Reviewerly/exHarmony huggingface dataset:
A Risk Assessment Algorithm for College Student Entrepreneurship Based on Big Data Analysis

College student entrepreneurship is a complex and dynamic process, in which the potential risks faced by entrepreneurial enterprises are interactive and diverse. The changes in risk assessment for college student entrepreneurship are also dynamic and nonlinear and are affected by many factors, which make the risk assessment process for college student entrepreneurship quite complicated. Big data analysis technology is a new product formed under the background of cloud computing and Internet technology, which has the characteristics of large data scale, multiple data types, and strong data value and provides more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. On the basis of summarizing and analyzing previous research results, this article expounded the research status and significance of the risk assessment algorithm for college student entrepreneurship, elaborated the development background, current status, and future challenges of big data analysis technology, introduced the basic principles of support vector machine (SVM) and hierarchical analytic process, constructed a risk assessment model for college student entrepreneurship based on big data analysis, analyzed the risk factors and assessment indicators of the entrepreneurial model, proposed a risk assessment algorithm for college student entrepreneurship based on big data analysis, performed the discrimination coefficient calculation and comprehensive correlation optimization, and finally conducted a case experiment and its result analysis. The study results show that the risk assessment algorithm for college student entrepreneurship based on big data analysis can effectively realize the comprehensive management of risk factors, make full use of the value of assessment parameter data, and significantly improve the accuracy and efficiency of the risk assessment for college student entrepreneurship, providing more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. The study results of this article provide a reference for further researches on the risk assessment algorithm of college student entrepreneurship based on big data analysis.
Facial Expression Detection by Combining Deep Learning Neural Networks

In this paper we detail the construction of a video processing system dedicated to identifying and understanding facial expressions of persons. Our approach implies detection of faciall and marks and analysis of their position to identify emotions. The paper describes a system based on three convolutional neural networks and how to combine them to give more accurate results in the field of facial expression recognition. We adapted the networks which were initially constructed to work on colored or grayscale images to work with black and white images containing facial landmarks. The training, validation and query datasets were also adapted and preprocessed from consecrated computer vision datasets, with the addition of several images acquired by ourselves. We present and comment our experimental results, pointing out advantages and disadvantages.
Extended User Preference Based Weighted Page Ranking Algorithm

Due to the massive amount of data and information available in web pages, users use search engines to find the desired information from the World Wide Web. Search engines encounter a very challenging task to retrieve relevant information efficiently. When a user inputs the search parameters and keywords into the search engine, there are many web pages that match the user's query. To overcome this challenge, the search engine utilizes ranking algorithm to rank the results. One of the most popular ranking algorithms is the Page Rank algorithm which we consider in this study. One of the challenges related to Page Rank algorithm is that many web pages resulted from the algorithm may not meet the intended search query. So, studies are required to develop and enhance such sort of algorithms including the Page Rank algorithm. This study presents the traditional Page Rank algorithm which focuses on the number of links, Hyperlink-Induced Topic Search algorithms which emphasizes hubs and authorities, and Weighted Page Rank algorithm which weighs in-links and out-links to rank pages. Additionally, it reviews the latest studies that improve page rank algorithms based on user behavior and interest. This study proposes an algorithm called Extended User Preference Based Weighted Page Ranking Algorithm (EUPWPR), which enhances the latest studies of page rank algorithms. Moreover, this study considers more parameters to compute the page rank and the results showed that considering more parameters makes the page rank values more precise.
Exploring Input Approximations for Control Panels in Virtual Reality

Today's availability of sophisticated consumer-grade Virtual Reality (VR) hardware provides affordable access to training simulation technology. In contrast to more traditional high fidelity training simulations utilizing physical replicas of control panels that allow natural interaction, users of virtual training often rely on handheld VR controllers to manipulate simulated controls. Therefore, control manipulations of virtual buttons and sliders must be mapped to approximations of real hand manipulations. For instance, the turning of a physical knob with thumb and index finger can be mapped to a gross motor forearm rotation, or a fine motor joystick manipulation when manipulating a controller. In this paper, we present an exploration of hand input approximations of real hands to manipulate the buttons, toggles, knobs and sliders using typical handheld VR controllers. We use common Oculus Quest controllers that rely on capacitive sensing to create basic, approximate hand gestures. We demonstrate the potential of our designs by performing an experiment in which we compare approximate hand gestures against using the controller's joystick that allows fine motor control using thumb input, and a baseline ray-casting interaction that is commonly used in VR applications. We provide a detailed analysis of our interaction designs using the Framework for Interactive Fidelity Analysis (FIFA), which allows us to discuss differences between input approximations and the real-world hand manipulations.
Stability-constrained mobile manipulation planning on rough terrain

Abstract This paper presents a hierarchical framework that allows online point-to-point dynamic-stability-constrained optimal trajectory planning of a mobile manipulator robot working on rough terrain. First, the kinematics model of a mobile manipulator robot and the zero moment point stability measure are presented as theoretical background. Then, a sampling-based quasi-static planning algorithm modified for stability guarantee and traction optimization in continuous dynamic motion is presented along with a mathematical proof. The robot’s quasi-static path is then used as an initial guess to warm start a nonlinear optimal control solver which may otherwise have difficulties finding a solution to the stability-constrained formulation efficiently. The performance and computational efficiency of the framework are demonstrated through an application to a simulated timber harvesting mobile manipulator machine working on varying terrain. The results demonstrate feasibility of online trajectory planning on varying terrain while satisfying the dynamic stability constraint. Qualitative and quantitative comparisons with existing methods are also presented.
A Blockchain Based Fast Authentication Framework for IoT Networks with Trusted Hardware

The Internet of Things, which connects users (devices) from various fields, has attracted great attention of the public nowadays. It is predicted that IoT will play an increasingly important role in people's daily life. The rapid expansion and extensive applications of IoT pose a significant challenge of authentication due to huge amount of IoT devices. Blockchain offers a promising approach for decentralized authentication in IoT networks with its inherent characteristics. To provide an efficient decentralized mutual authentication and privacy protection for IoT users, this paper presents a blockchain based authentication framework for IoT applications. We conduct security analysis and show that the proposed framework achieves sufficient security provision such as identity anonymity, perfect secrecy, traceability, and resistance to common attacks, etc. In addition, the framework is deployed to the Ethereum to verify its efficiency in terms of storage cost, computational and communication overheads.
Play fairway analysis for geothermal exploration in north-western Argentina

Play fairway analysis (PFA) is a statistically based data integration and exploration tool originating in the oil and gas industry that has recently been applied to geothermal exploration. Here, we apply geothermal PFA to a ~40,000 km2 area in the Argentine Puna of the South American Andes (average elevation >4,000 m aMSL) that has geothermal potential but no geothermal resource development to date. We develop a PF workflow customized to the local geological setting and availability of regional geoscience datasets. Evidence layers were grouped to model the spatial distribution of three primary geological attributes of a geothermal system: heat, permeability, and fluid. These layers included hot springs, geothermometry, age and distribution of volcanism, hydrothermal alteration, regional faults along with their kinematics and approximate age of most recent rupture, structural settings, and earthquakes. Spatial statistical methods were used to model and weight the distribution of these evidence layers, including distance buffers, point statistics, and weighted sum functions. Resulting favorability models were created using the veto (product) and voter (sum) methods, and both models indicate highest geothermal potential in Cerro Tuzgle/Aguas Calientes/Tocomar and west Coranzulí. Additional locations in the Puna study area may be prospective but are not resolved given the current data availability. Thus, further data collection to reduce the geological uncertainty is recommended, including acquisition of relatively low-cost datasets such as spring temperatures, fluid chemistry, detailed geological mapping, and structural characterization.
Hierarchical Classification with Confidence using Generalized Logits

We present a bottom-up approach to hierarchical classification based on posteriors conditioned with logits. Beginning with the output logits for a set of terminal labels from a base classifier, an initial hypothesis is repeatedly generalized (softened) to a weaker label until a particular confidence measure is achieved. As conditioning the probabilistic model with the full set of terminal logits quickly becomes intractable for large label sets, we propose an alternative approach employing “generalized logits” spanning relevant hypotheses within the label hierarchy. Experimental results are compared with related methods on multiple datasets and base classifiers. The proposed approach provides an efficient and effective hierarchical classification framework with monotonic, non-decreasing inference behavior.
Linear Computation Coding

We introduce the new concept of computation coding. For linear functions, we present an algorithm to reduce the computational cost of multiplying an arbitrary given matrix with an unknown vector. It decomposes the given matrix into the product of codebook and wiring matrices whose entries are either zero or signed integer powers of two.For a typical implementation of deep neural networks, the proposed algorithm reduces the number of required addition units several times. To achieve the accuracy of 16-bit signed integer arithmetic for 4k-vectors, no multipliers and only 1.5 adders per matrix entry are needed.
EnHMM: On the Use of Ensemble HMMs and Stack Traces to Predict the Reassignment of Bug Report Fields

Bug reports (BR) contain vital information that can help triaging teams prioritize and assign bugs to developers who will provide the fixes. However, studies have shown that BR fields often contain incorrect information that need to be reassigned, which delays the bug fixing process. There exist approaches for predicting whether a BR field should be reassigned or not. These studies use mainly BR descriptions and traditional machine learning algorithms (SVM, KNN, etc.). As such, they do not fully benefit from the sequential order of information in BR data, such as function call sequences in BR stack traces, which may be valuable for improving the prediction accuracy. In this paper, we propose a novel approach, called EnHMM, for predicting the reassignment of BR fields using ensemble Hidden Markov Models (HMMs), trained on stack traces. EnHMM leverages the natural ability of HMMs to represent sequential data to model the temporal order of function calls in BR stack traces. When applied to Eclipse and Gnome BR repositories, EnHMM achieves an average precision, recall, and F-measure of 54%, 76%, and 60% on Eclipse dataset and 41%, 69%, and 51% on Gnome dataset. We also found that EnHMM improves over the best single HMM by 36% for Eclipse and 76% for Gnome. Finally, when comparing EnHMM to Im.ML.KNN, a recent approach in the field, we found that the average F-measure score of EnHMM improves the average F-measure of Im.ML.KNN by 6.80% and improves the average recall of Im.ML.KNN by 36.09%. However, the average precision of EnHMM is lower than that of Im.ML.KNN (53.93% as opposed to 56.71%).
Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution

In this article, we present an approach to detect basic movements of cyclists in real world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet) architecture. To resolve occlusions of cyclists by other traffic participants or road structures, we use a wide angle stereo camera system mounted at a heavily frequented public intersection. We created a large dataset consisting of 1,639 video sequences containing cyclists, recorded in real world traffic, resulting in over 1.1 million samples. Through modeling the cyclists' behavior by a state machine of basic cyclist movements, our approach takes every situation into account and is not limited to certain scenarios. We compare our method to an approach solely based on position sequences. Both methods are evaluated taking into account frame wise and scene wise classification results of basic movements, and detection times of basic movement transitions, where our approach outperforms the position based approach by producing more reliable detections with shorter detection times. Our code and parts of our dataset are made publicly available.
An Automated Post-Mortem Analysis of Vulnerability Relationships using Natural Language Word Embeddings

The daily activities of cybersecurity experts and software engineers—code reviews, issue tracking, vulnerability reporting—are constantly contributing to a massive wealth of security-specific natural language. In the case of vulnerabilities, understanding their causes, consequences, and mitigations is essential to learning from past mistakes and writing better, more secure code in the future. Many existing vulnerability assessment methodologies, like CVSS, rely on categorization and numerical metrics to glean insights into vulnerabilities, but these tools are unable to capture the subtle complexities and relationships between vulnerabilities because they do not examine the nuanced natural language artifacts left behind by developers. In this work, we want to discover unexpected relationships between vulnerabilities with the goal of improving upon current practices for post-mortem analysis of vulnerabilities. To that end, we trained word embedding models on two corpora of vulnerability descriptions from Common Vulnerabilities and Exposures (CVE) and the Vulnerability History Project (VHP), performed hierarchical agglomerative clustering on word embedding vectors representing the overall semantic meaning of vulnerability descriptions, and derived insights from vulnerability clusters based on their most common bigrams. We found that (1) vulnerabilities with similar consequences and based on similar weaknesses are often clustered together, (2) clustering word embeddings identified vulnerabilities that need more detailed descriptions, and (3) clusters rarely contained vulnerabilities from a single software project. Our methodology is automated and can be easily applied to other natural language corpora. We release all of the corpora, models, and code used in our work.
RefinedFed: A Refining Algorithm for Federated Learning

Federated learning (FL) is a machine learning approach where the goal is to train a centralized model using a large number of clients that host private datasets. FL trains a smaller version of the model at each dataset site and then aggregates all the models at the server. In practice, clients (i.e., dataset holders) that participate in the learning process may possess corrupted or noisy datasets resulting in low accuracy models. Additionally, malicious clients may poison the data or carry out model discovery attacks.In this paper, we propose a refining algorithm called RefinedFed, to eliminate corrupted, low accuracy, and noisy models that can negatively impact the centralized model by reducing its accuracy or cause other malicious activities. Furthermore, RefinedFed reduces the uplink communication cost with the centralized server, which in return results in faster aggregation on the server side. Based on our preliminary experiments on the MNIST dataset, we observed that RefinedFed improved the global model accuracy from 84% to 91% while consuming less time for aggregation.
Multiscale Conditional Relationship Graph Network for Referring Relationships in Images

Images contain not only individual entities but also abundant visual relationships between entities. Therefore, conditioned on visual relationship triples <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${&lt; }subject-relationship-object{&gt;}$ </tex-math></inline-formula> that can be viewed as structured texts, entities (subjects or objects) can be localized in images without ambiguity. However, it is challenging to efficiently model visual relationships since a specific relationship usually has dramatic intraclass visual differences when involved with different entities, quite a number of which are in a small scale. In addition, the subject and the object in a relationship triple may have different best scales, and matching the subject and the object with different appropriate scales may improve prediction. To address these issues, a multiscale conditional relationship graph network (CRGN) is proposed in this article to localize entities based on visual relationships. Specifically, an attention pyramid network is first introduced to generate multiscale attention maps to capture entities with various sizes for entity matching. Then, a CRGN is further designed to aggregate and refine multiscale attention features to localize entities via passing relationship contexts between entity attention maps, which sufficiently utilizes the entity attention maps with the best scales. In order to mitigate the negative effects of intraclass visual differences of relationships, vision-agnostic relationship features are utilized in the proposed CRGN to indirectly model relationship contexts. The experiments demonstrate the superiority of the proposed method compared with the previous powerful frameworks on three challenging benchmark data sets, including CLEVR, Visual Genome, and VRD. The project page can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://mic.tongji.edu.cn/d9/5c/c9778a186716/page.htm</uri> .
Wavelet-Based Optical Flow Analysis for Background-Oriented Schlieren Image Processing

A wavelet-based optical flow analysis (wOFA) method for processing background-oriented schlieren (BOS) images is presented and demonstrated on synthetic and experimental data. Optical flow is inherently well suited to BOS, because the background pattern and lighting conditions are specified and controlled by the user and can be chosen to play to the strengths of optical flow processing. Analysis of the synthetic BOS data show that a two-dimensional sinusoidal background produces the highest reconstruction accuracy for both wOFA and iterative least squares (ILS) algorithms. wOFA outperforms ILS in terms of overall accuracy for displacement fields with sufficiently high spatial frequency content. In addition, wOFA provides higher spatial resolution, about an order of magnitude in terms of the total number of pixels in the final BOS image. Finally, wOFA is demonstrated on two sets of experimental data: a heat gun plume experiment with nearly ideal imaging characteristics, and experiments in a supersonic wind tunnel flow with more realistic restrictions on the acquisition of images. BOS images computed with wOFA are shown to have higher spatial resolution and sensitivity than ILS, without introducing additional noise. Therefore, wOFA of BOS images are able to reveal flow features not detected by ILS analysis.
GraphPEG

Due to massive thread-level parallelism, GPUs have become an attractive platform for accelerating large-scale data parallel computations, such as graph processing. However, achieving high performance for graph processing with GPUs is non-trivial. Processing graphs on GPUs introduces several problems, such as load imbalance, low utilization of hardware unit, and memory divergence. Although previous work has proposed several software strategies to optimize graph processing on GPUs, there are several issues beyond the capability of software techniques to address. In this article, we present GraphPEG, a graph processing engine for efficient graph processing on GPUs. Inspired by the observation that many graph algorithms have a common pattern on graph traversal, GraphPEG improves the performance of graph processing by coupling automatic edge gathering with fine-grain work distribution. GraphPEG can also adapt to various input graph datasets and simplify the software design of graph processing with hardware-assisted graph traversal. Simulation results show that, in comparison with two representative highly efficient GPU graph processing software framework Gunrock and SEP-Graph, GraphPEG improves graph processing throughput by 2.8× and 2.5× on average, and up to 7.3× and 7.0× for six graph algorithm benchmarks on six graph datasets, with marginal hardware cost.
Variable Coded Batch Matrix Multiplication

A majority of coded matrix-matrix computation literature has broadly focused in two directions: matrix partitioning for computing a single computation task and batch processing of multiple distinct computation tasks. While these works provide codes with good straggler resilience and fast decoding for their problem spaces, these codes would not be able to take advantage of the natural redundancy of re-using matrices across batch jobs. In this paper, we introduce the Variable Coded Distributed Batch Matrix Multiplication (VCDBMM) problem which tasks a distributed system to perform batch matrix multiplication where matrices are not necessarily distinct among batch jobs. Inspired in part by Cross-Subspace Alignment codes, we develop Flexible Cross-Subspace Alignments (FCSA) codes that are flexible enough to utilize this redundancy. We provide a full characterization of FCSA codes which allow for a wide variety of system complexities including good straggler resilience and fast decoding. We theoretically demonstrate that, under certain practical conditions, FCSA codes are within a factor of 2 of the optimal solution when it comes to straggler resilience. Furthermore, our simulations demonstrate that our codes can achieve even better optimality gaps in practice, even going as low as 1.7.