Reviewerly/exHarmony huggingface dataset:
Image-Text Pre-Training for Logo Recognition

Open-set logo recognition is commonly solved by first detecting possible logo regions and then matching the detected parts against an ever-evolving dataset of cropped logo images. The matching model, a metric learning problem, is especially challenging for logo recognition due to the mixture of text and symbols in logos. We propose two novel contributions to improve the matching model’s performance: (a) using image-text paired samples for pre-training, and (b) an improved metric learning loss function. A standard paradigm of fine-tuning ImageNet pre-trained models fails to discover the text sensitivity necessary to solve the matching problem effectively. This work demonstrates the importance of pre-training on image-text pairs, which significantly improves the performance of a visual embedder trained for the logo retrieval task, especially for more text-dominant classes. We construct a composite public logo dataset combining LogoDet3K, OpenLogo, and FlickrLogos-47 deemed OpenLogoDet3K47. We show that the same vision backbone pre-trained on image-text data, when fine-tuned on OpenLogoDet3K47, achieves 98.6% recall@1, significantly improving performance over pre-training on Imagenet1K (97.6%). We generalize the ProxyNCA++ loss function to propose ProxyNCAHN++ which incorporates class-specific hard negative images. The proposed method sets new state-of-the-art on five public logo datasets considered, with a 3.5% zero-shot recall@1 improvement on LogoDet3K test, 4% on OpenLogo, 6.5% on FlickrLogos-47, 6.2% on Logos In The Wild, and 0.6% on BelgaLogo.
Converting qubits

A quantum bit encoding converter between qubits of different forms is experimentally demonstrated, paving the way to efficient networks for optical quantum computing and communication.
Recent Advances in Chatbot Algorithms, Techniques, and Technologies

Intelligent conversational computer systems, known as chatbots, have always been at the forefront of artificial intelligence. They are made to sound like humans in order for machines to communicate with humans. Because of the rising benefits of chatbots, numerous sectors have adopted them to give virtual support to clients. They are also used as companions and virtual assistants. Natural language processing and deep learning are two artificial intelligence disciplines that are used in chatbots. This chapter will examine current advancements in chatbot algorithms, approaches, and technologies that use artificial intelligence and/or natural language processing.
Sentiment annotations for 3827 simplified Chinese characters

Sentiment analysis in Chinese natural language processing has been largely based on words annotated with sentiment categories or scores. Characters, however, are the basic orthographic, phonological, and in most cases, semantic units in the Chinese language. This study collected sentiment annotations for 3827 characters. The ratings demonstrated high levels of reliability, and were validated through a comparison with the ratings of some characters' word equivalents reported in a previous norming study. Relations with other lexico-semantic variables and character processing efficiency were investigated. Furthermore, analyses of the association between constituent character valence and word valence revealed semantic compositionality and sentiment fusion characteristic of larger Chinese linguistic units. These ratings for characters, expanding current Chinese sentiment lexicons, can be utilized for the purposes of more precise stimuli assessment in research on Chinese character processing and more efficient sentiment analysis equipped with annotations of single-character words.
Influence of size and location of buttons on the usability of interface on large touch screens

This study examined how large touchscreen buttons affect user task completion time, error rate, and subjective satisfaction (ease of pointing and clicking on targets). In this experiment, 31 participants completed button click tasks of square targets placed at 30 (5 × 6) different locations, with seven button sizes (10 mm, 30 mm, 50 mm, 70 mm, 90 mm, 110 mm, and 130 mm). Repeated measures ANOVAs showed that on a 65'' large touch screen (W 1428 × H 803 mm), 70 mm buttons achieved essentially the best performance in completion time, 50 mm in error rates, and 90 mm in subjective satisfaction, respectively. We also compare the differences in efficiency among different button areas and button sizes. The surrounding area and lower area require a bigger button size to achieve the best performance in terms of error rates and subjective satisfaction. The right area requires a bigger optimal button size than the left area considering error rates, while it is the opposite for subjective satisfaction.Practitioner summary: This study investigates how the size and location of buttons on the increasingly popular large touch screen affect user experience. The study reveals that 70, 50, 90 mm size buttons are recommended when speed, accuracy and satisfaction are priorities respectively. The interaction between button size and button area is significantly different. The surrounding area and lower area require a bigger button size to achieve the best performance in terms of error rate and subjective satisfaction. Considering error rates, the right area requires a bigger optimal button size than the left area, while it is opposite for subjective satisfaction. These findings could facilitate the optimal design of usable large touch screens.
Optically Readable Electrochromic-Based Microfiber Synaptic Device for Photonic Neuromorphic Systems

Optical synaptic devices possess great potential in both artificial intelligence and neuromorphic photonics. In this work, an optically readable electrochromic-based microfiber synaptic device was designed by the combination of a multimode fiber and an electrochromic device and using an external voltage to control the transmission of light in the fiber. The proposed synaptic device has the ability to imitate various basic functions of the biological synapses, such as synaptic plasticity, and paired-pulse facilitation (PPF), as well as the transition from short-term memory to long-term memory. Moreover, the proposed device decodes the output optical signal with the international Morse code to express the signal "HFUT" in two ways, and a 3 × 3 array composed of this device can simulate the perceptual learning process. The device can be easily prepared for a wide range of applications, and the incorporated microfibers can be replaced by planar optical waveguides, making it easy to be integrated into a more complex and versatile photonic neuromorphic system.
Q-gym

The high computation cost is one of the key bottlenecks for adopting deep neural networks (DNNs) in different hardware. When client data are sensitive, privacy-preserving DNN evaluation method, such as homomorphic encryptions (HE), shows even more computation cost. Prior works employed weight repetition in quantized neural networks to save the computation of convolutions by memorizing or arithmetic factorization. However, such methods fail to fully exploit the exponential search space from factorizing and reusing computation. We propose Q-gym, a DNN framework consisting of two components. First, we propose a compiler, which leverages equality saturation to generate computation expressions for convolutional layers with a significant reduction in the number of operations. Second, we integrate the computation expressions with various parallelization methods to accelerate DNN inference on different hardware. We also employ the efficient expressions to accelerate DNN inference under HE.
Towards Better Guided Attention and Human Knowledge Insertion in Deep Convolutional Neural Networks

Attention Branch Networks (ABNs) have been shown to simultaneously provide visual explanation and improve the performance of deep convolutional neural networks (CNNs). In this work, we introduce Multi-Scale Attention Branch Networks (MSABN), which enhance the resolution of the generated attention maps, and improve the performance. We evaluate MSABN on benchmark image recognition and fine-grained recognition datasets where we observe MSABN outperforms ABN and baseline models. We also introduce a new data augmentation strategy utilizing the attention maps to incorporate human knowledge in the form of bounding box annotations of the objects of interest. We show that even with a limited number of edited samples, a significant performance gain can be achieved with this strategy.
Subsequence and distant supervision based active learning for relation extraction of Chinese medical texts

In recent years, relation extraction on unstructured texts has become an important task in medical research. However, relation extraction requires a large amount of labeled corpus, manually annotating sequences is time consuming and expensive. Therefore, efficient and economical methods for annotating sequences are required to ensure the performance of relational extraction. This paper proposes a method of subsequence and distant supervision based active learning. The method is annotated by selecting information-rich subsequences as a sampling unit instead of the full sentences in traditional active learning. Additionally, the method saves the labeled subsequence texts and their corresponding labels in a dictionary which is continuously updated and maintained, and pre-labels the unlabeled set through text matching based on the idea of distant supervision. Finally, the method combines a Chinese-RoBERTa-CRF model for relation extraction in Chinese medical texts. Experimental results test on the CMeIE dataset achieves the best performance compared to existing methods. And the best F1 value obtained between different sampling strategies is 55.96%.
Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges

The expanding digitalization of routine diagnostic histological slides holds a potential to apply artificial intelligence (AI) to pathology, including bone marrow (BM) histology. In this perspective, we describe potential tasks in diagnostics that can be supported, investigations that can be guided, and questions that can be answered by the future application of AI on whole-slide images of BM biopsies. These range from characterization of cell lineages and quantification of cells and stromal structures to disease prediction. First glimpses show an exciting potential to detect subtle phenotypic changes with AI that are due to specific genotypes. The discussion is illustrated by examples of current AI research using BM biopsy slides. In addition, we briefly discuss current challenges for implementation of AI-supported diagnostics.
Beyond Transactional Democracy: A Study of Civic Tech in Canada

Technologies are increasingly enrolled in projects to involve civilians in the work of policy-making, often under the label of 'civic technology'. But conventional forms of participation through transactions such as voting provide limited opportunities for engagement. In response, some civic tech groups organize around issues of shared concern to explore new forms of democratic technologies. How does their work affect the relationship between publics and public servants? This paper explores how a Civic Tech Toronto creates a platform for civic engagement through the maintenance of an autonomous community for civic engagement and participation that is casual, social, nonpartisan, experimental, and flexible. Based on two years of action research, including community organizing, interviews, and observations, this paper shows how this grassroots civic tech group creates a civic platform that places a diverse range of participants in contact with the work of public servants, helping to build capacities and relationships that prepare both publics and public servants for the work of participatory democracy. The case shows that understanding civic tech requires a lens beyond the mere analysis or production of technical artifacts. As a practice for making technologies that is social and participatory, civic tech creates alternative modes of technology development and opportunities for experimentation and learning, and it can reconfigure the roles of democratic participants.
A robust phase extraction method for overcoming spectrum overlapping in shearography

The advantage of spatial phase-shifting shearography is its ability to extract the phase from a single speckle pattern; however, it often faces spectrum overlapping, which seriously affects phase quality. In this paper, we propose a shearography phase-extraction method based on windowed Fourier ridges, which can effectively extract phase information even in the presence of severe spectrum overlapping. A simple and efficient method was applied to determine the parameters of the windowed Fourier ridges, and a linear variation window was used to match the phase-extraction requirements for different frequency coordinates. A numerical simulation was quantitatively conducted to compare the phase-extraction results of the proposed method with those of the conventional method for various cases, and a shearography system was built with two types of objects to demonstrate the feasibility of the proposed method.
Communicative Psychosemantics of Cognitive-Noetic Development of the Personality

A comprehensive analysis and description was conducted of communicative psychosemantics as a universal basis for constructing a structural-dynamic model of the cognitive-noetic potential for the development of a professional personality in the modern educational process. The study used the main humanities communicative sense-forming processes of qualitative methods: hermeneutics, phenomenology, content analysis and experimental psychosemantics. It was found that the relevance of these methods depends on if they take into account authentic communicative factors. For hermeneutic understanding, these are universal factors of contradictions in the communicative situation due to the doubling of mismatched expectations and ideas among partners: on the one hand, about the subject and their linguistic representations; on the other hand, about each other and how each portrays themself. For phenomenological immersion in directly conscious experiences, this is a communicative moment of meeting with the other, the unknown, which gives rise to an ambivalent stress-transformation. It is they who actualize the intuitive-creative cycle of peak experiences: catharsis - imprinting -ecstasy - insight. Content analysis takes into account specific communicative contexts, but needs psychosemantic contextual markers. We created “Communicative psychosemantics” to integrate all high-quality meaning-oriented research methods based on the sociability of the mental and spiritual life of a person in line with the structural-dynamic model of the cognitive-noetic potential of personality development. The intentional dynamic vector of the model is presented as a cumulative cycle of integration of qualitative psychological modalities: motivation - perception - imagination - emotion. The transcendental structural-level vector of communicative psychosemantics is represented by holarchic levels: value-semantic formations - problematic conceptual target solutions - constructive compositional objectivism -archetypal symbolic expressive incarnations. This structural-dynamic model of communicative psychosemantics is translated into an equivalent model of 16 basic positional strategies of the educational process, focused on the cognitive-noetic development of a person's professional and personal potential.
To make yourself invisible with Adversarial Semantic Contours

Modern object detectors are vulnerable to adversarial examples, which may bring risks to real-world applications. The sparse attack is an important task which, compared with the popular adversarial perturbation on the whole image, needs to select the potential pixels that is generally regularized by an $\ell_0$-norm constraint, and simultaneously optimize the corresponding texture. The non-differentiability of $\ell_0$ norm brings challenges and many works on attacking object detection adopted manually-designed patterns to address them, which are meaningless and independent of objects, and therefore lead to relatively poor attack performance. In this paper, we propose Adversarial Semantic Contour (ASC), an MAP estimate of a Bayesian formulation of sparse attack with a deceived prior of object contour. The object contour prior effectively reduces the search space of pixel selection and improves the attack by introducing more semantic bias. Extensive experiments demonstrate that ASC can corrupt the prediction of 9 modern detectors with different architectures (\e.g., one-stage, two-stage and Transformer) by modifying fewer than 5\% of the pixels of the object area in COCO in white-box scenario and around 10\% of those in black-box scenario. We further extend the attack to datasets for autonomous driving systems to verify the effectiveness. We conclude with cautions about contour being the common weakness of object detectors with various architecture and the care needed in applying them in safety-sensitive scenarios.
Low Power Coherent Ising Machine Based on Mechanical Kerr Nonlinearity

Finding a reliable Ising machine for solving nondeterministic polynomial-class problems has attracted great attention in recent years, where an authentic system can be expanded with polynomial-scaled resources to find the ground state Ising Hamiltonian. In this Letter, we propose an extremely low power optomechanical coherent Ising machine based on a new enhanced symmetry breaking mechanism and highly nonlinear mechanical Kerr effect. The mechanical movement of an optomechanical actuator induced by the optical gradient force greatly increases the nonlinearity by a few orders and significantly reduces the power threshold using conventional structures capable of fabrication via photonic integrated circuit platforms. With the simple but strong bifurcation mechanism and remarkably low power requirement, our optomechanical spin model opens a path for chip-scale integration of large-size Ising machine implementations with great stability.
Code red: A nuclear nightmare-navigating ransomware response at an Eastern European power plant

Over the past decade, the number and scale of ransomware attacks has grown. These attacks have stretched across multiple industries globally and caused billions of dollars of damages. Recent attacks have disrupted critical infrastructure and have served as prelude to war. Studies have shown that there is a lack of awareness among industry practitioners surrounding appropriate responses to ransomware. This failure during a career-defining moment is largely due to the gap between cyber security academic knowledge and industry practice. We therefore provide a learning resource for ransomware response education that is academically adequate, practically viable, and ultimately illustrates a process for proper employment of technology. This case, exposes students to technical requirements for a ransomware response and explores ransomware best practices. Discussion questions and a wargaming-style purple team exercise for individuals or groups extend the opportunity for delivering additional technical depth.
Learning-Aided Adaptation - A Case Study from Wellness Ecosystem

Modern businesses are being subjected to an unprecedented variety of change drivers that cannot be predicted such as new regulations, emerging business models, and changing needs of stakeholders. This creates new demands on enterprises to meet stated goals in a dynamic and uncertain environment that translate to demands on the enterprise’s software systems. In our earlier work, we proposed an architecture for dynamic adaptation under uncertainty. In this paper, we provide validation of our adaptation architecture through a case study of a wellness ecosystem comprising multiple stakeholders with evolving goals and behavior that need to be continuously learnt and adapted. We present experimentation results from our case study that serve as an initial step towards validating our approach.
Robust Fuzzy Neural Network With an Adaptive Inference Engine

Fuzzy neural networks (FNNs) have been very successful at handling uncertainty in data using fuzzy mappings and if-then rules. However, they suffer from generalization and dimensionality issues. Although deep neural networks (DNNs) represent a step toward processing high-dimensional data, their capacity to address data uncertainty is limited. Furthermore, deep learning algorithms designed to improve robustness are either time consuming or yield unsatisfactory performance. In this article, we propose a robust fuzzy neural network (RFNN) to overcome these problems. The network contains an adaptive inference engine that is capable of handling samples with high-level uncertainty and high dimensions. Unlike traditional FNNs that use a fuzzy AND operation to calculate the firing strength for each rule, our inference engine is able to learn the firing strength adaptively. It also further processes the uncertainty in membership function values. Taking advantage of the learning ability of neural networks, the acquired fuzzy sets can be learned from training inputs automatically to cover the input space well. Furthermore, the consequent layer uses neural network structures to enhance the reasoning ability of the fuzzy rules when dealing with complex inputs. Experiments on a range of datasets show that RFNN delivers state-of-the-art accuracy even at very high levels of uncertainty. Our code is available online. https://github.com/leijiezhang/RFNN
DIDACTIC TRANSFORMATIONS OF THE DISTANCE EDUCATIONAL PROCESS IN UNIVERSITIES IN ENGINEERING IN (POST) PANDEMIC TIMES

As university education and, in particular, initial training of engineers are the priority directions of strategic development at the national and European level, in the current epidemiological conditions it is clear the need to support the digital transformation of the traditional engineering university education system into a valid one to be implemented remotely. In order to solve the problems listed above, within the call "Offers of research-innovation solutions on combating and mitigating the impact of the COVID-19 pandemic", launched by the National Agency for Research and Development of the Republic of Moldova, a team of researchers from the Technical University of Moldova (TUM) initiated a project, called DIGIFORME, which aims to develop solutions of an institutional, informational and methodological nature corresponding to the curricular specificities of engineering and technological specialties within the beneficiary university, such as laboratories remote, simulation laboratories, methods and algorithms for analyzing the interaction between the actors of the teaching process, etc. This research comes with the description of a viable didactic model to be implemented in the educational process in universities in engineering both in a pandemic context and in a post-pandemic context. The model design and analyzed by the author provides for the implementation of certain methodological digital transformations, the presence of which is argued both from a theoretical and applied perspective of teaching-learning-evaluation of engineering disciplines in a distance learning format. The solutions presented in the article are meant to reduce the vulnerabilities of a university with a profile in engineering education, such as the lack of a basis for carrying out distance work with students (from all types of studies); insufficient training of teachers for the full conduct of online lessons; the inadequacy of the informational and methodological support for the number of didactic activities specific to the specialties with engineering and technological profile. In the conclusion of the research, the author outlines the perspectives of capitalizing the experience of the TUM based on the given didactical model.