Reviewerly/exHarmony huggingface dataset:
Real-time Active Vision for a Humanoid Soccer Robot using Deep Reinforcement Learning

In this paper, we present an active vision method using a deep reinforcement learning approach for a humanoid soccer-playing robot.The proposed method adaptively optimises the viewpoint of the robot to acquire the most useful landmarks for self-localisation while keeping the ball into its viewpoint.Active vision is critical for humanoid decision-maker robots with a limited field of view.To deal with active vision problem, several probabilistic entropy-based approaches have previously been proposed which are highly dependent on the accuracy of the self-localisation model.However, in this research, we formulate the problem as an episodic reinforcement learning problem and employ a Deep Q-learning method to solve it.The proposed network only requires the raw images of the camera to move the robot's head toward the best viewpoint.The model shows a very competitive rate of 80% success rate in achieving the best viewpoint.We implemented the proposed method on a humanoid robot simulated in Webots simulator.Our evaluations and experimental show that the proposed method outperforms the entropy-based methods in the RoboCup context, in cases with high self-localisation errors.
Joint shape and centroid position tracking of a cluster of space debris by filtering on Lie groups

Space surveillance aims at detecting and tracking pieces of debris that are orbiting around the Earth. When the latter are sufficiently close to each other to form a compact cluster, they can be considered as a single extended object. State-of-the-art random-matrix methods estimate the kinematics of the object shape and centroid by assuming that it is ellipsoidal and that the sensor observations are randomly distributed within its volume. However, in accordance with the laws of orbital motion, space debris scatters taking a specific curvature. To intrinsically capture the resulting cluster shape, we propose a novel Lie-group-based parameterization of both the cluster and the sensor measurements. Then, we derive an iterated extended Kalman filter on Lie group to sequentially estimate both the centroid trajectory and the evolution of the extent parameters. Finally, numerical experiments validate the interest of the proposed method compared to a generic Gaussian-process-based extended-object tracking algorithm.
Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation

This paper proposes a formal approach to online learning and planning for agents operating in a priori unknown, time-varying environments. The proposed method computes the maximally likely model of the environment, given the observations about the environment made by an agent earlier in the system run and assuming knowledge of a bound on the maximal rate of change of system dynamics. Such an approach generalizes the estimation method commonly used in learning algorithms for unknown Markov decision processes with time-invariant transition probabilities, but is also able to quickly and correctly identify the system dynamics following a change. Based on the proposed method, we generalize the exploration bonuses used in learning for time-invariant Markov decision processes by introducing a notion of uncertainty in a learned time-varying model, and develop a control policy for time-varying Markov decision processes based on the exploitation and exploration trade-off. We demonstrate the proposed methods on four numerical examples: a patrolling task with a change in system dynamics, a two-state MDP with periodically changing outcomes of actions, a wind flow estimation task, and a multi-armed bandit problem with periodically changing probabilities of different rewards.
A Framework for Privacy-Preserving Genomic Data Analysis Using Trusted Execution Environments

As the quantity and quality of genomic records continue to grow, geneticists at disparate institutions are increasingly motivated to integrate and analyze genomic data collections. While these endeavors are often hampered by various privacy concerns, recently developed techniques can address privacy concerns via trusted execution environments (TEEs) (e.g., hardware-enabled secure enclaves). TEEs provide confidentiality for sensitive data and means to attest to programs' correctness and their corresponding inputs. However, due to the profound consequences of private genomic data leakage for individuals, integrating TEEs for genomic analytics requires extra assurances to ensure that the program's execution does not leak participants' private information in a direct or indirect form. One major drawback of state-of-the-art TEEs is related to their shortcomings in hiding memory access patterns due to performance ramifications. As a result, the TEE's computation must be data oblivious, i.e., independent of how it accesses the sensitive private data through its course. In this paper, we present our data oblivious and privacy-preserving genomic data analysis framework using two popular genomic tasks. The first application involves securely performing a multi-institution test for the most significant single nucleotide polymorphism (SNP) in an association study. The second application focuses on the growing trend in utilizing Deep Learning (DL) based inference for genomics data. In this application, we assume that genomic data and the model are private. In both of these applications, we show that our framework can enable efficient processing and analysis of encrypted genomic data without disclosing sensitive memory access patterns. We also discuss the future considerations needed to use TEE based privacy-preserving genomic data analysis in practice.
On the little secondary Bruhat order

Let $R$ and $S$ be two sequences of positive integers in nonincreasing order having the same sum. We denote by ${\cal A}(R,S)$ the class of all $(0,1)$-matrices having row sum vector $R$ and column sum vector $S$. Brualdi and Deaett (More on the Bruhat order for $(0,1)$-matrices, Linear Algebra Appl., 421:219--232, 2007) suggested the study of the secondary Bruhat order on ${\cal A}(R,S)$ but with some constraints. In this paper, we study the cover relation and the minimal elements for this partial order relation, which we call the little secondary Bruhat order, on certain classes ${\cal A}(R,S)$. Moreover, we show that this order is different from the Bruhat order and the secondary Bruhat order. We also study a variant of this order on certain classes of symmetric matrices of ${\cal A}(R,S)$.
Explaining Neural Networks Using Attentive Knowledge Distillation

Explaining the prediction of deep neural networks makes the networks more understandable and trusted, leading to their use in various mission critical tasks. Recent progress in the learning capability of networks has primarily been due to the enormous number of model parameters, so that it is usually hard to interpret their operations, as opposed to classical white-box models. For this purpose, generating saliency maps is a popular approach to identify the important input features used for the model prediction. Existing explanation methods typically only use the output of the last convolution layer of the model to generate a saliency map, lacking the information included in intermediate layers. Thus, the corresponding explanations are coarse and result in limited accuracy. Although the accuracy can be improved by iteratively developing a saliency map, this is too time-consuming and is thus impractical. To address these problems, we proposed a novel approach to explain the model prediction by developing an attentive surrogate network using the knowledge distillation. The surrogate network aims to generate a fine-grained saliency map corresponding to the model prediction using meaningful regional information presented over all network layers. Experiments demonstrated that the saliency maps are the result of spatially attentive features learned from the distillation. Thus, they are useful for fine-grained classification tasks. Moreover, the proposed method runs at the rate of 24.3 frames per second, which is much faster than the existing methods by orders of magnitude.
Entity Alignment across Power Knowledge Graphs

Power knowledge graph (PKG) plays an essential role in the electricity power field to integrate multi-source heterogeneous power data in a structural form. Recently, PKG is widely used for management and analysis of electric power big data. However, a single PKG can only provide limited power knowledge, which restricted its functions in downstream applications. In this paper, we merge multiple power knowledge graphs by aligning the entities which belong to different sources or languages respectively. To align entities, a Typed Graph Attention Network method is proposed to learn neighbor-level embedding through the weight of relation types in power knowledge graph. By incorporating structural information and relation type information simultaneously, the proposed method can accurately capture entity features in different Power knowledge graph.
A Decentralized Multiple MAV Collision Avoidance Trajectory Planning Method

In this paper, a decentralized algorithm for solving combinatorial optimization problems is used to solve the collision avoidance trajectory planning problem of multiple micro air vehicles (MAVs). According to the algorithm, the MAV avoids generating mutual interference trajectories through negotiation with its neighbors. The MAV and its neighbors transfer trajectory schemes to each other, extract track points from them, and construct virtual obstacles at these points. RRT* algorithm is used to generate trajectories to avoid these virtual obstacles to achieve the purpose of collision avoidance. In order to improve the efficiency of conflict resolution, we proposed a method based on the maximum clique algorithm to solidify the trajectory schemes of some MAVs in the system and only modify the trajectory schemes of other MAVs. We tested the effectiveness of the algorithm in a simulation environment and successfully avoided collisions in all tests.
AKSEL: Fast Byzantine SGD

Modern machine learning architectures distinguish servers and workers. Typically, a d-dimensional model is hosted by a server and trained by n workers, using a distributed stochastic gradient descent (SGD) optimization scheme. At each SGD step, the goal is to estimate the gradient of a cost function. The simplest way to do this is to average the gradients estimated by the workers. However, averaging is not resilient to even one single Byzantine failure of a worker. Many alternative gradient aggregation rules (GARs) have recently been proposed to tolerate a maximum number f of Byzantine workers. These GARs differ according to (1) the complexity of their computation time, (2) the maximal number of Byzantine workers despite which convergence can still be ensured (breakdown point), and (3) their accuracy, which can be captured by (3.1) their angular error, namely the angle with the true gradient, as well as (3.2) their ability to aggregate full gradients. In particular, many are not full gradients for they operate on each dimension separately, which results in a coordinate-wise blended gradient, leading to low accuracy in practical situations where the number (s) of workers that are actually Byzantine in an execution is small (s < < f). We propose Aksel, a new scalable median-based GAR with optimal time complexity (𝒪(nd)), optimal breakdown point (n > 2f) and the lowest upper bound on the expected angular error (𝒪(√d)) among full gradient approaches. We also study the actual angular error of Aksel when the gradient distribution is normal and show that it only grows in 𝒪(√dlog{n}), which is the first logarithmic upper bound ever proven on the number of workers n assuming an optimal breakdown point. We also report on an empirical evaluation of Aksel on various classification tasks, which we compare to alternative GARs against state-of-the-art attacks. Aksel is the only GAR reaching top accuracy when there is actually none or few Byzantine workers while maintaining a good defense even under the extreme case (s = f). For simplicity of presentation, we consider a scheme with a single server. However, as we explain in the paper, Aksel can also easily be adapted to multi-server architectures that tolerate the Byzantine behavior of a fraction of the servers.
Intelligent Decision Support System of Emergency Language Based on Fog Computing

In recent years, various emergencies have frequently occurred worldwide, which has forced relevant service departments to pay more attention to decision-making and emergency management. Since emergency events are characterized by complex environments, unstable events, and time constraints, events usually involve multiple factors and promptly correct errors in the decision-making process. In fact, in many cases, emergency decision-making needs to select an optimal one from multiple alternatives for execution. The fog algorithm decision-making method can solve the problem of optimal solution selection, and it has been widely used in many fields. This article evaluates the emergencies that have occurred in the past 10 years. The evaluation indicators include direct economic loss, indirect reputation loss, ecological environment indicators, and healthy living indicators. The first two are cost-based indicators. The index value of direct economic loss and indirect reputation loss is as small as possible, while the index value of ecological environment index and healthy living index is the larger the better. Among the many selected emergencies, only the index evaluation scores of fires are reliable ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.01</mn> </math> ), and the evaluation scores of other emergencies belonging to natural disasters are a bit wrong ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> <mo>&gt;</mo> <mn>0.05</mn> </math> ). The reason for this may be that the direct economic losses caused by natural disasters are not well counted, and the families involved and the environment are too wide. Therefore, the emergency language intelligent decision support system based on fog computing has a good development prospect.
High Efficient Bandwidth Utilization Hardware Design and Implement for AI Deep Learning Accelerator

This paper proposes a neural network accelerator for Tiny-Yolo V2. The data format of input feature maps, output feature maps, and weight kernels are converted to uint8 through a quantization strategy to reduce the data size and make the hardware utilization more efficient. Moreover, we propose an input feature maps placement method to reduce bandwidth utilization and improve PE utilization. To verify the hardware implementation, the Xilinx ZCU102 platform is used to verify the hardware architecture. Synthesis results show that the proposed architecture implements in 90nm can achieve 14.4GOPS@100Mhz with area efficiency by 99 GOPS/M-gates.
Recognition of Abnormal Behavior of Crowd based on Spatial Location Feature

Recognition of anomalous behavior for crowd gathering and dispersal faces challenges such as target scale change, target short-term disappearance, and target occlusion, which can result in low recognition accuracy. We propose an algorithm based on features of spatial location relationships for the recognition of crowd behaviors to solve the problems mentioned above. We first modified the network based on YOLOV3 by removing the 32x downsampling layer and replacing it with a 4x downsampling layer to further improve the detection of small scale targets, and we re-select the anchor boxes via K-means clustering algorithm to make the network more robust in training. We then perform data association by using the Kalman filter and Hungarian algorithm for target matching between frames to continuously track pedestrians and capture the motion trajectory. The trajectories of pedestrians are transformed to calculate the features of spatial position relationships, and ultimately used as a basis for determining whether abnormal behavior has occurred in the crowd. We validate our approach on PETS2009 dataset and it achieves an 89.0% accuracy with a processing speed of 29.7FPS It is of great significance to maintain the safety of people's lives and reduce the casualties caused by emergencies in public places.
Making a Case for 3D Convolutions for Object Segmentation in Videos.

The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of information. On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance. In this work, we show that 3D CNNs can be effectively applied to dense video prediction tasks such as salient object segmentation. We propose a simple yet effective encoder-decoder network architecture consisting entirely of 3D convolutions that can be trained end-to-end using a standard cross-entropy loss. To this end, we leverage an efficient 3D encoder, and propose a 3D decoder architecture, that comprises novel 3D Global Convolution layers and 3D Refinement modules. Our approach outperforms existing state-of-the-arts by a large margin on the DAVIS'16 Unsupervised, FBMS and ViSal dataset benchmarks in addition to being faster, thus showing that our architecture can efficiently learn expressive spatio-temporal features and produce high quality video segmentation masks. Our code and models will be made publicly available.
The yoneda reduction of polymorphic types

In this paper we explore a family of type isomorphisms in System F whose validity corresponds, semantically, to some form of the Yoneda isomorphism from category theory. These isomorphisms hold under theories of equivalence stronger than βη-equivalence, like those induced by parametricity and dinaturality. We show that the Yoneda type isomorphisms yield a rewriting over types, that we call Yoneda reduction, which can be used to eliminate quantifiers from a polymorphic type, replacing them with a combination of monomorphic type constructors. We establish some sufficient conditions under which quantifiers can be fully eliminated from a polymorphic type, and we show some application of these conditions to count the inhabitants of a type and to compute program equivalence in some fragments of System F.
Investigating alignment interpretability for low-resource NMT

The attention mechanism in Neural Machine Translation (NMT) models added flexibility to translation systems, and the possibility to visualize soft-alignments between source and target representations. While there is much debate about the relationship between attention and the yielded output for neural models (Jain and Wallace 2019; Serrano and Smith 2019; Wiegreffe and Pinter 2019; Vashishth et al. 2019), in this paper we propose a different assessment, investigating soft-alignment interpretability in low-resource scenarios. We experimented with different architectures (RNN (Bahdanau et al. 2015), 2D-CNN (Elbayad et al. 2018), and Transformer (Vaswani et al. 2017)), comparing them with regards to their ability to produce directly exploitable alignments. For evaluating exploitability, we replicated the Unsupervised Word Segmentation (UWS) task from Godard et al. (2018). There, source words are translated into unsegmented phone sequences. Posterior to training, the resulting soft-alignments are used for producing segmentation over the target side. Our results showed that a RNN-based NMT model produced the most exploitable alignments in this scenario. We then investigated methods for increasing its UWS scores by comparing the following methodologies: monolingual pre-training, input representation augmentation (hybrid model), and explicit word length optimization during training. We reached the best results by using the hybrid model, which uses an intermediate monolingual-rooted segmentation from a non-parametric Bayesian model (Goldwater 2007) to enrich the input representation before training.
Detachable Second-Order Pooling: Toward High-Performance First-Order Networks

Second-order pooling has proved to be more effective than its first-order counterpart in visual classification tasks. However, second-order pooling suffers from the high demand for a computational resource, limiting its use in practical applications. In this work, we present a novel architecture, namely a detachable second-order pooling network, to leverage the advantage of second-order pooling by first-order networks while keeping the model complexity unchanged during inference. Specifically, we introduce second-order pooling at the end of a few auxiliary branches and plug them into different stages of a convolutional neural network. During the training stage, the auxiliary second-order pooling networks assist the backbone first-order network to learn more discriminative feature representations. When training is completed, all auxiliary branches can be removed, and only the backbone first-order network is used for inference. Experiments conducted on CIFAR-10, CIFAR-100, and ImageNet data sets clearly demonstrated the leading performance of our network, which achieves even higher accuracy than second-order networks but keeps the low inference complexity of first-order networks.
Face Recognition System for Indoor Applications Based on Video with the LNMF and NMFsc Methods

Face detection and face recognition are among the most important research topics in computer vision, as many applications use faces as objects of biometric technology. One of the main issues in applying face recognition is recording the attendance of active participants in a room. The challenge is that recognition through video with multiple object conditions in one frame may be difficult to perform. The Principal Component Analysis method is commonly used in face detection. Principal Component Analysis still has shortcomings: the accuracy decreases when it is applied to large datasets and performs slowly in real-time applications. Therefore, this study simulates a face recognition system installed in a room based on video recordings using Non-negative Matrix Factorization suppressed carrier and Local Non-negative Matrix Factorization methods. Data acquisition is obtained by capturing video in classrooms with a resolution of 640 x 480 pixels in RGB, .avi format, video frame rate of 30 fps, and video duration of ±10 seconds. The proposed system can perform face recognition in which the average accuracy value of the Local Non-negative Matrix Factorization method is 71.61% with a computation time of 152,634 seconds. By contrast, the average accuracy value of the Non-negative Matrix Factorization suppressed carrier method is 86.76% with a computation time of 467,785 seconds. The proposed system is expected to be used for simultaneously finding and identifying faces in real time.