Reviewerly/exHarmony huggingface dataset:
The application of dual algorithms in IC card

This paper introduces the current application status and main security issues of IC card in the field of urban public transport internationally and domestically, and proposes to strengthen the application security of IC card by using dual algorithms. Dual algorithms mean the two most popular types of encryption algorithms currently used: algorithms 3DES and SM4 in terms of symmetric keys; RSA algorithm and SM2 algorithm based on ECC in terms of asymmetric keys. The two types of encryption algorithms have their own characteristics: algorithms 3DES and RSA, as internationally agreed cryptographic algorithms, have the advantages of good applicability and compatibility. However, the technological progresses pose a risk of being decoded constantly. If we enhance the decoding difficulty by increasing the encryption length, the computing speed will become lower and resources consumption higher. Algorithms SM2 and SM4 which are the cryptographic algorithms independently researched and developed in China, are superior to the algorithms 3DES and RSA in terms of encryption and decryption speed, resource consumption and security, but they don’t bring enough applicability. In view of the features of the two types of cryptographic algorithms, this Paper proposes to integrate the two types of algorithms into one IC card, and to switch the algorithm by the algorithm indicator or the algorithm switching instruction, so as to achieve the adaptive execution of appropriate algorithm based on practical conditions. Therefore, it can enhance the application security of IC card while ensure the wide application range.
Learning adaptive control in dynamic environments using reproducing kernel priors with bayesian policy gradients

One of the most distinctive characteristics in biological evolution is to not only learn and reinforce knowledge from prior experience, but also develop a solution in (pseudo) real-time for future events by studying the past choices. Inspired by this observation, we aim at developing a systematic methodology of dynamically learning effective control policy for robotic manipulators that deftly avoid dynamic obstacles in fast-changing environments. Unfortunately, dynamical obstacles present time-varying statistical sensory irregularities, making learning based on prior experience much less productive. Furthermore, off-the-shelf policy gradient methods often become computationally expensive, and sometimes intractable, to adapt existing policy for dynamically changing environments. In this paper, to mitigate both of these challenges, we propose to use vector-valued kernel embedding (instead of parameter vectors) to represent policy distribution as features in non-decreasing Euclidean space. Furthermore, we develop policy search algorithm over Bayesian posterior estimation derived from inner-product of a priori Gaussian kernels, allowing the search space to be defined as high (possibly infinite) dimensional Reproducing Kernel Hilbert Space (RKHS).
Unpacking linguistic devices and discursive strategies in online social movement organizations: Evidence from anti-vaccine online communities

This study investigates linguistic devices and discursive strategies employed by online social movement organizations (SMOs) in attempts to deinstitutionalize long-standing, institutionalized behaviors. The research draws from an in-depth analysis of public discourse within anti-vaccine online communities in Italy and contributes to the social movement literature on framing and the theory of discursive institutionalization. It employs semi-automated text-analysis methods and interpretive analysis of textual data from seven anti-vaccine social media communities, before and subsequent to the 2017 regulatory intervention of the Italian government to increase vaccination rates. This intervention followed a phase of intense debate centered on the decrease in vaccination coverage and the spread of anti-vaccine ideas in social media as well as in the broader public discourse. The study analyzes the discursive strategies and linguistic devices of community leaders (moderators) and followers (members), and investigates shifts in micro-level online anti-vaccine discursive strategies that developed after the government regulation. The findings suggest that anti-vaccine online SMOs employ specific sets of linguistic devices, namely rhetorical fallacies, that support well-defined discursive strategies such as those aiming to delegitimize actors that endorse vaccines. Furthermore, the evidence shows that these linguistic devices and discursive strategies, after the government regulation, shift from an evidence-based stance towards values and emotions-based argumentations.
Generalized Few-Shot Learning For Wearable Sensor-based Human Activity Recognition

Few-Shot Learning (FSL) aims to create a classifier which generalizes to classes not present in the training set given just a few samples from each new class. Generalized FSL recognizes samples from classes that are both present and not present in the training set. We developed the first generalized FSL system for Human Activity Recognition (HAR) based on data from wearable sensors. This enabled the classification of new human activities without the high cost of collecting data and allowed for increased personal variation of performing certain activities. We implemented prototypical networks and a center loss based model for FSL. We trained and evaluated two additional classifiers. The first one recognizes source classes and the second determines if a target is from source/target domain. We evaluated our model on three publicly available datasets (UTWENTE, PAMAP2 and OPP) and showed that our methods significantly outperformed the state-of-the-art on the FSL task.
Category attentive graph neural networks for session-based recommendation

Session-based recommendation is a challenging field in the research network-based behavior modeling, mainly due to the complex transfer of user interests between items and the limited information. The previous methods model the session as a sequence or a graph, which takes into account the role of time attention in session-based recommendation and achieve satisfactory performances, they still ignore the latent relationship between user interest transfer and item category. In this study, we propose a category attentive graph neural network (CAGNN) model for session-based recommendation. According to the item category, the category attention is embedded into the latent vector of the items to adaptively capture different user interests, which effectively improves the expressiveness and performance of the model. The numerical results of two real datasets show that CAGNN outperforms the state-of-the-art session-based recommendation methods.
Efficient Algorithms for Minimizing Compositions of Convex Functions and Random Functions and Its Applications in Network Revenue Management

In this paper, we study a class of nonconvex stochastic optimization, where the objective function is a composition of a convex function and a random function. Leveraging an (implicit) convex reformulation via a variable transformation, we develop stochastic gradient-based algorithms and establish their sample and gradient complexities for achieving an $\epsilon$-global optimal solution. Interestingly, our proposed Mirror Stochastic Gradient (MSG) method operates only in the original decision space using gradient estimators of the original nonconvex objective and achieves $\tilde{\mathcal{O}}(\epsilon^{-2})$ sample and gradient complexities, which matches the lower bounds for solving stochastic convex optimization problems. Under booking limits control, we formulate the air-cargo network revenue management (NRM) problem with random two-dimensional capacity, random consumption, and routing flexibility as a special case of the stochastic nonconvex optimization, where the random demand truncates the booking limit decision. Extensive numerical experiments demonstrate the superior performance of our proposed MSG algorithm for booking limit control with higher revenue and lower computation cost than state-of-the-art bid-price-based control policies, especially when the variance of random capacity is large.
Senti‐eXLM: Uyghur enhanced sentiment analysis model based on XLM

In the field of public opinion analysis, sentiment analysis is an important basic research branch. Previous studies have successfully proved that the advanced transformer pre-training model can be applied to this scenario in Uyghur and other low-resource language scenarios. However, the majority of these studies are based on the traditional language anchor point and rely on the pre-training model's cross-lingual understanding ability. The Senti-eXLM model proposed in this paper employs a method that allows for adaptively expanding the model's knowledge domain and dynamically adjusting the model for Uyghur language in order to improve the language's understanding and representation capability, thereby increasing the accuracy of text emotion analysis. Experiments on publicly available data sets demonstrate that when compared to the original model, the model's emotion classification accuracy is improved by 6.17%, the training convergence speed is increased by 27%, and the average reasoning time is increased by only 11%.
Statistical analysis of multi job processing in Hadoop environment using schedulers

Hadoop schedulers facilitate effective sharing of system resources among multiple tasks for the purpose of effective completion of jobs. Upon submission of a job, the same will be transferred onto a queue by Hadoop scheduler. Once the transfer is complete, aforesaid job will be split up into multiple tasks and is further distributed among various computing nodes. Herein, performance is directly related to time taken up by aforesaid tasks to complete the assigned job. Optimum distribution of system resources among such tasks play a vital role in reduction of job completion time, and thus can ensure improved performance in relation to the same. Hadoop, by virtue uses FIFO scheduler. In the proposed experiment, FIFO scheduler’s job execution time is discussed and compared with the job execution time of Fair scheduler and Capacity scheduler.
Negotiating Open Access

In this chapter, the authors interrogate the discursive terrain of the open access phenomenon to position the processual as well as the discourse communities that open access is inevitably enmeshed in. The essay explores the current climate of open access and investigates the ethical dilemmas that its subversive sibling of guerrilla open access foregrounds. Further, the essay also recommends a viable model that can be deployed by state players as an exemplar of academic socialism that is flexible, accommodative, and a true reflection of the open-access philosophy which also counters the development of otherwise illegal and ‘pirate' models of open access.
Wolf sheep predation

The NetLogo Wolf Sheep Predation model is well suited for instructional use due to its use of familiar agents, concise source code, and simple graphics. As part of a graduate course surveying modeling and simulation methods, a team of four students reimplemented the Wolf Sheep Predation model, treating the NetLogo version as the simuland, or system their model should simulate. The reimplementation used the Python programming language and the Mesa framework. Mesa is a framework for agent-based modeling written in Python, with features that are roughly equivalent to NetLogo. This paper documents their implementation process, compares the reimplemented Python/Mesa version to the original NetLogo version, and reports the students' and instructor's assessments of Wolf Sheep Predation as an instructional exercise.
An Improved and Efficient and Dynamic Load Balancing Approach in Cloud Computing Environments

Cloud Computing Environment (CCE) is a newly rudimentary knowledge in the IT industry. Central to these problems lies in the establishment of an active LB algorithm. The network load can be CPU load, memory limit, postponement, or system load. In the proposed algorithm, Load Balancing (LB) done by considering priority policy. An Efficient and Dynamic Load Balancing Approach in Cloud Computing (EDBA-CC) proposed, and the related algorithm executed on CloudSim (CS). A resource allocation approach that takes into account Resource Utilization (RU) would lead to better energy efficiency. The outcomes indicate the effectiveness of the proposed algorithm. The outcomes exhibited that the EDBA-CC algorithm reduced Response Time (RT). The decreasing in RT is about 2.23 ms when the instruction length was 275 Byte, and the decreasing became more obviously with 10.12 ms for RT and 5.54 ms for RT when the instruction length was 2000 Byte.
A new scrum and CMMI level 2 compatible model for small software firms in order to enhance their software quality

Advancement of the technological sector is far more progressive than ever before in Bangladesh. This means there is an influx of software companies requiring the adaptation of a structured process management framework (i.e CMMI) to attain successful projects and to enhance software quality.
Research and application of accident hazard prediction based on matrix decomposition

Safe production is related to the company's economic benefits or even survivals, and an important goal pursued by the company. It is basis to predict hazard precisely for controlling hidden dangers, eliminating hidden dangers, and improving the company's safety production capacity effectively. In this paper, the example of safety production hazard investigation is studied based on cloud service. First, the company's existing hazard data is used to classify statistics, normalize data and convert to grade. Second, the matrix decomposition method is introduced to predict the hazard degree of the company with hazard data of company and safety supervision department. By comparing the prediction data with the actual hidden data, it is show that the matrix decomposition method can be well used in hazard prediction in the enterprise to better eliminate hidden dangers.
Using distributional thesaurus to enhance transformer-based contextualized representations for low resource languages

Transformer-based language models recently gained large popularity in Natural Language Processing (NLP) because of their diverse applicability in various tasks where they reach state-of-the-art performance. Even though for resource-rich languages like English, performance is very high, there is still headroom for improvement for low resource languages. In this paper, we propose a methodology to incorporate Distributional Thesaurus information using a Graph Neural Network on top of pretrained Transformer models to improve the state-of-the-art performance for tasks like semantic textual similarity, sentiment analysis, paraphrasing, and discourse analysis. In this study, we attempt various NLP tasks using our proposed methodology for five languages - English, German, Hindi, Bengali, and Amharic - and show that by using our approach, the performance improvement over transformer models increases as we move from resource-rich (English) to low-resource languages (Hindi, Bengali, and Amharic).
POSLAN

Most online message threads inherently will be cluttered and learning to de-clutter or disentangle the threads create new avenues for analysing them. The need for disentangling the clutter is much higher in platforms, such as Telegram, wherein the discussion is taking place does not provide functions to retrieve reply relations of the messages. This introduces an interesting problem referred to as a structural learning problem. In this work we try to disentangle chats using post embeddings. Our main contribution is the vector embeddings for posts in a thread so that it captures both linguistic and positional features in relation to a context of where a given message is in. Using these embeddings for posts we compute a similarity based connectivity matrix which then converted into a graph. After pruning the resultant graph can be used to discover the reply relation for the posts in the thread. The process of discovering the reply-relation or disentangling chat is kept as an unsupervised mechanism. We present our experimental results on a data set obtained from Telegram with limited meta-data and evaluate our methods on a self collected dataset from Twitter where the reply structure is given. We show our methods have promising results by conducting ablation tests.
Use transformer encoder for KPI anomaly detection

KPI anomaly detection has always been a very important task in AIOps (Artificial Intelligence for IT Operations). Operations Engineers judge whether the service is stable by monitoring various KPIs. However, there are many challenges to build a KPI anomaly detection system, and the existing anomaly detection models are not strong. Given the effectiveness of the Transformer model in a series of fields such as NLP and CV, it has attracted great interest from researchers. Therefore, in this work, we build an end-to-end system named KPI_TransformerE for KPI anomaly detection. In particular, for the Class Imbalance problem, we use Focal Loss to solve it. Finally, we test our method on public datasets. The experimental results show that the method can achieve an F1 score of 0.963 in the test set which proves the power of our model.
Particle swarm optimization for procedural content generation in an endless platform game

Given the ever-increasing competition in the digital gaming industry, induced by a market of an exponentially growing gamer population, the production of creative, coherent, and appealing games has become inherently more complex. Creating game content by hand is both costly and time-consuming. By automating or assisting programmers and designers in their tasks, the techniques of procedural content generation (PCG) for games may address these challenges. PCG is not new, being active for several decades. However, the more traditional (and popular) form of PCG is somehow limited. It relies on some basic techniques ranging from simple pseudo-random number generators, generative grammars, image filtering, and spatial algorithms. The most advanced forms of PCG may utilize the modeling and simulation of complex systems and techniques from Artificial Intelligence (AI). In this context, this paper proposes a novel PCG approach, which is based on an optimization algorithm, known as Particle Swarm Optimization (PSO). Our approach is tested on a 2D endless platform runner game. The game structure (based on the Godot Game Engine) and a previous solution using Genetic Algorithms (GA) were first proposed and evaluated in an earlier work. In this paper, besides proposing the novel solution using PSO, we performed a comparative evaluation between the novel and previous solutions. The fitness function, utilized by both algorithms, takes into account the game’s environment aesthetics, physics, and some rules associated with gameplay, so that the generated environments are both enjoyable and playable. Our experiments evaluated time viability for in-game real-time generation and convergence to high/stable fitness values. We discovered hyper parameter ranges that yielded viable solutions. In the end, PSO has proven to be better suited to the investigated PCG task than the GA, since it presented faster convergence time and higher fitness function values.
Transpose-xen

Cloud systems continue to rise in popularity due to their ability to provide access to flexible, scalable systems to be shared among all their users. Several tasks can be executed simultaneously within a server, but have varying requirements for completion. While some jobs may have latency-critical quality-of-service (QoS) requirements, others may have stricter real-time constraints for maximum deadline misses. The introduction of hard real-time tasks, where zero deadline misses are acceptable, results in scheduling concurrent jobs becoming increasingly difficult.
What you see is not what you get

People usually think that digital screens are reliable devices. Unfortunately, attackers can exploit this blind trust to persuade a user to perform unintended actions. In this paper, we present a novel type of Man-in-the-Middle attack named Man-in-the-Video. Man-in-the-Video intercepts the video stream flowing between a computer and its screen and modifies it on-the-fly. The objective of such attack is to distort the perception of reality and to induce improper user behaviour. We implemented HackDMI, a Man-in-the-Video attack performed over an HDMI cable. We applied this attack to a realistic threat scenario (i.e., phishing) and we evaluated it with quantitative measures. HackDMI is able to deceptively modify a 720p video stream, while maintaining a frame-rate of 14FPS. We also recorded three demo videos for qualitative evaluation.
Similarity-based uncertainty scores for interpretable deep learning in scientific applications

There is a significant effort to increase the adoption of Deep Neural Networks (DNNs) in fields such as criminal justice and medicine. Yet, DNNs commonly fall short in these applications due to their "black box" nature. DNN research often focuses more on achieving a higher classification accuracy than traditional Machine Learning (ML) algorithms, but this does not apply well to data sets that are inherently difficult to label and classify, hence interpretability is crucial. In such cases, scientists not only expect a prediction but also an explanation and/or a measure of confidence per prediction. In this work, we propose a novel uncertainty metric based on clustering and data similarity learned through a Siamese Convolutional Neural Network (SCNN). Rather than using the silhouette coefficient as a quality metric for clustering, we use it to independently provide an uncertainty score and supplement each prediction. These uncertainty scores serve to better inform the end-user's decision, in our case an application domain expert. We evaluate our uncertainty scores using a K-Nearest Neighbors (KNN) classifier built on top of SCNN features. We show that the top 15% most uncertain points---flagged per our metric---capture over 50% of KNN errors (misclassified cases) for three different data sets, exhibiting the versatility and functionality of our uncertainty metric in detecting difficult-to-classify samples.
Issue of Studying Semantic Reading in Students with Health Limitations in the Context of Education Digitalisation: A Theoretical Review

The paper aims to provide an overview of modern Russian and foreign theoretical and experimental sources on the study of semantic reading among students with health limitations in the setting of education digitalisation. The paper analyses the changes in the field of educational texts in the conditions of education digitalisation, presents an analysis of the results of modern research on semantic reading among students with health limitations, as well as a theoretical justification for the need to modify diagnostic tools for the study of semantic reading in schoolchildren with health limitations. Scientific novelty lies in carrying out a comprehensive analysis of the little-studied issue of perception and comprehension of texts of various types and formats in the process of reading by students with health limitations. As a result, it has been found that it is important for correctional pedagogy to update the tools for diagnostic assessment and elimination of difficulties in semantic reading, taking into account the conceptual modifications of the educational and methodological material used in teaching.