Reviewerly/exHarmony huggingface dataset:
A Deep Learning CNN based Approach to the Problem of Crypto-Currency Prediction

After the invention of Bitcoin by a man named Satoshi Nakamoto along with other blockchain-based person-to-person payment systems, the cryptocurrency market has instantly gained popularity. Because of this, that is, the volatility of the various cryptocurrency prices. This attracts much attention from both the investors and the researchers. The task of forecasting the prices of crypto-currencies because of the static prices and the arbitrary effects in the market is quite challenging. Cryptocurrency price forecasting models that are available now mainly focus on analyzing extrinsic factors, like macro-financial indicators, data linked to the blockchain, and data from social media – with the goal of enhancing the prediction accuracy. However, the intrinsic noise present in the raw data, caused by market and political conditions worldwide, is complex to interpret. In our research we propose a multiple input convolutional neural network model, specifically a convolutional neural network model for the prediction of future cryptocurrency price. Generally, RNNs and LSTMs are used for problems dealing with timeseries data. We used the concept of residual networks on 1-Dimensional convolutional networks to solve the problem of predicting the price of Bitcoin, the most popular cryptocurrency out there at the moment. Furthermore, we conduct additional experiments on ether, the cryptocurrency of Ethereum to further confirm that even CNNs can work equally well, if not better in comparison to the widely used LSTM neural network models.
The Relationship Between the Proof Complexities of Linear Proofs in Quantified Sequent Calculus and Substitution Frege Systems

It has formerly been proved that there is an exponential speed-up in the number of lines of the quantified propositional sequent calculus over substitution Frege systems when considering proofs as trees. This paper shows that a linear proof of any quantifierfree tautology in quantified propositional sequent calculus can be transformed into a linear proof of the same tautology in a substitution Frege systems with no more than polynomially increasing proof lines and size.
Broadcasting single-qubit and multiqubit entangled states: Authentication, cryptography, and distributed quantum computation

Quantum entanglement assisted with measurements provides various pathways to communicate information to parties within a network. In this work, we generalize a previous broadcasting protocol and present schemes to broadcast product and multipartite entangled quantum states, where in the latter case the sender can remotely add phase gates or abort distributing the states. We first focus on the broadcasting of product quantum states in a network, and generalize the basic protocol to include an arbitrary basis rotation and allow for multiple receivers and senders. We show how to add and delete senders from the network. The generalization also includes the case where a phase to be applied to the broadcast states is not known in advance but is provided to a sender encoded in another quantum state. Applications of broadcasting product states include authentication and three-state quantum cryptography. In the second part, we study the distribution of a single multiqubit state shared among several receivers entangled with multiqubit phase gates, which includes the graph states as an example. We show that by coordinating with the sender, the receivers can assist in performing remote, distributed measurement-based quantum computation with the Pauli-$X$ basis measurement alone. As another application of this, we discuss the distribution of the multiqubit Greenberger-Horne-Zeilinger state.
Abnormal driving behavior detection based on an improved ant colony algorithm

As one of the most serious hazards in the world, more than 80% of traffic accidents are caused by driver misconduct. The detection of abnormal behavior of drivers is important to improve safety in public transportation. The anomaly measurement is not only determined by objective rules such as laws, but also distinguished due to the biological characteristics. The same driving behavior may present completely opposite judgment results for different categories of drivers. In this paper, we propose a novel detection method that measures the preference path length of drivers for various driving operations via pheromones, and identifies abnormal driving behavior by calculating the cumulative conversion probability of operation switching. An improved ant colony algorithm based on fixed point simplicial theory is proposed to improve the convergence efficiency by optimizing the initial population state. Experimental results show that the proposed method can effectively detect abnormal driving behavior and significantly reduce false alarms.
Robust Human Activity Recognition using Equilibrium Optimizer with Deep Extreme Learning Machine Model

Recently, Human Activity Recognition (HAR) is becoming one of the prevalent study fields. HAR is a powerful tool for monitoring a person's dynamism, and it can be accomplished through machine learning (ML) techniques. HAR is a technique of automatically analysing and recognizing human activities depending on information from several wearable devices and smartphone sensors, like location, accelerometer, gyroscope, duration, and other environmental sensors. This study introduces a new Robust Human Activity Recognition using Equilibrium Optimizer with Deep Extreme Learning Machine (RHAR-EODELM) model. The presented RHAR-EODELM technique mainly identifies different classes of human activities. It follows a three-stage process. Initially, the RHAR-EODELM technique employs a min-max normalization process for scaling the activity data. Next, the RHAR-EODELM technique exploits a deep extreme learning machine with a radial basis function (DELM-RBF) model for the prediction process.Finally, the EO approach is enforced to adjust the parameters associated with the DELM-RBF method.A large-scale simulating process highlights the improved HAR results of the RHAR-EODELM method.The experimental values signify that the RHAR-EODELM method reaches improved predictive outcomes over other models.
Temporal Perceiver: A General Architecture for Arbitrary Boundary Detection

Generic Boundary Detection (GBD) aims at locating the general boundaries that divide videos into semantically coherent and taxonomy-free units, and could serve as an important pre-processing step for long-form video understanding. Previous works often separately handle these different types of generic boundaries with specific designs of deep networks from simple CNN to LSTM. Instead, in this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Temporal Perceiver</i> , a general architecture with Transformer, offering a unified solution to the detection of arbitrary generic boundaries, ranging from shot-level, event-level, to scene-level GBDs. The core design is to introduce a small set of latent feature queries as anchors to compress the redundant video input into a fixed dimension via cross-attention blocks. Thanks to this fixed number of latent units, it greatly reduces the quadratic complexity of attention operation to a linear form of input frames. Specifically, to explicitly leverage the temporal structure of videos, we construct two types of latent feature queries: boundary queries and context queries, which handle the semantic incoherence and coherence accordingly. Moreover, to guide the learning of latent feature queries, we propose an alignment loss on the cross-attention maps to explicitly encourage the boundary queries to attend on the top boundary candidates. Finally, we present a sparse detection head on the compressed representation, and directly output the final boundary detection results without any post-processing module. We test our Temporal Perceiver on a variety of GBD benchmarks. Our method obtains the state-of-the-art results on all benchmarks with RGB single-stream features: SoccerNet-v2 (81.9 percent average-mAP), Kinetics-GEBD (86.0 percent average-f1), TAPOS (73.2 percent average-f1), MovieScenes (51.9 percent AP and 53.1 percent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M_{iou}$</tex-math></inline-formula> ) and MovieNet (53.3 percent AP and 53.2 percent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M_{iou}$</tex-math></inline-formula> ), demonstrating the generalization ability of our Temporal Perceiver. To further pursue a general GBD model, we combined various tasks to train a class-agnostic Temporal perceiver and evaluate its performance across all benchmarks. Results show that the class-agnostic Perceiver achieves comparable detection accuracy and even better generalization ability compared to dataset-specific Temporal Perceiver.
Identification Of Covid-19 Patients' Effect On Education Outcomes In Islam Majority Student Using Spatial Analysis

The COVID-19 epidemic has had an impact on the educational landscape, particularly the move to a remote learning model utilizing internet media. This system has so many issues that we need to do an extensive educational assessment of the subject. In order to create an educational map of the mathematics learning scores of the Islam Majority Student population during the COVID-19 epidemic in SMP Pekanbaru City, Indonesia, this study used spatial analysis. The distribution of the number of patients who tested positively was related to the geographical analysis of the learning score. The majority of the city's western and eastern regions had few patients and did not improve the score for mathematics education, according to a comparison of the two maps. On the other hand, a small percentage of the northern and western regions revealed that the few patients raised the Mathematics education score. A tiny portion of the southern region discovered that the score for mathematics education fell as the proportion of positive patients rose. Furthermore, the fewest patients are found in tree-lined, deserted locations, yet there are still few schools there. In Pekanbaru City, the majority of the schools are still located in urban areas devoid of pleasant open spaces.
Cyclic Convolutional Neural Network Model Based on Artificial Intelligence

Abstract This paper mainly discusses the internal correlation between meshless discrete data and learning samples, meshless dynamic analysis recursive operation and information transmission mode in cyclic convolutional neural networks. This paper establishes a cyclic convolutional neural network based on the meshless method. This paper demonstrates an agent model of cyclic convolutional neural network based on dynamic characteristics. This method combines the advantages of the flexible configuration of meshless nodes in the discrete model. The universality and adaptability of cyclic convolutional neural networks are improved. In addition, because of the unique historical memory characteristics of the periodic module, it can analyze continuous data efficiently. The solution of dynamic analysis is accelerated without affecting the calculation accuracy. Based on a group of examples, the accuracy and effectiveness of this method are studied experimentally.
Lightweight Reputation Management for Multi-Role Internet of Vehicles

With the rapid development of the Internet of Vehicles (IoV), smart vehicles can fulfill multiple roles in either the information-centric IoV or the task-oriented IoV. However, malicious vehicles may undermine the trustiness of vehicles towards each other, and further damage these IoV networks. Given the multiple roles undertaken by vehicles in IoV networks, we aim to design a lightweight reputation-based mechanism for a hybrid IoV network in this article. This mechanism can realize real-time reputation updates and synchronization in the device-edge-cloud continuum. Simulation is conducted to validate the reputation management strategy in this article. We also discuss some opportunities and challenges to shed a light on the future research directions in this topic.
A Framework for Decentralized, Real-Time Reputation Aggregation in IoV

With the rapid increase in the number of connected and autonomous vehicles, there is a growing concern about the potential road accidents and collisions caused by malicious vehicles. A reputation system can help to mitigate these concerns and allow users to have safe journeys by providing a way to identify and estimate the behaviour of individual vehicles and to take appropriate actions in case of any malicious behaviour. Centralised reputation systems are widely used for reputation aggregation, but this setup requires Peer trust and could be a single point of attack. The alternative to a centralised system is the decentralised reputation system for IoV, in which the reputation information is collected and maintained by the vehicles rather than a central authority. There are several key considerations when designing a secure reputation aggregation system for the IoV. These include: i) It should ensure that vehicle feedback about other vehicles is kept private; ii) vehicles' interaction networks and positions should be protected; and iii) computations should be decentralised and not resource-intensive. Adopting a decentralised reputation system within IoV using blockchain can enhance security and privacy and mitigate many security concerns. In this article, we proposed a blockchain-based reputation system which ensures the privacy of participants and provides secure and resilient reputation computation. The reputation value reflects the aggregate trustworthiness of vehicles and this is computed via feedback provided by the vehicles in a decentralized way. We analysed the security and privacy of the proposed system and provided the computation and communication performance.
SOTERIA: Preserving Privacy in Distributed Machine Learning

We propose Soteria, a system for distributed privacy-preserving Machine Learning (ML) that leverages Trusted Execution Environments (e.g. Intel SGX) to run code in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The conducted experimental evaluation validates that our approach reduces the runtime of ML algorithms by up to 41%, when compared to previous related work. Our protocol is accompanied by a security proof, as well as a discussion regarding resilience against a wide spectrum of ML attacks.
CORE: A Global Aggregation Service for Open Access Papers

Abstract This paper introduces CORE, a widely used scholarly service, which provides access to the world’s largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE’s continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services.
A Self-Supervised Human Activity Recognition Approach via Body Sensor Networks in Smart City

In smart cities, pervasive sensing and wearable computing techniques are increasingly being employed to monitor and recognize human activities through body sensor networks, which have been widely used in urban safety, healthcare, and manufacturing. However, most researchers regard human activity recognition (HAR) as a high-cost research task requiring a large amount of labeled data, which is often unrealistic in real-world applications. To address this problem, we propose a self-supervised learning framework for HAR (SS-HAR). SS-HAR initially takes the unlabeled data generated by data augmentation as the input of the network, mines the supervised information of the unlabeled data under the effect of self-supervised learning, and uses the obtained backbone network as a feature extractor to extract activity features for subsequent classification. After that, we use part of the labeled data as the training set and extract the activity features using the backbone network for training and fitting the classifier. Then we utilize the rest of the data to verify the feasibility and effectiveness of the proposed self-supervised learning method. We have conducted multiple experiments on three publicly available datasets and one self-collected basketball activity dataset SZU_HAD_Basketball. The experimental results show that the SS-HAR method is able to achieve higher classification accuracy and stability than supervised and semi-supervised methods. Specifically, on the UCI dataset, SS-HAR achieves better classification performance compared to other approaches which improve the classification accuracy by 1% over the supervised method and by 5%-6% over the semi-supervised method respectively.
Desain UI UX Aplikasi Penjualan dengan Menyelaraskan Kebutuhan Bisnis menggunakan Pendekatan Design Thinking

Alignment between business needs and Information Technology (IT) strategy is one of the main factors for the success of a business. One of them is about the interface design of the application used by the user. The current condition in the company is that marketing and sales activities only use social media such as WhatsApp and Facebook. These cause problems in two business activities, namely a less broad market coverage, frequent barriers to communication, less efficient delivery, and an obstacle to the recording by employees. This research aims to design a sales application user interface that suits business needs using a Design Thinking approach by collecting user characteristics using the User Persona tool. The test uses Usability Testing by giving 4 User Scenarios to each respondent to run and complete on the prototype. The test results show that respondents can complete all User Scenarios in the first iteration by 100%. Based on the test results, the design is good, in line with business needs, and accepted by potential users.
PROSES PEMBENTUKAN AKRONIM DAN SINGKATAN PADA BERITA HARIAN DETIK.COM

This research is motivated by the phenomenon of the various uses of acronyms and abbreviations in society. This research aims to find out how acronyms and abbreviations are formed in the January-March 2022 edition of detik.com daily news. This type of research is descriptive qualitative research. The subject of this research is detik.com daily news January-March 2022 edition, while the object of this research is acronyms and abbreviations in detik.com daily news January-March 2022 edition. The data collection method is the reading method with note-taking technique, the research instruments used are data cards and note-taking equipment, the data analysis method is the agih method with the basic technique of BUL (Bagi Elemen Langsung) and the advanced technique of lesap. The results of this study are in the form of, (1) forms of acronyms and abbreviations with 3 forms of acronyms and 3 forms of abbreviations, (2) the formation process of acronyms and abbreviations with 4 processes of acronym formation and 2 processes of abbreviation formation.
SP-E: Security Evaluation Framework of In-vehicle Infotainment System based on Threat Analyses and Penetration Tests

Abstract With the increasing requirement of people, the functions of in-vehicle infotainment systems are becoming more and more abundant, and their security also affects the safety of vehicles. Therefore, it is more and more important to evaluate the security of the IVI system. This paper proposes a security evaluation framework for in-vehicle infotainment systems based on threat analyses and penetration tests. By constructing the data flow diagram of application scenarios, analyzing threats, combing the attack link diagram, combining white-box audit and black-box test, we use the characteristics of high efficiency of automatic tools and high accuracy of manual methods to set factor sets, and then the whole IVI system is evaluated by Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation.
Face Sketch Creation and Identification

Forensic face sketching is a technique used by Investigatory organizations to help identify suspects in criminal investigations.This paper will discuss the process of forensic sketch construction and identification.The paper will begin by providing an over view of the history of forensic sketching and its role in criminal investigations.It will then describe the process of creation of a face sketch, including the initial interview, sketching techniques, and finalizing the sketch.The paper will also discuss the challenges and limitations of forensic sketching and identify potential solutions to these challenges.The paper will conclude by giving a summary of the present status of the research in this field and proposing possible are as for further study.
Object Based Vehicle Track Navigation System

An Intelligent object and tracing autonomous vehicles are required in various applications such as space, transportation, industry, and defence.Mobile robot vehicles can also handle material handling, disaster relief, patrolling, and rescue operations.Therefore, a simple and reliable vehicle is required to travel freely in a static or dynamic environment.Smooth and safe navigation of mobile vehicles through the cluttered environment from the start position to the goal position with following a safe path and producing optimal length is the main aim of tracking object navigation.Regarding this matter, researchers have explored several techniques for navigation path planning, out of which this sensor and tracing of already decided track navigation is adopted here in the implementation.This system has tried to develop navigation techniques that are well-suited for static and dynamic environments and can be implemented for the real-time navigation of mobile vehicles.
Fixpoint Theory -- Upside Down

Knaster-Tarski's theorem, characterising the greatest fixpoint of a monotone function over a complete lattice as the largest post-fixpoint, naturally leads to the so-called coinduction proof principle for showing that some element is below the greatest fixpoint (e.g., for providing bisimilarity witnesses). The dual principle, used for showing that an element is above the least fixpoint, is related to inductive invariants. In this paper we provide proof rules which are similar in spirit but for showing that an element is above the greatest fixpoint or, dually, below the least fixpoint. The theory is developed for non-expansive monotone functions on suitable lattices of the form $\mathbb{M}^Y$, where $Y$ is a finite set and $\mathbb{M}$ an MV-algebra, and it is based on the construction of (finitary) approximations of the original functions. We show that our theory applies to a wide range of examples, including termination probabilities, metric transition systems, behavioural distances for probabilistic automata and bisimilarity. Moreover it allows us to determine original algorithms for solving simple stochastic games.