Reviewerly/exHarmony huggingface dataset:
Vision Pair Learning: An Efficient Training Framework for Image  Classification

Transformer is a potentially powerful architecture for vision tasks. Although equipped with more parameters and attention mechanism, its performance is not as dominant as CNN currently. CNN is usually computationally cheaper and still the leading competitor in various vision tasks. One research direction is to adopt the successful ideas of CNN and improve transformer, but it often relies on elaborated and heuristic network design. Observing that transformer and CNN are complementary in representation learning and convergence speed, we propose an efficient training framework called Vision Pair Learning (VPL) for image classification task. VPL builds up a network composed of a transformer branch, a CNN branch and pair learning module. With multi-stage training strategy, VPL enables the branches to learn from their partners during the appropriate stage of the training process, and makes them both achieve better performance with less time cost. Without external data, VPL promotes the top-1 accuracy of ViT-Base and ResNet-50 on the ImageNet-1k validation set to 83.47% and 79.61% respectively. Experiments on other datasets of various domains prove the efficacy of VPL and suggest that transformer performs better when paired with the differently structured CNN in VPL. we also analyze the importance of components through ablation study.
The Mathematics of Writing Every Tune

This chapter aims to produce, in a systematic manner, every possible tune that can be made within limited parameters derived from the opening two bar phrase of Beethoven’s 5th Symphony, and then to locate Beethoven’s melody within that system. Our aim is not merely to count the number of possible tunes within the parameters but to produce a system that can place them in a logical sequence, and that each tune can be given a label within that system.
Text mining to Discover Design Features for Cybersecurity tools: The case of Password Management Systems

Username and password are the most common authentication approach used today in cybersecurity. Most people have more than one online account protected by passwords. Managing passwords is one of the daunting tasks for most people. Therefore, users may rely on password management systems PMS to help creating, managing, retrieving, and storing passwords. However, there is still a lack of relying on and using such systems to manage users’ passwords. This study aims to use a text mining approach to investigate the factors that influence users’ behavior to use PMSs which leads to improve their security. This study adopts a text mining approach to uncover the design principles (features) of PMSs extracted from online users’ reviews and feedbacks to improve the adoption of PMSs. Specifically, our study used a topic modeling algorithm to analyze users’ feedback by collecting and analyzing the reviews of nine password management systems. The results indicate the need to address principles and design features related to compatibility, security, usefulness, ease of use, and customer care services.
KPDrop: An Approach to Improving Absent Keyphrase Generation

Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. The generated keyphrases can be either present or absent from the text of the given document. While the extraction of present keyphrases has received much attention in the past, only recently a stronger focus has been placed on the generation of absent keyphrases. However, generating absent keyphrases is very challenging; even the best methods show only a modest degree of success. In this paper, we propose an approach, called keyphrase dropout (or KPDrop), to improve absent keyphrase generation. We randomly drop present keyphrases from the document and turn them into artificial absent keyphrases during training. We test our approach extensively and show that it consistently improves the absent performance of strong baselines in keyphrase generation.
ROMEO: Exploring Juliet through the Lens of Assembly Language

Automatic vulnerability detection on C/C++ source code has benefitted from the introduction of machine learning to the field, with many recent publications considering this combination. In contrast, assembly language or machine code artifacts receive little attention, although there are compelling reasons to study them. They are more representative of what is executed, more easily incorporated in dynamic analysis and in the case of closed-source code, there is no alternative. We propose ROMEO, a publicly available, reproducible and reusable binary vulnerability detection benchmark dataset derived from the Juliet test suite. Alongside, we introduce a simple text-based assembly language representation that includes context for function-spanning vulnerability detection and semantics to detect high-level vulnerabilities. Finally, we show that this representation, combined with an off-the-shelf classifier, compares favorably to state-of-the-art methods, including those operating on the full C/C++ code.
Overcome Anterograde Forgetting with Cycled Memory Networks

Learning from a sequence of tasks for a lifetime is essential for an agent towards artificial general intelligence. This requires the agent to continuously learn and memorize new knowledge without interference. This paper first demonstrates a fundamental issue of lifelong learning using neural networks, named anterograde forgetting, i.e., preserving and transferring memory may inhibit the learning of new knowledge. This is attributed to the fact that the learning capacity of a neural network will be reduced as it keeps memorizing historical knowledge, and the fact that conceptual confusion may occur as it transfers irrelevant old knowledge to the current task. This work proposes a general framework named Cycled Memory Networks (CMN) to address the anterograde forgetting in neural networks for lifelong learning. The CMN consists of two individual memory networks to store short-term and long-term memories to avoid capacity shrinkage. A transfer cell is designed to connect these two memory networks, enabling knowledge transfer from the long-term memory network to the short-term memory network to mitigate the conceptual confusion, and a memory consolidation mechanism is developed to integrate short-term knowledge into the long-term memory network for knowledge accumulation. Experimental results demonstrate that the CMN can effectively address the anterograde forgetting on several task-related, task-conflict, class-incremental and cross-domain benchmarks.
The role of the media in shaping the worldview of young people in the context of globalization

This article discusses the role of the media in shaping the worldview of young people in the context of globalization. The media has been analyzed as an important factor in shaping an individual's worldview. The role of social media in the internet in recent years has been discussed.
SCD: Self-Contrastive Decorrelation for Sentence Embeddings

In this paper, we propose Self-Contrastive Decorrelation (SCD), a self-supervised approach. Given an input sentence, it optimizes a joint self-contrastive and decorrelation objective. Learning a representation is facilitated by leveraging the contrast arising from the instantiation of standard dropout at different rates. The proposed method is conceptually simple yet empirically powerful. It achieves comparable results with state-of-the-art methods on multiple benchmarks without using contrastive pairs. This study opens up avenues for efficient self-supervised learning methods that are more robust than current contrastive methods.
Feature Set Analysis of Linear Predicted Code and Its Extended Algorithms for Speech Signal Processing

Speech is an important communication for human. Processing of this speech signal plays a vital role in the present era. Extracting features from the speech signal reduce the complexity of processing the complete data. Different feature extraction techniques are available in the state of art; the most simple and used feature extraction technique are LPC algorithm. In this paper, linear predicted code algorithm and its extended versions performance analysis are discussed and the results show that as the improved version of linear predicted code gives better results over the basic model.
Hybrid Quantum-Classical Multi-cut Benders Approach with a Power System  Application

Leveraging the current generation of quantum devices to solve optimization problems of practical interest necessitates the development of hybrid quantum-classical (HQC) solution approaches. In this paper, a multi-cut Benders decomposition (BD) approach that exploits multiple feasible solutions of the master problem (MP) to generate multiple valid cuts is adapted, so as to be used as an HQC solver for general mixed-integer linear programming (MILP) problems. The use of different cut selection criteria and strategies to manage the size of the MP by eliciting a subset of cuts to be added in each iteration of the BD scheme using quantum computing is discussed. The HQC optimization algorithm is applied to the Unit Commitment (UC) problem. UC is a prototypical use case of optimization applied to electrical power systems, a critical sector that may benefit from advances in quantum computing. The validity and computational viability of the proposed approach are demonstrated using the D-Wave Advantage 4.1 quantum annealer.
Synthetic Unknown Class Learning for Learning Unknowns

This paper addresses the open set recognition (OSR) problem, where the goal is to correctly classify samples of known classes while detecting unknown samples to reject. In the OSR problem, "unknown" is assumed to have infinite possibilities because we have no knowledge about unknowns until they emerge. Intuitively, the more an OSR system explores the possibilities of unknowns, the more likely it is to detect unknowns. Thus, this paper proposes a novel synthetic unknown class learning method that generates unknown-like samples while maintaining diversity between the generated samples and learns these samples. In addition to this unknown sample generation process, knowledge distillation is introduced to provide room for learning synthetic unknowns. By learning the unknown-like samples and known samples in an alternating manner, the proposed method can not only experience diverse synthetic unknowns but also reduce overgeneralization with respect to known classes. Experiments on several benchmark datasets show that the proposed method significantly outperforms other state-of-the-art approaches. It is also shown that realistic unknown digits can be generated and learned via the proposed method after training on the MNIST dataset.
Assistive Technology: Design and Implementation of an Eye Tracking Based Electric Wheelchair Control System for Children with Cerebral Palsy

Cerebral palsy (CP) is a set of non-progressive alterations which affect muscle tone, posture, and movement. CP is produced by a brain injury or malformation in one or more brain areas that control movement. It is the most common motor disability in childhood. It affects the autonomy and socialization of children and generates great pressure on the family; therefore, children with CP require the aid of a wheelchair, which is usually driven by the user or pushed by another person. Even using a wheelchair controlled with a joystick becomes complicated because it depends on the control of gross and fine motor skills. Then, there is necessary a more amicable and accessible way to control a wheelchair. Since the ocular muscles are one of the few that still fulfill their function in people with CP, in this paper, we present the design and implementation of an eye-tracking-based electric wheelchair control system for children with spastic and mixed CP. This system includes a low-cost electric wheelchair built with PCV tubes, a Microsoft Surface Pro Tablet where the software is running, and a PCEye Mini device used to monitor the gaze. The PCEeye Mini sends the signs to the mainboard that controls the electric motors, allowing the children to steer the wheelchair by themselves using their gaze. The software has three components: the first manages the application data, the second represents the model graphically displayed on the screen, and the third receives and manages user inputs. The application has three interfaces for the start of the system that children can access since the home screen, and each one gives the user different control system parameters options. Eight children with CP participated in three experiments to validate the efficiency and safety of the system. Each experiment was developed in an internal and external environment with three different floor soils: cement, ceramic, and wood. The results showed a 100% efficiency and safety of the system for children use. The calibration process was fast and simple, the activation of directional arrows on the screen with the gaze gave 100% performance, the power consumption did not show a difference between the type of floor, and the electric wheelchair did not slip on any floor. The software maintained stable communication with the hardware through a serial communication protocol, allowing a correct execution of the commands. This work shows that the implementation of the eye-tracking technology in the electric wheelchair works successfully. This system operates effectively and is easy to control for children with CP, improving the children’s autonomy in terms of mobility. This software can be implemented on any PC, and more functions can be added to the same software without affecting its performance. This developed system offers an intelligent and different option to help children with motor deficits, which is a great contribution to society.
Quantum Chaos and Circuit Parameter Optimization

We explore quantum chaos diagnostics of variational circuit states at random parameters and study their correlation with the circuit expressibility and the optimization of control parameters. By measuring the operator spreading coefficient and the eigenvalue spectrum of the modular Hamiltonian of the reduced density matrix, we identify the universal structure of random matrix models in high-depth circuit states. We construct different layer unitaries corresponding to the GOE and GUE distributions and quantify their VQA performance. Our study also highlights a potential tension between the OTOC and BGS-type diagnostics of quantum chaos.
On Learning Domain-Invariant Representations for Transfer Learning with  Multiple Sources

Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e.g., learning domain-invariant representations and its trade-off. However, it seems not the case for the multiple source DA and domain generalization (DG) settings which are remarkably more complicated and sophisticated due to the involvement of multiple source domains and potential unavailability of target domain during training. In this paper, we develop novel upper-bounds for the target general loss which appeal to us to define two kinds of domain-invariant representations. We further study the pros and cons as well as the trade-offs of enforcing learning each domain-invariant representation. Finally, we conduct experiments to inspect the trade-off of these representations for offering practical hints regarding how to use them in practice and explore other interesting properties of our developed theory.
Training Efficiency and Robustness in Deep Learning

Deep Learning has revolutionized machine learning and artificial intelligence, achieving superhuman performance in several standard benchmarks. It is well-known that deep learning models are inefficient to train; they learn by processing millions of training data multiple times and require powerful computational resources to process large batches of data in parallel at the same time rather than sequentially. Deep learning models also have unexpected failure modes; they can be fooled into misbehaviour, producing unexpectedly incorrect predictions. In this thesis, we study approaches to improve the training efficiency and robustness of deep learning models. In the context of learning visual-semantic embeddings, we find that prioritizing learning on more informative training data increases convergence speed and improves generalization performance on test data. We formalize a simple trick called hard negative mining as a modification to the learning objective function with no computational overhead. Next, we seek improvements to optimization speed in general-purpose optimization methods in deep learning. We show that a redundancy-aware modification to the sampling of training data improves the training speed and develops an efficient method for detecting the diversity of training signal, namely, gradient clustering. Finally, we study adversarial robustness in deep learning and approaches to achieve maximal adversarial robustness without training with additional data. For linear models, we prove guaranteed maximal robustness achieved only by appropriate choice of the optimizer, regularization, or architecture.
Expanded Roles: Qualities That Serialists Can Bring to Other Aspects of Information Work

Increasingly today, serials librarians have to accept additional responsibilities outside their primary areas of expertise. The skills serialists have gained in handling the complexities of serials cataloging make a solid foundation upon which to build proficiency in other areas of work within as well as outside the traditional library. Two professionals show how such skills are needed in the changing world of information services. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com ]
Method Description for CCKS 2021 Task 3: A Classification Approach of Scholar Structured Information Extraction from HTML Web Pages

Information extractions is a fundamental mission when constructing a scholar profile with the rapid development of AI and big data, especially in the searching task and recommendation system. Therefore, extracting, tagging and statistical analysis the precision facts of experts and scholar can be applied in academic searching. In this paper, a structured information extraction and match approach for structured scholar portrait from HTML web pages based on classification models is demonstrated in detail.
A Supervised Feature Selection Method For Mixed-Type Data using  Density-based Feature Clustering

Feature selection methods are widely used to address the high computational overheads and curse of dimensionality in classifying high-dimensional data. Most conventional feature selection methods focus on handling homogeneous features, while real-world datasets usually have a mixture of continuous and discrete features. Some recent mixed-type feature selection studies only select features with high relevance to class labels and ignore the redundancy among features. The determination of an appropriate feature subset is also a challenge. In this paper, a supervised feature selection method using density-based feature clustering (SFSDFC) is proposed to obtain an appropriate final feature subset for mixed-type data. SFSDFC decomposes the feature space into a set of disjoint feature clusters using a novel density-based clustering method. Then, an effective feature selection strategy is employed to obtain a subset of important features with minimal redundancy from those feature clusters. Extensive experiments as well as comparison studies with five state-of-the-art methods are conducted on SFSDFC using thirteen real-world benchmark datasets and results justify the efficacy of the SFSDFC method.
Scaling Up Query-Focused Summarization to Meet Open-Domain Question  Answering

Query-focused summarization (QFS) requires generating a textual summary given a query using a set of relevant documents. However, in practice, such relevant documents are not readily available but should be first retrieved from a document collection. Therefore, we show how to extend this task to make it more realistic. Thereby the task setup also resembles the settings of the open-domain question answering task, where the answer is a summary of the top-retrieved documents. To address this extended task, we combine passage retrieval with text generation to produce the summary of the retrieved passages given the input query. We demonstrate the first evaluation results on the proposed task and show that a few samples are sufficient to fine-tune a large generative model with retrieved passages.
Works by Afghan Classical Poet ‘Abd al-Hamid Momand: Stylometric Results

‘Abd al Hamid Momand (d. ca. 1732), a greatest Afghan classical poet at the turn of the 17th and 18th centuries, was the author of Pashto poetry. He created a unique individual style, which was different from that of his predecessors and greatly influenced subsequent Afghan Pashto poetry. However, because of the complexity and eloquence of his style, his work has been understudied inside and outside Afghanistan. The authorship of a set of ghazals by Hamid is questioned by the authors of this article, with some forty works defined as controversial. Methods of digital stylometry are applied to determine the authorship of these ghazals. As a result of this experiment, the question is clarified as to whether Hamid's controversial ghazals are indeed authentic.
Improved sparse PCA method for face and image recognition

Face recognition is the very significant field in pattern recognition area. It has multiple applications in military and finance, to name a few. In this paper, the combination of the sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and will be applied to solve the face recognition problem. Experimental results illustrate that the accuracy of the combination of the sparse PCA method (using the proximal gradient method and the FISTA method) and one specific classification system may be lower than the accuracy of the combination of the PCA method and one specific classification system but sometimes the combination of the sparse PCA method (using the proximal gradient method or the FISTA method) and one specific classification system leads to better accuracy. Moreover, we recognize that the process computing the sparse PCA algorithm using the FISTA method is always faster than the process computing the sparse PCA algorithm using the proximal gradient method.
Prosody Labelled Dataset for Hindi using Semi-Automated Approach

This study aims to develop a semi-automatically labelled prosody database for Hindi, for enhancing the intonation component in ASR and TTS systems, which is also helpful for building Speech to Speech Machine Translation systems. Although no single standard for prosody labelling exists in Hindi, researchers in the past have employed perceptual and statistical methods in literature to draw inferences about the behaviour of prosody patterns in Hindi. Based on such existing research and largely agreed upon theories of intonation in Hindi, this study attempts to first develop a manually annotated prosodic corpus of Hindi speech data, which is then used for training prediction models for generating automatic prosodic labels. A total of 5,000 sentences (23,500 words) for declarative and interrogative types have been labelled. The accuracy of the trained models for pitch accent, intermediate phrase boundaries and accentual phrase boundaries is 73.40%, 93.20%, and 43% respectively.