Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Extractive Summarization with Attention

Automatic summarization aims to extract important information from large amounts of textual data in order to create a shorter version of the original texts while preserving its information. Training traditional extractive summarization models relies heavily on human-engineered labels such as sentence-level annotations of summary-worthiness. However, in many use cases, such human-engineered labels do not exist and manually annotating thousands of documents for the purpose of training models may not be feasible. On the other hand, indirect signals for summarization are often available, such as agent actions for customer service dialogues, headlines for news articles, diagnosis for Electronic Health Records, etc. In this paper, we develop a general framework that generates extractive summarization as a byproduct of supervised learning tasks for indirect signals via the help of attention mechanism. We test our models on customer service dialogues and experimental results demonstrated that our models can reliably select informative sentences and words for automatic summarization.
Network intrusion detection method based on deep learning

Abstract With the rapid development of the network, network transmission encryption technologies such as SSL and SSH have emerged. Network traffic has grown exponentially, and transmission encryption has become an important means of protecting data security and privacy. However, encrypted data also brings hidden dangers that are not easily detectable to network security. Identifying the encrypted network traffic can effectively solve this problem. However, the current recognition probability is not high enough and the time delay caused by the recognition together makes it impossible to accurately detect and warn the network traffic. An encrypted network traffic recognition method based on deep learning is proposed. Experimental verification shows that the method is applied in the network. The accuracy of encrypted network traffic identification is 97.02%, which can meet actual needs.
Expert System in Identifying Tooth Damage in Children Using Forward Chaining and Certainty Factor Methods

Tooth decay in children is one of the most common problems found. This damage has a long-term effect on children's dental health. The limitation of expert doctor's service hours and the disproportionate number of doctors and patients makes the service to patients not optimal. Then we need an expert system to help the role of expert doctors in diagnosing tooth decay. One of them carried out treatment or prevention measures from the start. In this study forward chaining and certainty factor methods are used, in which this expert system can assist an expert in diagnosing tooth decay based on the symptoms experienced by the patient. The forward chaining method will be collaborated with the certainty factor method to calculate the accuracy of the type of tooth decay experienced. The use of these two methods aims to provide better results in overcoming or preventing tooth decay in children. From the test results obtained knowledge for patients in dealing with or preventing tooth decay with an accuracy rate of 91.20%. The application of an expert system can be used for early action in overcoming or preventing tooth decay in children.
Szegedy Walk Unitaries for Quantum Maps

Szegedy developed a generic method for quantizing classical algorithms based on random walks (Szegedy, in: 45th annual IEEE Symposium on Foundations of Computer Science, pp 32–41, 2004. https://doi.org/10.1109/FOCS.2004.53 ). A major contribution of his work was the construction of a walk unitary for any reversible random walk. Such unitary posses two crucial properties: its eigenvector with eigenphase 0 is a quantum sample of the limiting distribution of the random walk and its eigenphase gap is quadratically larger than the spectral gap of the random walk. It was an open question if it is possible to generalize Szegedy’s quantization method for stochastic maps to quantum maps. We answer this in the affirmative by presenting an explicit construction of a Szegedy walk unitary for detailed balanced Lindbladians—generators of quantum Markov semigroups—and detailed balanced quantum channels. We prove that our Szegedy walk unitary has a purification of the fixed point of the Lindbladian as eigenvector with eigenphase 0 and that its eigenphase gap is quadratically larger than the spectral gap of the Lindbladian. To construct the walk unitary we leverage a canonical form for detailed balanced Lindbladians showing that they are structurally related to Davies generators. We also explain how the quantization method for Lindbladians can be applied to quantum channels. We give an efficient quantum algorithm for quantizing Davies generators that describe many important dynamics of open quantum systems, for instance, the relaxation of a quantum system coupled to a bath. Our algorithm extends known techniques for simulating dynamics of quantum systems on a quantum computer.
Blockchain for edge-enabled smart cities applications

Modern industrial systems are enriched by cyber–physical devices and interconnections with business processes that enable flexible production, remote monitoring, control and maintenance. These systems are typically subject to multiple authorities which must cooperate with each other, as in the case of segmented industrial environments and supply chains. In similar contexts, voluntary or unintentional damages may be caused by cyber attacks or by misbehaving authorized parties. We propose an original architecture that regulates accesses to industrial systems’ resources through authorization delegation procedures. It guarantees several benefits that include the possibility of auditing authorizations released by delegated third parties, of detecting misconducts and possible attacks, and of assuring attribution of misconducts. The proposed solution is compatible with constraints characterizing industrial environments and with security and performance requirements of industrial architectures. The performance and latencies of the auditing mechanisms are evaluated through a prototype.
KARAKTERISTIK, POTENSI GENERASI MILENIAL DAN PERSPEKTIF PENGEMBANGAN PERTANIAN PRESISI DI INDONESIA

&lt;p&gt;Precision agriculture requires appropriate characters of human resources to implement it. It is an integrated agricultural system based on information and production to increase business efficiency, productivity and profitability. The concept of precision agriculture, as one of the latest agricultural technology packages, was born along with the emergence of the millennial generation, namely those born between 1980 and 2000.This paper discusses the character of precision agriculture and necessity to apply it and its link to the millennial generation in terms of their character suitability and capacity. Application of precision agriculture requires the millennial generation’s ability to create, engineer and operate modern agricultural systems based on this new technology. Applying precision agriculture in Indonesia deals with various characteristics of the millennial generation due to different regional and socio-economic conditions. The government should provide infrastructure and conduct millennial farmers training to achieve social, economic, and environmental benefits of precision agriculture implementation.&lt;/p&gt;
On wavelet-based algorithms for solving differential equations

We describe an order N method for computing the Green’s function of the two-point boundary value problem for elliptic differential operators in the wavelet “system of coordinates.” For simplicity, we consider the ordinary O(h2) finite-difference scheme, and use wavelets only to perform the “linear algebra.” Our main tool is the diagonal preconditioning available for the periodized differential operators in the wavelet bases.
The Quality of Public Information on Twitter: Yogyakarta And DKI Jakarta Regional Government Comparison

Twitter is considered one of the most appropriate media or platforms to realize transparency, participation, and accountability in governance. Social media adoption is also carried out by the Provincial Government of DKI Jakarta and the Special Region of Yogyakarta (DIY).  This study aimed to explain the quality of public information of the Yogyakarta Provincial Government and the DKI Jakarta Provincial Government through Twitter in 2019. The method used was descriptive qualitative by depicting a comparison of public information quality of both regional governments. The content analysis was processed with Nvivo application 12 Plus by collecting research data through Twitter accounts, online media, and literature reviews.  The results showed that the DKI Jakarta Provincial Government and the Yogyakarta Provincial Government actively used Twitter to convey information. The use of Twitter @humas_jogja by the Yogyakarta Provincial Government has fulfilled five public information quality variables: transparency (52%), participation (53%), trust (32%), human resource capacity (50%), and accountability (62%). Meanwhile, the DKI Jakarta Provincial Government Twitter account had a transparency value (47%), participation (48%), trust (68%), human resource capacity (49%), and accountability (37%). Each provincial government had a relationship with these variables. Therefore, it could be seen that the public information on Twitter of the Yogyakarta Provincial Government and the DKI Jakarta Provincial Government had the quality. This article contributes to the government's evaluation of Twitter adoption and its relationship to the quality of public information provided to the public.
Theoretical and Methodical Aspects of the Organization of Students’ Independent Study Activities Together with the Use of ICT and Tools

In the article the possibilities and classification of ICTs and tools that can be used in organizing students’ independent study activities of higher education institutions has been explored. It is determined the students’ independent study activities is individual, group, collective activity and is implemented within the process of education under the condition of no pedagogy’s direct involvement. It complies with the requirements of the curriculum and syllabus and is aimed at students’ acquisition of some social experiences in line with the learning objectives of vocational training. The analysis of the latest information and technological approaches to the organization of students’ independent study activities made it possible to determine the means of realization of the leading forms of organization for this activity (independent and research work, lectures, consultations and non-formal education), to characterize and classify the ICTs and tools that support presentation of teaching materials, electronic communication, mastering of learning material, monitoring of students’ learning and cognitive activity, such as ones that serve for the sake of development and support of automated training courses, systems of remote virtual education with elements of artificial intelligence, which implement the principle of adaptive management of learning and the organization of students’ independent study activities.
R-enum: Enumeration of Characteristic Substrings in BWT-runs Bounded Space.

Enumerating characteristic substrings (e.g., maximal repeats, minimal unique substrings, and minimal absent words) in a given string has been an important research topic because there are a wide variety of applications in various areas such as string processing and computational biology. Although several enumeration algorithms for characteristic substrings have been proposed, they are not space-efficient in that their space-usage is proportional to the length of an input string. Recently, the run-length encoded Burrows-Wheeler transform (RLBWT) has attracted increased attention in string processing, and various algorithms for the RLBWT have been developed. Developing enumeration algorithms for characteristic substrings with the RLBWT, however, remains a challenge. In this paper, we present r-enum (RLBWT-based enumeration), the first enumeration algorithm for characteristic substrings based on RLBWT. R-enum runs in $O(n \log \log (n/r))$ time and with $O(r \log n)$ bits of working space for string length $n$ and number $r$ of runs in RLBWT, where $r$ is expected to be significantly smaller than $n$ for highly repetitive strings (i.e., strings with many repetitions). Experiments using a benchmark dataset of highly repetitive strings show that the results of r-enum are more space-efficient than the previous results. In addition, we demonstrate the applicability of r-enum to a huge string by performing experiments on a 300-gigabyte string of 100 human genomes.
LZ1904 at SemEval-2021 Task 5: Bi-LSTM-CRF for Toxic Span Detection using Pretrained Word Embedding

Recurrent Neural Networks (RNN) have been widely used in various Natural Language Processing (NLP) tasks such as text classification, sequence tagging, and machine translation.Long Short Term Memory (LSTM), a special unit of RNN, has the advantage of memorizing past and even future information in a sentence (especially for bidirectional LSTM).In the shared task of detecting toxic spans in texts, we first apply pretrained word embedding (GloVe) to generate the word vectors after tokenization.Then we construct Bidirectional Long Short Term Memory-Conditional Random Field (Bi-LSTM-CRF) model by Baidu research to predict whether each word in the sentence is toxic or not.We tune hyperparameters of dropout rate, number of LSTM units, embedding size with 10 epochs and choose the epoch with best validation recall.Our model achieves an F1 score of 66.99% on test dataset.
Gapped Indexing for Consecutive Occurrences

The classic string indexing problem is to preprocess a string S into a compact data structure that supports efficient pattern matching queries. Typical queries include existential queries (decide if the pattern occurs in S), reporting queries (return all positions where the pattern occurs), and counting queries (return the number of occurrences of the pattern). In this paper we consider a variant of string indexing, where the goal is to compactly represent the string such that given two patterns $$P_1$$ and $$P_2$$ and a gap range $${[}\alpha , \beta ]$$ we can quickly find the consecutive occurrences of $$P_1$$ and $$P_2$$ with distance in $${[}\alpha , \beta ]$$ , i.e., pairs of subsequent occurrences with distance within the range. We present data structures that use linear space and query time $${\widetilde{O}}(|P_1|+|P_2|+n^{2/3})$$ for existence and counting and $${\widetilde{O}}(|P_1|+|P_2|+n^{2/3}\hbox {occ}^{1/3})$$ for reporting. We complement this with a conditional lower bound based on the set intersection problem showing that any solution using $${\widetilde{O}}(n)$$ space must use $${\widetilde{\Omega }}(|P_1| + |P_2| + \sqrt{n})$$ query time. To obtain our results we develop new techniques and ideas of independent interest including a new suffix tree decomposition and hardness of a variant of the set intersection problem.
Towards sample-efficient policy learning with DAC-ML

The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation by adding memory systems and architectural biases to improve learning speed, such as in Episodic Reinforcement Learning. However, despite achieving incremental improvements, their performance is still not comparable to how humans learn behavioral policies. In this paper, we capitalize on the design principles of the Distributed Adaptive Control (DAC) theory of mind and brain to build a novel cognitive architecture (DAC-ML) that, by incorporating a hippocampus-inspired sequential memory system, can rapidly converge to effective action policies that maximize reward acquisition in a challenging foraging task.
Neurometrics Applied to Banknote and Security Features Design

The aim of this paper is to present a methodology on the application of neuroanalysis to the design of banknotes and security features. Traditionally, evaluation of the perception of banknotes is based on explicit personal responses obtained through questionnaires and interviews. The implicit measures refer to methods and techniques capable of capturing people's implicit mental processes. Neuroscience has shown that, in most brain processes regulating emotions, attitudes, behaviours and decisions, human consciousness does not intervene. That is to say, these implicit processes are brain functions that occur automatically and without conscious control.The methodology on neuroanalysis can be applied to the design of banknotes and security features, and used as an effective analysis tool to assess people's cognitive processes, namely: visual interest, attention to certain areas of the banknote, emotions, motivation and the mental load to understand the design and level of stimulation. The proposed neuroanalysis methodology offers a criterion for making decisions about which banknote designs and security features have a more suitable configuration for the public. It is based on the monitoring of conscious processes, using traditional explicit measures, and unconscious processes, using neurometric techniques.The neuroanalysis methodology processes quantifiable neurometric variables obtained from the public when processing events, such as eye movement, sight fixation, facial expression, heart rate variation, skin conductance, etc. A neuroanalysis study is performed with a selected group of people representative of the population for which the design of a banknote or security features is made. In the neurometric study, suitably prepared physical samples are shown to the participants to collect their different neurometric responses, which are then processed to draw conclusions.
Analytical hierarchical prospectivity mapping using integration of exploratory data in the Anarak region, Central Iran

This paper aims to show the kind of corporations, Analytical Hierachy Process (AHP) for determining significant weights of evaluation criteria with Technique for Order Preference by Similarity to Ideal (TOPSIS) and Simple Additive Weighting (SAW) methods for ranking of geo-dataset for Mineral Prospectivity Mapping (MPM) of Cu in Anarak region, Central Iran. This operation was carried out by integration of remote sensing, geophysical, geochemical and geological data. The Anarak region has a high potential for copper mineralization because the studied district is located in the NW of the Central East Iranian Microplate (CEIM) that is an important ore mineralized zone in Central Iran. The integration approaches are complex, Multi-Criteria Decision Making (MCDM), and knowledge-driven methods that they named AHP-TOPSIS and AHP-SAW. In addition, there are three variant models of TOPSIS method including conventional, adjusted and modified. The AHP has been carried out on geological/alteration data, structural data, airborne geophysical data and stream sediment geochemical layer in the studied area. These data are classified by fractal modeling for the generation of geo-dataset layers to a relationship of copper occurrences in the Anarak region. Consequently, the produced MPMs by the AHP-TOPSIS and AHP-SAW have adequately matching and sufficient correlation with copper mines and main copper deposits/occurrences in the Anarak region.
Blockchain: Concept and Emergence

Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) are incredible instances of the technology paradigm and have brought about revolutionary changes in the communication, internet, and digital sectors coalesced into one and induced a landslide shift in the lifestyle of mankind. But the privacy concerns of the individuals can be trustfully and transparently addressed only by the use of ingenious contemporary technology, the Blockchain. Blockchain is a kind of distributed ledger technology that uses a peer-to-peer, distributed network to make a continuous, growing chain of time-stamped records to form a digital ledger. It is decentralized, which means that it can not be controlled by a central authority. It has numerous advantages like transparency, immutability, decentralization and can be incorporated in almost any sector of the society to increase the security of the transactions being carried out. This chapter predominantly focuses on the concepts of Blockchain and aims to do a comprehensive survey on its advantages and applications and also highlights the future prospects because even though it is so popular today, this technology is still in its infancy.
Machine Learning: Review

Machine Learning is that the core subarea of AI . It makes computers get into a self-learning mode without explicit programming. When fed new data, these computers learn, grow, change, and develop by themselves. The concept of machine learning has been around for a short time now. However, the power to automatically and quickly apply mathematical calculations to big data is now gaining a touch of momentum. Machine learning has been utilized in several places just like the self-driving Google car, the web recommendation engines – friend recommendations on Facebook, offer suggestions from Amazon, and in cyber fraud detection. In this paper, we will learn basic terms in machine learning.
MOBILE APPLICATIONS FOR EDUCATION USED DURING THE PANDEMIC OF COVID 19

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 9585-9594ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1931Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Broad Learning Based on Subgraph Networks for Graph Classification

Many real-world systems can be naturally represented by networks, such as biological networks, collaboration networks, software networks, social networks, etc., where subgraphs or motifs can be considered as network building blocks with particular functions to capture mesoscopic structures. Most existing studies ignored the interaction between these subgraphs, which could be of particular importance to represent the global structure at the subgraph level. In this chapter, the concept of subgraph network (SGN) is introduced and applied to network models, with algorithms designed for constructing the 1st-order and 2nd-order SGNs, which can be easily extended to build higher-order ones. Furthermore, these SGNs are used to expand the structural feature space of the underlying network, beneficial for network classification. The experiments demonstrate that the structural features of SGNs can complement that of the original network for better network classification. However, SGN model lacks diversity and is of high time-complexity, making it difficult to be widely applied in practice. Then, sampling strategies are introduced into SGNs and a novel sampling subgraph network (S2GN) model is designed, which is scale-controllable and of higher diversity. Further, a broad learning system (BLS) is introduced into graph classification, which fully utilizes the information provided by the S2GNs of different sampling strategies and thus can capture various aspects of the network structure more efficiently. Extensive experiments demonstrate that, by comparing with the SGN model, the S2GN model has much lower time-complexity, which together with BLS can enhance various graph classification methods.