Reviewerly/exHarmony huggingface dataset:
Requisite Skills Profile of Software Development Professionals for Startups

Startups are on the rise every day. They contribute to global economies by creating innovative products and services, yet the phenomenon of software development skills for start-ups is not fully researched. A key challenge startup companies face is recruiting software development professionals with the right skills. Current software development skill studies largely focus on a broader level with very little focus on startup companies, which operate differently to well-established companies. In this study, the Delphi method was employed to create a skills profile of software development professionals for startups. Four of the top five skills identified pertain to soft skills, i.e. problem-solving, working well as a team player, being self-motivated, and having learning agility. The only technical skill ranked in the top five pertained to the core development task of building new software.
Multi-Object Tracking Via Multi-Attention

Data association plays a crucial role in Multi-Object Tracking(MOT), but it is usually suppressed by occlusion. In this paper, we propose an online MOT approach via multiple attention mechanism(Multi-Attention) to handle the frequent interactions between targets. Specifically, the proposed Multi-Attention consists of spatial-attention, channel-attention, and temporal-attention three modules. The spatial-attention module lets the network focus on visible local areas by generating a visibility map, and the channel-attention module combines texture information and context information adaptively to build a recognizable object descriptor, then the temporal-attention module pays different attention to objects in the same trajectory avoiding the suppress caused by contaminated samples. Besides, a multiple branch convolutional block called receptive filed module(RFModule) is introduced to learn multiple levels of information for Multi-Attention. The experimental results on MOTChallenging benchmarks demonstrate the effectiveness of the proposed MOT algorithm against both online and offline trackers.
Knowledge Grounded Pre-Trained Model For Dialogue Response Generation

Teaching machine to answer arbitrary questions is a long-term goal of natural language processing. In real dialogue corpus, informative words like named entities can often be infrequent and hard to model, and one primary challenge of dialogue system is how to promote the model's capability of generating high-quality responses with those informative words. In order to address this problem, we propose a novel pre-training based encoder-decoder model, which can enhance the multiturn dialogue response generation by incorporating external textual knowledge. We adopt BERT as encoder to merge external knowledge into dialogue history modeling, and a multi-head attention based decoder is designed to incorporate the semantic information from both knowledge and dialogue hidden representations into decoding process to generate informative and proper dialogue responses. Experiments on two response generation tasks indicate our model to be superior over competitive baselines on both automatic and human evaluations.
Supporting Resilience in Rural Emergency Preparedness and Response Through Improved Information Access

Recent disasters have highlighted the gap between existing urbancentric resilience models and the needs of rural communities, which are frequently marginalized. Large rural constituencies, encompassing vast and sparsely populated areas, lack broadband connectivity and rely predominantly on volunteer-based emergency workforce. The above are some of the key factors hampering rural first responders? ability to access, act upon and disseminate emergency-related information. This has an adverse effect on both residents and the agencies that serve them, as it limits residents? ability to prepare for emergencies and compromises the safety of first responders. Furthermore, the unique socio-economic structure of rural areas makes them particularly vulnerable to the effects of incipient or unfolding disasters. Hence, rural communities often develop self-reliance capabilities by creating tightknit social structures and taking charge of their own technological progress through community-driven efforts.
Score Normalization of X-Vector Speaker Verification System for Short-Duration Speaker Verification Challenge

In this paper we present our contribution to the task 2 of the short-duration speaker verification (SdSV) challenge. The main task for this challenge is to find new technologies for text-dependent and text-independent speaker verification in short duration scenario. Some of the approaches used by the authors during participation in the challenge are presented. Described speaker verification systems include baseline x-vector system with PLDA backend and score normalization, x-vector system with neural PLDA backend and fusion of both systems.The main goal of this paper is to analyze influence of different score normalization methods on x-vector based speaker verification systems performance. We found that system with PLDA backend and ZT-normalization method (single system) gives superior performance in Farsi trials, but gives lower performance improvement in English trials. Overall, in terms of minDCF single system performs 46.3% better than baseline x-vector system. We found that enroll data augmentation is useless for Neural PLDA backend, as performance of the system does not improve after adding augmented enroll data. Single system with ZT-score normalization and additional enroll audio augmentation performs 14.8% better than Neural PLDA backend system.
Learning Heuristic A: Efficient Graph Search using Neural Network

In this paper, we consider the path planning problem on a graph. To reduce computation load by efficiently exploring the graph, we model the heuristic function as a neural network, which is trained by a training set derived from optimal paths to estimate the optimal cost between a pair of vertices on the graph. As such heuristic function cannot be proved to be an admissible heuristic to guarantee the global optimality of the path, we adapt an admissible heuristic function for the terminating criteria. Thus, proposed Learning Heuristic A* (LHA*) guarantees the bounded suboptimality of the path. The performance of LHA* was demonstrated by simulations in a maze-like map and compared with the performance of weighted A* with the same suboptimality bound.
Tiny-Hourglassnet: An Efficient Design For 3d Human Pose Estimation

Existing deep learning based methods for 3D human pose estimation cannot be deployed on resource-constrained devices. In this paper, we propose a Tiny-HourglassNet for 3D human pose estimation that improves the efficiency of Stacked Hourglass Networks with a guarantee of estimation performance. To be concrete, we develop an efficient Tiny-Hourglass backbone with two different types of ShuffleNet V2 blocks. At the meantime, we present a Simple Estimation Head (SEH) to reduce the resource utility for depth prediction. Furthermore, we develop two feature enhancement modules, namely Feature Enhancement Module (FEM) and Intermediate Enhancement Module (IEM), to improve the feature representation ability of Tiny-HourglassNet without evident increment of resource usage and computational complexity. Experimental results demonstrate that Tiny-HourglassNet achieves equivalent accuracy with a reduction of 80% complexity (FLOPs) in comparison to the baselines.
Biologically Inspired Sleep Algorithm for Variational Auto-Encoders

Variational auto-encoders (VAEs) are a class of likelihood-based generative models that operate by providing an approximation to the problem of inference by introducing a latent variable and encoder/decoder components. However, the latent codes usually have no structure, are not informative, and are not interpretable. This problem is amplified if these models need to be used for auxiliary tasks or when different aspects of the generated samples need to be controlled or interpreted by humans. We address these issues by proposing a biologically realistic sleep algorithm for VAEs (VAE-sleep). The algorithm augments the normal training phase of the VAE with an unsupervised learning phase in the equivalent spiking VAE modeled after how the human brain learns, using the Mirrored Spike Timing Dependent Plasticity learning rule. We hypothesize the proposed unsupervised VAE-sleep phase creates more realistic feature representations, which in turn lead to increase a VAE’s robustness to reconstruct the input. We conduct quantitative and qualitative experiments, including comparisons with the state-of-the-art on three datasets: CelebA, MNIST, and Fashion-MNIST. We show that our model performs better than the standard VAE and varitional sparse coding (VSC) on benchmark classification task by demonstrating improved classification accuracy and significantly increased robustness to the number of latent dimensions. As a result of experiments suggest, the proposed method shows improved performance in comparison with other widely used methods and performs favorably under the metrics PSNR, SSIM, LPIPS. The quantitative evaluations also suggest that our model can generate more realistic images compared to the state of arts when tested on disturbed or noisy inputs.
Unsupervised Learning From Limited Available Data by β-NMF and Dual Autoencoder

Unsupervised Learning (UL) models are a class of Machine Learning (ML) which concerns with reducing dimensionality, data factorization, disentangling and learning the representations among the data. The UL models gain their popularity due to their abilities to learn without any predefined label, and they are able to reduce the noise and redundancy among the data samples. However, generalizing the UL models for different applications including image generation, compression, encoding, and recognition faces different challenges due to limited available data for learning, diversity, and complex dimensions. To overcome such challenges, we propose a partial learning procedure by utilizing the β-Non Negative Matrix Factorization (β-NMF), which maps the data into two complementary subspaces constituting generalized driven priors among the data. Moreover, we employ a dual-shallow Autoencoder (AE) to learn the subspaces separately or jointly for image reconstruction and visualization tasks, where our model performance shows superior results to the literary works when learning the model with a small amount of data and generalizing it for large-scale unseen data.
Unified Multi-Domain Learning and Data Imputation using Adversarial Autoencoder

We present a novel framework that can combine multi-domain learning (MDL), data imputation (DI) and multi-task learning (MTL) to improve performance for classification and regression tasks in different domains. The core of our method is an adversarial autoencoder that can: (1) learn to produce domain-invariant embeddings to reduce the difference between domains; (2) learn the data distribution for each domain and correctly perform data imputation on missing data. For MDL, we use the Maximum Mean Discrepancy (MMD) measure to align the domain distributions. For DI, we use an adversarial approach where a generator fill in information for missing data and a discriminator tries to distinguish between real and imputed values. Finally, using the universal feature representation in the embeddings, we train a classifier using MTL that given input from any domain, can predict labels for all domains. We demonstrate the superior performance of our approach compared to other state-of-art methods in three distinct settings, DG-DI in image recognition with unstructured data, MTL-DI in grade estimation with structured data and MDMTL-DI in a selection process using mixed data.
Concept for the Large Scale Deployment of Ambient Assisted Living Systems

This paper deals with the problem of equipping 100 households in Lower Bavarian with sensors using different interfaces and protocols. This sensor set in the households is used in the context of the research project DeinHaus 4.0 of Deggendorf Institute of Technology. This project focuses on supporting technologies to enable elderly people to live as long as possible in their own house or apartment. Therefore, a set of sensors and Ambient Assisted Living (AAL) technologies are used to build a supporting system. Furthermore, the system collects, anonymizes and stores the sensor data for further technical and social analysis.
Efficient integration of generative topic models into discriminative classifiers using robust probabilistic kernels

We propose an alternative to the generative classifier that usually models both the class conditionals and class priors separately, and then uses the Bayes theorem to compute the posterior distribution of classes given the training set as a decision boundary. Because SVM (support vector machine) is not a probabilistic framework, it is really difficult to implement a direct posterior distribution-based discriminative classifier. As SVM lacks in full Bayesian analysis, we propose a hybrid (generative–discriminative) technique where the generative topic features from a Bayesian learning are fed to the SVM. The standard latent Dirichlet allocation topic model with its Dirichlet (Dir) prior could be defined as Dir–Dir topic model to characterize the Dirichlet placed on the document and corpus parameters. With very flexible conjugate priors to the multinomials such as generalized Dirichlet (GD) and Beta-Liouville (BL) in our proposed approach, we define two new topic models: the BL–GD and GD–BL. We take advantage of the geometric interpretation of our generative topic (latent) models that associate a K-dimensional manifold (K is the size of the topics) embedded into a V-dimensional feature space (word simplex) where V is the vocabulary size. Under this structure, the low-dimensional topic simplex (the subspace) defines a document as a single point on its manifold and associates each document with a single probability. The SVM, with its kernel trick, performs on these document probabilities in classification where it utilizes the maximum margin learning approach as a decision boundary. The key note is that points or documents that are close to each other on the manifold must belong to the same class. Experimental results with text documents and images show the merits of the proposed framework.
A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer’s Disease

Graph theory has been used extensively to investigate information exchange efficiency among brain regions represented as graph nodes. In this work, we propose a new metric to measure how the brain network is robust or resilient to any attack on its nodes and edges. The metric measures redundancy in the sense that it calculates the minimum number of independent, not necessarily shortest, paths between every pair of nodes. We adopt this metric for characterizing (i) the redundancy of time-varying brain networks, i.e., chronnectomes, computed along the progression of Alzheimer’s disease (AD), including early mild cognitive impairment (EMCI), and (ii) changes in progressive MCI compared to stable MCI by calculating the probabilities of having at least 2 (or 3) independent paths between every pair of brain regions in a short period of time. Finally, we design a learning-based early AD detection framework, coined “REdundancy Analysis of Dynamic functional connectivity for Disease Diagnosis (READ\(^3\))”, and show its superiority over other AD early detection methods. With the ability to measure dynamic resilience and robustness of brain networks, the metric is complementary to the commonly used “cost-efficiency” in brain network analysis.
Process Inspection Support

Organizational factors such as team structure, coordination among engineers, or processes have a significant impact on software quality and development progress. Projects often take much longer to complete than planned and miscommunications among engineers are common. Yet, the process for exploring the project-specific or organization-specific root causes why this happens is still poorly supported. Investigations are cumbersome and require significant effort. In the context of this industrial case study, our industry partner was interested in measuring and assessing how the organization structure and issue handling processes ultimately affected software quality and time. Reducing the effort of such investigations/retrospectives and speeding up fact finding is important as it allows for more frequent, informed engineering process improvements and feedback to managers, team leads, and engineers. This paper describes our approach of pairing process metrics with visual historical inspection of issues. Stakeholders such as managers, team leads, or quality assurance engineers inspect metrics (and deviations from expected values) for individual issues and utilize a historical visualization of the affected (and related) issues to obtain insights into the reason for the metric (deviation) and its root cause. We demonstrate the usefulness of our approach based on our ProcessInspector prototype providing access to data on four real industry projects and a qualitative evaluation with team leads and group leads from our industry partner.
Flexible Model for Organizing Blended and Distance Learning

The proposed flexible model for the organization of mixed and distance learning involves the creation of an individual learning path for student testing before starting training. Based on the learning outcomes, the student is credited to the learning path. The learning path consists of mandatory and optional modules for training, optional modules can be skipped by passing the test successfully. The training model is represented using the ontological model, and the decision-making rules for the model are logical rules.
Implementation of Computer Vision in Detecting Human Poses

Developing strong core muscles are important for children. Children with strong core muscles allow them to do any kinds of activities that mostly involve physical movement. The work tested on an education organisation that aims to strengthen core muscles by providing yoga-like poses. There are certified trainers that will coach the students along the way. However, mistakes could be made during the coaching because of different trainer's justification and whether the coaching processes were done correctly. Therefore, a solution is proposed to develop a computerized pose detector package which allows the trainers to improve the coaching with the students. The result is promising where the standardized pose could be implemented and compared to observed students' poses, however it is found that due to the uniqueness of the poses, it generates several unidentified results.
Experiments and optimizations for TVM on RISC-V Architectures with P Extension

TVM is a AI compiler supports both graph-level and operator-level optimization on machine learning models. It provides an optimizable flow to deploy on diverse target devices. By exploiting TVM schedule, we can optimize the codegen behavior for our RISC-V architecture. Since RISCV is configurable with the selection on different extension, we can enable multiple extensions depends on the application scene. In our work, we present the flow for enabling and optimizing the RISC-V P extension toward QNN models from TVM. With support from LLVM and a customized deep learning runtime (DLR), we verified our work on both FLOAT32 and prequantized models from Tensorflow Lite. Experiments shows that comparing with FLOAT32 models, our work can achieve 2.7-7.0 times of performance improvement with regard to total instruction count at runtime for pre-quantized version with a set of benchmarks including Mobilenet and Inception-v3. As for accuracy issue, the degradation is tiny for quantization version among 500 images. All experiments are running on RISC-V simulator, Spike with P extension support.
Patterns for Success in the Adoption and Execution of Feature‐based Product Line Engineering: A Report from Practitioners

Abstract Systems and Software Product Line Engineering (PLE) is a general approach to engineer a portfolio of related products in an efficient manner, taking advantage of the products’ similarities while respecting and managing their differences. The approach manages a product portfolio as a single entity, as opposed to a multitude of separate products. Numerous resources describe the organizational benefits associated with incorporating PLE techniques and tools. Feature‐based System and Software Product Line Engineering is a specific form of PLE that is powered by commercial off‐the‐shelf automation, fully defined processes, and a formal language of variation based on features. Many case studies show the efficacy of Feature‐based PLE and the improvements in cost, schedule, and quality that can come with it. In this paper, practitioners from four of world's six largest defense companies highlight their experience with the practices that enable and inhibit success with this powerful engineering discipline.
Black-box Evolutionary Search for Adversarial Examples against Deep Image Classifiers in Non-Targeted Attacks

Machine learning models exhibit vulnerability to adversarial examples i.e., artificially created inputs that become misinterpreted. The goal of this paper is to explore non-targeted black-box adversarial attacks on deep networks performing image classification. The original evolutionary algorithm for generating adversarial examples is proposed that employs a guided multi-objective search through the space of perturbed images. The efficiency of attacks is validated by experiments with the CIFAR-10 data set. The experimental results verify the usability of our approach against deep convolutional neural networks.
Design of Intelligent Mechanical Arm Based on STM32

With the deepening research of artificial intelligence technology, intelligent mechanical arm is widely used in industrial production. This paper designs an intelligent mechanical arm for simulating the process of U disk production in teaching. The mechanical arm is designed based on stm32f407 development platform, including three driving motors of elbow, wrist and finger, and six pairs of alignment sensors, Based on the algorithm PID and fuzzy control, combined with the precise PWM motor speed regulation technology, the intelligent mechanical arm in experiment can work accurately, smoothly and stably.
Simulation of Autonomous UAV Navigation with Collision Avoidance and Space Awareness

This research developed a safe navigation system of an autonomous UAV within a comprehensive simulation framework. The navigation system can find a collision-free trajectory to a randomly assigned 3D target position without any prior map information. It contains four main components: mapping, localization, cognition, and control. The cognition system makes execution command based on the perceived position information about obstacles and the UAV from mapping and localization system. The control system is responsible for executing the input command made by the cognition system. Three case studies for real-life scenarios, such as space awareness, static obstacle avoidance, and dynamic obstacle avoidance, are conducted. The experiments demonstrate that the UAV can determine a collision-free trajectory under all three cases of environments. All simulated components are designed to match their real-world counterparts' dynamics and properties. Ideally, the simulated navigation framework can be transferred to a real UAV without any changes. As the navigation system is implemented modularly, it is easier to test and validate to ensure its performance. Moreover, the system has excellent readability, maintainability, and extensibility. Hence, the simulation framework provides an excellent platform for future robotic research.