Reviewerly/exHarmony huggingface dataset:
Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems

Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while protecting data privacy. However, when deploying FL in real-time edge systems, the heterogeneity of devices among systems has a severe impact on the performance of the inferred model. Existing optimizations on FL focus on improving the training efficiency but fail to speed up inference, especially when there is a latency constraint. In this work, we propose Collate, a novel training framework that collaboratively learns heterogeneous models to meet the latency constraints of multiple edge systems simultaneously. We design a dynamic zeroizing-recovering method to adjust each local model architecture for high accuracy under its latency constraint. A proto-corrected federated aggregation scheme is also introduced to aggregate all heterogeneous local models, satisfying the latency constraint of different systems with only one training process and maintaining high accuracy. Extensive experiments indicate that, compared to state-of-the-art methods and under a latency constraint, our extended models can improve the accuracy by 1.96% on average, and our shrunk models can also obtain a 3.09% accuracy improvement on average, with almost no extra training overhead. The related codes and data will be available at https://github.com/ntuliuteam/Collate.
How Does Virtual Reality Compare? The Effects of Avatar Appearance and Medium on Self-Disclosure

Digital communication methods such as social media, texting, video conferencing account for a significant amount of social interaction traffic; however, in the coming decade, virtual reality (VR) is likely to appropriate much of this space from traditional 2D computer-mediated applications. The current series of studies first compared how different digital communication mediums (i.e., voice only, video chat, and VR) affect self-disclosure and then, looking specifically at VR, evaluated how avatar appearance affects self-disclosure. Overall, study one indicated that VR users are more likely to reveal highly personal facts, attitudes, opinions, and emotions than video chat or voice only communication users. Looking only at VR communication, study two demonstrated that individuals with a human avatar are more likely to reveal emotional experiences and highly personal facts, attitudes, and emotions about themselves and their experiences compared to individuals using a robot avatar.
Implicit Feature Decoupling with Depthwise Quantization

Quantization has been applied to multiple domains in Deep Neural Networks (DNNs). We propose Depthwise Quantization (DQ) where quantization is applied to a de-composed sub-tensor along the feature axis of weak statis-tical dependence. The feature decomposition leads to an exponential increase in representation capacity with a linear increase in memory and parameter cost. In addition, DQ can be directly applied to existing encoder-decoder frame-works without modification of the DNN architecture. We use DQ in the context of Hierarchical Auto-Encoders and train end-to-end on an image feature representation. We provide an analysis of the cross-correlation between spatial and channel features and propose a decomposition of the image feature representation along the channel axis. The improved performance of the depthwise operator is due to the increased representation capacity from implicit feature decoupling. We evaluate DQ on the likelihood estimation task, where it outperforms the previous state-of-the-art on CIFAR-10, ImageNet-32 and ImageNet-64. We progressively train with increasing image size a single hierarchical model that uses 69% fewer parameters and has faster convergence than the previous work.
The manuscript evaluation through Artificial Intelligence using Natural Language Processing and Machine Learning

Peer reviewing plays an important role if a re-searcher wants to publish his/her manuscript. Peer review is the impartial evaluation of your research article by subject matter experts in your area. Its objective is to assess the manuscript's quality and publishing readiness. This paper will be considering these peer reviews and use computational linguistics to relate them with acceptance of the manuscript. This paper will focus on text conversions in depth and discuss the flow of natural language processing. Natural language processing is a field of computer science and artificial intelligence that studies how computers interact with human languages, In specific it is an automated process of analyzing human language in order to gain information from it. Automation is the ultimate goal for efficiency driven organisations and Natural language processing is playing an important role in it. A machine can fully understand humans only when it communicates it with human language, its like communicating to someone who speaks your native language versus someone who speaks language you hardly can speak or understand. Natural language processing removes this gap. Text processing and text summarization, Automatic translation of languages, user interfaces, speech recognition and expert systems are examples of natural language processing applications. Chal-lenges for processing the complex language system of humans are far too many as human emotions and expressions through voice and body language are not fully decode in data. In near future everyone will have to communicate with a machine for household work as well as space work, so it becomes important to understand the working of Artificial Intelligence and Natural language processing is an important part of it.
AexPy: Detecting API Breaking Changes in Python Packages

With the popularity of the Python language, com-munity developers create and maintain a lot of third-party packages. APIs change frequently during the package evolving. Package developers need keeping backward compatibility of APIs to avoid breaking client code. Detecting breaking changes in Python packages is challenging because of Python's dynamic features and flexible designs, such as dynamic typing, API aliases, confusing public boundary and flexible argument passing. Despite the language's popularity, there have been few tools aiming to detect breaking changes, and existing approaches lack sufficient consideration of the mentioned challenges, resulting in imprecise and incomplete detection. Briefly, we propose an API-model-based systematic approach to address this problem. We design a Python-specific API model and classify API changes in different breaking levels. Based on the model, we obtain APIs with their types by a robust hybrid analysis and detect graded changes by checking constraints on API pairs. We implement a prototype, AexPy. Thanks to the more comprehensive model and hybrid analysis adopted, AexPy outperforms the state-of-the-art techniques for Python with an 86.9% recall on a dataset of 61 known breaking changes. Besides, AexPy detects 405 (manually verified) high and medium breaking changes with a precision of 93.5% on the latest versions of 45 packages. Specifically, in addition to 291 documented breaking changes, AexPy detects 114 undocumented changes. We report 63 undocumented breaking changes to active package developers, and 31 have been confirmed.
News Recommendation Model Based on Long-Term and Short-Term Interests

Nowadays internet users can receive massive amounts of information every day. Providing users with personalized news that they are concerned about together with improving their reading interest has become a hot and difficult research topic. Long-term interests of users are usually relatively stable, but short-term interests are easy to change, and usually related to hot events in a certain period of time. Existing research on news recommendation methods usually focuses on learning long-term interest representations of users and omits the short-term one. In this paper, a news recommendation system based on user's long-term and short-term interests is proposed. The proposed method learns the long-term interest representation from all the articles that the user has browsed, and at the same time, by finding the users whose long-term interests are most similar to the user in history, it learns the news type representation they have recently read together as the short-term interest representation of the current user. Finally, the long-term interest representation vector and the short-term interest representation vector are jointly considered to obtain the final long-term and short-term interest representation. Extensive experiments are conducted to show the efficiency of the proposed model in improving the accuracy of the news recommendation system.
Multi-Class Hypersphere Anomaly Detection

Machine learning-based classification algorithms typically operate under assumptions that assert that the underlying data generating distribution is stationary and draws from a finite set of categories. In some scenarios, these assumptions might not hold, but identifying violating inputs - here referred to as anomalies - is a challenging task. Recent publications propose deep learning-based approaches that perform anomaly detection and classification jointly by (implicitly) learning a mapping that projects data points to a lower-dimensional space, such that the images of points of one class reside inside of a hypersphere, while others are mapped outside of it. In this work, we propose Multi-Class Hypersphere Anomaly Detection (MCHAD), a new hypersphere learning algorithm for anomaly detection in classification settings, as well as a generalization of existing hypersphere learning methods that allows incorporating example anomalies into the training. Extensive experiments on competitive benchmark tasks, as well as theoretical arguments, provide evidence for the effectiveness of our method. Our code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
TemporalUV: Capturing Loose Clothing with Temporally Coherent UV Coordinates

We propose a novel approach to generate temporally coherent UV coordinates for loose clothing. Our method is not constrained by human body outlines and can capture loose garments and hair. We implemented a differentiable pipeline to learn UV mapping between a sequence of RGB inputs and textures via UV coordinates. Instead of treating the UV coordinates of each frame separately, our data generation approach connects all UV coordinates via feature matching for temporal stability. Subsequently, a generative model is trained to balance the spatial quality and temporal stability. It is driven by supervised and unsupervised losses in both UV and image spaces. Our experiments show that the trained models output high-quality UV coordinates and generalize to new poses. Once a sequence of UV co-ordinates has been inferred by our model, it can be used to flexibly synthesize new looks and modified visual styles. Compared to existing methods, our approach reduces the computational workload to animate new outfits by several orders of magnitude.
Revisiting the challenges and surveys in text similarity matching and detection methods

The massive amount of information from the internet has revolutionized the field of natural language processing. One of the challenges was estimating the similarity between texts. This has been an open research problem although various studies have proposed new methods over the years. This paper surveyed and traced the primary studies in the field of text similarity. The aim was to give a broad overview of existing issues, applications, and methods of text similarity research. This paper identified four issues and several applications of text similarity matching. It classified current studies based on intrinsic, extrinsic, and hybrid approaches. Then, we identified the methods and classified them into lexical-similarity, syntactic-similarity, semantic-similarity, structural-similarity, and hybrid. Furthermore, this study also analyzed and discussed method improvement, current limitations, and open challenges on this topic for future research directions.
PCMCR: A Novel Conflict Resolution Strategy based on Possibility Theory for Human Activity Recognition

DCR is a Distributed Collaborative Reasoning multi-agent model that aims to recognize human activities in smart homes from distributed, heterogeneous and dynamic sensor data. In this model, distributed agents with diverse classifiers, detect sensor stream data, make local predictions, communicate and collaborate to identify current activities. Conflict resolution strategies are applied to generate one final predicted activity when the local predicted activity of an agent is different from received predicted activities of other agents. In this paper, a possibilistic conflict resolution strategy, PCMCR, is proposed. Possibility theory is particularly efficient in combining multiple agents predictions that can be incomplete, uncertain, and conflicting. The PCMCR strategy deals with uncertainty factor which can be presented in the predictions of poor agents. It can take advantage of the complementary information given by each agent, even the weak ones. We experimentally test this strategy by performing an evaluation study on Aruba dataset. The obtained results indicate an enhancement in terms of accuracy, F-measure and G-mean metrics compared to the existing conflict resolution method max-trust of DCR, to the centralized model and to an existing distributed system.
A Cloud 3D Dataset and Application-Specific Learned Image Compression in Cloud 3D

In Cloud 3D, such as Cloud Gaming and Cloud Virtual Reality (VR), image frames are rendered and compressed (encoded) in the cloud, and sent to the clients for users to view. For low latency and high image quality, fast, high compression rate, and high-quality image compression techniques are preferable. This paper explores computation time reduction techniques for learned image compression to make it more suitable for cloud 3D. More specifically, we employed slim (low-complexity) and application-specific AI models to reduce the computation time without degrading image quality. Our approach is based on two key insights: (1) as the frames generated by a 3D application are highly homogeneous, application-specific compression models can improve the rate-distortion performance over a general model; (2) many computer-generated frames from 3D applications are less complex than natural photos, which makes it feasible to reduce the model complexity to accelerate compression computation. We evaluated our models on six gaming image datasets. The results show that our approach has similar rate-distortion performance as a state-of-the-art learned image compression algorithm, while obtaining about 5x to 9x speedup and reducing the compression time to be less than 1 s (0.74s), bringing learned image compression closer to being viable for cloud 3D. Code is available at https://github.com/cloud-graphics-rendering/AppSpecificLIC.
Co-rating Aware Evidential User-Based Collaborative Filtering Recommender System

Recommendation systems (RS) are decision support tools created to deal with information overload, which is the main challenge of the modern digital world. The aim of RS is to provide users with interesting items based on their preferences. Collaborative filtering (CF) is the most implemented recommendation technique, it is based on the idea that similar users have similar preferences. Evidential CF is a subclass of classical CF handling uncertainty using the framework of Dempster–Shafer Theory (DST). Evidential CF recommenders (ECFRS) are suitable for critical domains such as healthcare and threat assessment, where uncertainty management remains a major challenge. In this paper, we developed a user-based evidential CF system, where the number of the co-rated items is considered in predictions generation. The proposed approach is based on Evidential KNN where the Jaccard factor is used in the neighborhood selection. Our approach is tested using Movielens dataset. Experimental results show the importance of introducing a co-rating factor in improving the recommendation quality of traditional ECFRS.
Leveraging Long Short-Term User Preference in Conversational Recommendation via Multi-agent Reinforcement Learning

Conversational recommender systems (CRS) endow traditional recommender systems with the capability of dynamically obtaining users' short-term preferences for items and attributes through interactive dialogues. There are three core challenges for CRS, including the intelligent decisions for what attributes to ask, which items to recommend, and when to ask or recommend, at each conversation turn. Previous methods mainly leverage reinforcement learning (RL) to learn conversational recommendation policies for solving one or two of these three decision-making problems in CRS with separated conversation and recommendation components. These approaches restrict the scalability and generality of CRS and fall short of preserving a stable training procedure. In the light of these challenges, we tackle these three decision-making problems in CRS as a unified policy learning task. In order to leverage different features that are important to each sub-problem and facilitate better unified policy learning in CRS, we propose two novel multi-agent RL-based frameworks, namely Independent and Hierarchical Multi-Agent UNIfied COnversational RecommeNders (IMA-UNICORN and HMA-UNICORN), respectively. In specific, two low-level agents enrich the state representations for attribute prediction and item recommendation, by combining the long-term user preference information from the historical interaction data and the short-term user preference information from the conversation history. A high-level meta agent is responsible for coordinating the low-level agents to adaptively make the final decision. Experimental results on four benchmark CRS datasets and a real-world E-Commerce application show that the proposed frameworks significantly outperform state-of-the-art methods. Extensive analyses further demonstrate the superior scalability of the MARL frameworks on the multi-round conversational recommendation.
A Framework for User-Requirements Analysis and Development of Creative Design Concepts

This paper describes a framework and process for user-centred design for interactive products with an emphasis on creativity in designing solutions and the development of multiple solution concepts. The aim is to encourage more radical ideas to be put forward and for the resulting design concepts to be compared objectively using defined user requirements specification statements. The process consists of 6 stages including: identification of user problem or need, user research to generate insights and empathy, development of user requirements specification, generation of ideas synthesised into design concepts, comparison and choice of best concept, and prototyping of the concept to develop a design proposition. Classroom testing of the framework indicated that the students could use it effectively and were able to produce effective designs.
Gray Level Co-Occurrence Matrix Algorithm and Backpropagation Neural Networks for Herbal Plants Identification

Leaves are one of the most crucial plant structures. Leaves function as absorption, processing food through photosynthesis, and as a means of transpiration. Various medicinal plants have been known to the public for a long time. However, it is still difficult for ordinary people to remember that all leaves are relatively the same color, green, and do not know the characteristics of the leaves. In this paper, we will detect the types of herbal leaves of medicinal plants by identifying leaves using the GLCM (Gray Level Co-occurrence Matrix) algorithm and artificial neural networks backpropagation. The dataset obtained from the Kaggle-Leafsnap dataset has as many as 50 types of herbal plant leaves. The GLCM algorithm is a method with probability and statistics. This method extracts leaf images, converts the image into grayscale, and then changes the shape of the image data into numerical. The GLCM algorithm is combined with Backpropagation in classifying data. There are several stages in the classification process in this study, namely data retrieval, data preprocessing, data classification, and evaluation method. This research aims to achieve high accuracy through the proposed method.
Modeling &amp; Verification of an Adaptive IoT System using Uppaal

Design time verification of Internet Of Things (IoT) systems helps in detection of errors at an early stage. This study focuses on the modeling and verifying IoT systems that are self-adaptive. Self Adaptive Systems (SAS) adapt autonomously to changing environment and internal dynamics to achieve required functionalities. The adopted case study is the Smart Home Automation System. A self adaptive smart home system is modelled using Uppaal, an integrated tool used for modeling and verification of real-time systems. In Uppaal, systems are modelled using a network of timed automata and verified using statements in Linear Temporal Logic. Two subsystems of smart home, fire detection and automated lighting system are modelled and its properties are verified to ensure correctness of the system. A typical SAS has a feedback loop consisting of MAPE-K (M-Monitor, A-Analyze, P-Plan, E-Execute, K-Knowledge) components, for keeping track of changes in the system, automatically update the knowledge base and execute corrective actions to cope up with the changes in the system. Self adaptive nature of fire detection subsystem in the case study enables the system to automatically switch to inputs from temperature sensors for fire detection in case of failure/ malfunctioning smoke sensor, thus preventing system failure. Automated lighting system changes the light intensity depending on changing activity of a person in a particular area. Modelling and verification of SAS is essential for avoiding run-time failures of safety-critical systems.
GitDelver enterprise dataset (GDED)

Conducting socio-technical software engineering research on closed-source software is difficult as most organizations do not want to give access to their code repositories. Most experiments and publications therefore focus on open-source projects, which only provides a partial view of software development communities. Yet, closing the gap between open and closed source software industries is essential to increase the validity and applicability of results stemming from socio-technical software engineering research. We contribute to this effort by sharing our work in a large company counting 4,800 employees. We mined 101 repositories and produced the GDED dataset containing socio-technical information about 106,216 commits, 470,940 file modifications and 3,471,556 method modifications from 164 developers during the last 13 years, using various programming languages. For that, we used GitDelver, an open-source tool we developed on top of Pydriller, and anonymized and scrambled the data to comply with legal and corporate requirements. Our dataset can be used for various purposes and provides information about code complexity, self-admitted technical debt, bug fixes, as well as temporal information. We also share our experience regarding the processing of sensitive data to help other organizations making datasets publicly available to the research community.