Reviewerly/exHarmony huggingface dataset:
When Friends Become Strangers: Understanding the Influence of Avatar Gender on Interpersonal Distance in Virtual Reality

In this paper, we investigate how mismatches between biological gender and avatar gender affect interpersonal distance (IPD) in virtual reality (VR). An increasing number of VR experiences and online platforms like Rec Room and VRChat allow users to assume other genders through customized avatars. While the effects of acquaintanceship and gender have been studied with regard to proxemic behavior, the effect of changed genders remains largely unexplored. We conducted a user study (N = 40, friends = 20, strangers = 20) where users played a two-player collaborative game in Rec Room using both male and female avatars. We found that with swapped avatar genders, the preferred distance increased between friends but not between strangers. We discuss how our results can inform researchers and designers in the domain of multi-user VR.
Identifying map users with eye movement data from map-based spatial tasks: user privacy concerns

Individuals with different characteristics exhibit different eye movement patterns in map reading and wayfinding tasks. In this study, we aim to explore whether and to what extent map users’ eye movements can be used to detect who created them. Specifically, we focus on the use of gaze data for inferring users’ identities when users are performing map-based spatial tasks. We collected 32 participants’ eye movement data as they utilized maps to complete a series of self-localization and spatial orientation tasks. We extracted five sets of eye movement features and trained a random forest classifier. We used a leave-one-task-out approach to cross-validate the classifier and achieved the best identification rate of 89%, with a 2.7% equal error rate. This result is among the best performances reported in eye movement user identification studies. We evaluated the feature importance and found that basic statistical features (e.g. pupil size, saccade latency and fixation dispersion) yielded better performance than other feature sets (e.g. spatial fixation densities, saccade directions and saccade encodings). The results open the potential to develop personalized and adaptive gaze-based map interactions but also raise concerns about user privacy protection in data sharing and gaze-based geoapplications.
An Empirical Examination of the Impact of Bias on Just-in-time Defect Prediction

Background: Just-In-Time (JIT) defect prediction models predict if a commit will introduce defects in the future. DeepJIT and CC2Vec are two state-of-the-art JIT Deep Learning (DL) techniques. Usually, defect prediction techniques are evaluated, treating all training data equally. However, data is usually imbalanced not only in terms of the overall class label (e.g., defect and non-defect) but also in terms of characteristics such as File Count, Edit Count, Multiline Comments, Inward Dependency Sum etc. Prior research has investigated the impact of class imbalance on prediction technique's performance but not the impact of imbalance of other characteristics. Aims: We aim to explore the impact of different commit related characteristic's imbalance on DL defect prediction. Method: We investigated different characteristic's impact on the overall performance of DeepJIT and CC2Vec. We also propose a Siamese network based few-shot learning framework for JIT defect prediction (SifterJIT) combining Siamese network and DeepJIT. Results: Our results show that DeepJIT and CC2Vec lose out on the performance by around 20% when trained and tested on imbalanced data. However, SifterJIT can outperform state-of-the-art DL techniques with an average of 8.65% AUC score, 11% precision, and 6% F1-score improvement. Conclusions: Our results highlight that dataset imbalanced in terms of commit characteristics can significantly impact prediction performance, and few-shot learning based techniques can help alleviate the situation.
ALSEE: a framework for attribute-level sentiment element extraction towards product reviews

Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results.
Skills and fuzzy knowledge structures

In knowledge space theory (KST), knowledge structure is an effective feature to evaluate individuals’ knowledge and guide future learning. How to construct knowledge structures is one of the key research problems in KST. At present, the knowledge structure has been generalized to the polytomous knowledge structure. This article mainly focuses on the special polytomous knowledge structures delineated by skills, which are called fuzzy knowledge structures. We consider how to construct fuzzy knowledge structures based on the relationship between items and skills, and how to find the learning paths for specific knowledge domains. First, we construct knowledge structures in four models, which are the conjunctive model of skill maps, the disjunctive and conjunctive models of fuzzy skill maps, and the competency model of fuzzy skill multimaps. Second, we assess individuals’ skills and find the learning paths for the specific knowledge domains in the first three models. Finding the learning paths for a specific knowledge domain can guide learning and improve the learning efficiency of individuals. Finally, we analyze some data sets to show that the algorithms proposed are effective and applicable. These works can be applied to adaptive learning systems, which bring great convenience for assessing individuals’ knowledge and guiding future learning.
Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy With Repelling Subpopulations

The covariance matrix self-adaptation evolution strategy with repelling subpopulations (RS-CMSA-ES) is one of the most successful multimodal optimization (MMO) methods currently available. However, some of its components may become inefficient in certain situations. This study introduces the second variant of this method, called RS-CMSA-ESII. It improves the adaptation schemes for the normalized taboo distances of the archived solutions and the covariance matrix of the subpopulation, the termination criteria for the subpopulations, and the way in which the infeasible solutions are treated. It also improves the time complexity of RS-CMSA-ES by updating the initialization procedure of a subpopulation and developing a more accurate metric for determining critical taboo regions. The effects of these modifications are illustrated by designing controlled numerical simulations. RS-CMSA-ESII is then compared with the most successful and recent niching methods for MMO on a widely adopted test suite. The results obtained reveal the superiority of RS-CMSA-ESII over these methods, including the winners of the competition on niching methods for MMO in previous years. Besides, this study extends RS-CMSA-ESII to dynamic MMO and compares it with a few recently proposed methods on the modified moving peak benchmark functions.
Automatic Classical and Quantum Rebound Attacks on AES-Like Hashing by Exploiting Related-Key Differentials

Collision attacks on AES-like hashing (hash functions constructed by plugging AES-like ciphers or permutations into the famous PGV modes or their variants) can be reduced to the problem of finding a pair of inputs respecting a differential of the underlying AES-like primitive whose input and output differences are the same. The rebound attack due to Mendel et al. is a powerful tool for achieving this goal, whose quantum version was first considered by Hosoyamada and Sasaki at EUROCRYPT 2020. In this work, we automate the process of searching for the configurations of rebound attacks by taking related-key differentials of the underlying block cipher into account with the MILP-based approach. In the quantum setting, our model guide the search towards characteristics that minimize the resources (e.g., QRAM) and complexities of the resulting rebound attacks. We apply our method to Saturnin-hash, SKINNY, and Whirlpool and improved results are obtained.
Generalization in Multimodal Language Learning from Simulation

Neural networks can be powerful function approximators, which are able to model high-dimensional feature distributions from a subset of examples drawn from the target distribution. Naturally, they perform well at generalizing within the limits of their target function, but they often fail to generalize outside of the explicitly learned feature space. It is therefore an open research topic whether and how neural network-based architectures can be deployed for systematic reasoning. Many studies have shown evidence for poor generalization, but they often work with abstract data or are limited to single-channel input. Humans, however, learn and interact through a combination of multiple sensory modalities, and rarely rely on just one. To investigate compositional generalization in a multimodal setting, we generate an extensible dataset with multimodal input sequences from simulation. We investigate the influence of the underlying training data distribution on compostional generalization in a minimal LSTM-based network trained in a supervised, time continuous setting. We find compositional generalization to fail in simple setups while improving with the number of objects, actions, and particularly with a lot of color overlaps between objects. Furthermore, multimodality strongly improves compositional generalization in settings where a pure vision model struggles to generalize.
MVCom: Scheduling Most Valuable Committees for the Large-Scale Sharded Blockchain

In a large-scale sharded blockchain, transactions are processed by a number of parallel committees collaboratively. Thus, the blockchain throughput can be strongly boosted. A problem is that some groups of blockchain nodes consume large latency to form committees at the beginning of each epoch. Furthermore, the heterogeneous processing capabilities of different committees also result in unbalanced consensus latency. Such unbalanced two-phase latency brings a large cumulative age to the transactions waited in the final committee. Consequently, the blockchain throughput can be significantly degraded because of the large transaction's cumulative age. We believe that a good committee-scheduling strategy can reduce the cumulative age, and thus benefit the blockchain throughput. However, we have not yet found a committee-scheduling scheme that works for accelerating block formation in the context of blockchain sharding. To this end, this paper studies a fine-balanced tradeoff between the transaction's throughput and their cumulative age in a large-scale sharded blockchain. We formulate this tradeoff as a utility-maximization problem, which is proved NP-hard. To solve this problem, we propose an online distributed Stochastic-Exploration (SE) algorithm, which guarantees a near-optimal system utility. The theoretical convergence time of the proposed algorithm as well as the performance perturbation brought by the committee's failure are also analyzed rigorously. We then evaluate the proposed algorithm using the dataset of blockchain-sharding transactions. The simulation results demonstrate that the proposed SE algorithm shows an overwhelming better performance comparing with other baselines in terms of both system utility and the contributing degree while processing shard transactions.
Design Space Exploration of SABER in 65nm ASIC

This paper presents a design space exploration for SABER, one of the finalists in NIST's quantum-resistant public-key cryptographic standardization effort. Our design space exploration targets a 65nm ASIC platform and has resulted in the evaluation of 6 different architectures. Our exploration is initiated by setting a baseline architecture which is ported from FPGA. In order to improve the clock frequency (the primary goal in our exploration), we have employed several optimizations: (i) use of compiled memories in a 'smart synthesis' fashion, (ii) pipelining, and (iii) logic sharing between SABER building blocks. The most optimized architecture utilizes four register files, achieves a remarkable clock frequency of 1GHz while only requiring an area of 0.314mm2. Moreover, physical synthesis is carried out for this architecture and a tapeout-ready layout is presented. The estimated dynamic power consumption of the high-frequency architecture is approximately 184mW for key generation and 187mW for encapsulation or decapsulation operations. These results strongly suggest that our optimized accelerator architecture is well suited for high-speed cryptographic applications.
Weakly-Supervised Action Segmentation and Alignment via Transcript-Aware Union-of-Subspaces Learning

We address the problem of learning to segment actions from weakly-annotated videos, i.e., videos accompanied by transcripts (ordered list of actions). We propose a framework in which we model actions with a union of low-dimensional subspaces, learn the subspaces using transcripts and refine video features that lend themselves to action subspaces. To do so, we design an architecture consisting of a Union-of-Subspaces Network, which is an ensemble of autoencoders, each modeling a low-dimensional action subspace and can capture variations of an action within and across videos. For learning, at each iteration, we generate positive and negative soft alignment matrices using the segmentations from the previous iteration, which we use for discriminative training of our model. To regularize the learning, we introduce a constraint loss that prevents imbalanced segmentations and enforces relatively similar duration of each action across videos. To have a real-time inference, we develop a hierarchical segmentation framework that uses subset selection to find representative transcripts and hierarchically align a test video with increasingly refined representative transcripts. Our experiments on three datasets show that our method improves the state-of-the-art action segmentation and alignment, while speeding up the inference time by a factor of 4 to 13. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Position-robust optronic convolutional neural networks dealing with images position variation

Through taking maximum advantage of fast data processing characteristic of optical computing, optronic convolutional neural networks (OPCNNs) make all computation operations executed in optics to reduce calculation burden in electronic to the maximum extent and successfully performing classification tasks on both hand-written digit dataset and fashion product dataset. However, practical performance of OPCNNs is sensitive to the position variation of testing images caused by whether the alignment error of components or the translation of classified target. The classification performance reduction in such situations severely limits the application scenarios of OPCNNs. In this paper, we improve the original architecture and achieve position-robust optronic convolutional neural networks (PROPCNNs) with translation invariance through replacing strided convolutional operation and fully connected operation with spectral pooling and global average pooling. PROPCNNs contain less optical components and experiments demonstrate their streamlined architecture is capable of maintaining prediction performance in majority situation of images position variation. This consistent performance provides support for applying our architecture in more complex environment such as loaded on moving vehicles or transported in portable way.
Towards General Models of Player Experience: A Study Within Genres

To which degree can abstract gameplay metrics capture the player experience in a general fashion within a game genre? In this comprehensive study we address this question across three different videogame genres: racing, shooter, and platformer games. Using high-level gameplay features that feed preference learning models we are able to predict arousal accurately across different games of the same genre in a large-scale dataset of over 1, 000 arousal-annotated play sessions. Our genre models predict changes in arousal with up to 74% accuracy on average across all genres and 86% in the best cases. We also examine the feature importance during the modelling process and find that time-related features largely contribute to the performance of both game and genre models. The prominence of these game-agnostic features show the importance of the temporal dynamics of the play experience in modelling, but also highlight some of the challenges for the future of general affect modelling in games and beyond.
Sharpening Local Interpretable Model-Agnostic Explanations for Histopathology: Improved Understandability and Reliability

Being accountable for the signed reports, pathologists may be wary of high-quality deep learning outcomes if the decision-making is not understandable. Applying off-the-shelf methods with default configurations such as Local Interpretable Model-Agnostic Explanations (LIME) is not sufficient to generate stable and understandable explanations. This work improves the application of LIME to histopathology images by leveraging nuclei annotations, creating a reliable way for pathologists to audit black-box tumor classifiers. The obtained visualizations reveal the sharp, neat and high attention of the deep classifier to the neoplastic nuclei in the dataset, an observation in line with clinical decision making. Compared to standard LIME, our explanations show improved understandability for domain-experts, report higher stability and pass the sanity checks of consistency to data or initialization changes and sensitivity to network parameters. This represents a promising step in giving pathologists tools to obtain additional information on image classification models. The code and trained models are available on GitHub.
The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects

Defect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of “dormant defects,” i.e., of defects discovered several releases after their introduction. This can cause a class to be labeled as defect-free while it is not, and is, therefore “snoring.” In this article, we investigate the impact of snoring on classifiers' accuracy and the effectiveness of a possible countermeasure, i.e., dropping too recent data from a training set. We analyze the accuracy of 15 machine learning defect prediction classifiers, on data from more than 4,000 defects and 600 releases of 19 open source projects from the Apache ecosystem. Our results show that on average across projects (i) the presence of dormant defects decreases the recall of defect prediction classifiers, and (ii) removing from the training set the classes that in the last release are labeled as not defective significantly improves the accuracy of the classifiers. In summary, this article provides insights on how to create defects datasets by mitigating the negative effect of dormant defects on defect prediction.
Towards Energy-efficient Federated Edge Intelligence for IoT Networks

Federated edge intelligence (FEI) is an emerging framework that implements federated learning (FL)-based learning solutions in an edge networking and computing system. It has attracted significant interest due to its potential to enable machine learning (ML)-based smart services and applications in the next-generation wireless systems. Despite its potential, the environmental impact of implementing energy-consuming ML-based solutions in a large networking system has been considered as one of the major challenges for the sustainability of future digital networking systems. This paper proposes energy-efficient FEI (EE-FEI), an energy-efficient framework, that jointly optimizes multiple key parameters to minimize the overall energy consumption of an FEI-supported Internet of Things (IoT) network. We establish models to quantify the relationship between the total energy consumption of FEI and the key parameters including the number of edge servers, the number of local model training rounds, and the number of global coordination rounds. We formulate the energy consumption minimization problem and prove its approximation problem is biconvex. Alternate Convex Search (ACS) algorithm for solving the key parameters to minimize the energy consumption of an FEI system has been used. Finally, we evaluate our theoretical results using a hardware prototype. Numerical results have shown that EE-FEI can significantly reduce the energy consumption of FEI systems by 49.8%.
Self-Defined Text-Dependent Wake-Up-Words Speaker Recognition System

In recent years, wake-up-words (WUW) technology is highly developed in some speaker recognition system. It is the progress of verifying a person’s claimed identity from their voice characteristics, and can be efficiently deployed in some consumer applications. In this paper, we proposed a self-defined text-dependent wake-up-words (WUW) speaker recognition system and its implementation. The whole system is divided into two phases: training phase and testing phase. In the training phase, a wake-up word by language is recorded, and the voice segment is cut out by using Voice Activity Detection (VAD). Then we use the Mel-Frequency Cepstral Coefficients (MFCC) as the pre-processing to extract the speech features. After obtaining the speech features, we use Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) simultaneously for training. In the testing phase, we build GMM and HMM continuously and use the Levenshtein Distance (LD) to calculate the differences of the state sequences between the dataset and the unknown speech input. If the unknown speech input passes the threshold, then it means a wake-up event is derived. The experimental results show that the average accuracy is 93.31 %, 82.42% and 3.38 % in 10dB, 5dB and 0dB of Signal Noise Ratio (SNR) respectively. The CPU and memory usage of entire system is around 757 MIPS and 40MB respectively.