Reviewerly/exHarmony huggingface dataset:
A Mobile Application for Earthquake Education Targeting Foreigners Intending to Visit Japan

Japan is in the Pacific Rim Seismic Belt, and earthquakes happen frequently. Foreigners who lack the necessary earthquake knowledge and escape skills may not survive strong earthquakes in Japan. Therefore, it is essential to provide earthquake education to these foreigners. Since rich-formed earthquake education has been available for foreigners living in Japan, this research works to improve the earthquake education situation for foreigners who intend to visit Japan. Receiving earthquake education in advance helps enhance foreigners’ earthquake risk awareness, gain more opportunities to master earthquake survival knowledge, and build earthquake survival confidence. This paper proposed a learning model called ‘FOE+G’ to achieve this research goal. ‘FOE’ means the frequency of occurrence of earthquakes in Japan, and ‘G’ means gamification. A prototype system in the form of a cross-platform application has been developed to confirm that the learning model improves earthquake education for the target audience. The application delivers an earthquake notification to target objects each time an eligible earthquake occurs in Japan. The High-frequency earthquakes in Japan lead to relatively frequent updates, which enable users to build earthquake awareness, realise the importance of earthquake education, and participate more actively in it. Furthermore, gamification is employed in the application to prompt participation in earthquake education.
Impact of Social Media Usage in e-learning towards Students’ Academic Achievements during Pandemic Covid19

With the advancement of technology, social media positively and negatively influence students' academic achievement, mainly in higher institutions. The increased number of social media accounts shows that teenager mostly spends their time on social media. Thus, this paper will discuss and assess the impact of social media usage in an e-learning environment on student academic achievement in a higher education institution. Social media could be a tool or learning aid for them to improve their academic by joining online group learning, virtual meetings, and video conferencing to communicate with an expert within their field. The published and unpublished work has been dug out through online platforms to know and understand the impact of social media on student academic achievement. Keywords: Social Media, Student, Academic Achievement, Social Networking Sites (SNS) eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by E-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of AMER (Association of Malaysian Environment-Behavior Researchers), ABRA (Association of Behavioral Researchers on Asians), and cE-Bs (Centre for Environment-Behavior Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia.
Online Music Teaching Resource Sharing Algorithm Based on Blockchain Technology

Blockchain technology provides technical support for cloud computing data sharing in data storage and data access, and ensures the safe and effective transmission of data sharing. The online music teaching resource platform based on blockchain technology faces the problems of low data throughput, large delay and unstable performance. Therefore, a blockchain-based sharing algorithm (Practical Byzantine Fault Tolerance, PBFT), PBFT algorithm is proposed. Consensus allows 1/3 of cheating points in the blockchain, and authenticated transactions are confirmed through a confirmation mechanism to ensure data security. However, the traditional PBFT algorithm lacks an efficient node joining and exiting mechanism, and has problems such as low data throughput. Therefore, on the basis of the traditional PBFT algorithm, a blockchain sharing algorithm (WBFT) that improves the weighted Byzantine fault tolerance mechanism is proposed. A weighting mechanism is added in the consensus process. After the consensus, the node behavior will dynamically adjust the weights to reduce the adverse impact of malicious nodes on the data and improve the overall data sharing effect. Experimental performance tests show that the improved WBFT algorithm performs best in multi-algorithm delay performance tests. In the data average throughput test, the performance of the improved WBFT algorithm is 20.2% higher than that of the traditional PBFT algorithm. In the security test, the improved WBFT algorithm has the best performance. The proposed improved WBFT algorithm can well meet the needs of building an online music teaching resource sharing platform. The research content has important reference value for the application of blockchain technology in the education industry, and is conducive to promoting the development of modern education.
The Readability of Political Party Manifestos of the 2016 General Elections in Ghana

The objective of the study was to determine the readability of the manifestos that three political parties, namely National Democratic Congress (NDC), New Patriotic Party (NPP) and Convention People’s Party (CPP), in Ghana used in the 2016 general elections. These parties were chosen because they were the only ones in Ghana with parliamentary representation at the time of the study. The readability indexes used were the Coleman-Liau Index and the Gunning Fog Index. 12 random sections of each manifesto were selected for analysis. Then, texts of no less than 300 words were sampled from each section. The readability formulas used were available on the internet. The statistics show that all three manifestos were written at difficulty levels that were quite above the reading abilities of the average Ghanaian voter. Further, it was found that the manifestos did not differ significantly from each other, statistically speaking. Keywords: readability, readability formulas, manifestos, political parties, linguistic complexity
Contrastive Learning Reduces Hallucination in Conversations

Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the problem of "hallucination:" they may generate plausible-looking statements that are irrelevant or factually incorrect. To address this problem, we propose a contrastive learning scheme, named MixCL. A novel mixed contrastive objective is proposed to explicitly optimize the implicit knowledge elicitation process of LMs, and thus reduce their hallucination in conversations. We also examine negative sampling strategies of retrieved hard negatives and model-generated negatives. We conduct experiments on Wizard-of-Wikipedia, a public, open-domain knowledge-grounded dialogue benchmark, and assess the effectiveness of MixCL. MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality. We show that MixCL achieves comparable performance to state-of-the-art KB-based approaches while enjoying notable advantages in terms of efficiency and scalability.
Correction to “Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices” [Feb 20 1817-1829]

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Precise Zero-Shot Dense Retrieval without Relevance Labels

While dense retrieval has been shown effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no relevance label is available. In this paper, we recognize the difficulty of zero-shot learning and encoding relevance. Instead, we propose to pivot through Hypothetical Document Embeddings~(HyDE). Given a query, HyDE first zero-shot instructs an instruction-following language model (e.g. InstructGPT) to generate a hypothetical document. The document captures relevance patterns but is unreal and may contain false details. Then, an unsupervised contrastively learned encoder~(e.g. Contriever) encodes the document into an embedding vector. This vector identifies a neighborhood in the corpus embedding space, where similar real documents are retrieved based on vector similarity. This second step ground the generated document to the actual corpus, with the encoder's dense bottleneck filtering out the incorrect details. Our experiments show that HyDE significantly outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers, across various tasks (e.g. web search, QA, fact verification) and languages~(e.g. sw, ko, ja).
Performance of Uncoded Implementation of Grover's Algorithm on Today's Quantum Processors

This work tests the performance of Grover's search circuits on some IBM superconducting quantum devices in case of the size of search space $N=2^4$ and $N=2^5$. Ideally, we expect to get an outcome probability distribution that is clearly peaked at the goal (marked) state. However, the quantum circuit executed on real devices is vulnerable to noise which leads to fluctuations in the results. The contributions of the paper are therefore the following: a) it presents two new Grover's search circuits for $N=16$ which were not yet reported in the state of the art; b) it shows performance difference between simulation results and results obtained on real devices; c) it shows the need of adding error-correction on the circuit for $N\ge 2^5$.
A Survey of Deep Learning for Mathematical Reasoning

Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The development of artificial intelligence (AI) systems capable of solving math problems and proving theorems has garnered significant interest in the fields of machine learning and natural language processing. For example, mathematics serves as a testbed for aspects of reasoning that are challenging for powerful deep learning models, driving new algorithmic and modeling advances. On the other hand, recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning. In this survey paper, we review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. We also evaluate existing benchmarks and methods, and discuss future research directions in this domain.
Masking Speech Feature to Detect Adversarial Examples for Speaker Verification

Adversarial examples of speaker verification (SV) systems are the clean audio recordings added with imperceptible perturbation. They are generated to manipulate the decision of SV, which poses a serious threat to the security of SV. Therefore, many adversarial example detection methods have been proposed to defend against such adversarial attacks. However, existing methods either require additional training of detection models or are time-consuming. In this paper, we propose a non-training and effective method to detect adversarial examples. It simply masks the parts of the input speech features (e.g. LogFBank) that contain less speaker information. The masked parts will inevitably have a small impact on genuine examples, and large impact on adversarial examples. Therefore, the adversarial examples can be detected by analyzing the absolute alteration of scores before and after masking. Experimental results on ResNet34 showed that our method outperforms the training-dependent Parallel-Wave-GAN baseline, and only consumes 1/10 of the detection time of the baseline.
Blur Detection for Surveillance Camera System

Surveillance cameras, which are often placed in unconstrained environments, can be tampered with due to many environmental and human factors. It results in degraded surveillance videos and affects the subsequent smart applications that make use of the videos in decision-making. Blur anomaly is one of the most typical problems in those videos, which have the target objects in the videos significantly blurred or partially occluded. Such blur anomalies should be detected as soon as possible to ensure the integrity of the video data. In this study, a novel self-supervised blur detection model is proposed. The model focuses on the detection of four commonly found blur anomalies in surveillance videos. They are the natural blur, defocus blur, dirt blur, and spray-paint blur. By using the self-supervised learning method, we can fully make use of the abundant positive samples to improve detection accuracy. Since, in many situations, camera anomaly detection needs to be carried out with edge devices, we also propose a compressed residual network, which incorporates a color attention module, to reduce the model complexity so that it can be applied to edge applications. Our experimental results show that the proposed model significantly improves over the existing approaches in terms of complexity and accuracy.
Association Rules Discovery of Deviant Events in Multivariate Time Series: An Analysis and Implementation of the SAX-ARM Algorithm

In this work, we propose an open-source Python implementation of the SAX-ARM algorithm introduced by Park and Jung (2019).This algorithm mines association rules efficiently among the deviant events of multivariate time series.To do so, the algorithm combines two existing methods, namely the Symbolic Aggregate approXimation (SAX) from Lin et al. ( 2003) -a symbolic representation of time series -and the Apriori algorithm from Agrawal et al. (1996) -a data mining method which outputs all frequent itemsets and association rules from a transactional dataset.A detailed description of the underlying principles is given along with their numerical implementation.The choice of relevant parameters is thoroughly discussed and evaluated using a public dataset on the topic of temperature and energy consumption.
A Link Prediction-based Recommendation System using Transactional Data

Abstract Recommending relevant items to users has become an important task in many systems due to the increased amount of data produced. For this purpose, transaction datasets such as credit card transactions and e-commerce purchase histories can be used in recommendation systems to understand underlying user interests by exploiting user-item interactions, which can be a powerful signal to perform this task. This study proposes a link prediction-based recommendation system combining graph representation learning algorithms and gradient boosting classifiers for transaction datasets. The proposed system generates a network where nodes correspond to users and items, and links represent their interactions. A use case scenario is examined on a credit card transaction dataset as a merchant prediction task that predicts the merchants where users can make purchases in the next month. Performances of common network embedding extraction techniques and classifier models are evaluated via various experiments conducted and based on these evaluations, a novel system is proposed, and a matrix factorization-based alternative recommendation method is compared with the proposed model. The proposed method has shown superior performance to the alternative method in terms of receiver operating characteristic curves, area under the curve, and mean average precision metrics. The use of transactional data for a recommendation system is found to be a powerful approach to making relevant recommendations.
EXPLORING THE POTENTIAL OF VOICE INTERFACES FOR EMERGING TECHNOLOGIES: OPPORTUNITIES AND CHALLENGES

Abstract Successful human-technology interactions require that people be able to communicate their intentions to the technology. Emergent technologies such as virtual assistants, digital home assistant, smart phones, robots, and smart appliances are enabling voice activation and communication. This is potentially a positive trend as people often express a preference for using familiar methods of communicating with other people in their interactions with technologies. Presumably they expect these methods to be easier to learn and more effective. However, current voice technology interfaces are often dependent on specific language, cadence, and terminology that pose challenges for older adults to master. Moreover, age-related changes in voice characteristics, speech patterns, and the variability between individuals may lead to comprehension failures on the part of the technology. We have utilized research findings to develop implementation guidelines for use of voice interfaces with older adults. We also identify research gaps to provide a roadmap for future efforts.
Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization

Answering complex logical queries on incomplete knowledge graphs is a challenging task, and has been widely studied. Embedding-based methods require training on complex queries, and cannot generalize well to out-of-distribution query structures. Recent work frames this task as an end-to-end optimization problem, and it only requires a pretrained link predictor. However, due to the exponentially large combinatorial search space, the optimal solution can only be approximated, limiting the final accuracy. In this work, we propose QTO (Query Computation Tree Optimization) that can efficiently find the exact optimal solution. QTO finds the optimal solution by a forward-backward propagation on the tree-like computation graph, i.e., query computation tree. In particular, QTO utilizes the independence encoded in the query computation tree to reduce the search space, where only local computations are involved during the optimization procedure. Experiments on 3 datasets show that QTO obtains state-of-the-art performance on complex query answering, outperforming previous best results by an average of 22%. Moreover, QTO can interpret the intermediate solutions for each of the one-hop atoms in the query with over 90% accuracy. The code of our paper is at https://github.com/bys0318/QTO.
OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments

Unlike human daily activities, existing publicly available sensor datasets for work activity recognition in industrial domains are limited by difficulties in collecting realistic data as close collaboration with industrial sites is required. This also limits research on and development of AI methods for industrial applications. To address these challenges and contribute to research on machine recognition of work activities in industrial domains, in this study, we introduce a new large-scale dataset for packaging work recognition called OpenPack. OpenPack contains 53.8 hours of multimodal sensor data, including keypoints, depth images, acceleration data, and readings from IoT-enabled devices (e.g., handheld barcode scanners used in work procedures), collected from 16 distinct subjects with different levels of packaging work experience. On the basis of this dataset, we propose a neural network model designed to recognize work activities, which efficiently fuses sensor data and readings from IoT-enabled devices by processing them within different streams in a ladder-shaped architecture, and the experiment showed the effectiveness of the architecture. We believe that OpenPack will contribute to the community of action/activity recognition with sensors. OpenPack dataset is available at https://open-pack.github.io/.
Limits of quantum speed-ups for computational geometry and other  problems: Fine-grained complexity via quantum walks

Many computational problems are subject to a quantum speed-up: one might find that a problem having an O(n^3)-time or O(n^2)-time classic algorithm can be solved by a known O(n^1.5)-time or O(n)-time quantum algorithm. The question naturally arises: how much quantum speed-up is possible? The area of fine-grained complexity allows us to prove optimal lower-bounds on the complexity of various computational problems, based on the conjectured hardness of certain natural, well-studied problems. This theory has recently been extended to the quantum setting, in two independent papers by Buhrman, Patro, and Speelman (arXiv:1911.05686), and by Aaronson, Chia, Lin, Wang, and Zhang (arXiv:1911.01973). In this paper, we further extend the theory of fine-grained complexity to the quantum setting. A fundamental conjecture in the classical setting states that the 3SUM problem cannot be solved by (classical) algorithms in time O(n^{2-a}), for any a>0. We formulate an analogous conjecture, the Quantum-3SUM-Conjecture, which states that there exist no sublinear O(n^{1-b})-time quantum algorithms for the 3SUM problem. Based on the Quantum-3SUM-Conjecture, we show new lower-bounds on the time complexity of quantum algorithms for several computational problems. Most of our lower-bounds are optimal, in that they match known upper-bounds, and hence they imply tight limits on the quantum speedup that is possible for these problems.
An Unhealthy Webpage Discovery System Based on Convolutional Neural Network

Currently, with the popularity of the internet, people are surrounded by a large number of unhealthy pages which have a serious impact on the physical and mental health of visitors. To protect the legitimate rights and interests of internet users from infringement and maintain the harmonious and stable development of society, a new unhealthy webpage discovery system is needed. First, this paper proposed the knowledge of unhealthy webpages and web crawlers, and then the whole system's plan and design were introduced. The test results show that the unhealthy webpage discovery system can meet the needs of users. This experiment uses a CNN algorithm to classify the text and completes the collection and classification of unhealthy information through URL acquisition and URL filtering. The experimental results show that the unhealthy webpage discovery system based on a convolutional neural network can greatly improve the accuracy of unhealthy webpage discovery and reduce the omission rate, which can meet the needs of users for unhealthy webpage discovery.
Uncrewed Ground Vehicles (UGVs) and Nature-Inspired Designed Robot DIGIT and SPOT: A Review

Researchers around the world are developing autonomous robot systems to assist humans with tedious, hazardous, complex, and timeconsuming tasks.Today, autonomous robots are used for a variety of applications, ranging from delivering food to operating on patients.A robotic system can be operated with or without human assistance, depending on whether it is manned or unmanned.Additionally, the robot may be classified as an Unmanned Aerial Vehicle (UAV), Unmanned Surface Vehicle (USV), or Unmanned Ground Vehicle (UGV), depending on its operational conditions.An autonomous robot may be equipped with manipulators and advanced sensors, such as a LiDAR camera, which can collect 3D spatial data.The present study reviews recent research and development of unmanned ground vehicles, including two nature-inspired designed robots called SPOT and DIGIT and presents its technical data.In addition, this study introduces the challenges and applications of these robots.A novel type of comparison was made in this study between some UGVs, human and animalinspired designed robots, where researchers were able to get an idea of different robot basic specifications that could be used to select these robots for future research.As a result of this study, two more studies will be conducted in the future.