Reviewerly/exHarmony huggingface dataset:
A news text classification method based on the BiLSTM-Attention

Because of the large number and disorder of news on the Internet, which is difficult to classify and manage accurately, a text classification method based on BiLSTM (bi-directional long short term memory) and attention mechanism is applied in this paper. Firstly, each word segment of Chinese news content is embedded into a word vector through word2vec. Then, after the feature preprocessing of the BiLSTM layer, which can learn two-way long-term dependence, the attention weight is updated by the attention mechanism. Finally, after ReLU and fully connected layers, the classifier classified the news tags. In the experiment, the THUCNews data set is used to verify the effectiveness of the method. The accuracy rate of the test set of 10000 samples is as high as 97.46%, the recall rate is 97.47%, and the F1 score is 97.45%. These three balance indexes are higher than the traditional CNN, BiLSTM, and BiLSTM+pooling classification models. The experimental results show that the BiLSTM+attention fusion model can positively affect Chinese long news text classification.
Classification of Changes Based on API

In software maintenance process, software libraries are occasionally updated, and their APIs may also be updated. API changes can be classified into two categories: changes that break backward compatibility (in short, breaking changes) and changes that maintain backward compatibility (in short, maintaining changes). Detecting API changes and determining whether each is a breaking or maintaining change is useful for code reviews and release note generations. Since it is burdensome to check API changes manually, research on automatic detection of API changes has been conducted. APIDiff is a tool that automatically detects API changes and classifies the detected changes into breaking and maintaining ones. APIDiff takes two versions of a Java library as input, and it detects API changes based on the similarity of the input code. Each detected change is classified into the two kinds of changes. However, since APIDiff identifies breaking changes for each type of change, it tends to fail to correctly classify changes if multiple changes were conducted to a single API. On the other hand, our proposed technique in this paper groups changes by APIs and checks whether each group contains changes that break backward compatibility. Classifying API changes more correctly by our technique will be helpful for release note generations in maintenance process. We conducted experiments on eight open-source software and confirmed that our technique could detect API changes more correctly than APIDiff. We also confirmed that the proposed technique could classify API changes more correctly into breaking and maintaining ones than APIDiff.
Efficient Exploitation of Noise Leakage for Template Attack

Because of its strong leakage characterization ability and high-key-recovery efficiency, template attack (TA) is the strongest side-channel attack from the information-theoretic point-of-view. In light of this, TA can be used as a powerful tool to evaluate the security of a crypto device in real scenarios. In fact, by evaluating the key-recovery efficiency of TA, the security of a crypto device can be quantified. Therefore, it is very meaningful to optimize the key-recovery efficiency of TA in real scenarios. In this work, we show that the leakage exploitation style can significantly influence the key-recovery efficiency of TA. In detail, rather than exploiting the leakage of the noise part, the leakage of the squared noise part can be used in TA. Theoretically, by exploiting the leakage of the squared noise part, the key-recovery efficiency of TA can be optimized under the normalized difference metric. Empirically, with the evaluation data provided by DPA Contest and ASCAD, the key-recovery efficiency of TA can be optimized under the success rate metric. Overall, this contribution may help one to accurately evaluate the security of a crypto device in real scenarios.
Beyond the Design Code: Critical Design and Democratic Rationalizations

This chapter suggests a few touchpoints between Andrew Feenberg’s critical constructivism and current work in critical design, with the hope of initiating a productive dialogue between the two. Taking Feenberg’s notion of the design code as an invitation for such an encounter, the chapter illustrates how designers can take an active, meaningful role in facilitating what Feenberg calls “democratic rationalizations” in three different ways: in (co)design processes designers facilitate collaboration and nurture collective creativity while maintaining as much autonomy for participants to make the design process and its outcomes their own. In (re)design processes designers may be forced to respond to their users’ needs post-hoc, but they may also anticipate users’ needs and even embrace opportunities to provoke users to reflect on the social, cultural and political implications of the technologies they use. Lastly, and most radically, (un)designDesign(un)design processes ask designers to let go of (some of) their intentionality and seed potentials for users to appropriate the technologies they design. In Feenberg’s terms, this equals an almost complete relinquishing of “operational autonomy” and the valorization of technical rationality that characterizes it.
Concept Maps as a Support for Learning:

Knowledge Organization has Knowledge Organization Systems (KOS) to represent, organize and retrieve information. Among these systems are the concept maps, which represent ideas from the illustration of concepts that are linked together. Originating from pedagogy, concept maps began to be used in several areas as learning methods, including in the context of private institutions. Therefore, the demand to implement the rules of the Brazilian General Law for Personal Data Protection (LGPD in Portuguese) requires learning from agents involved in the treatment of this type of data, especially in corporations. The general objective of this article is to prepare a concept map that represents the main rules of the LGPD regarding personal data processed based on the consent of its data subjects. As specific objectives, it seeks to investigate the main characteristics and typologies of concept maps, in addition to analyzing the aspects and fundamental rules of the LGPD. The purpose of the proposal is to use the map to study the aforementioned law. The methodology of construction of input and output concept maps was used, in addition to Data Life Cycle, which interprets the “life” of data in four phases: collection, storage, retrieval and disposal. As a result, a simplified map was arrived at, but with a broad view of personal data management from collection to disposal. The constructed map serves as a representative model of the treatment addressed and can be adjusted according to the needs of each institution.
Dual-network Multi-attention Collaborative Classification Based on Fine-grained Vision

Fine-grained visual classification aims to identify images that belong to multiple subcategories within the same category. Most existing methods use a single network to extract image features or learn fine-grained features by locating and scaling key regions. Due to the limited number of components, this can ignore valuable clues or cause performance degradation. This paper proposes an efficient method to solve this problem: dual networks encourage the model to learn as many global features as possible. Considering the importance of attention, we utilize the attention generated by the gated interaction mechanism between different channels or the same channel to focus on key feature regions in the global feature map. Discover more discriminative feature maps. The gating vector in the gating mechanism is generated by a multilayer perceptron (MLP) and exploits its interaction with the original feature channel to generate attention. Similarly, bilinear is used to calculate the relationship between the locations and channels of feature maps to find common attention regions between them. The proposed model performs well on three datasets: CUB-200-2011, Stanford car, and FGVC aircraft.
A novel focus logic infused metaheuristic optimization approach based on swarm movement and human eye behavior

A novel global optimization method is described in this paper. The proposed metaheuristic approach is loosely based on the previously published technique, the Weighted Attraction Method. The main idea behind the proposed algorithm is the combination the local searching capabilities of the swarm movement-based metaheuristics and quasi-random global search. The quasi-random search technique applied is a novel concept, described as focus logic. The explosion step of the Weighted Attraction Method is modified in a way, that allows a wider area search preventing the algorithm to stuck at a local minimum point for multiple optimization cycles. The behavior of the proposed approach is similar, as the human eye observes its environment. It focuses on a specific area, while the peripheral vision is still giving information about the surrounding area. The proposed method can be applied to solve problems described by a continuous, moderately smooth objective functions rapidly. An objective function with such parameters can be found in any research field, from antenna design through model parameter identifications to Internet of Digital Reality (IoD) applications. In the first chapter the paper introduces the reader to the field of metaheuristic optimizations. The second chapter presents the detailed algorithmic structure of the proposed approach and describes the parameters that could be used to fine tune the behavior of the optimization process. The last technical chapter shows the performance of the proposed technique through comparing the convergence progress of multiple optimization methods on various test functions.
Knowledge Dissemination in Telecom Industry: A Case of Nepal

This study attempts to evaluate knowledge dissemination in the Nepalese telecom industry. It is based on cross section analysis of 85 observations for analyzing relationship between knowledge dissemination statuses in Nepalese telecom industry. The study is based on Kohli et al. (1993) and Daroch (2003) methodologies. Survey instruments are organized into one part and four factors. Spearman Rank Correlation, Analysis of variance (ANOVA), Cronbach's alpha etc. is used for data analysis. The valid response rate of 70.7 % is found. Among the factors that designed for knowledge dissemination and combination has less mean score and socialization has higher mean score than the other factors. In case of socialization, Nepalese Telecom industry analyzes the social contribution of their products or services. Employees of Nepal Telecom has rated higher mean value in knowledge dissemination practices, which means Nepal Telecom appeared better in knowledge dissemination practices than Ncell. There is a significant difference between Nepal Telecom and Ncell in term of knowledge dissemination. Nepal Telecom has better KD (Knowledge Dissemination) practices. The correlation matrix is created to determine the extent to which the components or research variables are related.
Design of Gaussian filter for nonlinear system with correlated noises, randomly delayed measurements, packet dropouts and communication constraints

To deal with the non-ideal conditions for state estimation problem, in this paper, a novel state estimation problem for nonlinear system with correlated noised, randomly delayed measurement, packet dropouts and communication constraints is considered. Using the alternative framework, we deduce the augmented filter, where state and measurement noises are estimated associated with state. To show the effectiveness of the proposed filter, the numerical realization on the basis of third-degree spherical-radial cubature rule is given, and the result is applied to deal with the univariate non-stationary growth model(UNGM). What's more, the superiority is shown through the simulation results.
An Interval Construction Belief Rule Base with Interpretability for Complex Systems

Abstract: Complex system modeling by subjective judgment alone can not meet the needs of larger and more complex actual systems. The expert knowledge base in the belief rule base (BRB) can participate in complex system modeling well, but there are still the following problems. On the one hand, BRB cannot deal with the explosion of combination rules caused by the excessive number of complex system attributes. On the other hand, parameters may lose their interpretability after BRB modeling, reasoning, and optimization. In view of the above two problems, an interval construction belief rule base with interpretability for complex systems (IBRB-i) model is proposed. The IBRB-i model constructs belief tables in the form of interval addition combination rules and introduces rule reliability, which perfectly solves the problem of combination rule explosion in traditional BRB. In addition, interpretable constraints are added to the IBRB-i model to ensure its interpretability. In the case study, the validity and accuracy of the proposed model are verified and analyzed by two examples of power fault detection and liquid launch vehicle structure safety assessment.
Il progetto SEPA360 nella percezione degli studenti

This chapter focuses on the perception of the students about the video 360° and their didactic value in the academic teaching. The results of an online survey, administered to more than 200 students in three European countries who followed an academic course where a video 360° was used, are then presented and discussed highlighting how, despite the relative novelty of this tool for academic teaching, the results seem quite promising for their further adoption in the Higher Education.
A Deep-Learning-based System for Indoor Active Cleaning

Cleaning public areas like commercial complexes is challenging due to their sophisticated surroundings and the vast kinds of real-life dirt. Robots are required to distinguish dirts and apply corresponding cleaning strategies. In this work, we proposed an active-cleaning framework by utilizing deep-learning methods for both solid wastes detection and liquid stains segmentation. Our system consists of 4 components: a Perception module integrated with deep-learning models, a Post-processing module for projection, a Tracking module for map localization, and a Planning and Control module for cleaning strategies. Compared with classic approaches, our vision-based system significantly improves cleaning efficiency. Besides, we released the largest real-world indoor hybrid dirt cleaning dataset (HD10K) containing 10K labeled images, together with a track-level evaluation metric for better cleaning performance measurement. The proposed deep-learning based system is verified with extensive experiments on our dataset, and deployed to Gaussian Robotics's robots operating globally. Dataset is available at: https://gaussianopensource.github.io/projects/active_cleaning.
An Adversarial Video Moment Retrieval Algorithm

In one-stage methods for video moment retrieval, the common representations indirectly supervised by boundary prediction fail to fully preserve the inherent characteristic of the video and query, which limits the retrieval accuracy. To solve this problem, an Adversarial Video Moment Retrieval (AVMR) algorithm is proposed to learn the common representations with modality invariance and cross-modal similarity. AVMR is implemented through the process of adversarial learning between a feature projector and a modality classifier. The feature projector tries to generate a modality-invariant common representation and to confuse the modality classifier. The modality classifier tries to discriminate between different modalities based on the generated representation by the feature projector. The triplet constraints are further imposed on the feature projector to preserve the underlying cross-modal semantic structure of data. The experimental results show that AVMR surpasses the baseline Attentive Cross-modal Relevance Matching (ACRM) by 1.10% and 1.73% in the “mIoU” metric on two public datasets Charades-STA and TACoS, respectively.
Attractive image artifacts suppression in color transfer image

This work brings in a colour transfer method is proposed to of transfer colour of and reference image to colour of target image.RGB color space to lαβ colour space method is used for colour transformation. While transferring the colour, the artifacts may occur. Joint Statistical Modellingmethod is discussed to remove artifacts from colour and contrast modifications. It combines both local andnon localstatistical smoothness self similarity. The performance of proposed methods is analyzed by Split Bregman-Based Iterative Algorithmand Kullback-Leibler distance calculation methods. It is used to convert unrestrained minimization problem in to restrain one.Kullback-Leibler distance calculates the distance between the target image and colour transferred image.
TTP-Aided Secure Computation Using (k, n) Threshold Secret Sharing With a Single Computing Server

Secure computation can be divided into those using homomorphic encryption and secret sharing. The advantage of the former method is that the computation can be performed on a single computing server and the computation process can be made public if the encryption key used is securely managed. However, the latter is computationally light and capable of high-speed processing, but it requires multiple independently managed computing servers, and processes of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> or more servers cannot be disclosed. When considering a business model for implementing secure computation that uses secret sharing, private information can be leaked if computing servers are managed by the same organization. Therefore, we need a complex business model in which multiple companies without conflicts of interest manage each computing server independently and make a profit. However, this approach is difficult to implement in practice. In this study, we proposed a secure computation using secret sharing. Moreover, by effectively using TTP, we demonstrated that it is possible to realize secure computation with a single computing server. In addition, we demonstrated that, if the security keys used are securely managed, the entire computational process can be made public. In other words, we realize a method that addresses all the drawbacks of the aforementioned methods, and it is possible to realize faster and more secure computations than conventional methods using secret sharing.
On-Device User-Adaptive Next Word Prediction System

Recent advancements in natural language processing have contributed to the success of language modeling in the task of next word prediction (NWP). The NWP systems are widely used on mobile devices, where deploying huge language models for offline prediction is a major bottleneck due to the limitation of storage and computing capacity. Moreover, due to the diversity of users, different users could have varying typing styles within the same language. Hence a system that does not adapt itself to the user’s style has an adverse effect on the user experience. This work proposes a low latency 3-gram NWP system using the Markov model with a ranking mechanism. The ranking mechanism ranks the next word prediction words in a way that the users preferred words gradually get a higher rank. With a 50K vocabulary size, our NWP system has an average latency of 6.41ms and a size of 3. 2MB.
Creation of a converged learning environment of information support for students to make decision when choosing the university educational entry trajectory

This article considers approaches to create a converged learning environment for information support for students to make decision at the stage of choosing an educational direction and a specific organization of higher education for admission, and describes the general architecture of a converged environment and an integrated information system, as well as methods and technological approaches to integrate and organize an interaction in the proposed environment of various information systems, resources and services, including all-Russian, departmental services and specialized services for vocational guidance testing and testing in the Unified State Exam. The considered approaches make it possible in the proposed convergent environment to implement decision support procedures in the formation of related stages of the individual educational trajectory of an applicant, including : choice of a future profession, that is on demand; personal professional abilities and inclinations studied during professional testing; preparation for passing the Unified State Exam, preliminary trial testing and passing the exam; choosing the appropriate direction of training and the university for admission.The interrelation to solve problems at the above stages is ensured, in particular, through the use of data from the digital trail of the applicant and the integration of information and software services provided by several systems through unified interaction interfaces (API). And there is given brief information about the implementation of the elements of the integrated system as well.
Real-Time Robust Single Sperm Tracking via Adaptive Particle Filtering

Assisted reproductive technology is commonly used to treat infertility. Motility-based selection of high-quality sperms is the key to improve the successful rate of artificial assisted reproduction. Visually tracking the sperms on optical microscopic video frames is essential to evaluate their motility before the selection. Unfortunately, current methods easily fail to precisely track the sperms in real time. This work is to accurately and robustly detect and track single sperm based on microscopic video frames. We propose a modified background subtraction method to detect multiple sperms in successive frames. We also introduce an adaptive particle filtering method to accurately and robustly track the trajectory of a single sperm in real time. Specifically, this method models the sperm movement by comparing its histogram information at different positions on microscopic images and uses adaptive particle filtering to approximate the optimal state of the sperm. The experimental results demonstrate that our method can achieve much better tracking accuracy than other visual tracking methods, providing more reliable sperm motility analysis. In particular, our method can successfully re-track the same sperm when it appears again on the microscopic focal plane after disappearing in a few frames, while the other compared tracking methods usually fail to re-track the same sperm after its back.
3D Deformable Kernels for Video super-resolution

Video super-resolution are drawing increasing attention in the computer vision community. Temporal modeling is crucial for video super-resolution. A challenge for video super-resolution to fully mining temporal-spatial information in video sequence. In this work, we propose 3D deformable kernels for video super-resolution (DK3Dnet). Specifically, we introduce 3D deformable kernels (DK3D) to integrate deformable convolution with 3D convolution to enhance spatio-temporal modeling capability. To enhance the quality of subsequent restoration. we use a Temporal and Spatial Attention fusion module (TSA fusion), in which attention is applied both temporally and spatially. Finally, we use channel-wise attention residual block (CARB) to enhance the quality of video frame in DK3Dnet reconstruction module. Experimental results show that DK3Dnet can exploiting spatio-temporal information to improve the performance of video super-resolution.