Reviewerly/exHarmony huggingface dataset:
Why does the experiment fail with students? An Experience

Experimental studies in the Software Engineering (SE) domain play a vital role in identifying shreds of evidence about the effectiveness of the technique in a specific context. Successful execution of such experiments with all threats to validity considerations requires a lot of effort, planning, preparation, and experience. The selection, motivation, and experience of the subjects (specifically with the students) are considered a major potential threat. In this paper, we report various challenges, recommendations, and experiences gained while conducting an experimental study with students as subjects. The experiment was conducted to assess the effectiveness of tool support developed to evaluate the quality of requirements specification. Our results have revealed several outliers in the data and suggested to go back and see what went wrong in the design and execution process.
Nearest Neighbor Classification Based on Activation Space of Convolutional Neural Network

In this paper, we propose a new image classifier based on the incorporation of the nearest neighbor algorithm and the activation space of convolutional neural network. The classifier has been successfully implemented on some state-of-the-art models and further improve their performance. The main technique tool we use is convex hull based classification and its acceleration. We find several phenomena which we believe are of both theoretical and application interest: 1) in several cases, the new classifier outperforms original CNN by reaching higher accuracy; 2) the classifier can work more efficiently by combining with sampling strategy; 3) centroid of each convex hull shows surprising ability in classification. Most of the work have strong geometric meanings, which helps us have a new understanding about convolutional layers.
Automatic Machine Translation for Bangla and English Resolving Ambiguities

The strategic borderless knowledge sharing and development of communication interacts with dialects. Significant factors such as education, medical, business, research and others are vastly diffused over the world based on various lingoes. Bilingual or multilingual expression is the standard of having unknown/new linguistic along with its resources. The initial endeavor of the study is to implement the MT (machine translation) approaches for English to Bangla language processing and vice-versa. The emphasis of the study is the distinct ambiguities are identified along with their best solutions. Certain machine translation approaches such as word-to-word, direct, transfer, interlingua, corpus-based and statistical translation are surviving and, few of them are deployed in this Smart Natural Language Processing (SNLP) for dispatching the source to the target language and vice-versa. Two different dictionaries (bilingual and monolingual) are developed for the execution process. An eminent resource Stanford POS Tagger (as a toolkit) is used for identifying the grammatical structure of the source (English) dialect. This research also focuses on output acquiring through performance analyzing of different translation models.
Graph-based Bayesian Meta Relation Extraction

Meta-learning methods accomplish rapid adaptation to a new task using few samples by first learning an internal representation that matches with similar tasks. In this paper, we focus on few-shot relation extraction. Previous works in few-shot relation extraction aim at predicting the relation for a pair of entities in a sentence by training with a few labeled examples in each relation. However, these algorithms implicitly require that the meta-training tasks be mutually-exclusive, such that no single model can solve all of the tasks at once, which hampers the generalization ability of these methods. To more effectively generalize to new relations, in this paper we address this challenge by designing a meta-regularization objective. We propose a novel Bayesian meta-learning approach to effectively learn the prototype vectors of relations via regularization on weights, and a graph neural network (GNN) is used to parameterize the initial prior of the prototype vectors on the global relation graph. Our approach substantially outperforms standard algorithms, and experiments demonstrate the effectiveness of our proposed approach.
Application of Convolution BLS in AI Face-Changing Problem

AI face-changing is a new technology that is developing in recent years, and its appearance may bring unnecessary trouble to certain fields of human society. On special occasions, there is an urgent need for a model that can autonomously determine whether an image has undergone AI face-exchanging processing.Proposed two convolutional structures based on the braod learning system (BLS), and give the specific algorithm flow. Using the convolution method for the basic BLS structure, try to directly convolve the feature mapping layer and the input data. The experimental results show that the BLS with convolutional structure performs well on small face data sets, and a very simple structure can achieve satisfactory accuracy and training time on the data set. It reflects the simplicity and reliability of the BLS-Convolution structure.
Disparity estimation method of electric inspection robot based on lightweight neural network

The image depth information can be used to understand the geometric relationship of image scenes, and has important applications in robots, scene understanding, three-xdimensional reconstruction and other fields. Recent work has proved that depth estimation from stereo RGB image pairs can be realized by convolution neural network. However, the mainstream depth estimation deep learning algorithms rely on patch-based Siamese networks, which lacks the ability to comprehensively utilize the context information and the environment texture information, and their performance is poor in complex regions and illposed regions. The working environment of electric inspection robot is complex and the occlusion problem is serious, so the classical methods are difficult to be directly applied. In addition, the real-time performance of neural network is very important for electric inspection robot. In this paper, a disparity estimation neural network for electric inspection robot is proposed, which consists of two main parts: PSMNet module and lightweight cutting module. Experiments show that the proposed method can be effectively applied to electric inspection robot.
IMPLEMENTATION OF BLOCKCHAIN FOR INCREASING TRACEABILITY AT VEHGRO SUPPLY CHAIN

Blockchain is a new technology where the presence is emerging around many business sectors. Although the origin of blockchain was to facilitate business in financial sectors, the technology has developed to suit other sectors besides financial so that it can help the organization to be more effective and efficient. The technology which acts as a distributed ledger enables transparency in the usage. In the middle of current situation where consumer is deman ding more and more from an organization to provide information, even worst by some cases of food contamination, this new technology that originally creates for financial sectors has evolved to help business in the food sector and others. If implemented properly to the supply chain management of a business, it can be beneficial in many ways. This thesis aims to see the effectiveness and efficiency of the implementation of blockchain in VehGro company. The author used literature review as a research method which analysed scientific journals, reports, and research.
Research on Education Big Data Security Strategy under Network Environment

In recent years, big data in education has become an important force to promote educational reform. However, the current research at home and abroad mainly focuses on how to use big data in education and pays insufficient attention to the information security problems it faces, which may lead to information security risks. The birth of cloud computing technology provides favorable conditions for the deployment of big data in education, but also brings challenges to the security of big data in education. Based on this, this paper studies the current education big data information security problems, find the existing problems, and targeted puts forward education under the network environment big data security policies, including storage security, access control, security strategies and virtual safety security strategy, and establish a safety technical framework, using standard and safety supervision system as the main content of the guarantee system, ensure the safety of the big data cloud environment education. The research results show that the network is a developing technology, so security problems often occur in the network environment. Only by formulating strict education big data security strategies and effectively ensuring the security of education big data, can education big data be rapidly developed and the legitimate rights and interests of users of education resources and the healthy development of big data be ensured.
Hunter-Gatherer Approach to Math Education - Everyday Mathematics in a San Community and Implications on Technology Design

K-12 math education is struggling as despite obvious job market benefits, several students choose to discontinue math education when given the possibility. At the same time, advances in learning technologies now enable modes of learning that were impossible up until ten years ago. In this study we analyse math education from scratch by returning to the hunter-gatherer time period and empirically observing how mathematics is present the lives of a San tribe in Southern Namibia with ethnographic analysis. With this work, we propose two high level design considerations for integrating math learning technologies with the hunter-gatherer way of living: (1) integration of learning technologies and real world objects; and (2) the introduction of physical activity and social communication to math education. We discuss how these two design considerations could boost students’ motivation to continue learning math also beyond the formal school environment.
Predicting the Mortality of Female Patients suffering from Myocardial Infarction using Data Mining Methods: A Comparison

Myocardial Infarction (MI) better known as heart attack, is considered as one of the most alarming diseases that used to harm a great percentage of the male populace. However, the number of female patients suffering from the condition that was formerly known as the “old-man disease” is gradually increasing at the present time. Taking that into consideration, the researchers gathered enough data to come up with a predictive model that could be utilized in identifying the risk indicators for the mortality of female patients suffering from MI. By using different tools in data mining that contribute to a lot of great data discoveries up to date, the researchers made use of logistic regression, random forest, and decision tree to evaluate which technique can generate a diagnostic model with a higher accuracy rate. The generated prognostic models were based on a total of 9 significant attributes that were used to determine the risk indicators for the mortality of female patients suffering from MI. Upon conclusion, it turns out that among the three data mining techniques used in this study, logistic regression has the highest accuracy rate of 79% while random forest and decision tree resulted in 77% and 73% respectively. Medical practitioners could also use this study in discovering the characteristics that made up the clusters or groups of Myocardial Infarction patients that survived and characteristics that made up the clusters or groups of Myocardial Infarction patients that didn't. Determining the risk indicators of a female patient surviving MI tailors a more personalized way of treating the disease.
Construction of Perceptual Security Dataset for Video Selective Encryption Based on Double-Blind Subjective Experiment

The development of information technology has provided opportunities for the application of Ultra High Definition (UHD) video, but the security problems such as privacy leakage cannot be ignored. Video selective encryption algorithm is an efficient and effective way to protect the privacy of users. However, the previous studies on the performance evaluation of video selective encryption scheme are insufficient. PSNR and SSIM, which are commonly used to evaluate the image quality, cannot fully reflect the encryption effect of selective encryption algorithm, which makes it difficult to evaluate and compare the security protection ability of selective encryption scheme for video content. To build an accurate security evaluation scheme of video selective encryption algorithm needs to rely on a large number of encrypted video data, but the existing datasets have the problems of small number of videos, single video resolution and selective encryption method, and the subjective experiment scheme is lack of scientific. In order to solve this problem, this paper selects a variety of resolutions and common scene video sequences, and uses different selective encryption schemes to encrypt them, so as to build a dataset for selective encryption. The method of double-blind subjective experiment provides a real and accurate subjective score for the perception security and visual quality of the video in the dataset. The subjective score shows that the dataset covers the encryption sequence of each security level, which can provide an effective reference for the follow-up research on video selective encryption and perceptual security index.
Performance Analysis of Object Detection Framework: Evolution from SIFT to Mask R - CNN

In a near of wide spread technological change that has givena positive impact to the society andhelpedinbuildingauser-friendly environment, object detection framework, an importantpart of Computer Vision (CV) plays a vital role. Starting fromasimpleautomaticattendancesystemforstudentsusingfacedetection, recognizing the presence of tumors in medical images,helping with automatic surveillance of cctv cameras to identifypeoplewhobreakstrafficrulescausingroadaccidentstobeingthecentral mechanism behind self-driving cars, object detection haswide range of applications and assist building an easy to cope withsmart environments. This in turn urges the need to evaluate theperformanceofthetechniquesbehindtheseframeworks.Thecentralideabehindthemodern-dayobjectdetectionandclassification is Convolutional Neural Network (CNN) which triesto mimic the occipital lobe, the visual cortex of the human brain.CNNhaswiderangeofvariationsandhascomethroughalongwaystarting from basic CV techniques like Scale Invariant FeatureTransform(SIFT),HistogramsofOrientedGradientsn(HOG)tillRegionbasedCNN’s(R-CNN).Theperformanceofeachandeverymethodthathas led throughthe evolutionofobjectdetectionmethods, its advantages and the disadvantages which has pavedway for the innovation of next technique has been discussed andrepresentedindetail.
Improved Connected Component Labeling Algorithm for Remote Sensing Image Classification

The main objective of this paper is to carry out a detailed analysis of the most popular Connected Component Labeling (CCL) algorithms for remote sensing image classification. This algorithm searches line-by-line, top to bottom to assign a splotch label to each current pixel that is connected to a splotch. This paper presents two new strategies that can be used to greatly improve the speed of connected component labeling algorithms. It assigns a label to a new object, most labeling algorithms use a scanning step that examines some of its neighbors. The first strategy deeds the dependencies among the neighbors to reduce the number of neighbors examined. The second strategy uses an array to store the equivalence information among the labels. This replaces the pointer based deep rooted trees used to store the same equivalence information. It reduces the memory required and also produces consecutive final labels. The connected component labeling assigns labels to a pixel such that adjacent pixels of the same features are assigned the same label. The paper presents a modification of this algorithm that allows the resolution of merged labels and experimental results demonstrate that proposed method is much more efficient than conventional methods for various kinds of color images. This method is improving the labeling algorithms and also benefits for other applications in computer vision and pattern recognition
Optimally selecting the top <i>k</i> values from <i>X</i> + <i>Y</i> with layer-ordered heaps

Selection and sorting the Cartesian sum, X + Y , are classic and important problems. Here, a new algorithm is presented, which generates the top k values of the form . The algorithm relies on layer-ordered heaps, partial orderings of exponentially sized layers. The algorithm relies only on median-of-medians and is simple to implement. Furthermore, it uses data structures contiguous in memory, cache efficient, and fast in practice. The presented algorithm is demonstrated to be theoretically optimal.
New Approach to A Disruptive Business Model with Dynamic Capability Under the Blockchain Technology

This chapter aims to develop a holistic view of the blockchain business model framework with the role of dynamic capability. The study conceptualizes a dynamic capability framework with blockchain properties and business model understanding. The traditional approach to a business model with new technological improvements is the lack of defining the necessary business values that are captured and created from the digital environment. Specifically, blockchain technology generates additional properties that can even disrupt digital business processes. Therefore, it is necessary to build a new business model framework other than digitalization for blockchain technology to disclose disruptive values for guidance on business strategy. The study explains the detailed properties of the blockchain and classical business model and its logic. Later, a dynamic capability framework is combined with these views to establish a new business model for blockchain. This framework is the beginning for businesses that invest in blockchain to understand holistically how to extract the disruptive values out of blockchain technology and applications. Therefore, the study contributes to the businesses that invest in blockchain technology to realize the new benefits by changing traditional processes and distinctive capability which they will gain with the blockchain technology.
A method of using standard flat objects to calibrate the spatial position of the sensor

Determining the relationship between coordinate systems during measurement is one of the key works to ensure the accuracy of the measurement system. According to the traditional calibration method of standard spherical surface, because of the problem of measuring angle of surface features such as spherical surface, and the interference of specular reflection is easy to occur, the measurement of spherical surface by structural light often cannot fully describe the calibration feature or produce errors. In this paper, a unified calibration method based on the standard plane is proposed. Because plane features are easier to obtain and easier to control the appearance of specular reflection interference, and the calibration of plane geometry features used in this paper can completely avoid the distortion caused by the pending calibration measurement system. Therefore, the method can fully meet the measurement accuracy requirements of the measurement system.
Analysis of Intelligent making of Schools Based on Swot model

The present research was to analyze the strengths, weaknesses, opportunities and threats facing the smart schools and  appropriate strategies provided in this field. The present study is an applied research and in terms of data collecting is descriptive-survey. Of the statistical population (2625 primary school teachers in the city of Kerman), using cluster sampling, a sample size of 217 teachers were selected. An author-developed questionnaire (48 question) was used as data collection instrument.  The validity of the questionnaire was determined by experts and the reliability coefficient of the questionnaire was 95 points. Using descriptive statistics, Swat's model analyzed data and presented important strategies for school intelligence. The results show that school intelligence strategies are close to conservative strategies based on teachers' viewpoints, which means that, by practicing the conservative strategies presented in this research, the authorities can largely influence the strengths of school intelligence to the maximum and minimize its threats.
Adding More Building Blocks of Human Reasoning to the Knowledge Problem

Induction and deduction, as discussed in the previous chapter, have received a great deal of attention from multiple quarters. In the ninteenth century, the philosophers William Whewell and Charles Sanders Peirce focused on retroduction as a distinct mode of reasoning. Retroduction had been recognized by Aristotle as a separate entity with specific properties; however, it wasn’t until Whewell and Peirce that a strong distinction between retroduction and induction was emphasized.1 Retroduction is an essential part of human reasoning, without which ideas of causal relationships essentially could not expand, as induction and deduction can only get one so far. Indeed, Peirce (who, it can be argued, was most instrumental in recognizing the role of retroduction in science) described this mode of reasoning as “the only logical operation which introduces any new idea and commented: “[N]ot the smallest advance can be made in knowledge beyond the stage of vacant staring, without making an abduction [retroduction] at every step.2”
Heuristic Evaluation Method Applied to the Usability Assessment of Smart Homes Applications

The usability of smart homes applications is crucial to ensure their correct and pleasant use, thus contributing to the safety, comfort, and entertainment of the inhabitants of a smart home. Since heuristic evaluation is a widespread method for usability assessment, the objective of the study reported by the present paper was to verify the applicability of this method to assess the usability of smart homes applications. Therefore, an experience was conducted to apply the heuristic evaluation method to assess the usability of three different smart homes applications. This evaluation involved three evaluators and was based on the Heuristic Evaluation System Checklist (HESC). The results reveal that the application of the heuristic evaluation method using the HESC is a demanding and time consuming task, since 292 sub-heuristic should be considered. In turn, the results also show that heuristic evaluation is effective in the exhaustive identification of the concrete flaws of the interfaces of smart homes applications and generates a large quantity of objective information valuable for the improvement of the applications.
Patch assembly for real-time instance segmentation

The paradigm of sliding window is proven effective for the task of visual instance segmentation in many popular research works. However, it still suffers from the bottleneck of inference time. To accelerate existing instance segmentation approaches which are dense sliding window based, this work introduces a novel approach, called patch assembly, which can be integrated into bounding box detectors for segmentation without extra up-sampling computations. A well-designed detector named PAMask is proposed to verify the effectiveness of the proposed approach. Benefitting from the simple structure as well as a fusion of multiple representations, PAMask has the ability to run in real time while achieving competitive performances. Besides, another effective technique called Center-NMS is designed to reduce the number of boxes for intersection of union calculation, which can be fully parallelized on device and contributes 0.6% mAP improvement both in detection and segmentation for free.