Reviewerly/exHarmony huggingface dataset:
On the Lack of Consensus Among Technical Debt Detection Tools

A vigorous and growing set of technical debt analysis tools have been developed in recent years-both research tools and industrial products-such as Structure 101, SonarQube, and DV8. Each of these tools identifies problematic files using their own definitions and measures. But to what extent do these tools agree with each other in terms of the files that they identify as problematic? If the top-ranked files reported by these tools are largely consistent, then we can be confident in using any of these tools. Otherwise, a problem of accuracy arises. In this paper, we report the results of an empirical study analyzing 10 projects using multiple tools. Our results show that: 1) these tools report very different results even for the most common measures, such as size, complexity, file cycles, and package cycles. 2) These tools also differ dramatically in terms of the set of problematic files they identify, since each implements its own definitions of "problematic". After normalizing by size, the most problematic file sets that the tools identify barely overlap. 3) Our results show that code-based measures, other than size and complexity, do not even moderately correlate with a file's change-proneness or error-proneness. In contrast, co-change-related measures performed better. Our results suggest that, to identify files with true technical debt-those that experience excessive changes or bugs-co-change information must be considered. Code-based measures are largely ineffective at pinpointing true debt. Finally, this study reveals the need for the community to create benchmarks and data sets to assess the accuracy of software analysis tools in terms of commonly used measures.
AVMSN: An Audio-Visual Two Stream Crowd Counting Framework Under Low-Quality Conditions

Crowd counting is considered as the essential computer vision application that uses the convolutional neural network to model the crowd density as the regression task.However, the vision-based models are hard to extract the feature under low-quality conditions.As we know, visual and audio are used widely as media platforms for human beings to touch the physical change of the world.The cross-modal information gives us an alternative method of solving the crowd counting task.In this case, in order to solve this problem, a model named the Audio-Visual Multi-Scale Network (AVMSN) is established to model the unconstrained visual and audio sources for completing the crowd counting task in this paper.Based on the Feature extraction and Multi-modal fusion module, in order to handle the objects of various sizes in the crowd scene, the Sample Convolutional Blocks are adopted by the AVMSN as the multi-scale Vision-end branch in the Feature extraction module to calculate the weighted-visual feature.Besides, the audio, which is the temporal domain transformed into the spectrogram information and the audio feature is learned by the audio-VGG network.Finally, the weighted-visual and audio features are fused by the Multi-modal fusion module, which adopts the cascade fusion architecture to calculate the estimated density map.The experimental results show the proposed AVMSN achieves a lower mean absolute error than other state-of-art crowd counting models under the low-quality conditions.
Dynamic path planning of mobile robot based on artificial potential field

Aiming at the problems of gravity imbalance, local minimum and local oscillation in traditional artificial potential field method, an improved artificial potential field algorithm is proposed in this paper. Firstly, the potential field function model is reconstructed; secondly, the pose threshold gain is introduced to overcome the linear interference; finally, the simulated annealing algorithm is used to optimize, and the escape local minimum module is designed to obtain the global optimal solution iteratively, so as to ensure the robot to reach the target quickly and stably. The experimental results show that in the complex environment, the improved artificial potential field method can effectively solve the gravity imbalance, local minimum and local oscillation problems existing in the traditional artificial potential field method, and can make the robot avoid dynamic obstacles and reach the desired target accurately and quickly.
Less is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging

End-to-end models that condition the output sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech recognition (ASR). Since distinct label histories correspond to distinct models states, such models are decoded using an approximate beam-search which produces a tree of hypotheses.In this work, we study the influence of the amount of label context on the model’s accuracy, and its impact on the efficiency of the decoding process. We find that we can limit the context of the recurrent neural network transducer (RNN-T) during training to just four previous word-piece labels, without degrading word error rate (WER) relative to the full-context baseline. Limiting context also provides opportunities to improve decoding efficiency by removing redundant paths from the active beam, and instead retaining them in the final lattice. This path-merging scheme can also be applied when decoding the baseline full-context model through an approximation. Overall, we find that the proposed path-merging scheme is extremely effective, allowing us to improve oracle WERs by up to 36% over the baseline, while simultaneously reducing the number of model evaluations by up to 5.3% without any degradation in WER, or up to 15.7% when lattice rescoring is applied.
A ReLU Dense Layer to Improve the Performance of Neural Networks

We propose ReDense as a simple and low complexity way to improve the performance of trained neural networks. We use a combination of random weights and rectified linear unit (ReLU) activation function to add a ReLU dense (ReDense) layer to the trained neural network such that it can achieve a lower training loss. The lossless flow property (LFP) of ReLU is the key to achieve the lower training loss while keeping the generalization error small. ReDense does not suffer from vanishing gradient problem in the training due to having a shallow structure. We experimentally show that ReDense can improve the training and testing performance of various neural network architectures with different optimization loss and activation functions. Finally, we test ReDense on some of the state-of-the-art architectures and show the performance improvement on benchmark datasets.
Any-to-One Sequence-to-Sequence Voice Conversion Using Self-Supervised Discrete Speech Representations

We present a novel approach to any-to-one (A2O) voice conversion (VC) in a sequence-to-sequence (seq2seq) framework. A2O VC aims to convert any speaker, including those unseen during training, to a fixed target speaker. We utilize vq-wav2vec (VQW2V), a discretized self-supervised speech representation that was learned from massive unlabeled data, which is assumed to be speaker-independent and well corresponds to underlying linguistic contents. Given a training dataset of the target speaker, we extract VQW2V and acoustic features to estimate a seq2seq mapping function from the former to the latter. With the help of a pretraining method and a newly designed postprocessing technique, our model can be generalized to only 5 min of data, even outperforming the same model trained with parallel data.
AttentionLite: Towards Efficient Self-Attention Models for Vision

We propose a novel framework for producing a class of parameter and compute efficient models called AttentionLite suitable for resource constrained applications. Prior work has primarily focused on optimizing models either via knowledge distillation or pruning. In addition to fusing these two mechanisms, our joint optimization framework also leverages recent advances in self-attention as a substitute for convolutions. We can simultaneously distill knowledge from a compute heavy teacher while also pruning the student model in a single pass of training thereby reducing training and fine tuning times considerably. We evaluate the merits of our proposed approach on the CIFAR-10, CIFAR-100 and Tiny-ImageNet datasets. Not only do our AttentionLite models significantly outperform their unoptimized counterparts in accuracy, we find that in some cases, that they perform almost as well as their compute-heavy teachers while consuming only a fraction of the parameters and FLOPs. Concretely, AttentionLite models can achieve up to 30× parameter efficiency and 2× computation efficiency with no significant accuracy drop compared to their teacher.
Method for Exploring Generative Adversarial Networks (GANs) via Automatically Generated Image Galleries

Generative Adversarial Networks (GANs) can automatically generate quality images from learned model parameters. However, it remains challenging to explore and objectively assess the quality of all possible images generated using a GAN. Currently, model creators evaluate their GANs via tedious visual examination of generated images sampled from narrow prior probability distributions on model parameters. Here, we introduce an interactive method to explore and sample quality images from GANs. Our first two user studies showed that participants can use the tool to explore a GAN and select quality images. Our third user study showed that images sampled from a posterior probability distribution using a Markov Chain Monte Carlo (MCMC) method on parameters of images collected in our first study resulted in on average higher quality and more diverse images than existing baselines. Our work enables principled qualitative GAN exploration and evaluation.
Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet

Introducing a knowledge graph into a recommender system as auxiliary information can effectively solve the sparse and cold start problems existing in traditional recommender systems. In recent years, many researchers have performed related work. A recommender system with knowledge graph embedding learning characteristics can be combined with a recommender system of the following three forms: one-by-one learning, joint learning, and alternating learning. For current knowledge graph embedding, a deep learning framework only has one embedding mode, which fails to excavate the potential information from the knowledge graph thoroughly. To solve this problem, this paper proposes the Ripp-MKR model, a multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet, which combines joint learning and alternating learning of knowledge graphs and recommender systems. Ripp-MKR is a deep end-to-end framework that utilizes a knowledge graph embedding task to assist recommendation tasks. Similar to the MKR model, in the Ripp-MKR model, two tasks are associated with cross and compress units, which automatically share latent features and learn the high-order interactions among items in recommender systems and entities in the knowledge graph. Additionally, the model borrows ideas from RippleNet and combines the knowledge graph with the historical interaction record of a user's historically clicked items to represent the user's characteristics. Through extensive experiments on real-world datasets, we demonstrate that Ripp-MKR achieves substantial gains over state-of-the-art baselines in movie, book, and music recommendations.
Horizontal Review on Video Surveillance for Smart Cities: Edge Devices, Applications, Datasets, and Future Trends

The automation strategy of today’s smart cities relies on large IoT (internet of Things) systems that collect big data analytics to gain insights. Although there have been recent reviews in this field, there is a remarkable gap that addresses four sides of the problem. Namely, the application of video surveillance in smart cities, algorithms, datasets, and embedded systems. In this paper, we discuss the latest datasets used, the algorithms used, and the recent advances in embedded systems to form edge vision computing are introduced. Moreover, future trends and challenges are addressed.
Domain-Aware Neural Language Models for Speech Recognition

As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains. We present a domain-aware rescoring framework suitable for achieving domain-adaptation during second-pass rescoring in production settings. In our framework, we fine-tune a domain-general neural language model on several domains, and use an LSTM-based domain classification model to select the appropriate domain-adapted model to use for second-pass rescoring. This domain-aware rescoring improves the word error rate by up to 2.4% and slot word error rate by up to 4.1% on three individual domains – shopping, navigation, and music – compared to domain general rescoring. These improvements are obtained while maintaining accuracy for the general use case.
Poros: Configurable Proxies for Distant Interactions in VR

A compelling property of virtual reality is that it allows users to interact with objects as they would in the real world. However, such interactions are limited to space within reach. We present Poros, a system that allows users to rearrange space. After marking a portion of space, the distant marked space is mirrored in a nearby proxy. Thereby, users can arrange what is within their reachable space, making it easy to interact with multiple distant spaces as well as nearby objects. Proxies themselves become part of the scene and can be moved, rotated, scaled, or anchored to other objects. Furthermore, they can be used in a set of higher-level interactions such as alignment and action duplication. We show how Poros enables a variety of tasks and applications and also validate its effectiveness through an expert evaluation.
Global-Attention-Based Neural Networks for Vision Language Intelligence

In this paper, we develop a novel global-attention-based neural network (GANN) for vision language intelligence, specifically, image captioning (language description of a given image). As many previous works, the encoder-decoder framework is adopted in our proposed model, in which the encoder is responsible for encoding the region proposal features and extracting global caption feature based on a specially designed module of predicting the caption objects, and the decoder generates captions by taking the obtained global caption feature along with the encoded visual features as inputs for each attention head of the decoder layer. The global caption feature is introduced for the purpose of exploring the latent contributions of region proposals for image captioning, and further helping the decoder better focus on the most relevant proposals so as to extract more accurate visual feature in each time step of caption generation. Our GANN is implemented by incorporating the global caption feature into the attention weight calculation phase in the word predication process in each head of the decoder layer. In our experiments, we qualitatively analyzed the proposed model, and quantitatively evaluated several state-of-the-art schemes with GANN on the MS-COCO dataset. Experimental results demonstrate the effectiveness of the proposed global attention mechanism for image captioning.
MirrorBlender: Supporting Hybrid Meetings with a Malleable Video-Conferencing System

In hybrid meetings, multiple co-located participants communicate with remote participants through video. But video communication inhibits non-verbal cues, and this often causes remote participants to feel excluded. To address this issue, we built MirrorBlender: a What-You-See-Is-What-I-See video-conferencing system for blending, repositioning, and resizing mirrors. Mirrors here denote shared video feeds of people and screens. In a qualitative study of MirrorBlender with three hybrid meeting sessions, we found that the shared control of mirrors supported users in negotiating a blended interpersonal space. Moreover, it enabled diverse acts of inclusion of remote participants. In particular, remote participants brought attention to themselves by manipulating the position, scale, and translucency of their camera and screen feeds. Participants also embodied and leveraged their mirror images for deictic gestures and playful interactions. Based on these findings, we discuss new opportunities for supporting video-mediated collaboration.
The Most Social Platform Ever? A Survey about Activities &amp; Motives of Social VR Users

We present online survey results on social virtual reality (social VR) users' activities and usage motives. Based on content analysis of users' free-text responses, we found that most users, in fact, use these applications for social activities and to satisfy diverse social needs. The second most frequently mentioned categories of activities and motives relate to experiential aspects such as entertainment activities. Another important category of motives, which has only recently been described in related work, relates to the self, such as personal growth. Our results indicate that while social VR provides a superior social experience than traditional digital social spaces, like games or social media, users still desire better and affordable tracking technology, increased sensory immersion, and further improvement concerning social features. These findings complement related work as they come from a comparatively large sample (N= 273) and summarize a general user view on social VR. Besides confirming an intuitive assumption, they help identify use cases and opportunities for further research on social VR.
Generating the Presence of Remote Mourners: a Case Study of Funeral Webcasting in Japan

Funerals are irreplaceable events, especially for bereaved family members and relatives. However, the COVID-19 pandemic has prevented many people worldwide from attending their loved ones’ funerals. The authors had the opportunity to assist one family faced with this predicament by webcasting and recording funeral rites held near Tokyo in June, 2020. Using our original 360-degree Telepresence system and smartphones running Zoom, we enabled the deceased’s elder siblings to remotely attend the funeral and did our utmost to make them feel present in the funeral hall. Despite the webcasting via Zoom contributing more to their remote attendances than our system, we discovered thoughtful findings which could be useful for designing remote funeral attendances. From the findings, we also discuss how HCI designers can contribute to this highly sensitive issue, weaving together knowledge from various domains including techno-spiritual practices, thanato-sensitive designs; and other religious and cultural aspects related to death rituals.
The Technology-Mediated Reflection Model: Barriers and Assistance in Data-Driven Reflection

Current personal informatics models consider reflection as an important stage in users' journeys with trackers. However, these models describe reflection from a meta perspective and it remains unclear what this stage entails. To design interactive technologies that support reflection, we need a more thorough understanding of how people reflect on their personal data in practice. To that end, we conducted semi-structured interviews with users of fitness trackers and an online survey to study practices in reflecting on fitness data. Our results show that users reported reflecting on data despite lacking reflection support from their tracking technology. Based on our results, we introduce the Technology-Mediated Reflection Model, which describes conditions and barriers for reflection on personal data. Our model consists of the temporal and conceptual cycles of reflection and helps designers identify the possible barriers a user might face when using a system for reflection.
Feature hallucination via Maximum A Posteriori for few-shot learning

Few-shot learning aims to train an effective classifier in a small data regime. Due to the scarcity of training samples (usually as small as 1 or 5), traditional deep learning solutions often suffer from overfitting. To address this issue, an intuitive idea is to augment or hallucinate sufficient training data. For this purpose, in this paper, we propose a simple yet effective method to build a model for novel categories with few samples. Specifically, we assume that each category in the base set follows a Gaussian distribution, so that we can employ Maximum A Posteriori (MAP) to estimate the distribution of a novel category with even one example. To achieve this goal, we first transform each base category into Gaussian form with power transformation for MAP estimation. Then, we estimate the Gaussian mean of the novel category under the Gaussian prior given few samples from it. Finally, each novel category is represented by a unique Gaussian distribution, where sufficient trainable features can be sampled to obtain a highly accurate classifier for final predictions. Experimental results on four few-shot benchmarks show that it significantly outperforms the baseline methods on both 1- and 5-shot tasks. Extensive results on cross-domain tasks and visualization of estimated feature distribution also demonstrate its effectiveness.
I Like My Relational Machine Teacher: An AI Instructor’s Communication Styles and Social Presence in Online Education

New advancements in technology have made machines teachers, or technology-powered robots or AI that assist in the overall learning experience, a possibility. Though adoption rates are currently low, colleges and universities will likely incorporate some aspects of machine teachers (e.g., AI, robots) in their curriculums in the foreseeable future. However, little is known about how to create an effective machine teacher-based education. As an initial step, the present study examines whether an AI instructor’s communication style would have an impact on students’ perceptions about an AI instructor-based education. To test this inquiry, the study conducted an online experiment using a 2 (communication styles: functional vs. relational) x 2 (course topic: natural science vs. social science) between-subjects design. Primary results indicate that students develop more favorable perceptions about an AI instructor-based education when the AI instructor is relational rather than functional. This tendency is particularly strong when listening to a social science lecture. Further, social presence of an AI instructor functions as a mediator, which explains the reason why a relational AI instructor leads to more favorable perceptions about an AI instructor-based education is because of one’s social presence of an AI instructor. Collectively, the study’s findings indicate the importance of communication styles and social presence of an AI instructor.
Query-By-Example Keyword Spotting System Using Multi-Head Attention and Soft-triple Loss

This paper proposes a neural network architecture for tackling the query-by-example user-defined keyword spotting task. A multi-head attention module is added on top of a multi-layered GRU for effective feature extraction, and a normalized multi-head attention module is proposed for feature aggregation. We also adopt the softtriple loss - a combination of triplet loss and softmax loss - and showcase its effectiveness. We demonstrate the performance of our model on internal datasets with different languages and the public Hey-Snips dataset. We compare the performance of our model to a baseline system [1] and conduct an ablation study to show the benefit of each component in our architecture. The proposed work shows solid performance while preserving simplicity.