Reviewerly/exHarmony huggingface dataset:
STAN: Adversarial Network for Cross-domain Question Difficulty Prediction

In intelligent education systems, question difficulty prediction (QDP) is a fundamental task of many applications, such as personalized question recommendation and test paper analysis. Previous work mainly focus on data-driven QDP methods, which are heavily relied on the large-scale labeled dataset of courses. To alleviate the labor intensity, an intuitive method is to introduce domain adaptation into QDP and consider each course as a domain. In educational psychology, there are two factors influencing difficulty common to different courses: the obstacles of comprehending the question and generating a response, namely stimulus and task difficulty. To this end, we propose a novel Stimulus and Task difficulty-based Adversarial Network (STAN) that models question difficulty from the views of stimulus and task. Then, in order to align the difficulty distribution of the source domain and the target domain, we utilize the conditional adversarial learning with readability-enhanced pseudo-labels. Meanwhile, we proposed a sampling method based on density estimation to implicit alignment. Finally, we conduct experiments on the real questions datasets to evaluate the effectiveness of our QDP model and domain adaptation method. Our method significantly improves accuracy over state-of-the-art methods on real-world question data of multiple courses.
Construction of Functional Structure Model of University Smart Gymnasium Under the Background of “Internet +”

In order to overcome the problems of low utilization rate and low service quality of traditional university gymnasiums, this paper adopts the field investigation method and semi-structured interview method to investigate the construction scheme of new smart university gymnasiums from the perspective of advanced technologies such as Internet, cloud computing, big data and 5G communication. It is found that the construction of smart gymnasium in colleges and universities should contain three functional modules, namely, smart building, smart management and smart service, to solve the three core needs of automation of building equipment, digitalization of daily management and intelligent service for teachers and students respectively. Based on this, this paper attempts to build a functional structure model of smart gymnasium in universities based on “Internet+” and other advanced technologies. This model is of great value and significance to effectively improve the utilization rate of university gymnasiums and gymnasiums, reduce the management cost and improve the service quality.
Diffusion: Analysis of Many-to-Many Transactions in Bitcoin

Bitcoin is a decentralized cryptocurrency that enables entities to transfer funds psuedo-anonymously. Investigative services like the FBI, however, have been able to subvert this using various techniques. To counter this, the Bitcoin community uses several methods to obscure transactions, including shared send transactions among others. We attempt to peer into these transactions through a pairing of address clustering and shared send untangling and test our experimental results through path finding between addresses. We then implement our methodology to test its effectiveness. Our findings show that while using clustering can improve path-finding results and shared send untangling, we recommend applying heuristics in combination to increase effectiveness.
SOSA-SHACL: Shapes Constraint for the Sensor, Observation, Sample, and Actuator Ontology

The explosive growth of the Linked Data on the Web has greatly facilitated collecting data from remote sensors, from air quality sensors spread out across a city, to seismograph stations spread across the entire world. Integrating these heterogeneous data can be quite challenging; however one can achieve this through the use of available W3C standards to create a knowledge graph. For this use case, the W3C also provides a standard, the Sensor, Observation, Sample, Actuator (SOSA) Ontology, that allows for the semantic encoding of sensors and their observations. However, even with the guidance of this standard, it may be difficult to produce a correct graph with high fidelity from heterogeneous sources. In this paper we present a set of (data) shape constraints, called SOSA-SHACL, for the SOSA ontology using a data validation language, namely the W3C standard SHACL (Shape Constraint Language). These constraints enable us to evaluate whether the modeled observations in our Knowledge Graph comply with the SOSA recommendations. Furthermore, we show through several case studies how the closed world assumption plays a role in the process of designing such shape constraints, especially as SOSA is based on the open world assumption.
A Survey of Vietnamese Automatic Speech Recognition

In this paper, we survey Vietnamese automatic speech recognition (ASR). The objective of this survey is to provide an overview of the current status and remaining challenges of implementing a Vietnamese ASR. Recently, there are some studies on ASR for Vietnamese language; however, these studies encounter some obstacles and the results obtained compared to other languages such as English or Mandarin are lower. With regards to Vietnamese speech recognition, we will examine the methods of building a system along with speech data and text data collection techniques and available speech resources. We review both the methods applied to acoustic modeling and those used to language modeling. In addition, we convey some directions for future research.
Robust Image Encryption Based on Double Hyper Chaotic Rabinovich System

an essential characteristic of chaotic systems is the difficulty in predicting the path of the chaotic time series and the size of the keyspace, which is very large compared to other encryption methods. This paper presents a new image encryption technique depending on a random bitstream generation method through a double Rabinovich hyperchaotic system. The proposed algorithm (Hyper Chaotic Rabinovich System (HCRS)) has been achieved in several ways. Each technique was examined in balanced and delay environments. Then an XOR operation will be done between the bits with the same index of the original image bits and the generated bits to obtain the encrypted image. The HCRS results showed that the balance in random bits between Zeros and Ones are equal in all proposed methods. The Pick Signal to Noise Ratio (PSNR) between the original and the encrypted images is (8.6262). The second significant result in this paper is that the keyspace is very large, more potent than 10ˆ288.
Exploratory Study for Introducing and Applying an AI-based Intelligent Learning System on Basic Science －Focusing on General Chemistry Class Case

Artificial Intelligence is the technical foundation of the Fourth Industrial Revolution, leading innovation and change in various areas of society as a whole. Even in the education area, AI-based learning systems have the potential to be used in adaptive classes and to take into consideration the students’ learning situations. This study focused on the use of ALEKS as an AI-based system on an adapted learning system in a general chemistry class for the second semester of 2020 and the first semester of 2021, and suggested factors to consider when operating AI-applied classes. According to our study, AI-based adaptive learning systems have contributed to the improvement of conceptual learning through individual learning support and learning interest and through class operations measuring the students’ learning progress. However, in order for these exploratory studies to produce real and meaningful effects, flexible access is requires in the use of course content, learning data and systems. In addition, it seems that creative class composition and curriculum development are needed, with the application and expansion of concepts taking place in various interactions.&lt;/br&gt;Finally, our exploratory research will be the basis for the introduction and application of a basic science class involving AI based adaptive learning systems.
Linear Attacks On SNOW 3G And SNOW-V Using Automatic Search

Abstract In this paper, a linear attack model of SNOW 3G and SNOW-V based on automatic search technology is proposed. We first describe the linear approximation of Finite State Machine transformation, which allows a wider range of automatic search, then model it with the automatic search technology based on SAT/SMT program. Adopting this generic method, we seek out a binary linear approximation of SNOW 3G with correlation of $2^{-21.92}$ which has been verified by test. Treating this binary approximation as a mask of an 8-bit distribution in a fixed field, we provide a method to obtain the 8-bit distribution. The binary approximation is used in a fast correlation attack with expected time and memory complexity $2^{184.67}$, given $2^{173.96}$ key stream words. For the full version of SNOW-V, considering the linear relationship between Linear Feedback Shift Register parts at three successive moments, we search out a distinguisher with correlation of $2^{-175.51}$, which results in a distinguishing attack with an expected complexity of $2^{351.02}$.
Literature Survey on Adaptive Virtual Machine Scheduling Strategy to Optimize Load Balancing in Cloud Environment

Cloud computing has become a widely accepted technology that provides on-demand services. Resource man-agement is critical in cloud computing to provide required resources to the subscribers. Cloud computing uses virtualization technology to optimize resource utilization. Resource allocation is done based on virtual machines. Load balancing based on virtual machine strategy is a more challenging problem. Heavy Workload of the physical machine decreases the VM s performance and increases the job completion time. It enables virtual machine migration techniques with the help of load balancing methods to remove hotspots and associations for the improvement of resource utilization and energy efficiency. In this paper, the survey is done on various load balancing mechanisms and their shortcomings are presented.
Compact representation of graphs with bounded bandwidth or treedepth

We study the problem of compact representation of graphs that have small bandwidth as well as graphs that have small treedepth. We present navigation oracles that support degree and adjacency queries in constant time and neighborhood queries in constant time per neighbor. For graphs of size n and bandwidth k, our oracle takes (k+⌈log⁡2k⌉)n+o(kn) bits. We also show that (k−5k−4)n−O(k2) bits are required to encode such graphs. For graphs of size n and treedepth k, we present an oracle that takes (k+⌈log⁡k⌉+2)n+o(kn) bits and complement it with lower bounds that show our oracle is compact for specific ranges of k. Our navigation oracles for both treedepth and treedepth parameters can be augmented, with additional n+o(n) bits, to support connectivity queries in constant time.
Indihome's New Post Activation Control Application Based On Website With Telegram Features

Development of an application to control the activation of new indihome pairs at PT. Telkom Sidoarjo is a necessity that needs to be realized in order to support the performance of helpdesk employees in the process of activating indihome services. In developing the application, a method is needed to be able to design and build applications to meet expectations. Long polling method is one method used to get updates on bots. With the long-polling method, the server will periodically check to the bot whether there are incoming messages. If there is an incoming message, the server will execute based on the request message sent by the user. If there is no message then the server condition is idle (idle). Based on the results of planning, making and testing applications that have been carried out. It was concluded that the Indihome New Install Activation Control Application can input customer data via the SIKPRO bot, check order progress status and the Helpdesk can update order status and provide order feedback through the SIKPRO website. Bots can refuse order requests in case of double requests with the same registration number. Supervisors and Team Leaders can also recap the indihome service activation data based on daily and monthly periods
A Survey on Automatic Design Methods for Swarm Robotics Systems

Abstract Swarm robots are a branch of robotics that draws inspiration from biological swarms to mimic their collective behavior. Automatic design methods are part of swarm engineering, depend on artificial intelligence algorithms to produce the collective behavior of robots. In general, they follow two-approach evolutionary algorithms like practical swarm optimization and reinforcement learning. This paper studies these approaches, illustrating the effect of modifications and enhancements of algorithms for both directions, showing important parameters considered for the best performance of the swarm, and explaining the methods and advantages of using deep learning to reinforcement learning.
Quantum Layer-Inspired Deep Learning for Mechanical Parts Classification

Quantum level matter physics is a macroscopic level of study for particle behavior. Simultaneously, deep learning algorithms help in training the machines to behave as the human brain do. Presently, deep learning in combination with quantum theory is achieving higher peaks. In this paper, we had implemented a quantum layer-inspired deep learning model for efficient supervised classification through images of different mechanical parts. In the implementation, a quantum layer was added before the basic Convolutional Neural Network (CNN) which pre-process data using quantum particle concepts. The implemented classification application measured the scale of accuracy and loss during training and testing. In end, based on comparative parameters one of the best methods had proposed.
PUPoW: A Framework for Designing Blockchains with Practically-Useful-Proof-of-Work &amp; VanityCoin

Bitcoin is the first of its kind, a truly decentralized and anonymous cryptocurrency. To realize it, it has developed a blockchain technology using the concept of ‘Proof of Work’ (PoW). The miners, nodes responsible for writing transaction database, solve a cryptographic puzzle to claim the right to write to the database. Though bitcoin and many other relevant cryptocurrencies such as ether use revolutionary ideas, the main criticism involves the computing resource and energy consumption to solve the puzzles that have otherwise no use. There are attempts to use the PoW to do something useful, commonly referred to as Proof-of-Useful-Work (PoUW). In this paper, we attempt to (i) make PoUW more usable - describe how a central problem setter can crowdsource their work as PoUW and (ii) in the true spirit of blockchains, decentralize the role of problem setter, whom we call puzzlers. We propose a formal framework to do so, namely PUPow. PUPoW has an inbuilt provision of payments from puzzler to the miner who solves its puzzle. Additionally, miners have the option to not rely on continuous feed of the puzzles and instead use original PoW puzzles. We also propose a way to use PUPoW for solving TOR vanity URL generation and bitcoin vanity address generation problems. We call this PUPoW blockchain solving vanity address generation problems as VanityCoin. Both the problems need to generate public keys from private keys such that resultant addresses are of interest. Such key pairs are found only by a brute force search. However, there are privacy concerns that miners would know the private keys of the puzzlers. We resolve this by splitting the private keys, and the miners would know only one part of it. In summary, we are proposing how PoW can be made practically useful, and we believe such an approach is needed for PoW blockchains to survive.
Regularization

Many ML methods use the principle of ERM (see Chap. 4 ) to learn a hypothesis out of a hypothesis space by minimizing the average loss (training error) on a set of labeled data points (training set).
Combining Imputation Method and Feature Weighting Algorithms to Improve the Classification Accuracy of Incomplete Data

Abstract It is a common problem that datasets in pattern classification tasks contain missing values. In order to deal with missing values, the K -Nearest Neighbor Imputation method with Euclidean distance is often used to find K nearest neighbors, and then the mean of these samples is considered as the filling value. Although this method can deal with the problem of missing values well, it cannot effectively deal with the classification task with noise and redundant features because it ignores the importance of features. In order to do this, the paper proposes a novel imputation method. In our method, missing values are firstly filled by using the K -Nearest Neighbor Imputation method based on Euclidean distance, then a feature weighting vector can be obtained by using a feature weighting algorithm trained on the initially filled data, and finally the K -Nearest Neighbor Imputation with weighted Euclidean distance is used to fill missing values. The experimental results on six datasets show that the proposed method can improve the classification accuracy of incomplete data.
Naive Importance Weighting for Data Stream with Intermediate Latency

In the literature, it is often assumed that given a data stream, every instance is provided with its correct label after the prediction for performance evaluation (i.e., the null latency scenario). However, in several real applications, this assumption is invalid. For example, in the problem of predicting if it will rain tomorrow, the actual label will only be available the day after tomorrow, i.e., the actual label will be made available with an unavoidable delay. This scenario is called intermediate latency. Unfortunately, learning with intermediate latency is little investigated in the literature. Furthermore, the data stream can be non-stationary, i.e., their data distribution can change over time. This phenomenon is called concept drift. Therefore, a classification model in a data stream with concept drift will need a set of recent instances with their respective labels to adapt to the new concept. However, in an intermediate latency scenario, the delayed instance labels may not be timely for concept drift detection. Thus, the classification model will not be capable of adapting immediately, implying a reduction of performance. In this paper, we propose a framework that can improve the performance of a classification model in a data stream with intermediate latency through a domain adaptation approach called importance weighting. The framework is called NIW-DSIL (Naive Importance Weighting for Data Stream with Intermediate Latency). The experiments showed that our approach is promising in dealing with this scenario. For the real datasets, the NIW - DSIL got better results in 6 of 8 intermediate latency scenarios.
Design of the service robot applied to undergraduate laboratory of pharmacy major

During the COVID-19 outbreak, service robots have provided good service for teachers and students in universities. In order to improve the safety and convenience of pharmaceutical undergraduate laboratory, a service robot for pharmaceutical undergraduate teaching laboratory was designed from the aspects of path planning, positioning and navigation, human-computer interaction etc., which can solve practical problems such as epidemic prevention and control, laboratory safety etc.
Towards Interpretability and Personalization: A Predictive Framework for Clinical Time-series Analysis

Clinical time-series is receiving long-term attention in data mining and machine learning communities and has boosted a variety of data-driven applications. Identifying similar patients or subgroups from clinical time-series is an essential step to design tailored treatments in clinical practice. However, most of the existing methods are either purely unsupervised that tend to neglect the patient outcome information or cannot generate personalized patient representation through supervised learning, thus may fail to identify ‘truly similar patients’ (i.e., patients who similar in both outcomes and individual outcome-related clinical variables). To tackle these limitations, we propose a novel predictive clinical time-series analysis framework. Specifically, our framework uses task-specific information to rule out the task-irrelevant factors in each patient data individually and generates the contribution scores that reveal the factors’ importance for the patient outcome. Then a patient representation construction method is proposed to generate task-related and personalized representations by combining remained factors and their contribution scores. At last, similarity measurement or cluster analysis can be conducted. We evaluate our framework on three real-world clinical time-series datasets, empirically demonstrate that our framework achieves improvements in prediction performance, similarity measurement, and clustering, thus potentially benefiting patient-similarity-based precision medicine applications.
Study on the Influence of Interaction Style on Learners

High-quality mobile learning resources are an important support for mobile learning. Interactivity is an important feature of mobile learning, which directly affects the effect of the interaction between learners and learning resources, and is a key indicator for evaluating the quality of learning resources. This article uses brain-computer interface technology to explore the changes in learning attention and learning effectiveness of mobile learners in different presentation forms and interactive methods of learning resources, including plain text, video and interactive methods represented by dubbing. Research shows that: First, different forms of knowledge presentation and interaction have a significant impact on attention. Second, in the interactive mode represented by dubbing, the learner's attention is higher and the learning effect is the best. Therefore, when designing mobile learning resources in the future, the inter activity of resources should be considered to maintain learners' higher learning attention and achieve better mobile learning effects.
Capping Mobile Data Access Creates Value for Bottom-of-the-Pyramid Consumers – Experimental Evidence from a Mumbai Settlement

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CKGSE: A Prototype Search Engine for Chinese Knowledge Graphs

Abstract Nowadays, with increasing open knowledge graphs (KGs) being published on the Web, users depend on open data portals and search engines to find KGs. However, existing systems provide search services and present results with only metadata while ignoring the contents of KGs, i.e., triples. It brings difficulty for users' comprehension and relevance judgement. To overcome the limitation of metadata, in this paper we propose a content-based search engine for open KGs named CKGSE. Our system provides keyword search, KG snippet generation, KG profiling and browsing, all based on KGs' detailed, informative contents rather than their brief, limited metadata. To evaluate its usability, we implement a prototype with Chinese KGs crawled from OpenKG.CN and report some preliminary results and findings.