Reviewerly/exHarmony huggingface dataset:
SmartVP: Viewpoint Optimization Based on Individual Preference for Watching 3D Boxing Punch Videos

When broadcasting the 3D boxing punch videos, spectators may have different preferences for spectating. Some people prefer to see the hitting point, while others want to see the punching form. It is not easy to provide a uniformly optimal viewpoint. In this paper, we introduce a novel method to select an optimal viewpoint for watching punching moments in VR that can handle different spectator preferences. We customize a visibility model that utilizes 8 bounding boxes to account for the visibility of upper body parts. We use a neural network classification model to reproduce the optimal viewpoint selection based on the features of body parts visibility, punch side, and punch offset. We collected spectators’ preferred viewpoints from 24 participants under 3 controlled preference conditions: seeing the punch Arm Form clearly (AF), seeing the Facial Expression clearly (FE), and no additional restriction (None). We conducted a user study to evaluate the empirical performance of our system. The training and user study results show that our method can reproduce the optimal viewpoint with spectator preference.
Understanding the importance of animation in human-computer interfaces and its relation to user experience

Animation is increasingly used in multimedia user interfaces (UI). Ever more applications are including animation to improve their UI and user experience (UX). Animation seems to play a key role to develop user-friendly, easy-to-use and easy-to-understand apps that achieve a satisfactory UX. This work attempts to explain animation performance in multimedia UI and how this affects the UX. We found that: (1) animation is a key element of multimedia UI that strongly affects UX and can improve it, if applied correctly; (2) animation enhances: reduced perceived waiting time, explanatory and cognitive support, efficient feedback and increased user attention; (3) animation's major properties lie in adapting its intensity, function, time and aesthetics to the context of action. Additionally, we identified two categories of animation aspects in multimedia UI: internal aspects and external features. Furthermore, this study demonstrates that the design of animated multimedia interfaces does not have a standardised methodology. Animation is incorporated ad-hoc, under the individual empirical experience of the creators, and without any a priori guarantee of usefulness or success. Therefore, our future work aims to generate an integral model where the variables related to animation in multimedia UI that modulate UX are analysed, and thus improve human-computer interfaces.
On the Relationship between HCI Researchers and Creators---Or How I Became a Toolsmith

Research on creativity support tools in human-computer interaction often focuses on novel interaction design, but that is just the tip of the iceberg. Let's dive deeper and help creative activities "in the wild."
Random forest algorithm using quartile-pattern bootstrapping for a class imbalanced problem

A classification is a problem of identifying the category (or the class) of an unknown-class observation using past historical data. One important issue in a classification is a class imbalanced problem which typically finds in a classification where the proportion of the target class is significantly smaller than others. A traditional classifier normally misclassifies an instance from this target class, called the minority class, as noise due to the small number of instances. Modification of the classification algorithm to handle a class imbalanced problem is a challenging task, especially for a random forest. In the random forest algorithm, the bootstrapping step is used to generate several subsets from a training data by random sampling uniformly with replacement. Most bootstrapping subsets may not even contain instances from the minority class which guarantee decision tree components to misclassify instances from the minority class. A random tree algorithm that needs to generate the bootstrapping subsets for each decision tree must assure the distribution of minority instances. This paper proposes a random forest algorithm using quartile-pattern bootstrapping by leveraging mass-ratio-variance outlier factor and minority condensation decision tree to handle this problem. The mass-ratio-variance outlier factor is a score assigned to each instance that will give a large value to an outlier and give a low value to instances surrounded by other instances in the same class. To evaluate the performance of this proposed algorithm, two synthesized datasets are used in the experiments. The experimental results show significant improvement when a dataset is imbalanced. The performance from the test dataset via F1 with the proposed algorithm is better than the performance from the traditional random forest algorithm.
Physical-Digital Programming

Scientists, artists, and engineers are innovating with digital fabrication machines, yet they lack effective tools to program machines for unconventional tasks. We argue for programming language foundations to empower these practitioners to build bespoke fabrication workflows for themselves.
Adaptive and Personalized Persuasive Technologies (ADAPPT 2023) Workshop

The Adaptive and Personalized Persuasive Technologies (ADAPPT’23) workshop holding in Cyprus this year is the third edition of the ADAPPT series, which commenced in 2019. The workshop is organized in conjunction with the 31st Association for Computer Machinery (ACM) Conference on User Modeling, Adaptation and Personalization (UMAP). In this preface to the third edition, we summarize the papers accepted for publication in the adjunct proceedings. Finally, we present a list of the members of the organizing and program committees that made the workshop a success.
Expected and Experienced Utility of Points of Interest in Tourism Recommender Systems

In this paper we analyze users’ preferences for Points of Interest (POIs), before and after the POIs are experienced: these are called expected and experienced utilities. State-of-the-art research in other domains, has shown that the difference between the user’s expected utility, which is estimated at the choice stage, and the experienced utility, obtained after the consumption of the item, is domain dependent. This discrepancy is here measured in the tourism domain by focusing on the POI recommendation problem. We have designed and implemented an interactive web-based survey to collect the two types of utility data. The analysis of the data collected from a sample of users shows that the two utilities express quite different preferences. This result may be exploited to design novel POI recommendation models that can optimally leverage both types of preference signals.
Branching Preferences: Visualizing Non-linear Topic Progression in Conversational Recommender Systems

Recent advances in AI allow complex, natural user–system dialogue flow in NLP-based conversational recommender systems (CRS). While this enables users to express complex intents to the system, its usual linear GUI representation as a chat log fails to account for two non-linear aspects of natural conversation: humans can switch between topics as customary; and, especially in decision-making contexts, topics discussed are structurally related. As early work, we motivate and present a GUI design approach that aims to exploit these phenomena for CRS by conveying topic progression, and discuss several design variants, their trade-offs, and open questions. Our approach aims to help users orientate while exploring and comparing multiple preference model variants and corresponding recommendations in complex, natural ways, also accounting for different explanation types. Such orientation could benefit users for achieving complex goals using CRS, like thoroughly-informed decision making, getting inspiration for novel consumable items, and exploring their own preferences.
CHARM: A Group Recommender ChatBot

Group recommender systems (GRSs) are tools that support a group to find items that the whole group would enjoy experiencing jointly. There are two main lines of research in this field. The first line of research focuses on methods that combine the preferences of individual group members to obtain a group preference model and generate appropriate recommendations. The second line of research is more holistic and aims to support groups in all the phases of their decision-making process. The majority of the approaches of the second type use a simple conversational approach, which is critiquing. However, nowadays people heavily rely on social and chat platforms to make group decisions, and we believe that these platforms could be a valuable mean for building more effective GRSs. To this end, we have designed a framework tool that extends standard chat platforms by augmenting it with a chat-bot. The chat-bot enables the communication between the users on one side and the group recommender agent on the other. Our goal is a new holistic approach to group recommendations that would be the more beneficial than previous proposed conversational approaches. We aim to provide the proposed framework as an open environment for researchers to prototype their own GRSs.
Group Adapted Avatar Recommendations for Exergames

Exergames are a promising way to encourage physical activity in the population. Especially competitive gaming has been shown to boost physical activity during gameplay. However, differences in physical abilities and fitness can lead to anxiety, fear of failure, or frustration. One way to mitigate these inhibitors is to balance the exergaming difficulty between competing players. This paper investigates the expectations and attitudes towards adaptivity in sports games, both in real life and with digital support. To that end, we present a survey with 421 participants investigating the general reaction to group adaptivity in sports games as well as a focus group discussing the reactions to group adaptive avatar recommendations within the game Mario Tennis Aces. Our results show that there is potential for group adaptive exergames to increase engagement, especially for non-sporty and female users, and that the first prototypical implementation was perceived positively regarding fairness and expected physical activity.
With Shared Microexponents, A Little Shifting Goes a Long Way

This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning. It enables comparison of popular quantization standards, and through BDR, new formats based on shared microexponents (MX) are identified, which outperform other state-of-the-art quantization approaches, including narrow-precision floating-point and block floating-point. MX utilizes multiple levels of quantization scaling with ultra-fine scaling factors based on shared microexponents in the hardware. The effectiveness of MX is demonstrated on real-world models including large-scale generative pretraining and inferencing, and production-scale recommendation systems.
LAORAM: A Look Ahead ORAM Architecture for Training Large Embedding Tables

Memory access patterns have been demonstrated to leak critical information such as security keys and a program's spatial and temporal information. This information leak poses a significant privacy challenge in machine learning models with embedding tables. Embedding tables are used to learn categorical features from training data. The address of an embedding table entry carries privacy sensitive information since the address of an entry discloses features associated with a user. Oblivious RAM (ORAM), and its enhanced variants, such as PathORAM, have emerged as viable solutions to hide leakage from memory access streams. PathORAM fetches an entire path of memory blocks for every memory fetch request, thereby leading to substantial bandwidth and performance overheads.
Exploring cognitive models to augment explainability in Deep Knowledge Tracing

Adaptive learning systems allow a personalized adaptation based on the characteristics of the student. Tracing the progress of knowledge and skills during the learning process through cognitive models is essential so that these systems can make appropriate decisions when carrying out personalization. This is the objective of Knowledge Tracing, which studies how to infer a cognitive model from the answers given to a sequence of questions or exercises. The incorporation of Deep Learning techniques in this field has given rise to Deep Knowledge Tracing (DKT) which usually has excellent predictive outcomes. The problem is that this increase in accuracy comes with a lack of explainability since Deep Learning models can be considered black boxes from which it is difficult to build interpretations or explanations. By contrast, traditional Knowledge Tracing methods are based on underlying learning models and provide a solid basis for explainability. In this paper we describe an ongoing research to build DKT models with a good trade-off between accuracy and explainability. To this end, we propose to use a loss function based on a mixup approach where the ground truth is a mix between the dataset labels and the predictions of a surrogate explainable model. The approach has potential to improve, not only explainability through the use of the surrogate, but also accuracy thanks to regularization effects. We will validate the approach by exploring, for different cognitive models, the trade-off curve that is obtained by plotting accuracy against explainability for different mixup values.
ISA-Grid: Architecture of Fine-grained Privilege Control for Instructions and Registers

Isolation is a critical mechanism for enhancing the security of computer systems. By controlling the access privileges of software and hardware resources, isolation mechanisms can decouple software into multiple isolated components and enforce the principle of least privilege. While existing isolation systems primarily focus on memory isolation, they overlook the isolation of instruction and register resources, which we refer to as ISA (Instruction Set Architecture) resources. However, previous works have shown that exploiting ISA resources can lead to serious security problems, such as breaking the system's memory isolation property by abusing x86's CR3 register. Furthermore, existing hardware only provides privilege-level-based access control for ISA resources, which is too coarse-grained for software decoupling. For example, ARM Cortex A53 has several hundred system instructions/registers, but only four exception levels (EL0 to EL3) are provided. Additionally, more than 100 instructions/registers for system control are available in only EL1 (the kernel mode). To address this problem, this paper proposes ISA-Grid, an architecture of fine-grained privilege control for instructions and registers. ISA-Grid is a hardware extension that enables the creation of multiple ISA domains, with each domain having different privileges to access instructions and registers. The ISA domain can provide bit-level fine-grained privilege control for registers. We implemented prototypes of ISA-Grid based on two different CPU cores: 1) a RISC-V CPU core on an FPGA board and 2) an x86 CPU core on a simulator. We applied ISA-Grid to different cases, including Linux kernel decomposition and enhancing existing security systems, to demonstrate how ISA-Grid can isolate ISA resources and mitigate attacks based on abusing them. The performance evaluation results on both x86 and RISC-V platforms with real-world applications showed that ISA-Grid has negligible runtime overhead (less than 1%).
Performance of Low-Latency HTTP-based Streaming Players

Reducing end-to-end streaming latency is critical to HTTP-based live video streaming. There are currently two main technologies in this domain: Low-Latency HTTP Live Streaming (LL-HLS) and Low-Latency Dynamic Adaptive Streaming over HTTP (LL-DASH). These protocols are now supported by many popular streaming players, including HLS.js, DASH.js, Video.js, Shaka player, THEO player, and others. With some players, such as DASH.js and HLS.js, there are also several different rate-adaptation algorithms that may be deployed. This paper is dedicated to the evaluation of the performance of such low-latency players and their adaptation methods. The evaluation is based on a series of live streaming experiments, repeated using identical video content, encoders, encoding profiles, and network conditions, emulated by using traces of real-world networks. A variety of system performance metrics, such as average stream bitrate, the amounts of downloaded data, streaming latency, as well as buffering and switching statistics have been captured and reported. These results are used to describe the observed differences in the performance of low-latency players and systems.
Design and research of library space reservation system based on microservice architecture

Library is an irreplaceable part of the public cultural service system. Nowadays, the informatization of library reservation management is the general trend. Many university libraries and social public libraries have begun to introduce management systems such as library reservation systems, in order to save manpower and material resources while improving the efficiency of the library. This system is developed using C# language, adopts B/S structure, Asp.Net Core MVC web application framework for development, uses SQL Server as the database, and applies microservice architecture. The system realizes the reasonable allocation of library space resources, saves users' time, and makes the library's reservation management more efficient and convenient.
Research on the transformation of unmanned automatic control production system in offshore oil and gas fields during typhoon

The communication and control system of an oilfield in the South China Sea has been modified and optimized in order to complete the remote control production of an offshore oilfield during typhoons. The oilfield communication antenna is equipped with glass fiber reinforced plastic sheath to reduce the vibration of satellite antenna caused by strong winds. The oilfield thermal backup and terminal power amplifier ensure continuous connection. Through the remote control transformation of on-site equipment, control system optimization, the construction of land remote monitoring center and other ways to complete the remote control of production during the typhoon. Meanwhile, through the automatic control of one-button seawater displacement function to ensure the replacement of pipelines after the production of oil fields, fire and gas fire fighting system during the typhoon production is still normal operation, to ensure the safety of on-site equipment. In the process of typhoon remote control, due to the remote communication control, there is a certain delay, the operator needs to minimize the man-machine interface of the operation control system, and try to make the equipment run automatically. The stable operation of typhoon remote control production is ensured through the transformation and optimization of the oilfield.
Rank-Metric Codes, Semifields, and the Average Critical Problem

.We investigate two fundamental questions intersecting coding theory and combinatorial geometry, with emphasis on their connections. These are the problem of computing the asymptotic density of MRD codes in the rank metric, and the Critical Problem for combinatorial geometries by Crapo and Rota. In the first part of the paper, we use methods from semifield theory to derive two lower bounds for the density function of full-rank, square MRD codes. The first bound is sharp when the matrix size is a prime number and the underlying field is sufficiently large, while the second bound applies to the binary field. We then take a new look at the Critical Problem for combinatorial geometries, approaching it from a qualitative, often asymptotic, viewpoint. We illustrate the connection between this very classical problem and that of computing the asymptotic density of MRD codes. Finally, in the third part of the paper we study the asymptotic density of some special families of codes in the rank metric, including the symmetric, alternating, and Hermitian ones. In particular, we show that the optimal codes in these three contexts are sparse.Keywordsrank-metric codesemifieldcritical problemasymptotic densityenumerative combinatoricsdensityMRD codeMSC codes94B0594B9905A1605B2511T71
TaskFusion: An Efficient Transfer Learning Architecture with Dual Delta Sparsity for Multi-Task Natural Language Processing

The combination of pre-trained models and task-specific fine-tuning schemes, such as BERT, has achieved great success in various natural language processing (NLP) tasks. However, the large memory and computation costs of such models make it challenging to deploy them in edge devices. Moreover, in real-world applications like chatbots, multiple NLP tasks need to be processed together to achieve higher response credibility. Running multiple NLP tasks with specialized models for each task increases the latency and memory cost latency linearly with the number of tasks. Though there have been recent works on parameter-shared tuning that aim to reduce the total parameter size by partially sharing weights among multiple tasks, computation remains intensive and redundant despite different tasks using the same input. In this work, we identify that a significant portion of activations and weights can be reused among different tasks, to reduce cost and latency for efficient multi-task NLP. Specifically, we propose TaskFusion, an efficient transfer learning software-hardware co-design that exploits delta sparsity in both weights and activations to boost data sharing among tasks. For training, TaskFusion uses ℓ1 regularization on delta activation to learn inter-task data redundancies. A novel hardware-aware sub-task inference algorithm is proposed to exploit the dual delta sparsity. We then designed a dedicated heterogeneous architecture to accelerate multi-task inference with an optimized scheduling to increase hardware utilization and reduce off-chip memory access. Extensive experiments demonstrate that TaskFusion can reduce the number of floating point operations (FLOPs) by over 73% in multi-task NLP with negligible accuracy loss, while adding a new task at the cost of only < 2% parameter size increase. With the proposed architecture and optimized scheduling, Task-Fusion can achieve 1.48--2.43× performance and 1.62--3.77× energy efficiency than those using state-of-the-art single-task accelerators for multi-task NLP applications.
Ethical issues in explanations of personalized recommender systems

Explanations provide means to increase transparency and trust in personalized recommender systems. However, the design of explanations can raise new ethical problems. This article aims to raise awareness about the ethical considerations that should be taken into account when designing, implementing, and evaluating explanations for personalized recommender systems.