Reviewerly/exHarmony huggingface dataset:
On generalized algebraic theories and categories with families

Abstract We give a syntax independent formulation of finitely presented generalized algebraic theories as initial objects in categories of categories with families (cwfs) with extra structure. To this end, we simultaneously define the notion of a presentation Σ of a generalized algebraic theory and the associated category CwF Σ of small cwfs with a Σ-structure and cwf-morphisms that preserve Σ-structure on the nose. Our definition refers to the purely semantic notion of uniform family of contexts, types, and terms in CwF Σ . Furthermore, we show how to syntactically construct an initial cwf with a Σ-structure. This result can be viewed as a generalization of Birkhoff’s completeness theorem for equational logic. It is obtained by extending Castellan, Clairambault, and Dybjer’s construction of an initial cwf. We provide examples of generalized algebraic theories for monoids, categories, categories with families, and categories with families with extra structure for some type formers of Martin-Löf type theory. The models of these are internal monoids, internal categories, and internal categories with families (with extra structure) in a small category with families. Finally, we show how to extend our definition to some generalized algebraic theories that are not finitely presented, such as the theory of contextual cwfs.
Using Feedforward Neural Networks for Parameter Modeling of a 4G Link for Unmanned Aerial Vehicles

A prediction of link parameters based on positional information contributes to a safe operation of Unmanned Aerial Vehicles (UAVs). For logistical and economical reasons, it makes sense to establish the wireless link via the fourth generation mobile standard Long Term Evolution (LTE) as it offers an already existing and stable infrastructure. The usual non line of sight (NLOS) connection in such a scenario poses challenges in parameter modeling, for which artificial neural networks are shown to be a suitable solution in this paper. The connection quality between transmitter station and UAV is mainly described by the parameters Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), which are tried to be predicted via an Artificial Neural Network (ANN) in this work. This approach takes the UAVs position (either Cartesian or polar coordinates) as input and maps them to the respective parameter.
Remote sensing images target detection based on deep learning

Target detection based on deep learning methods has important applications in the field of remote sensing images target detection. Aiming at the disadvantages of the small target scale for remote sensing images, this paper proposes an improved YOLOv4 algorithm to improve the detection effect of remote sensing images. The experimental results show that the algorithm proposed in this paper has an average accuracy rate of 2.25% higher than that of the original YOLOv4 algorithm. The accuracy of detecting small-scale targets has been significantly improved, and the amount of model parameters has also been reduced compared to the original algorithm.
HOPASS: A two-layer control framework for bandwidth and delay guarantee in datacenters

In data center networks (DCNs), flows with different objectives coexist and compete for limited network resources (such as bandwidth and buffer space). Without harmonious resource planning, chaotic competition among these flows would lead to severe performance degradation. Furthermore, low latency is critical for many emerging applications such as augmented reality(AR), virtual reality(VR) and telepresence, making the network control problem even more challenging. To address the above issues, this paper novelly proposes a receiver-driven two-layer control framework called HOPASS, which incorporates a slow control layer and a fast control layer to strike a balance among multiple network sharing objectives and achieve low latency. The slow control layer ensures bandwidth guarantee in an aggregated flow level by solving a multi-objective network utility maximization (NUM) problem using an online learning approach. Then the results will be dispatched to the data plane by configuring weights in the switches with weighted fair queue functionality. Under the configuration dictated by the slow control layer, the fast control layer leverages the token packets sent by receivers to dynamically probe and reserve network capacity, so that it can proactively prevent network congestion and guarantee low latency data delivery. To evaluate the proposed framework, we have implemented HOPASS in ns-3 and conduct extensive experiments under various network scenarios. The simulation results show that HOPASS achieves near-optimal performance in terms of bandwidth allocation in multi-objective scenarios and also guarantees low end-to-end delay. Moreover, it outperforms DCTCP and NewReno in terms of average bandwidth utilization and global total network utility at the aggregated flow level. Therefore, we conclude that HOPASS provides an effective framework for DCNs when considering both multi-objective optimization and a low latency network.
Optimal Object Placement for Minimum Discontinuity Non-revisiting Coverage Task

This work considers the optimal non-revisiting coverage tasks with a single non-redundant manipulator for the case when the object can be positioned at a predefined set of locations within the workcell. The scenario is often encountered in typical industrial settings, for instance when the object presents itself along a conveyor belt and its surface can not be serviced at a single location - the object being large or complex for that endeavour. Given the non-bijective nature of manipulator kinematics between task and joint space, without explicit consideration of joint-space continuity during its construction, a continuous coverage path designed in task-space may easily be truncated into intermittent segments where the manipulator needs to adopt a different configuration to continue the task, resulting in manipulator motions where the end-effector will need to lift off the surface, an altogether undesirable characteristic affecting the quality of the final product for smooth operations on objects such as polishing, painting or deburring. In this work, a novel algorithm to optimally partition the task-space whilst considering the various finite locations where the object may be stationed is proposed that ensures joint-space coverage continuity with minimal lift-offs. Results from the algorithm being challenged to achieve coverage of a number of objects, both in simulation and in real tests with an industrial manipulator, prove the effectiveness of the proposed planner when compared with classical coverage strategies faced with the same problem.
Design, Analysis and Obstacle Avoidance Control of a Smart Wheelchair

The wheelchair users are generally exposed to physical and psychological health problems, e.g., pressure sores and pain in the hip joint, associated with seating posture, or being inactive in a wheelchair for a long time. A smart wheelchair with back, thigh, and leg adjustment helps in daily life activities and health preservation. The seat elevating function of a wheelchair allows the user (lower limb amputation) to reach different heights. Thus, this paper proposed the design, analysis and obstacle avoidance control of a smart wheelchair with sit-sleep-seat elevation functions. A 3D model of the wheelchair is designed in SolidWorks which is later used in MSC Adams (Automated Dynamic Analysis of Mechanical Systems) to examine and prove the multi-body dynamics of a wheelchair. A fuzzy algorithm-based driving control system is designed to avoid the obstacle by getting information in the form of distance from the ultrasonic sensor and user-specified direction from the joystick's operation. The driving control system mainly focuses on the direction and velocity of the wheelchair. The designed driving control system has been implemented and tested in Gazebo simulator using Robot operating System (ROS). Simulation results verify the non-accidental behavior of the smart wheelchair.
A Multi-Level Network for Human Pose Estimation

Although multi-person human pose estimation has made great progress in recent years, the challenges such as various scales of persons, occluded keypoints, and crowded backgrounds in complex scenes are still remained to be solved. In this paper, we propose a novel multi-level pose estimation network (MLPE) to learn multi-level features that can preserve both the strong semantic clues and spatial resolution for keypoint prediction and location. More specifically, a multi-level prediction network with a feature enhancement strategy is first proposed to learn multi-level features to achieve a good trade-off between the global context information and spatial resolution. We then build a high-resolution fine network to restore high spatial resolution information based on transposed convolutions to accurately locate the keypoints. We have conducted extensive experiments on the challenging MS COCO dataset, which has proved the effectiveness of our proposed method. Code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> and the experimental results are publicly online available for further research.
AI Coach for Battle Royale Games

Battle Royale is a popular online multiplayer game genre that combines last-man-standing shooter gameplay with survival, combat, and scavenging elements. Since several abilities beyond basic combat skills affect player performance in game, the learning curve can be steep for many players. To enhance the gameplay experience, we propose the AI Coach algorithm to provide personalized and adaptive coaching suggestions to Apex Legends players. The proposed algorithm first models player game skills with a comprehensive set of in-game attributes using a random forest. Then, it artificially perturbs individual player data to search for impactful candidate attributes and the associated actions that an individual player can use to improve their game skill. The AI Coach technique provides automated and objective suggestions based on players’ recent gameplay data, optimizing expected game outcomes.
A Webshell Detection Model Based On Bayes

Webshell is a command-page document that exists in the form of execution of the program, also known as a backdoor, is an essential means of hacking website uses. Due to the great harm of webshell, webshell detection has become a key research direction in network security. At present, there are many types of research and results in webshell detection, such as the commonly used detection tools such as “safe dog”, “Dshield”, and “Hippo”. Most of these tools are based on the signature to detect, and the detection accuracy is not ideal. By analyzing the current status of Webshell detection methods, this paper proposes a Webshell detection method based on the Naive Bayes algorithm, and uses Python for implementation and results analysis. This model can effectively detect Webshell that has been fuzzy coded, and improve the accuracy of detection.
An Android Application for Clinical Diagnosis Using NLP and Fuzzy Logic

The application of natural language processing (NLP) methods to designing conversational frameworks for health diagnosis improves patients’ access to medical information. An Android application based on fuzzy logic rules and fuzzy inference was created in this research. In Ghana, the service assesses the symptoms of diseases. The android application is built with the Support Vector Machine learning technique, with the aim of improving the model’s accuracy and performance. Natural Language Processing is often used by the machine to achieve the conversational style of asking the users for their symptoms. People can spend less time in hospitals and get low-cost or free care by using this technique, which is mainly used in Ghana’s rural areas.
Prognostics and Management of Mental Stress by AIoT monitoring and Schlegel Diagrams

Aim of the paper is to illustrate how wearable body sensor networks (BSNs) powered by Machine Learning (ML) algorithms and a suitable visualization tool may be used to timely discover risky situations due to high mental stress. Although many algorithms and wearable devices are able to measure the stress, algorithms predictive of a trend towards, or pointing out timely, risky stress situations need the availability of sequences in time of multiple sensed data depending on context. The paper shows how this can be obtained by means of a wearable body sensor network and two cooperating neural networks: one resident on the edge devices and the other on a remote control center. The former to test the current situation and to possibly alert assistance people, the latter to learn the mental stress model and to predict risky stress situations for the subject under test. Recognition of the incipience of a risky situation is obtained by visualizing how the stress status evolves in the Rn space containing the Rn stress feature clustering by means of its projections into the Rn-1 using the Schlegel diagram. The case study illustrates how the mental stress prognostics may be done by visualizing the projections in the 3D Schlegel diagram of a 4D hypercube (called tesseract) containing the stress features clustered in a R4 space.
A design of command and control system based on neural network decision-making support algorithm

In order to optimize the decision-making process of the traditional auxiliary decision-making model, the author used the feature of big data mining can extract or excavate hidden valuable information from complex and multi-scale massive data, designed a big data decision support model which is based on deep learning algorithm and improved the original “six libraries” decision model. Its principle and operation process were described deeply. The process provided intelligent and informational auxiliary methods for commander’s decision making, and improved the real-time and accuracy of Command and Control (CC) decision making. Furthermore, the further applications of data mining and deep learning algorithm in CC Information System were discussed.
FHA: Fast Heuristic Attack Against Graph Convolutional Networks

Recent years have witnessed a significant growth in Graph Convolutional Networks (GCNs). Being widely applied in a number of tasks, the safety issues of GCNs have draw the attention of many researchers. Recent studies have demonstrated that GCNs are vulnerable to adversarial attacks such that they are easily fooled by deliberate perturbations and a number of attacking methods have been proposed. However, state-of-the-art methods, which incorporate meta learning techniques, suffer from high computational costs. On the other hand, heuristic methods, which excel in efficiency, are in lack of satisfactory attacking performance. In order to solve this problem, it is supposed to find the patterns of gradient-based attacks to improve the performance of heuristic algorithms.In this paper, we take advantage of several patterns discovered in untargeted attacks to propose a novel heuristic strategy to attack GCNs via creating viscous edges. We introduce the Fast Heuristic Attack (FHA) algorithm, which deliberately links training nodes to nodes of different classes. Instead of linking nodes fully randomly, the algorithm picks a batch of training nodes, which are of the same type, and links them to another class each time. Experimental studies show that our proposed method is able to achieve competitive attacking performance when attacking against various GCN models while significantly outperforming Mettack, which is the state-of-the-art untargeted structure attack, in terms of runtime.
DRDF

In multimodal tasks, the importance of text and image modal information often varies for different input cases. To model the difference of importance of different modal information, we propose a high-performance and highly general Dual-Router Dynamic Framework (DRDF), consisting of Dual-Router, MWF-Layer, experts and expert fusion unit. The text router and image router in Dual-Router take text modal information and image modal information respectively, and MWF-Layer is responsible to determine the importance of modal information. Based on the result of the determination, MWF-Layer generates fused weights for the subsequent experts fusion. Experts can adopt a variety of backbones that match the current multimodal or unimodal task. DRDF features high generality and modularity, and we test 12 backbones such as Visual BERT and their corresponding DRDF instances on the multimodal dataset Hateful memes, and unimodal datasets CIFAR10, CIFAR100, and TinyImagenet. Our DRDF instance outperforms those backbones. We also validate the effectiveness of components of DRDF by ablation studies, and discuss the reasons and ideas of DRDF design.
Authentic QR Codes for Traceability and Tamper Detection in IoT Enabled Waste Management Networks

With a population of around 136 crores, India is one of the world’s largest developing nations. According to the Ministry of Housing and Urban Affairs, it is also one of the largest producers of solid waste, generating around 150,000 tons per day. The main problem lies with the management of the generated waste efficiency. Although the government has taken up several measures to ensure proper waste management techniques, most of them have not given the desired outputs. IoT (Internet of Things), being called one of the dominant infrastructure in the technological domain, has dynamic characteristics such as interoperability, scalability, self-configuring, and unique identity in the connected network. On the other hand, QR codes are machine-readable barcodes that contain an authenticated specific code that provides the tracking and routing information for the IoT enabled devices in the connected network. On applying the request-response communication model, the scanned QR data can be exchanged with the connected devices in the network. They can also be communicated with the centralised cloud server. Confidentiality, integrity, and Authenticity (CIA) can be integrated with the QR code using the Hash chaining principle.
Comparing Chat Methods for Remote Collaborative Live-Coding Music

This study analyzes the impact of text versus audio chat in a remote collaborative live-coding music environment. Drawing on the literature discussing collaborative live-coding music, we wanted to examine the impacts of different chat methods on collaboration and coordination. This paper will explore the benefits and drawbacks of two different chat methods, and how they affect remote performances. We conducted a study with 5 participants, each of these sessions containing two 15 minute performance tasks where only one chat method was allowed to be used for collaboration. After the performance tasks, we interviewed the participants about their experience and how each chat method affected their performance. Audio-chat proved useful for real-time coordination and complex explanations, while text-chat was beneficial in its silence and recorded nature for future review of ideas. This paper will discuss these merits for a remote collaborative live-coding environment.
Augmented Reality Mobile Application Base On Marker Object

Bali has very diverse tourist objects, including natural tourism, cultural tourism, educational tourism, and marine tourism. The factor that causes Bali to become a mainstay tourist destination in Indonesia is its uniqueness in its tourism objects, both natural tourism and cultural tourism. Besides, complete tourism facilities and infrastructure also support tourism in Bali. Digital-based promotion is also a blessing and a challenge for the tourism industry. It is a blessing when tourist visits increase due to easy, cheap, and efficient access to tourist information. In this study, researchers developed a tourist attraction visualization application using marker-based Augmented Reality (AR) to promote tourism potential to attract tourists to visit. The Augmented Reality (AR) technology offered can describe real tourist objects by packaging potential tourist objects attractively, so that they can provide different experiences and education in understanding each place or tourist attraction visited. The results of system testing determine the reliability of applications made in this study using three tests, namely 99.6% functional suitability, 100% compatibility, and 90.7% usability.
A Study on the Introduction of Cross Cultural Communication in English Reading Teaching from the Perspective of Cross Culture

English reading course aims to cultivate students' ability to obtain information in English, which is highly practical. Students' English language ability and cross culture awareness are relatively weak. In the teaching process, they often pay attention to the cultivation of language knowledge and reading ability and ignore the teaching of cultural knowledge, which makes the cultural differences between China and the West become an obstacle to reading, resulting in the inability to improve students' overall reading ability. In view of this, from the perspective of cross-cultural communication, this paper puts forward a teaching method of integrating the four elements of culture introduction, cultural context creation, cultural contrast and cultural acquisition in college English reading class, integrating cultural teaching into language teaching, and cultivating students' awareness and ability of cross-cultural communication while teaching students reading skills, so as to promote English reading teaching and improve its connotation construction.
Development Process for Self-adaptive Applications of the Internet of Health Things based on Movement Patterns

This study proposes a development process for self-adaptive IoHT applications based on movement pattern data for mobile devices. The proposed process adapts the component reuse-oriented development process by adding elements related to the construction of self-adaptive systems. Also, we propose five artifacts to support this process: a taxonomy relating data from sensors and health status to assist; a correlation graph between sensor data, movement features, and health situations, to assist the choice of components and serving as a knowledge base for self-adaptive application; a template for building adaptation rules; a model based on the MAPE-K adaptation cycle; and a framework based on the model, for assisting the implementation phase. The evaluation of the proposed process will be done using proofs of concept and an experiment.
Dark Image Enhancement Based on Virtual Exposure Fusion

Image processing technology plays an important role in the field of monitoring, medical treatment and remote sensing. However, in some low light environment, the image captured by hardware will have the characteristics of loss of detail information and low contrast, which limits the practical application. In order to improve the brightness of low illumination image, alleviate the image contour distortion and restore more details of the image, a low light image enhancement algorithm based on virtual exposure fusion is proposed in this paper. In this method, the dark image is processed by histogram equalization, gamma correction and virtual exposure to generate three images which details and brightness are enhanced. The three processed images and the original image are weighted and fused by weight matrix afterwards to get the final enhanced image.Through the comparison of objective experiments, the algorithm has a certain improvement effect on image enhancement and has a better effect on dark image enhancement.