Reviewerly/exHarmony huggingface dataset:
Relevant Concepts, Key Technologies and Typical Application Scenarios of AI + Education

Abstract Artificial intelligence (AI) + education has become an indispensable part of China’s national strategy with regard to AI. This paper redefines relevant concepts of AI + education, studies the core technology of AI + education, and proposes a four-level systematic framework of AI + education. Besides, this paper probes into the application scenarios, typical models and main technologies of AI + education.
Multicriteria Decision Making to Continuous Software Improvement Based on Quality Management, Assurance, and Metrics

Software process improvement methodologies support to incessantly regulate and advance the process of software for its performance development. This development arises in terms of software product quality, time, changes reduction, and so on. The aim of software process improvement is to make software process effective and to increase quality of software product through continuous evaluation. Various approaches and frameworks are in practice for software process improvement. Early decision making concerning continuous software improvement can ultimately lead to successful software developments and growing software industry and business of software. Keeping in view the early decision making regarding continuous software improvement, the proposed study has considered a decision support system (DSS) for making decision based on multicriteria against quality management, assurance, and metrics. The necessary factors and alternatives for continuous software improvement were considered. The process of the proposed study was experimentally conducted, and results were good enough for supporting the study. The study will help practitioners in early decision making regarding software improvement based on quality management.
Simon @ DravidianLangTech-EACL2021: Meme Classification for Tamil with BERT

In this paper, we introduce the system for the task of meme classification for Tamil, submitted by our team. In today’s society, social media has become an important platform for people to communicate. We use social media to share information about ourselves and express our views on things. It has gradually developed a unique form of emotional expression on social media – meme. The meme is an expression that is often ironic. This also gives the meme a unique sense of humor. But it’s not just positive content on social media. There’s also a lot of offensive content. Meme’s unique expression makes it often used by some users to post offensive content. Therefore, it is very urgent to detect the offensive content of the meme. Our team uses the natural language processing method to classify the offensive content of the meme. Our team combines the BERT model with the CNN to improve the model’s ability to collect statement information. Finally, the F1-score of our team in the official test set is 0.49, and our method ranks 5th.
Verbalink-A Gesture Based Speech Generating Device

This paper represents design of Speech Generating Device (SGD) to help mute, deaf and paralyzed people communicate with others. Speech generation is controlled by physical gesture and using the gestures particular voice commands are played in a loudspeaker. To recognize physical gestures Flex Sensors, Accelerometer, Gyroscope and other Inertial Measurement Unit (IMU) sensors can be used. Accelerometer sensor is the most economic one which has been used for our purpose.
MUCS@DravidianLangTech-EACL2021:COOLI-Code-Mixing Offensive Language Identification

This paper describes the models submitted by the team MUCS for Offensive Language Identification in Dravidian Languages-EACL 2021 shared task that aims at identifying and classifying code-mixed texts of three language pairs namely, Kannada-English (Kn-En), Malayalam-English (Ma-En), and Tamil-English (Ta-En) into six predefined categories (5 categories in Ma-En language pair). Two models, namely, COOLI-Ensemble and COOLI-Keras are trained with the char sequences extracted from the sentences combined with words as features. Out of the two proposed models, COOLI-Ensemble model (best among our models) obtained first rank for Ma-En language pair with 0.97 weighted F1-score and fourth and sixth ranks with 0.75 and 0.69 weighted F1-score for Ta-En and Kn-En language pairs respectively.
A GMM-Based Anomaly IP Detection Model from Security Logs

The intrusion prevention system (IPS) is a widely used security system which generates logs for the attacks blocked by it for management personnel to review and conduct further processing. However, most of the entries in the actual IPS logs are not attack entries, which makes it impossible for us to obtain the attacker’s IP through simple log analysis. The traditional log analysis methods rely on the administrator to manually analyze the log text. So it is necessary to use anomaly detection methods for analysis. The majority of existing log data-based automatic detection methods for anomalies cannot get an satisfying result while ensuring computational requirements and the interpretability of the model. This paper chose the Gaussian Mixture Model (GMM) to detect abnormal IP on the log dataset. The GMM method provides better detection results while ensuring relatively low computational requirements, and maintains the interpretability of the model. Experiments show that the ability of GMM method to detect abnormal IP is strong and the GMM is a suitable log data-based automatic detection method for detecting abnormal IP.
Special Issue on Novel Technology of Autonomous Drone

In the past three years, there has been rapid progress in the use of drones in society. Drones, which were previously used only experimentally in various industrial fields, are now being used in earnest in everyday operations. Drones are becoming indispensable tools in several industrial fields, such as surveying, inspection, and agriculture. At the same time, there has also been dramatic progress in autonomous drone technology. With the advancement of image processing, simultaneous localization and mapping (SLAM), and artificial intelligence technologies, many intelligent drones that apply these technologies are being researched. At the same time, our knowledge of multi-rotor helicopters, the main type of drones, has continued to deepen. As the strengths and weaknesses of multi-rotor helicopters have gradually become clearer, drones with alternate structures, such as flapping-wing drones, have come to attract renewed attention. In addition, the range of applications for drones, including passenger drones, has expanded greatly, and research on unprecedented drone operations, as well as research on systems and controls to ensure operational safety, is actively being conducted. This special issue contains the latest review, research papers, and development reports on autonomous drones classified as follows from the abovementioned perspectives. · Research on drone airframes and structures · Research on drone navigation and recognition with a focus on image processing · Research on advanced drone controls · Research and development of drone applications We hope that the readers will actively promote the use of drones in their own research and work, based on the information obtained from this special issue.
Design of Automatic Rain Gauge Prototype (ARG) As An Early Warning Indicator for Cold Lava Flood Based on The Internet of Things (IoT)

Abstract This study aims to design a prototype of automatic rain gauge, characterize the automatic rain gauge, rainfall classification as an indicator of cold lava flood, tested the automatic rain gauge prototype. The design of this tool includes three parts, the first is the reed switch block design. Second, designing an automatic rain gauge block by designing a tipping bucket. The third is designing IoT block and Android applications. The automatic rain gauge prototype was successfully designed with a 0.8 cm reed switch and a tipping bucket with a funnel area of 380 cm 2 with a resolution of 0.1 mm and a maximum capacity of 9.4 mm/minute. The classification of rainfall as an indicator of cold lava flood is normal 0-15 mm, advisory 16-20 mm, watch 21-48 mm and warning &gt; 48 mm, so that the early warning system prototype can display rainfall charts and warning alarms on the android application.
Subject independent evaluation of eyebrows as a stand‐alone biometric

Considering challenges such as occlusions due to face coverings, ocular biometrics have risen as a potential remedy. Visible light ocular modalities use features extracted from in and around the eyes from ‘selfie’ captures for personal identification. A less-visited ocular neighbourhood is the eyebrows region. Previous works on eyebrows have been mostly using subject-specific verification protocols where identities overlap between the training and test sets and do not directly address the issue of doppelgangers and twins. Here, the evaluation of five deep learning models, lightCNN, ResNet, DenseNet, MobileNetV2, and SqueezeNet, for eyebrow-based user authentication in a subject independent environment across different data sets, lighting conditions, resolutions, and facial expressions is done. Also a challenging simulated identical twins scenario in our training and testing data sets is presented. Our results show a 4.2% equal error rate (EER) and 0.993 area under the curve (AUC) on a subset of the FACES data set (154 subjects) and 6.8% EER, 0.978 AUC on a subset of the VISOB data set (350 subjects). Despite achieving promising accuracy during short-term evaluations, the proposed methods showed lower accuracy when matching against long-term data or when facing data captured under varying illumination and facial expressions.
Planning Advanced Treatment of Tap Water Consumption in Universitas Pertamina

The wastewater treatment plant (WWTP) in Universitas Pertamina’s area is operating very well. The existence of a green campus program with a wastewater recycling indicator has become one of the challenges. Improving the WWTP effluent quality by adding an advanced treatment unit. This study aims to design advanced processing units and estimate the effluent yields and required costs. This study was conducted by conducting a field survey, collecting water quality data, and literature study. With planning discharge (Qpeak) of 8.45 m3 /hour, the land area required for the addition of advanced treatment is 105.85 m2 . Advanced treatment consists of 1 unit of Equalization Tank, 2 units of slow sand filter, 1 unit of sand washer, 2 units of microfiltration membrane, 1 disinfection body, and 2 reservoir units. These units it is expected to make effluent quality meets the drinking water quality standards with a TSS value of 0 mg/L, Ammonia (NH3-N) 0.35 mg/L, Organic Substances (KmnO4) 0.513 mg/L, Total Dissolved Solids (TDS) 23 mg/L, and Total Coliform 0 Total/100 ml of sample. The total cost needed to build an advance treatment for tap water consumption is Rp 374,727,334.
8.2 Open Science and the Future of Metrics

With regard to open science developments, scientometrics might play an important role. Scientometrics enable displays of changes in research practices and possibly measurements of the impact of open science initiatives. Conversely, metrics influence researchers’ practices and might be applied to achieve a widespread adoption of open science practices. This chapter discusses open science initiatives and goals, the establishment of policies and influences on researchers’ practices. With regard to these developments, metrics will be discussed as an evaluation tool for open practices on the one hand, and as an incentive to foster open science on the other. The last part of the chapter refers to the openness of metric analyses.
A scalable algorithm for the optimization of neural network architectures

We propose a new scalable method to optimize the architecture of an artificial neural network. The proposed algorithm, called Greedy Search for Neural Network Architecture, aims to determine a neural network with minimal number of layers that is at least as performant as neural networks of the same structure identified by other hyperparameter search algorithms in terms of accuracy and computational cost. Numerical results performed on benchmark datasets show that, for these datasets, our method outperforms state-of-the-art hyperparameter optimization algorithms in terms of attainable predictive performance by the selected neural network architecture, and time-to-solution for the hyperparameter optimization to complete.
Fast Adaptation with Linearized Neural Networks

The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings. We study the inductive biases of linearizations of neural networks, which we show to be surprisingly good summaries of the full network functions. Inspired by this finding, we propose a technique for embedding these inductive biases into Gaussian processes through a kernel designed from the Jacobian of the network. In this setting, domain adaptation takes the form of interpretable posterior inference, with accompanying uncertainty estimation. This inference is analytic and free of local optima issues found in standard techniques such as fine-tuning neural network weights to a new task. We develop significant computational speed-ups based on matrix multiplies, including a novel implementation for scalable Fisher vector products. Our experiments on both image classification and regression demonstrate the promise and convenience of this framework for transfer learning, compared to neural network fine-tuning. Code is available at this https URL.
Quantum tomography of three-qubit generalized Werner states

In this article, we introduce a numerical framework for quantum tomography and entanglement quantification of three-qubit generalized Werner states. The scheme involves the single-qubit SIC-POVM, which is then generalized to perform three-qubit measurements. We introduce random errors into the scheme by imposing the Poisson noise on the measured photon counts. The precision of state estimation is quantified and presented on graphs. As a special case, we compare the efficiency of the framework for the pure three-qubit generalized Werner state and the W state. The reconstructed states are presented graphically and discussed.
Toward Identity Preserving Face Synthesis Between Sketches and Photos Using Deep Feature Injection

Identity preservation is crucial for the practical application of face sketch and photo synthesis, such as law enforcement and entertainment. However, existing methods mainly focus on generating faces with good visual quality, leading the evaluation of identity preservation is partial and insufficient. Besides, it is difficult to simultaneously synthesize photos and sketches that have good visual quality and identity preservation. In this article, we propose to utilize auxiliary deep features extracted by an off-the-shelf face classifier to inject into the synthesis processes, so that the synthesized faces have more identity information. Furthermore, we propose a light interpolated convolutional neural network that has a shared encoder and two face-specialized decoders to simultaneously complete the transformations between sketches and photos. Evaluations on identity preservation and visual quality show our method is superior to existing methods in synthesizing both sketches and photos, and is qualified in practical application.
OCTOPUS: Overcoming Performance and Privatization Bottlenecks in Distributed Learning

The diversity and quantity of data warehouses, gathering data from distributed devices such as mobile devices, can enhance the success and robustness of machine learning algorithms. Federated learning enables distributed participants to collaboratively learn a commonly shared model while holding data locally. However, it is also faced with expensive communication and limitations due to the heterogeneity of distributed data sources and lack of access to global data. In this paper, we investigate a practical distributed learning scenario where multiple downstream tasks (e.g., classifiers) could be efficiently learned from dynamically updated and non-iid distributed data sources while providing local data privatization. We introduce a new distributed/collaborative learning scheme to address communication overhead via latent compression, leveraging global data while providing privatization of local data without additional cost due to encryption or perturbation. This scheme divides learning into (1) informative feature encoding, and transmitting the latent representation of local data to address communication overhead; (2) downstream tasks centralized at the server using the encoded codes gathered from each node to address computing overhead. Besides, a disentanglement strategy is applied to address the privatization of sensitive components of local data. Extensive experiments are conducted on image and speech datasets. The results demonstrate that downstream tasks with the compact latent representations with the privatization of local data can achieve comparable accuracy to centralized learning.
Research Artifact: The Potential of Meta-Maintenance on GitHub

This is a research artifact for the paper “Same File, Different Changes: The Potential of Meta-Maintenance on GitHub”. This artifact is a data repository including a list of studied 32,007 repositories on GitHub, a list of targeted 401,610,677 files, the results of the qualitative analysis for RQ2, RQ3, and RQ4, the results of the quantitative analysis for RQ5, and survey material for RQ6. The purpose of this artifact is enabling researchers to replicate our mixed-methods results of the paper, and to reuse the results of our exploratory study for further software engineering research. This research artifact is available at https://github.com/NAIST-SE/MetaMaintenancePotential and https://doi.org/10.5281/zenodo.4456668.
Case-based reasoning adaptation based on fuzzy gravitational search algorithm for disaster emergency plan

Disasters can result in substantial destructive damages to the world. Emergency plan is vital to deal with these disasters. It is still difficult for the traditional CBR to generate emergency plans to meet requirements of rapid responses. An integrated system including Case-based reasoning (CBR) and gravitational search algorithm (GSA) is proposed to generate the disaster emergency plan. Fuzzy GSA (FGSA) is developed to enhance the convergence ability and accomplish the case adaptation in CBR. The proposed algorithm dynamically updates the main parameters of GSA by introducing a fuzzy system. The FGSA-CBR system is proposed, in which fitness function is defined based on the effectiveness of disaster emergency management. The comparison results have revealed that the proposed algorithm has good performances compared with the original GSA and other algorithms. A gas leakage accident is taken as an empirical study. The results have demonstrated that the FGSA-CBR has good performances when generating the disaster emergency plan. The combination of CBR and FGSA can realize the case adaptation, which provides a useful approach to the real applications.
Similarity search of graph database based on Fuzzy Logic Support vector machine (PSO-SVM) algorithm and computer application

Support vector machine (SVM) has excellent learning performance, has become a popular research method in the field of machine learning, and has been successfully applied in many fields. In recent years, more and more modeling methods have been put forward by relevant scholars to solve problems such as classification identification, risk prediction, and effectiveness evaluation. This article briefly introduces the support vector machine (SVM), and expounds the algorithm of support vector machine in graph database similarity search and computer application for readers' reference.
Flow-Based Attribution in Graphical Models: A Recursive Shapley Approach

We study the attribution problem in a graphical model, wherein the objective is to quantify how the effect of changes at the source nodes propagates through the graph. We develop a model-agnostic flow-based attribution method, called recursive Shapley value (RSV). RSV generalizes a number of existing node-based methods and uniquely satisfies a set of flow-based axioms. In addition to admitting a natural characterization for linear models and facilitating mediation analysis for non-linear models, RSV satisfies a mix of desirable properties discussed in the recent literature, including implementation invariance, sensitivity, monotonicity, and affine scale invariance.
GADTs, Functoriality, Parametricity: Pick Two

GADTs can be represented either as their Church encodings a la Atkey, or as fixpoints a la Johann and Polonsky. While a GADT represented as its Church encoding need not support a map function satisfying the functor laws, the fixpoint representation of a GADT must support such a map function even to be well-defined. The two representations of a GADT thus need not be the same in general. This observation forces a choice of representation of data types in languages supporting GADTs. In this paper we show that choosing whether to represent data types as their Church encodings or as fixpoints determines whether or not a language supporting GADTs can have parametric models. This choice thus has important consequences for how we can program with, and reason about, these advanced data types.
Asymmetric Encryption Scheme to Protect Cloud Data Using Paillier-Cryptosystem

Cloud computing is used for large shared resources to facilitate execution and storage. So there is a need of resolving crucial security issues to avoid data theft. Hence cloud security provides data encryption for security parameters to change plain-text to cipher-text. The homomorphic encryption technique is used for performing operations on encrypted data. To manage the huge and growing informational collections that are being prepared these days, great encryption execution is a significant advance for the common sense of homomorphic encryption techniques, the Paillier cryptosystem is also used by researchers for securing the decimal digits of information. In the present work, a hybrid Paillier cryptosystem technique is used for reducing the bit length of the cipher-text by performing hex code operations on encryption. The proposed method has been implemented in the use of two object-oriented programming languages i.e. C++ and Python programming languages. The simulated results show the minimum encrypted bit length as well as provide more secure data. And we have also analyzed our algorithm based on the two parameters namely space complexity and time complexity which are represented in the form of tables and graphs given below.
Addendum: FosSahul 2.0, an updated database for the Late Quaternary fossil records of Sahul

The original publication described FosSahul 2.0, the updated version of the FosSahul database comprising collated and quality-rated megafauna fossil ages of the Late Quaternary from Sahul, as well as R code to run the algorithm that rated the quality of each age based on criteria established by Rodriguez-Rey et al. Since the paper was published, we received some useful feedback to improve the R code to ensure an objective and consistent rating of ages, which are outlined below. (This record updates FosSahul 2.0, an updated database for the Late Quaternary fossil records of Sahul .)