Reviewerly/exHarmony huggingface dataset:
Family-Aware Convolutional Neural Network for Image-based Kinship Verification

Faces is a unique region in our body that can be used as a biometric identity. Furthermore, the face between two people that have a kinship relationship may share the same face features which can be used to decide whether two people have a kinship relationship or not. In this paper, we proposed a family-aware convolutional neural network (CNN) for the visual kinship verification problem. Our proposed classifier is constructed by paralleling the state-of-the-art face recognition model and attaching two additional networks, a family-aware network, and a kinship verification network. The family-aware network weights adjusted by learning features specific to the family using deep metric learning loss while the kinship verification network use softmax loss to learn the kinship verification problem. One of the advantages of our proposed classifier is that the output of the classifier is normalized and can be represented as the probability of two images being kin or non-kin. To preserve the face recognition features extraction ability in the state-of-the-art face recognition model, we freeze the weights of the convolutional layers in the classifier for the training process. In the testing process, the family-aware network is detached to construct the final classifier. Experiments on FIW (Families In the Wild) dataset show that our proposed classifier performs better comparing with classifiers that trained without a family-aware network and the ensemble version of the classifier is comparable with several state-of-the-art methods with an average accuracy of 68.84%.
Emotion Based Music Recommendation System

This paper demonstrates the Bezier- approximation technique as a facial expression recognition method. The system is predicated on facial expression choices, faces pure mathematics knowledge, and approximated by Bezier curves of third order representing the relation between feature motion and expression changes. For face detection, color segmentation has been used to help the novel Fuzzy classification plan which manipulates color ambiguity. The experimental findings indicate that the facial expressions with Associate in nursing accuracy of 90 cases are recognized by this technique. Finally, a manipulator golem and face gesture commands were used to execute the System. We have a propensity to split in three regions from the external part of the body, namely the left eye, the right eye and the mouth. Facial identification first comes so that a skin layer is identified. We prefer to maintain the face skin region and attach the wide area of the skin to the outside body area of the skin.Keywords:Face expression Detection, Viola Jones Algorithm, audio player, Facial recognition.
Implementation of leakage elimination operators and subspace protection

Abstract Decoherence-induced leakage errors can potentially damage physical or logical qubits embedded in a subspace of the entire Hilbert space by coupling them to other system levels. Here we report the first experimental implementation of Leakage Elimination Operators (LEOs) that aims to reduce this undermining. LEOs are a type of dynamical decoupling control that have been previously introduced to counteract leakage from a chosen subspace into the rest of a Hilbert space, and have been widely explored theoretically. Different from other error correction strategies, LEOs are compatible with any gate sequence in a code space, and thus, compatible with universal quantum computation. Using IBM’s cloud quantum computer (QC), we design three potentially applicable examples of subspaces in two- and three-qubit Hilbert spaces and derive the explicit forms of the corresponding LEOs for these subspaces. For the first time, we experimentally demonstrate that these LEOs significantly suppress leakage. The results also show that the LEO time-scale condition can be satisfied with noise in the IBM’s cloud QC and pave a way for quantum setups to get rid of leakage trouble.
The challenges of German archival document categorization on insufficient labeled data

Document exploration in archives is often challenging due to the lack of organization in topic-based categories. Moreover, archival records only provide short text which is often insufficient for capturing the semantic. This paper proposes and explores a dataless categorization approach that utilizes word embeddings and TF-IDF to categorize archival documents. Additionally, it introduces a visual approach built on top of the word embeddings to enhance the exploration of data. Preliminary results suggest that current vector representations alone do not provide enough external knowledge to solve this task.
Routine Family Planning Data in the Low- and Middle-Income Country Context: A Synthesis of Findings From 17 Small Research Grants

<h3>ABSTRACT</h3> Health information systems rely on high-quality data to measure, track, and inform decision making. Currently, the quality, uptake, and use of family planning data in routine health information systems is limited, presenting an opportunity for improvement on many levels. The current synthesis assessed findings from 17 small grants that MEASURE Evaluation issued to low- and middle-income country research teams between 2015 and 2019. Main findings from that research were collaboratively categorized in 4 major themes: (1) the enabling environment for managing and using family planning information; (2) barriers to integration of family planning in routine health information systems; (3) gaps in the analysis, interpretation, and use of routine family planning data; and (4) family planning data use in management, programmatic, and budgetary decisions. Data quality at the systemic, organizational, technical, and output levels was a crosscutting theme. Collectively, the findings outline barriers to and opportunities for improved integration of family planning data and subsequent strengthening of routine health information systems.
An Investigation into the Effectiveness of MALL during COVID-19 at the Higher Education in Pakistani EFL Classrooms

The main aim and objective of this study is to understand the effectiveness of MALL during Covid-19 in the Pakistani context through the insights gathered from the views of EFL learners at the higher education level. The theoretical framework employed in the research is the Technology Acceptance Model (TAM) proposed by Davis (1986). The framework employed in the study is of TAM; its two factors were mainly focused, which are perceived usefulness (PU) and perceived ease of use (PEOU). The purpose of this model in the paper is to understand the behavior and attitude of the learners towards mobile technology. This research is mixed in its nature as it encompasses both qualitative and quantitative research designs. The data have been gathered from fifty students of the undergraduate and postgraduate level of English Department at the Women University Multan. The results of the study have revealed that the factors of PEOU and PU have been found to be working in this study as the learners’ views about the smartphones indicate it.
Expanding Side Touch Input on Mobile Phones

We investigate the performance of one-handed touch input on the side of a mobile phone. A first experiment examines grip change and subjective preference when reaching for side targets using different fingers. Results show all locations can be reached with at least one finger, but the thumb and index are most preferred and require less grip change for positions along the sides. Two following experiments examine taps and flicks using the thumb and index finger in a new two-dimensional input space. A side-touch sensor is simulated with a combination of capacitive sensing and motion tracking to distinguish touches on the lower, middle, or upper edges. When tapping, index and thumb speeds are similar with thumb more accurate and comfortable, and the lower edge is most reliable with the middle edge most comfortable. When flicking with the thumb, the upper edge is fast and rated highly.
First Steps Towards State Representation Learning for Cognitive Robotics

In this work we have implemented and tested a framework for training and analyzing auto-encoding architectures for the purpose of encoding state space representations into latent space, and thus compressing the essential space properties depicted in the training image dataset. We explored the possibility of incorporating a forward model in between encoder and decoder to predict the next state in series of temporally arranged images, with the aim of testing if it is able to predict meaningful representations (even if they are not interpretable by visual inspection). The application domain of this approach is that of cognitive robotics, where having a state representation learning system that can operate in an open-ended fashion is necessary. State representation learning has been a challenging topic in autonomous robotics for a long time, and some promising approaches have been developed. Anyway, when applying them to open-ended learning, as is the case of cognitive robotics, current solutions fail, so new systems must be implemented. The work presented here is a first approach towards this objective.
An Integrated Approach To The Study Of Semantics Of Proper Names

The article demonstrates the possibilities of a comprehensive study of the semantics of proper names according to the methodology developed by the Russian scientific theoretical and linguistic school in Voronezh and tested in numerous works.As an example, the results of a comprehensive semasiological study of the toponym "Grozny" are presented.Using the methodology for the unification of lexicographic definitions and the methods of psycholinguistic associative experiments, based on the principle of multiplicity (non-uniqueness) of the description of mental units and the principle of complementarity of semantic descriptions.The integrated lexicographic sememe (ILS) and the psycholinguistic sememe (PLS) of this understand.Corresponding dictionary entries are given, their structure is described.The brightness indices of these ILS and PLS are calculated, allowing to clarify their brightness in the composition of the corresponding seeds.A comparative analysis was carried out (as part of the third stage of the study), as a result of which seeds were found (a) present in both ILS and PLS (b) present in ILS and absent in PLS (c) absent in ILS, but present in PLS.Option (a) revealed ILS semes relevant to the linguistic consciousness of native speakers of the Russian language in the city of Grozny.Option (b) -ILS semes that are not relevant for the linguistic consciousness of native speakers of the Russian language in the city of Grozny; option (c)seeds that are not present in ILS, but which are still relevant to the linguistic consciousness of native speakers of the Russian in Grozny.
Errata to Sound Hashing Modes of Arbitrary Functions, Permutations, and Block Ciphers

In ToSC 2018(4), Daemen et al. performed an in-depth investigation of sound hashing modes based on arbitrary functions, permutations, or block ciphers. However, for the case of invertible primitives, there is a glitch. In this errata, we formally fix this glitch by adding an extra term to the security bound, q/2b−n, where q is query complexity, b the width of the permutation or the block size of the block cipher, and n the size of the hash digest. For permutations that are wider than two times the chaining value this term is negligible. For block cipher based hashing modes where the block size is close to the digest size, the term degrades the security significantly.
Specificity Of Student Creative Activity

This article explores the components of a student’s creative abilities, descriptions of each, in performing a problematic experiment in physics.
A Proposal for Securing Terrestrial Radio-Navigation Systems

The security of terrestrial radio-navigation systems (TRNS) has not yet been addressed in the literature. This proposal builds on what is known about securing global navigation satellite systems (GNSS) to address this gap, re-evaluating proposals for GNSS security in light of the distinctive properties of TRNS. TRNS of the type envisioned in this paper are currently in their infancy, unburdened by considerations of backwards compatibility: security for TRNS is a clean slate. This paper argues that waveform- or signal-level security measures are irrelevant for TRNS, preventing neither spoofing nor unauthorized use of the service. Thus, only security measures which modify navigation message bits merit consideration. This paper proposes orthogonal mechanisms for navigation message encryption (NME) and authentication (NMA), constructed from standard cryptography primitives and specialized to TRNS: message encryption allows providers to offer tiered access to navigation parameters on a bit-by-bit basis, and message authentication disperses the bits of a message authentication code across all data packets, posing an additional challenge to spoofers. The implementation of this proposal will render TRNS more secure and resilient than traditional civil GNSS.
MWPD2020: Semantic Web Challenge on Mining the Web of HTML-embedded Product Data.

This paper gives an overview of the Semantic Web Challenge on Mining the Web of HTML-embedded Product Data (MWPD2020) which has been conducted as part of the International Semantic Web Conference (ISWC2020). The challenge consists of two tasks: product matching and product classification. In the first task, participants need to identify offers for the same product originating from different websites. The goal of the second task is to categorize offers from different websites into the GS1 GPC product hierarchy. Six teams from the USA, China, Japan, and Germany participated in the challenge. The winning system in Task 1, PMap, achieved an F1 score of 86.05 using an ensemble oftransformer-based language models. Task 2 was won by team Rhinobird achieving a weighted average F1 score of 88.62 using a BERT-based ensemble which considers the dependencies among different category levels.
Complementary Language Model and Parallel Bi-LRNN for False Trigger Mitigation

False triggers in voice assistants are unintended invocations of the assistant, which not only degrade the user experience but may also compromise privacy. False trigger mitigation (FTM) is a process to detect the false trigger events and respond appropriately to the user. In this paper, we propose a novel solution to the FTM problem by introducing a parallel ASR decoding process with a special language model trained from out-of-domain data sources. Such language model is complementary to the existing language model optimized for the assistant task. A bidirectional lattice RNN (Bi-LRNN) classifier trained from the lattices generated by the complementary language model shows a $38.34\%$ relative reduction of the false trigger (FT) rate at the fixed rate of $0.4\%$ false suppression (FS) of correct invocations, compared to the current Bi-LRNN model. In addition, we propose to train a parallel Bi-LRNN model based on the decoding lattices from both language models, and examine various ways of implementation. The resulting model leads to further reduction in the false trigger rate by $10.8\%$.
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing

Object detection in videos is an important task in computer vision for various applications such as object tracking, video summarization and video search. Although great progress has been made in improving the accuracy of object detection in recent years due to the rise of deep neural networks, the state-of-the-art algorithms are highly computationally intensive. In order to address this challenge, we make two important observations in the context of videos: (i) Objects often occupy only a small fraction of the area in each video frame, and (ii) There is a high likelihood of strong temporal correlation between consecutive frames. Based on these observations, we propose Pack and Detect (PaD), an approach to reduce the computational requirements of object detection in videos. In PaD, only selected video frames called anchor frames are processed at full size. In the frames that lie between anchor frames (inter-anchor frames), regions of interest (ROIs) are identified based on the detections in the previous frame. We propose an algorithm to pack the ROIs of each inter-anchor frame together into a reduced-size frame. The computational requirements of the detector are reduced due to the lower size of the input. In order to maintain the accuracy of object detection, the proposed algorithm expands the ROIs greedily to provide additional background around each object to the detector. PaD can use any underlying neural network architecture to process the full-size and reduced-size frames. Experiments using the ImageNet video object detection dataset indicate that PaD can potentially reduce the number of FLOPS required for a frame by 4×. This leads to an overall increase in throughput of 1.25× on a 2.1 GHz Intel Xeon server with a NVIDIA Titan X GPU at the cost of 1.1% drop in accuracy.
CASTLE: Regularization via Auxiliary Causal Graph Discovery

Regularization improves generalization of supervised models to out-of-sample data. Prior works have shown that prediction in the causal direction (effect from cause) results in lower testing error than the anti-causal direction. However, existing regularization methods are agnostic of causality. We introduce Causal Structure Learning (CASTLE) regularization and propose to regularize a neural network by jointly learning the causal relationships between variables. CASTLE learns the causal directed acyclical graph (DAG) as an adjacency matrix embedded in the neural network's input layers, thereby facilitating the discovery of optimal predictors. Furthermore, CASTLE efficiently reconstructs only the features in the causal DAG that have a causal neighbor, whereas reconstruction-based regularizers suboptimally reconstruct all input features. We provide a theoretical generalization bound for our approach and conduct experiments on a plethora of synthetic and real publicly available datasets demonstrating that CASTLE consistently leads to better out-of-sample predictions as compared to other popular benchmark regularizers.
Service-oriented Text-to-SQL Parsing

The information retrieval from relational database requires professionals who has an understanding of structural query language such as SQL.TEXT2SQL models apply natural language inference to enable user interacting the database via natural language utterance.Current TEXT2SQL models normally focus on generating complex SQL query in a precise and complete fashion while certain features of real-world application in the production environment is not fully addressed.This paper is aimed to develop a service-oriented Text-to-SQL parser that translates natural language utterance to structural and executable SQL query.We introduce a algorithmic framework named Semantic-Enriched SQL generator (SE-SQL) that enables flexibly access database than rigid API in the application while keeping the performance quality for the most commonly used cases.The qualitative result shows that the proposed model achieves 88.3% execution accuracy on WikiSQL task, outperforming baseline by 13% error reduction.Moreover, the framework considers several service-oriented needs including low-complexity inference, out-oftable rejection, and text normalization.
Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries

Reducing rates of early hospital readmission has been recognized and identified as a key to improve quality of care and reduce costs.There are a number of risk factors that have been hypothesized to be important for understanding re-admission risk, including such factors as problems with substance abuse, ability to maintain work, relations with family.In this work, we develop RoBERTa-based models to predict the sentiment of sentences describing readmission risk factors in discharge summaries of patients with psychosis.We improve substantially on previous results by a scheme that shares information across risk factors while also allowing the model to learn risk factor-specific information.
Older Adults and Voice Interaction: A Pilot Study with Google Home

In this paper we present the results of an exploratory study examining the potential of voice assistants (VA) for some groups of older adults in the context of Smart Home Technology (SHT). To research the aspect of older adults' interaction with voice user interfaces (VUI) we organized two workshops and gathered insights concerning possible benefits and barriers to the use of VA combined with SHT by older adults. Apart from evaluating the participants' interaction with the devices during the two workshops we also discuss some improvements to the VA interaction paradigm.
Optimizing Mode Connectivity via Neuron Alignment

The loss landscapes of deep neural networks are not well understood due to their high nonconvexity. Empirically, the local minima of these loss functions can be connected by a learned curve in model space, along which the loss remains nearly constant; a feature known as mode connectivity. Yet, current curve finding algorithms do not consider the influence of symmetry in the loss surface created by model weight permutations. We propose a more general framework to investigate the effect of symmetry on landscape connectivity by accounting for the weight permutations of the networks being connected. To approximate the optimal permutation, we introduce an inexpensive heuristic referred to as neuron alignment. Neuron alignment promotes similarity between the distribution of intermediate activations of models along the curve. We provide theoretical analysis establishing the benefit of alignment to mode connectivity based on this simple heuristic. We empirically verify that the permutation given by alignment is locally optimal via a proximal alternating minimization scheme. Empirically, optimizing the weight permutation is critical for efficiently learning a simple, planar, low-loss curve between networks that successfully generalizes. Our alignment method can significantly alleviate the recently identified robust loss barrier on the path connecting two adversarial robust models and find more robust and accurate models on the path.
Coherence and Entanglement Dynamics in Training Variational Quantum Perceptron

In quantum computation, what contributes supremacy of quantum computation? One of the candidates is known to be a quantum coherence because it is a resource used in the various quantum algorithms. We reveal that quantum coherence contributes to the training of variational quantum perceptron proposed by Y. Du et al., arXiv:1809.06056 (2018). In detail, we show that in the first part of the training of the variational quantum perceptron, the quantum coherence of the total system is concentrated in the index register and in the second part, the Grover algorithm consumes the quantum coherence in the index register. This implies that the quantum coherence distribution and the quantum coherence depletion are required in the training of variational quantum perceptron. In addition, we investigate the behavior of entanglement during the training of variational quantum perceptron. We show that the bipartite concurrence between feature and index register decreases since Grover operation is only performed on the index register. Also, we reveal that the concurrence between the two qubits of index register increases as the variational quantum perceptron is trained.