Reviewerly/exHarmony huggingface dataset:
An Inquiry into the Semantic Transparency and Productivity of German Particle Verbs and Derivational Affixation

This study addresses the relation between morphological productivity and semantic transparency. Us- ing distributional semantics, we compare German word formation with particles with derivational word formation. We observed that derivational suffixes, but not particles, tend to make strong independent semantic contributions to their carrier words. In two-dimensional t-SNE maps, complex words show clus- tering by affix, but not by particle. Furthermore, the semantic vectors of suffixed words are predictable from their base words with higher accuracy than is possible for particle verbs. This holds irrespective of whether the semantic vectors of complex words are predicted using a generalization of the FRACCS model, or whether a more recent, and more precise, additive model (CosClassAvg) is used. For particle verbs, but not affixed verbs, a greater semantic similarity within the set of complex words correlated negatively with the number of types. Only for particle verbs, a greater number of types predicted a reduced probability of unseen types. Considered jointly, the particle verbs are characterized by quan- titative properties that are very similar to those observed for Mandarin compounds. We propose that particle verbs, just as compounds, primarily serve the onomasiological function of labeling, resulting in highly distinct semantic vectors for particle verbs. By contrast, derivational affixation groups complex words closely together in semantic space while maintaining strong and consistent semantic relations with the base words. This enables these words to serve not only as labels, but also allows them to be used for syntactic recategorization.
Revisiting Self-Supervised Monocular Depth Estimation

Self-supervised learning of depth map prediction and motion estimation from monocular video sequences is of vital importance -- since it realizes a broad range of tasks in robotics and autonomous vehicles. A large number of research efforts have enhanced the performance by tackling illumination variation, occlusions, and dynamic objects, to name a few. However, each of those efforts targets individual goals and endures as separate works. Moreover, most of previous works have adopted the same CNN architecture, not reaping architectural benefits. Therefore, the need to investigate the inter-dependency of the previous methods and the effect of architectural factors remains. To achieve these objectives, we revisit numerous previously proposed self-supervised methods for joint learning of depth and motion, perform a comprehensive empirical study, and unveil multiple crucial insights. Furthermore, we remarkably enhance the performance as a result of our study -- outperforming previous state-of-the-art performance.
A High Flexible Shift Transformation Unit Design Approach for Coarse-Grained Reconfigurable Cryptographic Arrays

Shift transformations are the fundamental operation of cryptographic algorithms, and the arithmetic unit implementing different types of shift transformations are utilized in the coarse-grain reconfigurable cryptographic architectures (CGRCA) to meet the different cryptographic algorithms. In this paper, a reconfigurable shift transformation unit (RSTU) is proposed to meet the complicated shift requirement of CGRCA, which achieves high flexibility and a good cost–performance ratio. The mathematical properties of shift transformation are analyzed, and several theorems are introduced to design a reconfigurable shifter. Furthermore, the reconfigurable data path of the proposed unit is presented to implement the random combination of shift operations in different granularity, and configuration word and routing algorithms are proposed to generate control information for RSTU. Moreover, the control information generation module is designed to invert the configuration word into the control information, according to the routing algorithms. As a proof-of-concept, the proposed RSTU is built using the CMOS 65 nm technology. The experimental results show that RSTU supports more shift operations, increases 18.2% speed at most, and reduces 13% area occupation, compared to the existing shifters.
INTERNET OF THINGS: PERSONAL DATA PROTECTION

The internet of things is becoming more and more popular with time.IOT personal devices are increasing day by day.There are so many devices related to IoT have come into market.Such as contact lenses, fitness bands, microchips, smart watches, under skin, among others.The extremely low cost of sensors is taking the growth of the IOT on steroids.However, as the IoT continues to evolve and develop, it has been subjected to certain threats and vulnerabilities.The common vulnerabilities are there in IoT such as security, privacy and data protection concerns.And as we know that how privacy and security are the biggest concern in this technology.So, this paper is presents on how we can protect personal data .aswell as inside this paper we will look different methods of protecting personal data.
Ensemble Self-Paced Learning Based on Adaptive Mixture Weighting

Self-paced learning (SPL) is a learning mechanism inspired by human and animal learning processes that gives variable weights to samples, gradually introducing simple to more complicated samples into the learning set as the “age” parameter increases. To regulate the learning process, a self-paced weighting regularization term with an “age” parameter is introduced to the learning function. Several self-paced weighting methods have been proposed, and different regularization terms might result in varied learning performance. However, on the one hand, it is difficult to select a suitable weighting method for SPL. On the other hand, it is challenging to determine the “age” parameter, and it is easy for SPL to obtain poor results as the “age” of the model increases. To solve the aforementioned difficulties, an ensemble SPL approach with an adaptive mixture weighting mechanism is proposed in this study. First, as the “age” parameter increases, a set of base classifiers is collected to produce a new data set, which is used to learn the second-level classifier. Then, the ensemble model is used to generate the final output to avoid the selection of the optimal “age” parameter. An adaptive mixture weighting method is designed to reduce the dependence of parameters on human experience. The previous methods find it difficult to determine the “age” parameters or self-paced parameters. In this paper, these parameters can be adjusted adaptively during the learning process. In comparison with the previous SPL techniques, the proposed method achieves the best results in 27 of the 32 datasets in the experiments with the adaptive parameters. The statistical tests are carried out to show that the proposed method is superior to other state-of-the-art algorithms.
Person Entity Alignment Method Based on Multimodal Information Aggregation

Entity alignment is used to determine whether entities from different sources refer to the same object in the real world. It is one of the key technologies for constructing large-scale knowledge graphs and is widely used in the fields of knowledge graphs and knowledge complementation. Because of the lack of semantic connection between the visual modality face attribute of the person entity and the text modality attribute and relationship information, it is difficult to model the visual and text modality into the same semantic space, and, as a result, that the traditional multimodal entity alignment method cannot be applied. In view of the scarcity of multimodal person relation graphs datasets and the difficulty of the multimodal semantic modeling of person entities, this paper analyzes and crawls open-source semi-structured data from different sources to build a multimodal person entity alignment dataset and focuses on using the facial and semantic information of multimodal person entities to improve the similarity of entity structural features which are modeled using the graph convolution layer and the dynamic graph attention layer to calculate the similarity. Through verification on the self-made multimodal person entity alignment dataset, the method proposed in this paper is compared with other entity alignment models which have a similar structure. Compared with AliNet, the probability that the first item in the candidate pre-aligned entity set is correct is increased by 12.4% and average ranking of correctly aligned entities in the candidate pre-aligned entity set decreased by 32.8, which proves the positive effect of integrating multimodal facial information, applying dynamic graph attention and a layer-wise gated network to improve the alignment effect of person entities.
Cryptographic Rational Secret Sharing Schemes over General Networks

We propose cryptographic rational secret sharing protocols over general networks. In a general network, the dealer may not have direct connections to each player, and players may not have direct connections to each of the other players. We present conditions on the network topology for which our proposed protocols are computational strict Nash equilibria and (k−1)-resilient, along with analysis on their round and communication complexity. We also present new notions of equilibria such as Φ-resilient computational Nash equilibria, whereby a protocol is resilient to coalitions that satisfy conditions in Φ, regardless of the coalition’s size. We also propose (n−1)-key leakage-tolerant equilibria applicable to cryptographic protocols involving secret keys, whereby the equilibrium holds even if some players acquire (n−1) tuples of secret keys.
High Performance on Atari Games Using Perceptual Control Architecture Without Training

Abstract Deep reinforcement learning (DRL) requires large samples and a long training time to operate optimally. Yet humans rarely require long periods of training to perform well on novel tasks, such as computer games, once they are provided with an accurate program of instructions. We used perceptual control theory (PCT) to construct a simple closed-loop model which requires no training samples and training time within a video game study using the Arcade Learning Environment (ALE). The model was programmed to parse inputs from the environment into hierarchically organised perceptual signals, and it computed a dynamic error signal by subtracting the incoming signal for each perceptual variable from a reference signal to drive output signals to reduce this error. We tested the same model across three different Atari games Breakout, Pong and Video Pinball to achieve performance at least as high as DRL paradigms, and close to good human performance. Our study shows that perceptual control models, based on simple assumptions, can perform well without learning. We conclude by specifying a parsimonious role of learning that may be more similar to psychological functioning.
Transformer-Based Abstract Generation of Medical Case Reports

A medical case report gives medical researchers and healthcare providers a thorough account of the symptoms, treatment, and diagnosis of a specific patient. This clinical data is essential because they aid in diagnosing novel or uncommon illnesses, analyzing specific medical occurrences, and enhancing knowledge of current medical education. The summary of the medical case report is needed so that one can decide on further reading as going through the entire contents of a medical case report istime-consuming. In this paper, we present a deep learning methodology for the generation of the automatic summaries of the medical case reports. The final proposed fine-tuned summarizer on the test data set generated a mean precision of 0.4481 and Rouge-1 Score of 0.2803.
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence

Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters. This architecture, however, inevitably results in a low-resolution matching field that additionally requires an ad-hoc interpolation process as a post-processing for converting it into a high-resolution one, certainly limiting the overall performance of matching results. To overcome this, inspired by recent success of implicit neural representation, we present a novel method for semantic correspondence, called Neural Matching Field (NeMF). However, complicacy and high-dimensionality of a 4D matching field are the major hindrances, which we propose a cost embedding network to process a coarse cost volume to use as a guidance for establishing high-precision matching field through the following fully-connected network. Nevertheless, learning a high-dimensional matching field remains challenging mainly due to computational complexity, since a naive exhaustive inference would require querying from all pixels in the 4D space to infer pixel-wise correspondences. To overcome this, we propose adequate training and inference procedures, which in the training phase, we randomly sample matching candidates and in the inference phase, we iteratively performs PatchMatch-based inference and coordinate optimization at test time. With these combined, competitive results are attained on several standard benchmarks for semantic correspondence. Code and pre-trained weights are available at https://ku-cvlab.github.io/NeMF/.
Orthogonal Non-negative Matrix Factorization: a Maximum-Entropy-Principle Approach

In data-driven control and machine learning, a common requirement involves breaking down large matrices into smaller, low-rank factors that possess specific levels of sparsity. This paper introduces an innovative solution to the orthogonal nonnegative matrix factorization (ONMF) problem. The objective is to approximate input data by using two low-rank nonnegative matrices, adhering to both orthogonality and $\ell_0$-norm sparsity constraints. the proposed maximum-entropy-principle based framework ensures orthogonality and sparsity of features or the mixing matrix, while maintaining nonnegativity in both. Additionally, the methodology offers a quantitative determination of the ``true'' number of underlying features, a crucial hyperparameter for ONMF. Experimental evaluation on synthetic and a standard datasets highlights the method's superiority in terms of sparsity, orthogonality, and computational speed compared to existing approaches. Notably, the proposed method achieves comparable or improved reconstruction errors in line with the literature.
Towards Better Semantic Understanding of Mobile Interfaces

Improving the accessibility and automation capabilities of mobile devices can have a significant positive impact on the daily lives of countless users. To stimulate research in this direction, we release a human-annotated dataset with approximately 500k unique annotations aimed at increasing the understanding of the functionality of UI elements. This dataset augments images and view hierarchies from RICO, a large dataset of mobile UIs, with annotations for icons based on their shapes and semantics, and associations between different elements and their corresponding text labels, resulting in a significant increase in the number of UI elements and the categories assigned to them. We also release models using image-only and multimodal inputs; we experiment with various architectures and study the benefits of using multimodal inputs on the new dataset. Our models demonstrate strong performance on an evaluation set of unseen apps, indicating their generalizability to newer screens. These models, combined with the new dataset, can enable innovative functionalities like referring to UI elements by their labels, improved coverage and better semantics for icons etc., which would go a long way in making UIs more usable for everyone.
Variational quantum one-class classifier

One-class classification is a fundamental problem in pattern recognition with a wide range of applications. This work presents a semi-supervised quantum machine learning algorithm for such a problem, which we call a variational quantum one-class classifier (VQOCC). The algorithm is suitable for noisy intermediate-scale quantum computing because the VQOCC trains a fully-parameterized quantum autoencoder with a normal dataset and does not require decoding. The performance of the VQOCC is compared with that of the one-class support vector machine (OC-SVM), the kernel principal component analysis (PCA), and the deep convolutional autoencoder (DCAE) using handwritten digit and Fashion-MNIST datasets. The numerical experiment examined various structures of VQOCC by varying data encoding, the number of parameterized quantum circuit layers, and the size of the latent feature space. The benchmark shows that the classification performance of VQOCC is comparable to that of OC-SVM and PCA, although the number of model parameters grows only logarithmically with the data size. The quantum algorithm outperformed DCAE in most cases under similar training conditions. Therefore, our algorithm constitutes an extremely compact and effective machine learning model for one-class classification.
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision

Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to synthesize training labels efficiently. The core component of PWS is the label model, which infers true labels by aggregating the outputs of multiple noisy supervision sources abstracted as labeling functions (LFs). Existing statistical label models typically rely only on the outputs of LF, ignoring the instance features when modeling the underlying generative process. In this paper, we attempt to incorporate the instance features into a statistical label model via the proposed FABLE. In particular, it is built on a mixture of Bayesian label models, each corresponding to a global pattern of correlation, and the coefficients of the mixture components are predicted by a Gaussian Process classifier based on instance features. We adopt an auxiliary variable-based variational inference algorithm to tackle the non-conjugate issue between the Gaussian Process and Bayesian label models. Extensive empirical comparison on eleven benchmark datasets sees FABLE achieving the highest averaged performance across nine baselines.
Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer

Developing neural architectures that are capable of logical reasoning has become increasingly important for a wide range of applications (e.g., natural language processing). Towards this grand objective, we propose a symbolic reasoning architecture that chains many join operators together to model output logical expressions. In particular, we demonstrate that such an ensemble of join-chains can express a broad subset of ''tree-structured'' first-order logical expressions, named FOET, which is particularly useful for modeling natural languages. To endow it with differentiable learning capability, we closely examine various neural operators for approximating the symbolic join-chains. Interestingly, we find that the widely used multi-head self-attention module in transformer can be understood as a special neural operator that implements the union bound of the join operator in probabilistic predicate space. Our analysis not only provides a new perspective on the mechanism of the pretrained models such as BERT for natural language understanding but also suggests several important future improvement directions.
Interplay of nonlocality and incompatibility breaking qubit channels

Incompatibility and nonlocality are not only of foundational interest but also act as important resources for quantum information theory. In the Clauser-Horne-Shimony-Holt (CHSH) scenario, the incompatibility of a pair of observables is known to be equivalent to Bell nonlocality. Here, we investigate these notions in the context of qubit channels. The Bell-CHSH inequality has a greater perspective--compared to any genuine tripartite nonlocality scenario--while determining the interplay between nonlocality breaking qubit channels and incompatibility breaking qubit channels. In the Bell-CHSH scenario, we prove that if the conjugate of a channel is incompatibility breaking, then the channel is itself nonlocality breaking and vice versa. However, this equivalence is not straightforwardly generalized to multipartite systems, due to the absence of an equivalence relation between incompatibility and nonlocality in the multipartite scenario. We investigate this relation in the tripartite scenario by considering some well-known states like Greenberger-Horne-Zeilinger and $W$ states and using the notion of Mermin and Svetlichny nonlocality. By subjecting the parties in question to unital qubit channels, we identify the range of state and channel parameters for which incompatibility coexists with nonlocality. Further, we identify the set of unital qubit channels that is Mermin or Svetlichny nonlocality breaking irrespective of the input state.
Double-Ended Palindromic Trees: A Linear-Time Data Structure and Its Applications

The palindromic tree (a.k.a. eertree) is a linear-size data structure that provides access to all palindromic substrings of a string. In this paper, we propose a generalized version of eertree, called double-ended eertree, which supports linear-time online double-ended queue operations on the stored string. At the heart of our construction, is a class of substrings, called surfaces, of independent interest. Namely, surfaces are neither prefixes nor suffixes of any other palindromic substrings and characterize the link structure of all palindromic substrings in the eertree. As an application, we develop a framework for range queries involving palindromes on strings, including counting distinct palindromic substrings, and finding the longest palindromic substring, shortest unique palindromic substring and shortest absent palindrome of any substring. In particular, offline queries only use linear space. Apart from range queries, we enumerate palindromic rich strings with a given word in linear time on the length of the given word.
Solution of SAT Problems with the Adaptive-Bias Quantum Approximate Optimization Algorithm

The quantum approximate optimization algorithm (QAOA) is a promising method for solving certain classical combinatorial optimization problems on near-term quantum devices. When employing the QAOA to 3-SAT and Max-3-SAT problems, the quantum cost exhibits an easy-hard-easy or easy-hard pattern respectively as the clause density is changed. The quantum resources needed in the hard-region problems are out of reach for current NISQ devices. We show by numerical simulations with up to 14 variables and analytical arguments that the adaptive-bias QAOA (ab-QAOA) greatly improves performance in the hard region of the 3-SAT problems and hard region of the Max-3-SAT problems. For similar accuracy, on average, ab-QAOA needs 3 levels for 10-variable 3-SAT problems as compared to 22 for QAOA. For 10-variable Max-3-SAT problems, the numbers are 7 levels and 62 levels. The improvement comes from a more targeted and more limited generation of entanglement during the evolution. We demonstrate that classical optimization is not strictly necessary in the ab-QAOA since local fields are used to guide the evolution. This leads us to propose an optimization-free ab-QAOA that can solve the hard-region 3-SAT and Max-3-SAT problems effectively with significantly fewer quantum gates as compared to the original ab-QAOA. Our work paves the way for realizing quantum advantages for optimization problems on NISQ devices.