Reviewerly/exHarmony huggingface dataset:
Few-Shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems

Existing work on generating hints in Intelligent Tutoring Systems (ITS) focuses mostly on manual and non-personalized feedback. In this work, we explore automatically generated questions as personalized feedback in an ITS. Our personalized feedback can pinpoint correct and incorrect or missing phrases in student answers as well as guide them towards correct answer by asking a question in natural language. Our approach combines cause–effect analysis to break down student answers using text similarity-based NLP Transformer models to identify correct and incorrect or missing parts. We train a few-shot Neural Question Generation and Question Re-ranking models to show questions addressing components missing in the student’s answers which steers students towards the correct answer. Our model vastly outperforms both simple and strong baselines in terms of student learning gains by 45% and 35% respectively when tested in a real dialogue-based ITS. Finally, we show that our personalized corrective feedback system has the potential to improve Generative Question Answering systems.
QoS-aware dynamic resource allocation with improved utilization and energy efficiency on GPU

Although GPUs have been indispensable in data centers, meeting the Quality of Service (QoS) under task consolidation on GPU is extremely challenging. Previous works mostly rely on the static task or resource scheduling and cannot handle the QoS violation during runtime. In addition, existing works fail to exploit the computing characteristics of batch tasks, and thus waste the opportunities to reduce power consumption while improving GPU utilization. To address the above problems, we propose a new runtime mechanism SMQoS that can dynamically adjust the resource allocation during runtime to meet the QoS of latency-sensitive (LS) tasks and determine the optimal resource allocation for batch tasks to improve GPU utilization and power efficiency. We implement the proposed mechanism on both simulator (SMQoS) and real GPU hardware (RH-SMQoS). The experimental results show that both SMQoS and RH-SMQoS can achieve better QoS for LS tasks and higher throughput for batch tasks compared to the state-of-the-art works. With hardware extension, the SMQoS can further reduce the power consumption by power gating idle computing resources.
Quick Response (QR) Code Assisted Learning Media on System Materials for Distance Learning Sky Coordinates

Abstract This study discusses whether there is an increase in learning carried out by students on the use of multimedia assisted by QR code on the material eye of the sky coordinate system which in its implementation uses the google meet platform. This study used experimental research methods by pretest and posttest. Based on the results of the evaluation that had been carried out on 10 students of Senior High School 51 Jakarta, Indonesia using the pretest and posttest, the average pretest results were 78.00 and the average posttest results were 87.12 using the Paired sample T-test. It can be concluded that there is a difference in the average learning outcomes between the pretest and posttest, meaning that there is an increase in learning in the material of the sky coordinate system after using multimedia assisted by QR code. This online learning tool can be said to be very good and suitable for use in the material of the sky coordinate system. So, multimedia assisted by QR code is effective and suitable for the learning process of the sky coordinate system.
Self-Balanced R-CNN for instance segmentation

Current state-of-the-art two-stage models on instance segmentation task suffer from several types of imbalances. In this paper, we address the Intersection over the Union (IoU) distribution imbalance of positive input Regions of Interest (RoIs) during the training of the second stage. Our Self-Balanced R-CNN (SBR-CNN), an evolved version of the Hybrid Task Cascade (HTC) model, brings brand new loop mechanisms of bounding box and mask refinements. With an improved Generic RoI Extraction (GRoIE), we also address the feature-level imbalance at the Feature Pyramid Network (FPN) level, originated by a non-uniform integration between low- and high-level features from the backbone layers. In addition, the redesign of the architecture heads toward a fully convolutional approach with FCC further reduces the number of parameters and obtains more clues to the connection between the task to solve and the layers used. Moreover, our SBR-CNN model shows the same or even better improvements if adopted in conjunction with other state-of-the-art models. In fact, with a lightweight ResNet-50 as backbone, evaluated on COCO minival 2017 dataset, our model reaches 45.3% and 41.5% AP for object detection and instance segmentation, with 12 epochs and without extra tricks. The code is available at https://github.com/IMPLabUniPr/mmdetection/tree/sbr_cnn.
One-shot emotional voice conversion based on feature separation

The task of emotional voice conversion (EVC) aims to convert speech from one emotional state into another, while keeping linguistic content, speaker identity and other emotion-independent information unchanged. Because previous studies were limited to a specific set of emotions, it is challenging to realize the conversion of emotions never seen in training stage. In this paper, we propose a one-shot emotional voice conversion model based on feature separation. The proposed method could control emotional characteristics with Global Emotion Embeddings (GEEs), and introduce activation guidance (AG) and mutual information (MI) minimization to reduce the correlations between emotion embedding and emotion-independent representation. At run-time conversion, it could produce the desired emotional utterance from a single pairwise utterance without any emotion labels, whether the target emotion appears in the training set or not. The subjective and objective evaluations validate the effectiveness of our proposed model for both the degree of feature separation and emotion expression, even it could achieve unseen emotion conversion.
Effect of The Composition of Hydrophobic Agents on The Contact Angle and Strength of Mortal Cement

Abstract Mortar cement resistance has serious economic implications in the form of maintenance costs and replacement of a structure. Therefore it is necessary to make cement mortar that is waterproof or hydrophobic. The purpose of this study was to look at the effect of the composition of hydrophobic agents on the large contact angle and strength of mortal cement. The hydrophobic agent used is Tetrahydrofuran (THF) with polyurethane (PU) with variations of 5:6molar, 1:10molar, 1:10.9 molar with total each variation being 11 molars. The control variables in this study are the amount of composition of otawa sand, aquades and silica sand 20-30 mesh which is 4:11:2 mold 5 cm x 5cm cement mortar cubes, long immersion with Ca(OH) 2 10% for 7 days, temperature 60 °C and heating length 1 hour. The large angle of contact is measured using the caessyal drop method and the hardness value using the hardness vickers tool. The results showed that the higher the concentration of Polyuteran, the smaller the contact angle of mortar cement produced. The largest contact angle occurs at the concentration of THF variation: PU 6:5 mol. The optimum contact angle is 126° and the mortal seemen are hydrophobic.
Specific Security Issues in the Integration between Sensor Network and Cloud Structure

The security of integrating sensor network data into cloud structures is a significant challenge for IoT development. This issue is the subject of serious research by industry and academia. In the paper, an approach is proposed to increase the security of the generated sensor data through the mechanisms for encryption of communication channels via SSL. An experiment was planned and conducted. The influence of the number of transmitted data packets and their parameters through encrypted protocols HTTPS, MQTT-S and MQTT-SN-S were studied. The obtained results give an advantage to the MQTT-S protocol under identical conditions: data delay (speed), CPU and RAM load. This trend is more pronounced when transmitting a smaller number of data packets specific to WSN.
Research on Information Sharing Mechanism Based on Blockchain Technology

Information sharing is becoming popular for its efficiently processing and modeling in intelligent era. During the information sharing procedure, especially in the Industrial Internet area, some problems should be considered, such as storage centralization, low integration efficiency, data isolation, weak trust, poor security performance, and privacy protection, which are the challenges for forming free circulation. For this purpose, blockchain technology could be used to realize the decentralized storage, distrust and traceability of information. This paper proposes a sharing mechanism of information based on blockchain technology, for the processes of collection, storage and application. It is concluded that building a multi-collaborative information sharing mechanism based on blockchain, can satisfy the requirement of safe and efficient sharing.
Differential Search Algorithm by XOR Gate in Binary Optimization Problems

There are many meta-heuristic algorithms proposed by researchers, and the general feature of these algorithms is that they are modeled by being inspired by the intelligent behaviors of living things in nature, individually or as a group. Basic versions of meta-heuristic algorithms are frequently introduced to solve continuous optimization problems. However, due to their flexibility, they can be adjusted for use in various types of optimization problems. In this research, an improved version of the Differential Search Algorithm (DSAX), which is a population-based meta-heuristic algorithm that has shown its success in solving optimization problems, is proposed to use in solving binary optimization problems. The DSAX has been tested on large-scale binary optimization problems. Moreover, it is compared with the XOR-Based version of the Tree-Seed Algorithm, which is well known in the literature. According to the obtained results and the comparisons, it is seen that the DSAX algorithm proposed in this study is superior.
Counterexample-guided inductive repair of reactive contracts

Executable implementations are ultimately the only dependable representations of a software component's behavior. Incorporating such a component in a rigorous model-based development of reactive systems poses challenges since a formal contract over its behaviors will have to be crafted for system verification. Simply hypothesizing a contract based on informal descriptions of the component is problematic: if it is too weak, we may fail in verifying valid system-level contracts; if it is too strong or simply erroneous, the system may fail in operation. Thus, establishing a valid and strong enough contract is crucially important.
Learning CI Configuration Correctness for Early Build Feedback

Continuous Integration (CI) allows developers to check whether their code can build successfully and pass tests across various system environments with every commit. To use a CI platform, a developer must provide configuration files within a code repository to specify build conditions. Incorrect configuration settings lead to CI build failures, which can take hours to run, wasting valuable developer time and delaying product release dates. Debugging CI configurations is a slow and error-prone process. The only way to check the correctness of CI configurations is to push a commit and wait for the build result. We present VeriCI, the first system for localizing CI configuration errors at the code level. VeriCI runs as a static analysis tool, before the developer sends the build request to the CI server. Our key insight is that the commit history and the corresponding build histories available in CI environments can be used both for build error prediction and build error localization. We leverage the build history as a labeled dataset to automatically derive customized rules describing correct CI configurations, using supervised machine learning techniques. To more accurately identify root causes, we train a neural network that filters out constraints that are less likely to be connected to the root cause of build failure. We evaluate VeriCI on real world data from GitHub and achieve 91&#x0025; accuracy of predicting a build failure and correctly identify the root cause in 75&#x0025; of cases. We also conducted a between-subjects user study with 20 software developers, showing that VeriCI significantly helps users in identifying and fixing errors in CI.
Goal and Policy Based Code Generation and Deployment of Smart Contracts

The emergence of smart contracts in blockchain environments allows for a wide spectrum of transaction oriented trusted applications to be developed. The majority of smart contracts are implemented as scripts written in specialized machine interpretable languages such as Solidity. However recent efforts allow for more general purpose languages such as GoLang and JavaScript to be used. It is therefore evident, that smart contract applications will quickly become more complex, and an interesting question that arises is how we can ensure that the smart contract code achieves and complies with the required goals and policies set by the system&#x0027;s stakeholders. In this short paper we outline a model-based approach for the automatic generation and deployment of smart contract code by using extended goal models to capture the tasks and policies set by various stakeholders, and by applying a code generator to produce Solidity code that corresponds to the model.
Do Developers Refactor Data Access Code? An Empirical Study

Developers often refactor code to improve the maintainability and comprehension of the software. There are many studies on refactoring activities in traditional software systems. However, refactoring in data-intensive systems is not well explored. Understanding the refactoring practices of developers is important to develop efficient tool support. We conducted a longitudinal study of refactoring activities in data access classes using 12 data-intensive subject systems. We investigated the prevalence and evolution of refactorings and the association of refactorings with data access smells. We also conducted a manual analysis of over 378 samples of data access refactoring instances to identify the functionalities of the code that are targeted by such refactorings. Our results show that (1) data access refactorings are prevalent and different in type. Rename variable is the most prevalent data access refactoring. (2) The prevalence and type of refactorings vary as systems evolve in time. (3) Most data access refactorings target codes that implement data fetching and insertion. (4) Data access refactorings do not generally touch SQL queries. Overall, the results show that data access refactorings focus on improving the code quality but not the underlying data access operations. Hence, more work is needed from the research community on providing awareness and support to practitioners on the benefits of addressing data access smells with refactorings.
Modeling Multi-annotator Uncertainty as Multi-class Segmentation Problem

Medical image segmentation is a monotonous, time-consuming, and costly task performed by highly skilled medical annotators. Despite adequate training, the intra- and inter-annotator variations results in significantly differing segmentations. If the variations arise from the uncertainty of the segmentation task, due to poor image contrast, lack of expert consensus, etc., then the algorithms for automatic segmentation should learn to capture the annotator (dis)agreements. In our approach we modeled the annotator (dis)agreement by aggregating the multi-annotator segmentations to reflect the uncertainty of the segmentation task and formulated the segmentation as multi-class pixel classification problem within an open source convolutional neural architecture nnU-Net. Validation was carried out for a wide range of imaging modalities and segmentation tasks as provided by the 2020 and 2021 QUBIQ (Quantification of Uncertainties in Biomedical Image Quantification) challenges. We achieved high quality segmentation results, despite a small set of training samples, and at time of this writing achieved an overall third and sixth best result on the respective QUBIQ 2020 and 2021 challenge leaderboards.
Quantum Technologies I: Information, Communication, and Computation

In this chapter, we introduce some of the notions of quantum information science including aspects of information, information security, entanglement states, quantum gates, teleportation, direct secure communication, quantum secret sharing, quantum noise, quantum operations, quantum error correction, quantum circuits and quantum Toffoli gate. Most of these aspects are of importance in quantum enhanced technologies including quantum blockchain. The objective of this chapter is to introduce the basic notions of quantum information science aspects with its real-time need and usage, including some notes on how the above mentioned concepts and tools might be helpful in quantum blockchain technology. The chapter is organized into three major sections as follows. Starting from postulates of quantum mechanics, which set the basic rules of the theory which drastically deviates from the classical mechanics. Then we introduce, in Sect. 1 the basic notions of quantum information (QI) science as described in the abstract. Section 2 is dedicated to multiple controlled Toffoli gate, which may find its application in may areas of quantum computing and also in quantum blockchain. Section 3 is dedicated to certain aspects of quantum error correction, a scheme to protect qubits (units of quantum information) from environmental noise, which helps develop fault-tolerant quantum technologies. In Conclusion section, we note how the content in this chapter might be relevant to quantum blockchain technology. A table of symbols is given in Appendix.
A Comprehensive Overview of Quantum Internet: Architecture, Protocol and Challenges

The Quantum Internet is defined as the internetwork of remote quantum devices through Quantum communications. The major advantages of Quantum Internet include the independence of legacy internet, a wide range of secured information transmission, and the capability of cutting-edge bling computing. This chapter focuses on four primary objectives. The first section focuses on exploring the fundaments behind Quantum Computing and the Quantum Internet. The Quantum entanglement, Quantum bits, and Quantum states will be discussed in this section. The second section focuses on envisioning the Quantum Internet standard architecture. The third objective is to explore Quantum Teleportation as a Quantum communication protocol for Quantum Internet. Finally, the challenges of Quantum Internet will be discussed. The major challenges include de-coherence of quantum information, non-cloning information theory, quantum state collapsing, distributed networking of quantum devices.
Adaptive Kernel Learning Kalman Filtering With Application to Model-Free Maneuvering Target Tracking

Kernel method is a non-parametric linearization method for system modeling, which uses nonlinear projection from input data space to high-dimensional Hilbert feature space and employs kernel function for hiding the projection operator in a linear learner to replace inner product calculation in Hilbert space and to avoid the curse of dimensionality. Kernel method is data-driven and learnable to nonlinear model. For independency to a priori model, it provides a novel way of state estimation for nonlinear dynamic systems with model uncertainties. In this paper, an adaptive kernel learning Kalman filtering method is proposed and applied into the problem of maneuvering target tracking. Without use of a priori system model, the method performs Kalman filtering in a reproducing kernel Hilbert space (RKHS) by estimating conditional embedding operator (CEO) as system state transfer function from training data. For the stochastic uncertainty in system model, the maximum correntropy criterion (MCC) is introduced to obtain kernel parameter optimization and balance of estimation performance, while a sliding window is designed for online updating estimation of state transfer function to get adaptability to unknown system dynamics. Such the construction for kernel Kalman filtering (KKF) is helpful to extend application from time-series signal processing to state estimation of uncertain dynamical systems. Simulation scenarios include average sunspot prediction, hovering target tracking and hypersonic maneuvering target tracking, corresponding to verification in low-dynamic, periodic-dynamic and high-dynamic systems. Numerical results have illustrated that the proposed adaptive KKF can realize model-free tracking for target that has nonlinear dynamical motion, shown adaptability to non-cooperative target maneuver and better precision and convergence speed than typical model-based algorithms.
Quantum Generative Modelling and Its Use Cases

A generative model describes how a dataset is generated according to a probability model and how new data can be generated by sampling this model. In the following work, we present the generative models' approach in quantum along with other algorithms that have been developed for the optimization of generative models as well for a quantum generative model-based. We have described a use case of molecular simulation and optimization using variational quantum eigen solver. The quantum generative model that we describe has several important use cases in which we discuss the generation of probability distributions, drugs, and option pricing for financial application. Various applications and their impact evaluations have been reviewed to determine if the use of quantum generator algorithms is more appropriate than traditional machine learning methods. The use of quantum generator algorithms has proven to be more appropriate than traditional machine learning methods according to previous works. This leads us to the place of quantum development in the field of artificial intelligence.
AE-Net:Adjoint Enhancement Network for Efficient Action Recognition in Video Understanding

Action recognition in video understanding is a challenging task, largely because of the complexity and difficulty in temporal modeling, making it suffer from motion information loss and misalignment of temporal attention in spatial dimensions. To overcome these difficulties, we propose a novel temporal modeling method called <i>Adjoint Enhancement Network</i> (AE-Net), which can fully explore clues of motion and time in the long-range structure. The AE-Net mainly consists of two new modules: the <i>Initial Adjoint Enhancement Module</i> (IAE-Module), which deals with shallow features; and the <i>Global Adjoint Enhancement Module</i> (GAE-Module), which deals with global features. With a novel mechanism of parallel spatio-temporal convolution and difference fusion, the IAE-Module is to enhance the degree of motion transformation in shallow network features, exciting the potential of motion flow and avoiding motion information loss. The GAE-Module is proposed to improve the local temporal representation in long-range structures by feeding the enhanced feature differences into a spatial cascade module with residuals to resolve the misalignment of temporal attention in the spatial dimension.The experimental results show that our AE-Net can achieve state-of-the-art results in Something-Something V1, UCF-101 and HMDB-51 datasets.