Reviewerly/exHarmony huggingface dataset:
SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning

This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM).We propose a distant supervision method to improve on this task.Specifically, we design grammar and reasoning rules to automatically generate a spatial description of visual scenes and corresponding QA pairs.Experiments show that further pretraining LMs on these automatically generated data significantly improves LMs' capability on spatial understanding, which in turn helps to better solve two external datasets, bAbI, and boolQ.We hope that this work can foster investigations into more sophisticated models for spatial reasoning over text.
MERCI: efficient embedding reduction on commodity hardware via sub-query memoization

Deep neural networks (DNNs) with embedding layers are widely adopted to capture complex relationships among entities within a dataset. Embedding layers aggregate multiple embeddings — a dense vector used to represent the complicated nature of a data feature— into a single embedding; such operation is called embedding reduction. Embedding reduction spends a significant portion of its runtime on reading embeddings from memory and thus is known to be heavily memory-bandwidth-bound. Recent works attempt to accelerate this critical operation, but they often require either hardware modifications or emerging memory technologies, which makes it hardly deployable on commodity hardware. Thus, we propose MERCI, Memoization for Embedding Reduction with ClusterIng, a novel memoization framework for efficient embedding reduction. MERCI provides a mechanism for memoizing partial aggregation of correlated embeddings and retrieving the memoized partial result at a low cost. MERCI substantially reduces the number of memory accesses by 44% (29%), leading to 102% (74%) throughput improvement on real machines and 40.2% (28.6%) energy savings at the expense of 8×(1×) additional memory usage.
A new blind image denoising method based on asymmetric generative adversarial network

Image denoising is a classical topic in computer vision. In recent years, with the development of deep learning, image denoising methods based on discriminative learning have received more attention. In this paper, a new blind image denoising method based on the asymmetric generative adversarial network (ID-AGAN) is proposed. In the new method, the adversarial learning is used to optimise the high-dimensional image information denoising, so as to balance the noise removal and detail retention. In order to overcome the unstability of the GAN training and improve the discriminative ability of the discriminating model, an image downsampling layer is added between the generating model and the discriminating model. Moreover, a multi-scale feature downsampling layer is utilised to extract the feature of the entire image and reducing the effect of noise on training images. Extensive experiments are conducted to verify the performance of the ID-AGAN algorithm. The results demonstrate that authors' method has high performance and flexibility.
Comparative analysis of methods for cloud segmentation in ground-based infrared images

The increasing penetration of photovoltaic systems in the power grid makes it vulnerable to cloud shadow projection. Real-time cloud segmentation in ground-based infrared images is important to reduce the noise in intra-hour global solar irradiance forecasting. We present a comparison between discriminative and generative models for cloud segmentation. The performances of supervised and unsupervised learning methods in cloud segmentation are evaluated. The discriminative models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. Infrared image preprocessing to remove stationary artifacts increases the overall performance in the analyzed methods. The inclusion of features from neighboring pixels in the feature vectors leads to a performance improvement in some of the cases. Markov Random Fields achieve the best performance in both unsupervised and supervised generative models. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation. Generative and discriminative models are comparable when preprocessing is applied to the infrared images.
Towards More Fine-grained and Reliable NLP Performance Prediction

Performance prediction, the task of estimating a system's performance without performing experiments, allows us to reduce the experimental burden caused by the combinatorial explosion of different datasets, languages, tasks, and models.In this paper, we make two contributions to improving performance prediction for NLP tasks.First, we examine performance predictors not only for holistic measures of accuracy like F1 or BLEU, but also fine-grained performance measures such as accuracy over individual classes of examples.Second, we propose methods to understand the reliability of a performance prediction model from two angles: confidence intervals and calibration.We perform an analysis of four types of NLP tasks, and both demonstrate the feasibility of fine-grained performance prediction and the necessity to perform reliability analysis for performance prediction methods in the future.We make our code publicly available:
Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool

Ben Swanson, Kory Mathewson, Ben Pietrzak, Sherol Chen, Monica Dinalescu. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. 2021.
Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning

Recent advancements in data-to-text generation largely take on the form of neural end-toend systems.Efforts have been dedicated to improving text generation systems by changing the order of training samples in a process known as curriculum learning.Past research on sequence-to-sequence learning showed that curriculum learning helps to improve both the performance and convergence speed.In this work, we delve into the same idea surrounding the training samples consisting of structured data and text pairs, where at each update, the curriculum framework selects training samples based on the model's competence.Specifically, we experiment with various difficulty metrics and put forward a soft edit distance metric for ranking training samples.Our benchmarks show faster convergence speed where training time is reduced by 38.7% and performance is boosted by 4.84 BLEU.
Cost-effective End-to-end Information Extraction for Semi-structured Document Images

A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its development and maintenance cost.One can instead consider an endto-end model that directly maps the input to the target output and simplify the entire process.However, such generation approach is known to lead to unstable performance if not designed carefully.Here we present our recent effort on transitioning from our existing pipeline-based IE system to an end-to-end system focusing on practical challenges that are associated with replacing and deploying the system in real, large-scale production.By carefully formulating document IE as a sequence generation task, we show that a single end-to-end IE system can be built and still achieve competent performance.
Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks

The increasing demand for surveillance systems has resulted in an unprecedented rise in the volume of video data being generated daily. The volume and frequency of the generation of video streams make it both impractical as well as inefficient to manually monitor them to keep track of abnormal events as they occur infrequently. To alleviate these difficulties through intelligent surveillance systems, several vision-based methods have appeared in the literature to detect abnormal events or behaviors. In this area, convolutional neural networks (CNNs) have also been frequently applied due to their prevalence in the related domain of general action recognition and classification. Although the existing approaches have achieved high detection rates for specific abnormal behaviors, more inclusive methods are expected. This paper presents a CNN-based approach that efficiently detects and classifies if a video involves the abnormal human behaviors of falling, loitering, and violence within uncrowded scenes. The approach implements a two-stream architecture using two separate 3D CNNs to accept a video and an optical flow stream as input to enhance the prediction performance. After applying transfer learning, the model was trained on a specialized dataset corresponding to each abnormal behavior. The experiments have shown that the proposed approach can detect falling, loitering, and violence with an accuracy of up to 99%, 97%, and 98%, respectively. The model achieved state-of-the-art results and outperformed the existing approaches.
Load balancing in Cloud Computing: Issues and Challenges

The evolution of IT led Cloud computing technology emerge as a new prototype in providing the services to its users on rented basis at any time or place. Considering the flexibility of cloud services, innumerable organizations switched their businesses to the cloud technology by setting up more data centers. Nevertheless, it has become mandatory to provide profitable execution of tasks and appropriate resource utilization. A few approaches were outlined in literature to enhance performance, job scheduling, storage resources, QoS and load distribution. Load balancing concept permits data centers to avert over-loading or under-loading in virtual machines that as such is an issue in cloud computing domain. Consequently, it necessitate the researchers to layout and apply a proper load balancer for cloud environment. The respective study represents a view of problems and threats faced by the current load balancing techniques and make the researchers find more efficient algorithms.
A Neuron Model with Dendrite Morphology for Classification

Recent neurological studies have shown the importance of dendrites in neural computation. In this paper, a neuron model with dendrite morphology, called the logic dendritic neuron model (LDNM), is proposed for classification. This model consists of four layers: a synaptic layer, a dendritic layer, a membrane layer, and a soma body. After training, the LDNM is simplified by proprietary pruning mechanisms and is further transformed into a logic circuit classifier. Moreover, to address the high-dimensional challenge, feature selection is employed as the dimension reduction method before training the LDNM. In addition, the effort of employing a heuristic optimization algorithm as the learning method is also undertaken to speed up the convergence. Finally, the performance of the proposed model is assessed by five benchmark high-dimensional classification problems. In comparison with the other six classical classifiers, LDNM achieves the best classification performance on two (out of five) classification problems. The experimental results demonstrate the effectiveness of the proposed model. A new perspective for solving classification problems by the proposed LDNM is provided in the paper.
Circular Complement Network for RGB-D Salient Object Detection

With the supplement of texture and geometry cues in depth maps, some difficult scenes of salient object detection (SOD) in 2D images can be overcome. However, some distractors in the depth maps with relatively poor quality may interfere with SOD. Thus, how to suppress the interference of depth maps and extract valuable depth cues, is a critical issue to serve as effective complements to RGB cues. Aiming at addressing this issue, we propose a predict-refine scheme based Circular Complement Network (CCNet), which consists of a prediction subnetwork and a refinement subnetwork. On one hand, since RGB images generally contain more essential information for SOD, we propose a strategy which employs higher-level RGB feature maps to suppress the interference of depth feature maps. With this strategy, a novel Circular Feature Complement (CFC) module is specifically designed to enhance depth feature maps as well as to promote mutual complementarity between RGB feature maps and depth feature maps. The CFC modules are embedded into two subnetworks to achieve the cross-modal interactions at three levels. On the other hand, for the sake of the integration of two subnetworks, a Transmission Bridge (TB) module is proposed to effectively transfer the feature maps of the prediction subnetwork to the refinement subnetwork. The non-salient regions are thus further suppressed in the TB module. Comprehensive experiments on six benchmark datasets show that the proposed CCNet outperforms 13 state-of-the-art models.
Speak with signs: Active learning platform for Greek Sign Language, English Sign Language, and their translation

Sign Language is used to facilitate the communication between Deaf and non-Deaf people. It uses signs-words with basic structural elements such as handshape, parts of face, body or space, and the orientation of the fingers-palm. Sign Languages vary from people to people and from country to country and evolve as spoken languages. In the current study, an application which aims at Greek Sign Language and English Sign Language learning by hard of hearing people and talking people, has been developed. The application includes grouped signs in alphabetical order. The user can find Greek Sign Language signs, English sign language signs and translate from Greek sign language to English sign language. The written word of each sign, and the corresponding meaning are displayed. In addition, the sound is activated in order to enable users with partial hearing loss to hear the pronunciation of each word. The user is also provided with various tasks in order to enable an interaction of the knowledge acquired by the user. This interaction is offered mainly by multiple-choice tasks, incorporating text or video. The current application is not a simple sign language dictionary as it provides the interactive participation of users. It is a platform for Greek and English sign language active learning.
Underwater image enhancement based on colour correction and fusion

Underwater image processing has always been a very challenging problem. Under the influence of environmental factors, underwater images are prone to some problems, such as colour cast, low visibility, and few edge details. Here, an image enhancement algorithm is proposed to improve image degradation mainly caused by the absorption of light. First, colour compensation and white balance algorithm are used to restore the natural appearance of the image. Then the improved dark channel prior (DCP) is used to improve the visibility and avoid blocking artifacts which appear in traditional DCP. Unsharp masking (USM) is applied to enhance the texture features of the DCP image. Finally, wavelet fusion is used to fuse the DCP image and DCP+USM image. The fusion algorithm not only further improves the visibility and texture features, but also reduces the noise of DCP+USM. Compared with other methods, quantitative analysis results show that the enhanced images have higher visibility, more details and edge information.
KAB: A new k-anonymity approach based on black hole algorithm

K-anonymity is the most widely used approach to privacy preserving microdata which is mainly based on generalization. Although generalization-based k-anonymity approaches can achieve the privacy protection objective, they suffer from information loss. Clustering-based approaches have been successfully adapted for k-anonymization as they enhance the data quality, however, the computational complexity of finding an optimal solution has shown as NP-hard. Nature-inspired optimization algorithms are effective in finding solutions to complex problems. We propose, in this paper, a novel algorithm based on a simple nature-inspired metaheuristic called Black Hole Algorithm (BHA), to address such limitations. Experiments on real data set show that data utility has been improved by our approach compared to k-anonymity, BHA-based k-anonymity and clustering-based k-anonymity approaches.
Enhancement of Speed and Accuracy Trade-Off for Sports Ball Detection in Videos—Finding Fast Moving, Small Objects in Real Time

The detection and localization of the ball in sport videos is crucial to better understand events and actions occurring in those sports. Despite recent advances in the field of object detection, the automatic detection of balls remains a challenging task due to the unsteady nature of balls in images. In this paper, we address the detection of small, fast-moving balls in sport video data and introduce a real-time ball detection approach based on the YOLOv3 object detection model. We apply specific adjustments to the network architecture and training process in order to enhance the detection accuracy and speed: We facilitate an efficient integration of motion information, avoiding a complex modification of the network architecture. Furthermore, we present a customized detection approach that is designed to primarily focus on the detection of small objects. We integrate domain-specific knowledge to adapt image pre-processing and a data augmentation strategy that takes advantage of the special features of balls in images in order to improve the generalization ability of the detection network. We demonstrate that the general trade-off between detection speed and accuracy of the YOLOv3 model can be enhanced in consideration of domain-specific prior knowledge.
Efficient Trainability of Linear Optical Modules in Quantum Optical Neural Networks

The existence of "barren plateau landscapes" for generic discrete-variable quantum neural networks, which obstructs efficient gradient-based optimization of cost functions defined by global measurements, would be surprising in the case of generic linear optical modules in quantum optical neural networks due to the tunability of the intensity of continuous variable states and the relevant unitary group having exponentially smaller dimension. We demonstrate that coherent light in m modes can be generically compiled efficiently if the total intensity scales sublinearly with m, and extend this result to cost functions based on homodyne, heterodyne, or photon detection measurement statistics, and to noisy cost functions in the presence of attenuation. We further demonstrate efficient trainability of m mode linear optical quantum circuits for variational mean field energy estimation of positive quadratic Hamiltonians for input states that do not have energy exponentially vanishing with m.
Virtual Reality as a Reflection Technique for Public Speaking Training

Video recording is one of the most commonly used techniques for reflection, because video allows people to know what they look like to others and how they could improve their performance, but it is problematic because some people easily fall into negative emotions and worry about their performance, resulting in a low benefit. In this study, the possibility of applying a simple VR-based reflection method was explored. This method uses virtual reality (VR) and a head-mounted display (HMD) to allow presenters to watch their own presentations from the audience’s perspective and uses an avatar, which hides personal appearance, which has low relevance to the quality of presentation, to help reduce self-awareness during reflection. An experimental study was carried out, considering four personal characteristics—gender, personal anxiety, personal confidence and self-bias. The goal of this study is to discuss which populations can benefit more from this system and to assess the impact of the avatar and HMD-based VR. According to the results, the individuals with low self-confidence in their public speaking skills could benefit more on self-evaluation from VR reflection with HMD, while individuals with negative self-bias could reduce more anxiety by using an avatar.
User-Interactive Robot Skin With Large-Area Scalability for Safer and Natural Human-Robot Collaboration in Future Telehealthcare

With the fourth revolution of healthcare, i.e., Healthcare 4.0, collaborative robotics is spilling out from traditional manufacturing and will blend into human living or working environments to deliver care services, especially telehealthcare. Because of the frequent and seamless interaction between robots and care recipients, it poses several challenges that require careful consideration: 1) the ability of the human to collaborate with the robots in a natural manner; and 2) the safety of the human collaborating with the robot. In this regard, we have proposed a proximity sensing solution based on the self-capacitive technology to provide an extended sense of touch for collaborative robots, allowing approach and contact measurement to enhance safe and natural human-robot collaboration. The modular design of our solution enables it to scale up to form a large-area sensing system. The sensing solution is proposed to work in two operation modes: the interaction mode and the safety mode. In the interaction mode, utilizing the ability of the sensor to localize the point of action, gesture command is used for robot manipulation. In the safety mode, the sensor enables the robot to actively avoid obstacles.
Critical slowing down in circuit quantum electrodynamics

A saturation of critical slowing down in the bistable regime of a microwave resonator coupled to a transmon is observed.
Application of VR Technology in Jewelry Display

As a special symbolic cultural carrier that reflects people’s material life and spiritual state, jewelry plays an increasingly important role in life. How can we accelerate brand promotion, promote jewelry product sales, and establish a rapid market response mechanism? High efficiency, high quality, and low cost to meet consumers’ increasingly personalized and diversified needs are the problems we currently need to solve. The purpose of this article is to explore the application of VR technology in jewelry displays and provide a brand new idea for jewelry display. In order to realize the virtualization of the jewelry design process, this article uses the Cult 3D VR platform to complete the design and realization of the interactive function of the jewelry virtual model, uses the Photoshop software to design the jewelry virtual display system interface, and finally completes the jewelry virtual display system in the Dreamweaver software integration and release. Through detailed example application, the feasibility of the viewpoint of this subject was effectively verified. In this paper, the two algorithms BRSK and SURF are used in conjunction, and the multiscale expression characteristics of BRISK in space and the rotation-invariant characteristics of SURF are used. Studies have shown that the experimental results of the rotation performance of the method in this paper show that the accuracy is improved by 60%, and the time-consuming is relatively less. Therefore, under the premise of ensuring the rapidity, the method in this paper can guarantee the accuracy and time cost of control matching.