Reviewerly/exHarmony huggingface dataset:
Intuitive Feature Engineering and Machine Learning Performance Improvement in the Banking Domain

Performance tuning of the machine learning models is very important, especially in the banking domain. In line with the new age, they are moving away from their conventional methods to target customers for credit cards, loans, etc. products. The transactional data, customer information, which was collected over the years, have a huge scope of applying data mining techniques to extract useful information for maximizing the return on investment, cost optimization and fraud detection. For a successful deployment of a machine learning model multiple out of time validations are performed and stability is strictly evaluated. Here, we propose a cardinal method for intuitive use case related feature engineering, tuning the hyper parameters, best model selection and diagnosing the model for further improvements. The stability of a model plays a huge factor, as we expect the deployed model to work well for the next 6–8 months, then up for re-tuning based on the data distribution and model performance. Statistical and data driven methods are used to develop sophisticated features and achieved minimal accuracy variation across time periods. Implementation of our methods for the use cases like customer attrition from the bank in the next 6 months and detection of the in-bound calls from the customer to the call centre to enquire about balance, transaction details, etc. are discussed. Achieved 47% & 55% recall score in the top 2 deciles respectively for the use cases.
Neutralization, coagulation and filtration process in peat water

Abstract The research purpose is to find out about neutralization, coagulation and filtration processes in peat water. Water is an environmental component that has a considerable role and the requirements in terms of quality and quantity. This research method uses experimental design and the variables are the processes of neutralization, coagulation, and filtration of peat water. Descriptive statistical analysis is an analysis carried out to assess the characteristics of a data. The results of the research were from the initial test obtained pH = 4.32. There are some changes from all parameters starting from dark black water colour (1088 PtCo) to 179 PtCo, high turbidity (13,2 NTU to 6,55 NTU, high iron content (1,68 mg/L) to 0,26 mg/L, high chromium (0,093) mg/L to 0,023 mg/L), low acidity (pH) (4,32) to pH 5,57 and high sulphate (744 mg/L) to 24 mg/L. The conclusion is that the results of processed Rasau Jaya Umum village peat water is feasible in accordance with the quality standards of the Regulation of the Minister of Health of the Republic of Indonesia No. 492/MENKES/PER/IV/2010, that the levels of iron, chromium and sulfate are already at a value permitted, but the turbidity of the water is still high and the pH of the water shows that the water is still acidic, amounting to 1080 PtCo. Therefore, water variations in the amount of material used in the peat water installation process still need to be improved in order to obtain maximum results.
Towards Visual Concept Learning and Reasoning: On Insights into Representative Approaches

The study of visual concept learning methodologies has been developed over the last years, becoming the state-of-the art research that challenges the reasoning capabilities of deep learning methods. In this paper we discuss the evolution of those methods, starting from the captioning approaches that prepared the transition to current cutting-edge visual question answering systems. The emergence of specially designed datasets, distilled from visual complexity, but with properties and divisions that challenge abstract reasoning and generalization capabilities, encourages the development of AI systems that will support them by design. Explainability of the decision making process of AI systems, either built-in or as a by-product of the acquired reasoning capabilities, underpins the understanding of those systems robustness, their underlying logic and their improvement potential.
Solving Errors Detected in Feature Modeling Languages: A Proposal

It is common to describe Software Product Lines and manage its variability with the aid of a feature model (FM). In this light, it shows that there are ambiguity issues concerning FM, which result in redundancy problems, anomalies, inconsistency, and mainly semantics issues. We propose a study regarding the feature modeling that considers the common aspects and the deficiencies in syntax, semantics, and semiotic clarity detected in the use of these modeling languages and the tools implemented from these. The initial results from this proposal show that the corrections of errors using feature modeling languages are feasible.
The Principle of Appropriate and Proportionate Remuneration of ART.18 Digital Single Market Directive: Some Thoughts for Its National Implementation

Directive (EU) 2019/790 of 17 April 2019 on Copyright and related rights in the Digital Single Market [DSMD] has taken a significant step towards improving fair remuneration of Authors and Performers across the EU, by establishing a set of harmonized mandatory principles and rights to strengthen their contractual position. Now, it is up to Member States to complete this job by implementing them in national laws. Member States must ensure that Authors and Performers effectively obtain fair remuneration for the exploitation of their works and performances. To that end, Member States must rely on a variety of mechanisms at their disposal. Statutory contract rules (already existing in most national laws) are necessary, but not enough. Sectorial collective bargaining, where available, may help define minimum remuneration schemes as well as other rules for the enforcement of the transparency, contractual adjustment, alternative resolution and revocation provisions mandated by the DSMD. However, conditions for a successful negotiation and enforcement of collective bargaining agreements are only a reality in a handful of countries and for specific sectors and, even then, with a limited scope (affiliated members and new productions). Statutory residual remuneration rights, granted by law as unwaivable (and inalienable) rights to receive remuneration subject to collective management (mandatorily, if necessary) and paid by users/licensees remain the best mechanism to secure effective fair remuneration for Authors and Performers, especially in the audiovisual and music sectors. Residual remuneration rights, retained by Authors and Performers after transferring their exclusive rights to producers, are in compliance with EU acquis and international instruments. They neither “duplicate” the transferred exclusive right nor “substitute” it for a statutory license. Residual remuneration rights are “merely” a statutory mechanism to secure that Authors and Performers receive fair remuneration for the transfer of their exclusive rights to Producers, especially when dealing with specific means of exploitation which - due to contractual and/or factual circumstances - are not conducive to provide fair remuneration for them. The principle of fair remuneration in Art.18 DSMD not only confirms the important role of national legislators in securing that goal, but obliges them to explore and implement the full potential of these several mechanisms, adjusting them to different sectors as necessary. Let’s hope national legislators successfully complete the important task started by the Directive.
Quantum Security for IoT to Secure Healthcare Applications and Their Data

Quantum computation has the ability to revolutionize the treatment of patients. Quantum computing can help to detect diseases by identifying and forecasting malfunctions. But there's a threat associated here (i.e., healthcare data among the most popular cybercriminal targets, IoT devices notoriously lacking in effective safeguards, and quantum computers on the brink of an encryption/decryption breakthrough). Health agencies need a security prognosis and treatment plan as soon as possible. Healthcare companies recently worry more about the quantum security threats. The biggest threat of healthcare data breaches has come in the form of identity theft. There should be a strong mechanism to combat the security gaps in existing healthcare industry. If the healthcare data are available on the network, an attacker may try to modify, intercept, or even view this data stream. With the use of quantum security, the quantum state of these photons changes alert the security pros that someone is trying to breach the link.
Active Access Control for the Operational Security in Industrial Control Systems

The Trusted Computing 3.0 architecture provides ways to guarantee a safe operation for industrial control systems (ICS). However, it is a critical issue to establish an access control strategy that conforms to the characteristics of the industrial control system on a trusted base. This paper proposes an access control strategy oriented to the security of operational behavior in ICS. It aims to the secure access of core data in the monitor workstation and data server, analyzes the typical abnormal behaviors of data access, and establishes fine-grained access control strategies from the aspect of the accessing of operating system kernel, the protection of stored data, and the control of critical instruction. Finally, the deployment scheme is given. The proposed access control strategy will improve the active defense theory based on the trusted computing in ICS.
A smart hand for VI: Resource-constrained assistive technology for visually impaired

Visually impaired face various difficulties in their daily routine tasks. Though there are several modern assistive devices available in the market, they are not meeting the consumer requirements in terms of the level of assistance. We are providing an assistive solution for visually impaired that reduces the fraud cases (such as expired medicine) and attacks by stray animals. In this chapter, we will discuss the technique of scanning the medicine names, manufactured date, expiry date, and cost printed on the medicines. Along with this, we will also discuss the technique of stray animal detection. Both the objectives have been implemented keeping in mind the idea of helping visually impaired. The objective is to assist the visually impaired people at the medical store while purchasing a medicine using the advantage of the scanner in the smart walking stick. For the safety of visually impaired people from stray animals, while walking on Indian roads, we have implemented the task of animal detection. We have used Efficient and Accurate Scene Text (EAST)-based optical character recognition for detection and extraction of medicine data. For stray animal detection, we have developed a model using Faster RCNN, RESNET 50, SSD Inception v2, and SSD Mobilenet v1, out of which Resnet50 performed best when these models have been trained to 10,000 epochs.
Effective Anomaly Detection in Smart Home by Analyzing Sensor Correlations

In a smart home environment, sensors generate events whenever activities of residents are captured. However, due to some factors, abnormal events could be generated, which are technically reasonable but contradict to real-world activities. To detect abnormal events, a number of methods has been introduced, e.g., clustering-based or snapshot-based approaches. However, they have limitations to deal with complicated anomalies which occur with large number of events and blended within normal sensor readings. In this paper, we propose a novel method of detecting sensor anomalies under smart home environment by considering spatial correlation and dependable correlation between sensors. Initially, we pre-calculate these correlations of every pair of two sensors to discover their relations. Then, from periodic sensor readings, if it has any unmatched relations to the pre-computed ones, an anomaly is detected on the correlated sensor. Through extensive evaluations with real datasets, we show that the proposed method outperforms previous approaches with 20% improvement on detection rate and reasonably low false positive rate.
Name in the Russian Bibliography: Irina Yurievna Fomenko (1953—2020)

The article is devoted to the memory of Irina Yurievna Fomenko (April 4, 1953 — May 30, 2020), philologist and book critic, the leading researcher in the Research Scientific Department of Rare Books (Book Museum) of the Russian State Library, responsible editor of four volumes of the “Union catalogue of Russian books. 1801—1825”. The authors give brief biography of I.Y. Fomenko, summarize information about her 150 scientific publications, which reflect the domestic publishing repertoire of the first quarter of the 19th century, relate to various aspects of working with early printed books, the subtleties of bibliographic description and book annotation. I.Y. Fomenko studied the creative heritage of M.N. Muravyev and defended PhD thesis on his prose. She wrote a number of articles for the Dictionary of Russian writers of the 18th century. With her participation, there were created catalogues of books of civil press and private owner’s collections from various holdings. Biographies of Russian writers of the 18th — 19th centuries, written by I.Y. Fomenko, were included in the collection of Russian literary studies.
Dissimilar Sensors Fusion of Airborne Early-warning and Surveillance System for Multi-targets Joint Detection, Tracking and Recognition

To realize the multi-targets detection, tracking and identification by the dissimilar sensors fusion in the air early-warning and surveillance system, the multi-targets joint detection, tracking and recognition strategy based on the random finite set model for the dissimilar sensors fusion is studied. Through the unified description of the target's kinematics state and target recognition attribute state, the multi-targets state is modeled as a global state described by a random finite set. By analyzing the kinematic sensor and attribute sensor models, the various sensors are modeled as a global sensor, and then each dissimilar sensor measurement is modeled as a global measurement described by a random finite set. According to the global state and the global measurement model, the process of multi-targets joint detection, tracking and recognition by dissimilar sensors fusion is described as a Bayesian filtering process, and the corresponding strategy of multi-targets joint detection, tracking and recognition has been proposed. Simulated experiment is given to prove the correctness and effectiveness of the proposed strategy.
Does Lifelong Learning Affect Mobile Robot Evolution?

Evolutionary Algorithms have been applied in robotics over the last quarter of a century to simultaneously evolve robot body morphology and controller. However, the literature shows that in this area, one is still unable to generate robots that perform better than conventional manual designs, even for simple tasks. It is noted that the main hindrance to satisfactory evolution is poor controller generated as a result of the simultaneous variation of the morphology and controller. As the controller is a result of pure evolution, it is not given a chance to improve before fitness is calculated, which is the equivalent of reproducing immediately after birth in biological evolution. Therefore, to improve co-evolution and to bring artificial robot evolution a step closer to biological evolution, this paper introduces Reinforced Co-evolution Algorithm (ReCoAl), which is a hybrid of an Evolutionary and a Reinforcement Learning algorithm. It combines the evolutionary and learning processes found in nature to co-evolve robot morphology and controller. ReCoAl works by allowing a direct policy gradient based RL algorithm to improve the controller of an evolved robot to better utilise the available morphological resources before fitness evaluation. The ReCoAl is tested for evolving mobile robots to perform navigation and obstacle avoidance. The findings indicate that the controller learning process has both positive and negative effects on the progress of evolution, similar to observations in evolutionary biology. It is also shown how, depending on the effectiveness of the learning algorithm, the evolver generates robots with similar fitness in different generations.
Online Technology: Effective Contributor to Academic Writing

This study explores the potential contributors to online technology in academic writing and designs a policy for using online technology. Focus group discussion and the survey (n = 151) were used for variable selection, questionnaire development, and data collection. A mini-experiment was conducted at the Department of Economics, Government Edward College, Pabna, Bangladesh and the survey was conducted at seven different colleges also in Pabna district. Students’ socio-economic-demographic characteristics like age, gender, religion, students’ parent income, students’ daily expenses, household composition, and level of education are major influential determinants of effective usage of online technology. Tutorial sessions for using online technology in writing, cost-effective internet package for students, institutional and infrastructural supports, motivation, students’ residential location, and electronic gadgets provided to the students at a subsidized price are important contributors to use online technology. Online technology-aided academic writing is a joint effort of students, teachers, college administration, University authority, and the Government.
Least Angle Regression Adaptive Incremental Broad Learning System

Broad Learning System (BLS) is a latest proposed flat network, which has an effective and efficient learning capability. Due to excellent performance, BLS has been applied to many fields. However, there is little attention on the structure simplification of BLS network. In this paper, drawing lessons from application of Least Angle Regression (LARS) in feature selection, we propose Least Angle Regression Adaptive Incremental Broad Learning System (LARS-BLS). Begin with a network with complex structure, LARS-BLS firstly ranks all nodes, then, the nodes are added to the empty network in turn according to the contribution rate from high to low. Then, the adaptive coefficient is calculated according to the accuracy change rate of the model to adjust the number of nodes added to the model accordingly. When the modeling requirements are met, stop adding nodes. Finally, a sparse BLS model without redundant nodes is constructed. The proposed method is applied to a real-world application and some common data sets. The results show that our proposed algorithm is effective and efficient.
MonoER - A Edge Refined Self-Supervised Monocular Depth Estimation Method

Estimating depth from a single image is a challenging but valuable job, as it has numerous applications. Recently, some works treat depth estimating as an image synthesis problem - using stereo pairs or video sequences to train the network. In this paper, we focus on the first case - training a network with stereo image pairs. Here, we propose our MonoER (Monocular depth estimation with Edge Refining), where edge refers to the contours of the objects. Existing monocular methods often suffer from blurred edges and fail to distinguish small objects. Our network aims to solve these problems. We test our network on the KITTI dataset, showing that it produces state of the art in self-supervised monocular depth prediction. We also transfer our ideas to some popular monocular methods and the experiment results show that our ideas have the ability to generalize.
Intelligent Acquisition and Analysis of Commodity Review Data

Abstract When shopping on online e-commerce platforms as they can only contact products through photos and other means, it is easy to have cognitive deviations on the products on online platforms. This paper makes intelligent acquisition and analysis of commodity review data by using web crawler tools. Through a series of operations such as comment data capture, data preprocessing, Chinese word segmentation, emotional tendency analysis, LDA model analysis, it analyzes buyers’ emotional tendency of this commodity to help consumers choose the commodities they really like.
Research and Algorithm Test of Adaptive Interbreeding Hybrid Particle Swarm Optimization

Particle swarm optimization (PSO) is a new evolutionary algorithm developed in recent years. It is easy to implement with few parameters, but it is often easy to fall into local optimization in the later period. This paper introduces the interbreeding algorithm in genetic algorithms (GA) to increase population size diversity, and uses the inertia weight adjustment method of the chaos algorithm mechanism to adjust the inertia factor, and proposes an adaptive interbreeding hybrid particle swarm optimization (AIHPSO). In this paper, six representative nonlinear experimental functions are used to simulate and compare the algorithms. The results prove that AIHPSO plays a better role in a complex optimization process. It can improve local development capabilities, enhance convergence speed and the accuracy is significantly improved, while avoiding the problems of premature maturity and local optimization.
Network Completion: Beyond Matrix Completion

Due to practical reasons such as limited resources and privacy settings specified by users on social media, most network data tend to be only partially observed with both missing nodes and missing edges. Thus, it is of paramount importance to infer the missing parts of the networks since incomplete network data may severely degrade the performance of downstream analyses. In this paper, we provide a comprehensive survey on network completion, which is a more challenging task than the well-studied low-rank matrix completion problem in the sense that a row and a column of an adjacency matrix shall be entirely unobservable when a node is completely missing from the given network. Specifically, we first define the problem of network completion. Then, we review two state-of-the-art algorithms for discovering the missing part of an underlying network, namely KronEM and DeepNC. We also show a performance comparison between the two algorithms via experimental evaluation. Finally, we discuss the potentials and limitations of the two algorithms.
Optimized Privacy Protection Method for Big Data Traffic

Data privacy leakages and accuracy declines are major problems in the field of big data. In this work, an adaptive space partition algorithm based on traffic history data under differential privacy is proposed to meet the privacy demands of big data traffic. This paper first uses the counting values of tree nodes in the quad-tree index structure at the first n moments by considering the spatial and temporal characteristics of traffic data and then applies the weighted moving average filter in exponential decay mode to predict the statistical values of the corresponding regions of the data to be released at the next moment. Then, an adaptive quadtree index structure based on the predicted values of each region and the heuristic judgment strategy is established by using the top-down approach and applied to the real data released at the next moment. Finally, we combine the differential privacy tree structure publishing technology and adjust the consistency constraint. Theoretical analyses and simulation experiments show that our algorithm protects data privacy and improves the accuracy of data query compared with existing algorithms.