OpenRaiser/Intern-Atlas huggingface dataset:
Langar: An Approach to Evaluate Reo Programming Language
Venue: arXiv cs.SE 2021
Year: 2021

Reo is a formal coordination language. In order to assess and evaluate its capabilities, we need a multi-perspective Language Evaluation Framework. Langar (Language Analysis for Reo) is a framework aimed to provide such an evaluation method. In this paper, we introduce Langar. Based on a review on various language evaluation methods, a tool-kit for useful evaluation techniques are provided. After Reo Evaluation, this method and tool-kit also could be used for another programming, computational and even natural languages. Furthermore, two suggestions for some future efforts and directions are provided for software engineering and software methodology communities.
Quasi-structured quadrilateral meshing in Gmsh -- a robust pipeline for complex CAD models
Venue: arXiv cs.CE 2021
Year: 2021

We propose an end-to-end pipeline to robustly generate high-quality quadrilateral meshes for complex CAD models. An initial quad-dominant mesh is generated with frontal point insertion guided by a locally integrable cross field and a scalar size map adapted to the small CAD features. After triangle combination and midpoint-subdivision into an all-quadrilateral mesh, the topology of the mesh is modified to reduce the number of irregular vertices. The idea is to preserve the irregular vertices matching cross-field singularities and to eliminate the others. The topological modifications are either local and based on disk quadrangulations, or more global with the remeshing of patches of quads according to predefined patterns. Validity of the quad mesh is guaranteed by monitoring element quality during all operations and reverting the changes when necessary. Advantages of our approach include robustness, strict respect of the CAD features and support for user-prescribed size constraints. The quad mesher, which is available in Gmsh, is validated and illustrated on two datasets of CAD models.
Anomaly Detection Based on Selection and Weighting in Latent Space
Venue: arXiv cs.LG 2021
Year: 2021

With the high requirements of automation in the era of Industry 4.0, anomaly detection plays an increasingly important role in higher safety and reliability in the production and manufacturing industry. Recently, autoencoders have been widely used as a backend algorithm for anomaly detection. Different techniques have been developed to improve the anomaly detection performance of autoencoders. Nonetheless, little attention has been paid to the latent representations learned by autoencoders. In this paper, we propose a novel selection-and-weighting-based anomaly detection framework called SWAD. In particular, the learned latent representations are individually selected and weighted. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD. On the benchmark datasets, the SWAD framework has reached comparable or even better performance than the state-of-the-art approaches.
A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels
Venue: arXiv cs.LG 2021
Year: 2021

Positive-unlabeled learning refers to the process of training a binary classifier using only positive and unlabeled data. Although unlabeled data can contain positive data, all unlabeled data are regarded as negative data in existing positive-unlabeled learning methods, which resulting in diminishing performance. We provide a new perspective on this problem -- considering unlabeled data as noisy-labeled data, and introducing a new formulation of PU learning as a problem of joint optimization of noisy-labeled data. This research presents a methodology that assigns initial pseudo-labels to unlabeled data which is used as noisy-labeled data, and trains a deep neural network using the noisy-labeled data. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods on several benchmark datasets.
Protect Against Unintentional Insider Threats: The risk of an employee's cyber misconduct on a Social Media Site
Venue: arXiv cs.SI 2021
Year: 2021

Social Media is a cyber-security risk for every business. What do people share on the Internet? Almost everything about oneself is shared: friendship, demographics, family, activities, and work-related information. This could become a potential risk in every business if the organization's policies, training and technology fail to properly address these issues. In many cases, it is the employees' behaviour that can put key company information at danger. Social media has turned into a reconnaissance tool for malicious actors and users accounts are now seen as a goldmine for cyber criminals. Investigation of Social Media is in the embryonic stage and thus, is not yet well understood. This research project aims to collect and analyse open-source data from LinkedIn, discover data leakage and analyse personality types through software as a service (SAAS). The final aim of the study is to understand if there are behavioral factors that can predicting one's attitude toward disclosing sensitive data.
Info-Evo: Using Information Geometry to Guide Evolutionary Program Learning
Venue: arXiv cs.NE 2021
Year: 2021

A novel optimization strategy, Info-Evo, is described, in which natural gradient search using nonparametric Fisher information is used to provide ongoing guidance to an evolutionary learning algorithm, so that the evolutionary process preferentially moves in the directions identified as "shortest paths" according to the natural gradient. Some specifics regarding the application of this approach to automated program learning are reviewed, including a strategy for integrating Info-Evo into the MOSES program learning framework.
A Study on Fairness and Trust Perceptions in Automated Decision Making
Venue: arXiv cs.AI 2021
Year: 2021

Automated decision systems are increasingly used for consequential decision making -- for a variety of reasons. These systems often rely on sophisticated yet opaque models, which do not (or hardly) allow for understanding how or why a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield undesirable (e.g., unfair) outcomes because their sanity is difficult to assess and calibrate in the first place. In this work, we conduct a study to evaluate different attempts of explaining such systems with respect to their effect on people's perceptions of fairness and trustworthiness towards the underlying mechanisms. A pilot study revealed surprising qualitative insights as well as preliminary significant effects, which will have to be verified, extended and thoroughly discussed in the larger main study.
District Wise Price Forecasting of Wheat in Pakistan using Deep Learning
Venue: arXiv cs.LG 2021
Year: 2021

Wheat is the main agricultural crop of Pakistan and is a staple food requirement of almost every Pakistani household making it the main strategic commodity of the country whose availability and affordability is the government's main priority. Wheat food availability can be vastly affected by multiple factors included but not limited to the production, consumption, financial crisis, inflation, or volatile market. The government ensures food security by particular policy and monitory arrangements, which keeps up purchase parity for the poor. Such arrangements can be made more effective if a dynamic analysis is carried out to estimate the future yield based on certain current factors. Future planning of commodity pricing is achievable by forecasting their future price anticipated by the current circumstances. This paper presents a wheat price forecasting methodology, which uses the price, weather, production, and consumption trends for wheat prices taken over the past few years and analyzes them with the help of advance neural networks architecture Long Short Term Memory (LSTM) networks. The proposed methodology presented significantly improved results versus other conventional machine learning and statistical time series analysis methods.
Robust Model Checking with Imprecise Markov Reward Models
Venue: arXiv cs.LO 2021
Year: 2021

In recent years probabilistic model checking has become an important area of research because of the diffusion of computational systems of stochastic nature. Despite its great success, standard probabilistic model checking suffers the limitation of requiring a sharp specification of the probabilities governing the model behaviour. The theory of imprecise probabilities offers a natural approach to overcome such limitation by a sensitivity analysis with respect to the values of these parameters. However, only extensions based on discrete-time imprecise Markov chains have been considered so far for such a robust approach to model checking. We present a further extension based on imprecise Markov reward models. In particular, we derive efficient algorithms to compute lower and upper bounds of the expected cumulative reward and probabilistic bounded rewards based on existing results for imprecise Markov chains. These ideas are tested on a real case study involving the spend-down costs of geriatric medicine departments.
HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands
Venue: arXiv cs.RO 2021
Year: 2021

Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this perspective, advanced prosthetic hands of the future are anticipated to benefit from improved shared control between a robotic hand and its human user, but more importantly from the improved capability to infer human intent from multimodal sensor data to provide the robotic hand perception abilities regarding the operational context. Such multimodal sensor data may include various environment sensors including vision, as well as human physiology and behavior sensors including electromyography and inertial measurement units. A fusion methodology for environmental state and human intent estimation can combine these sources of evidence in order to help prosthetic hand motion planning and control. In this paper, we present a dataset of this type that was gathered with the anticipation of cameras being built into prosthetic hands, and computer vision methods will need to assess this hand-view visual evidence in order to estimate human intent. Specifically, paired images from human eye-view and hand-view of various objects placed at different orientations have been captured at the initial state of grasping trials, followed by paired video, EMG and IMU from the arm of the human during a grasp, lift, put-down, and retract style trial structure. For each trial, based on eye-view images of the scene showing the hand and object on a table, multiple humans were asked to sort in decreasing order of preference, five grasp types appropriate for the object in its given configuration relative to the hand. The potential utility of paired eye-view and hand-view images was illustrated by training a convolutional neural network to process hand-view images in order to predict eye-view labels assigned by humans.
Domain Adaptive Egocentric Person Re-identification
Venue: arXiv cs.CV 2021
Year: 2021

Person re-identification (re-ID) in first-person (egocentric) vision is a fairly new and unexplored problem. With the increase of wearable video recording devices, egocentric data becomes readily available, and person re-identification has the potential to benefit greatly from this. However, there is a significant lack of large scale structured egocentric datasets for person re-identification, due to the poor video quality and lack of individuals in most of the recorded content. Although a lot of research has been done in person re-identification based on fixed surveillance cameras, these do not directly benefit egocentric re-ID. Machine learning models trained on the publicly available large scale re-ID datasets cannot be applied to egocentric re-ID due to the dataset bias problem. The proposed algorithm makes use of neural style transfer (NST) that incorporates a variant of Convolutional Neural Network (CNN) to utilize the benefits of both fixed camera vision and first-person vision. NST generates images having features from both egocentric datasets and fixed camera datasets, that are fed through a VGG-16 network trained on a fixed-camera dataset for feature extraction. These extracted features are then used to re-identify individuals. The fixed camera dataset Market-1501 and the first-person dataset EGO Re-ID are applied for this work and the results are on par with the present re-identification models in the egocentric domain.
The Weakly-Labeled Rand Index
Venue: arXiv cs.CV 2021
Year: 2021

Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of transition between seabed types. Due to these regions, it is difficult to label and segment the imagery and, furthermore, challenging to score the image segmentations appropriately. While there are many approaches to quantify performance in standard crisp segmentation schemes, drawing hard boundaries in remote sensing imagery where gradients and regions of uncertainty exist is inappropriate. These cases warrant weak labels and an associated appropriate scoring approach. In this paper, a labeling approach and associated modified version of the Rand index for weakly-labeled data is introduced to address these issues. Results are evaluated with the new index and compared to traditional segmentation evaluation methods. Experimental results on a SAS data set containing must-link and cannot-link labels show that our Weakly-Labeled Rand index scores segmentations appropriately in reference to qualitative performance and is more suitable than traditional quantitative metrics for scoring weakly-labeled data.
Socio-Technical Root Cause Analysis of Cyber-enabled Theft of the U.S. Intellectual Property -- The Case of APT41
Venue: arXiv cs.CR 2021
Year: 2021

Increased connectivity has made us all more vulnerable. Cyberspace, besides all its benefits, spawned more devices to hack and more opportunities to commit cybercrime. Criminals have found it lucrative to target both individuals and businesses, by holding or stealing their assets via different types of cyber attacks. The cyber-enabled theft of Intellectual Property (IP), as one of the most important and critical intangible assets of nations, organizations and individuals, by foreign countries has been a devastating challenge of the United States (U.S.) in the past decades. In this study, we conduct a socio-technical root cause analysis to investigate one of the recent cases of IP theft by employing a holistic approach. It concludes with a list of root causes and some corrective actions to stop the impact and prevent the recurrence of the problem in the future. Building upon the findings of this study, the U.S. requires a detailed revision of IP strategies bringing the whole socio-technical regulatory system into focus and strengthen IP rights protection considering China's indigenous innovation policies. It is critical that businesses and other organizations take steps to reduce their exposure to cyber attacks. It is particularly important to train employees on how to spot potential threats, and to institute policies that encourage workers to report potential security failures so that action can be taken quickly. Finally, we discuss how cyber ranges can provide an efficient and safe platform for dealing with such challenges. The results of this study can be expanded to other countries in order to protect their IP rights and deter or prevent and respond to future incidents.
A Crash Course on Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

The emerging field of Reinforcement Learning (RL) has led to impressive results in varied domains like strategy games, robotics, etc. This handout aims to give a simple introduction to RL from control perspective and discuss three possible approaches to solve an RL problem: Policy Gradient, Policy Iteration, and Model-building. Dynamical systems might have discrete action-space like cartpole where two possible actions are +1 and -1 or continuous action space like linear Gaussian systems. Our discussion covers both cases.
Transparent Checkpointing for OpenGL Applications on GPUs
Venue: arXiv cs.DC 2021
Year: 2021

This work presents transparent checkpointing of OpenGL applications, refining the split-process technique[1] for application in GPU-based 3D graphics. The split-process technique was earlier applied to checkpointing MPI and CUDA programs, enabling reinitialization of driver libraries. The presented design targets practical, checkpoint-package agnostic checkpointing of OpenGL applications. An early prototype is demonstrated on Autodesk Maya. Maya is a complex proprietary media-creation software suite used with large-scale rendering hardware for CGI (Computer-Generated Animation). Transparent checkpointing of Maya provides critically-needed fault tolerance, since Maya is prone to crash when artists use some of its bleeding-edge components. Artists then lose hours of work in re-creating their complex environment.
Automatic Cause Detection of Performance Problems in Web Applications
Venue: arXiv cs.LG 2021
Year: 2021

The execution of similar units can be compared by their internal behaviors to determine the causes of their potential performance issues. For instance, by examining the internal behaviors of different fast or slow web requests more closely and by clustering and comparing their internal executions, one can determine what causes some requests to run slowly or behave in unexpected ways. In this paper, we propose a method of extracting the internal behavior of web requests as well as introduce a pipeline that detects performance issues in web requests and provides insights into their root causes. First, low-level and fine-grained information regarding each request is gathered by tracing both the user space and the kernel space. Second, further information is extracted and fed into an outlier detector. Finally, these outliers are then clustered by their behavior, and each group is analyzed separately. Experiments revealed that this pipeline is indeed able to detect slow web requests and provide additional insights into their true root causes. Notably, we were able to identify a real PHP cache contention using the proposed approach.
Digital Convex + Unimodular Mapping =8-Connected (All Points but One 4-Connected)
Venue: arXiv cs.CG 2021
Year: 2021

In two dimensional digital geometry, two lattice points are 4-connected (resp. 8-connected) if their Euclidean distance is at most one (resp. $\sqrt{2}$). A set $S \subset Z^2$ is 4-connected (resp. 8-connected) if for all pair of points $p_1, p_2$ in $S$ there is a path connecting $p_1$ to $p_2$ such that every edge consists of a 4-connected (resp. 8-connected) pair of points. The original definition of digital convexity which states that a set $S \subset Z^d$ is digital convex if $\conv(S) \cap Z^d= S$, where $\conv(S)$ denotes the convex hull of $S$ does not guarantee connectivity. However, multiple algorithms assume connectivity. In this paper, we show that in two dimensional space, any digital convex set $S$ of $n$ points is unimodularly equivalent to a 8-connected digital convex set $C$. In fact, the resulting digital convex set $C$ is 4-connected except for at most one point which is 8-connected to the rest of the set. The matrix of $SL_2(Z)$ defining the affine isomorphism of $Z^2$ between the two unimodularly equivalent lattice polytopes $S$ and $C$ can be computed in roughly $O(n)$ time. We also show that no similar result is possible in higher dimension.
Hardware error correction for programmable photonics
Venue: arXiv cs.ET 2021
Year: 2021

Programmable photonic circuits of reconfigurable interferometers can be used to implement arbitrary operations on optical modes, facilitating a flexible platform for accelerating tasks in quantum simulation, signal processing, and artificial intelligence. A major obstacle to scaling up these systems is static fabrication error, where small component errors within each device accrue to produce significant errors within the circuit computation. Mitigating this error usually requires numerical optimization dependent on real-time feedback from the circuit, which can greatly limit the scalability of the hardware. Here we present a deterministic approach to correcting circuit errors by locally correcting hardware errors within individual optical gates. We apply our approach to simulations of large scale optical neural networks and infinite impulse response filters implemented in programmable photonics, finding that they remain resilient to component error well beyond modern day process tolerances. Our results highlight a new avenue for scaling up programmable photonics to hundreds of modes within current day fabrication processes.
Uncorrelated binary sequences of lengths 2a3b4c5d7e11f13g based on nested Barker codes and complementary sequences
Venue: arXiv cs.IT 2021
Year: 2021

Certain applications require the use of signals that combine both the capability to operate with low signal-to-noise ratios and the ability to support multiple users without interference. In the case where many users have very different signal-to-noise ratios, it is necessary to consider coding schemes that can be used in a multi-user environment but with different noise immunity levels. Traditional detection systems based on the correlation function and coding sequences have significant limitations in satisfying both objectives, since the cross-correlation between coded signals corresponding with different users is linked to the use of the same coded sequences length. The research topic of binary sequences that have null cross-correlation and different length has not been studied in depth, but it has potential applications in multi-user environments. In this work an algorithm to generate binary sequences completely uncorrelated with certain sets of complementary sequences is presented. The proposed algorithm is based on nested Barker sequences, and it is compared with a previous proposal based on an iterative algorithm. This approach allows to generate more diversity of sequences of different length than the iterative approach, which it makes useful for applications based on binary sequences detection and expand the horizon of many applications.
Radio Resource and Beam Management in 5G mmWave Using Clustering and Deep Reinforcement Learning
Venue: arXiv cs.NI 2021
Year: 2021

To optimally cover users in millimeter-Wave (mmWave) networks, clustering is needed to identify the number and direction of beams. The mobility of users motivates the need for an online clustering scheme to maintain up-to-date beams towards those clusters. Furthermore, mobility of users leads to varying patterns of clusters (i.e., users move from the coverage of one beam to another), causing dynamic traffic load per beam. As such, efficient radio resource allocation and beam management is needed to address the dynamicity that arises from mobility of users and their traffic. In this paper, we consider the coexistence of Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile BroadBand (eMBB) users in 5G mmWave networks and propose a Quality-of-Service (QoS) aware clustering and resource allocation scheme. Specifically, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used for online clustering of users and the selection of the number of beams. In addition, Long Short Term Memory (LSTM)-based Deep Reinforcement Learning (DRL) scheme is used for resource block allocation. The performance of the proposed scheme is compared to a baseline that uses K-means and priority-based proportional fairness for clustering and resource allocation, respectively. Our simulation results show that the proposed scheme outperforms the baseline algorithm in terms of latency, reliability, and rate of URLLC users as well as rate of eMBB users.