Reviewerly/exHarmony huggingface dataset:
Global Distance-distributions Separation for Unsupervised Person Re-identification

Supervised person re-identification (ReID) often has poor scalability and usability in real-world deployments due to domain gaps and the lack of annotations for the target domain data. Unsupervised person ReID through domain adaptation is attractive yet challenging. Existing unsupervised ReID approaches often fail in correctly identifying the positive samples and negative samples through the distance-based matching/ranking. The two distributions of distances for positive sample pairs (Pos-distr) and negative sample pairs (Neg-distr) are often not well separated, having large overlap. To address this problem, we introduce a global distance-distributions separation (GDS) constraint over the two distributions to encourage the clear separation of positive and negative samples from a global view. We model the two global distance distributions as Gaussian distributions and push apart the two distributions while encouraging their sharpness in the unsupervised training process. Particularly, to model the distributions from a global view and facilitate the timely updating of the distributions and the GDS related losses, we leverage a momentum update mechanism for building and maintaining the distribution parameters (mean and variance) and calculate the loss on the fly during the training. Distribution-based hard mining is proposed to further promote the separation of the two distributions. We validate the effectiveness of the GDS constraint in unsupervised ReID networks. Extensive experiments on multiple ReID benchmark datasets show our method leads to significant improvement over the baselines and achieves the state-of-the-art performance.
Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction

Deep Interest Network (DIN) is a state-of-the-art model which uses attention mechanism to capture user interests from historical behaviors. User interests intuitively follow a hierarchical pattern such that users generally show interests from a higher-level then to a lower-level abstraction. Modeling such an interest hierarchy in an attention network can fundamentally improve the representation of user behaviors. We, therefore, propose an improvement over DIN to model arbitrary interest hierarchy: Deep Interest with Hierarchical Attention Network (DHAN). In this model, a multi-dimensional hierarchical structure is introduced on the first attention layer which attends to an individual item, and the subsequent attention layers in the same dimension attend to higher-level hierarchy built on top of the lower corresponding layers. To enable modeling of multiple dimensional hierarchies, an expanding mechanism is introduced to capture one to many hierarchies. This design enables DHAN to attend different importance to different hierarchical abstractions thus can fully capture user interests at different dimensions (e.g. category, price, or brand).To validate our model, a simplified DHAN has applied to Click-Through Rate (CTR) prediction and our experimental results on three public datasets with two levels of the one-dimensional hierarchy only by category. It shows the superiority of DHAN with significant AUC uplift from 12% to 21% over DIN. DHAN is also compared with another state-of-the-art model Deep Interest Evolution Network (DIEN), which models temporal interest. The simplified DHAN also gets slight AUC uplift from 1.0% to 1.7% over DIEN. A potential future work can be a combination of DHAN and DIEN to model both temporal and hierarchical interests.
Secure and Scalable Document Similarity on Distributed Databases: Differential Privacy to the Rescue

Abstract Privacy-preserving collaborative data analysis enables richer models than what each party can learn with their own data. Secure Multi-Party Computation (MPC) offers a robust cryptographic approach to this problem, and in fact several protocols have been proposed for various data analysis and machine learning tasks. In this work, we focus on secure similarity computation between text documents, and the application to k -nearest neighbors ( k -NN) classification. Due to its non-parametric nature, k -NN presents scalability challenges in the MPC setting. Previous work addresses these by introducing non-standard assumptions about the abilities of an attacker, for example by relying on non-colluding servers. In this work, we tackle the scalability challenge from a different angle, and instead introduce a secure preprocessing phase that reveals differentially private (DP) statistics about the data. This allows us to exploit the inherent sparsity of text data and significantly speed up all subsequent classifications.
A Novel Software Architecture for Mixed Criticality Systems

The advent of multi-core CPUs in nearly all embedded markets has prompted an architectural trend towards combining safety critical and uncritical software on single hardware units. We present a novel architecture for mixed criticality systems based on Linux that allows us to consolidate critical and uncritical parts onto a single hardware unit. CPU virtualisation extensions enable strict and static partitioning of hardware by direct assignment of resources, which allows us to boot additional operating systems or bare metal applications running aside Linux. The hypervisor Jailhouse is at the core of the architecture and ensures that the resulting domains may serve workloads of different criticality and can not interfere in an unintended way. This retains Linux’s feature-richness in uncritical parts, while frugal safety and real-time critical applications execute in isolated domains. Architectural simplicity is a central aspect of our approach and a precondition for reliable implementability and successful certification. While standard virtualisation extensions provided by current hardware seem to suffice for a straight forward implementation of our approach, there are a number of further limitations that need to be worked around. This paper discusses the arising issues, and evaluates the suitability of our approach for real-world safety and real-time critical scenarios.
Optimization and Implementation of Efficient and Universal Quantum Key Distribution

Since quantum key distribution (QKD) can provide proven unconditional security guaranteed by the fundamental laws of quantum mechanics, it has attracted increasing attention over the past three decades. Its low bit rate, however, cannot meet the requirements of modern applications. To solve this problem, recently, an efficient and universal QKD protocol based on chaotic cryptography and middleware technology was proposed, which efficiently increases the bit rate of the underlying QKD system. Nevertheless, we find that this protocol does not take the bit errors into account, and one error bit may lead to the failure of the protocol. In this paper, we give an optimized protocol and deploy it on a BB84 QKD platform. The experimental results show that the optimized version provides resistance to bit errors compared with the original version. And the statistical properties of the generated bits are fully assessed using different methods. The evaluation results prove that the proposed protocol can generate bits with outstanding properties.
Mobile applications in teaching foreign languages in tertiary education

Modern devices with many diverse applications have significantly raised interest in the concept of mobile or m-learning. The standard features of mobile phones include access to the Internet and therefore an unlimited access to educational material at any place and at any time. This paper presents the research conducted to examine students' attitudes about the advantages and disadvantages of using mobile language learning applications. The paper offers a description of the research methodology, data processing, and the interpretation of the results. In order to obtain the data, an anonymous survey was conducted, which was compiled by the authors for the research purposes and distributed to students via email. Apart from the quantitative part, a qualitative research, in the form of a focus group, was conducted as well to provide a clearer picture of students' habits when using mobile applications for foreign language learning. The research showed that the greatest advantage of using mobile applications is their availability at any place and at any time, as well as the fact that such applications are mostly free of charge and their use does not require additional costs for students/their families. On the other hand, the biggest disadvantages relate to the offered grammar exercises, which, according to the respondents, are not good enough. This research unequivocally confirmed that mobile applications for foreign language learning are very popular among the student population, but also that students' interest in this type of learning is not exploited enough. One of the recommendations is that different applications, depending on students' abilities and lesson goals, should be integrated in the teaching process and should serve as an addition to the planned curriculum, actively contributing to the quality and diversity of teaching.
Improvements on the Convergence and Stability of Fuzzy Grey Cognitive Maps

Fuzzy grey cognitive maps (FGCMs) are extensions of fuzzy cognitive maps (FCMs), where the causal connections between the concepts are represented by so-called grey numbers. Just like in classical FCMs, the inference is determined by an iteration process, which may converge to an equilibrium point, but limit cycles or chaotic behaviour may also show up. In this paper, based on network measures like in-degree, out-degree and connectivity, we provide new sufficient conditions for the existence and uniqueness of fixed points for FGCMs. Moreover, a tighter convergence condition is presented using the spectral radius of the modified weight matrix.
Automation of e-Workshop Project Control for Knowledge-Intensive Areas

This article discusses the issues of systematic processing of electronic workshops content to prepare competitive graduates, of various majors, in knowledge-intensive areas. The research proposes an approach to an automated workshop design for a specific student major considering numerous factors which influence the changes in the regulatory framework, student learning outcomes of the previous workshop passage, employers’ assessment, and the labor market needs. The analysis of expert assessment and the workshop content formation in this approach involves the implementation of the following stages, such as calculation of the confidence-in-experts degree, selection of subject learning tasks, development of a workshop project considering the relevance of each learning task, and the task complexity and evaluation (points) for each major. The result of this study is an information system for managing the project of an electronic workshop. It implements the approach to the formation of the actual practical part content of subjects for each major. The approach is based on the ADDIE model of pedagogical design. Using the information system, we have developed a workshop project of the major “Design of embedded systems” for the subject “Embedded systems.” The workshop was tested by the fourth-year bachelor students. The workshop results were entered into the system, processed, and used to make recommendations for changing the content of the workshop.
Efficient Architecture Search for Continual Learning

Continual learning with neural networks is an important learning framework in AI that aims to learn a sequence of tasks well. However, it is often confronted with three challenges: (1) overcome the catastrophic forgetting problem, (2) adapt the current network to new tasks, and meanwhile (3) control its model complexity. To reach these goals, we propose a novel approach named as Continual Learning with Efficient Architecture Search, or CLEAS in short. CLEAS works closely with neural architecture search (NAS) which leverages reinforcement learning techniques to search for the best neural architecture that fits a new task. In particular, we design a neuron-level NAS controller that decides which old neurons from previous tasks should be reused (knowledge transfer), and which new neurons should be added (to learn new knowledge). Such a fine-grained controller allows one to find a very concise architecture that can fit each new task well. Meanwhile, since we do not alter the weights of the reused neurons, we perfectly memorize the knowledge learned from previous tasks. We evaluate CLEAS on numerous sequential classification tasks, and the results demonstrate that CLEAS outperforms other state-of-the-art alternative methods, achieving higher classification accuracy while using simpler neural architectures.
Anomaly Detection with Tensor Networks

Originating from condensed matter physics, tensor networks are compact representations of high-dimensional tensors. In this paper, the prowess of tensor networks is demonstrated on the particular task of one-class anomaly detection. We exploit the memory and computational efficiency of tensor networks to learn a linear transformation over a space with dimension exponential in the number of original features. The linearity of our model enables us to ensure a tight fit around training instances by penalizing the model's global tendency to a predict normality via its Frobenius norm---a task that is infeasible for most deep learning models. Our method outperforms deep and classical algorithms on tabular datasets and produces competitive results on image datasets, despite not exploiting the locality of images.
Continuous Transfer Learning with Label-informed Distribution Alignment

Transfer learning has been successfully applied across many high-impact applications. However, most existing work focuses on the static transfer learning setting, and very little is devoted to modeling the time evolving target domain, such as the online reviews for movies. To bridge this gap, in this paper, we study a novel continuous transfer learning setting with a time evolving target domain. One major challenge associated with continuous transfer learning is the potential occurrence of negative transfer as the target domain evolves over time. To address this challenge, we propose a novel label-informed C-divergence between the source and target domains in order to measure the shift of data distributions as well as to identify potential negative transfer. We then derive the error bound for the target domain using the empirical estimate of our proposed C-divergence. Furthermore, we propose a generic adversarial Variational Auto-encoder framework named TransLATE by minimizing the classification error and C-divergence of the target domain between consecutive time stamps in a latent feature space. In addition, we define a transfer signature for characterizing the negative transfer based on C-divergence, which indicates that larger C-divergence implies a higher probability of negative transfer in real scenarios. Extensive experiments on synthetic and real data sets demonstrate the effectiveness of our TransLATE framework.
Infinite families of 2-designs from a class of non-binary Kasami cyclic codes

&lt;p style='text-indent:20px;'&gt;Combinatorial &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-designs have been an important research subject for many years, as they have wide applications in coding theory, cryptography, communications and statistics. The interplay between coding theory and &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-designs has been attracted a lot of attention for both directions. It is well known that a linear code over any finite field can be derived from the incidence matrix of a &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-design, meanwhile, that the supports of all codewords with a fixed weight in a code also may hold a &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-design. In this paper, by determining the weight distribution of a class of linear codes derived from non-binary Kasami cyclic codes, we obtain infinite families of &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ 2 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-designs from the supports of all codewords with a fixed weight in these codes, and calculate their parameters explicitly.&lt;/p&gt;
Preliminary Prediction of Rice Planting for Planting Season 2019, 2020, and 2021, Based Water Balance in Freshwater Swampland

Climate change that has occurred in recent years causes a shift at the beginning of rice planting. Rice crop failure due to flooding or drought is the main cause of the low Cropping Index in the freshwater swampland such as happened in Swampland Agro Science Park of Banjarbaru. Therefore, this study was carried out to determine the preliminary prediction of the rice planting season for planting season (MT) 2019, 2020, and 2021 based on water balance. As for what can be analyzed was the rainfall data as input compared to evapotranspiration through the Hargreaves approach. Then the output obtained in the form of net rate which used to measure the amount of rain every year. M eanwhile, the water requirement for rice was calculated during the period of growth to harvest. After that, the water balance simulation was performed from the difference in water bubbles (net yield rate) with the water needs in the rice field. Based on this water balance simulation obtained that the beginning of rice planting for MT 2019 is May IV-June IV, MT 2020 is June I-July I, and MT 2021 is June II-July II.
Accuracy enhancement in intra- and inter-frame example search for lossless video coding

We previously proposed a lossless video coding method based on intra/inter-frame example search and probability model optimization. In this method, several examples, i.e. a set of pels whose neighborhoods are similar to a local texture of the target pel to be encoded, are searched from already encoded areas of the current and previous frames with integer pel accuracy. Probability distribution of an image value at the target pel is then modeled as weighted sum of the Gaussian functions whose peaked positions are given by the individual examples. Furthermore, model parameters that control shapes of the Gaussian functions are numerically optimized so that the resulting coding rate can be a minimum. In this paper, the above example search process is enhanced to allow fractional-pel positions for more accurate probability modeling.
Translation, human–computer interaction and cognition 1

This chapter presents translation as a form of human–computer interaction. The evolution of translator–computer interaction is explored, and the benefits and challenges from a cognitive and professional perspective are enunciated. Particular attention is given to the increasing use of machine translation (MT), the growing fuzzy boundary between translation memory and MT, and new modes of interaction such as adaptive MT and interactive MT. The concept of cognitive ergonomics is drawn on to argue for a more caring and inclusive approach towards the translator by developers of translation technology. A case is also made for wider acceptance by the translation community of the benefits of the technology at their disposal and for more humanistic research on the impact of technology on the translator, the translation profession and the translation process.
DataverseNO: A National, Generic Repository and its Contribution to the Increased FAIRness of Data from the Long Tail of Research

Research data repositories play a crucial role in the FAIR (Findable, Accessible, Interoperable, Reusable) ecosystem of digital objects. DataverseNO is a national, generic repository for open research data, primarily from researchers affiliated with Norwegian research organizations. The repository runs on the open-source software Dataverse. This article presents the organization and operation of DataverseNO, and investigates how the repository contributes to the increased FAIRness of small and medium sized research data. Sections 1 to 3 present background information about the FAIR Data Principles (section 1), how FAIR may be turned into reality (section 2), and what these principles and recommendations imply for data from the so-called long tail of research, i.e. small and medium-sized datasets that are often heterogenous in nature and hard to standardize (section 3). Section 4 gives an overview of the key organizational features of DataverseNO, followed by an evaluation of how well DataverseNO and the repository application Dataverse as such support the FAIR Data Principles (section 5). Section 6 discusses how sustainable and trustworthy the repository is. The article is rounded up in section 7 by a brief summary including a look into the future of the repository.
The diophantine problem for addition and divisibility for subrings of rational functions over finite fields

It is shown that the positive existential theory of the structure ?S = (S?1F[t];=,F, 0, 1,+, |, f ? tf), where f ? tf is the multiplication by t map, S is non-empty a finite set of irreducible polynomials, and F is a finite field of odd characteristic, is undecidable.
A Virtual Reality Approach to Automatic Blood Sample Generation

Given the nature of the elements that make up blood, microscopes are fundamental to identifying blood cell morphology. These instruments are expensive and limited, and it is difficult to keep blood samples and their diversity in educational environments. We propose a solution based on virtual reality, that generates reliable blood samples using the automatic creation of images that simulate specific pathologies. We validated the proposal in three aspects: A method of adoption model, the validation of replicated samples and a performance test. The study and subsequent development of the mobile application were able to generate simulations of samples corresponding to a healthy adult and an adult with acute myeloid leukemia, where users can visualize, explore and obtain data on each of the elements that appear in the sample. This iteration not only verifies its technical feasibility but opens the way for future research on improving education or training processes.
Storytelling method using big book to improve children's listening skill

The low of listen ability in children aged 5-6 years is the background of this action research, so this research aims to improve these abilities. One method that has proven to be effective solve that problem is the method of storytelling with a big book. Data on classroom action research was collected using observation techniques and analyzed in quantitative descriptive. This study involved ten students as participants. The result of the study showed that storytelling with a big book can improve the listening skill of 5-6 years old children in PAUD Bintang Mutiara, Pekanbaru. Before applying storytelling method with big book, the participants’ listening skill was categorized as ‘has not been developed”. After applying the storytelling method with big book, their listening skill was categorized as “Developed as expected” with a score of 71.67%. The improvement of participants’ listening skill from ‘pre-cycle’ to ‘cycle 1’ was 21%. From cycle I to cycle II, their listening skill was improved by 64%. From pre-cycle to cycle II, the improvement was 89%. Overall, the improvement of listening skills was 75.56%.