Reviewerly/exHarmony huggingface dataset:
A Tankendo Robot that Defends Itself from Opponent Attacks by Moving Immediately before a Valid Strike or Thrust

The final goal of this paper was to realize swordplay (Tankendo) matches between humans and robots. For a mobile trolley robot that cannot suddenly start and stop like a human, we devised a robot system design method that would allow a robot to defend against human attack by moving immediately before a valid attack. In the preliminary experiment, of the control parameters for the mobile trolley robot used, the authors changed the direction of movement and rotational elements to find the best conditions for a robot to avoid taking defensive action until the last possible second to avoid an attack. In this experiment, we calculated the threshold from the movement speed of the center of gravity of the body during striking and thrusting by a human and designed a system to determine the robot defense initiation time.
Lifelog Image Retrieval Based on Semantic Relevance Mapping

Lifelog analytics is an emerging research area with technologies embracing the latest advances in machine learning, wearable computing, and data analytics. However, state-of-the-art technologies are still inadequate to distill voluminous multimodal lifelog data into high quality insights. In this article, we propose a novel semantic relevance mapping ( SRM ) method to tackle the problem of lifelog information access. We formulate lifelog image retrieval as a series of mapping processes where a semantic gap exists for relating basic semantic attributes with high-level query topics. The SRM serves both as a formalism to construct a trainable model to bridge the semantic gap and an algorithm to implement the training process on real-world lifelog data. Based on the SRM, we propose a computational framework of lifelog analytics to support various applications of lifelog information access, such as image retrieval, summarization, and insight visualization. Systematic evaluations are performed on three challenging benchmarking tasks to show the effectiveness of our method.
National Library of Latvia Subject Headings as Linked Open Data

This paper presents the linked data representation of the National Library of Latvia (NLL) authority data consisting of topical, geographic name and form/genre thesauri. It is an important step from a data silo (MARC 21 data) to linked open datasets, represented using SKOS, that can be reused both within and outside the library domain. The datasets are converted to SKOS using mc2skos and are published as linked data using the Skosmos application for publishing SKOS datasets. The paper describes the datasets, including information about their external links. The NLL topical terms dataset, in particular, contains many external links – 85% of its concepts have links to the Library of Congress subject headings dataset that is available as linked open data. The published datasets can be applied to interconnecting Latvia's cultural heritage information by describing museum and archive items with URIs from these datasets.
Workload and Renewable Energy Prediction in Cloud Data Centers with Multi-scale Wavelet Transformation

In recent years, cloud computing and big data services are widely adopted by large-scale enterprises. The energy consumption of cloud data centers (CDCs) has also increased dramatically. To effectively reduce the harm on the environment, a growing number of CDCs consider renewable energy instead of fossil energy, and concentrate on reducing idle time of servers by forecasting short-term workload demands for proactively provisioning computational resources and balancing server load in advance. However, due to temporal fluctuation in workload demands and renewable energy, it is a huge challenge to precisely predict their short-term trends. This work adopts basic methods in the field of signal processing and proposes a time series prediction method based on multi-scale wavelet transformation. Extensive experiments based on real-life datasets demonstrate that the proposed method achieves higher accuracy than several typical baseline methods.
Gesture-Based Human-Robot Interaction

The paper proposes a method for human robot interaction through gestures. Even if there are different methods that perform gesture recognition, many of them are tested in ideal conditions. This paper proposes a convolutional neural network that is used for gesture recognition applied for human-robot interaction in different environmental conditions: such as: blurring, low or high illumination, low quality image or losing part of frames. We started from a pre-trained ResNeXt 101 model and we increased the accuracy by adding a preprocessing stage for improving the quality of image, applying data augmentation, using different optimisers and learning rate scheduler. The proposed model obtained an improvement of 0.9% compared to the baseline model. Online evaluation of gesture recognition was performed using the Turtlebot robot.
Visual Analysis Application for the Error Messages Clustering Framework

The framework for clustering of error messages, ClusterLogs, was developed as a flexible and modular tool for the needs of large-scale distributed computing infrastructures. Various types of failures are being constantly registered during the execution of millions of computing operations daily. Monitoring systems are faced with a challenging task of the analysis of considerable amount of multi-sourced error messages. It is critical to present the information about errors to human experts in a way that makes them able to analyze it. ClusterLogs is a pipeline of methods that allows the discovering of common patterns in error messages and group similar messages for further analysis. This pipeline is able to cluster heterogeneous messages without any prior knowledge about textual pattern and can be executed in a fully automated way. In this work we describe a visual analytics web application that provides the analyst with the visual means of setting up clustering pipeline parameters and facilitates the interpretation and validation of the clustering results.
Environmentally Friendly Composable Multi-party Computation in the Plain Model from Standard (Timed) Assumptions

Starting with the work of Rivest et al. in 1996, timed assumptions have found many applications in cryptography, building e.g. the foundation of the blockchain technology. They also have been used in the context of classical MPC, e.g. to enable fairness. We follow this line of research to obtain composable general MPC in the plain model.
Standardizing Crash Carts Using Collaborative Design Methods And Usability Testing

Crash carts are mobile containers of medications and supplies used to resuscitate patients in emergencies within health care facilities. Contents and organization of carts often differ across hospitals and even departments within the same hospital. To support our team members in emergencies across our multi-hospital system, we created a system safety initiative to standardize and optimally organize adult crash carts. Our team established a three-phase user-centered research and design approach. First, we facilitated a collaborative design workshop with front line stakeholders to develop an initial cart organization. Second, 59 clinicians across 8 departments interacted with a cart mock-up and provided feedback. Finally, 18 clinicians participated in usability testing of the updated cart in 3 separate full simulations of realistic patient emergencies including an airway emergency (anaphylaxis), cardiac arrest (ventricular fibrillation), and sepsis using manikins. Observations and feedback from all three research phases contributed to a final medical supply list and crash cart design for our hospital system. This work also provides guidance for using co-design methods and usability testing within hospital environments.
On LCD codes over Z <sub>4</sub>

Abstract Linear complementary dual(LCD) codes are being utilized to protect cryptographic implementations from side-channel and fault attacks. In this work, LCD codes over the ring Z 4 are examined. It is observed that an LCD cyclic code over Z 4 of odd length is always reversible. However, the converse of this result does not hold. A suitable example to validate this statement is presented. Further, a necessary and sufficient condition for a reversible cyclic code of odd length to be an LCD code is obtained. Finally, it is found that among cyclic codes of length 2 k , no non-trivial LCD code exists.
Sentiment Analysis of Arabic Tweets Related to COVID-19 Using Deep Neural Network

Along with the Coronavirus pandemic, several other severe crises also spiraled worldwide. Different industries are getting irreparable scathed and many organizations succumbed to this havoc. There is an inevitable need to analyze different trends going on social media platforms to alleviate the fear and misconceptions among public. The research plays out a thorough investigation on the emotional directions of the Arabic public dependent on social media using Twitter platform particular. We have extracted data from Twitter from November 2020 to January 2021.There are tweets from different cities of Arab. Natural language processing NLP and Machine learning ML capabilities are used to analyze whether an opinion's sentiment is positive, negative, or neutral. This research scrapes around Arabic tweets and then after manual annotation to classify the tweets into different sentiments like negative, positive, neutral, etc. This research use TFIDF and word embedding as a feature vector and then use Long Short-Term Memory and Naïve Bayes as classification. This work using two advanced machine learning methods, present a learned long short term memory LSTM model and a Nave Bayes model on the collected tweets. In addition, compare the performance of the Nave Bayes and LSTM models. In comparison with the Naïve Bayes the LSTM model performs better with an accuracy of 99%. The work analysis helps different Government and private organizations to understand public sentiments, their behavior towards this pandemic and then act make strategic decisions accordingly. In addition, this research focuses on data visualization by displaying a sentiment plot and a word cloud.
Function translations and search-based transformation for MVL reversible circuit synthesis

Two new function translations and a novel bounded search transformation-based synthesis approach that finds a reversible circuit implementation for a given 3-valued reversible function are presented. Methods for simplifying the circuit post-synthesis and quantum circuit implementation constraints are also considered. Experimental results for all 2-input 3-valued functions show the effectiveness of the new approaches compared to earlier transformation-based synthesis approaches. Other examples are given to show both the effectiveness and limitations of the new approaches which point to a number of key areas for further research.
Who are the dominant players in the experimental field of quantum entanglement? A bibliometric analysis

Abstract Quantum entanglement is a major research resource in quantum information science, which has obtained increasing attention and has been intensively studied experimentally in recent years. Based on 7089 research papers in the experimental field of quantum entanglement obtained from the Web of Science Core Collection database, this study investigates the 400 top-cited papers which were written by 498 first or corresponding authors from188 institutions in 32 countries/territories. This study not only relies on traditional methods with first author full counting and corresponding author full counting to present the critical authors, institutions, and countries as designers or executors but also uses distinctive methods with first/corresponding author full counting and first/corresponding author fractional counting to find the dominant players in the experimental field of quantum entanglement. Considering authors, A. Zeilinger and J.W. Pan are the most dominant scientists in this field. The University of Vienna has the best institutional performance in this field, and other institutions such as the University of Science and Technology of China also do fairly well. The results show that the USA, Germany, Austria, the UK, and mainland China fit their reputations as dominant players in the field.
An Evaluation of Eye-Foot Input for Target Acquisitions

The multimodal interaction technique that combines eye and foot input not only provides a great opportunity for the user with busy hands or hand disabilities to interaction with computer, but can also overcome the drawbacks when using dwell time to solve the "Midas Problem" of gaze input. However, the user's capability of eye-foot coordination was still unclear. At the same time, the human performance in the basic task of target acquisitions by eye-foot input was also uncertain, and especially a proper performance model was lacking. Motivated by this situation, an eye pointing and foot tapping task experiment had been carried out to fill these gaps. A low-cost eye tracker and a USB foot pedal switcher were used as the input devices from different modalities. The experimental results indicated that the user was soon able to coordinate her/his foot with the eyes for target acquisitions, and that the user could respond fast to tap the foot pedal to finish a task trial in the level of 600 ms. The main performance measures of eye movement time (EMT) and eye pointing time (EPT) under the eye-foot multimodal input condition were significantly increased with the increase of saccadic amplitude A and/or the decrease of target width (size) W, and vice versa. Regression analysis shown that the $$ID_{eye}$$ model was more suitable than the standard Fitts' law to model the human performance in this multimodal interaction context.
Accelerating Fully Spectral CNNs with Adaptive Activation Functions on FPGA

Computing convolutional layers in frequency domain can largely reduce the computation overhead for training and inference of convolutional neural networks (CNNs). However, existing designs with such an idea require repeated spatial- and frequency-domain transforms due to the absence of nonlinear functions in the frequency domain, as such it makes the benefit less attractive for low-latency inference. This paper presents a fully spectral CNN approach by proposing a novel adaptive Rectified Linear Unit (ReLU) activation in spectral domain. The proposed design maintains the non-linearity in the network while taking into account the hardware efficiency in algorithm level. The spectral model size is further optimized by merging and fusing layers. Then, a customized hardware architecture is proposed to implement the designed spectral network on FPGA device with DSP optimizations for 8-bit fixed point multipliers. Our hardware accelerator is implemented on Intel's Arria 10 device and applied to the MNIST, SVHN, AT&T and CIFAR-10 datasets. Experimental results show a speed improvement of 6 × ~ 10 × and 4 × ~ 5.7 × compared to state-of-the-art spatial or FFT-based designs respectively, while achieving similar accuracy across the benchmark datasets.
Memory-Hard Puzzles in the Standard Model with Applications to Memory-Hard Functions and Resource-Bounded Locally Decodable Codes

We formally introduce, define, and construct memory-hard puzzles. Intuitively, for a difficulty parameter t, a cryptographic puzzle is memory-hard if any parallel random access machine (PRAM) algorithm with “small” cumulative memory complexity ( $$\ll t^2$$ ) cannot solve the puzzle; moreover, such puzzles should be both “easy” to generate and be solvable by a sequential RAM algorithm running in time t. Our definitions and constructions of memory-hard puzzles are in the standard model, assuming the existence of indistinguishability obfuscation ( $$i\mathcal {O}$$ ) and one-way functions (OWFs), and additionally assuming the existence of a memory-hard language. Intuitively, a language is memory-hard if it is undecidable by any PRAM algorithm with “small” cumulative memory complexity, while a sequential RAM algorithm running in time t can decide the language. Our definitions and constructions of memory-hard objects are the first such definitions and constructions in the standard model without relying on idealized assumptions (such as random oracles). We give two applications which highlight the utility of memory-hard puzzles. For our first application, we give a construction of a (one-time) memory-hard function (MHF) in the standard model, using memory-hard puzzles and additionally assuming $$i\mathcal {O}$$ and OWFs. For our second application, we show any cryptographic puzzle (e.g., memory-hard, time-lock) can be used to construct resource-bounded locally decodable codes (LDCs) in the standard model, answering an open question of Blocki, Kulkarni, and Zhou (ITC 2020). Resource-bounded LDCs achieve better rate and locality than their classical counterparts under the assumption that the adversarial channel is resource bounded (e.g., a low-depth circuit). Prior constructions of MHFs and resource-bounded LDCs required idealized primitives like random oracles.
Treatment of produced water from Niger Delta oil fields using sequential mixture of bio-adsorbents

Produced water (PW) exploited from the reservoir alongside oil often contains heavy metals and other impurities that are harmful to man and the environment. Most of the PW treatment technologies that have been in use for years have been reported to be ineffective in reducing some of the impurities and metal concentrations to allowable disposal levels. This study evaluated the effectiveness of four local materials combined for treating PW from Niger Delta oil fields. Orange peels (I), banana peels (II), sponge gourd (Luffa cylindrica) (III) and palm kernel fibers (IV) were washed with distilled water, sun-dried (24 hours) and oven-dried at 105 ± 5°C (3 hours, I and II), 150°C (30 minutes, III) and 80°C (3 hours, IV). They were ground into powder, sieved (150 microns, Group A) and (300 mcicrons, Group B), washed with 0.4 mol/L HNO3, filtered and rinsed with distilled water. Samples of PW were obtained from fields R, X, and Y in the Niger Delta and analyzed for heavy metals using an Atomic Absorption Spectrophotometer (AAS). The PW samples were treated with the bio-adsorbents sequentially in the adsorption column for over 7 hours. Langmuir and Freundlich models were used to evaluate the samples and the Data was analyzed based on regression method. The 150 micron size of sample R, lead (Pb), cadmium (Cd), copper (Cu), and chromium (Cr) were completely (100%) adsorbed from the PW after 8 hours of treatment while other metals (Ni, Fe, Mg, Zn, Mn, Ca, Ar, B, Sn, Ba) were found to be 86.11%, 90.76%, 39.25%, 94.40%, 43.55%, 99.26%, 54.62%, 44.53%, 84.52%, and 47.37%, respectively. while the reduction in 300 micron size were 23.48% for Pb, 80.20% for Ni, 71.42% for Cd, 19.73%, for Cu. 43.65% for Fe, 34.79% for Mg, 12.67% for Cr, 88.8% for Zn, 35.48% for Mn, 98.95% for Ca, 23.01% for Ar, 44.52% for B, 14.83% for Sn and 23.68% for Ba, respectively. The finer adsorbent with large surface area was more effective. Similar results were obtained for PW samples from the other fields. Sequential mixture of the bio-adsorbents selected proved valuable in the treatment of contaminants in PW from Niger Delta oil fields.
Blockchain-Sidechain Based Data Storage for Reimaging Electronic Health Record via Optimized Interplanetary File System

Modern healthcare systems are extremely complicated and expensive. This can be minimized through better monitoring of health records, using Blockchain technology. The first implementation of Blockchain was done to provide distributed records of money-related transactions which were not reliant on unified authorities or financial institutions and seem to provide the way for trend in the traditional healthcare industry benefitting by its unique features such as data isolation and transparency. The system proposes a Sidechain-Blockchain Technology to store and manage patient data and authentication of the same. In addition to that, the main blockchain can transmit or share the patient information record with other smart systems. The system is implemented using optimized Interplanetary File System in order to provide faster and safe access of information between the main Blockchain and side blockchain using Ethereum Dapp. The proposed model uses different development environments to build a highly effective EHR that is not controlled by a single point of authority and uses a decentralized system for storage. It’s worth noting that the implementation of Blockchain-sidechain on top of ethereum results in patient-centered access to the data and improves scalability.
Analysis of Cloud Providers' Pricing Strategy based on extended Hotelling Model

As more and more cloud providers attracting users to participate in technological innovation activities, we use the extended Hotelling model to analyze the influence of user participation on cloud provider's equilibrium price , profits and competitive strategy from the perspective of cloud market competition. On the basis of these, we apply the relevant knowledge of game theory and consider the costs and performance of user participation in innovation. In this paper, we conclude that user participation in innovation strategy can help cloud providers to make the reasonable strategy and obtain competitive advantage.
Improved Genetic Algorithm and Coding Method for Cooperative Search of UAV Group

In view of the multi-UAV cooperative area coverage search problem, the analysis and simplification are carried out. A number of improvement measures are proposed on the basis of the classic genetic algorithm to improve the adaptability of the algorithm and adapt to the engineering application. The “zero-adding method” and the “negative number method” are mainly proposed. These two encoding methods are used to simulate and verify the optimization solution of a typical scene. The simulation results prove that both methods achieve the balance of search optimization accuracy and speed. Among them, the effect of the “negative number method” is slightly better than the “zero-filling method”.
Big Data Technology in Intelligent Translation Algorithm in Computer Science

With the rapid development of science and technology, more and more communication and exchanges are transmitting information through the network, which has higher and higher requirements for the difficulty of intelligent translation. On this basis, this article will specifically analyze the main factors that affect computer network information communication, take the computer network information intelligent translation as the starting point, and improve the level of information intelligent translation based on the combination of intelligent translation algorithms. Based on traditional algorithms, this paper can use high-strength artificial fish school algorithms to collect and process the data in the network more efficiently, so as to prevent errors in information translation. The intelligent translation algorithm model model studied in this paper mainly includes information collection and processing and more efficient and comprehensive translation of information. Through the analysis, it can be understood that the intelligent algorithm is to transform the key data and the key string. With the rapid development of big data technology, intelligent translation in computer network information can efficiently and accurately translate confidence. Experimental research results show that in the intelligent translation of computer network information, the use of artificial fish swarm algorithms can effectively improve the efficiency of information translation and the accuracy of translation results, and the speed and accuracy of traditional translation have doubled.