Reviewerly/exHarmony huggingface dataset:
Sparse motion fields for trajectory prediction

Trajectory prediction is a crucial element of many automated tasks, such as autonomous navigation or video surveillance. To automatically predict the motion of an agent (e.g., pedestrian or car), the model needs to efficiently represent human motion and “understand” the external stimuli that may influence human behavior. In this work we propose a methodology to model the motion of agents in a video scene. Our method is based on space-varying sparse motion fields, which simultaneously characterize diverse motion patterns in the scene and implicitly learn contextual cues about the static environment, namely obstacles and semantic constraints. The sparse motion fields are applied to the task of long-term trajectory prediction using a probabilistic generative approach. Several benchmark data sets are used to demonstrate the potential of the proposed approach and show that our method achieves competitive state-of-the-art performances.
Large-scale intent analysis for identifying large-review-effort code changes

Context: Code changes to software occur due to various reasons such as bug fixing, new feature addition, and code refactoring. Change intents have been studied for years to help developers understand the rationale behind code commits. However, in most existing studies, the intent of the change is rarely leveraged to provide more specific, context aware analysis. Objective: In this paper, we present the first study to leverage change intent to characterize and identify Large-Review-Effort (LRE) changes—changes with large review effort. Method: Specifically, we first propose a feedback-driven and heuristics-based approach to identify change intents of code changes. We then characterize the changes regarding review effort by using various features extracted from change metadata and the change intents. We further explore the feasibility of automatically classifying LRE changes. We conduct our study on four large-scale projects, one from Microsoft and three are open source projects, i.e., Qt, Android, and OpenStack. Results: Our results show that, (i) code changes with some intents (i.e., Feature and Refactor) are more likely to be LRE changes, (ii) machine learning based prediction models are applicable for identifying LRE changes, and (iii) prediction models built for code changes with some intents achieve better performance than prediction models without considering the change intent, the improvement in AUC can be up to 19 percentage points and is 7.4 percentage points on average. Conclusion: The change intent analysis and its application on LRE identification proposed in this study has already been used in Microsoft to provide the review effort and intent information of changes for reviewers to accelerate the review process. To show how to deploy our approaches in real-world practice, we report a case study of developing and deploying the intent analysis system in Microsoft. Moreover, we also evaluate the usefulness of our approaches by using a questionnaire survey. The feedback from developers demonstrate its practical value.
A cooperative system for metaheuristic algorithms

Optimization problems are defined as the functions whereby the target is to find the optimum state depending on the parameters that have certain limitations. In the field of optimization, the aim is to find from among multiple alternative solutions the optimal solution or approximate solution that provides all the restrictions. Metaheuristic is an extremely effective method to find approximate solutions to optimization problems. However, when metaheuristics are used, there occurs an algorithm selection problem. This problem involves decision-making about which algorithm is to be used to solve the existing optimization problem with maximum performance. The objective of this study is to use a cooperative system that combines different metaheuristics to successfully deal with algorithm selection problems. An intelligent combination of different metaheuristics is expected to provide more flexible, more efficient and more robust approaches. However, such a combination requires less precision. The combination is generated through a methodology designed with soft computing. In addition to the algorithm selection problem, the adjustment of algorithm parameters has significant importance in obtaining good results. For this reason, the cooperative system proposed in this study offers fine-tuning of parameters based on soft computing techniques.
Learning adaptive geometry for unsupervised domain adaptation

Unsupervised domain adaptation is an effective approach to solve the problem of dataset bias. However, most existing unsupervised domain adaptation methods assume that the geometry structures of data distributions are similar in the source and target domains. This assumption is invalid in many practical applications, because the training and test datasets usually differ in the variability modes and/or variation degrees. This paper handles the problem of inconsistent geometries by aligning both data representations and geometries. To overcome the lack of target labels in aligning geometries, this paper proposes learning the adaptive geometry that is derived from the domain-shared label space. Source and target geometries are aligned by constraining them with the unified criteria of the adaptive geometry. Combining the adaptive geometry learning and adversarial learning techniques, we develop a geometry-aware dual-stream network to learn the geometry-aligned representations. Experimental results show that our method achieves good performance on cross-dataset recognition tasks.
Person image synthesis through siamese generative adversarial network

Photo-realistic image synthesis is an attracting idea for person re-identification (ReID) and data augmentation on human pose estimation. However, existing advances manipulating human image synthesis lack texture details for varying poses or appearances. This paper presents a person image synthesis Siamese generative adversarial network (PS2GAN), which re-synthesizes person image by changing the pose of that person to a given pose, modeled in a Siamese structure with image generative network and pair conditional discriminative networks in single-branch. For pose transfer, the proposed PS2GAN adopts Siamese structure consisting of two image generative networks and a novel contrastive-pose loss regularizing the generation process. Additionally, a nearest-neighbor loss computes the difference between fake and real images to make high-level information closer. Furthermore, the proposed PS2GAN is competitive to the state-of-the-art performance on Market-1501 and DeepFashion datasets via qualitatively and quantitatively comparing with prior works, and synthetic images of the PS2GAN can alleviate data insufficiency for person ReID.
Evaluating the threat to national information security

An effective strategy for managing the national information security with capabilities to resist information threats significantly impacts its further development. This study aims to assess the level of threat to the information security of countries based on the integral index. It is proposed to use five indicators characterizing individual areas of information security and 37 world development indicators, selected from the World Bank database. Correlation analysis selected 12 out of 37 development indicators relevant to security indicators for which the correlation coefficient exceeded 0.5 or –0.5. The Harrington-Mencher function is proposed to determine the information security threat index. Nonlinear normalization was carried out to bring the initial data to a comparable measurement. Canonical analysis was performed to determine the indicator weights. The data from 159 countries were taken for 2018 to assess the index. The result was presented on the map showing countries’ distribution by the information security threat index, thus forming five groups. The group with a “very well” resistance to threats includes economically developed countries with a high level of information security. The “well” group was formed by new industrial and developing countries with economic potential sufficient to prevent information threats and combat their consequences. The information security level in developing countries, where the results of overcoming information threats will affect the economic sphere, is defined as “acceptable”. Countries with a low level of development and information security formed groups designated as “bad” and “very bad”, which indicates a high level of threats to their information security. AcknowledgmentThis work is carried out with in the tax payer – funded researches: No. 0118U003574 “Cybersecurity in the banking fraud enforcement: protection of financial service consumers and the financial and economic security growth in Ukraine”.
Face Image Reflection Removal

Face images captured through glass are usually contaminated by reflections. The low-transmitted reflections make the reflection removal more challenging than for general scenes because important facial features would be completely occluded. In this paper, we propose and solve the face image reflection removal problem. We recover the important facial structures by incorporating inpainting ideas into a guided reflection removal framework, which takes two images as the input and considers various face-specific priors. We use a newly collected face reflection image dataset to train our model and compare with state-of-the-art methods. The proposed method shows advantages in estimating reflection-free face images for improving face recognition.
DenseNetX and GRU for the sussex-huawei locomotion-transportation recognition challenge

The Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge organized at the HASCA Workshop of UbiComp 2020 presents a large and realistic dataset with different activities and transportation. The goal of this human activity recognition challenge is to recognize eight modes of locomotion and transportation from 5-second frames of sensor data of a smartphone carried in the unknown position. In this paper, our team (We can fly) summarize our submission to the competition. We proposed a one-dimensional (1D) DenseNetX model, a deep learning method for transportation mode classification. We first convert sensor readings from the phone coordinate system to the navigation coordinate system. Then, we normalized each sensor using different maximums and minimums and construct multi-channel sensor input. Finally, 1D DenseNetX with the Gated Recurrent Unit (GRU) model output the predictions. In the experiment, we utilized four internal datasets for training our model and achieved averaged F1 score of 0.7848 on four valid datasets.
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent

This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-parametric generalized Bayesian inference framework for federated learning. DSVGD maintains a number of non-random and interacting particles at a central server to represent the current iterate of the model global posterior. The particles are iteratively downloaded and updated by a subset of agents with the end goal of minimizing the global free energy. By varying the number of particles, DSVGD enables a flexible trade-off between per-iteration communication load and number of communication rounds. DSVGD is shown to compare favorably to benchmark frequentist and Bayesian federated learning strategies in terms of accuracy and scalability with respect to the number of agents, while also providing well-calibrated, and hence trustworthy, predictions.
Enterprise API Security and GDPR Compliance: Design and Implementation Perspective

With the advancements in enterprise-level business development, the demand for new applications and services is overwhelming. For the development and delivery of such applications and services, enterprise businesses rely on Application Programming Interfaces (APIs). APIs provide interface to enable the communication among different applications. In essence, API is a double-edged sword; on one hand, API helps in expanding the business through sharing value and utility, but on the other hand, it raises security and privacy issues. Since the applications usually use APIs to retrieve important and critical data, it is extremely important to make sure that effective access control and security mechanisms are in place so that the data do not fall into wrong hands. In this context, in this article, we discuss the current state of the enterprise API security and the role of Machine Learning (ML) in an API security. We also discuss the General Data Protection Regulation (GDPR) Compliance and its effect on the API security.
What Drives the Reading Order of Programmers?

Background: The way how programmers comprehend source code depends on several factors, including the source code itself and the programmer. Recent studies showed that novice programmers tend to read source code more like natural language text, whereas experts tend to follow the program execution flow. But, it is unknown how the linearity of source code and the comprehension strategy influence programmers' linearity of reading order.
A Transparent and Trusted Property Registration System on Permissioned Blockchain

Performing various transactions on real estate records in the government organization is a cumbersome task and has many pitfalls. There are a number of problems with the existing land registry systems. First, the process of land registry and transferring of a land is currently manual. Even though, some of the processes and records are digitized, however, it still has many problems, such as, it is developed on a centralized system, it cannot maintain the complete history of the land and it does not have a built-in mechanism for record tempering detection. To overcome these problems there is no viable solution based on a centralized system. The decentralized systems, such as, Blockchain comes built-in with this feature. In this proposal, aforementioned problems are addressed. The lands are registered over Blockchain network by designing a smart contract. The proposed studies can provide several features to the stakeholders including efficiency, transparency, trustworthiness, integrity for various entities and processes involved in buying and selling a property. Essentially the framework offers services that will provide a detailed history and untampered information regarding a property to ensure that the record is not manipulated. There is an external link available via Restful to the traditional property dealing apps so they can extract the real-time record of the land, such as, dimension, location, price of that specific land. The proposed system will ultimately safeguard confidence in doing real estate business over the Internet.
Knowledge Transfer in Modern Code Review

Knowledge transfer is one of the main goals of modern code review, as shown by several studies that surveyed and interviewed developers. While knowledge transfer is a clear expectation of the code review process, there are no analytical studies using data mined from software repositories to assess the effectiveness of code review in "training" developers and improve their skills over time. We present a mining-based study investigating how and whether the code review process helps developers to improve their contributions to open source projects over time. We analyze 32,062 peer-reviewed pull requests (PRs) made across 4,981 GitHub repositories by 728 developers who created their GitHub account in 2015. We assume that PRs performed in the past by a developer D that have been subject to a code review process have "transferred knowledge" to D. Then, we verify if over time (i.e., when more and more reviewed PRs are made by D), the quality of the contributions made by D to open source projects increases (as assessed by proxies we defined, such as the acceptance of PRs, or the polarity of the sentiment in the review comments left for the submitted PRs). With the above measures, we were unable to capture the positive impact played by the code review process on the quality of developers' contributions. This might be due to several factors, including the choices we made in our experimental design.Additional investigations are needed to confirm or contradict such a negative result.
A fast instance selection method for support vector machines in building extraction

Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH.
Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms

Optimization lies at the heart of machine learning (ML) and signal processing (SP). Contemporary approaches based on the stochastic gradient (SG) method are nonadaptive in the sense that their implementation employs prescribed parameter values that need to be tuned for each application. This article summarizes recent research and motivates future work on adaptive stochastic optimization methods, which have the potential to offer significant computational savings when training largescale systems.
Categorizing human phishing difficulty: a Phish Scale

Abstract As organizations continue to invest in phishing awareness training programs, many chief information security officers (CISOs) are concerned when their training exercise click rates are high or variable, as they must justify training budgets to organization officials who question the efficacy of awareness training when click rates are not declining. We argue that click rates should be expected to vary based on the difficulty of the phishing email for a target audience. Past research has shown that when the premise of a phishing email aligns with a user’s work context, it is much more challenging for users to detect a phish. Given this, we propose a Phish Scale, so CISOs and phishing training implementers can easily rate the difficulty of their phishing exercises and help explain associated click rates. We base our scale on past research in phishing cues and user context, and apply the scale to previously published and new data from enterprise-based phishing exercises. The Phish Scale performed well with the current phishing dataset, but future work is needed to validate it with a larger variety of phishing emails. The Phish Scale shows great promise as a tool to help frame data sharing on phishing exercise click rates across sectors.
DMRlib: Easy-Coding and Efficient Resource Management for Job Malleability

Process malleability has proved to have a highly positive impact on the resource utilization and global productivity in data centers compared with the conventional static resource allocation policy.However, the non-negligible additional development effort this solution imposes has constrained its adoption by the scientific programming community.In this article, we present DMRlib, a library designed to offer the global advantages of process malleability while providing a minimalist MPI-like syntax.The library includes a series of predefined communication patterns that greatly ease the development of malleable applications.In addition, we deploy several scenarios to demonstrate the positive impact of process malleability featuring different scalability patterns.Concretely, we study two job submission modes (rigid and moldable) in order to identify the best-case scenarios for malleability using metrics such as resource allocation rate, completed jobs per second, and energy consumption.The experiments prove that our elastic approach may improve global throughput by a factor higher than 3x compared to the traditional workloads of non-malleable jobs.
Exploring role of moral disengagement and counterproductive work behaviours in information security awareness.

As security breaches in organisations are on the rise, developing an understanding of factors enabling and preventing such breaches is crucial. Even though previous studies have examined organisational aspects of information security, not much focus has been placed on human factors. In the present work we examined the tendency to morally disengage (MD), information security awareness (ISA), and counterproductive work behaviours (CWB), in a sample of 718 employees who used computers on daily basis, in order to establish predictors of CWB and the behavioural outcomes of ISA. The results showed that the propensity to morally disengage plays an important role in ISA, particularly the aspect of diffusion of responsibility. Secondly, ISA knowledge and ISA attitude, as expected, were part of a mediating mechanism underlying the relationship between MD and ISA behaviours, as well as MD and CWB. This demonstrates that ISA and CWB constructs overlap to a certain degree, and thus affecting one, should have effects also on the other. Targeted interventions need to consider ways of improving ISA knowledge and attitudes, as well as employees’ sense of responsibility for the information they work with.
Kernel-blending connection approximated by a neural network for image classification

Abstract This paper proposes a kernel-blending connection approximated by a neural network (KBNN) for image classification. A kernel mapping connection structure, guaranteed by the function approximation theorem, is devised to blend feature extraction and feature classification through neural network learning. First, a feature extractor learns features from the raw images. Next, an automatically constructed kernel mapping connection maps the feature vectors into a feature space. Finally, a linear classifier is used as an output layer of the neural network to provide classification results. Furthermore, a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network. Experimental results on three well-known image datasets illustrate that the proposed method has good classification accuracy and generalizability.