Reviewerly/exHarmony huggingface dataset:
Transformer Model Compression for End-to-End Speech Recognition on Mobile Devices

Transformer-based models have achieved state-of-the-art performance in various areas of machine learning, including automatic speech recognition. However, their cost in terms of computational power, memory or energy consumption can be exorbitant, hence the interest in compression techniques. Trans-former models are mostly composed of attention and feedforward components. In this paper, we propose to reduce the size of a transformer model in an end-to-end speech recognition system by decreasing the number and precision of linear layer parameters. Specifically, we investigate the impact of weight pruning on system performance. We then consider model quantization. To further reduce the model size, we address the combination of pruning and quantization methods. Experiments carried out on several speech datasets from different languages show that the memory footprint can be reduced by up to 84% with an insignificant loss of accuracy.
Application scenarios and usability for modern 360 degree video projection rooms in the MICE industry

360-degree image and movie content has gained popularity in the media and the MICE (Meeting, Incentive, Conventions, and Exhibitions) industry in the last few years.There are three main reasons for this development.On the one hand, it is the immersive character of this media form; on the other hand, recording and presentation technology development has made significant progress in resolution and quality.Third, after a decade of dynamic rising, the MICE Industry focuses on a disruptive change for more digitalbased solutions.360-degree panoramas are particularly widespread in VR and AR technology.However, despite the high immersive potential, these forms of presentation have the disadvantage that the users are isolated and have no social contact during the performance.Therefore, efforts have been made to project 360-degree content in specially equipped rooms or planetariums to enable a shared experience for the audience.One application area for 360-degree panoramas and films is conference rooms in hotels, conference centers, and other venues that create an immersive environment for their clients to stimulate creativity.This work aims to overview the various application scenarios and usability possibilities for such conference rooms.In particular, we consider applications in construction, control, tourism, medicine, art exhibition, architecture, music performance, education, partying, organizing and carrying out events, and video conferencing.These applications and use scenarios were successfully tested, implemented, and evaluated in the 360-degree conference room "Dortmund" in the Hotel Park Soltau in Soltau, Germany [1].Finally, the advantages, challenges, and limitations of the proposed method are described.
Multi-Agent Narrative Experience Management as Story Graph Pruning

In many intelligent interactive narratives, the player controls an avatar while an experience manager controls non-player characters (NPCs). The space of all stories can be viewed as a story graph, where nodes are states and edges are actions taken by the player, by NPCs, or by both jointly. In this paper, we cast experience management as a story graph pruning problem. We start with the full graph and prune intelligently until each NPC has at most one action in every state. Considering the entire graph allows us to foresee the long-term consequences of every pruning decision on the space of possible stories. By never pruning player actions, we ensure the experience manager can accommodate any choice. When used to control the story of an adventure game, players found our technique generally produced higher agency and more believable NPC behavior than a control.
ClueGen: An Exploration of Procedural Storytelling in the Format of Murder Mystery Games

In this paper I present ClueGen, a murder mystery game that generates its own narrative. I discuss the genre’s suitability for combining gameplay and story using procedural generation techniques, before explaining the implementation and rationale behind ClueGen, utilizing the genre’s common structure as an opportunity to define the scope of the generation requirements. In particular I detail a novel system for telegraphing deceptive characters in procedurally generated games with text-based dialogue, by using audible cues.
k-Robust Multi-Agent Path Finding

In the multi-agent path-finding (MAPF) problem a plan is needed to move a set of agents from their initial location to their goals without collisions. In this paper we introduce and study the k-robust MAPF problem, where we seek a plan that is robust to k unexpected delays per agent.
Accuracy Enhancement in Early Detection of Breast Cancer on Mammogram Images with Convolutional Neural Network (CNN) Methods using Data Augmentation and Transfer Learning

The advancement of computer technology has made it possible for computers to imitate the work of the human brain to make decisions that can be used in the healthcare sector. One of the uses is detecting breast cancer by using Machine Learning to increase the sensitivity and or specificity of detection and diagnosis of the disease. Convolutional Neural Network (CNN) is the most commonly used image analysis and classification method in machine learning. This study aims to improve the accuracy of early detection of breast cancer on mammogram images using the CNN method by adding the Data Augmentation and Transfer method. Learning. This study used a mammography image dataset taken from MIAS 2012. The dataset has seven classes with 322 image samples. The results of accuracy tests of early detection process of breast cancer using CNN by utilizing Data Augmentation and Transfer Learning show several findings: Model VGG-16, Model VGG-19, and ResNet-50 produced the same training accuracy rate of 86%, while for validation accuracy, Model ResNet-50 produced the highest level of accuracy (71%) compared to other models (VGG-16=64%, VGG-19=61%). The use of more image datasets may create better accuracy.
Transfer Learning for Automatic Image Orientation Detection Using Deep Learning and Logistic Regression

The number of images produced each day increased significantly.The ability to detect and correct an image's orientation can provide several advantages in computer vision.This paper presents a new framework based on a transfer learning technique for automatically detecting image orientation.To implement the power of deep neural networks, we applied a convolutional neural network model pretrained on the ImageNet database for feature extraction.Then, we built a multi-class logistic regression classifier to detect the four image orientation probabilities corresponding to the following orientations (0 for no orientation, 90, 180, and 270).We tested our model on the SUN-397 dataset, one of the most extensive data sets currently used for image-orientation detection tasks.We conducted a cross-dataset evaluation for in-depth testing and analysis.We also examined our model using different old and recent state-of-the-art convolutional neural network (CNN) baselines.We demonstrate that our model yields promising results based on transfer learning for feature extraction combined with a one-vs-rest logistic regression classifier.Our proposed model surpassed the state-of-the-art results in terms of accuracy and performance.
Differentially Private Federated Learning in Edge Networks: The Perspective of Noise Reduction

The proliferation of distributed sensitive data in recent years in network edge devices motivates the introduction of edge computing which moves machine learning (ML) applications from the data center to the edge of the network. On the other hand, recent demands on data privacy have called for federated learning (FL) as a new distributed paradigm. The inherent privacy protection nature of FL makes FL in edge computing a prospective framework, especially for application scenarios where privacy protection and resource utilization are critical. Nevertheless, FL also suffers from privacy leakage as the exchanged messages between edge devices and the edge server can be revealed. As such, differential privacy has drawn great attention for privacy protection in the edge FL system for its extremely low computation cost, which is readily implemented by adding well-designed noise to target data/models. However, the added noise will deteriorate learning performance, and it is challenging to get a satisfactory trade-off between privacy protection and learning performance. This article gives the first systematic study on the framework of differentially private FL in edge networks from the perspective of noise reduction. To this end, three noise reduction methods are summarized based on the intrinsic factors influencing the added noise scale, including privacy amplification, model sparsification, and sensitivity reduction. Furthermore, we discuss the ongoing challenges and propose some future directions where differential privacy can be implemented to obtain a better trade-off between privacy and learning performance.
Multi-Agent Cooperation in Games with Goal Oriented Action Planner: Use Case in WONDER Prototype Project

Multi-agent cooperation systems often rely on an external commander-like entity that will plan for all the agents it manages. When it comes to games development, this entity can be hidden from the player, which makes the player believe the characters are actually taking their own decisions. However, it has limitations. We believe that to achieve truly believable character-interactions between non-playable characters, real(non-scripted) communication between the agents is the key. We will introduce our multi-agent cooperation system where each AI-agent thinks for itself and communicates with the other agents to cooperate and achieves complex goals. The system will be demonstrated with a use case in our WONDER prototype project.
AssistQ: Affordance-Centric Question-Driven Task Completion for Egocentric Assistant

A long-standing goal of intelligent assistants such as AR glasses/robots has been to assist users in affordance-centric real-world scenarios, such as “how can I run the microwave for 1 min?”. However, there is still no clear task definition and suitable benchmarks. In this paper, we define a new task called Affordance-centric Question-driven Task Completion, where the AI assistant should learn from instructional videos to provide step-by-step help in the user’s view. To support the task, we constructed AssistQ, a new dataset comprising 531 question-answer samples from 100 newly filmed instructional videos. We also developed a novel Question-to-Actions (Q2A) model to address the AQTC task and validate it on the AssistQ dataset. The results show that our model significantly outperforms several VQA-related baselines while still having large room for improvement. We expect our task and dataset to advance Egocentric AI Assistant’s development. Our project page is available at: https://showlab.github.io/assistq/ .
A guideline proposal for minimizing cybersickness in VR-based serious games and applications

Head-mounted displays (HMDs) are popular immersive tools in general, not limited to entertainment but also for education, military, and serious games for health. While these displays have strong popularity, they still have user experience issues, triggering possible symptoms of discomfort to users. This condition is known as cybersickness (CS) and is one of the most popular research topics tied to virtual reality (VR) issues. We first present the main strategies focused on minimizing cybersickness problems in virtual reality. Following this, we propose a guideline framework based on CS causes such as locomotion, acceleration, the field of view, depth of field, degree of freedom, exposition use time, latency-lag, static rest frame, and camera rotation. Additionally, serious games applications and broader categories of games can also adopt it. Additionally, we categorized the imminent challenges for CS minimization into four different items. Conclusively, this work contributes as a consulting reference to enable VR developers and designers to optimize their VR users' experience and VR serious games.
Local Search for Efficient Causal Effect Estimation

Causal effect estimation from observational data is a challenging problem, especially with high dimensional data and in the presence of unobserved variables. The available data-driven methods for tackling the problem either provide an estimation of the bounds of a causal effect (i.e., nonunique estimation) or have low efficiency. The major hurdle for achieving high efficiency while trying to obtain unique and unbiased causal effect estimation is how to find a proper adjustment set for confounding control in a fast way, given the huge covariate space and considering unobserved variables. In this paper, we approach the problem as a local search task for finding valid adjustment sets in data. We establish the theorems to support the local search for adjustment sets, and we show that unique and unbiased estimation can be achieved from observational data even when there exist unobserved variables. We then propose a data-driven algorithm that is fast and consistent under mild assumptions. We also make use of a frequent pattern mining method to further speed up the search of minimal adjustment sets for causal effect estimation. Experiments conducted on extensive synthetic and real-world datasets demonstrate that the proposed algorithm outperforms the state-of-the-art criteria/estimators in both accuracy and time-efficiency.
Partnership pedagogical high school and school as a means of improving the process of preparing future teachers of chemistry

Н. В. Жукова, О. А
A Comparative Study for Supervised Learning Algorithms to Analyze Sentiment Tweets

Twitter popularity has increasingly grown in the last few years, influencing life’s social, political, and business aspects. People would leave their tweets on social media about an event, and simultaneously inquire to see other people's experiences and whether they had a positive/negative opinion about that event. Sentiment Analysis can be used to obtain this categorization. Product reviews, events, and other topics from all users that comprise unstructured text comments are gathered and categorized as good, harmful, or neutral using sentiment analysis. Such issues are called polarity classifications. This study aims to use Twitter data about OK cuisine reviews obtained from the Amazon website and compare the effectiveness of three commonly used supervised learning classifiers, Naive Bayes, Logistic Regression, and Support Vector Machine. This is achieved by using two method of feature selection involving count Vectorizer and Term-Frequency-Inverse Data Frequency. The findings showed that the support vector machine classifier had achieved the highest accuracy of 91%, by feature selection: Count Vectorizer. But it is time consuming. For both accuracy and execution time concentrates, logistic regression is recommended.
Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation

Detection of tumors in metastatic colorectal cancer (mCRC) plays an essential role in the early diagnosis and treatment of liver cancer. Deep learning models backboned by fully convolutional neural networks (FCNNs) have become the dominant model for segmenting 3D computerized tomography (CT) scans. However, since their convolution layers suffer from limited kernel size, they are not able to capture long-range dependencies and global context. To tackle this restriction, vision transformers have been introduced to solve FCNN’s locality of receptive fields. Although transformers can capture long-range features, their segmentation performance decreases with various tumor sizes due to the model sensitivity to the input patch size. While finding an optimal patch size improves the performance of vision transformer-based models on segmentation tasks, it is a time-consuming and challenging procedure. This paper proposes a technique to select the vision transformer’s optimal input multi-resolution image patch size based on the average volume size of metastasis lesions. We further validated our suggested framework using a transfer-learning technique, demonstrating that the highest Dice similarity coefficient (DSC) performance was obtained by pre-training on training data with a larger tumour volume using the suggested ideal patch size and then training with a smaller one. We experimentally evaluate this idea through pre-training our model on a multi-resolution public dataset. Our model showed consistent and improved results when applied to our private multi-resolution mCRC dataset with a smaller average tumor volume. This study lays the groundwork for optimizing semantic segmentation of small objects using vision transformers. The implementation source code is available at: https://github.com/Ramtin-Mojtahedi/OVTPS .
Authoring Tools for Procedural Modeling of Virtual Reality-Based Rehabilitation Exercises

Patient compliance is one of the key factors for rehabilitation programs to be effective. To increase patients’ engagement game technologies and virtual reality can be applied. However, the advantages provided by these techniques are not enough to be applied in real scenarios since experts do not always have the required technological skills. To overcome this limitation, a new virtual reality-based rehabilitation system that combines virtual reality, procedural modelling, and authoring tools, is proposed. The system provides easy-to-use editors for the experts to prepare rehabilitation exercises that will take place in an expanding and varied open virtual world created using procedural generation strategies. Patients using a virtual reality headset explore this immersive virtual scenario and solve challenges by applying actions according to their rehabilitation goals and receiving success/failure feedback. While the patient interacts with the exercises through immersive virtual reality, the expert supervises the patient’s actions via a computer monitor where the exercise is displayed at the same time as in the headset. The paper presents the system and the evaluation that has been carried out to set the system configuration parameters that allow non-experts the easy creation of varied open virtual worlds for rehabilitation purposes. In addition, it collects the first impressions of rehabilitators which have been very satisfactory.
Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Control

As public discourse continues to move and grow online, conversations about divisive topics on social media plat-forms have also increased. These divisive topics prompt both contentious and non-contentious conversations. Although what distinguishes these conversations, often framed as what makes these conversations contentious, is known in broad strokes, much less is known about the linguistic signature of these conversations. Prior work has shown that contentious content and structure can be a predictor for this task, however,most of them have been focused on conversation in general,very specific events, or complex structural analysis. Additionally, many models used in prior work have lacked interpretability, a key factor in online moderation. Our work fills these gaps by focusing on conversations from highly divisive topics (abortion, climate change, and gun control), operationalizing a set of novel linguistic and conversational characteristics and user factors, and incorporating them to build interpretable models. We demonstrate that such characteristics can largely improve the performance of prediction on this task, and also enable nuanced interpretability. Our case studies on these three contentious topics suggest that certain generic linguistic characteristics are highly correlated with contentiousness in conversations while others demonstrate significant contextual influences on specific divisive topics.
APPLICATION OF DATA MINING TECHNIQUES TO FIND RELATIONSHIPS BETWEEN THE DISHES OFFERED BY A RESTAURANT FOR THE ELABORATION OF COMBOS BASED ON THE PREFERENCES OF THE DINERS

Currently, blended food has been a common menu item in fast food restaurants. The sales of the fast-food industry grow thanks to several sales strategies, including the “combos”, so, specialty, regional, family and buffet restaurants are even joining combos’ promotions. This research paper presents the implementation of a system that will serve as support to elaborate combos according to the preferences of the diners using data mining techniques to find relationships between the different dishes that are offered in a restaurant. The software resulting from this research is being used by the mobile application Food Express, with which it communicates through webservices.
Child Safety and Protection in the Online Gaming Ecosystem

Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. Although the online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. There is a vital need to develop laws and policies to regulate and build minimum standards for the industry to safeguard and protect children online on the one hand, while promoting innovations in the gaming industry to preempt such threats. Many tools have been adapted to control threats against children in the form of content filtering and parental controls, thereby restricting contact with children to protect them from child predators. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. In this paper, we survey online threats to children in the gaming environment and present the limitations of existing solutions that address these threats. We also aimed to present the challenges that ML techniques face in protecting children against predatory behavior by presenting a systematic review of the available techniques in the literature. Therefore, this analysis provides not only recommendations to stakeholders to develop policies and practices that safeguard children when gaming, but also to the gaming industry to continue providing appropriate measures for a safe and entertaining gaming environment.
A Safety Planning and Control Architecture Applied to a Quadrotor Autopilot

This letter presents a safety trajectory planning and tracking architecture for a quadrotor autopilot. Motor saturation constraints are explicitly considered in obstacle avoidance mission. Two challenging cases are covered: agile flight with a short task time and stable flight with actuator degradation. By explicitly considering the real physical constraints, the proposed method can make better use of the quadrotor maneuverability. Specifically, from control aspect, a sliding mode observer (SMO) based safety controller is implemented on the autopilot. Subsequently, the actuator saturation problem is analyzed and modeled as axis-coupled constraints. The B-spline curve is used to deal with the constraints due to its convex hull property. As a result, the planning problem can be transformed into a quadratically constrained quadratic programming (QCQP) problem with the proposed minimum energy cost function. Experiments show that the resulting trajectories can yield improvements in tracking accuracy in comparison of conventional planning method.