OpenRaiser/Intern-Atlas huggingface dataset:
To pipeline or not to pipeline, that is the question
Venue: arXiv cs.DB 2020
Year: 2020

In designing query processing primitives, a crucial design choice is the method for data transfer between two operators in a query plan. As we were considering this critical design mechanism for an in-memory database system that we are building, we quickly realized that (surprisingly) there isn't a clear definition of this concept. Papers are full or ad hoc use of terms like pipelining and blocking, but as these terms are not crisply defined, it is hard to fully understand the results attributed to these concepts. To address this limitation, we introduce a clear terminology for how to think about data transfer between operators in a query pipeline. We show that there isn't a clear definition of pipelining and blocking, and that there is a full spectrum of techniques based on a simple concept called unit-of-transfer. Next, we develop an analytical model for inter-operator communication, and highlight the key parameters that impact performance (for in-memory database settings). Armed with this model, we then apply it to the system we are designing and highlight the insights we gathered from this exercise. We find that the gap between pipelining and non-pipelining query execution, w.r.t. key factors such as performance and memory footprint is quite narrow, and thus system designers should likely rethink the notion of pipelining vs. blocking for in-memory database systems.
Effect of top-down connections in Hierarchical Sparse Coding
Venue: arXiv cs.CV 2020
Year: 2020

Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images. The simplest solution to solve this computationally hard problem is to decompose it into independent layer-wise subproblems. However, neuroscientific evidence would suggest inter-connecting these subproblems as in the Predictive Coding (PC) theory, which adds top-down connections between consecutive layers. In this study, a new model called 2-Layers Sparse Predictive Coding (2L-SPC) is introduced to assess the impact of this inter-layer feedback connection. In particular, the 2L-SPC is compared with a Hierarchical Lasso (Hi-La) network made out of a sequence of independent Lasso layers. The 2L-SPC and the 2-layers Hi-La networks are trained on 4 different databases and with different sparsity parameters on each layer. First, we show that the overall prediction error generated by 2L-SPC is lower thanks to the feedback mechanism as it transfers prediction error between layers. Second, we demonstrate that the inference stage of the 2L-SPC is faster to converge than for the Hi-La model. Third, we show that the 2L-SPC also accelerates the learning process. Finally, the qualitative analysis of both models dictionaries, supported by their activation probability, show that the 2L-SPC features are more generic and informative.
Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Non-volatile memory, such as resistive RAM (RRAM), is an emerging energy-efficient storage, especially for low-power machine learning models on the edge. It is reported, however, that the bit error rate of RRAMs can be up to 3.3% in the ultra low-power setting, which might be crucial for many use cases. Binary neural networks (BNNs), a resource efficient variant of neural networks (NNs), can tolerate a certain percentage of errors without a loss in accuracy and demand lower resources in computation and storage. The bit error tolerance (BET) in BNNs can be achieved by flipping the weight signs during training, as proposed by Hirtzlin et al., but their method has a significant drawback, especially for fully connected neural networks (FCNN): The FCNNs overfit to the error rate used in training, which leads to low accuracy under lower error rates. In addition, the underlying principles of BET are not investigated. In this work, we improve the training for BET of BNNs and aim to explain this property. We propose straight-through gradient approximation to improve the weight-sign-flip training, by which BNNs adapt less to the bit error rates. To explain the achieved robustness, we define a metric that aims to measure BET without fault injection. We evaluate the metric and find that it correlates with accuracy over error rate for all FCNNs tested. Finally, we explore the influence of a novel regularizer that optimizes with respect to this metric, with the aim of providing a configurable trade-off in accuracy and BET.
Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections
Venue: arXiv cs.RO 2020
Year: 2020

Human input has enabled autonomous systems to improve their capabilities and achieve complex behaviors that are otherwise challenging to generate automatically. Recent work focuses on how robots can use such input - like demonstrations or corrections - to learn intended objectives. These techniques assume that the human's desired objective already exists within the robot's hypothesis space. In reality, this assumption is often inaccurate: there will always be situations where the person might care about aspects of the task that the robot does not know about. Without this knowledge, the robot cannot infer the correct objective. Hence, when the robot's hypothesis space is misspecified, even methods that keep track of uncertainty over the objective fail because they reason about which hypothesis might be correct, and not whether any of the hypotheses are correct. In this paper, we posit that the robot should reason explicitly about how well it can explain human inputs given its hypothesis space and use that situational confidence to inform how it should incorporate human input. We demonstrate our method on a 7 degree-of-freedom robot manipulator in learning from two important types of human input: demonstrations of manipulation tasks, and physical corrections during the robot's task execution.
Four Principles of Explainable AI as Applied to Biometrics and Facial Forensic Algorithms
Venue: arXiv cs.CV 2020
Year: 2020

Traditionally, researchers in automatic face recognition and biometric technologies have focused on developing accurate algorithms. With this technology being integrated into operational systems, engineers and scientists are being asked, do these systems meet societal norms? The origin of this line of inquiry is `trust' of artificial intelligence (AI) systems. In this paper, we concentrate on adapting explainable AI to face recognition and biometrics, and we present four principles of explainable AI to face recognition and biometrics. The principles are illustrated by $\it{four}$ case studies, which show the challenges and issues in developing algorithms that can produce explanations.
Detecting Fake News with Capsule Neural Networks
Venue: arXiv cs.CL 2020
Year: 2020

Fake news is dramatically increased in social media in recent years. This has prompted the need for effective fake news detection algorithms. Capsule neural networks have been successful in computer vision and are receiving attention for use in Natural Language Processing (NLP). This paper aims to use capsule neural networks in the fake news detection task. We use different embedding models for news items of different lengths. Static word embedding is used for short news items, whereas non-static word embeddings that allow incremental up-training and updating in the training phase are used for medium length or large news statements. Moreover, we apply different levels of n-grams for feature extraction. Our proposed architectures are evaluated on two recent well-known datasets in the field, namely ISOT and LIAR. The results show encouraging performance, outperforming the state-of-the-art methods by 7.8% on ISOT and 3.1% on the validation set, and 1% on the test set of the LIAR dataset.
Fake News Detection by means of Uncertainty Weighted Causal Graphs
Venue: arXiv cs.AI 2020
Year: 2020

Society is experimenting changes in information consumption, as new information channels such as social networks let people share news that do not necessarily be trust worthy. Sometimes, these sources of information produce fake news deliberately with doubtful purposes and the consumers of that information share it to other users thinking that the information is accurate. This transmission of information represents an issue in our society, as can influence negatively the opinion of people about certain figures, groups or ideas. Hence, it is desirable to design a system that is able to detect and classify information as fake and categorize a source of information as trust worthy or not. Current systems experiment difficulties performing this task, as it is complicated to design an automatic procedure that can classify this information independent on the context. In this work, we propose a mechanism to detect fake news through a classifier based on weighted causal graphs. These graphs are specific hybrid models that are built through causal relations retrieved from texts and consider the uncertainty of causal relations. We take advantage of this representation to use the probability distributions of this graph and built a fake news classifier based on the entropy and KL divergence of learned and new information. We believe that the problem of fake news is accurately tackled by this model due to its hybrid nature between a symbolic and quantitative methodology. We describe the methodology of this classifier and add empirical evidence of the usefulness of our proposed approach in the form of synthetic experiments and a real experiment involving lung cancer.
Concept Embedding for Information Retrieval
Venue: arXiv cs.IR 2020
Year: 2020

Concepts are used to solve the term-mismatch problem. However, we need an effective similarity measure between concepts. Word embedding presents a promising solution. We present in this study three approaches to build concepts vectors based on words vectors. We use a vector-based measure to estimate inter-concepts similarity. Our experiments show promising results. Furthermore, words and concepts become comparable. This could be used to improve conceptual indexing process.
Multi-label Relation Modeling in Facial Action Units Detection
Venue: arXiv cs.CV 2020
Year: 2020

This paper describes an approach to the facial action units detections. The involved action units (AU) include AU1 (Inner Brow Raiser), AU2 (Outer Brow Raiser), AU4 (Brow Lowerer), AU6 (Cheek Raise), AU12 (Lip Corner Puller), AU15 (Lip Corner Depressor), AU20 (Lip Stretcher), and AU25 (Lip Part). Our work relies on the dataset released by the FG-2020 Competition: Affective Behavior Analysis In-the-Wild (ABAW). The proposed method consists of the data preprocessing, the feature extraction and the AU classification. The data preprocessing includes the detection of face texture and landmarks. The texture static and landmark dynamic features are extracted through neural networks and then fused as the feature latent representation. Finally, the fused feature is taken as the initial hidden state of a recurrent neural network with a trainable lookup AU table. The output of the RNN is the results of AU classification. The detected accuracy is evaluated with 0.5$\times$accuracy + 0.5$\times$F1. Our method achieve 0.56 with the validation data that is specified by the organization committee.
A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task
Venue: arXiv cs.HC 2020
Year: 2020

Recently, smart home systems based on brain-computer interface (BCI) has attracted a wide range of interests in both industry and academia. However, the current BCI system has several shortcomings as it produces a comparatively lower accuracy for real-time implementations as well as the intuitive paradigm for the users cannot be well established here. Therefore, in this study, we proposed a highly intuitive BCI paradigm that combines event-related potential (ERP) with the speech-imagery task for the individual target objects. The decoding accuracy of the proposed paradigm was 88.1% (plus or minus 5.90) which is a much significant higher performance than a conventional ERP system. Furthermore, the amplitude of N700 components was significantly enhanced over frontal regions which are priory evoked by the speech-imagery task. Our results could be utilized to develop a smart home system so that it could be more user-friendly and convenient by means of delivering user's intentions both, intuitively and accurately.
Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining
Venue: arXiv cs.HC 2020
Year: 2020

Brain-computer interface (BCI) decodes brain signals to understand user intention and status. Because of its simple and safe data acquisition process, electroencephalogram (EEG) is commonly used in non-invasive BCI. One of EEG paradigms, motor imagery (MI) is commonly used for recovery or rehabilitation of motor functions due to its signal origin. However, the EEG signals are an oscillatory and non-stationary signal that makes it difficult to collect and classify MI accurately. In this study, we proposed a band-power feature refining convolutional neural network (BFR-CNN) which is composed of two convolution blocks to achieve high classification accuracy. We collected EEG signals to create MI dataset contained the movement imagination of a single-arm. The proposed model outperforms conventional approaches in 4-class MI tasks classification. Hence, we demonstrate that the decoding of user intention is possible by using only EEG signals with robust performance using BFR-CNN.
Physical Zero-Knowledge Proof for Numberlink Puzzle and $k$ Vertex-Disjoint Paths Problem
Venue: arXiv cs.CR 2020
Year: 2020

Numberlink is a logic puzzle with an objective to connect all pairs of cells with the same number by non-crossing paths in a rectangular grid. In this paper, we propose a physical protocol of zero-knowledge proof for Numberlink using a deck of cards, which allows a prover to convince a verifier that he/she knows a solution without revealing it. In particular, the protocol shows how to physically count the number of elements in a list that are equal to a given secret value without revealing that value, the positions of elements in the list that are equal to it, or the value of any other element in the list. Finally, we show that our protocol can be modified to verify a solution of the well-known $k$ vertex-disjoint paths problem, both the undirected and directed settings.
Selfish Robustness and Equilibria in Multi-Player Bandits
Venue: arXiv cs.LG 2020
Year: 2020

Motivated by cognitive radios, stochastic multi-player multi-armed bandits gained a lot of interest recently. In this class of problems, several players simultaneously pull arms and encounter a collision - with 0 reward - if some of them pull the same arm at the same time. While the cooperative case where players maximize the collective reward (obediently following some fixed protocol) has been mostly considered, robustness to malicious players is a crucial and challenging concern. Existing approaches consider only the case of adversarial jammers whose objective is to blindly minimize the collective reward. We shall consider instead the more natural class of selfish players whose incentives are to maximize their individual rewards, potentially at the expense of the social welfare. We provide the first algorithm robust to selfish players (a.k.a. Nash equilibrium) with a logarithmic regret, when the arm performance is observed. When collisions are also observed, Grim Trigger type of strategies enable some implicit communication-based algorithms and we construct robust algorithms in two different settings: the homogeneous (with a regret comparable to the centralized optimal one) and heterogeneous cases (for an adapted and relevant notion of regret). We also provide impossibility results when only the reward is observed or when arm means vary arbitrarily among players.
On Stochastic Automata over Monoids
Venue: arXiv cs.FL 2020
Year: 2020

Stochastic automata over monoids as input sets are studied. The well-definedness of these automata requires an extension postulate that replaces the inherent universal property of free monoids. As a generalization of Turakainen's result, it will be shown that the generalized automata over monoids have the same acceptance power as their stochastic counterparts. The key to homomorphisms is a commuting property between the monoid homomorphism of input states and the monoid homomorphism of transition matrices. Closure properties of the languages accepted by stochastic automata over monoids are investigated. matrices. Closure properties of the languages accepted by stochastic automata over monoids are investigated.
On the generation of periodic discrete structures with identical two-point correlation
Venue: arXiv cs.CE 2020
Year: 2020

Strategies for the generation of periodic discrete structures with identical two-point correlation are developed. Starting from a pair of root structures, which are not related by translation, phase inversion or axis reflections, child structures of arbitrary resolution (i.e., pixel or voxel numbers) and number of phases (i.e., material phases/species) can be generated by means of trivial embedding based phase extension, application of kernels and/or phase coalescence, such that the generated structures inherit the two-point-correlation equivalence. Proofs of the inheritance property are provided by means of the Discrete Fourier Transform theory. A Python 3 implementation of the results is offered by the authors through the Github repository https://github.com/DataAnalyticsEngineering/EQ2PC in order to make the provided results reproducible and useful for all interested readers. Examples for the generation of structures are demonstrated, together with applications in the homogenization theory of periodic media.
HiveMind: A Scalable and Serverless Coordination Control Platform for UAV Swarms
Venue: arXiv cs.DC 2020
Year: 2020

Swarms of autonomous devices are increasing in ubiquity and size. There are two main trains of thought for controlling devices in such swarms; centralized and distributed control. Centralized platforms achieve higher output quality but result in high network traffic and limited scalability, while decentralized systems are more scalable, but less sophisticated. In this work we present HiveMind, a centralized coordination control platform for IoT swarms that is both scalable and performant. HiveMind leverages a centralized cluster for all resource-intensive computation, deferring lightweight and time-critical operations, such as obstacle avoidance to the edge devices to reduce network traffic. HiveMind employs an event-driven serverless framework to run tasks on the cluster, guarantees fault tolerance both in the edge devices and serverless functions, and handles straggler tasks and underperforming devices. We evaluate HiveMind on a swarm of 16 programmable drones on two scenarios; searching for given items, and counting unique people in an area. We show that HiveMind achieves better performance and battery efficiency compared to fully centralized and fully decentralized platforms, while also handling load imbalances and failures gracefully, and allowing edge devices to leverage the cluster to collectively improve their output quality.
Multi-User Provisioning in Millimeter-Wave Urban Cellular Networks
Venue: arXiv cs.NI 2020
Year: 2020

In this paper we present the first comprehensive study of the multi-user capacity of millimeter-wave (mm-wave) urban cellular networks, using site-specific ray-tracing propagation data and realistic antenna array patterns. We compare the performance of TDMA and SDMA (time and spatial division multiple access, respectively) for diverse network scenarios and antenna configurations. We propose a greedy heuristic algorithm to solve the network-wide directional link allocation problem, thereby estimating the achievable capacity and coverage of multi-user mm-wave networks. Our results show that inter-cell interference is negligible, so that TDMA performance is strictly limited by air-time sharing. By contrast, the major limiting factor for SDMA is intra-cell interference, emphasizing the impact of real antenna array sidelobes. Nonetheless, SDMA significantly outperforms TDMA in terms of average UE throughput, by up to 2 Gbps using 8x8 arrays. As an important design insight, our results show that larger base station antenna arrays limit intra-cell interference while compensating for small UE arrays, reducing costs and beamforming requirements in practical SDMA networks. Our analysis also shows that the limited number of antenna sub-arrays in a practical hybrid beamforming architecture may force SDMA to drop UEs with good coverage, highlighting a tradeoff between base station densification and antenna resources.
Complexity limitations on one-turn quantum refereed games
Venue: arXiv cs.CC 2020
Year: 2020

This paper studies complexity theoretic aspects of quantum refereed games, which are abstract games between two competing players that send quantum states to a referee, who performs an efficiently implementable joint measurement on the two states to determine which of the player wins. The complexity class $\mathrm{QRG}(1)$ contains those decision problems for which one of the players can always win with high probability on yes-instances and the other player can always win with high probability on no-instances, regardless of the opposing player's strategy. This class trivially contains $\mathrm{QMA} \cup \text{co-}\mathrm{QMA}$ and is known to be contained in $\mathrm{PSPACE}$. We prove stronger containments on two restricted variants of this class. Specifically, if one of the players is limited to sending a classical (probabilistic) state rather than a quantum state, the resulting complexity class $\mathrm{CQRG}(1)$ is contained in $\exists\cdot\mathrm{PP}$ (the nondeterministic polynomial-time operator applied to $\mathrm{PP}$); while if both players send quantum states but the referee is forced to measure one of the states first, and incorporates the classical outcome of this measurement into a measurement of the second state, the resulting class $\mathrm{MQRG}(1)$ is contained in $\mathrm{P}\cdot\mathrm{PP}$ (the unbounded-error probabilistic polynomial-time operator applied to $\mathrm{PP}$).
Academic viewpoints and concerns on CSCW education and training in Latin America
Venue: arXiv cs.HC 2020
Year: 2020

Computer-Supported Cooperative Work, or simply CSCW, is the research area that studies the design and use of socio-technical technology for supporting group work. CSCW has a long tradition in interdisciplinary work exploring technical, social, and theoretical challenges for the design of technologies to support cooperative and collaborative work and life activities. However, most of the research tradition, methods, and theories in the field follow a strong trend grounded in social and cultural aspects from North America and Western Europe. Therefore, it is inevitable that some of the underlying, and established, knowledge in the field will not be directly transferrable or applicable to other populations. This paper presents the results of an interview study conducted with Latin American faculty on the feasability, viability, and prospect of a curriculum proposal for CSCW Education in Latin America: To this end, we conducted nine interviews with faculty currently based in six countries of the region, aiming to understand how a CSCW course targeted to undergraduate and/or graduate students in Latin America might be deployed. Our findings suggest that there are specific traits that need to be addressed in such a course, such as: tailoring foundational CSCW concepts to the diversity of local cultures, motivating the involvement of students by tackling relevant problems to their local communities, and revitalizing CSCW research and practice in the continent.