Reviewerly/exHarmony huggingface dataset:
Industrial Manometer Detection and Reading for Autonomous Inspection Robots

Autonomous mobile robots for industrial inspection can reduce cost for digitalization of existing plants by performing autonomous routine inspections. A frequent task is reading of analog gauges to monitor the health of the facility. Automating this process involves capturing image data with a camera sensor and processing the data to read the value. Detection algorithms deployed on a mobile robot have to deal with increased uncertainty regarding localization and environmental influences. This imposes increased requirements regarding robustness to viewing angle, lighting and scale variation on detection and reading. Current approaches based on conventional computer vision require high quality images or prior knowledge. We address these limitations by leveraging the advances of neural networks in the task of object detection and instance segmentation in a two-stage pipeline. Our method robustly detects and reads manometers without prior knowledge of object location or exact object type. In our evaluation we show that our approach can detect and read manometers from a distance of up to 3m and a viewing angle of up to 60° in different lighting conditions with needle angle estimation errors of ±2.2°. We publish the validation split of our training dataset for manometer and needle detection at https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2881.
Review representation learning and profiling for Recommendation System

The utility function of users and items in a traditional recommender system is based on rating. However, data sparsity is a fundamental issue in which the user rates a small number of items in comparison to the available items. This rating alone is sometimes insufficient for precisely understanding users' behaviours. A user's general preferences on an item can be shown through the overall ratings, yet they may be dissatisfied with a specific feature of the item. Since most commercial website nowadays, allows user to express their opinion through the review text, then there is an opportunity to precisely understand the user preferences via this element. However, it is essential to represent the review texts in a suitable representation to be used as one of the recommendation components. Therefore, this research aim is to create a relevant review profiling framework based on the data analytic assessment and some observation of the user behaviours reviewing pattern. The review texts are classified into groups of word count, and the consistency of the relevant category is identified. This selected category is used to generate a representation of reviews as one of the components of recommendation processes and resulted in less computational complexity and processing time. The experimental results show that review text count distribution fall in the range of 10 to 30 with a range from 20–30 word count has the highest number of users for almost all datasets.
Illumination-Aware Image Quality Assessment for Enhanced Low-Light Image

Images captured in a dark environment may suffer from low visibility, which may degrade the visual aesthetics of images and the performance of vision-based systems. Extensive studies have focused on the low-light image enhancement (LIE) problem. However, we observe that even though the state-of-the-art LIE methods may oversharpen the low-light image and introduce visual artifacts. To reduce the overshoot effects of LIE, this paper proposes an illumination-aware image quality assessment, called LIE-IQA, for the enhanced low-light images. Since directly using the IQA of degraded image may fail to perform well, the proposed LIE-IQA is an illumination-aware and learnable metric. At first, the reflectance and shading components of both the enhanced low-light image and reference image are extracted by intrinsic image decomposition. Then, we use the weighted similarity between the VGG-based feature of enhanced low-light image and reference image to obtain LIE-IQA, where the weight of the measurement can be learned from pairs of data on benchmark dataset. Qualitative and quantitative experiments illustrate the superiority of the LIE-IQA to measure the image quality of LIE on different datasets, including a new IQA dataset built for LIE. We also use the LIE-IQA as a regularization of a loss function to optimize an end-to-end LIE method, and the results indicate the potential of the optimization framework with LIE-IQA to reduce the overshoot effects of low-light enhancement.
Stone representation theorem for Boolean algebras in the topos <i>M</i><b>Set</b>

The famous Stone representation theorem for Boolean algebras represents them as subalgebras of the powerset Boolean algebras. In this paper, we consider a Stone type representation for Boolean algebras in the functor topos SetM , for a monoid M . We find an adjunction between the category M Boo of Boolean algebras in SetM and the dual of the topos SetM. It is proved that each Boolean algebra in SetM can be embedded into a power of the two element Boolean algebra if and only if M is a group. It is also seen that in contrast to the classic case, in our class of topoi, the Stone representation theorem for Boolean algebras does not coincide with the (internal) prime ideal theorem.
Simulation of an All-Terrain Vehicle Driving Experience Using Virtual Reality

The present work consists of the creation of a virtual driving experience of an all-terrain vehicle that includes the use of an industrial robot in order to promote engineering careers at a private Mexican university. In its development, a product design and development methodology were used that consisted of virtualizing the route, in this case an off-road track, using computer-aided design and the Unity platform. The robot programming consisted of simulating seven physical movements of the path. For the validation of the experience, a questionnaire was created with 13 items that covered visual aspects such as the recreation of the vehicle’s interior, the route such as curves, ascents and descents, and the general experience in terms of duration, sounds and similarity. To a real environment, it was applied to a non-probabilistic sample for the convenience of undergraduate students and was validated based on its Cronbach alpha value. The findings show that the participant's experience and physical integrity are the strengths achieved with this development, a situation that allows its use for the stated purposes.
Automated Glossary Extraction from Collaborative Requirements Models

A glossary presents the terms of the common vocabulary of a project. Even though documentation in natural language is the primary source for these terms, requirements models are also valuable resources as they capture requirements and domain knowledge. Yet, models are often neglected when extracting glossary terms. This paper proposes a method to automatically extract a glossary from a set of models by applying natural language processing techniques. The output of our method is a list and a graph of glossary terms where related terms are connected based on their relatedness.
Joint Learning Appearance and Motion Models for Visual Tracking

Motion information is a key characteristic in the description of target objects in visual tracking. However, seldom of existing works consider the motion features and tracking performance is thus easily affected when appearance features are not reliable in challenging scenarios. In this work, we propose to leverage motion cues in a novel deep network for visual tracking. In particular, we employ the optical flow to effectively model motion cues and reduce background interferences. With a modest impact on efficiency, both appearance and motion features are used to significantly improve tracking accuracy and robustness. At the same time, we use a few strategies to update our tracker online so that we can avoid error accumulation. Extensive experiments validate that our method achieves better results against state-of-the-art methods on several public datasets, while operating at a real-time speed.
Principles for Engineering Social Interaction in Asynchronous Mobile Games

Since their first appearance, mobile devices have had exponential technological growth and have gained other functionalities besides standard calls and messaging services. With the advent of smartphones, the mobile gaming market has grown enormously to become one of the most widespread forms of gaming. This paper devises guidelines for engineering online multiplayer asynchronous mobile games. The findings emerge from the launch of a new game where, alternately, players try to guess the answers given by other players to a given set of questions. Departing from the meaning of usability in ISO 9241-11, the authors define idle-ability as the capacity of a product to realize operational and satisfaction goals in a specified context of sociotechnical idleness. Moreover, design principles are proposed to improve idle-ability in the selected genre of mobile games. Asynchronous games are traditionally integrated into social platforms and online communities. The work offers recommendations for its separate deployment in the market, exploring the potential of invisible social bonds among its players.
Multi-temporal remote sensing image registration based on siamese network

Multi-temporal remote sensing image registration aims to find the optimal alignment between images acquired from different times. The complexity and disparity of features in remote sensing images bring great difficulties to image registration. We propose a deep learning method based on the Siamese network to address this problem. Unlike traditional methods doing feature extraction and feature matching separately. We pair patches from sensed and reference images, and directly learn the mapping relationship between those image patch pairs and their matching labels. This end-to-end network architecture helps us optimize the entire network, which is what traditional methods lack. Besides, we use the spatial scale convolution layer in the feature extraction network to improve scale variations’ adaptability. Extensive experiments are conducted on a multi-temporal satellite image dataset from google earth. The results of the experiment indicate that our method can obtain more correct matched points and effectively improve the registration accuracy than traditional image registration methods.
Blockchain Technology in Cloud Computing: Challenges and Open Issues

Blockchain technology (BC) has recently emerged as a potential solution of security and privacy issues in cloud computing systems. Particularly, the decentralization concept is driven by Blockchain technology. This paper aims to define the cloud computing and Blockchain technologies as recent technological trends. It also surveys the latest trends of Blockchain inclusion within cloud computing. A systematic review method is conducted and evaluated in a dozen studies in time period between 2016 and 2019. The methodologies and results of these studies were explored, and it was found that these studies focused on two main aspects. First, does the data provenance, integrity, and privacy tackle in real data sets such as medical sets and business datasets? Second aspect is the data security in which various studies suggested and proposed for handling this issue. It has been noticed that there is a great variety of designs that are not contradictory, as well as variation of the interests of studies and adopted data sets. In this paper, the authors will analyze the Blockchain technology within cloud computing environments to demonstrate advantages and disadvantages in distributed data and protection.
Reducing Incidents in Microservices by Repaying Architectural Technical Debt

Architectural technical debt (ATD) may create a substantial extra effort in software development, which is called interest. There is little evidence about whether repaying ATD in microservices reduces such interest. Objectives: We wanted to conduct a first study on investigating the effect of removing ATD on the occurrence of incidents in a microservices architecture. Method: We conducted a quantitative and qualitative case study of a project with approximately 1000 microservices in a large, international financing services company. We measured and compared the number of software incidents of different categories before and after repaying ATD. Results: The total number of incidents was reduced by 84%, and the numbers of critical- and high-priority incidents were both reduced by approximately 90% after the architectural refactoring. The number of incidents in the architecture with the ATD was mainly constant over time, but we observed a slight increase of low priority incidents related to inaccessibility and the environment in the architecture without the ATD. Conclusion: This study shows evidence that refactoring ATDs, such as lack of communication standards, poor management of dead-letter queues, and the use of inadequate technologies in microservices, reduces the number of critical- and high-priority incidents and, thus, part of its interest, although some low priority incidents may increase.
Statistically significant? Let us recognize that estimates of tested effects are uncertain

Haven’t all of us dreamt of concluding that our results be statistically significant, that is, characterized by a p-value lying below an arbitrary threshold, most often [Formula: see text]? In this article, we, first, deplore that p has been largely misunderstood, and that its misinterpretation has entailed a fallacious dichotomization and an understatement of the uncertainty prevailing about the effect tested. Next, we introduce and explain a brand-new – direct – measure of the plausibility of the effect under study. Then, we illustrate the relevance of this indicator by revisiting a recently published marketing research case. We also insist on the necessity to contextualize it, using complementary credibility intervals graphically contrasted. Beyond making researchers aware of the exact meaning of test-related probabilities, the delineated approach invites them to formulate their inferences with prudence and modesty acknowledging how uncertain these are.
Boosting Few-shot Abstractive Summarization with Auxiliary Tasks

For summarization in niche domains, data is not enough to fine-tune the large pre-trained model. In order to alleviate the few-shot problem, we design several auxiliary tasks to assist the main task---abstractive summarization. In this paper, we employ BART as the base sequence-to-sequence model and incorporate the main and auxiliary tasks under the multi-task framework. We transform all the tasks in the format of machine reading comprehension [19]. Moreover, we utilize the task-specific adapter to effectively share knowledge across tasks and the adaptive weight mechanism to adjust the contribution of auxiliary tasks to the main task. Experiments show the effectiveness of our method for few-shot datasets. We also propose to firstly pre-train the model on unlabeled datasets, and the methods proposed in this paper can further improve the model performance.
Relative Jacobian-Based Cooperative Control of Multi-Rotor Drones

This paper presents a new formulation of controlling cooperative drones using relative Jacobian. This formulation affords relative movement of the end-effector drone with respect to the base drone, while the base drone moves with respect to the world frame. The relative movement releases unnecessary constraints in the movement of the drone with respect to each other, such that the relative movement between the drones now has increased degrees-of-freedom compared to absolute movement control. The implementation is shown in the Gazebo RotorS simulation platform using two firefly drones each with six propellers. The individual control of each firefly drone is using a new formulation that is also presented in this work using a Jacobian-based controller. This type of controller is versatile because it can lead towards Jacobian-based controllers of manipulators to be applied to controlling multi-rotor drones. This makes it possible to control more than one multi-rotor drone using a single controller. Frames of reference are assigned at the centre of gravity for each multi-rotor drone, and the derivation of individual drones Jacobian and the relative Jacobian are based on these reference frames. Simulation results are shown to demonstrate the effectiveness of this approach.
MGD-GAN: Text-to-Pedestrian Generation Through Multi-grained Discrimination

In this paper, we investigate the problem of text-to-pedestrian synthesis, which has many potential applications in art, design, and video surveillance. Existing methods for text-to-bird/flower synthesis are still far from solving this fine-grained image generation problem, due to the complex structure and heterogeneous appearance that the pedestrians naturally take on. To this end, we propose the Multi-Grained Discrimination enhanced Generative Adversarial Network, that capitalizes a human-part-based Discriminator (HPD) and a self-cross-attended (SCA) global Discriminator in order to capture the coherence of the complex body structure. A fined-grained word-level attention mechanism is employed in the HPD module to enforce diversified appearance and vivid details. In addition, two pedestrian generation metrics, named Pose Score and Pose Variance, are devised to evaluate the generation quality and diversity. We conduct extensive experiments and ablation studies on the caption-annotated pedestrian dataset, CUHK Person Description Dataset. The substantial improvement over the various metrics demonstrates the efficacy of MGD-GAN on the text-to-pedestrian synthesis scenario.
MESS: Manifold Embedding Motivated Super Sampling

Many approaches in the field of machine learning and data analysis rely on the assumption that the observed data lies on lower-dimensional manifolds. This assumption has been verified empirically for many real data sets. To make use of this manifold assumption one generally requires the manifold to be locally sampled to a certain density such that features of the manifold can be observed. However, for increasing intrinsic dimensionality of a data set the required data density introduces the need for very large data sets, resulting in one of the many faces of the curse of dimensionality. To combat the increased requirement for local data density we propose a framework to generate virtual data points that faithful to an approximate embedding function underlying the manifold observable in the data.
Architecture Support for Bitslicing

The bitsliced programming model has shown to boost the throughput of software programs. However, on a standard architecture, it exerts a high pressure on register access, causing memory spills and restraining the full potential of bitslicing. In this work, we present architecture support for bitslicing in a System-on-Chip. Our hardware extensions are of two types; internal to the processor core, in the form of custom instructions, and external to the processor, in the form of direct memory access module with support for data transposition. We present a comprehensive performance evaluation of the proposed enhancements in the context of several RISC-V ISA definitions (RV32I, RV64I, RV32B, RV64B). The proposed 14 new custom instructions use 1.5× fewer registers compared to the equivalent functionality expressed using RISC-V instructions. The integration of those custom instructions in a 5-stage pipelined RISC-V RV32I core incurs 10.21% and 12.72% overhead respectively in area and cell count using the SkyWater 130 nm standard cell library. The proposed bitslice transposition unit with DMA provides a further speedup, changing the quadratic increase in execution time of data transposition to linear. Finally, we demonstrate a comprehensive performance evaluation using a set of benchmarks of lightweight and masked ciphers.
Mapping of Local Government Policy in Sharing Operational Costs for Basic Education in the Pandemic Era

Abstract Adequacy of operational cost for basic education becomes the responsibility of the central and local government. Adequate funding is necessary for the success of compulsory basic education. The research aims to analyze and develop a framework for the local government policy in sharing operational cost adequacy for basic education in the study area. This research used a descriptive-quantitative approach. Data collecting was conducted by observation and interviews. The analysis used a descriptive one. There are findings in the research. Firstly, at the beginning of the year 2021, adequate funding for the elementary school in Pati regency is IDR 869,017 per student and it has been fulfilled with School Operational Assistance (BOS) IDR 900,000 provided by the central government but to cope with inflation during 2021, the local government should provide sharing operating cost IDR 38,538 per student yearly. Secondly, adequate funding for junior high school in Pati regency is IDR 1,063,797 per student at the beginning of the year 2021 and available funding from School Operational Assistance (BOS) is less than the needed funding. To satisfy adequate funding, the local government should provide sharing operating cost IDR 148,901 per student in 2021. To alleviate worsening problems in the pandemic era (Covid-19), local governments must take higher responsibility for boosting basic education by allocating more funds. It is useful to alleviate worsening problems such as dropping out of students in basic education.
A Privacy-Preserving Payment Model for EV Charging

Due to the rapid spread of electric vehicles worldwide, the importance of building an efficient and safe electric vehicle charging system is increasing. In order to implement the decentralized vehicle network, blockchain technology is being leveraged in the electric charging system. However, since the blockchain ledger is public, the charging payment and authentication information is visible. While there have been attempts by blockchain-based charging systems to safeguard an electric vehicle’s privacy and anonymity, the electric vehicle’s identity can still be revealed by the electric vehicle service provider. Thus, we proposed a privacy-preserving payment model for electronic charging. We applied a traceable ring signature so that the electric charging station and the electric vehicle service provider cannot specify the electric vehicle within the ring group. When the EV reuses the token, it will be discovered during the tracing process. In addition, by using a blockchain-based smart contract, malicious user information can be stored and shared in a blockchain. As a result, secure token authentication and energy trading are possible.
THE USE OF MODERN CLOUD TECHNOLOGIES IN THE EDUCATIONAL PROCESS OF HIGHER EDUCATION

The article considers modern cloud services for use in the educational process, namely Google Workspace for Education, Microsoft 365 Education and AWS Educate. The functional characteristics and services that teachers can use in their professional activities are analyzed. In particular, the most widespread cloud storage services are considered and a comparative characteristic is given to choose the most efficient service.
Extracting and categorising the reactions to COVID-19 by the South African public - A social media study

Social Media can be used to extract discussion topics during a disaster. With the COVID-19 pandemic impact on South Africa, we need to understand how the law and regulation promulgated by the government in response to the pandemic contrasts with discussion topics social media users have been engaging in. In this work, we expand on traditional media analysis by using Social Media discussions driven by or directed to South African government officials. We find topics that are similar as well as different in some cases. The findings can inform further study into social media during disaster settings in South Africa and beyond. This paper sets a framework for future analysis in understanding the opinions of the public during a pandemic and how these opinions can be distilled [in a semi-automated approach] to inform government communication in the future.