Reviewerly/exHarmony huggingface dataset:
Energy Complexity of Regular Language Recognition

The erasure of each bit of information by a computing device has an intrinsic energy cost. Although any Turing machine can be rewritten to be thermodynamically reversible without changing the recognized language, finite automata that are restricted to scan their input once in “real-time” fashion can only recognize the members of a proper subset of the class of regular languages in this reversible manner. We use a general quantum finite automaton model to study the thermodynamic cost per step associated with the recognition of different regular languages. We show that zero-error quantum finite automata have no energy cost advantage over their classical deterministic counterparts, and prove an upper bound for the cost that holds for all regular languages. We also demonstrate languages for which “error can be traded for energy”, i.e. whose zero-error recognition is associated with provably bigger energy cost per step when compared to their bounded-error recognition by real-time finite-memory quantum devices.
Status And Development Of Online Education Platforms In The Post-epidemic Era

At the beginning of 2020, COVID-19 began to sweep through China, major schools adhering to the concept of "no classrooms instead of classes" and launching online education. The major online education platforms received the "epidemic advantage" and grew. However, on the night of 24 July 2021, when Chinese authorities have introduced a guideline to ease the burdens of excessive homework and off-campus tutoring for students undergoing compulsory education, so that including online education platforms, received a severe blow. As part of the digital economy, the current transformation of the education sector to education informatization, quality education and vocational education may become the vane of the new era.
Toward Knowledge-Enriched Conversational Recommendation Systems

Conversational Recommendation Systems recommend items through language based interactions with users.In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie.We also propose an alignment loss to further integrate KG entities into the response generation network.Experiments on the large-scale REDIAL dataset demonstrate that the proposed system consistently outperforms state-of-the-art baselines.
Neural-Network-Based Model-Free Calibration Method for Stereo Fisheye Camera

The fisheye camera has a field of view (FOV) of over 180°, which has advantages in the fields of medicine and precision measurement. Ordinary pinhole models have difficulty in fitting the severe barrel distortion of the fisheye camera. Therefore, it is necessary to apply a nonlinear geometric model to model this distortion in measurement applications, while the process is computationally complex. To solve the problem, this paper proposes a model-free stereo calibration method for binocular fisheye camera based on neural-network. The neural-network can implicitly describe the nonlinear mapping relationship between image and spatial coordinates in the scene. We use a feature extraction method based on three-step phase-shift method. Compared with the conventional stereo calibration of fisheye cameras, our method does not require image correction and matching. The spatial coordinates of the points in the common field of view of binocular fisheye camera can all be calculated by the generalized fitting capability of the neural-network. Our method preserves the advantage of the broad field of view of the fisheye camera. The experimental results show that our method is more suitable for fisheye cameras with significant distortion.
Influence of psychological contract fulfillment on job outcomes: A case of the academic sphere in Jordan

Changes in the economy, politics, competitors, customers, and even the weather affect the trust relationship between employers and employees. Most organizations are striving to strengthen such relationships by focusing on psychological contract fulfillment that allows for trust leading to job stability and career advancement. An integrated framework was used to investigate the role of psychological contract fulfillment on academic employees in two Jordanian universities (Yarmouk and Jadara Universities). The research area is determined in universities due to a breach of psychological contracts. Using a quantitative method, responses from academic employees were collected to examine their perception of the influence of psychological contract fulfillment on job outcomes. Hypotheses were tested using the partial least square structural equation modeling (PLS-SEM) method on data gathered from 90 academic employees. The findings indicated that psychological contract fulfillment positively influences both employee productivity and satisfaction. In addition, the results revealed that psychological contract fulfillment negatively influences employee turnover. Furthermore, the study findings have managerial implications through enhancing organizational citizenship behavior that leads to increased employee productivity and satisfaction.
Problematic questions of software requirements engineering training

The key problems of training Requirement Engineering and the following ways to overcome the contradiction between the crucial role of Requirement Engineering in industrial software development and insufficient motivation to master it in the process of SE specialists professional training were identified based on a systematic research analysis on the formation of the ability of future software engineering (SE) specialists to identify, classify and formulate software requirements: use of activity and constructivist approaches, game teaching methods in the process of modeling requirements; active involvement of stakeholders in identifying, formulating and verifying requirements at the beginning of the project and evaluating its results at the end; application of mobile technologies for training of geographically distributed work with requirements; implementation of interdisciplinary cross-cutting projects on SE; involvement of students in real projects; stimulating the creation of interdisciplinary and age-old student project teams.
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract)

We resolve the min-max complexity of distributed stochastic convex optimization (up to a log factor) in the intermittent communication setting, where M machines work in parallel over the course of R rounds of communication to optimize the objective, and during each round of communication, each machine may sequentially compute K stochastic gradient estimates. We present a novel lower bound with a matching upper bound that establishes an optimal algorithm.
Hypergraph Structure Learning for Hypergraph Neural Networks

Hypergraphs are natural and expressive modeling tools to encode high-order relationships among entities. Several variations of Hypergraph Neural Networks (HGNNs) are proposed to learn the node representations and complex relationships in the hypergraphs. Most current approaches assume that the input hypergraph structure accurately depicts the relations in the hypergraphs. However, the input hypergraph structure inevitably contains noise, task-irrelevant information, or false-negative connections. Treating the input hypergraph structure as ground-truth information unavoidably leads to sub-optimal performance. In this paper, we propose a Hypergraph Structure Learning (HSL) framework, which optimizes the hypergraph structure and the HGNNs simultaneously in an end-to-end way. HSL learns an informative and concise hypergraph structure that is optimized for downstream tasks. To efficiently learn the hypergraph structure, HSL adopts a two-stage sampling process: hyperedge sampling for pruning redundant hyperedges and incident node sampling for pruning irrelevant incident nodes and discovering potential implicit connections. The consistency between the optimized structure and the original structure is maintained by the intra-hyperedge contrastive learning module. The sampling processes are jointly optimized with HGNNs towards the objective of the downstream tasks. Experiments conducted on 7 datasets show shat HSL outperforms the state-of-the-art baselines while adaptively sparsifying hypergraph structures.
Understanding and Improving Channel Attention for Human Activity Recognition by Temporal-Aware and Modality-Aware Embedding

Unlike image data, it is often hard to understand intricate sensor data for human activity, which generally contains heterogeneous sensor modalities from different body positions. The importance of every modality might also vary over time. Recent studies have witnessed the success of channel attention in boosting model performance. To maintain considerably low computational overhead, it utilizes global pooling operation to squeeze channel information, but neglects the importance of temporal-aware and modality-aware information that is very vital for activity recognition. In this paper, we propose a novel attention mechanism called TAMA (i.e., temporal-aware and modality-aware) to factorize global pooling operation into a pair of parallel activity feature embedding processes, which is able to simultaneously highlight the varying importance of temporal-aware and modality-aware information. Extensive ablation experiments verify that our TAMA attention can achieve competitive results on several standard HAR benchmarks without incurring extra computational burden. Moreover, a series of visualizing analysis is provided to show the improved interpretability by telling which temporal steps or which modalities is more determinant, which is in good line with human common intuition.
Continuous authentication on mobile devices using behavioral biometrics

As mobile device usage is increasing and users are storing large amounts of important and private data on these devices, security is becoming an increasingly important systemic aspect. If private information is leaked, this may have many negative consequences for the owner of a mobile device. Modern mobile devices are generally using only static, one-shot authentication, which does not provide a sufficient level of security throughout the entire usage session. Potential attackers who manage to unlock a mobile device (such as a smartphone) can use it and steal important data. To prevent such situations, a continuous biometrics system can be used which tries to identify the user and grant access to the right person throughout the entire session, thus improving security considerably. In this paper, we present an unobtrusive continuous authentication system which operates on behavioral biometrics and will identify users based on their hand movements when using the smartphone using built-in sensors and public APIs. We will also compare model-based and template-based approaches in terms of adding new users to the system, and test them on an actual device. Because this type of authentication does not attain an accuracy comparable to that of the best static authentication methods, it should not be treated as an alternative security mechanism but rather as another layer of security added to the existing static authentication system.
Incremental and parallel proximal SVM algorithm tailored on the Jetson Nano for the ImageNet challenge

Purpose This paper aims to propose the new incremental and parallel training algorithm of proximal support vector machines (Inc-Par-PSVM) tailored on the edge device (i.e. the Jetson Nano) to handle the large-scale ImageNet challenging problem. Design/methodology/approach The Inc-Par-PSVM trains in the incremental and parallel manner ensemble binary PSVM classifiers used for the One-Versus-All multiclass strategy on the Jetson Nano. The binary PSVM model is the average in bagged binary PSVM models built in undersampling training data block. Findings The empirical test results on the ImageNet data set show that the Inc-Par-PSVM algorithm with the Jetson Nano (Quad-core ARM A57 @ 1.43 GHz, 128-core NVIDIA Maxwell architecture-based graphics processing unit, 4 GB RAM) is faster and more accurate than the state-of-the-art linear SVM algorithm run on a PC [Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32 GB RAM]. Originality/value The new incremental and parallel PSVM algorithm tailored on the Jetson Nano is able to efficiently handle the large-scale ImageNet challenge with 1.2 million images and 1,000 classes.
Cubic nonlinear squeezing and its decoherence

Squeezed states of the harmonic oscillator are a common resource in applications of quantum technology. If the noise is suppressed in a nonlinear combination of quadrature operators below threshold for all possible up-to-quadratic Hamiltonians, the quantum states are non-Gaussian and we refer to the noise reduction as nonlinear squeezing. Non-Gaussian aspects of quantum states are often more vulnerable to decoherence due to imperfections appearing in realistic experimental implementations. Therefore, a stability of nonlinear squeezing is essential. We analyze the behavior of quantum states with cubic nonlinear squeezing under loss and dephasing. The properties of nonlinear squeezed states depend on their initial parameters which can be optimized and adjusted to achieve the maximal robustness for the potential applications.
Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?

When faced with changing environment, highly-configurable software systems need to dynamically search for promising adaptation plan that keeps the best possible performance, e.g., higher throughput or smaller latency &#x2014; a typical planning problem for self-adaptive systems (SASs). However, given the rugged and complex search landscape with multiple local optima, such a SAS planning is challenging especially in dynamic environments. In this paper, we propose LiDOS, a lifelong dynamic optimization framework for SAS planning. What makes LiDOS unique is that to handle the &#x201C;dynamic&#x201D;, we formulate the SAS planning as a multi-modal optimization problem, aiming to preserve the useful information for better dealing with the local optima issue under dynamic environment changes. This differs from existing planners in that the &#x201C;dynamic&#x201D; is not explicitly handled during the search process in planning. As such, the search and planning in LiDOS run continuously over the lifetime of SAS, terminating only when it is taken offline or the search space has been covered under an environment. Experimental results on three real-world SASs show that the concept of explicitly handling dynamic as part of the search in the SAS planning is effective, as LiDOS outperforms its stationary counterpart overall with up to 10&#x00D7; improvement. It also achieves better results in general over state-of-the-art planners and with 1.4&#x00D7; to 10&#x00D7; speedup on generating promising adaptation plans.
Learning from Very Few Samples: A Survey

Few sample learning (FSL) is significant and challenging in the field of machine learning. The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability. Despite the long history dated back to the early 2000s and the widespread attention in recent years with booming deep learning technologies, little surveys or reviews for FSL are available until now. In this context, we extensively review 300+ papers of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive survey for FSL. In this survey, we review the evolution history as well as the current progress on FSL, categorize FSL approaches into the generative model based and discriminative model based kinds in principle, and emphasize particularly on the meta learning based FSL approaches. We also summarize several recently emerging extensional topics of FSL and review the latest advances on these topics. Furthermore, we highlight the important FSL applications covering many research hotspots in computer vision, natural language processing, audio and speech, reinforcement learning and robotic, data analysis, etc. Finally, we conclude the survey with a discussion on promising trends in the hope of providing guidance and insights to follow-up researches.
Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users’ emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. The model of BiLSTM realizes the classification of negative emotions on Weibo and updates the parameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. The updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. The experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively.
Rise of Blockchain-Based Non-fungible Tokens (NFTs): Overview, Trends, and Future Prospects

Non-fungible tokens (NFTs) are blockchain-based unique, transferrable digital assets. NFTs have recently gained popularity due to their inherent ability of being traded and re-traded over the public blockchain. The financial market witnessed a huge spike in NFT trading in 2021. However, the development and research work in the area of NFTs is still in its early nascent stages. Therefore, in this work, we aim to discuss the working of NFT including its technical components, protocols, security, etc. We then discuss the key benefits as well as challenges facing the NFTs. Lastly, we provide a critical trend analysis to predict where this industry is headed and how it will bloom in upcoming years.
Double embedding and bidirectional sentiment dependence detector for aspect sentiment triplet extraction

Aspect sentiment triplet extraction (ASTE) is a popular subtask related to aspect-based sentiment analysis (ABSA). It extracts aspects and their associated opinion expressions and sentiment polarities from comment sentences. Previous studies have proposed a multitask learning framework that jointly extracts aspect and opinion terms and treats the sentiment analysis task as a table-filling problem. Although the multitask learning framework solves the problem of identifying overlapping opinion triples, the entire model cannot explicitly simulate interactions between aspects and opinions. Therefore, we propose a sentiment-dependence detector based on a dual-table structure that starts from two directions, aspect-to-opinion and opinion-to-aspect, to generate two sentiment-dependence tables dominated by two types of information. These complementary directions allow our framework to explicitly consider interactions between aspects and opinions and better identify triples. Moreover, we use a double-embedding mechanism—character-level and word-vector embeddings—in the model for triplet extraction that enables it to represent contexts at different granularity levels and explore high-level semantic features. To the best of our knowledge, this study presents the first bidirectional long short-term memory (BiLSTM) model based on double embedding used to perform ASTE tasks. Finally, our analysis shows that our proposed bidirectional sentiment-dependence detector and double-embedding BiLSTM model achieve more significant results than the baseline model for triples with multiple identical aspects or opinions.
Towards Single-Component and Dual-Component Radar Emitter Signal Intra-Pulse Modulation Classification Based on Convolutional Neural Network and Transformer

In the modern electromagnetic environment, the intra-pulse modulations of radar emitter signals have become more complex. Except for the single-component radar signals, dual-component radar signals have been widely used in the current radar systems. In order to make the radar system have the ability to classify single-component and dual-component intra-pulse modulation at the same period of time accurately, in this paper, we propose a multi-label learning method based on a convolutional neural network and transformer. Firstly, the original single channel sampled sequences are padded with zeros to the same length. Then the padded sequences are converted to frequency-domain sequences that only contain the amplitude information. After that, data normalization is employed to decrease the influence of amplitude. After radar signals preprocessing, a designed model which combines a convolutional neural network and transformer is used to accomplish multi-label classification. The extensive experiments indicate that the proposed method consumes lower computation resources and has higher accuracy than other baseline methods in classifying eight types of single and thirty-six types of dual-component intra-pulse modulation, where the overall accuracy and weighted accuracy are beyond 90%.
On shapley value in data assemblage under independent utility

In many applications, an organization may want to acquire data from many data owners. Data marketplaces allow data owners to produce data assemblage needed by data buyers through coalition. To encourage coalitions to produce data, it is critical to allocate revenue to data owners in a fair manner according to their contributions. Although in literature Shapley fairness and alternatives have been well explored to facilitate revenue allocation in data assemblage, computing exact Shapley value for many data owners and large assembled data sets through coalition remains challenging due to the combinatoric nature of Shapley value. In this paper, we explore the decomposability of utility in data assemblage by formulating the independent utility assumption. We argue that independent utility enjoys many applications. Moreover, we identify interesting properties of independent utility and develop fast computation techniques for exact Shapley value under independent utility. Our experimental results on a series of benchmark data sets show that our new approach not only guarantees the exactness of Shapley value, but also achieves faster computation by orders of magnitudes.