Reviewerly/exHarmony huggingface dataset:
Two-Stage Weighted Regularized Extreme Learning Machine for Class Imbalanced Learning

Compared to conventional machine learning techniques, extreme learning machine (ELM) which trains single-hidden-layer feedforward neural networks (SLFNs) shows faster-learning speed and better generalization performances. However, like most representative supervised learning algorithms, ELM tends to produce biased decision models when datasets are imbalanced. In this paper, two-stage weighted regularized ELM is proposed to address the aforementioned issue. The original regularized ELM (RELM) was proposed to handle adverse effects of outliers but not target the imbalanced learning problem. So we proposed a new weighted regularized ELM (WRELM) for class imbalance learning (CIL) in the first stage. Different from the existing weighted ELM which only considers the class distribution of datasets, the proposed algorithm also puts more focus on hard, misclassified samples in the second stage. The focal loss function is adopted to update weight by decreasing the weight of well-classified samples to focus more attention on the error of difficult samples. The final decision target is determined by the winner-take-all method. We assess the proposed method on 25 binary datasets and 10 multiclass datasets by 5-folder cross validations. The results indicate the proposed algorithm is an efficient method for CIL and exceed other CIL algorithms based on ELM.
Lessons and Ideas for Charting a New Development Path in Africa

Drawing on the theories of economic growth and development, facts and empirical evidence regarding economic growth in the long run, Africa’s legacy of history and lessons from the successful East Asian economies, Ocran put forward five ideas that African governments have to consider to hasten economic development. The rapid population growth rates in Africa have to be curbed. Domestic resource mobilisation has to be prioritised to generate resources for gross capital formation because no country has developed by relying solely on foreign capital. The fourth point is about the need to facilitate the transition of the informal economies into the mainstream economy and lastly, serious attention has to be paid to industrialisation by adopting “smart” industrial policies that provide some protection for selected local industries.
Scaling Across Functional Domains: A Case of Implementing an Electronic HIV Patient Information System in Sierra Leone

With adherence to treatment, HIV positives can live a normal life. Accordingly, investments are made and health systems are expanded to reach those at risk in developing countries, where HIV is reported to be most endemic. At the same time, many developing countries still rely heavily on paper-based tools which are found to be inefficient when large numbers of patients are involved and of limited use to support follow ups and assure adherence to treatment. In this paper, as we move from an existing paper base and to a digital and online information management system, we focus on improving our understanding of how to use an existing system made for collecting, aggregating and presenting population based routine data to support individual follow-up of HIV positives and their adherence to treatment. We approach this through an action research project in Sierra Leone where we have piloted a HIV patient information management system. We contribute insights on health information system scaling with emphasis on building on existing systems in developing new functionalities rather than introducing entirely new systems. Within this approach we observe the need for technological flexibility and organizational collaboration in utilizing existing resources for efficiency gains.
Nature-inspired algorithms for the optimal tuning of fuzzy controllers

This chapter describes the mechanisms occurring in four representative nature-inspired optimization algorithms: particle swarm optimization, gravitational search algorithms, charged systems search algorithms, and gray wolf optimizer algorithms. These algorithms are inserted in two steps of the design approach dedicated to the optimal tuning of simple Takagi-Sugeno proportional-integral fuzzy controllers involved in the position control of servo systems. Four optimization problems are solved and some results concerning the algorithms’ behavior are outlined.
Unambiguous discrimination among three linearly independent symmetric states

We investigate the unambiguous discrimination of three linearly independent symmetric states. We derive the minimum inconclusive probability by using an ancillary system and introducing a unitary transformation which acts on the input states and produces the output measured on the ancilla. We generalize to the case of discriminating the symmetric states with multiple copies. Our results show that the minimum inconclusive probabilities can be expressed as a form, not only dependent on the modular and the phase factors but also dependent on the number of the copies.
Graph matching using conformal module

Graph matching and classification play fundamental roles in computer vision. The computational complexity of the conventional method based on a spectrum method is high, which prevents it from handling large graphs in practice. This work proposes a novel framework for tackling the challenge by using conformal module. We apply the classical Hodge theory from differential manifold to the graph setting and compute the combinatorial conformal invariant of the graph, called as conformal module, which can be used as the fingerprint for the graph. The method is applicable for viewpoint classification and posture detection. The experimental results demonstrate the efficiency and efficacy of the proposed method.
Takagi-Sugeno Fuzzy Incremental State Model for Optimal Control of a Ball and Beam Nonlinear Model

The optimal control of a ball and beam by an approach based on Takagi-Sugeno incremental state model is proposed. The advantages of incremental state model in comparison with the non incremental one are that the control action cancels steady state errors, the affine terms disappear and incremental state solves the problem of computing the target state, choosing zero as an objective. A generalized version of Takagi-Sugeno identification method is applied. For an optimal control, Linear Quadratic Regulator and optimal state observer are used in each fuzzy rule. Simulation results over the ball and beam nonlinear model show a stable closed loop in the full range, zero steady state error and good transient response.
Performance Comparison Between Genetic Fuzzy Tree and Reinforcement Learning in Gaming Environment

Wargame systems are artificial combat simulation platforms which would be practical in game research. The widely used methods in wargame systems mostly rely on refined experience of human experts. We suppose to apply artificial intelligence methods rather than expert-experience-based methods to complicated game environments. Reinforcement learning methods provide a human-like normative way which guides agents upgrade their behaviors in game environments without expert experience. This paper reveals the performances of both experience-based models and reinforcement-learning-based models in game environments. This environment presented in this paper is a type of zero-sum game which means there only be one winner. Our experiments show that reinforcement-learning-based models is more robust and powerful than expert-experience-based methods but cost more time.
CDBV: A Driving Dataset With Chinese Characteristics From a Bike View

Public datasets are valuable for the development of the learning-based algorithms. In the field of autonomous driving, almost all the existing public datasets are collected by manually driving cars, and most of them are contributed by American and European researchers and represent the traffic scenarios typical for these countries. To diversify the public driving datasets and to provide data support for researches on the self-driving vehicles moving on bike lanes, such as wheelchairs, bikes, and some robots, we present the Chinese Driving from a Bike View (CDBV), which is a driving dataset collected by a bike in Beijing, China. The CDBV dataset contains annotated images, raw videos, and code tools. The annotations are for the object detection tasks and especially focus on the categories with Chinese characteristics, including the express tricycles, the motorbikes specializing in takeouts, the bicycle landmarks, the zebra crossings, and the traffic lights. We totally annotated 40 064 traffic elements in 13 427 images. The CDBV dataset is publicly available at http://www.sujingwang.name/CDBV.html.
Mirkwood: An Online Parallel Crawler

In this research we present Mirkwood, a parallel crawler for fast and online syntactic analysis of websites. Configured by default to behave as a focused crawler, analysing exclusively a limited set of hosts, it includes seed extraction capabilities, which allows it to autonomously obtain high quality sites to crawl. Mirkwood is designed to run in a computer cluster, taking advantage of all the cores of its individual machines (virtual or physical), although it can also run on a single machine. By analysing sites online and not downloading the web content, we achieve crawling speeds several orders of magnitude faster than if we did, while assuring that the content we check is up to date. Our crawler relies on MPI, for the cluster of computers, and threading, for each individual machine of the cluster. Our software has been tested in several platforms, including the Supercomputer Calendula. Mirkwood is entirely written in Java language, making it multi–platform and portable.
Tangent Host: An Uneasy Partnership

Tangent Host, a Canadian-based website services provider, was established in 2014 as a general partnership by two personal and professional friends. Each had a 50% ownership stake in profits and workload. In 2016, it had total revenue of US$121,440, divided evenly between web design (100 clients) and web hosting (375 clients). As a start-up, Tangent had taken advantage of growth opportunities and had overcome a number of operational challenges. By 2017, there were mounting unresolved issues: tension between partners, concerns with uncollected receivables, technical performance, and future direction. One of the partners seemed to be less committed to the partnership, shifting interest and time to other activities that generated personal not partnership income. The case provides thought-provoking issues confronted by a new venture and partnership: friction between partners, customer relations, service performance, increasing revenue, scaling a business, making strategic choices, and conflict resolution.
Color-Assisted Local Feature Pipeline for Three-Dimensional Object Retrieval

We propose a practical color-assisted local feature-based pipeline for 3D object instance recognition with a fixed imaging system which poses certain constrains for object viewing angle and distance. Assuming the objects are rigid and are put on a flat surface when being recognized, our approaches take advantages of the known system setup to reduce the number of samples needed for training, while achieving faster recognition with higher accuracy than general purpose methods. The system is first calibrated for object segmentation and white balancing, which removes background points and generate reliable object color information. Then we modify the traditional local feature pipeline for 3D object recognition by adding color similarity constrains both at the candidate object level and at the local feature points level. Experimental results show significant improvement on recognition accuracy. A demonstration system is created to show the real-time performance of our methods.
Teaching as a Collaborative Practice: Reframing Security Practitioners as Navigators

The need is growing for a workforce with both technical skills and the ability to navigate existing and emerging information security challenges. Practitioners can no longer depend upon process-driven approaches to people, processes and IT systems to manage information security. They need to be navigators of the entire environment to effectively integrate controls to protect information and technology. The research presented in this paper trialed an innovative tactile learning activity developed through the European Technology-supported Risk Estimation by Predictive Assessment of Socio-technical Security (TREsPASS) project with tertiary education students, designed to provide students with experience in real-world modelling of complex information security scenarios. The outcomes demonstrate that constructing such models in an educational setting are a means of encouraging exploration of the multiple dimensions of security. Such teaching may be a means of teaching social, organization and technical navigation skills necessary to integrate security controls in complex settings.
Enterprise Ontology for Service Interoperability in Socio-Cyber-Physical Systems

Enterprises are complex systems comprising three fundamental components: a physical sub-system, a decision sub-system, and an information sub-system. Humans in the enterprises are not just resources but the first consumers and providers of data, information, and knowledge. The present research considers enterprise as a technical system embedding social component. Socio-cyber-physical systems naturally embody this idea. The research considers such systems as an enterprise component that combines the cyber aspects of computing and communications with the dynamics and physics of physical enterprise. The enterprise interoperability problem is treated as the problem of service interoperability in socio-cyber-physical systems at the service level and process level of an enterprise. The research proposes ontologies of enterprise and socio-cyber-physical system, a task-oriented service interoperability model, and a service communication mechanism.
Satisfiability Calculus: An Abstract Formulation of Semantic Proof Systems

The theory of institutions, introduced by Goguen and Burstall in 1984, can be thought of as an abstract formulation of model theory. This theory has been shown to be particularly useful in computer science, as a mathematical foundation for formal approaches to software construction. Institution the ory was extended by a number of researchers, José Meseguer among them, who, in 1989, presented General Logics, wherein the model theoretical view of institutions is complemented by providing (categorical) structures supporting the proof theory of any given logic. In other words, Meseguer introduced the notion of proof calculus as a formalisation of syntactical deduction, thus “implementing” the entailment relation of a given logic. In this paper we follow the approach initiated by Goguen and introduce the concept of Satisfiability Calculus. This concept can be regarded as the semantical counterpart of Meseguer’s notion of proof calculus, as it provides the formal foundations for those proof systems that resort to model construction techniques to prove or disprove a given formula, thus “implementing” the satisfiability relation of an institution. These kinds of semantic proof methods have gained a great amount of interest in computer science over the years, as they provide the basic means for many automated theorem proving techniques.
A virtual window communication system with pointing function for remote communication

In recent years, video communication systems have found wide application along with cellphones and e-mails for remote communication. Using video communication, partner’s expression and personal space, which are beneficial in providing smooth communication between two remote locations, can be seen. However, it is difficult to perceive depth, since the projected images are only able to display two-dimensional information. Furthermore, during video communication, while user might move around, the view of partner remains unchanged. It is important to see an image including depth information for communication. It is expected that people will be able to communicate smoothly using images including depth information because feelings of shared place are increased by it. Therefore, in this paper, we develop a virtual window communication system by which an image of a remote location through a virtual window is projected based on the head position of the user. In this system, when the positional relationship between the head position of the user and the window changes, the projected image will also change accordingly. Thus, the user can feel as though he or she is looking directly at his or her conversational partner and the surrounding environment through the virtual window. Furthermore, remote pointing is realized using a pointing function in this communication system. This pointing function does not require calibration and can be placed at an arbitrary position using image processing. Then, sensory evaluations were performed to demonstrate the effectiveness of the developed virtual window communication system.
Increasing Code Reuse

Chapter 1 explained that a functional language like Haskell is characterized by its profuse use of functions as parameters or return values. However, Chapter 2 didn’t mention anything about this topic. I’ll rectify that here. In this chapter, you will focus on not one but three ways in which Haskell allows for a great amount of reuse.
Identifying Students’ Error in Writing Islamic Text at the Third Semester of English Study Program at IAIN Bone

This research aimed to find out the most common errors do the students made in their writing Islamic text at the third semester of English Study program IAIN Bone in Academic Years 2018-2019. The data were collected from the students’ writing of the third semester. The key instrument used was writing test result and interview. This research used descriptive research. It refers to the descriptive qualitative. Qualitative data described each category that occurred in the students’ writing. The researcher described the situation about the occurrence of the errors in the students’ writing. The total number of lexical errors that the researcher found in the students’ writing Islamic text was 150 errors that consisted of lexicon category was 68 errors and surface strategy was 82 errors. Most of the students’ errors in the lexical category were sentence pattern. Sentence pattern is the highest errors made by the students in writing Islamic text with 10 students or 62.50% made errors; the second was punctuation and article by 5 students or 31.25%; the third errors in spelling by 4 students or 25%; the fourth errors in vocabulary and preposition by 3 students or 18.75%; the last errors in tense, pronoun, and conjunction by 1 student or 6.25%. It is figured out that the students have less ability in writing a text and still have difficulties in learning grammatical structures in writing process.
A Survey on Deep Learning Techniques Used for Quality Process

In the current era, software development and software quality has become extensively important for implementing the real-world software application, and it will enhance the software functionality. Moreover, early prediction of expected error and fault level in the quality process is critical to the software development process. Deep learning techniques are the most appropriate methods for this problem, and this chapter carries out an extensive systematic survey on a variety of deep learning. These techniques are used in the software quality process along with a hypothesis justification for each of the proposed solutions. The deep learning and machine learning techniques are considered to be the most suitable systems for software quality prediction. Deep learning is a computational model made up of various hidden layers of investigation used to portray of information with the goal that researchers can better understand complex information issues.
Computational theory of semantics representation in scientific and technical texts

The work, described in this paper, continues the authors' research in the field of computational semantics of natural languages. The main focus of the paper is on the words processing in the sentence. Also the paper considers computational theory of the semantic interpretation of natural language texts. Furthermore, the computational model of a semantics representation in the form of the semantic schemes for the Russian language is discussed. The provided methodology is the constructed rules to compare semantic proximity of the semantic schemes. The obtained results show a qualitative difference between the proposed computational semantic model and the traditional model based on the frequency paradigm is shown. Finally, the provided examples of the semantic comparison demonstrated that the proposed computational semantic model is able to outperform models based on the frequency paradigm.
Application of Deep Learning in Speech Recognition

Speech is the vocalized form of communication used by humans and some animals. It is based upon the syntactic combination of items drawn from the lexicon. Each spoken word is created out of the phonetic combination of a limited set of vowel and consonant speech sound units (phonemes). Here, the authors propose a deep learning model used on tensor flow speech recognition dataset, which consist of 30 words. Here, 2D convolutional neural network (CNN) model is used for understanding simple spoken commands using the speech commands dataset by tensor flow. Dataset is divided into 70% training and 30% testing data. While running the algorithm for three epochs average accuracy of 92.7% is achieved.
Hand Gesture Recognition Using in Intelligent Transportation

Hand gesture recognition is important for intelligent transportation. In this paper, C3D neural networks and transfer learning related technologies are used to recognize the traffic police’s gestures recognition. Firstly, C3D algorithm is used to model the spatial and temporal information of the gesture. Then, external data set was used for training and the resulted model was saved. Finally, the model is fine-tuned with the idea of transfer learning, so that a good recognition rate can be achieved by training with a small amount of traffic police gesture video. The experimental results show efficacy of our method.
A Method of Attitude Control Based on Deep Deterministic Policy Gradient

The traditional methods of attitude control of satellite are represented by PID control, adaptive control, optimal control and intelligent control, etc. With these methods, lots of work of parameter adjustment and simulation needs to do on the earth. We proposed a method based on Deep Deterministic Policy Gradient (DDPG) to learn attitude control strategy in orbit in order to reduce the work and establish the ability of adapting to space environment. Through constructing training environment by using the attitude control system of satellite platform (ACSoSP), we trained an attitude control model and used the model to generate the strategy of attitude control. Validate the method by experiments in simulation environment.