Reviewerly/exHarmony huggingface dataset:
Build a New Era Innovation and Entrepreneurship Education Ecosystem for 2050

With the implementation of policies, such as: Opinions of the CPC Central Committee and the State Council on Deepening the Reform of Institutional Mechanisms and Accelerating the Implementation of Innovation-Driven Development Strategies,” “Opinions of the General Office of the State Council on Deepening the Reform of Innovation and Entrepreneurship Education in Colleges and Universities” and the State Council’s “Opinions on Promoting the High-quality Development of Innovation and Entrepreneurship to Create an Upgraded Version of Entrepreneurship and Innovation,” the enthusiasm of local governments and colleges and universities runs high, combined with the provincial situation, the implementation plan for deepening the reform of innovation and entrepreneurship education in higher education institutions is introduced. Under the policy support and active guidance of governments at all levels, the development of innovation and entrepreneurship in China’s colleges and universities has achieved remarkable results.
Resource-Efficient Visual Multiobject Tracking on Embedded Device

Multiobject tracking (MOT) is a crucial technology for security surveillance, which is computationally intensive due to the requirement of processing a large number of video streams within low latency in practice. The input video streams of MOT are processed on a cloud computing center with abundant computational capability, posing heavy pressures on delivering video streams to the cloud. Recent advances in the Internet-of-Things (IoT) technology provide edge-computing-based solutions for video analytics at scale. However, the gap between MOT’s high computational capability demand and IoT devices’ resource-constrained nature remains significant. In this article, a resource-efficient MOT (REMOT) method is proposed for real-time surveillance on IoT embedded devices, including an affinity measurement based on an appearance model with angular triplet loss and a motion association that substitutes the time-consuming graph-based data association stage. Considering the tradeoff between latency and accuracy, we design an optimization strategy on the parallel processing of deep learning models’ layers to accelerate the inference speed with less accuracy loss. Besides, we employ a model compression strategy for model size reduction. Experiments on MOT16 and MOT17 benchmarks demonstrate that REMOT reduces 2.4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> latency compared with the original implementation and achieves a running speed of 81 frames per second (fps) on an embedded device with only a marginal accuracy loss (6%), which meets the requirements of real-time processing and low-latency response for surveillance.
Sociolinguistic analysis of the communication style of users of social networks and messengers

The article presents a sociolinguistic analysis of the communication style of users in social networks and messengers. Based on the methods of sociolinguistics and indirect analysis methods of discourse, the usual moments of exchanging messages between users of a certain social group in Viber are compared with their interactions using the same environment in social networks during the occurrence of non-standard situations. The combination of case studies and the study of key moments when relevant offline moments affect the linguistic practices of a particular community can influence the understanding of current semi-social online communication. Accordingly, stylistic and linguistic practices of communication in social networks and messengers bear the imprint of the communication process and are directly related to the topic of the issues discussed. If, outside of a critical situation, such communication will receive an impression of ease and be confirmed by the usage of colloquial words, impulsive abbreviations, named sentences and a significant number of emojis.
Assemblies of neurons can learn to classify well-separated distributions.

Assemblies are patterns of coordinated firing across large populations of neurons, believed to represent higher-level information in the brain, such as memories, concepts, words, and other cognitive categories. Recently, a computational system called the Assembly Calculus (AC) has been proposed, based on a set of biologically plausible operations on assemblies. This system is capable of simulating arbitrary space-bounded computation, and describes quite naturally complex cognitive phenomena such as language. However, the question of whether assemblies can perform the brain's greatest trick -- its ability to learn -- has been open. We show that the AC provides a mechanism for learning to classify samples from well-separated classes. We prove rigorously that for simple classification problems, a new assembly that represents each class can be reliably formed in response to a few stimuli from it; this assembly is henceforth reliably recalled in response to new stimuli from the same class. Furthermore, such class assemblies will be distinguishable as long as the respective classes are reasonably separated, in particular when they are clusters of similar assemblies, or more generally divided by a halfspace with margin. Experimentally, we demonstrate the successful formation of assemblies which represent concept classes on synthetic data drawn from these distributions, and also on MNIST, which lends itself to classification through one assembly per digit. Seen as a learning algorithm, this mechanism is entirely online, generalizes from very few samples, and requires only mild supervision -- all key attributes of learning in a model of the brain.
Platform Z - Portal "Inverned" Mental Resources On The Principles Of Didactics

Industry Digital Educational Resources (DER) aims to create a variety of academic information content, learning tools and knowledge control for full-time and distance learning. However, only a small part of the DER meets the real needs of students (generation Z), corresponds to their psycho-physiological preferences. Therefore, there is a need to design new approaches to the creation of DERs that will ensure the maximum effect of their use in education. The article proposes to pay attention to the achievements of cognitive psychology and mental didactics in the field of creating educational content for the development of students' thinking. Purpose of the study- to justify the methodological basis of educational resources portal that focuses on the development of structural and computational thinking as the quality of the competitive expert in the smart society. The Platform Z - portal structurally represents a semantic network of fundamental questions, broken down into a tree of educational questions and tasks. For each question, short and clear answers are developed, as well as tutors for teaching problem solving, which can be arranged in different versions, according to the student's claims. This approach to educational content allows you to organize an individual learning path for each student. As the results of the study, prototypes with a high degree of satisfaction of the trainees and positive assessments of experts are given. The materials of the article may be of interest to DER developers, researchers of learning models, teachers wishing to use digital cognitive and mental learning technologies.
Investigation of Database Models for Evolving Graphs

We deal with the efficient implementation of storage models for time-varying graphs. To this end, we present an improved approach for the HiNode vertex-centric model based on MongoDB. This approach, apart from its inherent space optimality, exhibits significant improvements in global query execution times, which is the most challenging query type for entity-centric approaches. Not only significant speedups are achieved but more expensive queries can be executed as well, when compared to an implementation based on Cassandra due to the capability to exploit indices to a larger extent and benefit from in-database query processing.
Quantifying unlinkability in multi-hop wireless networks

Consider a multi-hop wireless network in which devices act as anonymizing routers. Even if devices anonymize their link transmissions, an adversary may still be able to infer key information by observing the traffic patterns in the network. In this work, we quantify how well an adversary can infer unlinkability, that is, the probability that different pairs of devices are communicating, from anonymized link transmissions. We first propose a metric to compute unlinkability using a Kalman-filter based adversary. Using this metric, we then evaluate how different network characteristics impact unlinkability. We assume that devices do not reorder packets to mix traffic and thereby increase unlinkability. Instead, we show that traffic mixing is still possible due to the use of multi-hop routing and broadcast transmissions, with the amount of mixing dependent on the network characteristics. In simulation, we find that (i) for unicast links, as network connectivity increases unlinkability decreases, while for broadcast links, as connectivity increases unlinkability increases, (ii) link dynamics tend to increase unlinkability with unicast links but decrease unlinkability with broadcast links, (iii) well-connected topologies, particularly with broadcast links, achieve the same level of unlinkability with fewer transmissions per packet delivered, (iv) a lattice topology has consistently good unlinkability in different scenarios, and (v) heterogeneous network traffic gives higher unlinkability and better anonymization efficiency than uniform traffic, even when the average rate of traffic is the same.
Enlargement of the Field of View Based on Image Region Prediction Using Thermal Videos

Various studies have been conducted for detecting humans in images. However, there are the cases where a part of human body disappears in the input image and leaves the camera field of view (FOV). Moreover, there are the cases where a pedestrian comes into the FOV as a part of the body slowly appears. In these cases, human detection and tracking fail by existing methods. Therefore, we propose the method for predicting a wider region than the FOV of a thermal camera based on the image prediction generative adversarial network version 2 (IPGAN-2). When an experiment was conducted using the marathon subdataset of the Boston University-thermal infrared video benchmark open dataset, the proposed method showed higher image prediction (structural similarity index measure (SSIM) of 0.9437) and object detection (F1 score of 0.866, accuracy of 0.914, and intersection over union (IoU) of 0.730) accuracies than state-of-the-art methods.
Macro Discourse Relation Recogniztion Based on Micro Discourse Structure and Self-Interactive Attention Network

Macro discourse relation recognition is an important task of macro discourse analysis. The existing models ignore the micro discourse structure within paragraphs and could not accurately grasp the paragraph semantics. In addition, the traditional pre-trained models only used the representation of the [CLS] token for macro relation classification, lacking more detailed semantic interaction between discourse units. To solve the above issues, we proposed a macro discourse relation recognition model based on Micro Discourse Structure and Self-Interactive Network (MDSSIN) that mines the semantic representation of important parts within a paragraph and enhances semantic interaction between paragraphs. Specially, we first automatically build the micro-structure discourse tree for each paragraph and get the core clause in each paragraph according to nuclearity. Then, we use the self-interactive attention mechanism to capture more detailed semantic interaction between discourse units and between the core clauses of discourse units. Experimental results on Chinese MCDTB show that our method achieves the SOTA performance.
RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging

Pruning Deep Neural Networks (DNNs) is a prominent field of study in the goal of inference runtime acceleration. In this paper, we introduce a novel data-free pruning protocol RED++. Only requiring a trained neural network, and not specific to any particular DNN, we exploit an adaptive data-free scalar hashing which exhibits redundancies among neuron weight values. We study the theoretical and empirical guarantees on the preservation of the accuracy from the hashing as well as the expected pruning ratio resulting from the exploitation of said redundancies. We propose a novel data-free pruning technique of DNN layers which removes the input-wise redundant operations. This algorithm is straightforward, parallelizable and offers novel perspective on DNN pruning by shifting the burden of large computation to efficient memory access and allocation. We provide theoretical guarantees on RED++ performance and empirically demonstrate its superiority over other data-free pruning methods and its competitiveness with data-driven ones on ResNets, MobileNets, and EfficientNets.
Information Algebras of Coherent Sets of Gambles in General Possibility Spaces.

In this paper, we show that coherent sets of gambles can be embedded into the algebraic structure of information algebra. This leads firstly, to a new perspective of the algebraic and logical structure of desirability and secondly, it connects desirability, hence imprecise probabilities, to other formalism in computer science sharing the same underlying structure. Both the domain-free and the labeled view of the information algebra of coherent sets of gambles are presented, considering general possibility spaces.
SSR: Explainable Session-based Recommendation

In recent years, session-based recommendation has attracted more and more attention. Previous work utilizing deep learning approaches has achieved significant progress in the accuracy of prediction. However, why the user would view the next item is not clear, though attention mechanism assigns different importance on items in a session. Meanwhile, some traditional data mining-based methods are able to provide explanations, but they mainly offer a narrow perspective like measuring item similarity and fail to achieve as good performance as deep learning-based approaches. To this end, we propose an explainable session-based recommendation by considering three factors including sequential patterns, repetition clicks, and item similarity. Concretely, we design a two-stage framework where candidate items are firstly selected according to users' sequential patterns and repeated behaviors, and then they are further ranked by considering short-term interest with the calculation of item similarity. The experimental results on two benchmark datasets show that our model not only achieves competitive recommendation performance with advanced deep learning based models, but also provides reasonable explanations.
Improved quantum lower and upper bounds for matrix scaling.

Matrix scaling is a simple to state, yet widely applicable linear-algebraic problem: the goal is to scale the rows and columns of a given non-negative matrix such that the rescaled matrix has prescribed row and column sums. Motivated by recent results on first-order quantum algorithms for matrix scaling, we investigate the possibilities for quantum speedups for classical second-order algorithms, which comprise the state-of-the-art in the classical setting. We first show that there can be essentially no quantum speedup in terms of the input size in the high-precision regime: any quantum algorithm that solves the matrix scaling problem for $n \times n$ matrices with at most $m$ non-zero entries and with $\ell_2$-error $\varepsilon=\widetilde\Theta(1/m)$ must make $\widetilde\Omega(m)$ queries to the matrix, even when the success probability is exponentially small in $n$. Additionally, we show that for $\varepsilon\in[1/n,1/2]$, any quantum algorithm capable of producing $\frac{\varepsilon}{100}$-$\ell_1$-approximations of the row-sum vector of a (dense) normalized matrix uses $\Omega(n/\varepsilon)$ queries, and that there exists a constant $\varepsilon_0>0$ for which this problem takes $\Omega(n^{1.5})$ queries. To complement these results we give improved quantum algorithms in the low-precision regime: with quantum graph sparsification and amplitude estimation, a box-constrained Newton method can be sped up in the large-$\varepsilon$ regime, and outperforms previous quantum algorithms. For entrywise-positive matrices, we find an $\varepsilon$-$\ell_1$-scaling in time $\widetilde O(n^{1.5}/\varepsilon^2)$, whereas the best previously known bounds were $\widetilde O(n^2\mathrm{polylog}(1/\varepsilon))$ (classical) and $\widetilde O(n^{1.5}/\varepsilon^3)$ (quantum).
Approximation Method for Optimization Problems in Gate-Model Quantum Computers

In near-term quantum computers, the computations are realized via unitary operators. The optimization problem fed into the quantum computer sets an objective function that is to be estimated via several measurement rounds. Here, we define a procedure for objective function approximation in gate-model quantum computers. The proposed solution optimizes the process of objective function estimation for optimization problems in gate-model quantum computers and quantum devices.
THE STUDENTS' PERCEPTION ON THE USE OF PADLET IN IMPROVING WRITING SKILLS

Technology use in teaching and learning process had become very popular especially in the 21st century. Padlet is one of online platforms which have simple design and well-integrated features to support learning process. The implementation of Padlet turns the learning process becomes students-centered. This study discusses the use of Padlet in improving students’ writing skills. It mainly describes how the Padlet affect students’ interest in the learning process especially writing. This study is a qualitative research which using questionnaires to gather the data. The questionnaires given to students are divided into two types, namely yes-no question and yes-no question with reason. Students were given six questions related to their experience in using Padlet. Based on the results, most of students agree that the implementation of Padlet in writing class can improve their interest and writing skills. Students give positive responds towards the use of Padlet because of the great features provided in Padlet and immediate feedback they get from teacher and peers.
CPD 2021: The 4th International Workshop on Combining Physical and Data-Driven Knowledge in Ubiquitous Computing

In the real-world ubiquitous computing systems, it is difficult to require a significant amount of data to obtain accurate information through pure data-driven methods. The performance of data-driven methods relies on the quantity and ‘quality’ of data. They perform well when a sufficient amount of data is available, which is regarded as ideal conditions. However, in real-world systems, collecting data can be costly or impossible due to practical limitations. On the other hand, it is promising to utilize physical knowledge to alleviate these issues of data limitation. The physical knowledge includes domain knowledge from experts, heuristics from experiences, analytic models of the physical phenomena and etc.
Location Based Twitter Emotion Classification for Disaster Management

Nowadays, smart phones and social media are very powerful. Twitter has become a major communication in social media, particularly in the modern world. In 2021, Twitter was found to be used by 330 million active users and 145 million daily users, mainly young people. Almost everyone's access to smart phones gives them easy access at any time to social media. This enables individuals to automatically report any emergency that happens to them on social media. The main aim of this project is to design a simple system which will be helpful in disaster identification and disaster recovery. Many disaster relief organizations are also involved in analyzing tweets, so it is currently becoming a hot research subject. The main problem is that with normal ML models it is difficult to identify the context from tweet texts. So, here NLP techniques are performed, and it is used with deep learning models. This system develops a deep learning model that classifies real disaster tweets and emotions based on location and then a recovery model is developed based on negative emotion to help the suffering peoples.
Structural Scheme Of Practice-Oriented Skills Formation In The Professional Training Process

The article identifies and substantiates the Russian universities EMERCOM cadets practice-oriented skills structure in their professional training process, considers possible ways and conditions for the practice-oriented skills effective formation. The Russian Emergency Situations Ministry higher educational institutions cadets concept "practice-oriented skills essence" is revealed and their list is specified. A practice-oriented skills formation structural diagram in the Russian Emergency Situations Ministry higher educational institutions cadets professional training process has been developed, which is represented by interrelated components: normative-target, content-technological, reflexive-evaluative. Practice-oriented skills formation the criteria, parameters and levels in the Russian Emergency Situations Ministry universities cadets professional training process being determined. Organizational and pedagogical conditions for the Russian universities EMERCOM cadets practice-oriented skills have been identified and developed, including cadets orientation to mastering actions aimed at successfully solving professional problems; knowledge enrichment, skills and competencies under the professional standards requirements by solving situational problems at a specialized training ground; cadets introduction to production and technological activities during practical training in fire departments, their effectiveness was checked.
Development of future imaging technology under the background of intelligent media fusion based on VR technology

Development of future imaging technology under the background of the intelligent media fusion based on VR technology is studied in this manuscript. In the traditional model, in the same image area, the image data connection is relatively simple. When the image areas are different, some situations will occur when connecting. In the process of image preparation, the geographic information data in the GIS system can be used, and elements such as the scale and spatial resolution of the map can be reasonably selected according to the different platforms and directions used by the map. To implement the system with the environment information, the VR and the intelligent system model are both combined. The proposed framework has been tested on the public collected data by which the performance has been validated.
Improving sentiment analysis using preprocessing techniques and lexical patterns

Sentiment analysis has recently gained considerable attention, since the classification of the emotional content of a text (online reviews, blog messages etc.) may have a relevant impact on market research, political science and many other fields. In this paper, we focus on the importance of the text preprocessing phase, proposing a new technique we termed lexical pattern-based feature weighting (LPFW) that allows one to improve sentence-level sentiment analysis by increasing the relevance of the features contained in particular lexical patterns. This approach has been evaluated on two sentiment classification datasets. We show that a systematic optimisation of the preprocessing filters is important for obtaining good classification accuracy. Also, we show that LPFW is effective in different application domains and with different training set sizes.