Reviewerly/exHarmony huggingface dataset:
A Benchmark for the UEQ+ Framework: Construction of a Simple Tool to Quickly Interpret UEQ+ KPIs

Questionnaires are a highly efficient method to compare the user experience (UX) of different interactive products or versions of a single product.Concretely, they allow us to evaluate the UX easily and to compare different products with a numeric UX score.However, often only one UX score from a single evaluated product is available.Without a comparison to other measurements, it is difficult to interpret an individual score, e.g. to decide whether a product's UX is good enough to compete in the market.Many questionnaires offer benchmarks to support researchers in these cases.A benchmark is the result of a larger set of product evaluations performed with the same questionnaire.The score obtained from a single product evaluation can be compared to the scores from this benchmark data set to quickly interpret the results.In this paper, the first benchmark for the UEQ+ (User Experience Questionnaire +) is presented, which was created using 3.290 UEQ+ responses for 26 successful software products.The UEQ+ is a modular framework that contains a high number of validated user experience scales that can be combined to form a UX questionnaire.Currently, no benchmark is available for this framework, making the benchmark constructed in this paper a valuable interpretation tool for UEQ+ questionnaires.
Non-iterative phase tilt interferometry for the tilt shift calculation and phase extraction

This study proposed a non-iterative phase tilt interferometry (NIPTI) for the tilt shift calculation and phase extraction, which can solve the random tilt-shift problem caused by external vibration. The method approximates the higher order terms of the phase to make it suitable for linear fitting. Then according to an estimated tilt, the accurate tilt shift can be obtained by least squares method without iteration, and the phase distribution can be calculated by it. The simulation results indicated that the root mean square error of the phase calculated by the NIPTI can reach up to 0.0002λ. The experimental results showed that the calculated phase exhibited no significant ripple when using the NIPTI for cavity measurements in a time-domain phase shift Fizeau interferometer. Moreover, the root mean square repeatability of the calculated phase reached up to 0.0006λ. It indicates that the NIPTI provides an efficient and high-precision solution for random tilt-shift interferometry under vibration.
Data Recovery and Backup management: A Cloud Computing Impact

Cloud computing is the collection of excellent services (storage, databases, etc.) provided to customers and organizations over the internet to fulfill their requirements efficiently. Everything depends on the internet, and you can access your data anywhere. On the other hand, cloud service clients must ensure that the outsourced backup is secure enough to entrust with their data. This research highlighted how the cloud has benefited people by providing unlimited, secure storage without any high cost of investment in hardware. Cloud storage also aids in the security of your data backup, allowing you to access your files and documents even if your hardware is lost or damaged. It is also known as disaster recovery. Google Photos and Google Drive are examples of adequately encrypted backup storage. This paper also discusses the core functionalities of Google photos and Google drive and their advantages in a more comprehensive form. This research’s main aim is to provide users with awareness regarding data loss and recovery. Furthermore, threats related to modern technology are also part of this research. In addition, the article also included some graphs and figures to explain the results and facts in detail.
Multi-Object Detection and Tracking Using Reptile Search Optimization Algorithm with Deep Learning

Multiple-Object Tracking (MOT) has become more popular because of its commercial and academic potential. Though various techniques were devised for managing this issue, it becomes a challenge because of factors such as severe object occlusions and abrupt appearance changes. Tracking presents the optimal outcomes whenever the object moves uniformly without occlusion and in the same direction. However, this is generally not a real scenario, particularly in complicated scenes such as dance events or sporting where a greater number of players are tracked, moving quickly, varying their speed and direction, along with distance and position from the camera and activity they are executing. In dynamic scenes, MOT remains the main difficulty due to the symmetrical shape, structure, and size of the objects. Therefore, this study develops a new reptile search optimization algorithm with deep learning-based multiple object detection and tracking (RSOADL–MODT) techniques. The presented RSOADL–MODT model intends to recognize and track the objects that exist with position estimation, tracking, and action recognition. It follows a series of processes, namely object detection, object classification, and object tracking. At the initial stage, the presented RSOADL–MODT technique applies a path-augmented RetinaNet-based (PA–RetinaNet) object detection module, which improves the feature extraction process. To improvise the network potentiality of the PA–RetinaNet method, the RSOA is utilized as a hyperparameter optimizer. Finally, the quasi-recurrent neural network (QRNN) classifier is exploited for classification procedures. A wide-ranging experimental validation process takes place on DanceTrack and MOT17 datasets for examining the effectual object detection outcomes of the RSOADL–MODT algorithm. The simulation values confirmed the enhancements of the RSOADL–MODT method over other DL approaches.
Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithm

The construction of smart villages has begun in many Indonesian villages, along with the advancement of technology and local economic growth. Villagers must participate in constructing the smart economy-smart village by becoming familiar with the characteristics of the village's inhabitants using the citizen science model. This study intends to categorize villagers so that researchers can assess and decide their level of readiness for a smart economy in an ecosystem based on a smart village. Clustering is required to find communities of residents who are ready based on their traits. Using fuzzy C-Means with a Davied Bouldin Index value of 0.129, the data were divided into 4 clusters. The most important variables were chosen using information from the test's 300 responders, and the Kaiser Mayer Olkin assumption of 0.975 was used to validate the results. Our paper provides new information on how smart village readiness is assessed by the citizen science cluster. It was decided to divide residents into four groups: those who are less prepared (24.33%), those who are somewhat prepared (29.33%), those who are ready ( 25.67%) %), those who are ready (level of participatory knowledge), and those who are very ready for the smart economy (20.67%) based on the cluster model.
Aspect Based Sentiment Analysis Marketplace Product Reviews Using BERT, LSTM, and CNN

Bukalapak is one of the largest marketplaces in Indonesia. Reviews on Bukalapak are only in the form of text, images, videos, and stars without any special filters. Reading and analyzing manually makes it difficult for potential buyers. To help with this, we can extract this review by using aspect-based sentiment analysis because an entity cannot be represented by just one sentiment. Several previous research stated that using LSTM-CNN got better results than using LSTM or CNN. In addition, using BERT as word embedding gets better results than using word2vec or glove. For this reason, this study aims to classify aspect-based sentiment analysis from the Bukalapak marketplace with BERT as word embedding and using the LSTM-CNN method, where LSTM is for aspect extraction and CNN for sentiment extraction. Based on testing the LSTM-CNN method, it gets better results than LSTM or CNN. The LSTM-CNN model gets an accuracy of 93.91%. Unbalanced dataset distribution can affect model performance. With the increasing number of datasets used, the accuracy of a model will increase. Classification without using stemming on datasets can increase accuracy by 2.04%.&#x0D;
Human Detection from Drone using You Only Look Once (YOLOv5) for Search and Rescue Operation

Drones are unmanned aerial vehicles that can be remotely operated to perform a variety of tasks. They have been used in search and rescue operations since the early 2000s and have proven to be invaluable tools for quickly locating missing persons in difficult terrain and environment. In certain cases, automated human detection on drone camera feed can help the responder to locate the victims more effectively. In this work, we propose the use of a deep learning method called You Only Look Once version 5, or YOLOv5. The YOLOv5 model is trained using data collected during a simulation of search and rescue operations, where mannequins were used to represent human victims. Video was acquired using DJI Matrice 300 drone with Zenmuse H20T camera which flew around an area with various terrains such as farms, ravines, and river of more than 15,000 m2, at a height of 40 meters. The drone used grid, circular and zigzag flying patterns, with three different levels of camera zooms, and the data was captured on different days and times. The total duration of the video collected at 1080p@30fps is 148 minutes 26 seconds. Five pretrained models of YOLOv5 with different complexities were trained and tested using this dataset. Results showed that pretrained yolov5l6 model delivered the best precision, recall and mAP50 rate at 0.668, 0.303 and 0.346 respectively. Besides, the experiment also showed that we can improve the overall performance by using images acquired at 6x zoom magnification level where precision, recall, and mAP50 rate are increased to 0.846, 0.543, and 0.591 respectively. yolov5l6 model also delivered an acceptable inference time of 43ms per 1920x1080 resolution image, thus it can run at a respectable 23fps.
Context-aware Answer Selection in Community Question Answering Exploiting Spatial Temporal Bidirectional Long Short-Term Memory

Community Question Answering (CQA) sites provide knowledge sharing facility as the users can post questions and other users can share their answers. The selection of top-quality answers from the set of answers in a thread is a significant and challenging task in Natural Language Processing (NLP). To address this issue, we propose a deep learning based spatial temporal Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm. The existing studies mainly focus only computing semantic similarity between questions and answers using votes given by the users. The proposed hybrid approach, based on both forward and backward, consider question to answer and answer to answer similarity. The forward LSTM captures the spatial impact of the answer to estimate the relevancy, whereas the backward LSTM learns temporal features with the answer to predict the best quality answer. Moreover, spatial Bi-LSTM captures past and future dependencies for a better understanding of context and to improve the effectiveness of answer selection. For extracting meaningful information from noisy text data, data is preprocessed following standard steps such as tokenization, parsing, lemmatization, stop words removal, part of speech tagging and entities extraction. Word embeddings-based Paragraph to vector (par2vec) has additional input nodes to represent paragraph information in vector for context understanding. The empirical analysis carried out on the SemEval CQA dataset shows that the proposed model outperforms state-of-art answer selection approaches.
On-Chip Generation and Collectively Coherent Control of the Superposition of the Whole Family of Dicke States

Integrated quantum photonics has recently emerged as a powerful platform for generating, manipulating, and detecting entangled photons. Multipartite entangled states lie at the heart of the quantum physics and are the key enabling resources for scalable quantum information processing. Dicke state is an important class of genuinely entangled state, which has been systematically studied in the light-matter interactions, quantum state engineering and quantum metrology. Here, by using a silicon photonic chip, we report the generation and collectively coherent control of the entire family of four-photon Dicke states, i.e. with arbitrary excitations. We generate four entangled photons from two microresonators and coherently control them in a linear-optic quantum circuit, in which the nonlinear and linear processing are achieved in a chip-scale device. The generated photons are in telecom band, which lays the groundwork for large-scale photonic quantum technologies for multiparty networking and metrology.
Object and it’s dimension detection in real time

Object and its dimension detection from images and videos can be very helpful for everyday use. This paper discusses the use of the system to detect an object in real time and provide its dimensions upon demand. The object dimension measurement and detection are some of the important topics of computer vision which helps in automating the manual tasks. Human beings are capable of recognizing and spotting objects in images and videos, but computers lack that ability with out prior training. To train the computer, we must use machine learning, computer vision, and object detection algorithms. This project provides the way to detect and measure an object’s dimension in real time from a webcam. To estimate the object’s dimension in real time, we have utilized the OpenCV and NumPy libraries. Computer vision provides support to computers to observe and understand. Computer vision helps the computer in understanding a 3D surrounding from a 2D image and trains the computer to perform different functions. It also helps in Human Computer Interaction effectively because it is able to differentiate the objects with surroundings and provide us with the key information.
Fast Semantic Segmentation of Remote Sensing Images Using a Network That Integrates Global and Local Information

Efficient processing of ultra-high-resolution images is increasingly sought after with the continuous advancement of photography and sensor technology. However, the semantic segmentation of remote sensing images lacks a satisfactory solution to optimize GPU memory utilization and the feature extraction speed. To tackle this challenge, Chen et al. introduced GLNet, a network designed to strike a better balance between GPU memory usage and segmentation accuracy when processing high-resolution images. Building upon GLNet and PFNet, our proposed method, Fast-GLNet, further enhances the feature fusion and segmentation processes. It incorporates the double feature pyramid aggregation (DFPA) module and IFS module for local and global branches, respectively, resulting in superior feature maps and optimized segmentation speed. Extensive experimentation demonstrates that Fast-GLNet achieves faster semantic segmentation while maintaining segmentation quality. Additionally, it effectively optimizes GPU memory utilization. For example, compared to GLNet, Fast-GLNet's mIoU on the Deepglobe dataset increased from 71.6% to 72.1%, and GPU memory usage decreased from 1865 MB to 1639 MB. Notably, Fast-GLNet surpasses existing general-purpose methods, offering a superior trade-off between speed and accuracy in semantic segmentation.
A Multi-Step Fusion Network for Semantic Segmentation of High-Resolution Aerial Images

The demand for semantic segmentation of ultra-high-resolution remote sensing images is becoming increasingly stronger in various fields, posing a great challenge with concern to the accuracy requirement. Most of the existing methods process ultra-high-resolution images using downsampling or cropping, but using this approach could result in a decline in the accuracy of segmenting data, as it may cause the omission of local details or global contextual information. Some scholars have proposed the two-branch structure, but the noise introduced by the global image will interfere with the result of semantic segmentation and reduce the segmentation accuracy. Therefore, we propose a model that can achieve ultra-high-precision semantic segmentation. The model consists of a local branch, a surrounding branch, and a global branch. To achieve high precision, the model is designed with a two-level fusion mechanism. The high-resolution fine structures are captured through the local and surrounding branches in the low-level fusion process, and the global contextual information is captured from downsampled inputs in the high-level fusion process. We conducted extensive experiments and analyses using the Potsdam and Vaihingen datasets of the ISPRS. The results show that our model has extremely high precision.
Lilac: A Modal Separation Logic for Conditional Probability

We present Lilac, a separation logic for reasoning about probabilistic programs where separating conjunction captures probabilistic independence. Inspired by an analogy with mutable state where sampling corresponds to dynamic allocation, we show how probability spaces over a fixed, ambient sample space appear to be the natural analogue of heap fragments, and present a new combining operation on them such that probability spaces behave like heaps and measurability of random variables behaves like ownership. This combining operation forms the basis for our model of separation, and produces a logic with many pleasant properties. In particular, Lilac has a frame rule identical to the ordinary one, and naturally accommodates advanced features like continuous random variables and reasoning about quantitative properties of programs. Then we propose a new modality based on disintegration theory for reasoning about conditional probability. We show how the resulting modal logic validates examples from prior work, and give a formal verification of an intricate weighted sampling algorithm whose correctness depends crucially on conditional independence structure.
Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction

One of the arts in Surakarta culture is batik cloth. A batik is a form of heritage from the nation's ancestors whose manufacturing process must use specific tools and materials. Surakarta's typical batik has many patterns and motifs, such as Sawat, Satriomanah, and Semenrante. The pattern is a picture framework whose results will display the type of batik. A batik may resemble one type and another, so a classification technique is needed to determine the type of batik. This study aims to develop a classification method for batik cloth using the Naïve Bayes classification technique. The feature extraction used is the Gray Level Co-Occurrence Matrix (GLCM) to obtain texture values in each image. The stages in this research include pre-processing, feature extraction, classification, and testing. The training data in this study were 200 images for each Sawat, Satriomanah, and Sementrante class obtained from the data augmentation method by flipping, zooming, cropping, shifting, and changing the brightness of the images. The total sample data is 600 images. The amount of training data and data testing was divided three times (60% training and 40% testing), (70% training and 30% testing), and (80% training and 20% testing) for accuracy. In this study, the Naïve Bayes method using WEKA 3.8.6 tools obtained the best accuracy of 97.22% using a 70% percentage split compared to using 80% and 60% percentage splits with a result of 96.66%, this difference occurs due to differences in training data and test data. The results of this study indicate that the Naïve Bayes method can be used to classify batik cloth patterns based on texture feature extraction.
Loop Rerolling for Hardware Decompilation

We introduce the new problem of hardware decompilation . Analogous to software decompilation, hardware decompilation is about analyzing a low-level artifact—in this case a netlist , i.e., a graph of wires and logical gates representing a digital circuit—in order to recover higher-level programming abstractions, and using those abstractions to generate code written in a hardware description language (HDL). The overall problem of hardware decompilation requires a number of pieces. In this paper we focus on one specific piece of the puzzle: a technique we call hardware loop rerolling . Hardware loop rerolling leverages clone detection and program synthesis techniques to identify repeated logic in netlists (such as would be synthesized from loops in the original HDL code) and reroll them into syntactic loops in the recovered HDL code. We evaluate hardware loop rerolling for hardware decompilation over a set of hardware design benchmarks written in the PyRTL HDL and industry standard SystemVerilog. Our implementation identifies and rerolls loops in 52 out of 53 of the netlists in our benchmark suite, and we show three examples of how hardware decompilation can provide concrete benefits: transpilation between HDLs, faster simulation times over netlists (with mean speedup of 6x), and artifact compaction (39% smaller on average).
Cakes That Bake Cakes: Dynamic Computation in CakeML

We have extended the verified CakeML compiler with a new language primitive, Eval, which permits evaluation of new CakeML syntax at runtime. This new implementation supports an ambitious form of compilation at runtime and dynamic execution, where the original and dynamically added code can share (higher-order) values and recursively call each other. This is, to our knowledge, the first verified run-time environment capable of supporting a standard LCF-style theorem prover design. Modifying the modern CakeML compiler pipeline and proofs to support a dynamic computation semantics was an extensive project. We review the design decisions, proof techniques, and proof engineering lessons from the project, and highlight some unexpected complications.
The Internet of Vehicles (IoV) — Security, Privacy, Trust, and Reputation Management for Connected Vehicles

The IEEE IoT Magazine hosted a Virtual Roundtable to discuss the technologies, business models, governance regimes, and public perceptions of the challenges and opportunities for solutions to issues of security, privacy, and trust for connected vehicles.
Imperative Role of Blockchain in The Taxation System

Today, governments levy various forms of taxes on economic activities, including revenue from wages or consumption of goods and services, as well as on properties, in order to obtain the funds necessary to pay their public expenditures and provide public goods and services. As a result, taxes have increasingly become a major source of public funding and a key fiscal weapon used by governments to promote or restrain economic growth and the development of specific economic sectors in many nations today. Block chain is made up of a dispersed database of informational blocks that are encrypted using specialized hashing algorithms to allow data monitoring. The purpose of this study is to look into the effects of using blockchain technology for taxation. In the study, the detailed explanation related to the usage of blockchain technology for taxation was presented. The review's findings support the study's conclusion that the legal and administrative studies are still being conducted to simplify tax administration, enable efficient revenue collection, and lower fiscal deficits. It is also concluded that blockchain has made taxation fast, accurate, and accessible to everyone and is helping in economic development. As a result of its transparency, blockchain technology will cause upheaval and innovation in global tax regimes. In the future work, we will be studying the implementation of blockchain for taxation by proposing an framework and also we will carry out the study to explain the blockchain usage in taxation in real time.