Reviewerly/exHarmony huggingface dataset:
From Event Tracking to Event Modelling: Understanding as a Paradigm Shift

In 1998, Topic Detection and Tracking (TDT), or event tracking, was only two years old. By then, however, event tracking’s pioneers had already realized that for algorithms to accomplish their task, to detect and track events in the news media accurately, they had to understand events. Yet it has taken event tracking the decades since then to resolve what it means to understand events and how. Simple interpretations of understanding often failed; the rest never fulfilled the potential of event tracking. Without understanding, event tracking continues to struggle with the same, early problems, which social media aggravated and contemporary applications of events compounded. Therefore in this position paper, we argue that now, more than ever, event tracking needs event understanding. By comparing event tracking’s interpretations of what it means to understand, we demonstrate how early understanding only failed event tracking because it remained too simple in its reliance on linguistics. We adopt a different definition of events, a structured and semantic description that revolves around Who did What, Where and When. Furthermore, we propose how event tracking can fill in the event structure automatically and ahead of time. In the end, we show that understanding remains a complex problem, but event tracking can find in event knowledge a new purpose and its much-awaited paradigm shift.
Qubit Reduction and Quantum Speedup for Wireless Channel Assignment Problem

In this paper, we propose a novel method of formulating an NP-hard wireless channel assignment problem as a higher-order unconstrained binary optimization (HUBO), where the Grover adaptive search (GAS) is used to provide a quadratic speedup for solving the problem. The conventional method relies on a one-hot encoding of the channel indices, resulting in a quadratic formulation. By contrast, we conceive ascending and descending binary encodings of the channel indices, construct a specific quantum circuit, and derive the exact numbers of qubits and gates required by GAS. Our analysis clarifies that the proposed HUBO formulation significantly reduces the number of qubits and the query complexity compared with the conventional quadratic formulation. This advantage is achieved at the cost of an increased number of quantum gates, which we demonstrate can be reduced by our proposed descending binary encoding.
A Method and Display System for Interacting with Virtual Scene Through VR Device

Virtual reality devices are connected to the virtual scene through cables. The user moves the VR headset and head to move the camera in real time, which will be reflected in the virtual scene. This interaction is called “indoor” or “immersive” virtual reality (VR). Compared with other types of virtual reality, this type of virtual reality has many advantages: it can provide users with a more realistic experience than other types; it can give users the opportunity to interact with the environment through mobile; it can be used for training purposes. Therefore, this paper studies an interactive method and device between VR interactive equipment and VR scene. The method includes obtaining the real scene information corresponding to the current real scene in the preset project; according to the real scene information, judge whether there are physical props in the real scene whose collision probability with the VR interactive device exceeds the preset collision threshold. By setting a non-explicit collision area at the front end of the virtual operator in the VR scene, the probability of collision between VR interactive equipment and physical props is reduced, and the VR interactive equipment is effectively prevented from being damaged.
A Study of Practical Approaches to Reduce Power Consumption Using Different Floor Layouts of Datacenter

For scalability and longevity, the server room flooring solution must be meticulously chosen. In a conventional raised-floor datacenter, cold air is provided to the computer room via perforated floor tiles by an under-floor plenum. Any datacenter's thermal management is mostly dependent on the rates of air flow through the tiles. A datacenter's power density capability and electrical efficiency are heavily influenced by its floor plan. The designers and administrators of datacenters must be able to change the rates of airflow through the perforated tiles to maximize the floor space and allow for flexibility in rearranging and retrofitting. The objective of this study is to describe how floor plans influence these qualities and to recommend an efficient technique for producing a floor layout specification. This research has been carried out over a period of three financial years to analyze the performance of a datacenter by the modification of floor plan. Three different protocols one in each financial year have been implemented. A substantial gain in power consumption (from 409,196 KWH to 357,308 KWH) and power usage efficiency (PUE) (from 2.23 to 1.97) have been observed. Moreover, this research concludes that efficient and optimized floor and equipment arrangement can enhance the cooling efficiency.
Dynamic Group Signatures with Message Dependent Opening and Non-Interactive Signing

The group signature with message dependent opening (GS-MDO) is a variant of the group signature in the sense that the opening authority is split into two parties called the opener and the admitter. Most known constructions of GS-MDO consider the static model. The only scheme using the dynamic model by Sun and Liu has a problem of the anonymity against the admitter in the real-world usage because the signing process requires the interaction between the signer and the admitter. In this paper, we restart the line of research of GS-MDO in the dynamic setting. We introduce the definition of the dynamic group signature with message dependent opening (DGS-MDO) with the security requirements and propose a generic construction. By instantiating our construction with appropriate primitives, we can obtain a DGS-MDO scheme with the standard model security, constant signature size and non-interactive signing process.
Research on Optimization of Resource Scheduling Algorithm Based on Responsive Web Front-End

Research on optimization of resource scheduling algorithm based on responsive web front-end. The purpose of this study is to design a resource scheduling algorithm, which can provide the best performance for applications in terms of response time and page loading time, while maintaining an acceptable quality of service level. The solution consists of two parts: (1) a new method to determine the optimal number of instances, and (2) an adaptive mechanism to allocate instances according to their priority. A new method was developed to determine the number of instances that should be scheduled for each request type. This study is very useful for those who use large-scale applications with high traffic and need to pay attention to their time to market. The proposed solution helps us achieve better performance, scalability, and reliability by using prediction algorithms that can predict future load or traffic patterns. It also provides a mechanism to monitor the time required for each request to complete.
Image Caption Generator Through Deep Learning

With the advent rise of social media, it has become a necessity to upload pictures. But, it is really hard to come up with unique captions every time you upload a photo. Hence, an AI-enabled image caption generator can really help to effectively generate captions. Image caption generator is an application of deep learning models that can automatically generate captions for images. Using LSTM and CNN we can uniquely identify images and generate captions for them. This paper approaches the caption generation problem using an Long Short-Term Memory (LSTM)-based RNN model and builds an architecture based on it to generate efficient and meaningful captions by effectively training the dataset. Our model was successfully trained using the Flicker8k dataset.
The cost of passing - using deep learning AIs to expand our understanding of the ancient game of Go

In addition to their playing skills, AI engines utilizing deep learning neural networks provide excellent tools for analyzing traditional board games if we define new measures based on their raw output. For the ancient game of Go, we develop a numerical tool for context-sensitive move-by-move performance evaluation and for automating the recognition of game features. We measure the urgency of a move by the cost of passing, which is the score value difference between the current configuration of stones and after a hypothetical pass in the same board position. In this paper, we investigate the properties of this measure and describe some applications for developing learning tools and analyzing a large number of games. As we use AI tools to gain new insights into the ancient game of Go and develop a more precise, quantified understanding, this work fits into the more significant and general project of utilizing superhuman AI engines for deepening human understanding and growing human knowledge.
Improvement of Miller Loop for a Pairing on FK12 Curve and Evaluation with other STNFS Curves

Pairing is carried out by two steps, Miller loop and final exponentiation. In this manuscript, the authors propose an efficient Miller loop for a pairing on the FK12 curve. A Hamming weight and bit-length of loop parameter have a great effect on the computational cost of the Miller loop. Optimal-ate pairing is used as the most efficient pairing on the FK12 curve currently. The loop parameter of optimal-ate pairing is 6z + 2 where z is the integer to make the FK12 curve parameter. Our method uses z which has a shorter bit-length than the previous optimal-ate pairing as the loop parameter. Usually, z has a low Hamming weight to make final exponentiation efficient. Therefore, the loop parameter in our method has a lower Hamming weight than the loop parameter of the previous one in many cases. The authors evaluate our method by the number of multiplications and execution time. As a result, the proposed algorithm leads to a 3.71% reduction in the number of multiplications and a 3.03% reduction in the execution time. In addition, the authors implement other STNFS secure curves and evaluate these curves from viewpoint of execution time.
Trust-based Negotiation in Multiagent Systems: A Systematic Review

In this work, we conducted a systematic review on trust-based negotiation in Multiagent Systems (MAS), through a bibliometric analysis over the past 25 years of research publications, on three of the most popular scientific databases (Google Scholar, Scopus, and Web of Science). Our analysis reveals that this research topic is regaining interest, after some oscillating years, and the impact of its contributions is equivalent to other equally important research variants like ontology and argumentation (in a negotiation scenario). Discarding the human-to-agent trust challenges, we only focus on agent-to-agent trust concepts, and we performed an analysis of the different types of trust dimensions, using the findings and concerns of past review works, as we identify and select the dimensions that, in our opinion, have the most potential to lead the research advances on the topic of trust in MAS. Furthermore, we discuss the current challenges and open issues associated with those trust dimensions, and how current advancements in the literature could provide insights for the solution of those challenges, or even the finding of new research paths.
A SPADE Multi-agent Application for Outdoor $$CO_2$$ Monitoring

The Internet of Things (IoT) has emerged as a powerful computation environment that interconnects diverse devices to decentralized systems and which can be deployed in numerous domains. Multi-Agent Systems (MAS) have been identified as a promising paradigm for the development of smart applications, including IoT devices. In this context, this paper introduces an air quality monitoring and prediction application which uses a multi-agent platform called SPADE. SPADE has recently incorporated an extension that allows for the seamless integration of IoT devices into multi-agent systems, called SPADE Artifact, which conforms with the theoretical A&A (Agents and Artifacts) Meta-Model. Furthermore, gas sensing, particularly measuring $$CO_2$$ concentration as an indicator of air quality. Outdoor air quality is a critical factor to consider for ensuring the health and safety of individuals in urban and suburban environments. By utilizing IoT artifacts to integrate gas sensing devices into MAS, it becomes feasible to accurately monitor and manage outdoor air quality, enabling decision-making on measures such as reducing traffic or controlling industrial emissions to improve air quality. This paper highlights the significance of integrating gas-sensing devices in IoT artifacts within MAS for effective monitoring and management of outdoor air quality in the era of IoT.
Guitar Thinking

Playing the guitar develops physical skills but also ways of listening and thinking about music. For example, guitarists often conceptualize chords as two-dimensional shapes—an approach that is foreign to pianists. What does it mean, then, to think like a guitarist? This article approaches “guitar thinking” through music theory and cognitive science. Psychological experiments help to reveal auditory, visual, and tactile aspects of guitar playing and to show how guitarists respond to the instrument’s affordances (i.e., its possibilities for action). Additionally, recent research in music theory models fretboard space and examines patterns of body-instrument interaction. To demonstrate this mode of analysis, the article discusses Leo Brouwer’s Estudios sencillos, nos. 1 and 7. Ultimately, this investigation suggests that the guitar is not only a tool for producing musical sound; it also produces musical knowledge.
STUDY OF SIGN LANGUAGE USING NEURAL NETWORKS

Hand gestures, facial expressions, and body language are all used in sign language to communicate. Either word level signs or fingerspelling make up a sign language. For the deaf-dumb community, it is their sole means of communication. However, hearing individuals never attempt to learn sign language. As a result, hearing-impaired people are unable to converse with hearing people without a sign language interpreter. Because of this, deaf persons are socially isolated. Therefore, a system that can decode sign language automatically is required. The execution of such a system gives hearing-impaired people a platform for communication without the help of anyone. One of the most traditional and organic kinds of language for communicating is sign language. Sign language based on fingerspelling technique employing neural networks. Deaf and dumb people use sign language, a kind of nonverbal communication, to communicate. D&amp;M individuals can only communicate through sign languages due to a single communication-related disability that prevents them from utilising spoken languages. Exchanging thoughts and messages through a variety of channels, such as speaking, signalling, behaviour, and visuals, is the act of communication. People (D&amp;M) without hearing or speech use their hands to produce a range of gestures to communicate with others.
Failure Point Estimation Using Rule-based Learning

NTT Access Network Service Systems Laboratories aims to achieve smart and advanced network operations supporting the digital transformation of the NTT Group.This article introduces a means of failure point estimation using rule-based learning that immediately presents potential failure points at the time of a failure.This estimation technique is based on technology for autonomously deriving cause-andeffect relationships (rules) between failure points and alarms.
Development and Assessment of Static Website for Digital Media Production

Internet education has a long history, and it has been used to learn everything. Since the advent of computers, digital learning has increased in popularity. Interest in online learning for education has also increased, with both teachers and students looking for online activities to supplement existing education. The “Development and Assessment of Static Website for Digital Media Production” is designed for faster, easier and paperless process of teaching students. This study is to promote an exciting approach to SP-ICT students, that ICT Subjects can be fun and interesting, by using an exciting approach that they are all familiar with, “the Internet”, by creating a website containing downloadable digital modules and assessment that are still related to the subjects learning competencies. This study used developmental research and descriptive research as the study conducted gathering of data in order to design and develop system or application and be evaluated by the respondents. Utilization of the developed website for digital media production is found Functional, Reliable, Usable, Efficient, Maintainable and Portable to the IT Professional respondents. In this time of pandemic, majority of the students prefer using cellphones for distance learning. Utilizing the website will be beneficial to them since it is found Portable, which means the website is accessible anywhere where an internet source is available using different gadgets with browser installed. Schools with SP-ICT in other Congressional District of DepED Nueva Ecija may utilize the website by making the admin multi-user with the permission of the researcher. Also, it was found that the application is Effective, Efficient, Satisfactory, Free from Risk and has sufficient Context Coverage.
Isogeny computation on Kummer lines and applications

Isogeny-based cryptography has been the hotspot in recent years owing to its resistance to quantum computing attacks. Unfortunately, efficiency is the main bottleneck that limits its cryptographic applications. Inspired by the efficient implementations of Kummer lines, we formalize the isogenies on Kummer lines. In particular, we derive the explicit formulae of distinct 2-isogenies, consisting of several transforms between different Kummer lines, followed by some appealing isomorphic properties. As an additional contribution, we establish the formulae of odd-degree isogenies and show that 3-isogenies can be decomposed into three similar operations. Furthermore, with appropriate optimizations, we present the algorithms of computing the 2-isogenies over Fp with the ability to obtain the images simultaneously. For the setup of Verifiable Delay Functions and Delay Encryptions, our method reduces the number of operations with the potential of more compact schemes.
Mining algorithm of accumulation sequence of unbalanced data based on probability matrix decomposition

Due to the inherent characteristics of accumulation sequence of unbalanced data, the mining results of this kind of data are often affected by a large number of categories, resulting in the decline of mining performance. To solve the above problems, the performance of data cumulative sequence mining is optimized. The algorithm for mining cumulative sequence of unbalanced data based on probability matrix decomposition is studied. The natural nearest neighbor of a few samples in the unbalanced data cumulative sequence is determined, and the few samples in the unbalanced data cumulative sequence are clustered according to the natural nearest neighbor relationship. In the same cluster, new samples are generated from the core points of dense regions and non core points of sparse regions, and then new samples are added to the original data accumulation sequence to balance the data accumulation sequence. The probability matrix decomposition method is used to generate two random number matrices with Gaussian distribution in the cumulative sequence of balanced data, and the linear combination of low dimensional eigenvectors is used to explain the preference of specific users for the data sequence; At the same time, from a global perspective, the AdaBoost idea is used to adaptively adjust the sample weight and optimize the probability matrix decomposition algorithm. Experimental results show that the algorithm can effectively generate new samples, improve the imbalance of data accumulation sequence, and obtain more accurate mining results. Optimizing global errors as well as more efficient single-sample errors. When the decomposition dimension is 5, the minimum RMSE is obtained. The proposed algorithm has good classification performance for the cumulative sequence of balanced data, and the average ranking of index F value, G mean and AUC is the best.
Web Base Online Election Management Systems: Technical Review

The implementation of advanced technologies in elections becomes more effectful, beneficial and valuable to eliminate the fraud attempts in voting, to get the accurate voting results and to raise the voter turnout. From last few decade numbers of countries are developing and improving their election management system to make it secure. But still maximum of countries are following old ballet paper system because of large population. Remaining countries are in concern about the security of online voting system. There are various types of E-Voting System that have introduced and implemented, but they have different vulnerabilities. Numbers of researchers dose a work for improving old election systems but each technique comes with different drawbacks and because of this still these techniques are struggling to get its acceptance in voting system management. In this review article we are going to analize some earlier researchers work in same specific domain. In this a critical we are going to review some popular election management techniques like: Fingerprint Electronic Voting System based on Android Application, Web Based Secure Internet Voting System for Corporate Election and Omni Ballot platform web base online voting system.
The Reform of Pronunciation Teaching in Colleges and Universities by Praat Software From the Perspective of Deep Learning

Due to the difficulties of speech signal processing, there is still a considerable gap between the ability of machines to correctly process and that of human beings. In order to overcome the defects of isolated learning and noise sensitivity of SOM, this paper proposes a new time self-organization model (TSOM) from the perspective of deep learning. On the basis of self-organizing mapping network, time enhancement mechanism is introduced to improve the system performance. This method makes up for the fixed spatial topology of the original self-organizing mapping network and the neglect of the time factor, which is crucial to the voice signal. At the same time, this paper makes full use of computer-aided technology and rich network resources to provide a comprehensive and systematic English pronunciation learning database and establish learners' pronunciation files. Once learners understand and master the operation of voice analysis software, they can conduct self-assessment and judgment to find out their blind spots and weaknesses in voice acquisition.