Reviewerly/exHarmony huggingface dataset:
What if This Modified That? Syntactic Interventions with Counterfactual Embeddings

Neural language models exhibit impressive performance on a variety of tasks, but their internal reasoning may be difficult to understand.Prior art aims to uncover meaningful properties within model representations via probes, but it is unclear how faithfully such probes portray information that the models actually use.To overcome such limitations, we propose a technique, inspired by causal analysis, for generating counterfactual embeddings within models.In experiments testing our technique, we produce evidence that suggests some BERT-based models use a tree-distancelike representation of syntax in downstream prediction tasks.
Hybrid Model Based on ReliefF Algorithm and K-Nearest Neighbor for Erythemato-Squamous Diseases Forecasting

Abstract Machine learning (ML) techniques have been used to solve real-world problems for decades. In the field of medical sciences, these approaches have been found to be useful in the diagnosis and prognosis of a variety of medical disorders. However, when dealing with voluminous, inconsistent, and higher-dimensional data, conventional ML approaches have failed to deliver the expected results. Researchers have suggested hybrid solutions to resolve these problems, which have been found to be more effective than conventional methods because these systems integrate their merits while reducing their drawbacks. In the current research article, hybrid model has been presented by coupling feature optimization with prediction approach. The proposed hybrid model has two stages: the first involves implementing the ReliefF Algorithm for optimum feature selection in erythemato-squamous diseases, and the second involves implementing k-nearest neighbor (KNN) for prediction of those selected optimum features. The experimentation was carried out on bench mark dataset for erythemato-squamous diseases. The presented hybrid model was also assessed with conventional KNN approach based on various metrics such as classification accuracy, kappa coefficient, recall, precision, and f-score.
More the Merrier: Towards Multi-Emotion and Intensity Controllable Response Generation

The focus on conversational systems has recently shifted towards creating engaging agents by inculcating emotions into them. Human emotions are highly complex as humans can express multiple emotions with varying intensity in a single utterance, whereas the conversational agents convey only one emotion in their responses. To infuse human-like behaviour in the agents, we introduce the task of multi-emotion controllable response generation with the ability to express different emotions with varying levels of intensity in an open-domain dialogue system. We introduce a Multiple Emotion Intensity aware Multi-party Dialogue (MEIMD) dataset having 34k conversations taken from 8 different TV Series. We finally propose a Multiple Emotion with Intensity-based Dialogue Generation (MEI-DG) framework. The system employs two novel mechanisms: viz. (i) determining the trade-off between the emotion and generic words, while focusing on the intensity of the desired emotions; and (ii) computing the amount of emotion left to be expressed, thereby regulating the generation accordingly. The detailed evaluation shows that our proposed approach attains superior performance compared to the baseline models.
VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words

Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries.In this paper, we propose VisualSparta, a novel (Visualtext Sparse Transformer Matching) model that shows significant improvement in terms of both accuracy and efficiency.VisualSparta is capable of outperforming previous stateof-the-art scalable methods in MSCOCO and Flickr30K.We also show that it achieves substantial retrieving speed advantages, i.e., for a 1 million image index, VisualSparta using CPU gets ∼391X speedup compared to CPU vector search and ∼5.4X speedup compared to vector search with GPU acceleration.Experiments show that this speed advantage even gets bigger for larger datasets because Visu-alSparta can be efficiently implemented as an inverted index.To the best of our knowledge, VisualSparta is the first transformer-based textto-image retrieval model that can achieve realtime searching for large-scale datasets, with significant accuracy improvement compared to previous state-of-the-art methods.
LRSC: Learning Representations for Subspace Clustering

Deep learning based subspace clustering methods have attracted increasing attention in recent years, where a basic theme is to non-linearly map data into a latent space, and then uncover subspace structures based upon the data self-expressiveness property. However, almost all existing deep subspace clustering methods only rely on target domain data, and always resort to shallow neural networks for modeling data, leaving huge room to design more effective representation learning mechanisms tailored for subspace clustering. In this paper, we propose a novel subspace clustering framework through learning precise sample representations. In contrast to previous approaches, the proposed method aims to leverage external data through constructing lots of relevant tasks to guide the training of the encoder, motivated by the idea of meta-learning. Considering limited layer structures of current deep subspace clustering models, we intend to distill knowledge from a deeper network trained on the external data, and transfer it into the shallower model. To reach the above two goals, we propose a new loss function to realize them in a joint framework. Moreover, we propose to construct a new pretext task for self-supervised training of the model, such that the representation ability of the model can be further improved. Extensive experiments are performed on four publicly available datasets, and experimental results clearly demonstrate the efficacy of our method, compared to state-of-the-art methods.
Sliding Spectrum Decomposition for Diversified Recommendation

Content feed, a type of product that recommends a sequence of items for users to browse and engage with, has gained tremendous popularity among social media platforms. In this paper, we propose to study the diversity problem in such a scenario from an item sequence perspective using time series analysis techniques. We derive a method calledsliding spectrum decomposition (SSD) that captures users' perception of diversity in browsing a long item sequence. We also share our experiences in designing and implementing a suitable item embedding method for accurate similarity measurement under long tail effect. Combined together, they are now fully implemented and deployed in Xiaohongshu App's production recommender system that serves the main Explore Feed product for tens of millions of users every day. We demonstrate the effectiveness and efficiency of the method through theoretical analysis, offline experiments and online A/B tests.
Design principles for constructing GDPR-compliant blockchain solutions

Blockchain is an incipient technology that offers several advantages over traditional systems, such as decentralization, transparency and traceability. The architecture and characteristics specific to this technology shall, however, have consequences on how personal data is stored and processed. We present a detailed analysis of the European data protection regulation and discuss the weaknesses and strengths of solutions built using blockchain technology regarding the security and privacy requirements established by that regulation. We study the integration of off-chain capabilities in blockchain-based solutions and put forward a methodological framework that can be used to design secure and privacy aware information systems that combine operational off-chain constructs with traditional blockchain functionalities.
Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball

We present a simple regularization of adversarial perturbations based upon the perceptual loss. While the resulting perturbations remain imperceptible to the human eye, they differ from existing adversarial perturbations in that they are semi-sparse alterations that highlight objects and regions of interest while leaving the background unaltered. As a semantically meaningful adverse perturbations, it forms a bridge between counterfactual explanations and adversarial perturbations in the space of images.We evaluate our approach on several standard explainability benchmarks, namely, weak localization, insertion-deletion, and the pointing game demonstrating that perceptually regularized counterfactuals are an effective explanation for image-based classifiers.
ULearnEnglish: An Open Ubiquitous System for Assisting in Learning English Vocabulary

Language learners often face communication problems when they need to express themselves and do not have the ability to do so. On the other hand, continuous advances in technology are creating new opportunities to improve second language (L2) acquisition through context-aware ubiquitous learning (CAUL) technology. Since vocabulary is the foundation of all language acquisition, this article presents ULearnEnglish, an open-source system to allow ubiquitous English learning focused on incidental vocabulary acquisition. To evaluate our proposal, 15 learners used the developed system, and 10 answered a survey based on the Technology Acceptance Model (TAM). Results indicate a favorable response to the application of incidental learning techniques in combination with the learner context. ULearnEnglish achieved an acceptance rate of 78.66% for the perception of utility, 96% for the perception of ease of use, 86.5% for user context assessment, and 88% for ubiquity. Among its main contributions, this study demonstrates a possible tool for ubiquitous use in the future in language learning; additionally, further studies can use the available resources to develop the system.
An Enhanced Semantic Focused Web Crawler Based on Hybrid String Matching Algorithm

Abstract Topic precise crawler is a special purpose web crawler, which downloads appropriate web pages analogous to a particular topic by measuring cosine similarity or semantic similarity score. The cosine based similarity measure displays inaccurate relevance score, if topic term does not directly occur in the web page. The semantic-based similarity measure provides the precise relevance score, even if the synonyms of the given topic occur in the web page. The unavailability of the topic in the ontology produces inaccurate relevance score by the semantic focused crawlers. This paper overcomes these glitches with a hybrid string-matching algorithm by combining the semantic similarity-based measure with the probabilistic similarity-based measure. The experimental results revealed that this algorithm increased the efficiency of the focused web crawlers and achieved better Harvest Rate (HR), Precision (P) and Irrelevance Ratio (IR) than the existing web focused crawlers achieve.
Changing your body changes your eating attitudes: embodiment of a slim virtual avatar induces avoidance of high-calorie food

The virtual-reality full-body illusion paradigm has been suggested to not only trigger the illusory ownership of the avatar's body but also the attitudinal and behavioral components stereotypically associated to that kind of virtual body. In the present study, we investigated whether this was true for stereotypes related to body size: body satisfaction and eating control behavior. Healthy participants underwent the full-body illusion paradigm with an avatar having either a larger or a slimmer body than their own, and were assessed for implicit attitudes towards body image and food calorie content at baseline and after each full-body illusion session. Results showed that the illusion emerged regardless of the avatar's body size, whereas the perceived dimension of the own body size changed according to the avatar's body size (i.e., participants felt to be slimmer after embodying their slim avatar and larger after embodying their large avatar). Crucially, we found that implicit attitudes towards food, but not those towards one's own body, were modulated by the size of the virtual body. Compared to baseline, ownership of a slimmer avatar increased the avoidance of high-calorie food, whereas ownership of a larger avatar did not induce changes. Our findings suggest that the illusory feeling of being slimmer drives also the food-related stereotypes associated with that body size, increasing the regulation of eating behaviors.
A Comparison of LSTM and GRU Networks for Learning Symbolic Sequences

We explore the architecture of recurrent neural networks (RNNs) by studying the complexity of string sequences that it is able to memorize. Symbolic sequences of different complexity are generated to simulate RNN training and study parameter configurations with a view to the network’s capability of learning and inference. We compare Long Short-Term Memory (LSTM) networks and gated recurrent units (GRUs). We find that an increase in RNN depth does not necessarily result in better memorization capability when the training time is constrained. Our results also indicate that the learning rate and the number of units per layer are among the most important hyper-parameters to be tuned. Generally, GRUs outperform LSTM networks on low-complexity sequences while on high-complexity sequences LSTMs perform better.
The effects of knowledge sharing, social capital and innovation on marketing performance

Women entrepreneurs and the informal sector are looking for footholds in the COVID-19 pandemic, which will lead women to develop creative businesses. This study examines the role of sharing knowledge and innovation in addressing gaps in social capital and marketing performance. Purposive sampling is used in the technique sample with 229 samples and Structural Equation Modeling (SEM-PLS) analysis techniques with SmartPLS is used for processing applications. The results show that social capital has a positive effect on the business performance of women entrepreneurs in Bali, Indonesia. The knowledge-sharing variable can be a mediator in the relationship between social capital and performance, and social capital has a significant positive effect on innovation, but innovation does not have a positive effect on marketing performance and knowledge sharing. In the end, women entrepreneurs will use knowledge sharing to create various innovations to meet market demand. However, opportunities for women entrepreneurs are very limited on capital due to the lack of guaranteed capital, and a lack of entrepreneurial skills in the era of technology, market access, bureaucracy, and legal matters. In addition, managerial skills, access to information technology, and the perspective that men should excel in Balinese culture and customs, limit business for women entrepreneurs.
University Science and Education in the Context of Artificial Intelligence

Computerization, digitalization, and robotization are the bright signs of modern life. In this regard, a long-standing scientific problem of artificial intelligence has now acquired a new sound and a variety of applied aspects. The higher education system has also become involved in the information technology orbit. Using the example of Voronezh State University, two main areas of informatization and computerization are revealed: first, the scientific development of artificial intelligence problems by university scientists and the use of artificial intelligence technologies for research, and, second, training of modern specialists in the field of IT technologies in specialized faculties and the expansion of the digital competence among the graduates of other training areas and specialties. The article discusses the need for students’ comprehensive acquaintance with this area of modern scientific knowledge which includes the capabilities of artificial intelligence technologies and its limitations as well.
Towards large scale automated algorithm design by integrating modular benchmarking frameworks

We present a first proof-of-concept use-case that demonstrates the efficiency of interfacing the algorithm framework ParadisEO with the automated algorithm configuration tool irace and the experimental platform IOHprofiler. By combing these three tools, we obtain a powerful benchmarking environment that allows us to systematically analyze large classes of algorithms on complex benchmark problems. Key advantages of our pipeline are fast evaluation times, the possibility to generate rich data sets to support the analysis of the algorithms, and a standardized interface that can be used to benchmark very broad classes of sampling-based optimization heuristics. In addition to enabling systematic algorithm configuration studies, our approach paves a way for assessing the contribution of new ideas in interplay with already existing operators -- a promising avenue for our research domain, which at present may have a too strong focus on comparing entire algorithm instances.
Dense Depth Estimation from Stereo Endoscopy Videos Using Unsupervised Optical Flow Methods

In the context of Minimally Invasive Surgery, estimating depth from stereo endoscopy plays a crucial role in three-dimensional (3D) reconstruction, surgical navigation, and augmentation reality (AR) visualization. However, the challenges associated with this task are three-fold: 1) feature-less surface representations, often polluted by artifacts, pose difficulty in identifying correspondence; 2) ground truth depth is difficult to estimate; and 3) an endoscopy image acquisition accompanied by accurately calibrated camera parameters is rare, as the camera is often adjusted during an intervention. To address these difficulties, we propose an unsupervised depth estimation framework (END-flow) based on an unsupervised optical flow network trained on un-rectified binocular videos without calibrated camera parameters. The proposed END-flow architecture is compared with traditional stereo matching, self-supervised depth estimation, unsupervised optical flow, and supervised methods implemented on the Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED) Challenge dataset. Experimental results show that our method outperforms several state-of-the-art techniques and achieves a close performance to that of supervised methods.
Spatial Coevolution for Generative Adversarial Network Training

Generative Adversarial Networks (GANs) are difficult to train because of pathologies such as mode and discriminator collapse. Similar pathologies have been studied and addressed in competitive evolutionary computation by increased diversity. We study a system, Lipizzaner, that combines spatial coevolution with gradient-based learning to improve the robustness and scalability of GAN training. We study different features of Lipizzaner’s evolutionary computation methodology. Our ablation experiments determine that communication, selection, parameter optimization, and ensemble optimization each, as well as in combination, play critical roles. Lipizzaner succumbs less frequently to critical collapses and, as a side benefit, demonstrates improved performance. In addition, we show a GAN-training feature of Lipizzaner: the ability to train simultaneously with different loss functions in the gradient descent parameter learning framework of each GAN at each cell. We use an image generation problem to show that different loss function combinations result in models with better accuracy and more diversity in comparison to other existing evolutionary GAN models. Finally, Lipizzaner with multiple loss function options promotes the best model diversity while requiring a large grid size for adequate accuracy.
Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teams

Multi agent strategies in mixed cooperative-competitive environments can be hard to craft by hand because each agent needs to coordinate with its teammates while competing with its opponents. Learning based algorithms are appealing but they require a competitive opponent to train against, which is often not available. Many scenarios require heterogeneous agent behavior for the team’s success and this increases the complexity of the learning algorithm. In this work, we develop a mixed cooperative-competitive multi agent environment called FortAttack in which two teams compete against each other for success. We show that modeling agents with Graph Neural Networks (GNNs) and training them with Reinforcement Learning (RL) from scratch, leads to the co-evolution of increasingly complex strategies for each team. Through competition in Multi-Agent Reinforcement Learning (MARL), we observe a natural emergence of heterogeneous behavior among homogeneous agents when such behavior can lead to the team’s success. Such heterogeneous behavior from homogeneous agents is appealing because any agent can replace the role of another agent at test time. Finally, we propose ensemble training, in which we utilize the evolved opponent strategies to train a single policy for friendly agents. We were able to train a large number of agents on a commodity laptop, which shows the scalability and efficiency of our approach. The code and a video presentation are available online (Code: https://github.com/Ankur-Deka/Emergent-Multiagent- Strategies , Video: https://youtu.be/ltHgKYc0F-E ).
Improvements to Quantum Search Techniques for Block-Ciphers, with Applications to AES

In this paper we demonstrate that the overheads (ancillae qubits/time/number of gates) involved with implementing quantum oracles for a generic key-recovery attack against block-ciphers using quantum search techniques can be reduced. In particular, if we require $$r \ge 1$$ plaintext-ciphertext pairs to uniquely identify a user's key, then using Grover's quantum search algorithm for cryptanalysis of block-ciphers as in [2, 3, 9, 13, 18] would require a quantum circuit which requires effort (either Time $$\times $$ Space product or number of quantum gates) proportional to r. We demonstrate how we can reduce this by a fine-grained approach to quantum amplitude amplification [6, 17] and design of the required quantum oracles. We furthermore demonstrate that this effort can be reduced to $$< r$$ with respect to cryptanalysis of AES-128/192/256 and provide full quantum resource estimations for AES-128/192/256 with our methods, and code in the Q# quantum programming language that extends the work of [13].