Reviewerly/exHarmony huggingface dataset:
A dual framework for implicit and explicit emotion recognition: An ensemble of language models and computational linguistics

One of the research domains in the field of sentiment analysis is automatic emotion recognition in texts which is a worthy topic in human-computer interaction. Text processing has always faced many challenges. The main one is the structural and semantic differences of sentences which have had a significant impact on the malfunction of auto-recognition systems. This problem becomes more prominent in short texts in which words and their concurrences are limited and insufficient. As a result of this, word frequency and TF-IDF weighing cannot well represent the relationship between words and the appropriate feature vector, leading to an undesirable accuracy of emotion recognition. Thus, different strategies should be applied to improve the feature vector and to formulate the features properly. The desired strategy should be able to identify the words that can distinguish between classes well and also to find the relationships between words and meaningful phrases using natural language processing concepts. In this paper, a combination of emotional models, categorical and hierarchical, are used for an emotional text recognition which could discover simultaneously explicit and implicit emotion in a short text. Our approach called DuFER, proposed a weighed method which improves the feature vector using language models and computational linguistics through applying a modified TF-IDF weighing to words as well as Maximum Likelihood Estimation weighing to expressions. Four implicit and explicit emotion datasets are used for the experiments. The results show that the accuracy of both implicit and explicit emotion recognition has increased and DuFER is actually the first successful dual framework in recognizing implicit and explicit emotions from text.
Silicon photonic neural network applications and prospects

Neural networks have enabled many applications in artificial intelligence and neuromorphic computing ranging from scientific computing, intelligent communications, security etc. Neural networks implemented in on digital platforms are limited in speed and energy efficiency. Neuromorphic (i.e., neuron-isomorphic) photonics aims to build processors in which optical hardware mimic neural networks in the brain. These processors promise orders of magnitude improvements in both speed and energy efficiency over purely digital electronic approaches. However, integrated optical neural networks are much smaller (hundreds of neurons) than electronic implementations (tens of millions of neurons). This raises a question: what are the applications where sub-nanosecond latencies and energy efficiency trump the sheer size of processor? We provide an overview of neuromorphic photonic systems and their real-world applications to machine learning and neuromorphic computing.
The same but different: A social semiotic analysis of website interactivity as discourse

The aim of this article is to explore website interactivity as discourse. Whereas the use of writing, images and layout in web design has been explored extensively, interactivity, that is, interactions between a web user and the website system, remains an underdeveloped area of discourse studies. To analyze interactivity as discourse, the article uses data from a research project on offline and online shopping for electronics, viewing the offline-online relationship as recontextualization in the sense that webshop interactivity (together with other design elements) represents and transforms in-store shopping actions. Using a methodology that combines analytical framework for interactive sites and approach to discourse analysis, the article maps cursor resources and interactive webshop features as actions and compares them to the actions that constitute in-store shopping. On this basis, the article offers reflections on how interactivity plays a defining role in the digital resemiotization of social practices.
Wall Size Prediction from 2D Images with the Help of Reference Objects

Digital image processing has been widely used in many applications in various science domains. Detection, recognition, extraction and tracking of an object can be given as examples of such applications on 2D images. In this study, we present an approach to predict the size of a wall in terms of width and height, on 2D images by using a reference object. The size of the reference object is already known beforehand and its size gives us hints to predict the size of the wall, which is called target object. The proposed approach has been tested and evaluated on a real world images and the performance results are presented at the end of the paper.
Application of cloud computing technology in computer network security storage

This article will start from the cloud computing technology, understand the current situation of modern network security management, aiming at the key technology of cloud computing technology in network security, and put forward effective and feasible security protection suggestions, strengthening the security of computer network storage. With the gradual popularization of the application of Computer Network Information Technology, cloud computing technology is now widely used in computer, Internet of things and other related fields, not only to facilitate people’s lives, but also to meet the needs of work, it makes the work more convenient, efficient and fast, but it also makes the network security risk increase constantly. Research the application of cloud computing technology in the computer network security storage, it is significant to reduce the network security risk of cloud computing technology application.
OLMNE+FT: Multiplex network embedding based on overlapping links

Network embedding or graph representation learning has recently attracted more researchers' attention and achieved state-of-the-art performance in many areas and tasks. Nevertheless, most of these methods are targeted for monolayer networks and ignore the multiplexity property of nodes which refers to the multifaceted relationships between two elements. Multiplexity provides multiple types of auxiliary information to refine the characteristics of nodes and can be modeled as a multiplex network. In this study, we propose a multiplex network embedding algorithm to learn a unique embedding for each node in each layer or each relation type. A biased path-dependency random walk strategy is adopted to generate node sequences for integrating different types of relations between nodes, which pays more attention to overlapping links and makes neighbor nodes in the sampling sequence more similar to each other. Then the skip-gram model is used to learn an overall embedding over node sequences. To strengthen the expressive power of the embedding in a specific layer, a fine tuning strategy with low time cost is employed to make the embedding comprise information of nodes at this particular layer and preserve their distinctive properties, and the unique embedding is achieved ultimately. To verify the effectiveness of our algorithms, we validate the performance of our algorithm and other baseline methods in the link prediction task. The results demonstrate that the learned embedding can capture the interlayer relationships and preserve the specific characteristics of nodes, and our algorithms can stably obtain better or comparable performance compared with other methods.
Highly Efficient and Revocable CP-ABE with Outsourcing Decryption for IoT

In IoT scenarios, computational and communication costs on the user side are important problems. In most expressive ABE schemes, there is a linear relationship between the access structure size and the number of heavy pairing operations that are used in the decryption process. This property limits the application of ABE. We propose an expressive CP-ABE with the constant number of pairings in the decryption process. The simulation shows that the proposed scheme is highly efficient in encryption and decryption processes. In addition, we use the outsourcing method in decryption to get better performance on the user side. The main burden of decryption computations is done by the cloud without revealing any information about the plaintext. We introduce a new revocation method. In this method, the users' communication channels aren't used during the revocation process. These features significantly reduce the computational and communication costs on the user side that makes the proposed scheme suitable for applications such as IoT. The proposed scheme is selectively CPA-secure in the standard model.
Generalization at Retrieval Using Associative Networks with Transient Weight Changes

Abstract Without having seen a bigram like “her buffalo”, you can easily tell that it is congruent because “buffalo” can be aligned with more common nouns like “cat” or “dog” that have been seen in contexts like “her cat” or “her dog”—the novel bigram structurally aligns with representations in memory. We present a new class of associative nets we call Dynamic-Eigen-Nets , and provide simulations that show how they generalize to patterns that are structurally aligned with the training domain. Linear-Associative-Nets respond with the same pattern regardless of input, motivating the introduction of saturation to facilitate other response states. However, models using saturation cannot readily generalize to novel, but structurally aligned patterns. Dynamic-Eigen-Nets address this problem by dynamically biasing the eigenspectrum towards external input using temporary weight changes. We demonstrate how a two-slot Dynamic-Eigen-Net trained on a text corpus provides an account of bigram judgment-of-grammaticality and lexical decision tasks, showing it can better capture syntactic regularities from the corpus compared to the Brain-State-in-a-Box and the Linear-Associative-Net. We end with a simulation showing how a Dynamic-Eigen-Net is sensitive to syntactic violations introduced in bigrams, even after the associations that encode those bigrams are deleted from memory. Over all simulations, the Dynamic-Eigen-Net reliably outperforms the Brain-State-in-a-Box and the Linear-Associative-Net. We propose Dynamic-Eigen-Nets as associative nets that generalize at retrieval, instead of encoding, through recurrent feedback.
Context-Aware Effective Communications

We investigate the effective communications problem, in which the goal of the transmitter is to impact the actions of the receiver over a time frame in order to maximize the prescribed reward function. This problem is formulated as a multi-agent partially-observable Markov decision process (MA-POMDP), where one agent can communicate to the other through a noisy communication channel. In this paper, we show that not only should the communication scheme be jointly designed with the underlying learning objective, but the context of the problem can also be exploited to achieve even greater effectiveness. Here, the context refers to a function of the state of the environment that is available to both agents. We then show that, using different communication schemes depending on the context is beneficial to the effectiveness of the solution. We emphasize that this is different from sending different messages at different states; with the proposed context-aware communication scheme, the same message is interpreted differently by the receiver depending on the context, similarly to human communications, where the meaning of a sentence may change depending on the context.
An Ensemble-based Outlier Detection Approach on Intrusion Detection

Outlier detection is becoming an utmost essential for data pre-processing and modeling. Commonly, the model cannot minimize the training errors during the learning process due to outliers in the training dataset. Several outlier detection approaches have been proposed to address a variety of problems in different disciplines. In this study, an ensemble-based outlier detection approach is proposed to detect the outliers on intrusion datasets. The proposed ensemble approach consists of a local outlier factor, isolation forest, and one-class SVM method to identify the outliers on intrusion datasets. The majority voting method is used to finalize the connection as an outlier by averaging the outlier indexes generated by the approach. The result of the proposed method is compared before and after detection of the outlier obtained from the popular supervised classification methods. Empirically, we have achieved a better classification result and drastically reduced the training error by eliminating the outliers during the learning process.
Social Distancing Violation Detection Using Pre-Trained Object Detection Models

The outbreak of COVID-19 pandemic has resulted in a devastating impact all around the world. The social distancing protocol has become the compulsory preventive measures in many countries for the purpose of avoiding physical contacts with other persons. This paper presents a drone based surveillance approach that uses Computer Vision and Deep Learning based techniques to check if two persons are violating social distancing norms. The implementation has been done using a Raspberry Pi based Drone where the camera captures the video from a height and detects the persons who are in near proximity with the help of an object detection algorithm and computes the distance between two persons in order to check whether they lie near to each other. The Euclidean distance between the two persons is calculated which is then compared with the given margin distance. If the distance is found to be below the margin., local authorities or law enforcement agencies can be notified through an automatically generated email alert.
An Approach to Converting Relational Database to Graph Database: from MySQL to Neo4j

At present, there are few research methods that can convert any relational database into a graph database, and most of them are based on a specific field data set to build a relational database, and then perform simple conversion through the characteristics of the data set.Aiming at this problem, a universal conversion method is proposed. Firstly, converted the most basic component tables name, records, and fields in the relational database into labels, nodes, and corresponding attributes of the nodes under the graph database; secondly, used the intermediate connection table method to convert the foreign keys in the relational database into the relationship of a graph database between the nodes; then some constraint issues in relational databases, such as multiple primary key issues, indexes, and no default values, were optimized to form a final graph database model that met expectations; finally, Realized the effective migration of large quantities of data in the relational database to the constructed graph database model. In the experiment, the above method was used to successfully convert a relational database to a graph database, and the database construction, data import, SQL query and Cypher language query were performed for the database before and after the conversion, and through the analysis and comparison of data integrity, time cost, result validity,which shows that the integrity and operability of the database before and after conversion are consistent, and the data processing efficiency of the database is much higher than that of the relational database, which verifies that the method in this paper is feasible.
Novel Training Methodology to Enhance Deep Learning Based Modulation Classification

Automatic Modulation Classification (AMC) is central to dynamic spectrum sensing. This work aims to improve the performance of deep learning (DL) models applied to AMC. Novel training methodology is introduced to improve performance. Over-the-air (OTA) data is collected using software-defined radio (SDR) over a range of modulation types and SNR levels. Collected dataset is partitioned into subsets across SNR levels. A group of identical models is trained on these multiple subsets and performance is compared against model trained on whole dataset. Efforts are taken to identify and isolate corrupted OTA data caused by interferences. Convolutional neural network (CNN) based architecture is used. We show an average improvement of 6% in the classifier’s performance on OTA data using this SNR partitioning approach.
Personalized Home BESS Recommender System Based on Neural Collaborative Filtering

Battery energy storage systems have been becoming increasingly prevalent in residential sector. With more and more home battery energy storage system (HBESS) products appeared in the market, it would be difficult for the users to choose the most suitable battery product from the market. This paper proposes a personalized HBESS recommender system that provides decision-making support to residential users by recommending suitable HBESSs to them. The system integrates a neural collaborative filtering technique, which uses a General Matrix Factorization (GMF) model and a Multi-Layer Perceptron (MLP) neural network to infer the target user’s preferences on different HBESS products through analyzing the preference trends of a group of the target user’s similar users on a set of HBESS products. Based on this, the system generated a recommendation list of HBESS products to the target user. Numerical simulations based on real-world data are conducted to validate the effectiveness of the proposed system.
Semi-supervised Nonnegative Matrix Factorization for Document Classification

We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators. The proposed SSNMF models simultaneously provide both a topic model and a model for classification, thereby offering highly interpretable classification results. We derive training methods using multiplicative updates for each new model, and demonstrate the application of these models to single-label and multi-label document classification, although the models are flexible to other supervised learning tasks such as regression. We illustrate the promise of these models and training methods on document classification datasets (e.g., 20 Newsgroups, Reuters).
Towards Intelligent Legal Advisors for Document Retrieval and Question-Answering in German Legal Documents

The legal system is one of the most important pillars of human society. While digitization is integrated into many areas of everyday life, the legal system is still very traditionally positioned. Recent advances in storing and processing large number of documents initiated the work of intelligent legal advisors. While first approaches in the English language suggest an enormous potential to generate knowledge from legal documents, there are no approaches in the German legal system. We present an intelligent legal advisor based on semantic document retrieval, to improve knowledge extraction from German legal documents. In addition, we set up a question-answering system. We implemented a BERT and a BM25-model for German document retrieval in legal documents. The approach is validated on a data set consisting of German question-answer pairs.
Digital Moral Outrage, Collective Guilt, And Collective Action: An Examination of How Twitter Users Expressed Their Anguish During India's Covid-19 Related Migrant Crisis

This paper examines the intersection of group-based expressions including digital moral outrage, collective guilt, and collective action on Twitter, following the tragic incident of 8 May 2020, in which 16 migrant workers were run over by a train after the Indian government imposed a sudden COVID-19-related lockdown. Twitter data were gathered immediately at three different times - May 8-15, May 16- 23, May 24-May 31, and 4598 tweets were manually coded. The analysis revealed that digital moral outrage was the most frequently expressed emotion. It, however, gradually decreased, signaling digital outrage fatigue. Collective guilt and sympathy constituted the second-largest portion of the total tweets, and tweets reflecting collective action by the community progressively increased. The network of relationships among different group-based emotions, the promotion of one-sided narratives and virtue signaling on social media platforms are discussed.
Coupling X-ray Absorption and Raman Spectroscopies to Characterize Iron Species in a Karst Pedosedimentary Record

The identification of iron (Fe) forms throughout a sediment sequence was investigated by X-ray Absorption Near Edge Spectroscopy (XANES) and Extended X-ray Absorption Fine Structure (EXAFS) at the Fe K-edge, paired with Raman micro-spectroscopy. The contribution of different organic and inorganic Fe-bearing compounds was quantified by Linear Combination Fitting (LCF) carried out on both XANES and EXAFS spectra. Fe-XANES showed that the Fe(II)/Fe(III) ratio of different Fe-bearing minerals in sediments can be quantified with reasonable accuracy. The main Fe species detected were ferrihydrite, goethite, hematite, clay minerals (smectite, illite, nontronite), and Fe(III)-organic matter (Fe(III)-OM). A more accurate quantification of ferrihydrite was possible with LCF conducted on Fe-EXAFS spectra. With the exception of hematite, the concentration of these mineral species does not have a clear trend with depth, probably because water infiltration caused continuous Fe reduction and oxidation cycles in these sediments. From an analytical perspective, Fe oxide compounds can be difficult to identify or distinguish unless multiple techniques are used. X-ray diffraction (XRD; previous work) and Raman spectroscopy turn out to be not particularly useful in identifying ferrihydrite, while they are best suited for a broad mineralogical analysis that requires integrative spectral studies for an accurate Fe speciation. In detail, XANES and EXAFS allowed for the detection of Fe-bearing clay minerals and a more refined identification of Fe species, including Fe(III)-OM. Thermal analysis was useful to confirm some mineralogical components observed using both XRD (data previously published) and Raman spectroscopy (e.g., goethite, todorokite). In conclusion, this study underlines how a multi-technique approach is required to investigate peculiar environments such as karst pedosequences.
A Study on Application Category Estimation Method Based on Burst Characteristics of Communication

As traffic on the Internet continues to increase, quality requirements are also diversifying with new applications that require lower latency and higher reliability, for example, in e-sports, industrial IoT, connected cars, and so on. To effectively use network resources, we must appropriately set quality targets for each communication flow. Application estimation is one key technology for resource control based on quality of service (QoS) requirements. However, in recent years, communication has been encrypted for security, making it difficult to estimate the applications of flows by payload analysis, such as deep packet inspection. For encrypted traffic, application estimation methods have been researched using just the observable information about IP addresses, port numbers, packet sizes, protocols, and packet arrival times. In this paper, we go further in this direction by focusing on burst features, which are a collection of packets with short arrival intervals, and show that application categories can be estimated for each flow, even from encrypted traffic. When one application transmits such different types of data as audio, video, and control signals to their dedicated flow, application categories can be identified for each flow that corresponds to its type of transferred data. We demonstrated the effectiveness of our method with actual encrypted Internet traffic.