OpenRaiser/Intern-Atlas huggingface dataset:
VisMaker: a Question-Oriented Visualization Recommender System for Data Exploration
Venue: arXiv cs.HC 2020
Year: 2020

The increasingly rapid growth of data production and the consequent need to explore data to obtain answers to the most varied questions have promoted the development of tools to facilitate the manipulation and construction of data visualizations. However, building useful data visualizations is not a trivial task: it may involve a large number of subtle decisions that require experience from their designer. In this paper, we present VisMaker, a visualization recommender tool that uses a set of rules to present visualization recommendations organized and described through questions, in order to facilitate the understanding of the recommendations and assisting the visual exploration process. We carried out two studies comparing our tool with Voyager 2 and analyzed some aspects of the use of tools. We collected feedback from participants to identify the advantages and disadvantages of our recommendation approach. As a result, we gathered comments to help improve the development of tools in this domain.
Deploying large fixed file datasets with SquashFS and Singularity
Venue: arXiv cs.DC 2020
Year: 2020

Shared high-performance computing (HPC) platforms, such as those provided by XSEDE and Compute Canada, enable researchers to carry out large-scale computational experiments at a fraction of the cost of the cloud. Most systems require the use of distributed filesystems (e.g. Lustre) for providing a highly multi-user, large capacity storage environment. These suffer performance penalties as the number of files increases due to network contention and metadata performance. We demonstrate how a combination of two technologies, Singularity and SquashFS, can help developers, integrators, architects, and scientists deploy large datasets (O(10M) files) on these shared systems with minimal performance limitations. The proposed integration enables more efficient access and indexing than normal file-based dataset installations, while providing transparent file access to users and processes. Furthermore, the approach does not require administrative privileges on the target system. While the examples studied here have been taken from the field of neuroimaging, the technologies adopted are not specific to that field. Currently, this solution is limited to read-only datasets. We propose the adoption of this technology for the consumption and dissemination of community datasets across shared computing resources.
The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
Venue: arXiv cs.AI 2020
Year: 2020

Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.
Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm
Venue: arXiv cs.LG 2020
Year: 2020

Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. Due to neglecting of requirement and significance of data preprocessing and disregarding the inadequacy of using a single predicting model, many traditional models have poor performance in wind speed prediction. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh, and Deylaman) to increase the accuracy of the subsequent hybrid model. The capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP model without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the standalone MLP model. The hybrid model had acceptable performances with lower amounts of the RMSE, SI and RE parameters and higher values of NSE, WI, and KGE parameters. It was concluded that the WOA optimization algorithm can improve the prediction accuracy of MLP model and may be recommended for accurate wind speed prediction.
Convolutional Neural Networks for Sentiment Analysis in Persian Social Media
Venue: arXiv cs.SI 2020
Year: 2020

With the social media engagement on the rise, the resulting data can be used as a rich resource for analyzing and understanding different phenomena around us. A sentiment analysis system employs these data to find the attitude of social media users towards certain entities in a given document. In this paper we propose a sentiment analysis method for Persian text using Convolutional Neural Network (CNN), a feedforward Artificial Neural Network, that categorize sentences into two and five classes (considering their intensity) by applying a layer of convolution over input data through different filters. We evaluated the method on three different datasets of Persian social media texts using Area under Curve metric. The final results show the advantage of using CNN over earlier attempts at developing traditional machine learning methods for Persian texts sentiment classification especially for short texts.
Cache Allocations for Consecutive Requests of Categorized Contents: Service Provider's Perspective
Venue: arXiv cs.NI 2020
Year: 2020

In wireless caching networks, a user generally has a concrete purpose of consuming contents in a certain preferred category, and requests multiple contents in sequence. While most existing research on wireless caching and delivery has focused only on one-shot requests, the popularity distribution of contents requested consecutively is definitely different from the one-shot request and has been not considered. Also, especially from the perspective of the service provider, it is advantageous for users to consume as many contents as possible. Thus, this paper proposes two cache allocation policies for categorized contents and consecutive user demands, which maximize 1) the cache hit rate and 2) the number of consecutive content consumption, respectively. Numerical results show how categorized contents and consecutive content requests have impacts on the cache allocation.
Open5G: A Software-Defined Networking Protocol for 5G Multi-RAT Wireless Networks
Venue: arXiv cs.NI 2020
Year: 2020

Mobile Networks today comprise of multiple Radio Access Technologies (RATs), e.g., 4G LTE, Wireless Local Area Network (WLAN), and the upcoming 5G-New Radio (5G-NR). The access networks of these RATs are controlled by RAT-specific entities, e.g., the resource management function located inside an individual LTE eNB is used for the eNB control, or access controllers are used for controlling WLAN Access Points. Even in the 3GPP's 5G architecture, which has a common Core supporting multiple RATs, radio access related decisions are taken independently within individual RATs. Due to the fragmented nature of control-plane in multi-RAT Radio Access Network (RAN), a unified global view of network resources is unavailable, hindering optimized allocation of resources. It also brings complexity to the features involving multiple RATs, e.g., dual connectivity. To address these issues, we introduced an SDN-based Multi-RAT RAN architecture (SMRAN) in our earlier work [arXiv:1812.11825], where the RAN control-plane is segregated from the data-plane. As part of the SMRAN architecture, we defined a logically centralized multi-RAT RAN Controller and individual RAT-specific data-plane functions. In the current work, we define a protocol, called Open5G, to be used for control and management of the SMRAN data-plane. Open5G is based on OpenFlow (OF) and OF-Config, which are commonly used protocols in the SDN-based wired networks and data centers.
Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined
Venue: arXiv cs.CV 2020
Year: 2020

Deep convolutional neural networks (DCNNs) trained for face identification develop representations that generalize over variable images, while retaining subject (e.g., gender) and image (e.g., viewpoint) information. Identity, gender, and viewpoint codes were studied at the "neural unit" and ensemble levels of a face-identification network. At the unit level, identification, gender classification, and viewpoint estimation were measured by deleting units to create variably-sized, randomly-sampled subspaces at the top network layer. Identification of 3,531 identities remained high (area under the ROC approximately 1.0) as dimensionality decreased from 512 units to 16 (0.95), 4 (0.80), and 2 (0.72) units. Individual identities separated statistically on every top-layer unit. Cross-unit responses were minimally correlated, indicating that units code non-redundant identity cues. This "distributed" code requires only a sparse, random sample of units to identify faces accurately. Gender classification declined gradually and viewpoint estimation fell steeply as dimensionality decreased. Individual units were weakly predictive of gender and viewpoint, but ensembles proved effective predictors. Therefore, distributed and sparse codes co-exist in the network units to represent different face attributes. At the ensemble level, principal component analysis of face representations showed that identity, gender, and viewpoint information separated into high-dimensional subspaces, ordered by explained variance. Identity, gender, and viewpoint information contributed to all individual unit responses, undercutting a neural tuning analogy for face attributes. Interpretation of neural-like codes from DCNNs, and by analogy, high-level visual codes, cannot be inferred from single unit responses. Instead, "meaning" is encoded by directions in the high-dimensional space.
Algorithmic Recourse: from Counterfactual Explanations to Interventions
Venue: arXiv cs.LG 2020
Year: 2020

As machine learning is increasingly used to inform consequential decision-making (e.g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at its decision, and also suggest actions to achieve a favorable decision. Counterfactual explanations -- "how the world would have (had) to be different for a desirable outcome to occur" -- aim to satisfy these criteria. Existing works have primarily focused on designing algorithms to obtain counterfactual explanations for a wide range of settings. However, one of the main objectives of "explanations as a means to help a data-subject act rather than merely understand" has been overlooked. In layman's terms, counterfactual explanations inform an individual where they need to get to, but not how to get there. In this work, we rely on causal reasoning to caution against the use of counterfactual explanations as a recommendable set of actions for recourse. Instead, we propose a shift of paradigm from recourse via nearest counterfactual explanations to recourse through minimal interventions, moving the focus from explanations to recommendations. Finally, we provide the reader with an extensive discussion on how to realistically achieve recourse beyond structural interventions.
I-nteract: A cyber-physical system for real-time interaction with physical and virtual objects using mixed reality technologies for additive manufacturing
Venue: arXiv cs.HC 2020
Year: 2020

This paper presents I-nteract, a cyber-physical system that enables real-time interaction with real and virtual objects in a mixed augmented reality environment to design 3D models for additive manufacturing. The system has been developed using mixed reality technologies such as HoloLens, for augmenting visual feedback, and haptic gloves, for augmenting haptic force feedback. The efficacy of the system has been demonstrated by generating 3D model using a novel scanning method to 3D print a customized orthopedic cast for human arm, by estimating spring rates of compression springs, and by simulating interaction with a virtual spring using hand.
Applying Depth-Sensing to Automated Surgical Manipulation with a da Vinci Robot
Venue: arXiv cs.RO 2020
Year: 2020

Recent advances in depth-sensing have significantly increased accuracy, resolution, and frame rate, as shown in the 1920x1200 resolution and 13 frames per second Zivid RGBD camera. In this study, we explore the potential of depth sensing for efficient and reliable automation of surgical subtasks. We consider a monochrome (all red) version of the peg transfer task from the Fundamentals of Laparoscopic Surgery training suite implemented with the da Vinci Research Kit (dVRK). We use calibration techniques that allow the imprecise, cable-driven da Vinci to reduce error from 4-5 mm to 1-2 mm in the task space. We report experimental results for a handover-free version of the peg transfer task, performing 20 and 5 physical episodes with single- and bilateral-arm setups, respectively. Results over 236 and 49 total block transfer attempts for the single- and bilateral-arm peg transfer cases suggest that reliability can be attained with 86.9 % and 78.0 % for each individual block, with respective block transfer speeds of 10.02 and 5.72 seconds. Supplementary material is available at https://sites.google.com/view/peg-transfer.
Memristive oscillatory circuits for resolution of NP-complete logic puzzles: Sudoku case
Venue: arXiv cs.ET 2020
Year: 2020

Memristor networks are capable of low-power and massive parallel processing and information storage. Moreover, they have presented the ability to apply for a vast number of intelligent data analysis applications targeting mobile edge devices and low power computing. Beyond the memory and conventional computing architectures, memristors are widely studied in circuits aiming for increased intelligence that are suitable to tackle complex problems in a power and area efficient manner, offering viable solutions oftenly arriving also from the biological principles of living organisms. In this paper, a memristive circuit exploiting the dynamics of oscillating networks is utilized for the resolution of very popular and NP-complete logic puzzles, like the well-known "Sudoku". More specifically, the proposed circuit design methodology allows for appropriate usage of interconnections' advantages in a oscillation network and of memristor's switching dynamics resulting to logic-solvable puzzle-instances. The reduced complexity of the proposed circuit and its increased scalability constitute its main advantage against previous approaches and the broadly presented SPICE based simulations provide a clear proof of concept of the aforementioned appealing characteristics.
UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation
Venue: arXiv cs.CV 2020
Year: 2020

With the recent success of the pre-training technique for NLP and image-linguistic tasks, some video-linguistic pre-training works are gradually developed to improve video-text related downstream tasks. However, most of the existing multimodal models are pre-trained for understanding tasks, leading to a pretrain-finetune discrepancy for generation tasks. This paper proposes UniVL: a Unified Video and Language pre-training model for both multimodal understanding and generation. It comprises four components, including two single-modal encoders, a cross encoder, and a decoder with the Transformer backbone. Five objectives, including video-text joint, conditioned masked language model (CMLM), conditioned masked frame model (CMFM), video-text alignment, and language reconstruction, are designed to train each of the components. We further develop two pre-training strategies, stage by stage pre-training (StagedP) and enhanced video representation (EnhancedV), to make the training process of the UniVL more effective. The pre-train is carried out on a sizeable instructional video dataset HowTo100M. Experimental results demonstrate that the UniVL can learn strong video-text representation and achieves state-of-the-art results on five downstream tasks.
Autonomous Unknown-Application Filtering and Labeling for DL-based Traffic Classifier Update
Venue: arXiv cs.NI 2020
Year: 2020

Network traffic classification has been widely studied to fundamentally advance network measurement and management. Machine Learning is one of the effective approaches for network traffic classification. Specifically, Deep Learning (DL) has attracted much attention from the researchers due to its effectiveness even in encrypted network traffic without compromising neither user privacy nor network security. However, most of the existing models are created from closed-world datasets, thus they can only classify those existing classes previously sampled and labeled. In this case, unknown classes cannot be correctly classified. To tackle this issue, an autonomous learning framework is proposed to effectively update DL-based traffic classification models during active operations. The core of the proposed framework consists of a DL-based classifier, a self-learned discriminator, and an autonomous self-labeling model. The discriminator and self-labeling process can generate new dataset during active operations to support classifier update. Evaluation of the proposed framework is performed on an open dataset, i.e., ISCX VPN-nonVPN, and independently collected data packets. The results demonstrate that the proposed autonomous learning framework can filter packets from unknown classes and provide accurate labels. Thus, corresponding DL-based classification models can be updated successfully with the autonomously generated dataset.
Recommendation of Move Method Refactoring Using Path-Based Representation of Code
Venue: arXiv cs.SE 2020
Year: 2020

Software refactoring plays an important role in increasing code quality. One of the most popular refactoring types is the Move Method refactoring. It is usually applied when a method depends more on members of other classes than on its own original class. Several approaches have been proposed to recommend Move Method refactoring automatically. Most of them are based on heuristics and have certain limitations (e.g., they depend on the selection of metrics and manually-defined thresholds). In this paper, we propose an approach to recommend Move Method refactoring based on a path-based representation of code called code2vec that is able to capture the syntactic structure and semantic information of a code fragment. We use this code representation to train a machine learning classifier suggesting to move methods to more appropriate classes. We evaluate the approach on two publicly available datasets: a manually compiled dataset of well-known open-source projects and a synthetic dataset with automatically injected code smell instances. The results show that our approach is capable of recommending accurate refactoring opportunities and outperforms JDeodorant and JMove, which are state of the art tools in this field.
Analysis on Computation-Intensive Status Update in Mobile Edge Computing
Venue: arXiv cs.IT 2020
Year: 2020

In status update scenarios, the freshness of information is measured in terms of age-of-information (AoI), which essentially reflects the timeliness for real-time applications to transmit status update messages to a remote controller. For some applications, computational expensive and time consuming data processing is inevitable for status information of messages to be displayed. Mobile edge servers are equipped with adequate computation resources and they are placed close to users. Thus, mobile edge computing (MEC) can be a promising technology to reduce AoI for computation-intensive messages. In this paper, we study the AoI for computation-intensive messages with MEC, and consider three computing schemes: local computing, remote computing at the MEC server, and partial computing, i.e., some part of computing tasks are performed locally, and the rest is executed at the MEC server. Zero-wait policy is adopted in all three schemes. Specifically, in local computing, a new message is generated immediately after the previous one is revealed by computing. While in remote computing and partial computing, a new message is generated once the previous one is received by the remote MEC server. With infinite queue size and exponentially distributed transmission time, closed-form average AoI for exponentially distributed computing time is derived for the three computing schemes. For deterministic computing time, the average AoI is analyzed numerically. Simulation results show that by carefully partitioning the computing tasks, the average AoI in partial computing is the smallest compared to local computing and remote computing. The results also indicate numerically the conditions on which remote computing attains smaller average AoI compared with local computing.
Kruskal-based approximation algorithm for the multi-level Steiner tree problem
Venue: arXiv cs.DS 2020
Year: 2020

We study the multi-level Steiner tree problem: a generalization of the Steiner tree problem in graphs where terminals $T$ require varying priority, level, or quality of service. In this problem, we seek to find a minimum cost tree containing edges of varying rates such that any two terminals $u$, $v$ with priorities $P(u)$, $P(v)$ are connected using edges of rate $\min\{P(u),P(v)\}$ or better. The case where edge costs are proportional to their rate is approximable to within a constant factor of the optimal solution. For the more general case of non-proportional costs, this problem is hard to approximate with ratio $c \log \log n$, where $n$ is the number of vertices in the graph. A simple greedy algorithm by Charikar et al., however, provides a $\min\{2(\ln |T|+1), \ell ρ\}$-approximation in this setting, where $ρ$ is an approximation ratio for a heuristic solver for the Steiner tree problem and $\ell$ is the number of priorities or levels (Byrka et al. give a Steiner tree algorithm with $ρ\approx 1.39$, for example). In this paper, we describe a natural generalization to the multi-level case of the classical (single-level) Steiner tree approximation algorithm based on Kruskal's minimum spanning tree algorithm. We prove that this algorithm achieves an approximation ratio at least as good as Charikar et al., and experimentally performs better with respect to the optimum solution. We develop an integer linear programming formulation to compute an exact solution for the multi-level Steiner tree problem with non-proportional edge costs and use it to evaluate the performance of our algorithm on both random graphs and multi-level instances derived from SteinLib.
PDDLGym: Gym Environments from PDDL Problems
Venue: arXiv cs.AI 2020
Year: 2020

We present PDDLGym, a framework that automatically constructs OpenAI Gym environments from PDDL domains and problems. Observations and actions in PDDLGym are relational, making the framework particularly well-suited for research in relational reinforcement learning and relational sequential decision-making. PDDLGym is also useful as a generic framework for rapidly building numerous, diverse benchmarks from a concise and familiar specification language. We discuss design decisions and implementation details, and also illustrate empirical variations between the 20 built-in environments in terms of planning and model-learning difficulty. We hope that PDDLGym will facilitate bridge-building between the reinforcement learning community (from which Gym emerged) and the AI planning community (which produced PDDL). We look forward to gathering feedback from all those interested and expanding the set of available environments and features accordingly. Code: https://github.com/tomsilver/pddlgym