Reviewerly/exHarmony huggingface dataset:
METER: A Mobile Vision Transformer Architecture for Monocular Depth Estimation

Depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. Deep learning algorithms for depth estimation have gained significant interest in recent years, owing to the potential benefits of this methodology in overcoming the limitations of active depth sensing systems. Moreover, due to the low cost and size of monocular cameras, researchers have focused their attention on monocular depth estimation (MDE), which consists in estimating a dense depth map from a single RGB video frame. State of the art MDE models typically rely on vision transformers (ViT) architectures that are highly deep and complex, making them unsuitable for fast inference on devices with hardware constraints. Purposely, in this paper, we address the problem of exploiting ViT in MDE on embedded devices. Those systems are usually characterized by limited memory capabilities and low-power CPU/GPU. We propose METER, a novel lightweight vision transformer architecture capable of achieving state of the art estimations and low latency inference performances on the considered embedded hardwares: NVIDIA Jetson TX1 and NVIDIA Jetson Nano. We provide a solution consisting of three alternative configurations of METER, a novel loss function to balance pixel estimation and reconstruction of image details, and a new data augmentation strategy to improve the overall final predictions. The proposed method outperforms previous lightweight works over the two benchmark datasets: the indoor NYU Depth v2 and the outdoor KITTI.
Domain-guided data augmentation for deep learning on medical imaging

While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Our objective was to test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found that using a context-preserving cut-paste strategy, we could create valid training data as measured by performance of the resulting trained model on the benchmark test dataset. When used in an online fashion, models trained on this hybrid data performed similarly to those trained using traditional data augmentation (FETAL-125 F-score 85.33 ± 0.24 vs 86.89 ± 0.60, p-value 0.014; OB-125 F-score 74.60 ± 0.11 vs 72.43 ± 0.62, p-value 0.004). Furthermore, the ability to perform augmentations during training time, as well as the ability to apply chosen augmentations equally across data classes, are important considerations in designing a bespoke data augmentation. Finally, we provide open-source code to facilitate running bespoke data augmentations in an online fashion. Taken together, this work expands the ability to design and apply domain-guided data augmentations for medical imaging tasks.
Multipartite Entanglement in a Microwave Frequency Comb

Significant progress has been made with multipartite entanglement of discrete qubits, but continuous variable systems may provide a more scalable path toward entanglement of large ensembles. We demonstrate multipartite entanglement in a microwave frequency comb generated by a Josephson parametric amplifier subject to a bichromatic pump. We find 64 correlated modes in the transmission line using a multifrequency digital signal processing platform. Full inseparability is verified in a subset of seven modes. Our method can be expanded to generate even more entangled modes in the near future.
The Effects of Teaching Presence on Students’ Motivation and Performance in A Long-term Online Gamified EFL Listening Course

Studies have proven that gamification in learning can increase motivation and engagement, however, since the pandemic broke out and instruction was held fully online for a long time, its effectiveness has been under question.The motivation was also reportedly decreased in long-term gamified classes.Nonetheless, in gamification research, teaching presence (TP) as the predictor of success in distance instruction has not adequately been included.The study investigated the effects of TP on students' motivation and performance in a long-term online gamified EFL listening class.Teaching Presence and eMUSIC motivation framework were used to guide the research.A mixed-method with quasiexperimental design and thematic analysis was employed to mine quantitative and qualitative data respectively.Two groups of treatment and control of 42 freshmen, 28 females and 14 males aged 17-19 taking Listening for General Communication 2 were involved in the study.Independent samples t-test and one-way repeated measures ANOVA by SPSS 27 were used for the analysis of quantitative data; meanwhile, thematic analysis with NVivo 12 was used for the analysis and interpretation of qualitative data.The study revealed that TP was relatively able to maintain students' motivation and enhance performance in a long-term gamified EFL listening class.The findings recommend similar research focusing on three other language skills and Caring subscale of eMUSIC motivation.
Social Network Analysis on Interpretable Compressed Sparse Networks

Big data are everywhere. World Wide Web is an example of these big data. It has become a vast data production and consumption platform, at which threads of data evolve from multiple devices, by different human interactions, over worldwide locations, under divergent distributed settings. Embedded in these big web data is implicit, previously unknown and potentially useful information and knowledge that awaited to be discovered. This calls for web intelligence solutions, which make good use of data science and data mining (especially, web mining or social network mining) to discover useful knowledge and important information from the web. As a web mining task, web structure mining aims to examine incoming and outgoing links on web pages and make recommendations of frequently referenced web pages to web surfers. As another web mining task, web usage mining aims to examine web surfer patterns and make recommendations of frequently visited pages to web surfers. While the size of the web is huge, the connection among all web pages may be sparse. In other words, the number of vertex nodes (i.e., web pages) on the web is huge, the number of directed edges (i.e., incoming and outgoing hyperlinks between web pages) may be small. This leads to a sparse web. In this paper, we present a solution for interpretable mining of frequent patterns from sparse web. In particular, we represent web structure and usage information by bitmaps to capture connections to web pages. Due to the sparsity of the web, we compress the bitmaps, and use them in mining influential patterns (e.g., popular web pages). For explainability of the mining process, we ensure the compressed bitmaps are interpretable. Evaluation on real-life web data demonstrates the effectiveness, interpretability and practicality of our solution for interpretable mining of influential patterns from sparse web.
Integrated cloud computing and blockchain systems: A review

Blockchain technology is one of the crypto-currency technologies that has received a lot of attention. It has also found use in various applications, including the Internet of Things (IoT) and Cloud computing. Nonetheless, Blockchain has a signiﬁcant scalability issue, restricting its ability to support services with various transactions. On the other hand, cloud computing is the on-demand availability of shared computer system resources, although issues now beset it in automation, processes, management, policies, and human aspects. Combining cloud computing and blockchain technology into a single system can improve network control, task scheduling, data integrity, resource management, pricing, fair payment, and resource allocation. In this article, we oﬀered a comprehensive and up-to-date survey of cloud computing and Blockchain integration, a critical service for business applications due to the beneﬁts of privacy, security, and service support. The lack of a comprehensive assessment examining the signiﬁcance of BaaS platforms used in cloud computing prompted this review. We focus on the various BaaS tools that are currently in use. This report also examines the most common BaaS platforms incorporating Blockchain as a cloud service, such as Alibaba, Oracle, Azure, Amazon, and IBM. Furthermore, this research highlighted some major technological issues associated with merging Blockchain with cloud computing.
The digital adoption of blockchain technology in the project management information systems: A value creation mediation model

Blockchain technology adoption among the businesses has become a new updated technological improvement and calls all businesses to care with it. This research aims to address its effect with project management information systems with a possible moderation effect of the value creation gained of this factor. The study used a quantitative research method to meet its objective and collected the data from Jordanian e-commerce enterprises with a total of 320 participants. PLS-SEM technique was used to perform the key analyses. The results found a significant effect of all hypothesized research assumptions and a significant moderating effect of value creation on the respective effect of study’s variables. The findings also expanded the current understanding and application of this technology and showed some implications and applicable insights that supported the existing literature and evidence in this field. The study would contribute to cover the limited research works and knowledge gaps with a suggestion to integrate this technology and application within different contextual settings.
TraVaS: Differentially Private Trace Variant Selection for Process Mining

Abstract In the area of industrial process mining, privacy-preserving event data publication is becoming increasingly relevant. Consequently, the trade-off between high data utility and quantifiable privacy poses new challenges. State-of-the-art research mainly focuses on differentially private trace variant construction based on prefix expansion methods. However, these algorithms face several practical limitations such as high computational complexity, introducing fake variants, removing frequent variants, and a bounded variant length. In this paper, we introduce a new approach for direct differentially private trace variant release which uses anonymized partition selection strategies to overcome the aforementioned restraints. Experimental results on real-life event data show that our algorithm outperforms state-of-the-art methods in terms of both plain data utility and result utility preservation.
Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information

Abstract Vehicle re-identification (ReID) aims to find a specific vehicle identity across multiple non-overlapping cameras. The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID dataset.
Development of an Educational Chatbot System for Enhancing Students’ Biology Learning Performance

&lt;p&gt;In traditional biology classrooms, teachers do not have enough class time to interact with students, and students often do not have the opportunities to raise questions in class. Therefore, out of class, students may not have sufficient ability to learn and review biology knowledge based on faulty foundations. To address the problem, it is important to provide suitable learning tools to support students conducting biology learning out of class. In recent years, chatbots have been adopted in various fields for providing instant interactions and supports to users. Therefore, by using chatbot technologies in biology education, students can interact with a chatbot out of class anytime and anywhere and further obtain instant learning supports. Bearing this in mind, the aim of this study is to propose a biology learning chatbot system that can support students to learn biology out of class. Moreover, a quasi-experiment was designed for a grade 7 biology course in a junior high school. 34 students were asked to participate in the experiment and divided into a control group and an experimental group. The experimental results indicated that the proposed chatbot system had a positive impact on students’ learning achievements in traditional biology learning.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Bidirectional LSTM and NLP Based Sentiment Analysis of Tweets

Twitter is one of the most popular social media platforms in today’s world. It is gaining more users and has already started to heavily impact people’s lives. Different people have different perspectives, and their thought processes are different as well. People have different reactions to different events, which can be both positive and negative. Sometimes the reaction can be neutral as well. As a popular social media platform, Twitter witnesses numerous sorts of reactions in the form of tweets. In the modern world, it is important to classify the types of sentiment in tweets. In this study, a natural language processing (NLP) based and bidirection LSTM implemented sentiment analysis of tweets has been proposed with an accuracy of 90%. This study looks at how people feel about a company’s products through microblogging sentiment analysis.
Energy Planning of Renewable Energy Sources in an Italian Context: Energy Forecasting Analysis of Photovoltaic Systems in the Residential Sector

Solar photovoltaic systems will play a key role in the country’s energy mix thanks to their ability to meet increasing energy needs while reducing greenhouse gas emissions. Despite the potential of solar photovoltaic energy, several criticalities remain, such as the intermittent nature and the need for significant land use for its implementation. In this regard, this work aimed at evaluating the photovoltaic potentiality in a national context by 2030 and 2050, considering only installations on the roof surfaces of existing buildings, i.e., without consuming additional land. This study has allowed the answering of three key points: (i) the roof surface could represent a valuable and alternative solution for new installations, since it could amount to around 450 km2, (ii) the national target cannot be reached by only using installations on existing buildings, although some regions could get close to the target by 2050, and (iii) long-term energy incentives should be implemented branching out to each national region, considering their photovoltaic potential. Finally, a regional potential index was also defined, capable of evaluating the photovoltaic potential in each region, helping policymakers to adopt the most suitable energy strategies.
Automatic Normalization of Temporal Expressions

Dates, periods and timespans are described in archaeological datasets using a number of different textual patterns for which myriad variations exist, rendering direct automated comparison difficult. The issue can occur even within records from the same dataset and is further compounded when attempting to integrate multilingual data – particularly where dates may be expressed in words rather than numbers. The same problem can be found in temporal metadata, whether manually entered or generated via Natural Language Processing (NLP) techniques from reports and grey literature. Resolving and normalizing dates and periods to internationally agreed standard formats enables efficient data integration, interchange, search, comparison and visualization. This paper reports on the design and implementation of a tool to normalize temporal expressions to a numerical time axis and reflects on key issues. Textual patterns for seven categories of temporal expression have been normalized: Ordinal named or numbered centuries; Year spans; Single year (with tolerance); Decades; Century spans; Single year with prefix; Named periods. The following languages are currently supported: Dutch, English, French, German, Italian, Norwegian, Spanish, Swedish, Welsh. Methods are described together with an (open source) normalization tool developed in Python and four applications of the method are discussed, together with limitations and future work. Results are presented from diverse data sets and languages. The input is a temporal text string and a language code (ISO639-1). The output is a tab delimited text file with start/end years (in ISO 8601 format), relative to Common Era (CE). The normalized outputs are provided as additional attributes along with the original text expression for consuming software to employ in end-user applications.
Fault-tolerant quantum computation of molecular observables

Over the past three decades significant reductions have been made to the cost of estimating ground-state energies of molecular Hamiltonians with quantum computers. However, comparatively little attention has been paid to estimating the expectation values of other observables with respect to said ground states, which is important for many industrial applications. In this work we present a novel expectation value estimation (EVE) quantum algorithm which can be applied to estimate the expectation values of arbitrary observables with respect to any of the system's eigenstates. In particular, we consider two variants of EVE: std-EVE, based on standard quantum phase estimation, and QSP-EVE, which utilizes quantum signal processing (QSP) techniques. We provide rigorous error analysis for both both variants and minimize the number of individual phase factors for QSPEVE. These error analyses enable us to produce constant-factor quantum resource estimates for both std-EVE and QSP-EVE across a variety of molecular systems and observables. For the systems considered, we show that QSP-EVE reduces (Toffoli) gate counts by up to three orders of magnitude and reduces qubit width by up to 25% compared to std-EVE. While estimated resource counts remain far too high for the first generations of fault-tolerant quantum computers, our estimates mark a first of their kind for both the application of expectation value estimation and modern QSP-based techniques.
The Effect of Instructional Media and Interpersonal Intelligence on Early Reading Skills

The interaction of kindergarten students with others is dominant in individual activities. It causes low early reading skills. The application of innovative learning media would attract students' interest in interacting with other children and optimize children's interpersonal intelligence. The study examined the effect of instructional media and interpersonal intelligence on early reading skills. This study used to design treatment by level 2 x 2. The selection of this method is based on the existence of treatment with variable attributes based on Interpersonal intelligence levels. Using simple random sampling 44 children were divided into four groups —data Analysis of Variance (ANOVA) and Tukey test. The study concluded that there was a significant difference between the effect of instructional media and media fun thinkers and big books for early reading skills. There was a significant interaction between instructional media and interpersonal intelligence on the early reading skills, (3) for a group that has a high degree of interpersonal intelligence and use fun thinkers media the effect early reading skills is higher than that used big books treatment media. For groups who have lower levels of interpersonal intelligence and used media fun thinkers treatment early reading skills lower than that used big books treatment media
PRM-D* Method for Mobile Robot Path Planning

Various navigation tasks involving dynamic scenarios require mobile robots to meet the requirements of a high planning success rate, fast planning, dynamic obstacle avoidance, and shortest path. PRM (probabilistic roadmap method), as one of the classical path planning methods, is characterized by simple principles, probabilistic completeness, fast planning speed, and the formation of asymptotically optimal paths, but has poor performance in dynamic obstacle avoidance. In this study, we use the idea of hierarchical planning to improve the dynamic obstacle avoidance performance of PRM by introducing D* into the network construction and planning process of PRM. To demonstrate the feasibility of the proposed method, we conducted simulation experiments using the proposed PRM-D* (probabilistic roadmap method and D*) method for maps of different complexity and compared the results with those obtained by classical methods such as SPARS2 (improving sparse roadmap spanners). The experiments demonstrate that our method is non-optimal in terms of path length but second only to graph search methods; it outperforms other methods in static planning, with an average planning time of less than 1 s, and in terms of the dynamic planning speed, our method is two orders of magnitude faster than the SPARS2 method, with a single dynamic planning time of less than 0.02 s. Finally, we deployed the proposed PRM-D* algorithm on a real vehicle for experimental validation. The experimental results show that the proposed method was able to perform the navigation task in a real-world scenario.
Lecturers’ Reasoning in Using Digital Technology: A Cognitive Approach in Learning Process

This research aims to investigate the reasoning of lecturers in using digital technology and its impact on cognitive approaches. Moreover, we find out the lecturer's use of digital technology in the learning process and determine the reasoning relationship between the use of digital technology and cognition. Problems occurred during the COVID-19 pandemic, where many lecturers needed to be stronger in digital mastering technology and low in its use. Some lecturers think that WhatsApp is online learning, where online learning requires interaction and not just sending news or subjects. Furthermore, many students feel pressured to be taught by lecturers like this, so several classes are closed because there are no students. This study used a mixed-method approach with 109 respondents. Data collection uses a questionnaire distributed through Google Forms, and the qualitative data uses interviews with eight respondents. The results show that 50.5% of respondents can use digital technology, and 49.5% must learn, practice, and receive digital technology training. Furthermore, this research implies that lecturers with critical thinking can quickly master digital technology and simultaneously teach fellow lecturers to use digital technology