Reviewerly/exHarmony huggingface dataset:
Analysis and Application of Tourists’ Sentiment Based on Hotel Comment Data

In today’s life, Online Travel Agency (OTA) platform gets more and more users. Tourists on OTA platform makes much more comments on hotels. Similarly, the authenticity also increases significantly. Real user feedback plays a guiding role in improving the quality of the hotel, but it needs to obtain useful information from the complex comment data and a series of data analysis processes. In this paper, the ‘Bazhuayu’ collector is used to gain data, making pre-processing of the dataset. Using jieba to finish segmentation. The Term Frequency-Inverse Document Frequency algorithm is used to extract keywords, and then the Bag-of-Words model is used to construct the word vector. Finally, subsampled is used to balance the dataset. The support vector machine, Naive Bayes and Long Short-Term Memory neural network model are established to classify and adjust the parameters, compare the classification performance of the models, put forward some suggestions for hotel self optimization and upgrading according to the classification result.
A framework for the clustering and categorization of CISA reports

This paper presents a framework for text clustering and categorisation. The proposed clustering approach is based on a modified existing similarity-based clustering algorithm, which was originally developed for well-structured data. In this study, the clustering algorithm is used to map text documents into clusters, in order to discover groups of topical documents. The clusters produced in this way are also used for the categorisation of new documents that are uploaded to the system. The algorithms are discussed using as an example the analysis of text documents including Industrial Control Systems (ICS) Advisory Reports and Common Vulnerabilities and Exposures (CVE) recommendations, together available and provided by the Cybersecurity and Infrastructure Security Agency (CISA). Experiments are carried out, although the main focus is on the clustering algorithm. Based on the experimental results, it can be concluded that the proposed similarity-based clustering algorithm can be considered as an alternative approach for text clustering.
Federated Learning from Demonstration for Active Assistance to Smart Wheelchair Users

Learning from Demonstration (LfD) is a very appealing approach to empower robots with autonomy. Given some demonstrations provided by a human teacher, the robot can learn a policy to solve the task without explicit programming. A promising use case is to endow smart robotic wheelchairs with active assistance to navigation. By using LfD, it is possible to learn to infer short-term destinations anywhere, without the need of building a map of the environment beforehand. Nevertheless, it is difficult to generalize robot behaviors to environments other than those used for training. We believe that one possible solution is learning from crowds, involving a broad number of teachers (the end users themselves) who perform demonstrations in diverse and real environments. To this end, in this work we consider Federated Learning from Demonstration (FLfD), a distributed approach based on a Federated Learning architecture. Our proposal allows the training of a global deep neural network using sensitive local data (images and laser readings) with privacy guarantees. In our experiments we pose a scenario involving different clients working in heterogeneous domains. We show that the federated model is able to generalize and deal with non Independent and Identically Distributed (non-IID) data.
Location First Non-Maximum Suppression for Uncovered Muck Truck Detection

Uncovered muck truck detection aims to detect the muck truck and distinguish whether it is covered or not by dust-proof net to trace the source of pollution. Unlike traditional detection problem, recalling all uncovered trucks is more important than accurate locating for pollution traceability. When two objects are very close in an image, the occluded object may not be recalled because the non-maximum suppression (NMS) algorithm can remove the overlapped proposal. To address this issue, we propose a Location First NMS method to match the ground truth boxes and predicted boxes by position rather than class identifier (ID) in the training stage. Firstly, a box matching method is introduced to re-assign the predicted box ID using the closest ground truth one, which can avoid object missing when the IoU of two proposals is greater than the threshold. Secondly, we design a loss function to adapt the proposed algorithm. Thirdly, a uncovered muck truck detection system is designed using the method in a real scene. Experiment results show the effectiveness of the proposed method.
Contrastive Learning in Wavelet Domain for Image Dehazing

Image dehazing remains a challenging problem because it is hard to restore a clean scene from a severely degraded hazy image. However, existing learning-based dehazing methods mostly ignore the fact that the interference of haze to an image is mainly concentrated in the low-frequency components. If all image components are processed indiscriminately, it is difficult to achieve a good restoration and accurate details cannot be guaranteed. In order to process the hazy images hierarchically, we propose a low-frequency sub-band contrastive regularization (LSCR) in the wavelet domain to ensure that the components of the restored image mainly affected by haze are pulled closer to the clear image and pushed far away from the hazy image. In addition, a high-frequency sub-band loss is also introduced to make high-frequency components of the restored image consistent with the clear image. Our method can better restore the haze-free image and achieve more accurate and rich details. The extensive experiments on synthetic and real-world datasets verify that the proposed method outperforms previous approaches.
Algorithm-Oriented SIMD Computer Mathematical Model and Its Application

This paper has designed a professional and practical SIMD computer mathematical model based on the SIMD physical machine model combined with the variable addition method. Furthermore, the model is applied in image collection, processing, and display operations, and a SIMD data parallel image processing system is finally established by absorbing the parallel computing advantages of the mathematical model. In addition, the data-parallel image processing algorithm is introduced and the convolutional neural network algorithm is optimized to promote the significant improvement of the main performance such as the accuracy of the application system. The final experimental results have shown that the highest accuracy of the data-parallel image processing algorithm reaches 93.3% and the lowest error rate reaches 0.11%, which proves the superiority of the SIMD computer mathematical model in image processing applications.
Using Extended Reality to Teach Protection of Civilians in Higher Military Education

The Norwegian Armed Forces educate future joint staff officers to better understand the role, utility, and limitations of military force in protecting civilians from violence in armed conflict. At the center of this education is the threat-based approach to protection of civilians that redefines and details the role armed forces play in protecting civilians from different perpetrators of violence. Educators have experienced obstacles in teaching this new approach, as it challenges existing doctrinal thinking about force in contemporary armed conflict. These challenges inspired a search for a new and innovative way to overcome learning thresholds, leading to the development of an Extended Reality (XR) application to support teaching of the threat-based approach. The application employs several immersive experiences, including a new form of embodiment that combines digital avatars and 360°-videos, an agent-based simulation, and a collaborative map visualization. The pedagogical approach is built on threshold concepts theory, empathy, and peer learning. Preliminary results point to faster and deeper learning, and a significant interest in using XR to improve learning in higher military education.
SHiFT

Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch. The emergence of rich model repositories, such as TensorFlow Hub, enables practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep growing exponentially, efficiently selecting a good model for the task at hand becomes paramount. However, a single generic search strategy (e.g., taking the model with the highest linear classifier accuracy) does not lead to optimal model selection for diverse downstream tasks. In fact, using hybrid or mixed strategies can often be beneficial. Therefore, we propose SHiFT, the first downstream task-aware, flexible, and efficient model search engine for transfer learning. Users interface with SHiFT using the SHiFT-QL query language, which gives users the flexibility to customize their search criteria. We optimize SHiFT-QL queries using a cost-based decision maker and evaluate them on a wide rang of tasks. Motivated by the iterative nature of machine learning development, we further support efficient incremental executions of our queries, which requires a special implementation when jointly used with our optimizations.
Quantitative causality analysis of viewing abandonment reasons using Shapley value

As adaptive bitrate streaming services are widely used, video streaming providers need to know what factors impact viewing abandonments. As quality and content-related factors are known to influence viewing abandonments, the reasons for viewing abandonments need to be analyzed while considering both factors. For this purpose, previous studies developed machine learning models, but most of them lack the interpretability of the relationships between explanatory variables and a target variable. In addition, causal relationships among explanatory variables need to be considered to disambiguate the effect of each variable. In this paper, we propose using Asymmetric Shapley value (ASV) to study the interpretability of a developed machine learning model and to take into account the effect of causalities among explanatory variables. We used a dataset collected in laboratory experiments on adaptive bitrate streaming and built binary classification models that classify viewing abandonment reasons into quality-induced or content-induced. We selected random forests as the best model and analyzed the relationships between explanatory variables (application quality, users' behavior, content attribute) and the target variable (viewing abandonment reason) with ASV. The results provided meaningful insights into the relationships and showed assuming causality is helpful to modify the estimation of the relationships
Deceiving Audio Design in Augmented Environments : A Systematic Review of Audio Effects in Augmented Reality

Recently, a lot of works show promising directions for audio design in augmented reality (AR). These works are mainly focused on how to improve user experience and make AR more realistic. But even though these improvements seem promising, these new possibilities could also be used as an input for manipulative design. This survey aims to analyze all recent discoveries in audio development regarding AR and argue what kind of “manipulative” effect this could have on the user. It can be concluded that even though there are many works explaining the effects of audio design in AR, very few works point out the risk of harm or manipulation toward the user. Future works could contain more awareness of this problem or maybe even systems to protect the user from any possible manipulation or harm.
Enhancing Privacy in Computer Vision Applications: An Emotion Preserving Approach to Obfuscate Faces

Computer vision offers many techniques to facilitate the extraction of semantic information from images. If the images include persons, preservation of privacy in computer vision applications is challenging, but undoubtedly desired. A common technique to prevent exposure of identities is to cover peoples’ faces with, for example, a black bar. Although emotions are crucial for reasoning in many applications, facial expressions may be covered, which hinders the recognition of actual emotions. Thus, recorded images containing obfuscated faces may be useless for further analysis and investigation. We introduce an approach that enables automatic detection and obfuscation of faces. To avoid privacy conflicts, we use synthetically generated faces for obfuscation. Furthermore, we reconstruct the facial expressions of the original face, adjust the color of the new face and seamlessly clone it to the original location. To evaluate our approach experimentally, we obfuscate faces from various datasets by applying blurring, pixelation and the proposed technique. To determine the success of obfuscation, we verify whether the original and the resulting face represent the same person using a state-of-the-art matching tool. Our approach successfully obfuscates faces in more than 97% of the cases. This performance is comparable to blurring, which scores around 96%, and even better than pixelation (76%). Moreover, we analyze how effectively emotions can be preserved when obfuscating the faces. For this, we utilize emotion recognizers to recognize the depicted emotions before and after obfuscation. Regardless of the recognizer, our approach preserves emotions more effectively than the other techniques while preserving a convincingly natural appearance.
Multiple Objective Optimization for Fully Adaptive Active Sonar

Conventional active sonar systems rely on the operator to decide how to best allocate resources to detect and track targets. This may place impractical demands on the operator, particularly when multiple competing objectives are present. A fully adaptive active sonar learns the dynamics of an environment by looking for information on signals of interest and then adapts the system parameters to achieve objectives specified by the user. Traditionally in active sonar, detection and tracking of targets are two common objectives that are carried out non-simultaneously. This paper demonstrates an operator-free simplified intelligent active sonar system capable of simultaneous detection and tracking by allocating pings using a Reinforcement Learning (RL) algorithm. The system uses the popular state-action-reward-state-action RL agent combined with a modified reward metric inspired by multiple objective optimization. Initial evaluation of the approach shows that the intelligent active sonar can swiftly adapt to changes in the dynamic environment when tracking and detecting targets.
Point-pixel fusion for object detection and depth estimation

Object detection and recognition is a meticulous procedure that demands a great deal of accuracy when it comes to sophisticated autonomous motion, especially for the autonomous drive in an intricate environment. This paper introduces a point-pixel fusion system for object detection and classification with depth information for an autonomous vehicle system. Point-pixel fuses the lidar points from the 3D coordinates onto 2D pixels of the camera image, similarly camera image is also transformed into 2D pixels frames from the 3D environment. Further, YOLO4 is used to detect the object as a region of interest (ROI) and then lidar points are blended into ROI to determine the depth of the objects detected by the YOLO4 detector. Lidar point only in the ROI are kept for depth estimation, rest of the lidar points are repudiated. The experimental results demonstrated that the proposed early sensor fusion method is an efficient and reliable solution for object detection, classification, and depth estimation for an autonomous driving system.
Benchmarking TensorFlow Lite Quantization Algorithms for Deep Neural Networks

Deploying deep neural network models on the resource constrained devices, e.g., lost-cost microcontrollers, is challenging because they are mostly limited in terms of memory footprint and computation capabilities. Quantization is one of the widely used solutions to reduce the size of a model. For parameter representation, it employs for example just 8-bit integer or less instead of 32-bit floating point. The TensorFlow Lite deep learning framework currently provides four methods for post-training quantization. The aim of this paper is to benchmark these quantization methods using various deep neural models of different sizes. The main outcomes of the paper are: (1) the compression ratio obtained for each quantization method for deep neural models of small, medium, and large sizes, (2) a comparison of the accuracy results relative to the original accuracy, and (3) a viewpoint for the decision to choose the quantization method depending on the model size.
Fast Skinny-128 SIMD Implementations for Sequential Modes of Operation

This paper reports new software implementation results for the Skinny-128 tweakable block ciphers on various SIMD architectures. More precisely, we introduce a decomposition of the 8-bit S-box into four 4-bit S-boxes in order to take advantage of vector permute instructions, leading to significant performance improvements over previous constant-time implementations. Since our approach is of particular interest when Skinny-128 is used in sequential modes of operation, we also report how it benefits to the Romulus authenticated encryption scheme, a finalist of the NIST LWC standardization process.
Immersive Environments in Higher Education: The Digital Well-Being Perspective

In less than half a century, digital environments have pervaded nearly all aspects of our lives. Presently, the importance of Immersive Environments (IE) in education is rapidly increasing, offering interesting new opportunities for learning in higher education, but at the same time presenting some risks related to their use. In particular, the IE pose some issues to be considered in terms of digital well-being, such as access and inclusion, cognitive overload, and physiological discomfort. Furthermore, it has to be considered that there is a lack of a general methodology and a theoretical grounded approach for the implementation and evaluation of IE for educational purposes. The main aspects to be considered for their impact on the digital well-being have been collected from the current literature and analysed, exploring 4 main dimensions: cognitive, physiological, social and educational. Based upon the findings a first set of guidelines on digital well-being for IE in education have been developed leading to the production of a learning scenario that has been evaluated, by several stakeholders. The results of this evaluation process are positive, underlining the compliance of the learning scenario with the digital well-being requirements for an effective IE integration in the educational context.
Explanation-by-Example Based on Item Response Theory

Intelligent systems that use Machine Learning classification algorithms are increasingly common in everyday society. However, many systems use black-box models that do not have characteristics that allow for self-explanation of their predictions. This situation leads researchers in the field and society to the following question: How can I trust the prediction of a model I cannot understand? In this sense, XAI emerges as a field of AI that aims to create techniques capable of explaining the decisions of the classifier to the end-user. As a result, several techniques have emerged, such as Explanation-by-Example, which has a few initiatives consolidated by the community currently working with XAI. This research explores the Item Response Theory (IRT) as a tool to explaining the models and measuring the level of reliability of the Explanation-by-Example approach. To this end, four datasets with different levels of complexity were used, and the Random Forest model was used as a hypothesis test. From the test set, 83.8% of the errors are from instances in which the IRT points out the model as unreliable.
Kind computing

Kindness can boost happiness and wellbeing. It can benefit individuals (e.g., increasing resilience) as well as society (e.g., increasing trust). With digital technology permeating our daily lives, there are increasing opportunities for such technology to enable, mediate, and amplify kindness in society. In this paper, we propose kind computing, a new computing paradigm that explicitly incorporates kindness into the development and use of digital technology. We envisage software engineering as a discipline that can deliver such technology. However, software engineering techniques do not provide explicit abstractions, formalisms, and tools to consider, analyse, and implement software that delivers such technology. With reference to related work, we elaborate on kind computing and the role of software engineering in enabling it, identify open research challenges, elicit three categories of kind computing requirements, and sketch a research agenda for future work.
A Formal Model of Checked C

We present a formal model of Checked C, a dialect of C that aims to enforce spatial memory safety. Our model pays particular attention to the semantics of dynamically sized, potentially null-terminated arrays. We formalize this model in Coq, and prove that any spatial memory safety errors can be blamed on portions of the program labeled unchecked; this is a Checked C feature that supports incremental porting and backward compatibility. While our model's operational semantics uses annotated (“fat”) pointers to enforce spatial safety, we show that such annotations can be safely erased. Using PLT Redex we formalize an executable version of our model and a compilation procedure to an untyped C-like language, as well as use randomized testing to validate that generated code faithfully simulates the original. Finally, we develop a custom random generator for well-typed and almost-well-typed terms in our Redex model, and use it to search for inconsistencies between our model and the Clang Checked C implementation. We find these steps to be a useful way to co-develop a language (Checked C is still in development) and a core model of it.
Design and Implementation of Weibo Public Opinion Analysis System

Public opinion refers to the public attitudes and emotions of the masses on social phenomena. As one of the most popular micro-blogging platforms, Sina Weibo serves as an important public opinion carrier. Understanding the public opinion helps the government departments such as contingency management, public security, and other decision makers to timely understand the social situation, and actively resolve social conflicts and promote social harmony. On the other hand, related departments can also identify criminals from online public opinions and effectively crack down on them. In this work, we design and implement a Weibo public opinion analysis system, which consists of data collecting subsystem, data pre-processing subsystem, and text analysis subsystem and demonstrate our discovery mainly in public security.
A District Energy Management Approach Based on Internet of Things and Blockchain

The energy consumption while satisfying user comfort in the District Energy Management is a current crucial problem. This paper proposes an innovative system to improve the user behaviour using an approach based on Internet of Things (IoT) components, Blockchain, Smart Contract and a reward and penalty strategy. In particular, the IoT tools and sensors provide data about the energy consumption in real time. Moreover, a specifically designed Smart Contract stores data and allows adding a basic feature of the Blockchain: the payment management subject to a reward and penalty strategy. Indeed, the users are classified in consumption classes and each class is characterized by multiplier coefficient that increases (penalty) or decreases (reward) the user energy costs. In order to show the applicability and performance of the proposed approach, a case study is presented, considering the energy consumption and IoT data of a 2000 users district.