Reviewerly/exHarmony huggingface dataset:
Can Lethal Autonomous Robots Learn Ethics?

When lethal autonomous robots (LARs) are used in warfare, the issue of how to ensure they behave ethically given military necessity, rules of engagement and laws of war raises important questions. This paper describes a novel approach in which LARs acquire their own knowledge of ethics through generating data for a wide variety of simulated battlefield situations. Unsupervised learning techniques are used by the LAR to find naturally occurring clusters equating approximately to ethically justified and ethically unjustified lethal engagement. These cluster labels can then be used to learn moral rules for determining whether its autonomous actions are ethical in specific battlefield contexts. One major advantage of this approach is that it reduces the probability of the LAR picking up human biases and prejudices. Another advantage is that an LAR learning its own ethical code is more consistent with the idea of an intelligent autonomous agent.
Deep Representation Decomposition for Feature Disentanglement.

Representation disentanglement aims at learning interpretable features, so that the output can be recovered or manipulated accordingly. While existing works like infoGAN and AC-GAN exist, they choose to derive disjoint attribute code for feature disentanglement, which is not applicable for existing/trained generative models. In this paper, we propose a decomposition-GAN (dec-GAN), which is able to achieve the decomposition of an existing latent representation into content and attribute features. Guided by the classifier pre-trained on the attributes of interest, our dec-GAN decomposes the attributes of interest from the latent representation, while data recovery and feature consistency objectives enforce the learning of our proposed method. Our experiments on multiple image datasets confirm the effectiveness and robustness of our dec-GAN over recent representation disentanglement models.
HAMBATAN DALAM PENERAPAN TEKNIK EVALUASI NON TES DI SDN PINANG 5 TANGERANG

The activity learns and teaching in the school, of course, involve evaluation activity, this activity does for measure how far student understands and know the learning done, until the teacher and school component finds out deficiency the learn system. Evaluation divided into 2 types, that is the test and the non-test. The test usually uses in school as the test, and non-test in the 2013 curriculum do to measure student attitude, through observation, questionnaire, and any more. Of course, there is the resistance in evaluation non-test, like teacher unpreparedness to do and make non-test instruments although students do not understand how to do this scoring. So we do this research to see how far the application of this evaluation in the school.
Comparison of threshold identification techniques for object-oriented software metrics

Quality assurance is a continuous process throughout the project lifecycle from inception till post-delivery. Software metrics are tools to help developers in achieving software quality objectives. Software metrics are used to predict the fault-proneness of classes in software using machine-learning and statistical techniques. However, these methodologies are difficult for daily tasks. Simpler and on the fly methodologies such as threshold values are needed. Metric thresholds can be used to control software quality and to recommend improvements on software code. Thresholds detect the parts of software that need more verification and validation. Many threshold identification techniques were proposed in previous research. However, the techniques do not provide consistent thresholds. The authors compare eight threshold identification techniques to diagnose software fault-proneness. The eight techniques are derived from diagnosis measures such as specificity, sensitivity, recall and precision. Five threshold identification techniques have derived thresholds that are skewed and have large standard deviations. Only three techniques are selected for threshold identification based on consistency and variation in selecting thresholds of software metrics in the systems under study. These techniques find thresholds that have the least variation among the studied techniques. The median of the 11 systems is selected as a representative of all thresholds.
Helmet Detection Algorithm Based on Single Pixel Zoom

Abstract The automatic detection technology of helmet wearing is of great significance to the safety monitoring of construction site. It is difficult to detect a helmet when the object is small, overlapped, or unclear. Therefore, we propose SPZ-Det model to improve the performance of detect small targets in helmet detection task. We redesign the backbone network layers and proposes a Single Pixel Zoom (SPZ) module. SPZ-Det introduces the lower-level feature map, while for the same scale feature map, we select the higher-level feature map. Then the important elements of each feature map layer are amplified by SPZ module to solve the problem of small object feature fading. In the attention module of SPZ, the residual mechanism is introduced to weaken the influence of the attention. The proposed model improves the detection accuracy and keeps the detection speed. On the Safety-Helmet-Wearing dataset, the mAP of SPZ-Det is 27.9% higher than Efficientdet-d0 and gets an 8.8% increase compares with YOLOv3.
Evaluation for Evolution: Using the ERMI Standards to Validate an Airtable ERMS

Electronic resource management systems (ERMS) have yet to resolve the difficulty of electronic resource management to the satisfaction of librarians, in spite of widespread approval of the standards proposed in the 2004 Electronic Resource Management Initiative Report. This article proposes using the report’s functional requirements as criteria to evaluate an ERMS systematically. Sharing local critiques of current and potential ERMS implementations enriches the library technology ecosystem with a shared understanding of common problems and potential solutions. This article illustrates this proposal using the author’s ERMS, “RELIC,” which is built on the Airtable platform.
Deep Learning Based Sequential Mining for User Authentication in Web Applications

Behavioral biometrics is a seamless and transparent way to authenticate or identify users through their interaction with electronic systems. It can serve as an additional security mechanism to existing security methods by continuously authenticating the users, for example when using pointing devices (e.g., mouse, touchscreen). These methods usually aim at extracting meaningful features such as curvature and acceleration using the raw mouse coordinates and ignore the specific elements the user interacts with during the movement. A possible improvement is to combine these methods with approaches that analyze the user path of elements throughout the session. One such previously suggested process proposes using a model-per-user approach, built using the traditional sequence mining algorithm Hidden Markov Model (HMM). In this paper we examine the use of deep learning sequential mining mechanisms for authentication, using mechanisms such as Long Short-Term Memory (LSTM), LSTM with Attention, and a Convolutional Neural Network (CNN). This method has the major advantage of one global model per web application, which drastically reduces the system’s required memory and storage resources. We demonstrate the competitive advantage by encouraging results in low false positive rates (FPR) ranges on an anonymized dataset collected by IBM from accounts of more than 2000 web application users.
A Retrospective on ASPires

In this paper, we describe the design and a prototypical implementation of an open system (ASPires) for the early prevention and early detection of forest fires. Forest fires cause huge, constantly increasing material damage and immaterial costs to humans, the environment and property. Among others, the use of new sensor and mobile communication technologies, the use of drones, data storage and analysis in a cloud, and direct connection with authorities reduces reaction time and thereby damage. Also, biodiversity is sustained in remote areas with rare and endemic species of flora and fauna. This has been tested and proven in 3 national parks in South East Europe.
Mind mapping functions for managing information, records, and archives

This chapter takes an ontological representation of the functionalities that should be performed by an archival system – the Ontology of Functional Activities for Archival Systems developed from an InterPARES Trust study – and applies it more broadly to an information system. The resulting mind map is applied specifically to the information, records, and archives programmes in international organizations to test its usefulness in supporting an information governance framework in that context. The authors designed and circulated a survey to information, records, and archives professionals working in international organizations. The vast majority responded positively to the mind map model and saw significant value in the proposed approach.
Good ICT Practices for the Integral Development of Ecuadorian Universities

The proliferation of information and communication technologies in all spheres of society, and especially in the area of education, has posed major challenges for developing countries. Bridging the gap in access to ICTs between different strata of society has become a constant task at all levels. The objective of this paper is to analyze the digital divide that exists between students and teachers in Ecuador’s university institutions. The research was of an exploratory type, with a field design. A questionnaire was applied to a sample of 133 teachers and 906 students from a universe of 1654 people, selected through a stratified probabilistic sampling. The result of the exploration carried out allowed to point out the findings from a descriptive investigation, supported, equally, in background and theoretical references. The results show an initial reduction of the gap in the use of basic technological tools and the need to continue deepening in the use of new more complex technologies such as Academic ERPs, as a management and integration core of good ICT practices to achieve a comprehensive and harmonious development between the teaching - learning and academic - administrative processes, in order to alleviate the computer deficiencies that are still apparent in institutional controls, and thus ensure that good practices are adopted by higher education institutions to guarantee their comprehensive development for the benefit of education and the society in which they are immersed.
Drone classification from RF fingerprints using deep residual nets

Detecting UAVs is becoming more crucial for various industries such as airports and nuclear power plants for improving surveillance and security measures. Exploiting radio frequency (RF) based drone control and communication enables a passive way of drone detection for a wide range of environments and even without favourable line of sight (LOS) conditions. In this paper, we evaluate RF based drone classification performance of various state-of-the-art (SoA) models on a new realistic drone RF dataset. With the help of a newly proposed residual Convolutional Neural Network (CNN) model, we show that the drone RF frequency signatures can be used for effective classification. The robustness of the classifier is evaluated in a multipath environment considering varying Doppler frequencies that may be introduced from a flying drone. We also show that the model achieves better generalization capabilities under different wireless channel and drone speed scenarios. Furthermore, the newly proposed model's classification performance is evaluated on a simultaneous multi-drone scenario. The classifier achieves close to 99 % classification accuracy for signal-to-noise ratio (SNR) 0 dB and at -10 dB SNR it obtains 5 % better classification accuracy compared to the existing framework.
El cerebro lector

espanolLa lectura constituye una de las actividades mas asequibles para mantener una buena salud cerebral, porque en este proceso intervienen muchas funciones cognitivas diferentes, como la percepcion, la atencion, la memoria o el razonamiento. Al leer, se activa una gran cantidad de circuitos neuronales y regiones concretas del cerebro que nos permiten, en milesimas de segundo, reconocer las letras, combinarlas para formar palabras, asignarles sonidos para poder pronunciarlas y dotarlas de significado. EnglishReading is one of the most affordable activities to maintain good brain health, because many different cognitive functions are involved in this process, such as perception, attention, memory or reasoning. When reading, a large number of neural circuits and specific areas of the brain are activated that allow us, in milliseconds, to recognize letters, combine them to form words, assign sounds to them to be able to pronounce them and give them meaning.
Research on Online and Offline Blended Teaching Mode Based on “MOOC +SPOC +flipped class”

MOOC and SPOC is the exploration of modern education reform based on Internet technology. For college education, the integration of MOOC, SOPC and flipped class is a high-quality exploration to tackle the existing teaching problems at present. How to obtain a better teaching effect and provide differentiated learning environments to achieve deep learning through integrating the online and offline classes based on MOOC and SPOC, is the main issue of this paper.
A Text Augmentation Approach using Similarity Measures based on Neural Sentence Embeddings for Emotion Classification on Microblogs

Machine learning models for fine-grained emotion classification can benefit from a larger pool of training data but manually expanding the emotion corpus for training is labor-intensive and time-consuming. While distant supervision provides a viable alternative, the self-labeled emotion corpus is susceptible to a high level of noise. This paper introduces a text augmentation method that can be used to efficiently expand the size of positive examples for the purpose of training by harnessing tweets collected from distant supervision (DS) that are similar to a small set of gold standard seed tweets. Tweets labeled with happiness in EmoTweet-28 (ET) are used as gold standard seeds to augment the training data to include similar DS tweets containing the happiness hashtags. Three pre-trained sentence encoders are used to encode the tweets into multidimensional vectors for similarity scoring between each DS:ET-seed pair. DS tweets with similarity scores exceeding a predefined threshold are added into an augmented set that is subsequently used to train a linear SVM classifier to distinguish between happiness and non-happiness. Our proposed text augmentation method proved to be a more effective approach that can leverage quality training data in larger quantities contributed by both carefully curated and distant supervision emotion corpora.
Automatic RGBD Object Segmentation Based on MSRM Framework Integrating Depth Value

In this paper, an automatic RGBD object segmentation method is described. The method integrates depth feature with the cues from RGB images and then uses maximal similarity based region merging (MSRM) method to obtain the segmentation results. Firstly, the depth information is fused to the simple linear iterative clustering (SLIC) method so as to produce superpixels whose boundaries are well adhered to the edges of the natural image. Meanwhile, the depth prior is also incorporated into the saliency estimation, which helps a more accurate localization of representative object and background seeds. By introducing the depth cue into the region merging rule, the maximal geometry weighted similarity (MGWS) is considered, and the resulting segmentation framework has the ability to handle the complex image with similar colour appearance between object and background. Extensive experiments on public RGBD image datasets show that our proposed approach can reliably and automatically provide very promising segmentation results.
Deinterlacing Network for Early Interlaced Videos.

With the rapid development of image restoration techniques, high-definition reconstruction of early videos has achieved impressive results. However, there are few studies about the interlacing artifacts that often appear in early videos and significantly affect visual perception. Traditional deinterlacing approaches are mainly focused on early interlacing scanning systems and thus cannot handle the complex and complicated artifacts in real-world early interlaced videos. Hence, this paper proposes a specific deinterlacing network (DIN), which is motivated by the traditional deinterlacing strategy. The proposed DIN consists of two stages, i.e., a cooperative vertical interpolation stage for split fields, and a merging stage that is applied to perceive movements and remove ghost artifacts. Experimental results demonstrate that the proposed method can effectively remove complex artifacts in early interlaced videos.
Learning Disentangled Intent Representations for Zero-shot Intent Detection.

Zero-shot intent detection (ZSID) aims to deal with the continuously emerging intents without annotated training data. However, existing ZSID systems suffer from two limitations: 1) They are not good at modeling the relationship between seen and unseen intents, when the label names are given in the form of raw phrases or sentences. 2) They cannot effectively recognize unseen intents under the generalized intent detection (GZSID) setting. A critical factor behind these limitations is the representations of unseen intents, which cannot be learned in the training stage. To address this problem, we propose a class-transductive framework that utilizes unseen class labels to learn Disentangled Intent Representations (DIR). Specifically, we allow the model to predict unseen intents in the training stage, with the corresponding label names serving as input utterances. Under this framework, we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents, and a similarity scorer, which estimates the connections among intents more accurately based on the learned intent representations. Since the purpose of DIR is to provide better intent representations, it can be easily integrated with existing ZSID and GZSID methods. Experiments on two real-world datasets show that the proposed framework brings consistent improvement to the baseline systems, regardless of the model architectures or zero-shot learning strategies.
Obtaining optimal cutoff values for tree classifiers using multiple biomarkers

Abstract In biomedical practices, multiple biomarkers are often combined using a prespecified classification rule with tree structure for diagnostic decisions. The classification structure and cutoff point at each node of a tree are usually chosen on an ad hoc basis, depending on decision makers' experience. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide the choice of optimal cutoff points in a pre‐specified classification tree. In this paper, we propose to search for and estimate the optimal decision rule through an approach of rank correlation maximization. The proposed method is flexible, theoretically sound, and computationally feasible when many biomarkers are available for classification or prediction. Using the proposed approach, for a prespecified tree‐structured classification rule, we can guide the choice of optimal cutoff points at tree nodes and estimate optimal prediction performance from multiple biomarkers combined.
Single-Shot Freestyle Dance Reenactment

The task of motion transfer between a source dancer and a target person is a special case of the pose transfer problem, in which the target person changes their pose in accordance with the motions of the dancer. In this work, we propose a novel method that can reanimate a single image by arbitrary video sequences, unseen during training. The method combines three networks: (i) a segmentation-mapping network, (ii) a realistic frame-rendering network, and (iii) a face refinement network. By separating this task into three stages, we are able to attain a novel sequence of realistic frames, capturing natural motion and appearance. Our method obtains significantly better visual quality than previous methods and is able to animate diverse body types and appearances, which are captured in challenging poses, as shown in the experiments and supplementary video.
ADAPTATION OF E-LEARNING IN HIGHER EDUCATION AFTER 2020 COVID19 CRISIS: DESIGNING PRINCIPLES AND DIGITAL COMPETENCES

ADAPTATION OF E-LEARNING IN HIGHER EDUCATION AFTER 2020 COVID19 CRISIS: DESIGNING PRINCIPLES AND DIGITAL COMPETENCES
Determinants of Accounting Practice in Micro and Small Enterprises: Evidence from Ethiopia

This study assesses the factors determining accounting recordkeeping practices among micro and small enterprises in Ethiopia's South Western Wollo region. It has been noted that, despite the enormous importance of accounting recordkeeping, the practice is still inadequate in micro and small businesses. This study used cross-sectional primary data collected from 343 micro and small enterprises. An ordered logistic regression analysis method was employed to analyze the explanatory variable's effect (education level, number of employees, frequency of transactions, size of the firm, and age of the firm) on micro and small enterprises' accounting practice. The study results show that the education level, the number of employees, the frequency of transactions, and the firm's size have a statistically significant and positive effect on the accounting practice of micro and small enterprises. On the other hand, the firm's age is found to have no statistically significant effect on micro and small enterprises' accounting practice. This study helps micro and small enterprises by showing the significant factors that affect their accounting practice and working towards better reporting and decision-making habits.
COMPARATIVE ANALYSIS OF DIFFERENTIAL INVARIANTS BASED ON THE SPLINE MODEL FOR VARIOUS IMAGE DISTORTION

In the task of finding the features of digital images, it is relevant to determine solutions that provide high processing speed. In the article experimental studies of the application of differential invariants based on the partial image model as a linear combination of B-splines that are close to the average interpolation. Such a model retains the properties of the Gaussian model in the frequency domain, but has less computational complexity, which allows us to better investigate its asymptotic properties and the properties of the corresponding partial derivatives used in the construction of differential invariants. The issue of differences in gradient magnitude, Lapsasian, Hessian determinant, and curvature of the scaling curve during image processing was studied, and the masks of low-frequency filters and operator masks were minimized based on differences of smoothing operators. It has been experimentally proved that smoothing of digital images and reduction of their linear sizes allows to formalize the process of feature selection on the basis of analysis of probability distributions of introduced differential invariants. The suggested approach may be recommended when searching for similar objects containing different images. The approach considered in the work has a low computational complexity, which makes it possible to recommend it for use in systems with a low computation speed, in particular for systems operating on single-board computers.