (ShotCSP) approach. Designed for generating promotional videos in ecommerce settings, ShotCSP considers three key film-making principles into the visual storyline generation pipeline: a) proximity-aware scene transition, b) sound logic flow, and c) graphic discontinuity. We propose two novel metrics to enhance viewing experience: 1) Semantic Distance, which measures how related a shot is to the product being promoted; and 2) Salient Region Ratio, which estimates attention to product details in a shot. Through large-scale user evaluation involving 1,748 pairwise comparisons against five state-of-the-art approaches, ShotCSP achieves significantly improved viewing experience. It is a promising approach to enable AI generated promotional videos to benefit e-commerce businesses.
Study on Eye Movements in Subjective Evaluation of Displayed Pictures

Eye movement was measured when food and wear pictures that consisted of four items with different positions were subjectively evaluated by seven-grade scale (like - dislike), and the relationship between the subjective evaluation and feature parameters obtained from eye fixation and scanpath was investigated. In results, a significant relationship between the saliency of pictures obtained by GBVS and the normalized total fixation time was not observed. And the eye movement at the initial state might be similar, independent of the saliency of items in the pictures. The fixation time to the item of the negative `dislike' evaluation became relatively longer, when the evaluation was negative irrespective of the experimental condition. These feature parameters would be useful for quantifying the evaluation of the pictures.
Research and Practice on Student-centered Curriculum Reform of "DSP Principle and Application"

It is an important content of the current educational reform to change the educational idea to take the student as the center, and it is also an important guarantee to realize the goal of the current educational reform. This paper studies the connotation of student-centered education concept, the characteristics of the current course "DSP principle and application" and the problems in teaching, this paper focuses on the student-centered reform of the course "DSP principle and application".
Real-World Image Super-Resolution Via Spatio-Temporal Correlation Network

Super-resolving real-world image is very challenging due to the degradations in real-world low-resolution images are highly complicated. In this paper, we propose a novel Spatio-temporal Correlation Network (STCN) for real-world single image super-resolution. Specifically, we adopt a very deep network which consists of several attention groups. Each attention group (AG) contains a series of residual channel attention blocks (RCABs) and one spatio-temporal correlation block (STCB). Notably, STCB mainly consists of a residual 3D convolution, and aims to fully explore the local spatial and temporal correlations between channels of feature maps generated by RCABs for selectively capturing more informative features. In addition, we propose an innovative dual restriction (DR) through a simple degradation model to reduce the possible space of mapping functions in super-resolution. Experiments conducted on two public available real-world datasets demonstrate the superior performance of our method.
Understanding join strategies in distributed systems

This article discusses the features of distributed computing in systems based on MapReduce, analyzes join strategies in Apache Spark, gives basic recommendations for optimizing join operation, and proposes an algorithm of the join operation for distributed data warehouses, which increases the processing speed. Experimental data represents that the method becomes more effective when the volume of initial data increases. So, for 2 TB data, the join operation was performed ~ 37% faster, for 7 TB data already by ~ 47%.
Optical parameter measurement of parallel plates based on weighted multi-step phase-shifting algorithm

Parallel plates are commonly used as optical transmission elements. Their main optical parameters include the optical homogeneity, optical thickness, and surface shape. If the optical parameters are not consistent, the transmissive wavefront will be changed, and the performance of the optical system will be reduced. Herein, a weighted multi-step phase-shifting algorithm is developed. The algorithm can measure the optical parameters with high accuracy based on wavelength tuning interferometry, and extracts the phase at the target frequency by weighting the sampling values. Both the simulated and experimental results show that the algorithm can suppress high-order harmonics and is insensitive to the phase-shifting errors caused by nonlinear wavelength tuning and coupling errors between phase-shifting and harmonic errors. Moreover, the algorithm requires fewer interferograms, improves the fringe contrast, and has a high detection efficiency. It is suitable for high-precision parallel plate optical parameter measurements.
An Approach for Resolving Double Character Segmentation in Sinhala Social Media Text Images

Sinhala is the official language of Sri Lanka, which has descended from the ancient Brahmi script. For Asian languages such as Sinhala and Tamil, only a little attention has been given to text extraction. Sinhala text in digital images have different fonts and font sizes, thus making the text extraction process harder. It is a challenging task to segment overlapping and touching Sinhala characters in digital images, and effectively recognize the segmented characters. This paper outlines a software system designed and implemented to extract Sinhala text from digital images. In this study, a connected components labelling algorithm was used for overlapping character segmentation, while a background thinning based approach was used for touching character segmentation. The proposed system is capable of extracting Sinhala text from digital images with nearly 76% accuracy.
Putting Humans at the Centre of the Governance of the Integration and Deployment of Artificial Intelligence in Peace Support Operations

The United Nations and the North Atlantic Treaty Organization (NATO) have put in place systems governing the integration and deployment of Artificial Intelligence (AI) in their Peace Support Operations (PSOs). Policy proposals and discourses have emphasized putting humans at the centre of those processes, often without effectively linking important ethical and normative considerations to the technical aspects of AI and the practical aspects of peacekeeping operations. This article mainly explains that the disconnection can be mapped out as follows: the lack of mechanisms to manage knowledge and mastery, less consideration of processes pertaining to trust, and very little concern about explainability: these are all significant elements that could improve accountability in those two organizations. In addition, while taking into account the promises and perils of AI in those operations at the strategic, tactical, and operational levels, the main arguments of this paper is that the governance systems in place aiming at integrating and deploying AI do not fully take humans into consideration. The arguments in this article follow the principles behind prioritizing the needs and concerns of civilians, whom these operations aim to protect, as well as the civilian, military, and police employees of the organizations that carry out the operations.
Stochastic Optimization for Motion Planning of Helicopter and Underwater Vehicles Reposition Task

We present a stochastic optimization based motion planner for use in aerial and underwater vehicles. Our approach draws from state of the art research in path planning for robotic arms, STOMP, in order to develop a motion planner which generates smooth feasible trajectories for repositioning tasks. We refer to repositioning as helicopter air-taxi or hover-taxi operation which is a preferred way of flight when movements require within an airport while avoiding flight over other aircraft, vehicles, and personnel. Our method, which we coin STOMP-RP (RP for RePosition), can be used in realistic scenarios where the vehicle can only view a small subset of obstacles due to sensor range limitations and can be used in dynamically changing environments. STOMP-RP is a practical motion planner for repositioning because (a) it generates paths that do not need to be post processed by a smoother, (b) optimizes trade-offs between path smoothness and obstacle clearance, and (c) can optionally be given an initial global plan path to be locally optimized. Additionally, STOMP- RP iteratively returns increasingly optimized paths which makes it desirable for real-time use.
The Empiricist Turn of Automation- Mathematisation: Large Corpora, Restricted Languages and Sublanguages

With large corpora, one can identify a second turn in the automation- mathematisation of linguistics in the early 1990s, brought about through drastic technological changes, the appearance of micro-computers and generalised use of the Internet. Large corpora have modified the linguists’s practical approaches towards data and mark a real renewal of empiricism in linguistics. However, this upheaval, which impacts many other domains of knowledge and society in general, does not justify that some linguists calling it a new discipline, “a new linguistics” and this for legitimisation purposes. In this chapter, three points will be examined: the British sources of corpus linguistics; the debates between the Chomskyans and the British empiricists on the use of corpora; and the new linguistic objects which appeared at the crossroads of the empiricist approach and automation.
Entrepreneurship and Economic growth: the mediation role of finance

The purpose of this paper is to examine the mediating role of finance in the relationship between economic growth and entrepreneurship in a sample of 17 OIC countries. We developed a model where banking finance as a proxy for Access to finance mediates the relationship between the total early stage entrepreneurship (TEA) as a proxy for entrepreneurship and economic growth. Correlation, Baron and Kenny approach (causal steps approach) and PROCESS Macro (normal test theory) developed by Hayes were used to find out the direct and indirect effects of financing between entrepreneurship and economic growth. The bootstrap mediation results indicated that finance was a significant predictor of entrepreneurship and entrepreneurship was a significant predictor of economic growth. These findings support the mediation hypothesis. In addition, findings showed that there is a positive relation between entrepreneurship and economic growth and a positive relation between finance and economic growth in OIC countries. Furthermore, the findings of this study indicate that the total association between entrepreneurship and economic growth in OIC countries is not only direct, but also that entrepreneurship contributes to levels of economic growth through the increased levels of finance. As a result, countries with higher levels of access to finance tended to experience entrepreneurship at higher levels, which in turn contributed to the emergence of increased levels of economic growth. The results indicated that the direct effect of entrepreneurship on the economic growth remained significant when controlling for finance, thus suggesting partial mediation. In other words, finance only mediates part of the effect of entrepreneurship on economic growth.
Dijkstra and A* Algorithms for Global Trajectory Planning in the TurtleBot 3 Mobile Robot

In this work, two global path planners will be evaluated, specifically the Dijkstra and A* algorithms. For this evaluation, a mobile robot that processes ROS (Robot Operating System) will be used. Tests were carried out in two different environments (symmetrical and asymmetric), in order to evaluate the performance of both algorithms. The mobile robot used was the TurtleBot 3 Burger, which has open source software. The results showed a small, but better performance of the Dijkstra algorithm, compared to the A* algorithm.
Same Scandal, Different Standards: The Effect of Partisanship on Expectations of News Reports about Whistleblowers

This experiment (N = 591) tests whether audiences adjust their standards for what qualifies as fair journalism based the transparency of news editors, the source of the news, and the target of an accusation. In the context of a whistleblower scandal, the results suggest the relationship between the audience member’s ideology and the news story publisher and target influence what details the audience thinks journalists should reveal. Additionally, we find transparency from editors can alter those perceptions.
Stacking with Dynamic Weights on Base Models

Stacking is used to combine models based on different techniques using a second-level model to come up with higher accuracy. The second-level model essentially uses the values predicted by different base-level models as independent variables, while the dependent variable remains the observed one. Though fit of the base-level models differ at various parts of the data, the second-level model uses same set of weights on base-level models on the whole data. We have derived two methods where we replace the second-level model by a linear combination of base model outputs where the weights vary. In our methods, we select a part of the data based on some predefined condition of proximity for classification of a new observation. Then, weights are assigned on different base models considering their accuracy in that part of the data. In one method, all points in the neighbourhood get equal importance, while in the other method, points get importance based on proximity. The algorithms apply same principle on each of the new observations which get their neighbourhoods in different parts of the data; thus, weights vary. The new ensemble methods are tried on different datasets from different fields and found to give better results than conventional stacking.
Quantitative Weighted Visual Cryptographic (k, m, n) Method

The weighted visual cryptographic scheme (WVCS) is a secret sharing technology, where weights are assigned to each shadow (participant) according to its importance. Among WVCS, the random grid-based WVCS (RGWVCS) is a frequently visited subject. It considers the premise of equality of all participants, without taking into account the existence of privileged people in reality. To address this problem of RGWVCS, this paper designs a new model, named as (k, m, n)-RGWVCS (where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>m</mi> <mo>&lt;</mo> <mi>k</mi> <mo>&lt;</mo> <mi>n</mi> </math> ), in which the secret is encrypted into n shares and sent to k participants. In the recovery end, the secret could be reconstructed by minimum m shares when the privileged join in; otherwise, k shares are needed. The experimental results show that our method has the advantage of no pixel expansion and no codebook design by means of random grid. Moreover, the contrast of our model increased by 32.85% on average compared with that of other WVCS.
Platform Construction and Application Strategy on Mobile Learning for College English

Applying mobile learning to college English teaching is conducive to enriching learning resources, developing personalized learning, stimulating students' interest, and integrating learning time. This article mainly conducts four aspects of research work: first, build technical framework consisting of "presentation layer, business logic layer, data access layer, and data storage layer". Second, functional framework consisting of main modules such as "resource management, learning space, public modules, and system management" is constructed. Third, learning process consisting of "pre-class preparation stage, class learning stage, and post-class consolidation stage" is designed. Fourth, the application countermeasures such as "reducing the negative impact of mobile terminals on students, improving the information literacy of teachers and students, developing diverse English mobile learning resources" are proposed.