Reviewerly/exHarmony huggingface dataset:
Building for the Future, Together: A Model for Bringing Emerging Products to Market, Using Anticipatory Ethnography and Mixed Methods Research

Applied ethnography practitioners are often charged with learning from existing or potential customers, for a product that is either familiar to them or close in nature to what they have used before. There are particular challenges for emerging technologies, where the market is much less defined. Applied ethnography has the potential to help with predicting future market scenarios, honing the value proposition and product definition. Done well, these contributions help align siloed teams, resulting in better products being delivered to the right customers, faster. This case study will outline the approaches used across all product development stages for new creative design tools in the emerging field of immersive media – specifically, 3D and Augmented Reality. A collaboration model and best practices are offered, to guide practitioners to address both overt challenges (e.g., identifying which audiences to focus on), and hidden challenges (e.g., creating a human‐centered dialogue in an ambiguous, technology‐centered space).
BERT-based Aggregative Group Representation for Group Recommendation

Group recommendation, as a type of recommender systems, mainly recommends items to a group of users. In this paper, inspired by Bidirectional Encoder Representation from Transformers (BERT), our goal is to explore a novel BERT-based group modeling. Therefore, we devise a novel BERT-based Group Recommendation approach, namely BGR. Specifically, BERT is adopted to capture group preferences by using sentence-level embedding from a global perspective. Based on this, we then integrate aggregation representation of group users to obtain final group representation. Finally, group recommendation is achieved in the framework of neural collaborative filtering. Experiments are conducted on two real-world datasets demonstrate that our BGR outperforms the baselines.
A Framework for Area-efficient Multi-task BERT Execution on ReRAM-based Accelerators

With the superior algorithmic performances, BERT has become the de-facto standard model for various NLP tasks. Accordingly, multiple BERT models have been adopted on a single system, which is also called multi-task BERT. Although the ReRAM-based accelerator shows the sufficient potential to execute a single BERT model by adopting in-memory computation, processing multi-task BERT on the ReRAM-based accelerator extremely increases the overall area due to multiple fine-tuned models. In this paper, we propose a framework for area-efficient multi-task BERT execution on the ReRAM-based accelerator. Firstly, we decompose the fine-tuned model of each task by utilizing the base-model. After that, we propose a two-stage weight compressor, which shrinks the decomposed models by analyzing the properties of the ReRAM-based accelerator. We also present a profiler to generate hyper-parameters for the proposed compressor. By sharing the base-model and compressing the decomposed models, the proposed framework successfully reduces the total area of the ReRAM-based accelerator without an additional training procedure. It achieves a 0.26 x area than baseline while maintaining the algorithmic performances.
SdcNet for object recognition

In this paper, a CNN architecture for object recognition is proposed, aiming at achieving a good processing-quality at the lowest computation-cost. The work includes the design of SdcBlock, a convolution module, for feature extraction, and that of SdcNet, an end-to-end CNN architecture. The module is designed to extract the maximum amount of high-density feature information from a given set of data channels. To this end, successive depthwise convolutions (Sdc) are applied to each group of data to produce feature elements of different filtering orders. To optimize the functionality of these convolutions, a particular pre-and-post-convolution data control is applied. The pre-convolution control is to organize the input channels of the module so that the depthwise convolutions can be performed with a single channel or a combination of multiple data channels, depending on the nature of the data. The post-convolution control is to combine the critical feature elements of different filtering orders to enhance the quality of the convolved results. The SdcNet is mainly composed of cascaded SdcBlocks. The hyper-parameters in the architecture can be adjusted easily so that each module can be tuned to suit its input signals in order to optimize the processing-quality of the entire network. Three different versions of SdcNet have been proposed and tested using CIFAR dataset, and the results demonstrate that the architecture gives a better processing-quality at a significantly lower computation cost, compared with networks performing similar tasks. Two other versions have also been tested with samples from ImageNet to prove the applicability of SdcNet in object recognition with images of ImageNet format. Also, a SdcNet for brain tumor detection has been designed and tested successfully to illustrate that SdcNet can effectively perform the detection with a high computation efficiency.
Model Diet: A Simple yet Effective Model Compression for Vision Tasks

Computer vision coupled with machine learning algorithms has greatly helped mobile robotic platforms become more intelligent and capable of performing in the real world. Specifically, Convolutional Neural Networks (CNNs) have achieved a high accuracy on a range of visual perception tasks (e.g., object detection, classification, segmentation, and similar others). One of the bottlenecks in CNNs is their high computational requirement. This makes most of them not easily deployable on robotic platforms, since their on-board computational power is limited. Recently, Involution successfully reduced the number of parameters of CNNs by replacing all the 3 &#x00D7; 3 convolution kernels with involution kernels, which use 1 &#x00D7; 1 convolution for the kernel generation. Filter pruning methods have also successively reduced the number of parameters in CNNs. Notably, however, Involution has reshaping layers and the kernel size is unknown when loading the pre-trained model. In this paper, we propose a pruning method named Model Diet that can be applied to Involution and other CNNs. We present experimental results showing that it has better results compared with randomly initialized weights.
Occlusion-Aware Path Planning to Promote Infrared Positioning Accuracy for Autonomous Driving in a Warehouse

Infrared positioning is a critical module in an indoor autonomous vehicle platform. In an infrared positioning system, the ego vehicle is equipped with an infrared emitter while the infrared receivers are fixed onto the ceiling. The infrared positioning result is accurate only when the number of valid infrared receivers is more than three. An infrared receiver easily becomes invalid if it does not receive light from the infrared emitter due to indoor occlusions. This study proposes an occlusion-aware path planner that enables an autonomous vehicle to navigate toward the occlusion-free part of the drivable area. The planner consists of four layers. In layer one, a homotopic A* path is searched for in the 2D grid map to roughly connect the initial and goal points. In layer two, a curvature-continuous reference line is planned close to the A* path using numerical optimal control. In layer three, a Frenet frame is constructed along the reference line, followed by a search for an occlusion-aware path within that frame via dynamic programming. In layer four, a curvature-continuous path is optimized via quadratic programming within the Frenet frame. A path planned within the Frenet frame may violate the curvature bounds in a real-world Cartesian frame; thus, layer four is implemented through trial and error. Simulation results in CarSim software show that the derived paths reduce the poor positioning risk and are easily tracked by a controller.
Sovremennoe pedagogicheskoe obrazovanie: analiz trendov razvitiia (po materialam luchshikh praktik raboty federal'nykh innovatsionnykh ploshchadok v 2021 godu)

The chapter of the monograph highlights effective models of activities in the field of teacher education based on the best practices of federal innovation platforms. The authors discuss the projects of federal innovation platforms of higher education institutions aimed at achieving a new quality and formats of education. The paper analyzes the innovative experience of Federal Innovation Platforms: State Budgetary Educational Institution of Additional Professional Education «Institute of Education Development» of the Krasnodar Krai, State Autonomous Institution of Additional Professional Education Institute of Education Development «of the Republic of Bashkortostan, Federal State Budget Educational Institution of Higher Education «Kemerovo State University», Federal State Budget Educational Institution of Higher Education «Vladimir state university», Federal State Budget Educational Institution of Higher Education «Tomsk State Pedagogical University», FGAOU VO «Immanuel Kant Baltic Federal University», Federal State Budget Educational Institution of Higher Education «Naberezhnye Chelny State Pedagogical University», FGBOU VO «Russian State Pedagogical University named after A.I. Herzen», the Starooskolsk branch of the Belgorod State National Research University, Federal State Budget Educational Institution of Higher Education Altai State Pedagogical University, Federal State Budget Educational Institution of Higher Education «Ryazan State University named after S.А. Yesenin».
Multi-accelerator Neural Network Inference in Diversely Heterogeneous Embedded Systems

Neural network inference (NNI) is commonly used in mobile and autonomous systems for latency-sensitive critical operations such as obstacle detection and avoidance. In addition to latency, energy consumption is also an important factor in such workloads, since the battery is a limited resource in such systems. Energy and latency demands of critical workload execution in such systems can vary based on the physical system state. For example, the remaining energy on a low-running battery should be prioritized for motor consumption in a quadcopter. On the other hand, if the quadcopter is flying through obstacles, latency-aware execution becomes a priority. Many recent mobile and autonomous system-on-chips embed a diverse range of accelerators with varying power and performance characteristics which can be utilized to achieve this fine trade-off between energy and latency.In this paper, we investigate Multi-accelerator Execution (MAE) on diversely heterogeneous embedded systems, where sub-components of a given workload, such as NNI, can be assigned to different type of accelerators to achieve a desired latency or energy goal. We first analyze the energy and performance characteristics of execution of neural network layers on different type of accelerators. We then explore energy/performance trade-offs via layer-wise scheduling for NNI by considering different layer-to-PE mappings. We finally propose a customizable metric, called multi-accelerator execution gain (MAEG), in order to measure the energy or performance benefits of MAE of a given workload. Our empirical results on Jetson Xavier SoCs show that our methodology can provide up to 28% energy/performance trade-off benefit when compared to the case where all layers are assigned to a single PE.
Deep Neural Network Security From a Hardware Perspective

Deep neural networks (DNNs) have been deployed on various computing platforms for acceleration, making the hardware security of DNNs an emerging concern. Several attacking methods related to the hardware accelerator of DNN have been introduced, which either affect the DNN inference accuracy or leak the privacy of DNN architectures and parameters. To provide a generic understanding of this emerging research area, in this survey, we systematically review the recent research progress of DNN security from a hardware perspective. Specially, we discuss the existing hardware-oriented attacks targeting different DNN acceleration platforms, and point out the potential vulnerabilities.
Comment on “Bipolar fuzzy relation equations systems based on the product t‐norm”

This note provides counterexamples to illustrate that Theorem 4 which is proposed by Cornejo, Lobo, and Medina (Bipolar fuzzy relation equations systems based on the product t‐norm. Mathematical Methods in the Applied Sciences. 2019,42(17):5779‐5793) is incorrect. The incorrect of Theorem 4 is due to the inaccuracy of the definition of feasible pair. Then, we present the correction of the definition of feasible pair to the contention.
Application of Web Crawler Technology Based on Python in Big Data Environment

With the transformation and upgrading of social economy and the application and development of high and new technology, it is an important trend of the development of China's higher vocational education to carry out the extension of higher vocational college diploma education to undergraduate level vocational education or even higher level vocational education, which is also the highlight of the construction of modern vocational education system. Undergraduate vocational education has the basic attributes of higher, technical and regional. Based on Python data crawler, this paper studies the application of data crawler in the demand of undergraduate vocational education.
A two-level Item Response Theory model to evaluate speech synthesis and recognition

Automatic speech recognition (ASR) systems should be tested ideally using diverse speech test data. A promising alternative to produce such test data is to synthesize speeches from diverse sentences and speakers. However, despite the great amount of test data that can be produced, not all speeches are equally relevant. This paper proposes a two-level Item Response Theory (IRT) model to simultaneously evaluate ASR systems, speakers and sentences. In the first level, the transcription rates obtained by a pool of ASR systems on a set of synthesized speeches are recorded and then analyzed to estimate: each speech’s difficulty and each ASR system’s ability. In the second level, each speech’s difficulty is decomposed as a function of two factors: the sentence’s difficulty and the speaker’s quality. Thus, the speech’s difficulty is high when generated from a difficult sentence and a bad speaker, while an ASR is good when it is robust to hard speeches. Performed experiments revealed useful insights on how the quality of speech synthesis and recognition can be affected by distinct factors (e.g., sentence difficulty and speaker ability).
Gamma-Ray Imaging with Spatially Continuous Intensity Statistics

Novel methods for the inference of radiation intensity functions defined over known surfaces are proposed, intended for use in surveying applications with mobile spectrometers. Previous approaches, based on the maximum likelihood expectation maximization (ML-EM) framework with Poisson likelihoods, are extended to better handle spatially continuous intensity statistics using ideas from Gaussian filtering. The resulting algorithm is evaluated against a classical ML-EM method, and a recently proposed sparse additive point source localization (APSL) algorithm in a Monte-Carlo simulation study. The new generalized ASPL (GASPL) is shown to compare favorably in terms of estimation accuracy when the true intensity is not well described by a set of point sources. Finally, the GASPL is used in an experiment where a detector is mounted to an unmanned aerial vehicle to estimate the intensity and location of radioactive sources placed in a meadow.
Multicriteria approaches based on a new discrimination criterions for feature selection

In machine learning, the presence of a large number of explanatory features leads to a greater complexity of the algorithms and to a strong degradation in the performance of the prediction models. For this, a selection of an optimal discriminating subset is necessary. Feature selection can be viewed as a multi-objective optimization problem, since, in the simplest case, it involves feature subset size minimization and performance maximization. In our work, we introduced approaches based on the theory of preordering that use association techniques between two heterogeneous features, introduced by S.Chah, H. Chamla1 [14], and between several ones, the first approach consists in selecting at each step a variable by maximizing two criteria, eliminating the effect of the variable in question, and moving on to the next step, the procedure stops when the second criterion is no longer significant. In the second approach, we adapted SFFS (Sequential Floating Forward Selection) and SFBS (Sequential Floating Backward Selection) to our problem. These methods consist of using the SFS (Sequential Forward Selection) algorithm once so as to add 1 features, then using the SBS (Sequential Backward Selection) algorithm for r times in order to remove r features. These steps are then repeated until the stop criterion is obtained. As the optimal values of these parameters cannot be determined theoretically, we proposed to leave them floating during the selection process in order to get as close as possible to the optimal solution. The proposed approaches were tested on several datasets and the experimental results were very satisfactory.
A Study of Biology-inspired Algorithms Applied to Long Short-Term Memory Network Training for Time Series Forecasting

Recent research introduced plenty of approaches to time series forecasting. Long short-term memory (LSTM) is a widely studied and effective recurrent artificial neural network architecture commonly used in time series prediction. LSTM networks are often trained using gradient-based methods. Such methods might be prone to premature convergence, and this affects prediction performance. In this paper, we consider a biology-inspired approach to LSTM loss function optimization. We compare the performance of different LSTM networks trained with backpropagation and using biology-inspired algorithms, including the Genetic Algorithm, Particle Swarm optimization, and Fish School Search. According to the obtained results, the LSTM network trained with the chaotic Fish School Search algorithm with exponential step decay produces the most accurate predictions in the considered time series forecasting problems.
Improved YOLOv5 for Remote Sensing Rotating Object Detection

Remote sensing image object detection is a challenging task owing to high resolution, dense distribution of small objects, and rotating objects at any angle. In this study, aiming to improve the performance of remote sensing image object detection, we propose a lightweight remote sensing rotating object detection model based on YOLOv5. To solve the problem of detecting rotating objects at any angle, we transform the angle regression problem into a classification problem, making the model predict one more angle information, which is used to rotate the horizontal bounding box to enclose the rotating objects at any angle. Aiming at the dense distribution of small objects in remote sensing images, we improve the detection layer structure of the model and add a object detection scale to pay more attention to small objects. Moreover, we also replace the BottleneckCSP module in the YOLOv5 Backbone network with the GhostBottleneck module to reduce the amount of model parameters and lighten the model. We conducted experiments on the public dataset DOTA. Experimental results show that our proposed model has achieved good detection results and the model size is only 13.4M, which can be effectively used for remote sensing object detection and can be easily deployed to the corresponding platform.
The Systematic Design of Responsibility Analysis by Abstract Interpretation

Given a behavior of interest, automatically determining the corresponding responsible entity (i.e., the root cause) is a task of critical importance in program static analysis. In this article, a novel definition of responsibility based on the abstraction of trace semantics is proposed, which takes into account the cognizance of observer, which, to the best of our knowledge, is a new innovative idea in program analysis. Compared to current dependency and causality analysis methods, the responsibility analysis is demonstrated to be more precise on various examples. However, the concrete trace semantics used in defining responsibility is uncomputable in general, which makes the corresponding concrete responsibility analysis undecidable. To solve this problem, the article proposes a sound framework of abstract responsibility analysis, which allows a balance between cost and precision. Essentially, the abstract analysis builds a trace partitioning automaton by an iteration of over-approximating forward reachability analysis with trace partitioning and under/over-approximating backward impossible failure accessibility analysis, and determines the bounds of potentially responsible entities along paths in the automaton. Unlike the concrete responsibility analysis that identifies exactly a single action as the responsible entity along every concrete trace, the abstract analysis may lose some precision and find multiple actions potentially responsible along each automaton path. However, the soundness is preserved, and every responsible entity in the concrete is guaranteed to be also found responsible in the abstract.
Human Activity Detection Using Deep Learning and Bracelet with Bluetooth Transmitter

The use of artificial intelligence, machine learning, and deep learning is finding its purpose in various fields nowadays. This paper describes a study in which Internet of Things and deep learning are used to implement human activity detection based on data collected from bracelet equipped with Bluetooth transmitter. The main focus of the study was development of a prediction model using deep learning that would help elderly people and their caretakers. Time series data about elderly people activity was collected from bracelet using a Bluetooth gateway and IoT platform, and later annotated based on the activity logs they kept in a form of diary. A neural network is trained to classify data into two groups (binary classification problem) corresponding to activity of the person wearing the bracelet. Initial study shows promising results of the presented approach for the use in human activity detection for elderly.
Validating Ontology-based Annotations of Biomedical Resources using Zero-shot Learning

Authoritative thesauri in the form of web ontologies offer a sound representation of domain knowledge and can act as a reference point for automated semantic tagging. On the other hand, current language models achieve to capture contextualized semantics of text corpora and can be leveraged towards this goal. We present an approach for injecting subject annotations using query term expansion against such ontologies in the biomedical domain. For the user to have an indication of the usefulness of these suggestions we further propose an online method for validating the quality of annotations using NLI models such as BART and XLM-R. To circumvent training barriers posed by very large label sets and scarcity of data we rely on zero-shot classification and show that semantic matching can contribute above-average thematic annotations. Also, a web-based validation service can be attractive for human curators vs. the overhead of pretraining large, domain-tailored classification models.