Reviewerly/exHarmony huggingface dataset:
BiSeNet with Depthwise Attention Spatial Path for Semantic Segmentation

This paper proposes a new structure to obtain similar results while reducing the computational amount of BiSeNet for Real-Time Semantic Segmentation. Among the Spatial Path and Context Path of BiSeNet, the study was conducted focusing on the large size kernel of the Spatial Path. Spatial Path has rich spatial information by creating a feature map 1/8 times the size of the original image through three convolution operations. The convolution operation used at this time is performed in the order of 7×7, 3×3, and 3×3. When a general convolution is used for a kernel of such a large size, the calculated cost increases due to a large number of parameters. To solve this problem, this paper uses Depthwise Separable Convolution. At this time, in Depthwise Separable Convolution, loss occurs in Spatial Information. To solve this information loss, an attention mechanism [1] was applied by elementwise summing between the input and output feature maps of depthwise separable convolution. To solve the dimensional difference between input and output, PPM: Pooling Pointwise Module is used. PPM uses Maxpooling to change the Spatial Dimension of input features and Channel Dimension through Pointwise Convolution (lx1 Convolution) [2]. This paper propose to use Depthwise Attention Spatial Path for BiSeNet using these methods. Through our proposed methods, mIoU in SS, SSC, MSF, and MSCF were 72.7%, 74.1 %, 74.3%, and 76.1 %. Proposed network can segment the part that the original one can't when using our Depthwise Attention Spatial Path.
Crossword Puzzle Resolution via Monte Carlo Tree Search

Although the development of AI in games is remarkable, intelligent machines still lag behind humans in games that require the ability of language understanding. In this paper, we focus on the crossword puzzle resolution task. Solving crossword puzzles is a challenging task since it requires the ability to understand natural language and the ability to execute a search over possible answers to find an optimal set of solutions for the grid. Previous solutions are devoted to exploiting heuristic strategies in search to find solutions while having limited ability to explore the search space. We propose a solution for crossword puzzle resolution based on Monte Carlo tree search (MCTS). As far as we know, we are the first to model the crossword puzzle resolution problem as a Markov Decision Process and apply the MCTS to solve it. We construct a dataset for crossword puzzle resolution based on daily puzzles from New York Times with detailed specifications on both the puzzle and clue database selection. Our method can achieve an accuracy of 97% on the dataset.
A Data-Driven Recipe Simulation for Synthetic Rubber Production

To manufacture synthetic rubber, rubber manufacturers require optimal recipes to ensure that it satisfies the required quality standards.Several experiments are required to create the optimal recipe, which adversely affects not only the cost and time required but also the health of workers.Suppose the experimental results can be predicted in advance at the recipe design stage before direct experimentation.In that case, the cost of the experiment can be reduced, and the workers' health can be significantly less impacted.For this purpose, a method called the prediction walk model using a machine learning model was developed to generate the temperature trajectory in a kneading machine.A cross-updating method to predict the quality of the kneading operation is also proposed.From the results of the experiment, it was confirmed that the performance of the proposed models was superior to that of the existing prediction models.
Big data analysis

“Big Data” can be considered as an expression which has spread suddenly. It could be depicted as a pioneering mechanism for saving, distributing, managing, visualizing and analyzing large data along with very high velocity and approaches for managing incompetent quantity of data. Here, the state-of-the-art and background of big data are reviewed. Every review-related technology and general background of big data are firstly reviewed, for example IoT, cloud computing, Hadoop and data centers. Then, we concentrate on every value chain phase belongs to big data such as data acquisition, data generation, data analysis and data storage. For the four phases, the general background is presented, the technicalities are discussed, and most recent advances are reviewed. Eventually, the various big data representative applications are examined, containing IoT, enterprise management, medial applications, online social networks, smart grid and collective intelligence. Those argumentations pursue to supply an overall overview for readers interested in this exciting area. The survey here is finalized with an argumentation of future directions and open problems.
Detecting Offensive Language in Romanian Social Media

Due to an exponential increase in the use of Internet by persons from different countries and educational backgrounds, the offensive online language detection has become a significant task facing natural language processing. Considering the major negative impact of this type of content in the case of youngers, detecting online toxic language to protect users’ online safety becomes an urgent issue. The project has two main goals: (1) developing an annotated corpus of offensive content for Romanian language and (2) testing various machine learning algorithms to identify a best approach. The proposed methods achieve results with a few percentages more than the accuracy of the current SoTA.
Benchmarks for Pathfinding Search: Iron Harvest

Pathfinding is a central topic in AI for games, with many approaches having been suggested. But comparing different algorithms is tricky, because design choices stem from different practical considerations; e.g., some pathfinding systems are grid-based, others rely on a navigation mesh or visibility graph and so on. Current benchmarks mirror this trend, focusing on one set of assumptions while ignoring the rest. In this work we present a new unified benchmark using data from the game Iron Harvest. For 35 different levels in the game we generate several complementary map representations (grid, mesh and obstacle-set) and we provide a common set of challenging instances. We describe and analyse the new benchmark and then compare several leading pathfinding algorithms that begin from different assumption sets. Our goal is to allow researchers and practitioners to better understand the relative strengths and weakness of competing techniques.
Measuring Media Bias via Masked Language Modeling

Bias in news reporting can lead to tribalism and division on important issues. Scalable and reliable measurement of such biases is an important first step in addressing them. In this work, based on the intuition that media bias is captured by the tone and word choices in articles, we propose a framework for modeling the relative bias of media outlets through masked token prediction via large-scale pretrained masked language models fine-tuned on articles form news outlets. Through experiments on five diverse and politically polarized topics we show that our framework can capture media bias towards these topics with high reliability. Additionally, our experiments show that our framework is general, in that language models fine-tuned on one topic can be applied to other topics with little drop in performance.
Strategic Management Indicators for Sustainable Road Traffic Management

Human is very critical and important asset of any nation. Human security is the prime need. Also, at organizational level, the business success depends on the human efficiency. Hence, strategic management is very important to achieve effective performance. This paper assessed various indicators and attributes of strategic management. However, the limitation of this study was to focus on strategic management used in traffic management used bypublic security sector ofAbu Dhabi. Evaluation involved 29 common attributes of strategic management identified from literature. These were categorized into five indicators aslearning &amp; training; traffic setting; emergency response; road engineering &amp; vehicle safety; and strategiesbeing enforced. Analysis of the data collected through Questionnaire survey with officials involved in traffic management was done with mean score method. Results of the analysis showed that review and develop a driver training program and licensing mechanismis the top ranked attribute in the domain of learning &amp; training. The respondents highlighted effective traffic control in pedestrian and cyclist safety areas and seatbelts as very useful in the indicator traffic setting. Top indicators in the indicator emergency response are development of effective systems for geographical positioning of traffic accidents andspreading a culture of prioritising emergency vehicles in emergency situations. Develop an integrated traffic safety management system (ITSMS) is the most useful attribute related to road engineering &amp; vehicle safety indicator. While in the indicator strategies being enforced, the respondents mentioned that the attribute at top rank is developing smart traffic settings to minimize the accidents.
Design, Analysis, and Application of Flipped Product Chaotic System

In this paper, a novel method is proposed to build an improved 1-D discrete chaotic map called flipped product chaotic system (FPCS) by multiplying the output of one map with the output of a vertically flipped second map. Two variants, each with nine combinations, are shown with trade-off between computational cost and performance. The chaotic properties are explored using the bifurcation diagram, Lyapunov exponent, Kolmogorov entropy, and correlation coefficient. The proposed schemes offer a wider chaotic range and improved chaotic performance compared to the constituent maps and several prior works of similar nature. Wide chaotic window and improved chaotic complexity are two desired characteristics for several security applications as these two characteristics ensure enhanced design space with elevated entropic properties. We present a general Field-Programmable Gate Array (FPGA) design framework for the hardware implementation of the proposed flipped-product schemes and the results show good qualitative agreement with the numerical results from MATLAB simulation. Finally, we present a new Pseudo Random Number Generator (PRNG) using the two variants of the proposed chaotic map and validate their excellent randomness property using four standard statistical tests, namely NIST, FIPS, TestU01, and Diehard.
Analysis of Privacy and Security Challenges in e-Health Clouds

Electronic Health Records (EHR) techniques are being used at an increasingly faster rate to store the patient data making it easier to retrieve, share and utilize it efficiently.This data can be used for research purposes, clinical trials and for studying epidemiology to come up with strategies for epidemic control.With a huge global inflation, the increasing costs of healthcare and the shortage of medicine, it becomes convenient for the healthcare organizations to migrate from the traditional healthcare system to a more sophisticated, cost effective and efficient cloud-based e-Health model.To optimize the full potential of an ICT-based e-Health system, it is imperative for the existing healthcare systems to be implemented in a fullfledged cloud environment.However, with numerous benefits of technology, it might pose some privacy and security threats as well.Therefore, the security and access control of such information is of vital significance.Nonetheless, with the increasing interest of healthcare organizations to migrate from the conventional healthcare systems to the modern cloud-based e-Health systems, not much care is being taken to address security and privacy issues effectively towards the protection of sensitive data.
Augmenting QAOA Ansatz with Multiparameter Problem-Independent Layer

The quantum approximate optimization algorithm (QAOA) promises to solve classically intractable computational problems in the area of combinatorial optimization. A growing amount of evidence suggests that the originally proposed form of the QAOA ansatz is not optimal, however. To address this problem, we propose an alternative ansatz, which we call QAOA+, that augments the traditional p=1 QAOA ansatz with an additional multiparameter problem-independent layer. The QAOA+ ansatz allows obtaining higher approximation ratios than p=1 QAOA while keeping the circuit depth below that of p = 2 QAOA, as benchmarked on the MaxCut problem for random regular graphs. We additionally show that the proposed QAOA+ ansatz, while using a larger number of trainable classical parameters than with the standard QAOA, in most cases outperforms alternative multiangle QAOA ansätze for fixed number of independent parameters.
FlexChain

While permissioned blockchains enable a family of data center applications, existing systems suffer from imbalanced loads across compute and memory, exacerbating the underutilization of cloud resources. This paper presents FlexChain , a novel permissioned blockchain system that addresses this challenge by physically disaggregating CPUs, DRAM, and storage devices to process different blockchain workloads efficiently. Disaggregation allows blockchain service providers to upgrade and expand hardware resources independently to support a wide range of smart contracts with diverse CPU and memory demands. Moreover, it ensures efficient resource utilization and hence prevents resource fragmentation in a data center. We have explored the design of XOV blockchain systems in a disaggregated fashion and developed a tiered key-value store that can elastically scale its memory and storage. Our design significantly speeds up the execution stage. We have also leveraged several techniques to parallelize the validation stage in FlexChain to further improve the overall blockchain performance. Our evaluation results show that FlexChain can provide independent compute and memory scalability, while incurring at most 12.8% disaggregation overhead. FlexChain achieves almost identical throughput as the state-of-the-art distributed approaches with significantly lower memory and CPU consumption for compute-intensive and memory-intensive workloads respectively.
Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection

Detecting mass in mammogram is significant due to the high occurrence and mortality of breast cancer. In mammogram mass detection, modeling pairwise lesion correspondence explicitly is particularly important. However, most of the existing methods build relatively coarse correspondence and have not utilized correspondence supervision. In this paper, we propose a new transformer-based framework CL-Net to learn lesion detection and pairwise correspondence in an end-to-end manner. In CL-Net, View-Interactive Lesion Detector is proposed to achieve dynamic interaction across candidates of cross views, while Lesion Linker employs the correspondence supervision to guide the interaction process more accurately. The combination of these two designs accomplishes precise understanding of pairwise lesion correspondence for mammograms. Experiments show that CL-Net yields state-of-the-art performance on the public DDSM dataset and our in-house dataset. Moreover, it outperforms previous methods by a large margin in low FPI regime.
A Bayesian Network Structure Learning Algorithm Based on Probabilistic Incremental Analysis and Constraint

To address the problem of low efficiency of the existing hill-climbing algorithm in Bayesian network structure learning, this paper proposes a Bayesian network structure learning algorithm based on probabilistic incremental analysis and constraints. The algorithm constructs a suitable measure for representing the degree of node association in Bayesian networks based on the principle of random forest feature extraction; then uses the method to construct the initial Bayesian network structure and constrains the search space by setting a corresponding threshold for the probability increment centered on each node; finally takes the initial Bayesian network as the starting point and learns it by the forbidden hill-climbing search and BIC scoring method to obtain the optimal Bayesian network structure. Experimental results show that the correlation degree measure and mutual information proposed in this paper have an approximate correlation expression effect; compared with other Bayesian network structure learning algorithms of the same type, the method in this paper has a faster operation speed while ensuring the quality of the learned network. The experimental results show that the Bayesian network structure learning algorithm based on probabilistic incremental analysis and constraints is an effective and efficient Bayesian network structure learning algorithm.
A Real Time Tracking System for School Forms Using QR Codes with Watermarking Algorithm

Document tracking is considered crucial, particularly in schools that still use traditional administration methods. Meanwhile, the usage of forms in a school's office, such as the Budget Division, is still necessary and should not be removed as per the school’s current policy. As a result, the school should have a solid monitoring system in place to ensure that these forms are stable and reliable. The tracking system is a flexible solution that may be used as a collaboration and communication tool, and it can aid in the construction of a traceable process. The researcher investigates the school's key concerns regarding using paper-based forms, as well as how the school can track its progress and location. The usage of a watermarking algorithm and a QR code-based tracking system was explored in this study. In addition, the structure, applications, security, and strategies for using a QR code are discussed. Existing watermarking technology, according to relevant research, has the opportunity for development in terms of imperceptibility and endurance in the future.
ePA*SE: Edge-Based Parallel A* for Slow Evaluations

Parallel search algorithms harness the multithreading capability of modern processors to achieve faster planning. One such algorithm is PA*SE (Parallel A* for Slow Expansions), which parallelizes state expansions to achieve faster planning in domains where state expansions are slow. In this work, we propose ePA*SE (Edge-Based Parallel A* for Slow Evaluations) that improves on PA*SE by parallelizing edge evaluations instead of state expansions. This makes ePA*SE more efficient in domains where edge evaluations are expensive and need varying amounts of computational effort, which is often the case in robotics. On the theoretical front, we show that ePA*SE provides rigorous optimality guarantees. In addition, ePA*SE can be trivially extended to handle an inflation weight on the heuristic resulting in a bounded suboptimal algorithm w-ePA*SE (Weighted ePA*SE) that trades off optimality for faster planning. On the experimental front, we validate the proposed algorithm in two different planning domains: 1) motion planning for 3D humanoid navigation and 2) task and motion planning for a dual-arm robotic assembly task. We show that ePA*SE can be significantly more efficient than PA*SE and other alternatives. The open-source code for ePA*SE along with the baselines is available here: https://github.com/shohinm/parallel_search
CREATION OF INFORMATION AND EDUCATIONAL ENVIRONMENT FOR THE TRAINING OF FUTURE TEACHERS USING VISUAL-DIGITAL APPROACH AND WEB 3.0 TECHNOLOGIES

The article reveals the features of creating and using an information and educational environment for the professional training of future teachers of labor training by means of a visual-digital approach and Web 3.0 technologies. It was established that the information and educational environment of the professional training of future teachers of labor education involves the use of certain Internet technologies, namely: specialized software, virtual and augmented reality technologies, as well as virtual network workshops, the Moodle distance learning platform, GoogleDrive cloud storage with professional oriented educational components. Such an environment determines the influence on the motivational, cognitive, and activity components of the readiness of the teacher of labor training to the formation of students’ subject competencies in the technological educational field.The possibilities of using the Moodle distance learning platform in the professional training of future teachers of labor training are presented. The possibilities of video conferences in the process of forming public speaking skills among future teachers of labor training are considered. The advantages of using virtual workshops in the individual work of students have been determined. The features and advantages of using cloud technologies, in particular GoogleDrive technology, in working with large arrays of information presented on the Internet (storage and exchange of information, joint work with documents, integration of materials from cloud storage on one’s own web resource, etc.) are analyzed. The advantages of the visual-digital approach in the training of future teachers of labor training (involving virtual and augmented reality tools) are revealed.In order to implement the visual-digital approach in education, the article defines the types of work, which should be used to supplement the traditional form of organization of educational work (searching for necessary information on the Internet, working with electronic didactic materials, online seminars, chats, Internet conferences, round tables discussions, video lessons, etc.). The method of using digital learning tools in the educational process of a higher education institution is presented.
A High-performance RNS LSTM block

The Residue Number System (RNS) has been proposed as an alternative to conventional binary representations for use in AI hardware accelerators. While it has been successfully utilized in applications targeting Convolutional Neural Networks (CNNs), its usage in other network models such as Recurrent Neural Networks (RNNs) has been set back due to the difficulty of implementing more complex activations functions like tanh and sigmoid ($\sigma$) in the RNS domain. In this paper, we seek to extend its usage in such models, and in particular LSTM networks, by providing efficient RNS implementations of the activation functions. To this aim, we derive improved accuracy piecewise linear approximations of the tanh and $\sigma$ functions using the minimax approach and propose a fully RNS-based hardware realization. We show that our approximations can effectively mitigate accuracy degradation in LSTM networks compared to naive approximations, while the RNS LSTM block can be up to 40% more efficient in terms of performance per area unit compared to a binary counterpart, when used in high performance-targeted accelerators.
E-Graphs: Bootstrapping Planning with Experience Graphs

In this paper, we develop an online motion planning approach which learns from its planning episodes (experiences) a graph, an Experience Graph. On the theoretical side, we show that planning with Experience graphs is complete and provides bounds on suboptimality with respect to the graph that represents the original planning problem. Experimentally, we show in simulations and on a physical robot that our approach is particularly suitable for higher-dimensional motion planning tasks such as planning for two armed mobile manipulation.