Reviewerly/exHarmony huggingface dataset:
Conductivity tensor in rectangular quantum wire for the case of electrons-optical phonon scattering

Conductivity tensor in a rectangular quantum wire with infinite potential is calculated in the presence of two external fields, laser field, and linearly polarized electromagnetic wave field. Conductivity tensor is calculated based on the theory of quantum kinetic equations for electrons in quantum wires. The conductivity tensor expression shows its dependence on the amplitude, frequency of the external field, and characteristic quantities for a rectangular quantum wire with infinite potential. This work also investigated and plotted the dependence of conductivity tensors to the external fields, the quantum wire studied here is the quantum wire GaAs / GaAsAl. The dependence of the conductivity tensor to the external field is nonlinear, at different frequency bands, the dependence is different. The conductivity tensor depends a lot on the laser field amplitude and temperature of the system.
Is It Necessary to Transfer Temporal Knowledge for Domain Adaptive Video Semantic Segmentation?

Video semantic segmentation is a fundamental and important task in computer vision, and it usually requires large-scale labeled data for training deep neural network models. To avoid laborious manual labeling, domain adaptive video segmentation approaches were recently introduced by transferring the knowledge from the source domain of self-labeled simulated videos to the target domain of unlabeled real-world videos. However, it leads to an interesting question – while video-to-video adaptation is a natural idea, are the source data required to be videos? In this paper, we argue that it is not necessary to transfer temporal knowledge since the temporal continuity of video segmentation in the target domain can be estimated and enforced without reference to videos in the source domain. This motivates a new framework of Image-to-Video Domain Adaptive Semantic Segmentation (I2VDA), where the source domain is a set of images without temporal information. Under this setting, we bridge the domain gap via adversarial training based only on the spatial knowledge, and develop a novel temporal augmentation strategy, through which the temporal consistency in the target domain is well-exploited and learned. In addition, we introduce a new training scheme by leveraging a proxy network to produce pseudo-labels on-the-fly, which is very effective to improve the stability of adversarial training. Experimental results on two synthetic-to-real scenarios show that the proposed I2VDA method can achieve even better performance on video semantic segmentation than existing state-of-the-art video-to-video domain adaption approaches.
More on Incorrigible Sets of Binary Linear Codes

This letter is concerned with incorrigible sets of binary linear codes. For a given binary linear code C, we represent the numbers of incorrigible sets of size up to ⌈3/2d - 1⌉ using the weight enumerator of C, where d is the minimum distance of C. In addition, we determine the incorrigible set enumerators of binary Golay codes G23 and G24 through combinatorial methods.
Edge-Labelled Graphs and Property Graphs - To the User, More Similar Than Different

The two dominant paradigms for graph databases, edge-labelled graphs and property graphs, may appear quite different. Yet, to the user, they have strong similarities. A usability study, comparing RDF-star/SPARQL-star and Cypher, found evidence for only limited differences in preferences between the modelling paradigms. This suggests the possibility of a convergence of the two paradigms; indeed in one case (Stardog) this is already happening. We also found little difference between the paradigms in users’ ability to detect valid and non-valid queries. In one specific case, the use of a reverse arrow in Cypher was interpreted significantly more accurately than the ^ symbol in the SPARQL query language; this argues, where possible, for the more intuitive Cypher notation.
A GAN-Based Framework For High-Fidelity Face Swapping

In recent years, face manipulation has attracted much attention which has both positive and negative effects for us. Face swapping is a face manipulation technique that modifies the identity information while preserving the attribute information of the face. It is necessary for us to know the advanced methods for high-quality face swapping and generate high-quality face swapping images to train the forgery detection algorithm. Currently, the face swapping algorithm faces many challenges, such as poor quality of the generated images or poor generalization of the algorithm, and sometimes the attribute features of the source image are also transferred to the target identity, resulting in the loss of attribute information of the generated images. In this paper, we propose a GAN-based face swapping framework which can obtain high-fidelity face swapping images. Firstly, this framework uses two separate encoders to decouple the identity and attribute feature of face images. Then a feature fusion module is used to coupled the identity and attribute feature of the face. Specifically, we treat the face swapping problem as a style transfer problem to solve the feature fusion problem, furthermore, an attention mechanism is introduced to guide the feature fusion process. The experimental results show that our method is effective.
Image Caption Generator using EfficientNet

A human can give a quick glance at a picture or a scenery and will be able to describe that scene very accurately and comprehensively. Image Captioning is a well-known but also a challenging task in research. The reason being that it is the intersection between Computer Vision and Natural Language Processing which should be able to develop and provide us with accurate description of an image. In this paper, we try to implement EfficientNet - the most powerful CNN model which uses less parameters and gives us a better prediction. We use the Microsoft: Common Objects in Context (MSCOCO) Dataset which contains around 330K images with 1.5M object instances and 80 object categories. We evaluate the model using BLEU-4 score, which indicates how similar the candidate text is to the reference texts, with values closer to one representing more similar texts. Our proposed model shows better understanding of the image and provides us with desirable results.
A Modular Architecture for Integrating Normative Advisors in MAS

This paper introduces a modular architecture for integrating norms in autonomous agents and multi-agent systems. As the interactions between norms and agents can be complex, this architecture utilizes multiple programmable components to model concepts such as adoption of personal and/or collective norms (possibly conflicting), interpretation and qualification as mappings between social and normative contexts, intentionally (non-)compliant behaviors, and resolution of conflicts between norms and desires (or other norms). The architecture revolves around normative advisors, that act as the bridge between intentional agents and the institutional reality. As a technical contribution, a running implementation of the architecture is presented based on the ASC2 (AgentScript) BDI framework and eFLINT normative reasoner.
Hierarchical Coding for Talking-Head Video

Talking-head video is very popular in video conference and social media, where the camera captures the movement of user’s head and the change of facial expression. In this paper, we propose a hierarchical coding scheme for the compression of talking-head video. In our proposed method, three data layers, including one base layer, one enhancement layer and one feature layer, are formed as the input of encoder. More specifically, the base layer is generated by spatially sub-sampling the source video. The enhancement layer is composed by the specific key frames and the feature layer is produced based on the extracted facial landmarks. These layers are separately compressed but fused together to reconstruct the video signal in the decoder side. To achieve a high-quality reconstruction, we design the multi-feature fusion network in which the feature layer is used to guide the fusion of base layer and enhancement layer. The experiment results demonstrate the good performance of our proposed method for the coding of talking-head video.
Web Security and Single Sign-On Protocols

Single Sign-On protocols (SSO) are used to grant a web user authenticated access to many web applications after a single manual login. On the client side, SSO protocols like OAuth, OpenID Connect, or SAML rely on standard web browser features. The blueprint for modern SSO protocols is the Kerberos protocol [56] described in chapter 14 .
Human-Centered Real-Time Instance Segmentation with Integration with Data Association and SOLO

Video instance segmentation (VIS) mainly has two methods in human-targeted tasks, one is based on mask propagation method, and the other is based on detection tracking mode method. Existing datasets pay less attention to all the characters appearing in the video, but focus on the key characters appearing in the video. Therefore, in this paper, an online joint detection and tracking model is proposed. Based on the single-stage segmentation method SOLO, appearance feature extraction and data association are added to realize the segmentation and tracking of human body in video. At the same time, in order to better evaluate the method, we extract a single person type on the basis of existing multi-type data sets, filter out high-quality annotations and various video scenes, and obtain the challenging data set PVIS. As a result, the method based on the PVIS dataset can achieve satisfied performance both for segmentation accuracy and tracking accuracy.
MSR-DARTS: Minimum Stable Rank of Differentiable Architecture Search

In neural architecture search (NAS), differentiable architecture search (DARTS) has recently attracted much attention due to its high efficiency. However, this method finds a model with the weights converging faster than the others, and such a model with fastest convergence often leads to overfitting. Accordingly, the resulting model cannot always be well-generalized. To overcome this problem, we propose a method called minimum stable rank DARTS (MSR-DARTS), for finding a model with the best generalization error by replacing architecture optimization with the selection process using the minimum stable rank criterion. Specifically, a convolution operator is represented by a matrix, and MSR-DARTS selects the one with the smallest stable rank. We evaluated MSR-DARTS on CIFAR-10 and ImageNet datasets. It achieves an error rate of 2.54% with 4.0M parameters within 0.3 GPU-days on CIFAR-10, and a top-1 error rate of 23.9% on ImageNet.
A Call for Research: Cryptocurrencies as a Disruptive Technology for Governments, Organizations, and Society

With the potential for significant effects on financial systems, societal order, and organizational strategy, cryptocurrencies and blockchain technologies stand as a potentially major disruptive force (Rossi et al. 2019). As such, Information Systems Research should focus resources on this still emerging technology. The objective of this research is to identify and analyze what research has found since these technologies have emerged in the last decade. An inductive study was conducted where articles from the AIS Basket of 8 journal list in the domain of cryptocurrencies and blockchain were examined and categorized into emergent themes. These themes were analyzed and supplemented with other research. Resultantly, the ramifications for future trends in research were discussed.
An Image Segmentation Based on a Piecewise Smooth Model in Deep Learning

We propose simple method that can solve the image segmentation problem in unsupervised manner. Our convolutional neural network is based on ResNet-18 to construct auto-encoder structure since the results with the ResNet-18 are reasonable. Our network optimizes the traditional segmentation algorithms: Chan-Vese algorithm and Mumford-Shah algorithm. Compared to some unsupervised segmentation algorithms that need additional modules to cover the inaccurate boundaries, our method can be trained end-to-end and consists of a simple objective function.
Joint Modeling of Document and Label with Clause Interaction Hypergraph for ICD Medical Code Assignment

Automatic medical code assignment for clinical records is the fundamental problem of medical statistical research and informatization. Due to the high dimension and sparse distribution of label space, it is necessary to make full use of the description information of the labels. However, most of the current work is based on similarity matching at the level of token or n-gram, and ignores information fusion with richer semantic structure and representation. In this paper, we propose a Clause Interaction HyperGraph (CIHG) to jointly model documents and label descriptions, which construct a richer semantic interaction at the level of clause. The CIHG models the high-order co-occurrence relationship between document and labels based on hypergraph, and uses the semantic structure of the document to constrain the encoding of labels. Experiments on widely used medical code assignment datasets show that our method successfully constrains the embedding of labels and significantly improves predictive precision <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The code is available at https://github.com/CKRE/CIHG.
A Design-Led Exploration of Material Interactions Between Machine Learning and Digital Portraiture

Design materials are defined as a combination of what they are, what they do, and the ways they interact with other materials. In this pictorial, we explore the interactions between machine learning (as a design material) and another design material—the human face—in the form of digital portraiture. Employing an exploratory Research through Design approach we consider how machine learning simultaneously enriches and subverts the materiality of the human face. Through a combination of images and text, we offer some considerations and provocations for further research.
Research and Application of Tibetan Pre-training Language Model Based on BERT

In recent years, pre-training language models have been widely used in the field of natural language processing, but the research on Tibetan pre-training language models is still in the exploratory stage. To promote the further development of Tibetan natural language processing and effectively solve the problem of the scarcity of Tibetan annotation data sets, the article studies the Tibetan pre-training language model based on BERT. First, given the characteristics of the Tibetan language, we constructed a data set for the BERT pre-training language model and downstream text classification tasks. Secondly, construct a small-scale Tibetan BERT pre-training language model to train it. Finally, the performance of the model was verified through the downstream task of Tibetan text classification, and an accuracy rate of 86% was achieved on the task of text classification. Experiments show that the model we built has a significant effect on the task of Tibetan text classification.
Engineering Pro-social Values in Autonomous Agents – Collective and Individual Perspectives

This doctoral thesis is concerned with the engineering of values with an explicit pro-social (as opposed to a personal) focus. To do so, two approaches are explored, each dealing with a different level at which interactions are studied and engineered in a multi-agent system. The first, referred to as the collective approach, leverages prescriptive norms as the promoting mechanisms of pro-social values. The second, referred to as the individual approach, deals with the internal reasoning scheme of agents and endows them with the ability to reason about others. This results in empathetic autonomous agents, who are able to take the perspective of a peer and understand the motivations behind their behaviour.
WASABI Contextual BOT

Conversational Artificial Intelligence (AI) chatbots are getting huge attention from Industries and AI communities. With the development of Natural Language Processing (NLP) and Deep Learning algorithms the application of artificial intelligence in chatbots is taking sharp turns. Today we are seeing a wave of conversational AI sweeping across industries and every brand is looking to add AI-based solutions. Natural Language is an intuitive way for users to interact with the technology. Conversational AI bots not only recognize the human requests but it also emphasizes on the context of the request. By understanding context, a chatbot can respond to different and unexpected user inputs and drive the conversation when user drifts from regular conversation path. Contextual chatbot can give better and appropriate response when user asks domain specific questions. In any organization, it becomes handy and useful for the users if they can access the data in a personalized way. Rasa is an open source machine learning framework which is used for building contextual AI assistants and chatbot. In this paper we discuss an efficient way to design a chatbot using RASA framework which made things easier for the employees during pandemic and continue to do so by providing them information related to their work in organization.
RDO-Q: Extremely Fine-Grained Channel-Wise Quantization via Rate-Distortion Optimization

Allocating different bit widths to different channels and quantizing them independently bring higher quantization precision and accuracy. Most of prior works use equal bit width to quantize all layers or channels, which is sub-optimal. On the other hand, it is very challenging to explore the hyperparameter space of channel bit widths, as the search space increases exponentially with the number of channels, which could be tens of thousand in a deep neural network. In this paper, we address the problem of efficiently exploring the hyperparameter space of channel bit widths. We formulate the quantization of deep neural networks as a rate-distortion optimization problem, and present an ultra-fast algorithm to search the bit allocation of channels. Our approach has only linear time complexity and can find the optimal bit allocation within a few minutes on CPU. In addition, we provide an effective way to improve the performance on target hardware platforms. We restrict the bit rate (size) of each layer to allow as many weights and activations as possible to be stored on-chip, and incorporate hardware-aware constraints into our objective function. The hardware-aware constraints do not cause additional overhead to optimization, and have very positive impact on hardware performance. Experimental results show that our approach achieves state-of-the-art results on four deep neural networks, ResNet-18, ResNet-34, ResNet-50, and MobileNet-v2, on ImageNet. Hardware simulation results demonstrate that our approach is able to bring up to 3.5 $$\times $$ and 3.0 $$\times $$ speedups on two deep-learning accelerators, TPU and Eyeriss, respectively.
A Reachability-Based Spatio-Temporal Sampling Strategy for Kinodynamic Motion Planning

By limiting the planning domain to “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Informed Set”, some sampling-based motion planners (SBMP) (e.g., Informed RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , BIT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ) can solve the geometric motion planning problems efficiently. However, the construction of informed set (IS) will be very challenging, when further differential constraints are considered. For the time-optimal kinodynamic motion planning problem, this paper defines a modified time informed set (MTIS) to limit the planning domain. Due to drawing inspiration from Hamilton-Jacobi-Bellman (HJB) reachability analysis, MTIS, compared with the original TIS, can not only help save the running time of SBMP, but also extend the applicable scope from linear systems to polynomial nonlinear systems with control constrains. On this basis, a spatio-temporal sampling strategy adapted to MTIS is proposed. Firstly, MTIS is used to estimate the optimal cost and the valid tree structure is reused, so that we do not need to provide a solution trajectory in advance. Secondly, this strategy is generic, allowing it to be combined with common SBMP (such as SST, etc.) to accelerate convergence and reduce the memory requirement. Several simulation experiments also demonstrate the effectiveness of proposed method.
A Machine Learning Based Command Voice Recognition Interface

As a trend in the tech industry, human-machine interaction has increasingly become friendly and intuitive. In this context, our work presents the implementation of a voice command recognition system based on Machine Learning composed of an audio capture system, phoneme segmentation, description of voice commands from standardized power density histograms, classifier training, and functional tests on a graphical interface. The results showed that the best classification algorithm when implementing different classifiers was Support Vector Machine (SVM), with an efficiency of 95.4%, as expected, given its optimal structure.
Educational Space and the Issues of Digitalization in the Mining Region

The solution of key tasks of the national program of transition to digital economy depends on the efficiency of interaction between the primary components of the national economy, the level of development of information and communication technologies, and the quality of the educational space. The conducted analysis of the mutual influence of production growth in the Republic of Sakha (Yakutia), geographically remote from the industrial centers of Russia, and the educational space accompanying the changes in the social life of society shows the radically changed nature of the formation of human capital in the region. The current inter-territorial and socio-economic asymmetry of digitalization introduces specific limitations, which must be overcome by improving the educational space of the region through the implementation of joint projects using digital technologies.
Neural Network-Based Model for the Quality Assessment of Object-Oriented Software

Software quality assessment is an important subject among the researchers in the software development domain. The quality assessment is generally done either at the design level through some of the design attributes or through code when the product is ready. These two types of software quality are referred to as design quality and product quality, respectively. Several techniques and tools are available that facilitate to assess the design as well as the product quality of software. In this paper, a neural network model is proposed for the assessment of quality of object-oriented software at the product level. The authors select a subset of existing object-oriented metrics that are normalized at three levels and used to find quality factors like understandability, reusability, flexibility, maintainability, reliability, extensibility, and modifiability for the model development. The model is validated by assessing quality levels of 33 open source object-oriented software of different design complexities and observing a high correlation between these quality levels in comparison with an existing model.