Reviewerly/exHarmony huggingface dataset:
A novel dynamic weight allocation method for multisource information fusion

In multisource information fusion, the weight assignment is critical to improve the fusion performance, especially under uncertainty and conflict situations. As for uncertainty, the probability distribution is a desirable method to model uncertainty. In this context, a novel dynamic weight allocation method is first proposed, which considers three situations: nonconflict, general conflict, and high conflict. In particular, according to the degree of conflict, different weight allocations are considered by means of generalized information quality and negation operation. After that, a novel dynamic weight allocation method is proposed that contains three subalgorithms: NonC algorithm, BNC algorithm, and BIC algorithm. In addition, a novel multisource information fusion method is presented based on the newly designed dynamic weight allocation method. Besides, some numerical examples illustrate its feasibility. Finally, an application in target recognition demonstrates the practicability of the proposed fusion method.
Analysis of Anchor-Based and Anchor-Free Object Detection Methods Based on Deep Learning

As one of the core mission of computer vision, object detection has been widely applied with the rapid development of computer technology, especially in the fields of face detection, behavior detection, auto driving, and intelligent monitoring. Aiming at the shortcomings of the traditional object detection, such as low detection accuracy, low efficiency and poor robustness, this article summarizes deep learning-based detectors of two kinds of modules: Anchor-Based and Anchor-Free. The performance of each detector is compared and analyzed in this paper as well. In addition, we summarize the development of object detection's key technologies from the aspect of improvement of Backbone, the optimization of NMS, imbalance of positive and negative samples' solution etc. Finally, the development trend of object detection is discussed from the prospect of lightweight detection model, weakly supervision detection and small object detection etc.
Copy-move forgery detection of digital images using football game optimization

Copy move forgery (CMF) of digital images is the frequently adapted tampering by simply copying a region of the image and pasting it on to the same image using user-friendly image processing tools. Most of the existing key-point (KP) based CMF detection algorithms involve a hefty number of KPs and huge sets of feature descriptors, which increase their computational burden. In addition, the identified KPs may not spread over all regions of the image and the classical clustering techniques may not optimally classify the feature space into cluster space, thereby affecting the accuracy of the results. This paper suggests a new CMF detection method that considers a small number of the strongest KPs, chosen from both Difference of Gaussian (DoG)-based KPs and FAST-corner KPs, evaluates Scale Invariant Features Transform (SIFT) descriptors, applies discrete wavelet transform for dimensionality reduction, and employs football game based optimization (FGBO). The FGBO belongs to the family of meta-heuristic optimization algorithms, and is used for effectively classifying the feature space into cluster space, with a view to overcoming the drawbacks of classical methods. This article exhibits the superior performances of the developed method over existing methods by presenting results on 500 digital images.
Investigating Efficient Learning and Compositionality in Generative LSTM Networks

When comparing human with artificial intelligence, one major difference is apparent: Humans can generalize very broadly from sparse data sets because they are able to recombine and reintegrate data components in compositional manners. To investigate differences in efficient learning, Joshua B. Tenenbaum and colleagues developed the character challenge: First an algorithm is trained in generating handwritten characters. In a next step, one version of a new type of character is presented. An efficient learning algorithm is expected to be able to re-generate this new character, to identify similar versions of this character, to generate new variants of it, and to create completely new character types. In the past, the character challenge was only met by complex algorithms that were provided with stochastic primitives. Here, we tackle the challenge without providing primitives. We apply a minimal recurrent neural network (RNN) model with one feedforward layer and one LSTM layer and train it to generate sequential handwritten character trajectories from one-hot encoded inputs. To manage the re-generation of untrained characters when presented with only one example of them, we introduce a one-shot inference mechanism: the gradient signal is backpropagated to the feedforward layer weights only, leaving the LSTM layer untouched. We show that our model is able to meet the character challenge by recombining previously learned dynamic substructures, which are visible in the hidden LSTM states. Making use of the compositional abilities of RNNs in this way might be an important step towards bridging the gap between human and artificial intelligence.
Social AR: Reimagining and Interrogating the Role of Augmented Reality in Face to Face Social Interactions

As augmented reality (AR) technologies become more pervasive, there is a growing interest in Social AR systems designed to support face to face interactions with collocated others. Prior research on AR often focuses on the technical aspects of the technology. However, at this early juncture, it is crucial to reflect and discuss the ethical, political, societal, and privacy implications of Social AR. This workshop aims to bring together industry practitioners and academic researchers to discuss the opportunities and challenges of social AR: from platforms to content creation, to self-representation. We aim to use a design fiction approach where workshop participants create speculative scenarios that interrogate the values imbued into Social AR. Based on these discussions, we will put together a set of initial recommendations for designers of Social AR technologies.
Correctness by Construction for Probabilistic Programs

The “correct by construction” paradigm is an important component of modern Formal Methods, and here we use the probabilistic Guarded-Command Language pGCL to illustrate its application to probabilistic programming. pGCL extends Dijkstra’s guarded-command language GCL with probabilistic choice, and is equipped with a correctness-preserving refinement relation $$(\mathrel \sqsubseteq )$$ that enables compact, abstract specifications of probabilistic properties to be transformed gradually to concrete, executable code by applying mathematical insights in a systematic and layered way. Characteristically for correctness by construction, as far as possible the reasoning in each refinement-step layer does not depend on earlier layers, and does not affect later ones. We demonstrate the technique by deriving a fair-coin implementation of any given discrete probability distribution. In the special case of simulating a fair die, our correct-by-construction algorithm turns out to be “within spitting distance” of Knuth and Yao’s optimal solution.
GazeWheels: Comparing Dwell-time Feedback and Methods for Gaze Input

We present an evaluation and comparison of GazeWheels: techniques for dwell time gaze input and feedback. In GazeWheel, visual feedback is shown to the user in the form of a wheel that is filled. When completely filled, a selection is made where the user is gazing. We compare three methods for responding to the user when gazing away from the target: Resetting GazeWheel, Pause-and-Resume GazeWheel, and Infinite GazeWheel. We also compare the position of the GazeWheel; Co-located Feedback: shown on the target being gazed at, and Remote Feedback: shown at the top of the interface. To this end, we report on results of a user study (N=19) that investigates the benefits and drawbacks of each method at different dwell times: 500ms, 800ms, and 1000ms. Results show that Infinite GazeWheel and Pause-and-Resume GazeWheel are more error prone but significantly faster than Resetting GazeWheel when using 800-1000 ms dwell time, even when including the time for correcting errors.
A Preliminary Study on Understanding Voice-only Online Meetings Using Emoji-based Captioning for Deaf or Hard of Hearing Users

In the midst of the coronavirus disease 2019 pandemic, online meetings are rapidly increasing. Deaf or hard of hearing (DHH) people participating in an online meeting often face difficulties in capturing the affective states of other speakers. Recent studies have shown the effectiveness of emoji-based representation of spoken text to capture such affective states. Nevertheless, in voice-only online meetings, it is still not clear how emoji-based spoken texts can assist DHH people to understand the feelings of speakers without perceiving their facial expressions. We therefore conducted a preliminary experiment to understand the effect of emoji-based text representation during voice-only online meetings by leveraging an emoji-based captioning system. Our preliminary results demonstrate the necessity of designing an advanced system to help DHH people understanding the voice-only online meetings more meaningfully.
A visual analysis method of randomness for classifying and ranking pseudo-random number generators

The development of new pseudo-random number generators (PRNGs) has steadily increased over the years. Commonly, PRNGs' randomness is "measured" by using statistical pass/fail suite tests, but the question remains, which PRNG is the best when compared to others. Existing randomness tests lack means for comparisons between PRNGs, since they are not quantitatively analysing. It is, therefore, an important task to analyze the quality of randomness for each PRNG, or, in general, comparing the randomness property among PRNGs. In this paper, we propose a novel visual approach to analyze PRNGs randomness allowing for a ranking comparison concerning the PRNGs' quality. Our analysis approach is applied to ensembles of time series which are outcomes of different PRNG runs. The ensembles are generated by using a single PRNG method with different parameter settings or by using different PRNG methods. We propose a similarity metric for PRNG time series for randomness and apply it within an interactive visual approach for analyzing similarities of PRNG time series and relating them to an optimal result of perfect randomness. The interactive analysis leads to an unsupervised classification, from which respective conclusions about the impact of the PRNGs' parameters or rankings of PRNGs on randomness are derived. We report new findings using our approach in a study of randomness for state-of-the-art numerical PRNGs such as LCG, PCG, SplitMix, Mersenne Twister, and RANDU as well as chaos-based PRNG families such as K-Logistic map and K-Tent map with varying parameter K.
Hate Speech Detection using Global Vector and Deep Belief Network Algorithm

Hate speeches are words, behaviors, and actions prohibited because they lead to acts that trigger violence and anarchist attitudes towards other individuals or groups. Since the 2014 presidential election, the term 'hater' has been widely known, marking people with a tendency to practice speech utterances in certain people and groups. Thus, the internet's ethics need to be emphasized, considering that the internet is a necessity for today's society. Nevertheless, more and more users are also many parties who abuse the internet to spread information about speech utterances such as ethnicity, race, and religion. In this project, a system will be created to detect hate speech in the form of tweets on twitter. The author's method is the Deep Belief Network method by weighing the Global Vector feature to increase accuracy before classification. The making of this system is expected to be able to find out and detect hate speech from the text previously in the form of tweets. By using the Deep Belief Network method, the results of this study were obtained with an accuracy =86,00%, precision =82,00%, recall =89,13% and Fl-Score =85,42%. After doing this research, it is expected that the computer can find out and classify the existence of hate speech in the text.
The quality of fisheries governance assessed using a participatory, multi-criteria framework: A case study from Murcia, Spain

Global policy initiatives, such as the UN Sustainable Development Goals, consider good governance a prerequisite for sustainable management of natural resources. Resource governance to address ecological and socioeconomic challenges, however, is complex and difficult to measure, as it involves both formal and informal structures and processes. To fill this need, a governance framework was developed that specifies governance quality criteria and indicators along multiple dimensions that were assessed by five stakeholder groups. The seven principles of good governance were adopted as criteria of governance quality and 29 governance indicators were developed to measure these criteria. Diverse stakeholders were asked to assess the importance and status of these criteria for fisheries in the Region of Murcia, Spain. To connect stakeholder perceptions of governance quality with possible policy interventions, a qualitative methodology, cognitive mapping, was used to assess the perceived linkages among indicators, as well as the perceived importance and status of each indicator. This was done to provide practical insights into how to improve governance quality, given its multi-dimensional and normative nature. The relationship between fisheries governance quality and fisheries sustainability also was explored. While applied to a specific case in Spain, this participatory, multi-criteria governance quality assessment framework can be adapted for any fishery.
A Study of Inductive Biases for Unsupervised Speech Representation Learning

Distributed representations, or embeddings, are commonly learned without supervision on very large unannotated corpora for natural language processing. In speech processing, deep network-based representations such as bottlenecks and x-vectors have had some success,but are limited to supervised or partly supervised settings where annotations are available and are not optimized to separate underlying factors. Here, we propose a generative model with deep encoders and decoders that can learn interpretable speech representations without supervision. Our inductive biases operate as prior distributions in a variational autoencoder model and allow us to separate several latent variables along a continuous range of time-scale properties, as opposed to binary oppositions or hierarchical factorization that have been previously proposed. On simulated data, we confirm that these biases enable the model to accurately recover phonetic and speaker underlying factors. On TIMIT and LibriSpeech, they yield representations that separate phonetic and speaker information, as evidenced by unsupervised results on downstream phoneme and speaker classification tasks using a simple k-means classifier.
Find right countenance for your input—Improving automatic emoticon recommendation system with distributed representations

Emoticons are popularly used to express user’s feelings in social media, blogs, and instant messaging. However, the number of emoticons existing in emoticon dictionaries which users select from is large, thus, it is difficult for users to find the desired emoticon that matches the content of their messages. In this paper, we propose a method that supports users’ emoticon selection by reordering 167 unique emoticons in the emoticon dictionary by applying pre-trained models learned from large data in Japanese. We evaluated whether adapting a pre-trained model to our emoticon recommendation system achieves better results than just learning surface patterns of text and emoticon. We collected sets of sentences and emoticons in Japanese from the Internet and pre-trained models (i.e. Word2vec, ELMo, and BERT) that learned from large Japanese textual data and used deep learning techniques such as BiLSTM and fine-tuning for learning. We confirmed that fine-tuning our data with BERT achieved the best recommendation accuracy of 52.98%, recommending the correct emoticon within the top 25 (top 15%) of the emoticons. Moreover, we confirmed our intuition that widely used Wikipedia-based pre-trained models are not the best voice for the facemark recommendations.
Hybrid Ensemble Pruning Using Coevolution Binary Glowworm Swarm Optimization and Reduce-Error

Ensemble pruning has been widely applied to improve the capacity of multiple learner system. Both diversity and classification accuracy of learners are considered as two key factors for achieving an ensemble with competitive classification ability. Considering that extreme learning machine (ELM) is characterized by excellent training rate and generalization capability, it is employed as the base classifier. For a multiple ELM system, when we increase its constituents’ diversity, the mean accuracy of the whole members must be decreased. Therefore, a compromise between them can ensure that the ELMs remain good diversity and high precision, but finding the compromise brings a heavy computational burden. It is hard to look for the exact result via the searching of intelligent algorithms or pruning of diversity measures. On the basis, we propose a hybrid ensemble pruning approach employing coevolution binary glowworm swarm optimization and reduce-error (HEPCBR). Considering the good performance of reduce-error (RE) in selecting ELMs with high diversity and precision, we try to employ RE to choose the satisfactory ELMs from the generated ELMs. In addition, the constituents are further selected via the proposed coevolution binary glowworm swarm optimization, which are utilized to construct the promising ensemble. Experimental results indicate that, compared to other frequently used methods, the proposed HEPCBR achieves significantly superior performance in classification.
A novel geometry error measurement methodology for coaxiality evaluation

In this paper, we proposed a new device for geometry errors measurement and coaxiality evaluation, and the corresponding methodology for coaxiality evaluation from measurement data is presented, which allows to characterize multiple holes at a time. Unlike traditional measurement system a laser sensor is mounted onto out of the holes so that multi-hole surfaces can be “seen” by the senor when it rotates around a fixed axis. First the intersections (or ellipse profiles) of the sensor’s scanning plane and holes, are computed by fitting. Then, the center coordinates and profile points of the ellipse are computed and transformed to the 3D global coordinate frame. Finally the centerline of the hole is determined from the 3D profile points by using a weighted least-squares fitting algorithm. In addition, to reduce the effect of noises on the measurement result, error analysis and compensation techniques are studied to improve the measurement accuracy. A case study is presented to validate the measurement principle and data processing approach.
A robust generative classifier against transfer attacks based on variational auto-encoders

Deep neural networks (DNNs) are vulnerable to adversarial examples. Even under black-box setting that is without access to the target model, transfer-based attacks can easily fool the DNNs. To alleviate this problem, we propose a robust classification model against transfer attacks based on the framework of variational Auto-Encoders (VAEs) which are probabilistic generative models and have been successfully used to a large mount of tasks. Specifically, our model simulates the data generative process with several multivariate Gaussian distributions and DNNs: (1) We assume that the latent embedding generated by an encoder (a DNN) of each category corresponds to a multivariate Gaussian distribution. (2) A decoder (a DNN) is proposed to decodes the latent embedding into an observable. (3) Theoretical analysis illustrates that our model can predict data's labels by maximizing the lower bound on the log-likelihood for each category utilizing Bayes' theorem with excellent robustness against transfer attacks. Inference in our model is done in a variational way so the Stochastic Gradient Variational Bayes (SGVB) estimator and reparamerization trick can be utilized to optimize the evidence lower bound (ELBO). The experiments with quantitative comparisons show that our approach reaches state-of-the-art with significantly better robustness.
IoTHound

As the Internet of Things (IoT) becomes more ingrained in our daily lives and environments, asset enumeration, characterization, and monitoring become crucial, yet challenging tasks. A vast number of gadgets in the market have a smartphone-based companion-app, making monitoring a variety of devices an overwhelming task for users. We propose IoTHound, an automated method to identify and monitor IoT devices in smart-homes.
Smells Like Team Spirit

Verbal communication is a central component in collaborative multiplayer gaming and creates a feeling of companionship among the players. In single-player games, this aspect is often missing. Advancements in speech recognition now open new potentials for voice-activated single-player experiences. In this work, we integrated voice interaction to a single-player virtual reality (VR) game. To create a sense of team spirit, we enabled players to talk to a multiplicity of agents using natural language. We hypothesize that conversing with only one agent cannot produce the same level of camaraderie. We conducted a preliminary qualitative user study (N=10) to explore how players experience talking with the in-game characters in the single-agent and the multi-agent condition. Early results suggest that our participants prefer interacting with the group of interlocutors. They perceived the multi-agent condition as more entertaining and liked the feeling of being part of a team.
Designing for Bodily Play Experiences Based on Danish Linguistic Connotations of "Playing a Game"

Designing for bodily play in HCI is increasingly gaining attraction, including research on the experiential dynamics leading to that. Within this research, however, there has been little investigation into the differences between bodily playing and bodily gaming and associated implications for design. This paper investigates such differences and proposes an understanding derived from the Danish linguistic connotations of the four different combinations of bodily "playing/gaming" a "play/game". We exemplify these through four different examples and extract four strategies for designers to implement in their future bodily designs. With our work, we hope we are able to expand the range of diverse bodily play and game experiences within HCI.
Playing Together while Apart

Sharing recreational experiences with loved ones can improve well-being, experience enjoyment, relationship quality, and feelings of closeness. Despite these benefits, study participants report numerous barriers to shared recreation with loved ones. Among others, these include differences in recreational preferences and safety measures associated with the Coronavirus disease (COVID-19). In this paper we examine how loved ones use, and wish to use, technology to play together while apart. We take into account reported barriers to shared recreation and present the Planet Runner technology probe, a two-player digital-physical game which mediates a shared interdependent asymmetric recreational experience involving the outdoors. We share early findings from an exploratory study and conclude by highlighting design considerations for asymmetric games intended to mediate shared remote play experiences involving the outdoors.