Reviewerly/exHarmony huggingface dataset:
Risk Mitigation Framework for Agile Global Software Development

The main goal of Agile development method is to reduce risks so that it leads to a more effective and successful information system. Analyzing priorities, identifying risks are, in fact, important activities in all development approaches, including the Agile development. However, some small studies seek to assess risk management on the basis of Agile global software development (GSD) and to provide mitigation measures to address specific risks. This paper aims to propose a new framework. The mechanism of the proposed framework combines conceptual framework of Global Software Development integrated with risk management in a Scrum framework. The strength of this framework is software development in an Agile process model as a technique to solve identified potential risk in an Agile global environment. As a result, this paper shows the application of Risk Mitigation Framework for Agile Global Software Development (RMAG) in a Scrum process model. Several Scrum practices and phases are used to mitigate risks. We setup an experiment to apply the RMAG in an Agile Global Software Development using Scrum. Each phases have different types of the risk and mitigation technique. Scrum practices help to reduce the risk. Scrum practices and Scrum phases are not provided the outsourcing category because Scrum does not cover the software development life cycle. Thus, this paper can be a reference to developers and Agile practitioners to mitigate or reduce the risk.
Bisphenol A Acts As Developmental Agonist in Culex Quinquefasciatus Say

Abstract Plastic – derived Bisphenol A (BPA) contaminated sewages in Trivandrum, Kerala, India are mosquito breeding sites. After summer rain, BPA in the stagnant water samples ranged between 0.86 and 1.14ppm. 1.0 ppm BPA is considered as environmentally relevant concentration. Present study revealed that BPA is a developmental agonist of Culex quinquefasciatus . Embryonic and larval development are shortened by BPA but pupal development is unaffected. Under the atmospheric temperature of 26 to 31 0 C lifecycle was completed within 312 hours but during summer it was completed within 278 hours, meanwhile atmospheric temperature ranged between 30 and 37 0 C. Reduction in the duration of development due to BPA was 50 to 70 hours during summer and 60 to 80 hours in other seasons. Larval – pupal stadium of the mosquito has three surges of 20-hydroxy ecdysone(20-HE), at 24,32 and 48th hour of the 4th instar. BPA resulted dose-dependent advancement of 20-HE peaks, Phospholipase A 2 induction and expression of Ecdysone receptor EcRA and Ecdysone inducible gene E75A, which culminated in early pupation. Adults emerging from 1.0 ppm BPA treatment did not show significant difference in sanguivory and fecundity compared to control but pupae developed in 2 and 4 ppm BPA were significantly small.
Using Unlabeled Data Set for Mining Knowledge from DDB

In this paper, two algorithms were introduced to describe two algorithms to describe and compare the applying of the proposed technique in the two types of the distributed database system. The First Proposed Algorithm is Homogeneous Distributed Clustering for Classification (HOMDC4C), which aim to learn a classification model from unlabeled datasets distributed homogenously over the network, this is done by building a local clustering model on the datasets distributed over three sites in the network and then build a local classification model based on labeled data that produce from clustering model. In the one computer considered as a control computer, we build a global classification model and then use this model in the future predictive. The Second Proposed Algorithm in Heterogeneous Distributed Clustering for Classification (HETDC4C) aims to build a classification model over unlabeled datasets distributed heterogeneously over sites of the network, the datasets in this algorithm collected in one central computer and then build the clustering model and then classification model. The objective of this work is to use the unlabeled data to introduce a set of labeled data that are useful for build a classification model that can predict any unlabeled instance based on that classification model. This was done by using the Clustering for Classification technique. Then presented this technique in distributed database environment to reduce the execution time and storage space that is required.
Reinforcement Learning-Based Solution to Power Grid Planning and Operation Under Uncertainties

With the ever-increasing stochastic and dynamic behavior observed in today’s bulk power systems, securely and economically planning future operational scenarios that meet all reliability standards under uncertainties becomes a challenging computational task, which typically involves searching feasible and suboptimal solutions in a highly dimensional space via massive numerical simulations. This paper presents a novel approach to achieving this goal by adopting the state-of-the-art reinforcement learning algorithm, Soft Actor Critic (SAC). First, the optimization problem of finding feasible solutions under uncertainties is formulated as Markov Decision Process (MDP). Second, a general and flexible framework is developed to train SAC agent by adjusting generator active power outputs for searching feasible operating conditions. A software prototype is developed that verifies the effectiveness of the proposed approach via numerical studies conducted on the planning cases of the SGCC Zhejiang Electric Power Company.
Unsupervised Graph-based Topic Modeling from Video Transcriptions.

To unfold the tremendous amount of audiovisual data uploaded daily to social media platforms, effective topic modelling techniques are needed. Existing work tends to apply variants of topic models on text data sets. In this paper, we aim at developing a topic extractor on video transcriptions. The model improves coherence by exploiting neural word embeddings through a graph-based clustering method. Unlike typical topic models, this approach works without knowing the true number of topics. Experimental results on the real-life multimodal data set MuSe-CaR demonstrates that our approach extracts coherent and meaningful topics, outperforming baseline methods. Furthermore, we successfully demonstrate the generalisability of our approach on a pure text review data set.
Teaching and learning how to make requests in different contexts in Italian as a foreign language

This study is motivated by the dearth of research in pragmatic instruction in Italian as a foreign language (IFL) with beginner learners. Based on the limitations in the way IFL textbooks present requests, instructional material was designed with the following objectives: to improve learners’ pragmalinguistic and sociopragmatic competences and to raise their awareness of request realisation across different contexts. The material, designed for a 60-minute pedagogical intervention, was then used in an Italian language school in Sao Paulo with five intact classes of beginner Brazilian learners of Italian. We implemented a pre- and post-test quasi-experimental methodological design, where three classes were randomly selected to serve as the experimental group, and two classes served as the control group. The data collected through written DCTs and simplified retrospective questionnaires revealed that the learners in the experimental group showed a more significant improvement in their socio pragmatic, pragmalinguistic and metapragmatic competence compared to the control group. These results suggest a positive effect of consciousness-raising activities on learners’ declarative knowledge in the short term.
pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models

Bayesian Knowledge Tracing, a model used for cognitive mastery estimation, has been a hallmark of adaptive learning research and an integral component of deployed intelligent tutoring systems (ITS). In this paper, we provide a brief history of knowledge tracing model research and introduce pyBKT, an accessible and computationally efficient library of model extensions from the literature. The library provides data generation, fitting, prediction, and cross-validation routines, as well as a simple to use data helper interface to ingest typical tutor log dataset formats. We evaluate the runtime with various dataset sizes and compare to past implementations. Additionally, we conduct sanity checks of the model using experiments with simulated data to evaluate the accuracy of its EM parameter learning and use real-world data to validate its predictions, comparing pyBKT's supported model variants with results from the papers in which they were originally introduced. The library is open source and open license for the purpose of making knowledge tracing more accessible to communities of research and practice and to facilitate progress in the field through easier replication of past approaches.
Efficient scalar multiplication of ECC using SMBR and fast septuple formula for IoT

Abstract In order to solve the problem between low power of Internet of Things devices and the high cost of cryptography, lightweight cryptography is required. The improvement of the scalar multiplication can effectively reduce the complexity of elliptic curve cryptography (ECC). In this paper, we propose a fast formula for point septupling on elliptic curves over binary fields using division polynomial and multiplexing of intermediate values to accelerate the computation by more than 14%. We also propose a scalar multiplication algorithm based on the step multi-base representation using point halving and the septuple formula we proposed, which significantly reduces the computational cost. The experimental results show that our method is more efficient over binary fields and contributes to reducing the complexity of ECC.
Preserving Symmetries for Variational Quantum Eigensolvers in the Presence of Noise

One of the most promising applications of noisy intermediate-scale quantum computers is the simulation of molecular Hamiltonians using the variational quantum eigensolver (VQE). We show that encoding symmetries of the simulated Hamiltonian in the VQE ansatz reduces both classical and quantum resources compared to other widely available ansatze. Through simulations of the ${\mathrm{H}}_{2}$ molecule and of a Heisenberg model on a two-dimensional lattice, we verify that these improvements persist in the presence of noise. This is done using both real IBM devices and classical simulations. We also demonstrate how these techniques can be used to find molecular excited states of various symmetries using a noisy processor. We use error-mitigation techniques to further improve the quality of our results.
Pengaruh Iklan Dan Citra Merek Terhadap Kepuasan Konsumen (Studi Pada Konsumen sepeda Motor Honda PT Helmi Hendra Motor Maja Majalengka)

This research is motivated by the problem of companies that still use print media to advertisegoods, while currently there is digital technology that is more efficient for advertising andbrand images. This study aims to determine the effect of advertising, brand image onconsumer satisfaction of Honda motorbikes at PT Helmi Hendra Motor Maja either partiallyor simultaneously, using descriptive and verifiative analysis approaches so as to increaseproduct sales and revenue. Testing instruments using validity and reliability tests with a litertscale. The analytical tool used in this research is the Classical Assumption test, theCoefficient of Determination and Multiple Linear Regression. While testing the hypothesisusing the t test for partial and f test for silmutan. It can be seen that advertising is in the goodcategory, brand image is in the good category, and customer satisfaction is in the highcategory. Partially that advertising has a significant effect on customer satisfaction, brandimage has a significant effect on customer satisfaction. Simultaneously, advertising and brandimage have a significant effect on customer satisfaction.
Implementation of a Service that Enables User to Cross-refer Earlier Convicted Felons in Vicinity

Human societies have always devised codes or rules to live by. These codes or rules are set in place to ensure the overall well-being of most of the people. But we cannot deny the fact that crime has always manifested itself by one way or another and can be traced back to as far in history as records go. The origin of crime can be traced to combination of various social, economic, demographic institutional and local factors. These factors contribute towards various aspects of an individual's life such as his education, employment, family relationships, emotional stability, mental health, anonymity etc. which play a huge role in further determining the nature, pattern, frequency and volume of crime. So, until now we have established that where there are human society's crime and criminals are inevitable. The major problem which arises is that in some underdeveloped or developing countries crimes of all nature are not talked about openly or acknowledged let alone be reported. People as individuals should be equipped to their best of abilities in order to protect themselves and their families from all possible aspects of what they perceive as evil and to do so we will use Liner search, Dijkstra's and A* algorithm for finding the convicts near you.
Explainable Autonomous Robots: A Survey and Perspective

Advanced communication protocols are critical to enable the coexistence of autonomous robots with humans. Thus, the development of explanatory capabilities is an urgent first step toward autonomous robots. This survey provides an overview of the various types of "explainability" discussed in machine learning research. Then, we discuss the definition of "explainability" in the context of autonomous robots (i.e., explainable autonomous robots) by exploring the question "what is an explanation?" We further conduct a research survey based on this definition and present some relevant topics for future research.
Lifelong Person Re-Identification via Adaptive Knowledge Accumulation

Person ReID methods always learn through a stationary domain that is fixed by the choice of a given dataset. In many contexts (e.g., lifelong learning), those methods are ineffective because the domain is continually changing in which case incremental learning over multiple domains is required potentially. In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains. Following the cognitive processes in the human brain, we design an Adaptive Knowledge Accumulation (AKA) framework that is endowed with two crucial abilities: knowledge representation and knowledge operation. Our method alleviates catastrophic forgetting on seen domains and demonstrates the ability to generalize to unseen domains. Correspondingly, we also provide a new and large-scale benchmark for LReID. Extensive experiments demonstrate our method outperforms other competitors by a margin of 5.8% mAP in generalising evaluation.
Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling

One of the difficulties of conversion rate (CVR) prediction is that the conversions can delay and take place long after the clicks. The delayed feedback poses a challenge: fresh data are beneficial to continuous training but may not have complete label information at the time they are ingested into the training pipeline. To balance model freshness and label certainty, previous methods set a short waiting window or even do not wait for the conversion signal. If conversion happens outside the waiting window, this sample will be duplicated and ingested into the training pipeline with a positive label. However, these methods have some issues. First, they assume the observed feature distribution remains the same as the actual distribution. But this assumption does not hold due to the ingestion of duplicated samples. Second, the certainty of the conversion action only comes from the positives. But the positives are scarce as conversions are sparse in commercial systems. These issues induce bias during the modeling of delayed feedback. In this paper, we propose DElayed FEedback modeling with Real negatives (DEFER) method to address these issues. The proposed method ingests real negative samples into the training pipeline. The ingestion of real negatives ensures the observed feature distribution is equivalent to the actual distribution, thus reducing the bias. The ingestion of real negatives also brings more certainty information of the conversion. To correct the distribution shift, DEFER employs importance sampling to weigh the loss function. Experimental results on industrial datasets validate the superiority of DEFER. DEFER have been deployed in the display advertising system of Alibaba, obtaining over 6.0% improvement on CVR in several scenarios. The code and data in this paper are now open-sourced {https://github.com/gusuperstar/defer.git}.
USING MACHINE LEARNING AS A TOOL TO HELP GUIDE UNDECLARED/UNDECIDED FIRST-YEAR ENGINEERING STUDENTS TOWARDS A DISCIPLINE

Supervised Machine Learning classification algorithms are used to analyze the potential inclination of the undecided/undeclared first-year engineering students. The data exploration task is possible by building a dataset that comprises of questions based on significant attributes. These attributes hover around different disciplines of engineering being offered at the University of Toronto. This qualitative survey is distributed to upperclassmen students (3rd, 4th year and graduate students, N = 54) and undecided first-year engineering students (N = 29) Multi-class classification is a technique that is used to categorize the data into two or more classes, in this case, the different disciplines of engineering at University of Toronto. The dataset that is built, based on the answers provided by the upperclassmen, is programmed into different classification algorithms such as Logistic Regression, KNN (K-nearest neighbors), Decision Tree and Random Forest classifier. The algorithms are compared so as to identify the most appropriate one that can determine the specific class label of the upperclassmen based on the answers provided in the qualitative survey. The accuracy of the various algorithms is an indicator of the favorable algorithm that can serve as a tool to suggest the potential majors that could be pursued by the undecided/undeclared students. Moreover, the answers given by the upperclassmen is visually analyzed for identifying the patterns of inclination of the students belonging to different disciplines of engineering.&#x0D;
Multipartite entanglement criterion via generalized local uncertainty relations

Abstract We study the detection of multipartite entanglement based on the generalized local uncertainty relations. A sufficient criterion for the entanglement of four-partite quantum systems is presented in terms of the local uncertainty relations. Detailed examples are given to illustrate the advantages of our criterion. The approach is generalized to general multipartite entanglement cases.
On the stability of deep convolutional neural networks under irregular or random deformations

The problem of robustness under location deformations for deep convolutional neural networks (DCNNs) is of great theoretical and practical interest. This issue has been studied in pioneering works, especially for scattering-type architectures, for deformation vector fields $τ(x)$ with some regularity - at least $C^1$. Here we address this issue for any field $τ\in L^\infty(\mathbb{R}^d;\mathbb{R}^d)$, without any additional regularity assumption, hence including the case of wild irregular deformations such as a noise on the pixel location of an image. We prove that for signals in multiresolution approximation spaces $U_s$ at scale $s$, whenever the network is Lipschitz continuous (regardless of its architecture), stability in $L^2$ holds in the regime $\|τ\|_{L^\infty}/s\ll 1$, essentially as a consequence of the uncertainty principle. When $\|τ\|_{L^\infty}/s\gg 1$ instability can occur even for well-structured DCNNs such as the wavelet scattering networks, and we provide a sharp upper bound for the asymptotic growth rate. The stability results are then extended to signals in the Besov space $B^{d/2}_{2,1}$ tailored to the given multiresolution approximation. We also consider the case of more general time-frequency deformations. Finally, we provide stochastic versions of the aforementioned results, namely we study the issue of stability in mean when $τ(x)$ is modeled as a random field (not bounded, in general) with with identically distributed variables $|τ(x)|$, $x\in\mathbb{R}^d$.
Video streaming over HTTP/2: Design and evaluation of adaptive server-paced push

By using the server push of the hypertext transfer protocol (HTTP) version 2.0, i.e., HTTP/2, along with the technique of server pacing, a novel scheme is proposed in this paper to deliver video segments.Furthermore, the load of bitrate adaptation is shifted to the server to avoid bandwidth competition and wastage caused by bitrate switching.It can be explicitly shown that no significant overhead is brought by our proposed scheme via complexity analysis.Moreover, our proposed scheme generates one HTTP request only.With extensive simulations, we successfully demonstrate that it is superior over the closely related schemes in terms of the average achievable bitrate, the number of buffer stalls, the ratio of unclaimed pushes, etc., in particular, when a harsh network condition arises.
Pedestrian Detection by Novel Axis-Line Representation and Regression Pattern

The pattern of bounding box representation and regression has long been dominant in CNN-based pedestrian detectors. Despite the method’s success, it cannot accurately represent location, and introduces unnecessary background information, while pedestrian features are mainly located in axis-line areas. Other object representations, such as corner-pairs, are not easy to obtain by regression because the corners are far from the axis-line and are greatly affected by background features. In this paper, we propose a novel detection pattern, named Axis-line Representation and Regression (ALR), for pedestrian detection in road scenes. Specifically, we design a 3-d axis-line representation for pedestrians and use it as the regression target during network training. A line-box transformation method is also proposed to fit the widely used box-annotations. Meanwhile, we explore the influence of deformable convolution base-offset on detection performance and propose a base-offset initialization strategy to further promote the gain brought by ALR. Notably, the proposed ALR pattern can be introduced into both anchor-based and anchor-free frameworks. We validate the effectiveness of ALR on the Caltech-USA and CityPersons datasets. Experimental results show that our approach outperforms the baseline significantly through simple modifications and achieves competitive accuracy with other methods without bells and whistles.