Reviewerly/exHarmony huggingface dataset:
Effects of Behavioral and Anthropomorphic Realism on Social Influence with Virtual Humans in AR

While many applications in AR will display embodied agents in scenes, there is little research examining the social influence of these AR renderings. In this experiment, we manipulated the behavioral and anthropomorphic realism of an embodied agent. Participants wore an AR headset and walked a path specified by four virtual cubes, designed to bring them close to either humans or objects rendered in AR. In addition there was a control condition with no virtual objects in the room. Participants were then asked to choose between two physical chairs to sit on—one with a virtual human or object on it, or one without any. We examined the interpersonal distance between participants and rendered objects, physical seat choice, body rotation direction while choosing a seat, and social presence ratings. For interpersonal distance, there was an effect of anthropomorphic realism but not behavioral realism—participants left more space for human-shaped objects than for non-human objects, regardless of how real the human behaved. There were no significant differences in seat choice and rotation direction. Social presence ratings were higher for agents high in both behavioral and anthropomorphic realism than for other conditions. We discuss implications for the social influence theory [5] and for the design of AR systems.
Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes

While visual object detection with deep learning has received much attention in the past decade, cases when heavy intra-class occlusions occur have not been studied thoroughly. In this work, we propose a Non-Maximum-Suppression (NMS) algorithm that dramatically improves the detection recall while maintaining high precision in scenes with heavy occlusions. Our NMS algorithm is derived from a novel embedding mechanism, in which the semantic and geometric features of the detected boxes are jointly exploited. The embedding makes it possible to determine whether two heavily-overlapping boxes belong to the same object in the physical world. Our approach is particularly useful for car detection and pedestrian detection in urban scenes where occlusions often happen. We show the effectiveness of our approach by creating a model called SG-Det (short for Semantics and Geometry Detection) and testing SG-Det on two widely-adopted datasets, KITTI and CityPersons for which it achieves state-of-the-art performance. Our code is available at https://github.com/ChenhongyiYang/SG-NMS .
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.
The Share Size of Secret-Sharing Schemes for Almost All Access Structures and Graphs

The share size of general secret-sharing schemes is poorly understood. The gap between the best known upper bound on the total share size per party of $$2^{0.64n}$$ (Applebaum et al., STOC 2020) and the best known lower bound of $$\varOmega (n/\log n)$$ (Csirmaz, J. of Cryptology 1997) is huge (where n is the number of parties in the scheme). To gain some understanding on this problem, we study the share size of secret-sharing schemes of almost all access structures, i.e., of almost all collections of authorized sets. This is motivated by the fact that in complexity, many times almost all objects are hardest (e.g., most Boolean functions require exponential size circuits). All previous constructions of secret-sharing schemes were for the worst access structures (i.e., all access structures) or for specific families of access structures. We prove upper bounds on the share size for almost all access structures. We combine results on almost all monotone Boolean functions (Korshunov, Probl. Kibern. 1981) and a construction of (Liu and Vaikuntanathan, STOC 2018) and conclude that almost all access structures have a secret-sharing scheme with share size $$2^{\tilde{O}(\sqrt{n})}$$ . We also study graph secret-sharing schemes. In these schemes, the parties are vertices of a graph and a set can reconstruct the secret if and only if it contains an edge. Again, for this family there is a huge gap between the upper bounds – $$O(n/\log n)$$ (Erdös and Pyber, Discrete Mathematics 1997) – and the lower bounds – $$\varOmega (\log n)$$ (van Dijk, Des. Codes Crypto. 1995). We show that for almost all graphs, the share size of each party is $$n^{o(1)}$$ . This result is achieved by using robust 2-server conditional disclosure of secrets protocols, a new primitive introduced and constructed in (Applebaum et al., STOC 2020), and the fact that the size of the maximal independent set in a random graph is small. Finally, using robust conditional disclosure of secrets protocols, we improve the total share size for all very dense graphs.
Intrusion Detection System Based on Gradient Corrected Online Sequential Extreme Learning Machine

Nowadays, Intrusion Detection System (IDS) is an active research topic with machine learning nature.A single-hidden layer feedforward neural network (SLFN) trained on the approach of extreme learning machine (ELM) is used for (IDS).The encouraging factors for its usage are its fast learning and supportability of sequential learning in its online sequential extreme learning machine (OSELM) variant.An issue with OSELM that has been addressed by researchers is its random weights nature of the input-hidden layer.Most approaches use the concept of metaheuristic optimisation for determining the optimal weights of OSELM and resolve the random weight.However, metaheuristic approaches require many trials to determine the optimal one.Hence, there is concern about the convergence aspect and speed.This article proposes a novel approach for finding the optimal weights of the input-hidden layer.This article presents an approach for an integration between OSELM and back-propagation designated as (OSELM-BP).After integration, BP changes the random weights iteratively and uses an iterated evaluation of the generated error for feedback correction of the weights.The approach is evaluated based on various scenarios of activation functions for OSELM on the one hand and the number of iterations for BP on the other.An extensive evaluation of the approach and comparison with the original OSELM reveal a superiority of OSELM-BP in reaching optimal accuracy with a small number of iterations.INDEX TERMS Online sequential extreme learning machine (OSELM), intrusion detection system (IDS), back-propagation (BP), activation function.
TriC: Distributed-memory Triangle Counting by Exploiting the Graph Structure

Graph analytics has emerged as an important tool in the analysis of large scale data from diverse application domains such as social networks, cyber security and bioinformatics. Counting the number of triangles in a graph is a fundamental kernel with several applications such as detecting the community structure of a graph or in identifying important vertices in a graph. The ubiquity of massive datasets is driving the need to scale graph analytics on parallel systems. However, numerous challenges exist in efficiently parallelizing graph algorithms, especially on distributed-memory systems. Irregular memory accesses and communication patterns, low computation to communication ratios, and the need for frequent synchronization are some of the leading challenges. In this paper, we present TriC, our distributed-memory implementation of triangle counting in graphs using the Message Passing Interface (MPI), as a submission to the 2020 Graph Challenge competition. Using a set of synthetic and real-world inputs from the challenge, we demonstrate a speedup of up to 90 x relative to previous work on 32 processor-cores of a NERSC Cori node. We also provide details from distributed runs with up to 8192 processes along with strong scaling results. The observations presented in this work provide an understanding of the system-level bottlenecks at scale that specifically impact sparse-irregular workloads and will therefore benefit other efforts to parallelize graph algorithms.
A BERT-based Hierarchical Model for Vietnamese Aspect Based Sentiment Analysis

Aspect based sentiment analysis (ABSA) is the task of identifying sentiment polarity towards specific entities and their aspects mentioned in customers' reviews. This paper presents a new and effective hierarchical model using the pre-trained language model, Bidirectional Encoder Representations from Transformers (BERT). This model integrates the context information of the previous layer (i.e. entity type) into the prediction for the following layer (i.e. aspect type) and optimizes the global loss functions to capture the entire information from all layers. Experimental results on two public benchmark datasets in Vietnamese showed that the proposed model is superior to the existing ones. Specifically, the model achieved 84.23% and 82.06% in the F1_micro scores in detecting entities and their aspects on the domains of restaurants and hotels, respectively. In identifying aspect sentiment polarity, the model gained 71.3% and 74.69% in the F1_micro scores on the domains of restaurants and hotels, respectively. These results outperformed the best submission of the campaign by a large margin and gained a new state of the art.
Ensemble Deep Learning on Wearables Using Small Datasets

This article presents an in-depth experimental study of Ensemble Deep Learning techniques on small datasets for the analysis of time-series data generated by wearable devices. Deep Learning networks generally require large datasets for training. In some health care applications, such as the real-time smartwatch-based fall detection, there are no publicly available, large, annotated datasets that can be used for training, due to the nature of the problem (i.e., a fall is not a common event). We conducted a series of offline experiments using two different datasets of simulated falls for training various ensemble models. Our offline experimental results show that an ensemble of Recurrent Neural Network (RNN) models, combined by the stacking ensemble technique, outperforms a single RNN model trained on the same data samples. Nonetheless, fall detection models trained on simulated falls and activities of daily living performed by test subjects in a controlled environment, suffer from low precision due to high false-positive rates. In this work, through a set of real-world experiments, we demonstrate that the low precision can be mitigated via the collection of false-positive feedback by the end-users. The final Ensemble RNN model, after re-training with real-world user archived data and feedback, achieved a significantly higher precision without reducing much of the recall in a real-world setting.
Study on BERT Model for Hate Speech Detection

The last decade has witnessed a massive rise in the problem of hate speech due to revolutionary growth in online content. Urgent countermeasures are being taken by government bodies, social websites, multinational companies, research communities to solve the problem of hate speech detection, which is a text classification problem, by the development of automated methods of hate speech detection. A lot of manual effort and time is dedicated to find the solution for the problem of detecting hate speech from online content, but the authors of the paper feel that there is a lot of scope to improve the accuracy of the existing automated methods of hate speech detection. The authors observed that recently, transfer learning models have exhibited good results in the area of text classification for question-answering, summarization, and nextword prediction, but these learning models have not been used for the problem of hate speech detection to date. The authors of the article anticipate that these networks may give better results in another task of text classification, i.e., hate speech detection. Therefore, this article proposes a novel method of hate speech detection based on the concept of attention networks using the BERT attention model.
Optimization of Sliding-DCT Based Gaussian Filtering for Hardware Accelerator

Gaussian filtering is a smoothing filter used in various tasks. The main disadvantage is the dependence of the processing time on its kernel radius. One solution is using a sliding-discreet cosine transform (DCT), a constant-time algorithm for the kernel radius, and it provides the best performance in terms of both speed and accuracy. However, the speed and accuracy differ according to the type of DCT used. We can also accelerate the sliding-DCT based Gaussian filter by hardware accelerators, but the acceleration requires modification of the algorithms. In this paper, we focus on the fused multiply-add (FMA) instruction of hardware accelerators in modern computer architectures. The FMA instruction simultaneously performs multiplication and addition, i.e.,ax+b. We proposed an acceleration method of the sliding-DCT based Gaussian filtering for the FMA instruction. Moreover, we evaluate the performance of it in terms of computational time and approximation accuracy.
The Effect of Goal Moderation on the Achievement and Satisfaction of Physical Activity Goals

Many self-trackers lose interest in, disengage from and ultimately withdraw from tracking. Reasons for this include poor motivation, unmet expectations and difficulty in attaining daily goals. To support users in reflecting on their goals more realistically, we developed FitReflect, an app that moderates physical activity goals by factoring in users' confidence in achieving the goals. The app also encourages users to reflect on their goals regularly and think about the factors affecting their achievement. We conducted a 4-week field experiment where we trialled the app with fourteen Fitbit users. We found that, compared with a non-moderated goal condition, participants with moderated goals achieved their goals more often, got closer to them, and adjusted them more frequently. Crucially, they were also more satisfied with their physical activity. More frequent goal updates were key to align user goals with their confidence and capabilities in achieving them.
InSet

Architectural smells (ASs) are architectural decisions that negatively affect the maintenance and evolution of software. Most of the existing tools able to identify AS rely on few metrics with fixed thresholds. However, it is not possible to define specific metrics and thresholds that meet all the cases, i.e., the classification of a piece of code in smell or not can depend on the domain, the experience of developers, organization patterns or even from a vast set of features - so there is a subjective ingredient in this decision. Machine Learning (ML) can help to make these decisions/classifications more precise by taking into consideration a vast set of features and also feedback from experts. This paper presents a machine learning-based tool to detect the architectural smells Unstable Dependency(UD) and God Component(GC). Our tool is able to take into consideration users' feedback to retrain the algorithms and constantly improve their performance. Our tool got good result in terms of accuracy, precision, recall, F-measure and Kappa's coefficient.
Waveguide integrated hot electron bolometer for classical and quantum photonics

The development of performant integrated detectors, which are sensitive to quantum fluctuations of coherent light, are strongly desired to realize a scalable and determinist photonic quantum processor based on continuous variables states of light. Here, we investigate the performance of hot electron bolometers (HEBs) fabricated on top of a silicon-on-insulator (SOI) photonic circuit showing responsivities up to 8600 V/W and a record noise equivalent temperature of 1.1 dB above the quantum limit. Thanks to a detailed analysis of the noise sources of the waveguide integrated HEB, we estimate 14.8 dBV clearance between the shot noise and electrical noise with just 1.1µW of local oscillator power. The full technology compatibility with superconducting nanowire single photon detectors (SNSPDs) opens the possibility of nonclassical state engineering and state tomography performed within the same platform, enabling a new class of optical quantum processors.
Genetic Fuzzy Based Scalable System of Distributed Robots for a Collaborative Task

This paper introduces a new genetic fuzzy based paradigm for developing scalable set of decentralized homogenous robots for a collaborative task. In this work, the number of robots in the team can be changed without any additional training. The dynamic problem considered in this work involves multiple stationary robots that are assigned with the goal of bringing a common effector, which is physically connected to each of these robots through cables, to any arbitrary target position within the workspace of the robots. The robots do not communicate with each other. This means that each robot has no explicit knowledge of the actions of the other robots in the team. At any instant, the robots only have information related to the common effector and the target. Genetic Fuzzy System (GFS) framework is used to train controllers for the robots to achieve the common goal. The same GFS model is shared among all robots. This way, we take advantage of the homogeneity of the robots to reduce the training parameters. This also provides the capability to scale to any team size without any additional training. This paper shows the effectiveness of this methodology by testing the system on an extensive set of cases involving teams with different number of robots. Although the robots are stationary, the GFS framework presented in this paper does not put any restriction on the placement of the robots. This paper describes the scalable GFS framework and its applicability across a wide set of cases involving a variety of team sizes and robot locations. We also show results in the case of moving targets.
DDaNet: Dual-Path Depth-Aware Attention Network for Fingerspelling Recognition Using RGB-D Images

Automatic fingerspelling recognition aims to overcome communication barriers between people who are deaf and those who can hear. RGB-D cameras are widely used to handle finger occlusion, which usually hinders fingerspelling recognition. However, color-depth misalignment, which is an intrinsic property of RGB-D cameras, hinders the simultaneous processing of color and depth images in the absence of intrinsic parameters of the camera. Furthermore, fine-grained hand gestures performed by various persons and captured from multiple views render the discriminative feature extraction difficult, due to intra-class variability and inter-class similarity. Inspired by the human visual mechanism, we propose a network to learn discriminative features related to fine-grained hand gestures while suppressing the effect of color-depth misalignment. Unlike existing approaches that independently process RGB-D images, a dual-path depth-aware attention network that learns a fingerspelling representation in separate RGB and depth paths, and progressively fuses the features learned from the two paths is proposed. As the hand is usually the closest object to the camera, depth information can contribute to emphasize the key fingers related to a letter sign. Thus, we develop a depth-aware attention module (DAM) to exploit spatial relations in the depth feature maps, refining the RGB and depth feature maps across a bottleneck structure. The module establishes a lateral connection of the RGB and depth paths and provides a depth-aware salient map to both paths. The experimental results demonstrated that the proposed network improved the accuracy (+0.83%) and F score (+1.55%) compared to state-of-the-art methods on a publicly available fingerspelling dataset. The visualization of the network processes demonstrates that the DAM facilitates the selection of representative hand regions from the RGB-D images. Furthermore, the number of parameters and computational overhead of the DAM are negligible in the network. The code is available at https://github.com/cweizen/cweizen-DDaNet_model_master.
Differentially private ensemble learning for classification

Training machine learning models requires large amounts of data, which may contain personal sensitive information. Machine learning based on privacy protection has become a research hotspot. In this paper, differential privacy is applied to the ensemble learning, a branch of machine learning, to prevent privacy leakage in the classification process. We propose a differentially private ensemble learning algorithm for classification, which achieves privacy protection while ensures prediction accuracy. Firstly, we adopt the Bag of Little Bootstrap technique and the Jaccard similarity coefficient to generate a set of training data sets, and construct corresponding differentially private base classifiers by adding a carefully chosen amount of perturbation noise with a privacy budget allocation strategy. Furthermore, to reduce the impact of perturbation noise on the accuracy of prediction, an effective ensemble algorithm is proposed. Specifically, the base classifiers are selected based on some criterion functions, and the corresponding weights are assigned simultaneously. Then, the final result of the classification is obtained by a weighted voting scheme. Experiments are executed on 9 real data sets from the UCI Machine Learning Repository to demonstrate that our differentially private ensemble classification algorithm achieves a better trade-off in terms of privacy protection and prediction accuracy.
Dynamic Interaction Graphs for Driver Activity Recognition

The drivers activities and the resulting distraction is relevant for all levels of vehicle automation. It is especially important for take-over scenarios in partially automated vehicles. To this end we investigate graph neuronal networks for pose based driver activity recognition. We focus on integrating additional input modalities like interior elements and objects and investigate how this data can be integrated in an activity recognition model. We test our approach on the Drive & Act dataset [1]. To this end we densely annotate and publish the bounding boxes of the dynamic objects contained in the dataset. Our results show that adding the additional input modalities boosts the recognition results of classes related to interior elements and objects by a large margin closing the gap to popular image based methods.