Reviewerly/exHarmony huggingface dataset:
The capacity of human memory: Is there any limit to human memory?

There exist some estimates of the capacity of human memory. Recent studies have proven the fact that Long Term Memory is subject to constant reconfigurations mostly at lower levels of neural clusters. There is no consensus on one definition for the capacity of memory. As far as retrieval of items present in memory is not the concern, it is reasonable to refrain from putting limits on capacity of human memory; otherwise, one must accept a number game which renders no fixed definite final estimation. Recently such capacity is defined as the amount of interference created by the item which must remain active in the memory.
An Enhanced Privacy protection scheme for Profile-based personalized search

With the information overload on the Internet, intelligent personalized systems come to play a critical role by providing tailor-made services to the user based on his interests.An example that recently becomes popular is the personalized search systems, offering users personalized answers.One of the major issues these systems face is the lack of user's trust due to the lack of privacy protection.Giving the user a personalized browsing experience usually comes at the cost of his privacy.Thus, most people are afraid of using such applications.To address this issue in this work, we propose a trade-off scheme between the personalization quality and the privacy risk, to keep the latter under control.We have studied the assets and drawbacks of the existing profile-based personalized search systems in general, from a privacy protection perspective.Furthermore, we present a new model to protect the user's privacy on different levels, using homomorphic encryption to enhance data protection.
Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism

The aim of aspect-level sentiment analysis is to identify the sentiment polarity of a given target term in sentences. Existing neural network models provide a useful account of how to judge the polarity. However, context relative position information for the target terms is adversely ignored under the limitation of training datasets. Considering position features between words into the models can improve the accuracy of sentiment classification. Hence, this study proposes an improved classification model by combining multilevel interactive bidirectional Gated Recurrent Unit (GRU), attention mechanisms, and position features (MI-biGRU). Firstly, the position features of words in a sentence are initialized to enrich word embedding. Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network. Thirdly, an attention mechanism is introduced so that the model can pay greater attention to those words that are important for sentiment analysis. Finally, four classic sentiment classification datasets are used to deal with aspect-level tasks. Experimental results indicate that there is a correlation between the multilevel interactive attention network and the position features. MI-biGRU can obviously improve the performance of classification.
EShield: protect smart contracts against reverse engineering

Smart contracts are the back-end programs of blockchain-based applications and the execution results are deterministic and publicly visible. Developers are unwilling to release source code of some smart contracts to generate randomness or for security reasons, however, attackers still can use reverse engineering tools to decompile and analyze the code. In this paper, we propose EShield, an automated security enhancement tool for protecting smart contracts against reverse engineering. EShield replaces original instructions of operating jump addresses with anti-patterns to interfere with control flow recovery from bytecode. We have implemented four methods in EShield and conducted an experiment on over 20k smart contracts. The evaluation results show that all the protected smart contracts are resistant to three different reverse engineering tools with little extra gas cost.
Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

In the year 2018, 18.9% of the population in Indonesia mentioned that the main reason for their use of the Internet is social media. One of the social media with an active user of 6.43 million users is Twitter. Based on the surge of information published via Twitter, it is possible that such information may contain the user's opinions on an object, such objects may be events around the community such as a product or service. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) such as Indihome. Through Twitter, users can discuss each other's complaints or satisfaction with Indihome's services. It takes a method of sentiment analysis to understand whether the textual data includes negative opinions or positive opinions. Thus, the authors use the Support Vector Machine (SVM) method in sentiment analysis on the opinions of the Indihome service user on Twitter, with the aim of obtaining a sentiment classification model using SVM, and to know how much accuracy the SVM method generates, which is applied to sentiment analysis, and to see how satisfied the Indihome service users are based on Twitter. After testing with SVM method The result is accuracy 87%, precision 86%, recall 95%, error rate 13%, and F1-score 90%
Leakage-Resilient Certificate-based Key Encapsulation Scheme Resistant to Continual Leakage

In the past, the security of most public-key encryption or key encapsulation schemes is shown in an ideal model, where private keys, secret keys and random values are assumed to be absolutely secure to adversaries. However, this ideal model is not practical due to side-channel attacks in the sense that adversaries could gain partial information of these secret values involved in decryption operations by perceiving energy consumption or execution timing. In such a case, these schemes under the ideal model could suffer from side-channel attacks. Recently, leakage-resilient cryptography resistant to side-channel attacks is an emerging research topic. Certificate-based encryption (CBE) or certificate-based key encapsulation (CB-KE) schemes are a class of important public-key encryption. However, little work addresses the design of leakage-resilient CBE (LR-CBE) or leakage-resilient CB-KE (LR-CB-KE) schemes. In this paper, we present the first LR-CB-KE scheme with overall unbounded leakage property which permits adversaries to continuously gain partial information of the system secret key of a trusted certificate authority (CA), the private keys and certificates of users, and random values. In the generic bilinear group model, formal security analysis is made to prove that the proposed LR-CB-KE scheme is secure against chosen ciphertext attacks.
MCCVA: A New Approach Using SVM and Kmeans for Load Balancing on Cloud

Nowadays, the demand of using resources, using services via the intranet system or on the Internet is rapidly growing.The respective problem coming is how to use these resources effectively in terms of time and quality.Therefore, the network QoS and its economy are people concerns, cloud computing was born in an inevitable trend.However, managing resources and scheduling tasks in virtualized data centres on the cloud are challenging tasks.Currently, there are a lot of Load Balancing algorithms applied in clouds and proposed by many authors, scholars, and experts.These existing methods are more about natural and heuristic, but the application of AI, or modern datamining technologies, in load balancing is not too popular due to the different characteristics of cloud.In this paper, we propose an algorithm to reduce the processing time (makespan) on cloud computing, helping the load balancing work more efficiency.Here, we use the SVM algorithm to classify the coming Requests, K -Mean to cluster the VMs in cloud, then the LB will allocate the requests into the VMs in the most reasonable way.In this way, request with the least processing time will be allocated to the VMs with the lowest usage.We name this new proposal as MCCVA -Makespan Classification & Clustering VM Algorithm.We have experimented and evaluated this algorithm in CloudSim, a cloud simulation environment, we obtained better results than some other wellknown algorithms.With this MCCVA, we can see the big potential of AI and datamining in Load Balancing, we can further develop LB with AI to achieve better and better results of QoS.
Jupyter Notebooks on GitHub: Characteristics and Code Clones

Jupyter notebooks has emerged as a standard tool for data science programming. Programs in Jupyter notebooks are different from typical programs as they are constructed by a collection of code snippets interleaved with text and visualisation. This allows interactive exploration and snippets may be executed in different order which may give rise to different results due to side-effects between snippets. Previous studies have shown the presence of considerable code duplication -- code clones -- in sources of traditional programs, in both so-called systems programming languages and so-called scripting languages. In this paper we present the first large-scale study of code cloning in Jupyter notebooks. We analyse a corpus of 2.7 million Jupyter notebooks hosted on GitHJub, representing 37 million individual snippets and 227 million lines of code. We study clones at the level of individual snippets, and study the extent to which snippets are recurring across multiple notebooks. We study both identical clones and approximate clones and conduct a small-scale ocular inspection of the most common clones. We find that code cloning is common in Jupyter notebooks -- more than 70% of all code snippets are exact copies of other snippets (with possible differences in white spaces), and around 50% of all notebooks do not have any unique snippet, but consists solely of snippets that are also found elsewhere. In notebooks written in Python, at least 80% of all snippets are approximate clones and the prevalence of code cloning is higher in Python than in other languages. We further find that clones between different repositories are far more common than clones within the same repository. However, the most common individual repository from which a Jupyter notebook contains clones is the repository in which itself resides.
BLOCKCHAIN: A POSSIBLE ALTERNATIVE TO ACHIEVING HEALTH INFORMATION EXCHANGE (HIE)

A patient may have access to one or more healthcare organizations, meaning that their health information is spread across separate EHR systems, resulting in data fragmentation.Thus, the medical history of a patient may not be complete, consistent or comprehensive if the records cannot be combined into a single consistent overview that may not be efficient for the future diagnosis of the patient.Health Information Exchange (HIE) relates to the electronic transfer of health care information between institutions and practitioners within a specific group, region or hospital.However, HIE is surrounded with various challenges such as data insecurity, threat to patient privacy, dependency on third parties and so on.Blockchain through its characteristics such as safety, privacy, decentralization, and immutability has the ability to transform health care, putting the patient at the core of the health care ecosystem, and enhancing health data security, privacy, and interoperability thereby offering HIE with excellent potential.In this paper Health Information Exchange (HIE) and Blockchain Technology are discussed and the possibility of using blockchain technology to achieve HIE is presented.
VR Panorama Mosaic Algorithm Based on Particle Swarm Optimization and Mutual Information

With the development of science and technology, Virtual Reality (VR) technology is more and more mature, and has been widely used in real life.In particular, the panorama technology in VR enables users to interact with the objects in the virtual environment in a natural way through the establishment of an artificial three-dimensional virtual environment.so as to get a good experience.Although VR technology has got some maturity, in response to the complex environment and increasing market demand, we put forward higher requirements for VR panorama technology.Panoramic mosaic technology has been studied for a long time at domestically and abroad.. Since the concept of image fusion was put forward, image fusion technology has been developed rapidly.However, the development of image mosaic technology still faces some problems to be studied and these problems were solved.Based on the existing research, present study proposes a VR panoramic image mosaic algorithm based on particle swarm optimization..The proposed algorithm has the advantages of small computation time, fast splicing speed, high splicing quality, and high splicing confidence.This algorithm is based on particle swarm optimization, combined with the advantages of traditional mutual information algorithm, according to the characteristics of virtual reality imaging, a new model is established.After a large number of comparative experiments, it was found thatproposed method significantly improved the ability of feature region block extraction in VR panoramic image mosaic when compared with the traditional method.At the same time of improving the accuracy, the mapping time is shortened by more than 1 / 3, and the mapping efficiency is improved.Present research work is an innovation of virtual reality panoramic technology.
Interaction Between Evolution and Learning in NK Fitness Landscapes

Artificial Life has a long tradition of studying the interaction between learning and evolution. And, thanks to the increase in the use of individual learning techniques in Artificial Intelligence,...
Path planning of UAVs based on improved Clustering Algorithm and Ant Colony System Algorithm

This paper studies the path planning problem of multi-UAVs with multiple missions under complicated constraints, and proposes a new approach to provide optimal paths for each UAV such that the task completion time would be minimized. First, with the model of UAVs, we analyze the object function and travelling constraints of the path planning problem. Then, by considering the limit of the maximum yaw angle of UAVs, we propose an efficient approach to solve the path planning problem by combining the improved Clustering by Fast Search and Find of Density Peaks algorithm (CFSDP) and ant colony system (ACS) algorithm together. The propose approach not only helps UAVs in covering the cruise valid areas, but also finds the shortest tasks completion time for each UAV to perform the searching task. Finally, we use simulation experiments randomly generated targets to verify the effectiveness of the proposed approach.
Effectiveness of Gamification for Flood Emergency Planning in the Disaster Risk Reduction Area

Natural disasters are something that is not easy to predict, and one of them is a flood. It is found that the frequency of floods that have occurred in Indonesia is 505 times the total of 1824 natural disasters. Disaster risk reduction is the concept of how people minimise accidents and victims affected by disasters. One example of disaster risk reduction is to provide knowledge to the public about disaster evacuation and prevention before an emergency occurs. The objective of this study is to utilise gamification to increase user effectiveness. The method of gamification is the science used to learn, along with the increasing interest in learning. Gamification is applicable in several fields; one of them is crisis management and emergency training. The gamification is applied by utilising mechanical games, dynamic games, and gamification elements. The results showed that gamification could increase the effectiveness in terms of time by 89.77% of information and education about disaster risk and disaster preparedness to the public. This re-search was conducted at the disaster preparedness participant school with 65 participants, along with 21 communities affected by the flood disaster. Based evaluations on aspects of usefulness (UU), Ease of use (EU), Ease of learning (UL), and satisfaction (US) aspects of usefulness have the highest value because with the application the participant feels helped and can learn the dangers of disaster and preparation for disaster.
Multi-obstacle Path Planning of UAV Based on Improved Ant Colony System Algorithm

Recently unmanned aerial vehicles (UAVs) have been widely adopted by military and civilian applications due to their strong autonomies and adaptabilities. Especially in some tasks, a complex path planning problem is that a UAV traverses and searches multiple targets in an environment with obstacles, which is a traveling salesman problem with NP-hard computational complexity. The traditional algorithms which do not consider environment with obstacles are not feasible in the actual flying environment. In this paper, we study the path planning problem of a UAV searching for multiple targets in the environment with irregular obstacles. First, we use an improved geometric algorithm to find an accessible path between any two targets. Then, we design an ant colony system(ACS)-based optimization strategy to adjust the accessible order of a UAV such that the task completion time obtained by our approach would be reduced as much as possible. Finally, experiments with randomly generated target points in an environment with obstacles are conducted to show the effectiveness of the algorithm.
The Blockchain Revolution: Paradigm Shifts in Traditional Voting Practices

Perhaps the most advanced application of the distributed ledger technology (DLT), the Blockchain is a decentralized system that is known to store immutable metadata with the use of robust cryptographic hashes and consensus mechanisms.The very foundation of the Blockchain is the establishment of trust-less transactions in peer-to-peer networks.Having been deemed to set off a whirlwind in Industry 4.0 as we know it and starting out with the groundbreaking Bitcoin, this relatively new technology is on the way to prove that it can find applications in almost every imaginable sector.While most people focus only on cryptocurrencies; this disruptive technology, in fact, offers utilities to many administrative operations, fintech procedures, and everyday services which could earlier only be done offline and/or in person, that can now be safely moved to the Internet as Software as a Service (SaaS) models.What makes Blockchain a powerful tool for digitalizing everyday facilities is the introduction of smart contracts, as brought forward foremost by the Ethereum platform.Considering today"s technology, Blockchain may create one of the most prominent alternatives to traditional voting in terms of security, consistency and speed.The Blockchain technology, fortified by Smart Contracts, enables enhanced data verifiability and lowered costs while maintaining the openness and transparency of the voting process.The anonymity of voters, the security of ballot transmission and the veracity of votes during the billing phase are the most fundamental requirements for voting.In this paper, a potential use case of Blockchain, an E-Voting protocol, is proposed, that utilizes the Blockchain as a transparent ballot box to cast votes.
“COVID-19” MEME IN SOCIAL MEDIA: STUDY OF ROLAND BARTHES SEMIOLOGY

ABSTRACT&#x0D; The objective of this research is to reveal the meaning of Covid-19 memes in social media using study of Roland Barthes semiology. In addition, this research also explains the social representation in Covid-19 memes. The method of the research is qualitative descriptive using technical analysis of Roland Barthes semiology. The technique of collecting data is based on two resources. The primary data are collections of memes about Covid 19 in some social media, such as Instagram, Facebook, and Web Blog. The secondary data are references supporting the topic of the research. In validity technique, the primary data are collected based on the topic of the discussion. After that, the data are processed descriptively by explaining, and comparing the information gained from many resources. Then, the data are reduced and presented. The result of the study shows that the creators tried to reveal social condition through Covid-19 memes. The social conditions were presented in some types of memes, namely critical memes, parodist memes, and motivational memes. In relation to meaning presentation, some social problems were revealed Covid-19 memes.&#x0D; Keywords: Meme, Covid-19, Social Media, Roland Barthes Semiology
Protecting the entanglement of two-qubit over quantum channels with memory via weak measurement and quantum measurement reversal*

Based on the quantum technique of the weak measurement and quantum measurement reversal (WMR), we propose a scheme to protect entanglement for an entangled two-qubit pure state from four typical quantum noise channels with memory, i.e. , the amplitude damping channel, the phase damping channel, the bit flip channel, and the depolarizing channel. For a given initial state | ψ 〉 = a | 00 〉 + d | 11 〉, it is found that the WMR operation indeed helps to protect entanglement from the above four quantum channels with memory, and the protection effect of WMR scheme is better when the coefficient a is small. For the other initial state | ϕ 〉 = b | 01 〉 + c | 10 〉, the effect of the protection scheme is the same regardless of the coefficient b and the WMR operation can protect entanglement in the amplitude damping channel with memory. Moreover, the protection of entanglement in quantum noise channels without memory in contrast to the results of the channels with memory is more effective. For | ψ 〉 or | ϕ 〉, we also find that the memory parameters play a significant role in the suppression of entanglement sudden death and the initial entanglement can be drastically amplified. Another more important result is that the relationship between the concurrence, the memory parameter, the weak measurement strength, and quantum measurement reversal strength is found through calculation and discussion. It provides a strong basis for the system to maintain maximum entanglement in the nosie channel.
INDONESIAN EFL STUDENTS’ PERSPECTIVES TOWARDS LEARNING MANAGEMENT SYSTEM SOFTWARE

The use of strategy takes an important role in language learning. The use of Learning Management System software (LMSs) in the EFL classroom enables the students to get their target language. LMSs has a feature that can enhance communication between student to student and between lecturer and students. This study generally explores students’ perspectives toward the use of LMSs as a language learning strategy. This qualitative research was conducted in Universities in Lampung Province, Indonesia. The subjects were 33 undergraduate students in the English Education study program, Faculty of Arts and Education. The data used were questionnaires and interviews. The findings show that the participants give positive feedback toward their experiences in using LMSs. By using LMSs, admitted that LMSs was given improvement to their speaking, reading, listening, and writing skill. However, some students revealed that there were some technical problems founds when they use LMSs, such as bad gateway connection. Nevertheless, learning a language using LMSs as the strategy helps the students to have self-learning experiences.