Reviewerly/exHarmony huggingface dataset:
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions

Machine learning models have become more and more complex in order to better approximate complex functions. Although fruitful in many domains, the added complexity has come at the cost of model interpretability. The once popular k-nearest neighbors (kNN) approach, which finds and uses the most similar data for reasoning, has received much less attention in recent decades due to numerous problems when compared to other techniques. We show that many of these historical problems with kNN can be overcome, and our contribution has applications not only in machine learning but also in online learning, data synthesis, anomaly detection, model compression, and reinforcement learning, without sacrificing interpretability. We introduce a synthesis between kNN and information theory that we hope will provide a clear path towards models that are innately interpretable and auditable. Through this work we hope to gather interest in combining kNN with information theory as a promising path to fully auditable machine learning and artificial intelligence.
A Novel Model for Aligning Knowledge Management Activities within the Process of Developing Quality Software

Background: Currently, the organization's competitive advantage is based on critical decisions to achieve their objectives by understanding the power of knowledge as a source within the organizations. However, there is a lack of qualitative models/frameworks for integrating Knowledge Life Cycle (KLC) within software development life cycle SDLC. Therefore, the goal of this research is to involve Knowledge Management activities within the SDLC in Information Technology companies to produce quality software. With the help of knowledge movements within the companies, the quality of provided software is used to improve organizations performance and products better and faster. Methods: This research highlights the importance of Knowledge Management activities during a typical software development process to provide the software as a final product/target. Moreover, the paper proposes a model to explain the relationships between knowledge management activities within the process of software development life cycle to produce quality software using three basic building blocks: people, organizations, and technologies. The success factors for the blocks are selected depending on the most recent literature occurrences and on their fitness to the nature of this study. Results: The research proposes a novel model for the success factors to evaluate the effects of the building blocks, and workflows during the software development processes. The selected success factors for the blocks are (Training, Leadership, Teamwork, Trust, IT Infrastructure, Culture, and strategies). Also, the research demonstrates the relationships between KM success factors and SDLC to produce quality software. Conclusion: In this research, we proposed a novel model to explain the relationships between knowledge management activities within the process of software development life cycle to produce quality software using three basic building blocks: people, organizations, and technologies. We selected seven success factors for the blocks depending on: 1) their importance and occurrence in a number of literature by many authors; and 2) their fitness to the nature of this study. The success factors (Training, Leadership, Teamwork, Trust, IT Infrastructure, Culture, and strategies) of the proposed model can be used to evaluate the effects of people, organizations, technologies, and workflows during the software development processes to obtain the required software quality. Finally, a quantitative study will be implemented to investigate the proposed hypothesis and to measure factors influencing the suggested model. By assessing to which degree these factors are present/ absent within the SDLC process the managers will be able to address the weakness by preparing a suitable plan and produce quality software.
Practicing Scrum in Institute Course

Scrum is one of the most popular agile methods following \textit{Manifesto for Agile Software Development}, and is a value-driven software development approach which focuses on maximizing the values of the customers. Many top software companies like Amazon.com, Apple, and Microsoft directly apply Scrum and other Agile methods for developing great software products. To raise the talents required by industry, teaching agile methods in university is necessary. However, with the limits of time, space, and experts in agile development, it can be difficult for students to learn the practices of agile methods in college. In this paper, we describe an experimental course in Feng Chia University that practices Scrum for term projects among five teams composed of 34 students. To the best of our knowledge the practices is the few attempts to practically apply all the factors described in Scrum framework such as sprint planning, daily scrum, review, retrospective meetings, product owner, and scrum master in institutional agile education. In this paper, the design and the process of the term projects based on Scrum are described, and the lessons learned from practicing Scrum in college are presented as discussion.
Adaptive Event Dispatching in Serverless Computing Infrastructures

Serverless computing is an emerging Cloud service model. It is currently gaining momentum as the next step in the evolution of hosted computing from capacitated machine virtualisation and microservices towards utility computing. The term "serverless" has become a synonym for the entirely resource-transparent deployment model of cloud-based event-driven distributed applications. This work investigates how adaptive event dispatching can improve serverless platform resource efficiency and contributes a novel approach that allows for better scaling and fitting of the platform's resource consumption to actual demand.
Flexible cryptographic access control through proxy re-encryption between groups

A cryptographic access control scheme enforces access control policy of the data owner on his encrypted data. Most widely used cryptographic access control mechanism is hierarchical access control. Set of users for which access of data is to be managed/controlled, is divided into disjoint partitions called "security classes" or simply "classes". A hierarchical key assignment scheme (HKAS) assigns encryption keys of the data items in such a way that a class can only access data intended for class(es) lower down in the hierarchy [1]. So, we say each class has "designated access" to data intended for class(es) lower in the hierarchy. Suppose a class Ci, not having designated access to another class Cj, wishes to temporarily have access to data intended for Cj for a finite duration of time and Cj agrees for the same. However, consider that Cj does not wish to share its designated access with Ci. That is, Cj wishes to allow Ci to access data intended directly for Cj but not the one intended for any of the classes to which Cj has designated access and Ci does not. This flexibility requirement has various practical applications. In health-care services for instance, it may be desired that patient's data accessible directly to a doctor is securely delegated to another doctor in another service center for a finite period of time for expert consultation. However, it may be desired that the two doctors do not share information of any other patient.
N. Luhmann’s Theory of Systems in the Application to the Analysis of Scientific Communication

This article represents the review of the Russian translation of N. Lumann's work which was published under the title Truth, knowledge, science as a system. In article N. Lumann's approach to consideration of science as one of the communicative systems of society performing function on development of knowledge is reconstructed. Within this approach we consider in a separate way the the truthconditional perspective which is expressed by means of terms of language of the theory symbolically the generalized of media of communications. Special attention is paid to the potential of the reconstructed theory in the analysis of modern scientific community and to the relation of scientific system to other communicative systems (its differentiation from them and the possibility of interaction).
Robust Anomaly Detection in Images using Adversarial Autoencoders

Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalies, where the reconstruction error exceeds some threshold. Here we analyze a fundamental problem of this approach when the training set is contaminated with a small fraction of outliers. We find that continued training of autoencoders inevitably reduces the reconstruction error of outliers, and hence degrades the anomaly detection performance. In order to counteract this effect, an adversarial autoencoder architecture is adapted, which imposes a prior distribution on the latent representation, typically placing anomalies into low likelihood-regions. Utilizing the likelihood model, potential anomalies can be identified and rejected already during training, which results in an anomaly detector that is significantly more robust to the presence of outliers during training.
On the Non-uniqueness of Representations of Coxian Phase-Type Distributions

Parameter estimation in Coxian phase-type models can be challenging due to their non-unique representation leading to a multi-modal likelihood. Since each representation corresponds to a different underlying data-generating mechanism, it is of interest to identify those supported by given data (i.e., find all likelihood modes). The standard approach is to simply refit using various initial values, but this has no guarantee of working. Thus, we develop new properties specific to this class of models, and employ these to determine all the equivalent model representations. The proposed approach only requires fitting the model once, and is guaranteed to find all representations.
Disentangling Video with Independent Prediction

We propose an unsupervised variational model for disentangling video into independent factors, i.e. each factor's future can be predicted from its past without considering the others. We show that our approach often learns factors which are interpretable as objects in a scene.
Typical Rainfall Distribution Pattern of Flood Event Caused by Tropical Cyclone at Bima City, West Nusa Tenggara, Indonesia

Tropical cyclones are the most serious meteorological phenomena that hit Bima city in December 2016. The strong winds and heavy precipitation associated with a typhoon significantly affect the weather in this city. The impact of a tropical cyclone on precipitation variability in Bima is studied using rainfall data for analyzing hourly rainfall distribution pattern during the event. Depend on the geographic situation and climate characteristic, the hourly rainfall distribution pattern of one area is different to others area. The research aims to analyze hourly rainfall distribution pattern in the form of the rainfall intensity distribution. This research is conducted using one automatic rainfall gauge in Bima city, West Nusa Tenggara province that obtained from Regional Disaster Management Agency (BPBD). The results showed that two events of rainfall were recorded. The first rainfall event was on 20th to 21st December 2016 with a total rainfall 191.4 mm. The second rainfall event occurred on 22nd to 23rd December 2016 with a total rainfall 126.2 mm. The rainfall distribution pattern has rainfall intensity peak at 45% of duration with cumulative rainfall reached 70%. It was found there is no common pattern of temporal rainfall distribution for rainfall induced by tropical cyclones.
Stroke-based sketched symbol reconstruction and segmentation.

Hand-drawn objects usually consist of multiple semantically meaningful parts. For example, a stick figure consists of a head, a torso, and pairs of legs and arms. Efficient and accurate identification of these subparts promises to significantly improve algorithms for stylization, deformation, morphing and animation of 2D drawings. In this paper, we propose a neural network model that segments symbols into stroke-level components. Our segmentation framework has two main elements: a fixed feature extractor and a Multilayer Perceptron (MLP) network that identifies a component based on the feature. As the feature extractor we utilize an encoder of a stroke-rnn, which is our newly proposed generative Variational Auto-Encoder (VAE) model that reconstructs symbols on a stroke by stroke basis. Experiments show that a single encoder could be reused for segmenting multiple categories of sketched symbols with negligible effects on segmentation accuracies. Our segmentation scores surpass existing methodologies on an available small state of the art dataset. Moreover, extensive evaluations on our newly annotated big dataset demonstrate that our framework obtains significantly better accuracies as compared to baseline models. We release the dataset to the community.
Study on the strategy of improving self-learning ability of College Students

College students are the main force of social development and construction in China, and at present, autonomous learning is an important topic in higher education research. Therefore, mastering autonomous learning ability is very important for contemporary college students. This paper discusses the reasons that affect college students' self-learning from the aspects of schools, teachers, students and society, and gives the strategies and measures to enhance the self-learning ability of college students according to reasons. It provides useful reference for quality colleges and universities to cultivate students.
Hybrid genetic algorithm for task scheduling in distributed real-time system

A distributed real-time system consists of a set of heterogeneous processors located at possibly different sites and connected by a communication link. Task scheduling in distributed real-time system attracts the attention of the researcher in many disciplines. Many researchers have been developed the solution of task scheduling problem by using different types of technique. In this paper, a hybrid genetic algorithm (HGA) is developed which is a combination of k-means and genetic algorithm to form the clusters of tasks in an effort to minimise the communication costs. A genetic algorithm is also developed to schedule the formed clusters of tasks onto set of processors to minimise the execution costs. The results of the algorithm have been compared with various existing techniques. Experiment results shows that the proposed algorithm achieves better efficiency than other existing techniques.
A Theoretical Validation of Component Point

The system-level size measures are important in software project management as tasks such as planning and estimating the cost and schedule of software development can be performed more accurately when a size estimate of the entire system is available.However, due to the black-box nature of software components, traditional software measures are not adequate for Component-Based Systems (CBS).We have developed a Function Point (FP) like measure, named Component Point (CP), for measuring the system-level size of a CBS specified in Unified Modeling Language.In this paper, we present a theoretical validation of the CP measure using mathematics and show that not only the CP measure holds all the mathematical conditions necessary for a size measure, but it can also be used in Component-Based Software Development (CBSD) in a similar way that FP and its extensions are used in other software development paradigms.
Learning Spatial Pyramid Attentive Pooling in Image Synthesis and Image-to-Image Translation

Image synthesis and image-to-image translation are two important generative learning tasks. Remarkable progress has been made by learning Generative Adversarial Networks (GANs)~\cite{goodfellow2014generative} and cycle-consistent GANs (CycleGANs)~\cite{zhu2017unpaired} respectively. This paper presents a method of learning Spatial Pyramid Attentive Pooling (SPAP) which is a novel architectural unit and can be easily integrated into both generators and discriminators in GANs and CycleGANs. The proposed SPAP integrates Atrous spatial pyramid~\cite{chen2018deeplab}, a proposed cascade attention mechanism and residual connections~\cite{he2016deep}. It leverages the advantages of the three components to facilitate effective end-to-end generative learning: (i) the capability of fusing multi-scale information by ASPP; (ii) the capability of capturing relative importance between both spatial locations (especially multi-scale context) or feature channels by attention; (iii) the capability of preserving information and enhancing optimization feasibility by residual connections. Coarse-to-fine and fine-to-coarse SPAP are studied and intriguing attention maps are observed in both tasks. In experiments, the proposed SPAP is tested in GANs on the Celeba-HQ-128 dataset~\cite{karras2017progressive}, and tested in CycleGANs on the Image-to-Image translation datasets including the Cityscape dataset~\cite{cordts2016cityscapes}, Facade and Aerial Maps dataset~\cite{zhu2017unpaired}, both obtaining better performance.
Exploration of Wireless Multimedia Sensor Network Based on Joint Photographic Experts Group Image Coding Algorithm

&lt;span lang="EN-US"&gt;to study the performance of image coding and image transmission of wireless video sensor node in wireless multimedia sensor network, the challenges and design objectives of image coding technology in wireless multimedia sensor network are discussed, and the classification and exploration are carried out from two aspects: individual source coding and distributed source coding. The compression of video monitoring image sequence in wireless multimedia sensor network is studied and a low complexity image coding scheme based on change detection and improved &lt;a name="_Hlk527536749"&gt;&lt;/a&gt;Joint Photographic Experts Group (JPEG) algorithm is proposed. The moving target in the monitoring image is located with the change detection algorithm, that is, the area of interest. Only the region of interest is transmitted to reduce the quantity of data transmission and adapt to the limited storage and forwarding capability of wireless sensor nodes&lt;/span&gt;&lt;span lang="EN-US"&gt;; The Discrete Cosine Transform (DCT) and quantization processes in Joint Photographic Experts Group (JPEG) algorithm are improved to reduce the computational complexity and adapt to the limited computing power of wireless sensor nodes.&lt;/span&gt;&lt;span lang="EN-US"&gt;The analysis of algorithm complexity and the results of simulation experiment show that the proposed method can effectively reduce the data communication amount of wireless sensor nodes and the energy consumption in the calculation process while ensuring the image quality required by the application.&lt;/span&gt;&lt;span lang="EN-US"&gt;Wireless multimedia sensor network is a new and interdisciplinary subject.&lt;/span&gt;&lt;span lang="EN-US"&gt;The study of it is also multidirectional and multi-angle.&lt;/span&gt;&lt;span lang="EN-US"&gt;The image coding algorithm of wireless multimedia sensor network is studied deeply.&lt;/span&gt;&lt;span lang="EN-US"&gt;Although some achievements have been made, there are still a lot of problems that need to be further studied.&lt;/span&gt;
Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison

An appropriate choice of the activation function (like ReLU, sigmoid or swish) plays an important role in the performance of (deep) multilayer perceptrons (MLP) for classification and regression learning. Prototype-based classification learning methods like (generalized) learning vector quantization (GLVQ) are powerful alternatives. These models also deal with activation functions but here they are applied to the so-called classifier function instead. In this paper we investigate successful candidates of activation functions known for MLPs for application in GLVQ and their influence on the performance.
Consensus Mechanism Design based on Structured Directed Acyclic Graphs

We introduce a structure for the directed acyclic graph (DAG) and a mechanism design based on that structure so that peers can reach consensus at large scale based on proof of work (PoW). We also design a mempool transaction assignment method based on the DAG structure to render negligible the probability that a transaction being processed by more than one miners. The result is a significant scale-up of the capacity without sacrificing security and decentralization.
Solving Initial Value Problems by Flower Pollination Algorithm

Differential equations are very important in modeling many phenomena mathematically. The aim of this paper is to consider Initial Value Problems (IVPs) in ordinary differential equations (ODEs) as an optimization problem, solved by using a meta-heuristic algorithm which is considered as an alternative way to find numerical approximation of (IVPs) since they can almost be solved simply by classical mathematical tools which are not very precise. By selecting a methodical way based on the use of recent and efficient algorithm, that is, Flower Pollination Algorithm (FPA), inspired by the pollination process of flowers plants to solve approximately an (IVP) when a specified example is selected that is the exponential problem which have an imperative role to describe many real problems. The effectiveness of the proposed method is tested via a simulation study between the exact results, the FPA results and Euler method which is considerate as a classical tool to solve numerically an (IVP). The final results and after a comparison between the performance of FPA and Euler method in terms of solution quality shows that FPA yields satisfactorily precise approximation of the solution. That ensures the ability of FPA to solve such important problems and highly complexes problems efficiently with minimal error.
Albanian Language Identification in Text Documents

In this work we investigate the accuracy of standard and state-of-the-art language identification methods in identifying Albanian in written text documents. A dataset consisting of news articles written in Albanian has been constructed for this purpose. We noticed a considerable decrease of accuracy when using test documents that miss the Albanian alphabet letters " Ë " and " Ç " and created a custom training corpus that solved this problem by achieving an accuracy of more than 99%. Based on our experiments, the most performing language identification methods for Albanian use a naïve Bayes classifier and n-gram based classification features.