Reviewerly/exHarmony huggingface dataset:
Estrategias de aprendizaje mediadas por tecnología para el aprendizaje significativo en la asignatura de Biología

Currently there is a fairly large number of educational platforms; therefore, it is crucial to enter that virtual universe and use it as a tool to develop learning strategies that contribute to the appropriation of knowledge. This research aims to identify technology-mediated learning strategies that promote previous concept and meaningful learning in the subject of Biology at the high school level. To analyze the object of this study, a quantitative methodology of exploratory-descriptive type was used to know the strategies mediated by technology that develop the appropriation of knowledge of Biology content. There was an experimental group and a controlled one to apply a pre-diagnosis, treatment and post-diagnosis. From these data, the results were: Significant difference in the experimental group after the didactic intervention, since the subjects showed mastery of basic concepts of Biology and considered that the application of this type of strategies are fundamental for the understanding and organization of main ideas in the construction of meaningful learning. Besides, they affirmed that the assimilation and appropriation of knowledge depend on the strategies mediated by technology.
Tibetan Sentiment Classification Method Based on Semi-Supervised Recursive Autoencoders

We apply the semi-supervised recursive autoencoders (RAE) model for the sentiment classification task of Tibetan short text, and we obtain a better classification effect. The input of the semi-supervised RAE model is the word vector. We crawled a large amount of Tibetan text from the Internet, got Tibetan word vectors by using Word2vec, and verified its validity through simple experiments. The values of parameter α and word vector dimension are important to the model effect. The experiment results indicate that when α is 0.3 and the word vector dimension is 60, the model works best. Our experiment also shows the effectiveness of the semi-supervised RAE model for Tibetan sentiment classification task and suggests the validity of the Tibetan word vectors we trained.
Digital-Analog Quantum Simulation of Fermionic Models

Simulating quantum many-body systems is a highly demanding task since the required resources grow exponentially with the dimension of the system. In the case of fermionic systems, this is even harder since nonlocal interactions emerge due to the antisymmetric character of the fermionic wave function. Here, we introduce a digital-analog quantum algorithm to simulate a wide class of fermionic Hamiltonians including the paradigmatic one-dimensional Fermi-Hubbard model. These digital-analog methods allow quantum algorithms to run beyond digital versions via an efficient use of coherence time. Furthermore, we exemplify our techniques with a low-connected architecture for realistic digital-analog implementations of specific fermionic models.
Quantum Aitchison geometry

Multiplying a likelihood function with a positive number makes no difference in Bayesian statistical inference, therefore after normalization the likelihood function in many cases can be considered as probability distribution. This idea led Aitchison to define a vector space structure on the probability simplex in 1986. Pawlowsky-Glahn and Egozcue gave a statistically relevant scalar product on this space in 2001, endowing the probability simplex with a Hilbert space structure. In this paper, we present the noncommutative counterpart of this geometry. We introduce a real Hilbert space structure on the quantum mechanical finite dimensional state space. We show that the scalar product in quantum setting respects the tensor product structure and can be expressed in terms of modular operators and Hamilton operators. Using the quantum analogue of the log-ratio transformation, it turns out that all the newly introduced operations emerge naturally in the language of Gibbs states. We show an orthonormal basis in the state space and study the introduced geometry on the space of qubits in details.
A Study of Manuscripts Evolution to Perfection

A manuscript, written by the researcher(s), undergoes a lot of revisions to improve its readability. The revision process eliminates grammatical mistakes, enhances presentation style and minimizes ambiguity. A better version of the research article is possible after a sufficient number of revisions. However, there is no specific criterion for the number of revisions. It takes a lot of time and efforts to prepare a draft. Hence, it is required to finalize a manuscript in minimum number of drafts. This article highlights the key mistakes made by most of the researchers while writing and revising their drafts. The first three drafts of ten different manuscripts have been investigated for common mistakes. The article also finds the relationship(s) among these mistakes. Association rule mining methods have been applied on the investigation dataset. The detected rules infer that the mistakes are interrelated.
Smart Cities: Drivers to Increase Context-Awareness Based on a Systematic Review of the Literature

The study reported in this article aimed to identify the most relevant context-aware applications supported in smart city infrastructures, the characteristics of the technologies being used, and the maturity level of their implementation. A systematic review was performed based on a search of the literature and a total of 19 articles was retrieved. The articles report context-aware applications for different domains, namely public health, information services, urban mobility, management of urban infrastructures, active citizenship, and improvement of shopping experience.
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation

A challenge for named entity disambiguation (NED), the task of mapping textual mentions to entities in a knowledge base, is how to disambiguate entities that appear rarely in the training data, termed tail entities. Humans use subtle reasoning patterns based on knowledge of entity facts, relations, and types to disambiguate unfamiliar entities. Inspired by these patterns, we introduce Bootleg, a self-supervised NED system that is explicitly grounded in reasoning patterns for disambiguation. We define core reasoning patterns for disambiguation, create a learning procedure to encourage the self-supervised model to learn the patterns, and show how to use weak supervision to enhance the signals in the training data. Encoding the reasoning patterns in a simple Transformer architecture, Bootleg meets or exceeds state-of-the-art on three NED benchmarks. We further show that the learned representations from Bootleg successfully transfer to other non-disambiguation tasks that require entity-based knowledge: we set a new state-of-the-art in the popular TACRED relation extraction task by 1.0 F1 points and demonstrate up to 8% performance lift in highly optimized production search and assistant tasks at a major technology company
Intersection Subtyping with Constructors

We study the question of extending the BCD intersection type system with additional type constructors. On the typing side, we focus on adding the usual rules for product types. On the subtyping side, we consider a generic way of defining a subtyping relation on families of types which include intersection types. We find back the BCD subtyping relation by considering the particular case where the type constructors are intersection, omega and arrow. We obtain an extension of BCD subtyping to product types as another instance. We show how the preservation of typing by both reduction and expansion is satisfied in all the considered cases. Our approach takes benefits from a "subformula property" of the proposed presentation of the subtyping relation.
Passive, Transparent, and Selective TLS Decryption for Network Security Monitoring

More and more Internet traffic is encrypted. While this protects the confidentiality and integrity of communication, it prevents network monitoring systems (NMS) from effectively analyzing the now encrypted payloads. Many enterprise networks have deployed man-in-the-middle (MitM) proxies that intercept TLS connections at the network border to examine packet payloads and to regain visibility. However, TLS interception via MitM proxies often reduces connection security and potentially introduces additional attack vectors. In this paper, we present a cooperative approach in which endpoints selectively send TLS keys to the NMS for decrypting TLS connections. This enables hosts to control which TLS connections an NMS can decrypt and lets users retain privacy for chosen connections. We implement a prototype based on the Zeek NMS that is able to receive key material from hosts, decrypt TLS connections, and analyze the cleartext. Meanwhile, our patch was merged into Zeek upstream and will be part of Zeek v4.3.0. In our evaluation, we initially compare our approach to MitM proxies and can deduce that it significantly reduces the computational overhead. Furthermore, our experimental results on real-world traffic indicate that our TLS decryption adds a runtime overhead of 2.5 times compared to the analysis of cleartext. Additionally, our results indicate that when buffering traffic for only short amounts of time at the NMS, all keys from the hosts arrive in time to completely decrypt 99,99% of all observed TLS connections.
Solving linear systems by quadratic unconstrained binary optimization on D-Wave quantum annealing device

Many quantum computing algorithms are being developed with the advent of quantum computers. Solving linear systems is one of the most fundamental problems in almost all of science and engineering. HHL algorithm, a monumental quantum algorithm for solving linear systems on the gate model quantum computers, was invented and several advanced variations have been developed. However, HHL-based algorithms have a lot of limitations in spite of their importance. We address solving linear systems on a D-Wave quantum annealing device. To formulate a quadratic unconstrained binary optimization (QUBO) model for a linear system solving problem, we make use of a linear least-square problem with binary representation of the solution. We validate this QUBO model on the D-Wave system and discuss the results.
Implementation of Mobile Phone Data Collection in the Conduct EPI Comprehensive Review in East and Southern African Countries

Mobile phone data collection tools are increasingly becoming very usable collecting, collating and analysing data in the health sector. In this paper, we documented the experiences with mobile phone data collection, collation and analysis in 5 countries of the East and Southern African, using Open Data Kit (ODK), where questionnaires were designed and coded on an XML form, uploaded and data collected using Android-Based mobile phones, with a web-based system to monitor data in real-time during EPI comprehensive review. The ODK interface supports in real-time monitoring of the flow of data, detection of missing or incomplete data, coordinate location of all locations visited, embedded charts for basic analysis. It also minimized data quality errors at entry level with the use of validation codes and constraint developed into the checklist. These benefits, combined with the improvement that mobile phones offer over paper-based in terms of timeliness, data loss, collation, and real-time data collection, analysis and uploading difficulties, make mobile phone data collection a feasible method of data collection that needs to be further explored in the conduct of all surveys in the organization.
Interrater disagreement resolution: A systematic procedure to reach consensus in annotation tasks

We present a systematic procedure for interrater disagreement resolution. The procedure is general, but of particular use in multiple-annotator tasks geared towards ground truth construction. We motivate our proposal by arguing that, barring cases in which the researchers’ goal is to elicit different viewpoints, interrater disagreement is a sign of poor quality in the design or the description of a task. Consensus among annotators, we maintain, should be striven for, through a systematic procedure for disagreement resolution such as the one we describe.
Research on AGV Navigation Simulation in Printing Wisdom Factory

AGV is an important part of the printing wisdom factory. Navigation technology is one of the key technologies of AGV. Path planning, obstacle avoidance and mapping are the core of the realization of AGV navigation. In order to solve the AGV navigation problem in the printing wisdom factory, this paper studies the mapping and path planning of the AGV in the printing wisdom factory. First, the physical simulation environment of the printing wisdom factory is established based on gazebo. Second, the two-dimensional grid map of the physical simulation environment of the printing wisdom factory is constructed by using the graph optimization SLAM algorithm, and then design a combined navigation scheme combining A* algorithm and Dynamic Window Approach (DWA) was developed. Finally, the simulation results verify the accessibility of global path planning and the feasibility of local path avoidance and obstacle avoidance.
Developing Software Signature Search Engines Using Paragraph Vector Model: A Triage Approach for Digital Forensics

Today, with the growth of information and communication technology, digital crimes have also spread. Advanced storage technologies and their low cost have led to a significant increase in their use. Therefore, the high volume of digital data to be analyzed is a challenge facing digital forensic investigators. Digital forensic triage solutions aim to alleviate the forensic backlog. A promising triage technique is to quickly find the software packages run on the target system to narrow down the search space. In this paper, we propose a software signature search engine (S3E) to identify software on the system under investigation. The document collection of this search engine consists of software signatures, and the query is the features extracted from the system’s hard disk. We propose a forensic differential analysis model to build software signatures. Besides, we use the paragraph vector model to construct the corresponding vectors of each software signature and find similarities between the query vector and the signature vectors. Different design parameters are involved in making software signature search engines, and distinct values of these parameters lead to different models. We have measured the performance of these S3E models against several controlled systems and some pseudo-real systems. The experimental results on both datasets show that some S3E models achieve perfect recall, and many of them have a recall of more than 90%. Besides, we find that the recall rate of the S3E models in both datasets is higher than the averaged word2vec model and the TF-IDF model.
Privacy Protection, Big Data Gathering and Public Health Issues: COVID-19 Tracking App Use in Italy

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A UAV Detection Algorithm Combined With Lightweight Network

Unmanned Aerial Vehicles (UAVs) are characterized by the small scale and fast flying speed, which brings certain challenges to the detection and supervision of UAVs. In response to this problem, this paper proposes an improved detection algorithm combined with a lightweight network for objects with fast speed. First of all, the original three feature scales that the You Only Look Once(YOLO) v3 algorithm detects are extended to five feature scales for detection, which improves the detection performance of scenes of small objects such as UAVs. Secondly, combined with the Ghost module in the lightweight network, a lightweight feature extraction network is constructed, and a series of cheap operations like linear transformations are applied to generate more feature maps with fewer parameters. To further improve the detection performance of the network, the channel attention mechanism is added to suppress the adverse information. Besides, a UAV dataset with an urban background for training and testing is made. The experimental results show that the improved method proposed in this paper effectively improves the detection accuracy of UAVs in a complex background of cities, meets the real-time requirements, and realizes the lightweight of the object detection algorithm, which provides the possibility to realize the object detection on the embedded platform.
IoT and Machine Learning - A Technological Combination for Smart Application

With the rapid developments in hardware, software and communication technologies, a new era of internet connected sensory devices has begun which provides a link between physical and cyber worlds. It is estimated that by 2020 internet connected devices being used will be between 25 and 50 billion. Internet connected devices referred as Internet of Things. IoT provides a huge range of services and applications covering all the domains ranging from infra-structure, retail, transportation and personal health care. Fughua etal. (2015) poised that IoT has empowered the development in internet protocols and communication technologies. In IoT applications, intelligent processing and analysis of big data act as key for their development. Data science technologies help to find new pattern and new insights from data to make IoT applications more intelligent. Data science with IoT is mainly used in various sectors dealing with volume, velocity and pattern recognition. Because of predictive analysis of machine learning, software becomes able to predict incoming desired and undesired events. Therefore, machine learning system is not only recognizing the abnormal behaviour but also help to understand and establish long term trends.A continue correction and supervision is required for effectiveness and efficiency in data analysis. This paper is having two sections- pertaining the different applications of IoT where machine learning helps to make the intelligent system and futuristic scope of IoT and machine learning and how these two technologies help in development of communication devices. This paper will provide the answers to questions such as what is the taxonomy of machine learning that can be adopted in IoT, how can machine learning be applied to IoT applications.
Visualization analysis method of data assets hotspots in power enterprises based on data middle platform

Aiming at the problem that the current visualization analysis method of power enterprise data assets does not consider the design of data visualization analysis model, which leads to the low accuracy of the line chart formed by visualization, this paper proposes a data assets hot spot visualization analysis method based on data platform. Starting from the three problems of data assets, such as lack, error and repetition, this paper preprocesses the data assets of power enterprises, adopts the technology of data middle platform, establishes the framework of data middle platform to mine the hot spots of data assets of power enterprises. This paper designed a data visualization analysis model, build the visualization system structure of electric power enterprise data assets hot, implements the hot visualization of electric power enterprise data assets. The experimental results show that the line chart formed by the visualization analysis method of power enterprise data assets hot spot has high accuracy.
Using contextual and cross-lingual word embeddings to improve variety in template-based NLG for automated journalism.

In this work, we describe our efforts in improving the variety of language generated from a rule-based NLG system for automated journalism. We present two approaches: one based on inserting completely new words into sentences generated from templates, and another based on replacing words with synonyms. Our initial results from a human evaluation conducted in English indicate that these approaches successfully improve the variety of the language without significantly modifying sentence meaning. We also present variations of the methods applicable to low-resource languages, simulated here using Finnish, where cross-lingual aligned embeddings are harnessed to make use of linguistic resources in a high-resource language. A human evaluation indicates that while proposed methods show potential in the low-resource case, additional work is needed to improve their performance.
ScreenID: Enhancing QRCode Security by Fingerprinting Screens

Quick response (QR) codes have been widely used in mobile applications due to its convenience and the pervasive built-in cameras on smartphones. Recently, however, attacks against QR codes have been reported that attackers can capture a QR code of the victim and replay it to achieve a fraudulent transaction or intercept private information, just before the original QR code is scanned. In this study, we enhance the security of a QR code by identifying its authenticity. We propose SCREENID, which embeds a QR code with information of the screen which displays it, thereby the QR code can reveal whether it is reproduced by an adversary or not. In SCREENID, PWM frequency of screens is exploited as the unique screen fingerprint. To improve the estimation accuracy of PWM frequency, SCREENID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that SCREENID can differentiate screens of different models, types, and manufacturers, thus improve the security of QR codes.
Reformulating Sentence Ordering as Conditional Text Generation.

The task of organizing a shuffled set of sentences into a coherent text is important in NLP and has been used to evaluate a machine's understanding of causal and temporal relations. We present Reorder-BART (RE-BART), a sentence ordering framework which leverages a pre-trained transformer-based model to identify a coherent order for a given set of shuffled sentences. We reformulate the task as a conditional text-to-marker generation setup where the input is a set of shuffled sentences with sentence-specific markers and output is a sequence of position markers of the ordered text. Our framework achieves the state-of-the-art performance across six datasets in Perfect Match Ratio (PMR) and Kendall's tau ($\tau$) metric. We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets. We additionally perform a series of experiments to understand the functioning and explore the limitations of our framework.