Reviewerly/exHarmony huggingface dataset:
Coloring anime line art videos with transformation region enhancement network

Automatic colorization of anime line art videos aims to produce color frames given line art frames and reference color images, which is challenging due to various motions and geometric transformations across frame sequences. Existing methods usually utilize the feature maps of reference images directly and treat all the regions in an image equally. However, this may overlook the details of the regions undergoing geometric transformations. To emphasize the regions with significant transformations between the reference and target frames, we propose a Transformation Region Enhancement Network (TRE-Net) to exploit useful reference information and enhance the colorization of key transformation regions with Region Localization Module (RLM) and Feature Enhancement Module (FEM). Specifically, we propose Multi-scale Euclidean Distance Difference (Multi-scale EDD) Maps in RLM which effectively locate geometric transformation regions by contrasting the Euclidean Distance Maps of two line arts and aggregating representations at multiple scales of the network. In addition, FEM is devised to enhance feature learning in the regions with geometric transformation and to ensure proper color alignment. FEM learns locally enhanced features through an attention-gating operation at a low computational cost. With the well-represented key geometric transformation regions, our method exploits the multi-scale reference information well for color alignment, thus produces perceptually pleasing frames. Comprehensive experimental results show that our proposed method is superior to existing methods in terms of the overall quality of colorized anime line art videos.
Design and Implementation of a Web-Based Canteen Management System using PHP and MySQL

The Mess Management System is a novel software tool that automates the manual process of managing various mess-related activities such as meal planning, food ordering, inventory management, and billing.The system is programmed using the PHP language and MySQL is used as the database management system.The fundamental goal of this system is to reduce the workload on mess staff, increase efficiency, and minimize errors in the mess management process.The present research paper is centered on the design and implementation of the Mess Management System and its effect on the management of college hostel mess.The development of the system was executed using the agile software development methodology, which offers flexibility and enables quick adaptation to any modifications in the requirements.The requirements gathering stage involved extensive research, interviews with the mess staff, and analysis of the existing manual systems.The system design stage involved the development of use cases, entity-relationship diagrams, class diagrams, and sequence diagrams.The implementation phase involved coding the system, integrating it with the database, and testing it for errors and bugs.The Mess Management System offers various features, including menu planning, food ordering, inventory management, and billing.The menu planning feature permits the mess staff to plan menus for a specified period, taking into consideration the preferences and dietary requirements of the students.The food ordering feature enables the mess staff to order raw materials from the vendors and track their delivery status.The inventory management feature helps the mess staff to manage inventory levels and avoid stockouts.The billing feature allows the mess staff to generate bills for the students based on their meal consumption, and students can view their bills online.A prototype of the system was deployed in a college hostel, and the results indicated that the system was user-friendly and efficient.The system significantly reduced the workload on the mess staff and improved the accuracy of billing.The students also found the system easy to use, and it reduced the time required to queue for meals.The system's scalability was also tested by adding new users to the system, and it was observed that the system could handle the increased load without any significant impact on performance.In conclusion, the Mess Management System is a cost-effective, efficient, and user-friendly software tool that automates the manual mess management process.The system can be tailored to meet the specific needs of different institutions and can significantly reduce the workload on mess staff.The system's scalability, flexibility, and ease of use make it a promising solution for college hostel mess management.Nevertheless, further research can be carried out to address the challenges associated with integrating the system with other campus management systems and assessing its long-term impact on the mess management process.
PELATIHAN PEMBUATAN MINUMAN PROBIOTIK THE KOMBUCHA BAGI GURU PAUD GUGUS CAHAYA JINGGA KABUPATEN BINTAN

Community service is one of the mandatory tasks of lecturers as part of the realization of the Tri Dharma activities of Higher Education. In line with this, Tri Dharma activities are focused on important activities in contributing to improving the quality of life of the community, especially in developing skills that can be used in a wider scope of society. One of the activities carried out was the training of kombucha tea making techniques for PAUD teachers of GUGUS CAHAYA JINGGA at Batam Tourism Polytechnic. This training aims to provide information to teachers of PAUD Cahaya Jingga Cluster regarding the ingredients and process of making kombucha tea, knowing the time needed to produce fermented products, and knowing the morphological and chemical characteristics of kombucha tea. The specific target to be achieved is to make PAUD GUGUS CAHAYA JINGGA teachers skilled in processing kombucha tea products and can develop them in entrepreneurship so as to support financially in the economy.
Utilisation of Service Robots to Assist Human Workers in Completing Tasks Such in Retail, Hospitality, Healthcare, and Logistics Businesses

Abstract: The proposed study has been developed to explore utilization of service robots in hospitality, logistics, retail, healthcare to assist humans. Service robots are capable of accelerating the movement of activities from these industries. Advanced techniques such as artificial intelligence, information technology and IoT are equipped in these robots. This equipment has increased physical strength and ability to perform better in these industries. Increasing capability helps to increase performance of the robots and for this reason firms from these industries get opportunities to improve customer service. In the healthcare sector, service robots are used to monitor elderly people to detect sudden actions such as falling down. This kind of detection helps to assist nurses to take necessary treatment for the patients. The discussion of this study found that in many cases service robots such as mobile healthcare robots work as medical experts and even as exercise coaches. In the other industries such as logistics and retail, firms use service robots as heavyweight lifters to assist human workers. However, for arranging products in warehouses, service robots are also used. The usages provide smooth customer service that helps to promote overall growth of the firms. In the hospitality and tourism industry, service robots are used as waiters, attendants of housekeeping, cooks, and hosts to improve customer service quality. Utilization of these kinds of robots help to improve service quality that plays an important role for financial growth of the hospitality industry. These facts have been discussed in this proposed study. Secondary qualitative data have been used in this study to make discussion. Online journals and articles have been approached to collect data or information related to utilization of service robots in hospitality, logistics, retail and healthcare to assist humans.
Context Matters: Adaptive Mutation for Grammars

This work proposes Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation. In SGE, the genotype of individuals contains a list for each non-terminal of the grammar that defines the search space. In our proposed mutation, each individual contains an array with a different, self-adaptive mutation rate for each non-terminal. We also propose Function Grouped Grammars, a grammar design procedure to enhance the benefits of the propose mutation. Experiments were conducted on three symbolic regression benchmarks using Probabilistic Structured Grammatical Evolution (PSGE), a variant of SGE. Results show our approach is similar or better when compared with the standard grammar and mutation.
Simulating Quantum Principles: Qiskit Versus Cirq

Quantum computing is a computing paradigm for addressing computer issues of classical systems by harnessing all the possibilities given by quantum physics concepts. In these computers, information is represented by quantum states and utilizes different quantum phenomena such as quantum superposition, entanglement, and interference provided by quantum physics. Quantum computations are based on the fundamental notion of reversible computing. Quantum algorithms are built based on quantum computational complexity. Various quantum algorithms have been developed so far, with the general conclusion that exploiting quantum physics effects results in a significant speedup over conventional algorithms. In this chapter, we have implemented some important quantum principles using different open-source software development kit for dealing with quantum computers with an aim to provide an understanding on how we can create and execute quantum programs on prototype quantum devices or simulate them on a local machine. The chapter deals in the implementation details of quantum principles along with providing other prime representations like Visual Circuit, State vector, Q-sphere, and Visual Probabilistic result.
Key-Recovery by Side-Channel Information on the Matrix-Vector Product in Code-Based Cryptosystems

The modern security protocols in most of our systems rely primarily on three basic functions of asymmetric cryptography: public key encryption, digital signature, and key exchange. Today, we only do key exchange (TLS 1.3) with the ECDH protocol. The confidentiality is persistent because the session keys are discarded at the end and to certify this key exchange, we sign it with RSA or ECDSA. However, these cryptosystems are at least theoretically attackable in a quantum computer model. Thus the NIST PQC standardization process has given significant momentum to research on code-based public-key cryptosystems specifically. Their security is based on the hardness of the syndrome decoding problem. In this article, we first propose a new formalism of the matrix-vector product in based-code cryptography. Second, we present a chosen-ciphertext attack on the first step of Niederreiter decryption by solving the matrix-vector product problem with side-channel information. Finally, we put this result to recover secret information in code-based cryptosystems including some candidates for the extension of the NIST PQC normalization process.
Smart-Contract Vulnerability Detection Method Based on Deep Learning

With the rapid development of blockchain technology, smart contracts (SCs) applied in digital currency transactions have been widely used. However, SCs often have vulnerability in their code that allow criminals to exploit them to steal associated digital assets. Benefiting from the development of machine learning technology and the improvement of hardware performance, one can use deep learning techniques to analyze code and detect vulnerabilities. This paper proposes an innovative combination of opcode sequences and abstract syntax trees for source code parsing. And a method based on the combination of self-attention mechanism and bidirectional long-short term memory neural network is proposed to detect the vulnerability of SCs after word embedding. Experimentation results show that the two parsing methods can complement each other and effectively improve the accuracy of vulnerability detection.
Fast Detection of Multi-Direction Remote Sensing Ship Object Based on Scale Space Pyramid

Ships in remote sensing images are usually arranged in arbitrary direction, small in size, and densely arranged. As a result, existing object detection algorithms cannot detect ships quickly and accurately. In order to solve the above problems, a lightweight object detection network for fast detection of ships is proposed. The network is composed of backbone network, four-scale fusion network and rotation branch. First, a lightweight network unit S-LeanNet is designed and used to build a low-computing and accurate backbone network. Then, a four-scale feature fusion module is designed to generate a four-scale feature pyramid, which contains more features such as ship shape and texture, and at the same time is conducive to the detection of small ships. Finally, a novel rotation branch module is designed, using balance L1 loss function and R-NMS for post-processing, to realize the precise positioning and regression of the rotating bounding box in one step. Experimental results show that the detection precision of our method in the DOT A remote sensing data set is compared with the latest SCRDet detection method, the precision is increased by 1.1%, and the operating speed is increased by 8 times, which can meet the fast detection requirements of ships.
A Trust Model Based on Characteristic Factors and SLAs for Cloud Environments

In view of the lack of research regarding characteristic factors (e.g., service cost, quality, etc.) in trust models for cloud environments, as well as the lack of negotiation and monitoring mechanisms, a trust model based on characteristic factors and service level agreements (SLAs) is proposed in this article. First, on the basis of comprehensive trust and self-recommended trust, we introduce the cost deviation trust and service quality coefficient, to simultaneously consider the influence of common and characteristic factors upon trust evaluations and thereby improve the accuracy of trust evaluations. Second, we establish a negotiation and monitoring mechanism whereby both parties sign an SLA before the trade and require SLA agent monitoring services to improve the accuracy of the service cost and quality evaluation and the efficiency of malicious entity identification. Finally, using the agreement quality, experience quality, and monitoring quality, we more accurately judge the trade result and update the recognition degree (which plays a key role in trust evaluation), to further improve the accuracy of trust evaluation and thereby the trade success rate. The results of experiments conducted upon real datasets show that our model can effectively resist spoofing, coordination, and defamation attacks from malicious entities, and it offers a high trade success rate.
Something rotten in the state of Denmark

The Copenhagen Network: The Birth of Quantum Mechanics from a Postdoctoral Perspective, Alexei Kojevnikov, Springer, 2020, $69.99 (paper)
Hierarchical federated learning with local model embedding

Federated learning can synergize the local model training with private data samples from geo-distributed users. Nevertheless, the unification process of a comprehensive global model through periodical parameter sharing can be time-consuming at a high cost. On the one hand, the data samples are collected from users with diverse preferences, and the data distribution can be non-independent and identically distributed (non-IID). On the other hand, the consequent model trained locally needs to communicate with a remote parameter server periodically for parameter synchronization, which leads to overwhelming communication and synchronization overhead given heterogeneous device capacities and network conditions of end-users. Generally, a hierarchical system design with a clustered group is ideal for accommodating diversity. Actually, it is still challenging to maintain the relationship of the local model training without knowing the data samples in advance for privacy concerns. Therefore, we present hierarchical federated model embedding to formulate the relationship between local data distributions. Initially, the local models are embedded through the global shared dataset to obtain feature latent representation vectors. The cloud server groups the clients according to the vectors, making clients with similar data distribution train collaboratively in a same group. Then, these vectors are used to train the predictor on the cloud server, which is utilized for efficient group assignment when new clients join the system. Compared with the baseline, the accuracy of the group model can be improved by 1.22%∼5.63% and that of the global model can be improved by 3.97%∼14.25% on different datasets.
Sampling and noise filtering methods for recommender systems: A literature review

In the era of online business, many e-commerce sites have evolved which recommend items according to one’s needs and interests. Plenty of data is available to be processed to make the recommender systems work effectively and efficiently. But, processing the entire dataset is a cumbersome process. So, there is a need to select a part of the data to be processed easily. Sampling is a way to select a subset of the entire dataset which contains all the attributes that the database can represent. It is important to understand which type of sampling method is suitable for a particular application in recommender systems. Thus, there is a need to study various sampling methods previously used in recommender systems. Also, before processing the data, we need to clean it up as it contains a certain amount of noise. This noise is described as either malicious noise or natural noise. Malicious noise is implicitly inserted in the system to alter the behavior of the system. This type of noise is termed as shilling attack. Natural noise enters the systems unknowingly due to the reluctance of users in giving proper ratings to the items. So, there is a need to filter these types of noise before making the data suitable for processing. In this paper, we have provided a review of 80 papers including both sampling and noise filtering methods. This is the first paper to the best of our knowledge, combining a literature review of both sampling and noise filtering methods.
Analysis of land use and spatial planning in the Upstream Citarum watershed of West Java based on remote sensing data

&lt;p&gt;The Upstream Citarum watershed has a critical essential role in maintaining the quality and quantity of its water resources, which are a source of drinking water, agriculture, fisheries, irrigation, and electricity generation. The upstream watershed acts as a water catchment area that contributes as a place to accommodate the availability of water, especially during the dry season, and also as a protector of the entire area, both downstream and middle, so that the carrying capacity of the ecological balance can be protected. The increasing total population was inversely related to the condition of land use which is decreasing in quality, so to fulfill these needs, there was a land conversion that caused changes in land use. The Landsat-8 satellite imagery, SPOT 7, and spatial planning maps were used to analyze the suitability and incompatibility of land use with spatial pattern plans, evaluation, and directions for controlling the use of space. The result of the most extensive existing land use in the Upstream Citarum watershed in 2021 used for fields is 30% of the total area of 66,831.66 ha. Meanwhile, the spatial pattern plan was classified into protected and cultivated areas. The built-up area has the largest area, 35% or 75,223.96 ha. The results of the suitability of land use classes obtained that the level of suitability between the existing land use and the spatial pattern plan from the Maps of Regional Spatial Planning with the appropriate class was 52.50% or 96,205.43 ha, and the unsuitable class was 47.50% or 87,028.75 ha.&lt;/p&gt;
Forward Backward Syndrome Computation: A Reduced Complexity CRC Code Decoder

This letter proposes a simplified method for correcting a single-bit error event in a message protected by a CRC code thanks to a new decoding method called Forward-Backward Syndrome Computation. The two key ideas of the letter are to show that: 1) syndrome computation can be performed in any direction (Forward and Backward), 2) consequently, it is possible to create a partition of the received message in a set of disjoint syndromes, thus allowing a direct identification of the error position. Evaluation in terms of number of 2-input binary logic functions shows a complexity reduction by a factor of up to 3 compared to conventional methods.
Improved BlendMask: Nuclei instance segmentation for medical microscopy images

Abstract Nuclei instance segmentation is an important task in medical image analysis involving cell‐level pathological analysis, which is of great significance for many biomedical applications. Nuclei segmentation is a challenging task due to edge adhesions and the distribution of numerous tiny dense nuclei. In this work, a nuclei instance segmentation framework, namely, the improved BlendMask is proposed. In this framework, in order to improve the performance of detection and segmentation of dense small objects and adhering nuclei, two components, including dilated convolution aggregation module (DCA) and context information aggregation module (CIA), are designed. The DCA constructs multi‐path parallel dilated convolution, which greatly increases the receptive field of the network and the ability to capture multi‐scale contextual information. The CIA reduces the information loss in the channel by endowing the network with high‐level multi‐scale spatial context information. In addition, a novel distributional ranking loss function is given that can effectively alleviate the imbalance between the target and the background. The proposed method is validated on the DSB2018 dataset. Compared to BlendMask, this network improves by 3.6% on AP segmentation metric, and the segmentation performance of this network is superior to that of several recent classic open‐source nuclei instance segmentation methods.
Sentiment Analysis for IMDb Movie Review Using Support Vector Machine (SVM) Method

Many researchers currently employ supervised, machine learning methods to study sentiment analysis. Analysis can be done on movie reviews, Twitter reviews, online product reviews, blogs, discussion forums, Myspace comments, and social networks. Support Vector Machines (SVM) classifiers are used to analyze the Twitter data set using different parameters. The analysis and discussion were undertaken to allow for the conclusion that SVM has been successfully implemented utilizing the IMDb data for this study (Support Vector Machine). To complete this study, the preprocessing phase, which consisted of filtering and classifying data using SVM with a total of 50.000 data points, was completed after collecting up to 40.000 reviews to use as training data and 10.000 reviews to use as testing data. 25.000 positive and 25.000 negative points make up the view. In this study, we adopted an evaluation matrix including accurate, precision, recall, and F1-score. According to the experiment report, our model achieved SVM with Bags of Word (BoW) used to get results for the highest accuracy test, which was 88,59% accurate. Then, using grid-search, optimize against the SVM parameters to find the best parameters that SVM models can use. Our model achieved Term Frequency–inverse Document Frequency (TF-IDF) was used to get results for the highest accuracy test, which was 91,27% accurate.&#x0D;