Reviewerly/exHarmony huggingface dataset:
CostCounter: A Better Method for Collision Mitigation in Cuckoo Hashing

Hardware is often required to support fast search and high-throughput applications. Consequently, the performance of search algorithms is limited by storage bandwidth. Hence, the search algorithm must be optimized accordingly. We propose a CostCounter (CC) algorithm based on cuckoo hashing and an Improved CostCounter (ICC) algorithm. A better path can be selected when collisions occur using a cost counter to record the kick-out situation. Our simulation results indicate that the CC and ICC algorithms can achieve more significant performance improvements than Random Walk (RW), Breadth First Search (BFS), and MinCounter (MC). With two buckets and two slots per bucket, under the 95% memory load rate of the maximum load rate, CC and ICC are optimized on read-write times over 20% and 80% compared to MC and BFS, respectively. Furthermore, the CC and ICC algorithms achieve a slight improvement in storage efficiency compared with MC. In addition, we implement RW, MC, and the proposed algorithms using fine-grained locking to support a high throughput rate. From the test on field programmable gate arrays, we verify the simulation results and our algorithms optimize the maximum throughput over 23% compared to RW and 9% compared to MC under 95% of the memory capacity. The test results indicate that our CC and ICC algorithms can achieve better performance in terms of hardware bandwidth and memory load efficiency without incurring a significant resource cost.
‘Easy Language’ for migrants in France

Abstract One of the target groups of Easy Language, a comprehensibility enhanced variety of a natural language, are migrants who have difficulties with the standard language of the host country. However, regarding French Easy Language, evidence-based linguistic criteria are lacking. Therefore, the present study aims at elaborating suitable linguistic criteria for French Easy Language intended for low-literate migrants. We asked 87 migrants in France to read four texts, two original and two rewritten according to specific criteria, and to answer several comprehension questions presented in a questionnaire. The score of correctly answered questions is significantly higher in the case of the rewritten texts. The article discusses the implications of this result in the context of Easy Language research and gives an outlook on current research desiderata.
The Effect of Cryptocurrency Returns and Cryptocurrency Volume on the Stock Price Index of Indonesia, Singapore, Thailand

The title of this study is to analyze the effect of cryptocurrency returns and cryptocurrency volume on the stock price indices of Indonesia, Singapore and Thailand. This research was conducted using the panel data regression method by combining cross section data with time series data using Eviews software. This study uses cryptocurrency returns and volumes in a weekly period from 1 January 2018 to 31 December 2021 for 48 weeks for each country with a total of 144 data. The results of this study indicate that cryptocurrency returns have a significant positive effect on the stock price indexes of Indonesia, Singapore, and Thailand. and cryptocurrency volume has a significant positive effect on the stock price indices of Indonesia, Singapore and Thailand.
Protecting controversial speakers plays key role in protecting free speech

Colleges and universities commonly have controversial speakers on campus, typically invited by a student organization. Student protests of controversial speakers are almost just as common. Such protests often involve students shouting down, interrupting, or engaging in disruptions that are aimed at canceling the event. These disruptions are commonly called a “heckler's veto.”
A Delphi-type study with medical doctors to develop a physical activity prescription

Multirelational recommendation usually considers multiple types of user–item interaction data. Recently, graph convolutional networks (GCNs) have been widely used for recommendation systems. However, most existing GCN-based recommendation models only utilize one type of interactive data, ignoring other types of interactive data. At the same time, it is worth noting that GCN-based recommendation models are prone to oversmoothing problems. This means that when GCN-based recommendation models stack more layers, they also import noisy messages from users who have no common interest with the target user, resulting in a recommendation performance bottleneck. Consequently, it is necessary to reform the traditional GCN to make it suitable for a multirelational recommendation. A novel multirelational recommendation model through an interest-oriented heterogeneous graph (IHG4MR) is proposed in this paper. To effectively capture information from high-level nodes in a heterogeneous graph, IHG4MR first utilizes user characteristics and graph structure to split users who have similar preferences and then employs these segmented users and their interacting items to build multiple new interactive graphs instead of the previous whole graph to implement graph convolution operations. IHG4MR reduces the negative information carried during embedding propagation of the graph convolutional layers, thereby mitigating the effect of oversmoothing. Extensive experimental results on two real world datasets show that IHG4MR generally outperforms existing state-of-the-art models, demonstrating the effectiveness of our model.
Charitable trusts and cryptoassets: opportunity, or the asset of discord?

Abstract In written evidence submitted to Parliament in 2022, the Charity Commission for England and Wales outlined three types of “cryptoasset-related risk exposure” that can directly affect charitable trusts. These include: (i) fundraising donations; (ii) moving funds internationally; and (iii) investment. This article’s purpose is to demonstrate that, out of these forms of risk exposure, “investment” is currently the greatest issue for charities. Charity trustees possess a broad investment power and may be tempted to invest in crypto to financially benefit their funds. However, extreme caution should be heeded before investing in cryptoassets, given their complexity and volatility.
Study on the Design of Algorithm Based on Machine Learning to Improve Cloud Computing

The on-demand availability of end-user resources, in particular data storage and processing power, without a direct or customer-defined organization is referred to as "cloud computing." Distributed computing is a term widely used yet may have different meanings to different people. Customers may access both public and private data using the cloud computing model. The potential of simultaneously requesting data from several clients of the same source, which slows down the source's response time, is the most significant security risk with cloud computing. Other security concerns with cloud computing include weaknesses in the client and connection. By reducing the delay between a client's request for data and the cloud source's answer, a method was developed in our recent research to enhance the performance of cloud computing. By requesting data from several clients from the same source at once or from multiple clients from the same source or from other sources at various times in the same network, four instances were shown. By testing request and response times while protecting data from loss and noise, the findings demonstrated the system's robustness.
Balancing Security and Information Management in the Digital Workplace

As the digital workplace becomes more prevalent, organizations are faced with the challenge of balancing security and information management. On one hand, there is a need to protect sensitive data and prevent cyberattacks, while on the other hand, organizations must enable employees to collaborate and share information effectively. Machine learning (ML) is a promising technology that can help organizations address this challenge. By analyzing data patterns and identifying potential security threats, ML algorithms can enhance security measures and mitigate risks. At the same time, ML can also facilitate information management by automating routine tasks and improving the accuracy of data analysis. In this paper, we explore the role of ML in balancing security and information management in the digital workplace. We propose a hybrid ML model that integrates autoencoder and convolutional subnetworks in unified architecture to help capturing and security threats in the digital workplace, without compromising the information management tasks. We also present a case study of a real-world implementation of ML in a digital workplace setting, highlighting the benefits and limitations of this approach. Our findings suggest that ML can be a valuable tool for achieving a balance between security and information management in the digital workplace, but its successful implementation requires careful consideration of organizational context and stakeholder needs.
Response properties of single neurons

Neurons are often viewed as biological filters that convert input signals to output signals. This perspective allows specific neural systems to be studied in isolation. For example, one may study sensory areas by assuming that they process and relay information from the sensory periphery to higher brain areas.
NutriFACT: An Android based Mobile Application for Intake of Food Nutrition during Rare Disease

In current era people are suffering from many problems including common and rare diseases. The common diseases like hypertension and diabetes can be curable and controllable using proper medical guidance and diet but there is some rare disease which people do not aware about. Some of the rare diseases are Wilson's disease, Hemochromatosis and Ricket's disease. The causes of these disease are food deficiency or excessive consumption of minerals. Proper Food intake must be necessary to control these rare diseases. Specific nutrition must be required to control these diseases. In this research paper we have proposed an application which can be used by thepatient suffering from these rare diseases. It is developed using Android Studio platform. In the application user can enter the rare disease type and based on selected type a user is guided forfood to be intake by them. User can get the information regarding the hospitals which provides treatments for these raredisease.
Guess the Syntax: Parsers and the Evolution of the Puzzle in Text Games

This paper was originally written as an invited chapter for a book that never happened, way back in 2014. Given the specialist nature of the text, the frame of its inquiry, and the style of its writing it represents a challenging prospect for more traditional publication. It is present then as a pre-print for those that may find it worthwhile. References have been updated as is appropriate, but otherwise the text is largely ’as is’.It could be argued that the first type of game to truly make puzzle solving a core part of the experience is the text adventure. Single player interactive fictions and their later multiplayer counterparts have a long history of incorporating complex and cognitively demanding problem solving. Such puzzles are provided as both a narrative conceit and an obstacle to advancement that must be overcome. While often made more difficult as a consequence of the specific limitations of the form, such as that represented by the ’hunt the syntax’ problem, the puzzles and quests within these games have considerable significance outside their own individualized context. In the first instance, they are significant from a historical perspective. In the second, they are significant in how the limitations of visual representation have influenced the ways in which players can interact with the puzzles and the greater world in which they are embedded.While representations of puzzle forms such as Minesweeper, Light’s Out and tile matching are common in text games, there are significant implications with regards to their presentation. Visually impaired players, often drawn to text gaming because of the comparative accessibility, are usually unable to engage with these challenges in the way in which the developer may have intended. This ensures an additional complexity of implementation that greatly influences the way in which puzzles have been represented and implemented within text environments.This paper sets out the historical roots of the text puzzle and the influence it has had on the evolution of video games. It begins with the first text adventures, moves on to the representation of the quest within multi-user dungeons, and discusses how pure interactive fiction gave way first to graphical hybrids and then to fully graphical adventures. Thus, the puzzle within the text game is the historical precursor to much of what we think of as narratively relevant puzzle based game-play. Alongside this discussion, it outlines the context of the implementation of puzzles within text that makes them distinct in interaction, representation, and the cognitive processes required to solve them. We conclude by discussing the way in which the role of the puzzle within interactive fiction has become underemphasised in recent years even as its role in other game genres has intensified.
Segmenting MR Images Through Texture Extraction and Multiplicative Components Optimization

The segmentation of MRI is a challenging task due to artifacts introduced by the acquisition process, like bias field and noise. In this paper, using a cartoon-texture decomposition of the image, we present a strategy that segments the cartoon processed by simultaneous bias correction and denoising. Preliminary numerical tests show that our method is effective in segmenting MRI data corrupted by noise.
Navigation

Robot navigation https://go.sn.pub/yLoW7Z is the problem of guiding a robot towards a goal. The goal might be specified in terms of some feature in the environment, for instance, moving toward a light, or in terms of some geometric coordinate or map reference. The human approach to navigation is to make maps and erect signposts, and at first glance it seems obvious that robots should operate the same way. However, many robotic tasks can be achieved without any map at all, using an approach referred to as reactive navigation – sensing the world and reacting to what is sensed. For example, the early robotic tortoise Elsie, shown in Fig. 5.1a, reacted to her environment and could seek out a light source and move around obstacles without having any explicit plan, or any knowledge of the position of herself or the light. Today, tens of millions of robotic vacuum cleaners are cleaning floors and many of them do so without using any map of the rooms in which they work. Instead, they do the job by making random moves and sensing only that they have made contact with an obstacle as shown in Fig. 5.2.
Advanced Visual Servoing

This https://go.sn.pub/jdeUql chapter builds on the previous one and introduces some advanced visual servo techniques and applications. Sect. 16.1 introduces a hybrid visual servo method that avoids some of the limitations of the IBVS and PBVS schemes described previously. Wide-angle cameras such as fisheye lenses and catadioptric cameras have significant advantages for visual servoing. Sect. 16.2 shows how IBVS can be reformulated for polar rather than Cartesian image-plane coordinates. This is directly relevant to fisheye lenses but also gives improved rotational control when using a perspective camera. The unified imaging model from Sect. 13.4 allows most cameras (perspective, fisheye and panoramic) to be represented by a spherical projection model, and Sect. 16.3 shows how IBVS can be reformulated for spherical cameras. Sect. 16.4 presents a number of brief application examples. These illustrate, using block diagrams, how visual servoing can be used with different types of cameras (perspective and spherical) and different types of robots (arm-type robots, mobile ground robots and aerial robots). Examples include a 6-axis robot manipulator with an eye-in-hand camera; a mobile robot moving to a specific pose which could be used for navigating through a doorway or docking; and a quadrotor moving to, and hovering at, a fixed pose with respect to a goal on the ground.
Vision-Based Control

The https://go.sn.pub/WETNOx task in visual servoing is to control the pose of the robot’s end effector, relative to some goal object, using visual features extracted from an image of the goal object. The features might be centroids of points, equations of lines or ellipses, or even brightness patterns of pixels. The image of the goal is a function of its camera-relative pose $${}^{C}{}{\boldsymbol{\xi}}_{G}$$ , and so to are the features extracted from the image. There are two possible configurations of camera and robot. The configuration of Fig. 15.1a has the camera mounted on the robot’s end effector observing the goal, and is referred to as end-point closed-loop or eye-in-hand. The configuration of Fig. 15.1b has the camera at a fixed point in the world, observing both the goal and the robot’s end effector, and is referred to as end-point open-loop. In the remainder of this book we will discuss only the eye-in-hand configuration. Vision-based control can work with other types of robots besides manipulator arms – the camera could be attached to a mobile ground robot or a aerial robot, and these are discussed in the next chapter.
Limits and Lessons of COVID-19 Apps

A crucial resource in the COVID-19 pandemic has been data. Data – on where and when the SARS-CoV-2 virus is spreading or mutating, where vulnerable or non-vaccinated people are, and how best citizens, firms and governments can react – have been assets to individuals and organizations faced with an exceptional health emergency. In the early days of COVID-19, however, the production, collection and analysis of such data unfolded in less than exceptional ways. This chapter traces how the first year of the COVID-19 pandemic witnessed a fall back on pre-existing ‘technosolutionist’ tendencies across Europe. A reliance on smartphone applications seemingly offered governments quick technological fixes to generate and collect ‘actionable data’. Representing something concrete being done to address the unfolding pandemic, COVID-19 apps offered much desired solutions. Yet, these fixes glossed over deeper structural problems and, in hindsight, have set the stage for similarly shallow responses to exceptional times to come. This chapter analyses the successes and failures of COVID-App solutionism, drawing out lessons learned from the limits of techno-fixes that skirt wider complex socio-political and ecological problems.
Human Activity Recognition

Human Activity Recognition (HAR) is the difficulty of quickly identifying strenuous exercise performed by people. It is feasible to sample some measures of a body's tangential acceleration and speed using inertial sensors and exercise them only to learn model skilled of incorrectly categorising behavior into the relevant categories.Detecting human activities the use of detectors in personal and portable devices has increased in order to better understand and anticipate human behaviour. Many specialists are working toward developing a classification that can distinguish between a user's behaviour and uncooked data while utilising as few reserves as possible.A Long-term Recurrent Convolutional Network (LRCN) is proposed as a comprehensive human action recognition system based on deep neural networks in this paper.
Images and Image Processing

Image https://go.sn.pub/XuZbLo processing is a computational process that transforms one or more input images into an output image. Image processing is frequently used to enhance an image for human viewing or interpretation, for example to improve contrast. Alternatively, and of more interest to robotics, it is the foundation for the process of feature extraction which will be discussed in much more detail in the next chapter. An image is a rectangular array of picture elements (pixels) so we will use a NumPy array to represent an image within a Python session. This allows us to use NumPy’s fast and powerful armory of functions and operators for array computing.
IoV and blockchain-enabled driving guidance strategy in complex traffic environment

Diversified traffic participants and complex traffic environment (e.g., roadblocks or road damage exist) challenge the decision-making accuracy of a single connected and autonomous vehicle (CAV) due to its limited sensing and computing capabilities. Using Internet of Vehicles (IoV) to share driving rules between CAVs can break limitations of a single CAV, but at the same time may cause privacy and safety issues. To tackle this problem, this paper proposes to combine IoV and blockchain technologies to form an efficient and accurate autonomous guidance strategy. Specifically, we first use reinforcement learning for driving decision learning, and give the corresponding driving rule extraction method. Then, an architecture combining IoV and blockchain is designed to ensure secure driving rule sharing. Finally, the shared rules will form an effective autonomous driving guidance strategy through driving rules selection and action selection. Extensive simulation proves that the proposed strategy performs well in complex traffic environment, mainly in terms of accuracy, safety, and robustness.
Anomaly Detection System of Power Consumption Data Based on Social Information Verification

Power consumption data has been widely used in many fields. The prediction and analysis of power system data such as power supply and power consumption in power plants plays a significant role in the rational layout and control of energy facilities and improving energy utilization. However, in real application scenarios, due to various reasons such as sensor damage, data transmission failure, memory damage, etc., some paragraphs of power consumption data may have persistent data anomalies. Therefore, how to effectively detect power data anomalies has become a practical and urgent need. In this paper, a spatiotemporal attention-based method SIDM is proposed to detect power consumption data with paragraph anomalies. The Transformer model has excellent performance in capturing the dependencies between variables, and researchers generally use its attention mechanism to capture the mutual influence of data at different times. The SIDM model adds additional spatial attention to its temporal attention to capture the impact of social information on power consumption. By incorporating social information factors such as similar users, related power-consuming equipment, and local climate into the model, verify whether the current data is abnormal. The experimental results on the actual collected power consumption data set show that the effect of the model proposed in this paper is significantly improved compared to the direct prediction without using social information, and compared with other social information fusion detection methods, it has a higher accuracyrate.
Intel SGX-Based Trust Framework Designed for Secure Machine Learning

As a branch of artificial intelligence science, machine learning (ML) uses big data in its training process, but data owners usually refuse to give their data away due to data rights and potential risks in data privacy. Instead of acquiring data from owners, transferring the ML model to data providers might be a safer way to access data, while avoiding data leakage. Two concerns about this safer way of access are data theft by the ML model and the theft of the ML model in the provider environment. Our newly designed trust execution framework based on Intel Software Guard Extensions (SGX) builds a secure communication mechanism between data users and providers and a trusted execution environment based on SGX hardware isolation, which protects the two-way security of both data and the ML model. The framework also realizes the porting of the python interpreter in SGX, which simplifies the programming of ML algorithms in SGX.