Reviewerly/exHarmony huggingface dataset:
Automatic Categorization of LGBT User Profiles on Twitter with Machine Learning

Privacy needs and stigma pose significant barriers to lesbian, gay, bisexual, and transgender (LGBT) people sharing information related to their identities in traditional settings and research methods such as surveys and interviews. Fortunately, social media facilitates people’s belonging to and exchanging information within online LGBT communities. Compared to heterosexual respondents, LGBT users are also more likely to have accounts on social media websites and access social media daily. However, the current relevant LGBT studies on social media are not efficient or assume that any accounts that utilize LGBT-related words in their profile belong to individuals who identify as LGBT. Our human coding of over 16,000 accounts instead proposes the following three categories of LGBT Twitter users: individual, sexual worker/porn, and organization. This research develops a machine learning classifier based on the profile and bio features of these Twitter accounts. To have an efficient and effective process, we use a feature selection method to reduce the number of features and improve the classifier’s performance. Our approach achieves a promising result with around 88% accuracy. We also develop statistical analyses to compare the three categories based on the average weight of top features.
The Study of Three-Dimensional Fingerprint Recognition in Cultural Heritage

Fingerprints play a central role in any field where person identification is required. In forensics and biometrics, three-dimensional fingerprint-based imaging technologies, and corresponding recognition methods, have been vastly investigated. In cultural heritage, preliminary studies provide evidence that the three-dimensional impressions left on objects from the past (ancient fingerprints) are of paramount relevance to understand the socio-cultural systems of former societies, to possibly identify a single producer of multiple potteries, and to authenticate the artist of a sculpture. These findings suggest that the study of ancient fingerprints can be further investigated and open new avenues of research. However, the potential for capturing and analyzing ancient fingerprints is still largely unexplored in the context of cultural heritage research. In fact, most of the existing studies have focused on plane fingerprint representations and commercial software for image processing. Our aim is to outline the opportunities and challenges of digital fingerprint recognition in answering a range of questions in cultural heritage research. Therefore, we summarize the fingerprint-based imaging technologies, reconstruction methods, and analyses used in biometrics that could be beneficial to the study of ancient fingerprints in cultural heritage. In addition, we analyze the works conducted on ancient fingerprints from potteries and ceramic/fired clay sculptures. We conclude with a discussion on the open challenges and future works that could initiate novel strategies for ancient fingerprint acquisition, digitization, and processing within the cultural heritage community.
Learning from class-imbalanced data: review of data driven methods and algorithm driven methods

<abstract> As an important part of machine learning, classification learning has been applied in many practical fields. It is valuable that to discuss class imbalance learning in several fields. In this research, we provide a review of class imbalanced learning methods from the data driven methods and algorithm driven methods based on numerous published papers which studied class imbalance learning. The preliminary analysis shows that class imbalanced learning methods mainly are applied both management and engineering fields. Firstly, we analyze and then summarize resampling methods that are used in different stages. Secondly, we provide a detailed instruction on different algorithms, and then we compare the results of decision tree classifiers based on resampling and empirical cost sensitivity. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the class imbalanced learning fields. </abstract>
The Application of Big Data in Enterprise Information Intelligent Decision-Making

With the continuous increase of mass information in the process of enterprise operation, information redundancy interference poses a challenge to enterprise information decision-making. Therefore, this paper applies big data analysis technology to enterprise information intelligent decision-making, and builds an enterprise information intelligent decision-making model based on big data analysis. The key data of enterprise is mined by using the density weight Canopy to improve the K-Gmedoids algorithm. After sorting, filtering, and transforming, the big data model designed in this paper uses interactive genetic algorithms to obtain the optimal decision-making strategy of enterprise information through experimental tests, which has a significant impact on the decision-making management and the competitiveness of the enterprise. In the process of enterprise information collection and intelligent decision-making, the interactive genetic algorithm generates a 95% match between the optimal business operation plan and the problems that need to be solved in the actual operation of the enterprise, which can better improve the ability of the enterprise to deal with problems. The number of iterations of the optimal decision-making plan obtained by the company is only 6 times, which has a good use effect while improving work efficiency.
Hybrid Movie Recommendation System Using Machine Learning

Every year new movies are released with a varied story-line or a genre which could be of potential interest to viewers. Various online movie or video streaming platforms can keep the customers engaged by recommending movies of the viewer's preference. A key research challenge for Recommender engines is make more targeted recommendations. This paper presents Filtering approaches including Content-based, which recommends items (movies) to the user (viewer) based on their previous history/ preferences and Collaborative-based which uses opinions and actions of other similar users (viewers) to recommend items (movies). In Collaborative filtering, User-based, Item based, SVD, and SVD++ algorithms have been implemented and the performance evaluated. Finally, a hybrid recommendation engine that stacks both the Content-based and SVD filtering models is shown to have optimal performance and improved movie recommendations to retain active viewer engagement with the service.
Research on the Application of Artificial Intelligence in Image Recognition Technology

Abstract Artificial intelligence is gradually widely used in our country, and with the development of the information age, the image recognition technology derived from artificial intelligence is also gradually known to people. The main principle of image recognition technology is the substitution of computer to artificial. These working principles can not be separated from the traction function of artificial intelligence, so as to realize many processing and judging of data information. Under the trend of gradual development of computer technology, the security and process of recognition data should be analyzed under the continuous extension of image recognition technology. In this paper, the image recognition technology in artificial intelligence will be analyzed and studied in depth, and the image recognition technology will be briefly summarized.
To click or not to click the link: the factors influencing internet banking users’ intention in responding to phishing emails

Purpose Despite internet banking’s popularity, there is a rise in phishing attacks related to online banking transactions. Phishing attacks involved the process of sending out electronic mails impersonating the valid banking institutions to their customers and demanding confidential data such as credential and transaction authorisation code. The purpose of this paper is to propose a theoretical model of individual and technological factors influencing Malaysian internet banking users’ intention in responding to malicious uniform resource locator (URL) in phishing email content. Design/methodology/approach It applied the protective motivation theory, the theories of reasoned action and planned behaviour, the habit theory and the trust theory to examine the factors influencing internet banking users’ intention to click URLs in phishing emails. The study identifies individual and technological factors with ten hypotheses. A total of 368 Malaysian respondents voluntarily participated in an online survey conducted in the first week of March 2021. The partial least squares method provided in SmartPLS-3 was used to model the data. Findings The results revealed that individual factors, namely, internet banking experience, understanding the phishing meaning, response cost, trust and perceived ability were the significant influencing factors of internet banking users’ intention to click the link in phishing emails. This study also suggested that technological factors were not relevant in describing the behavioural intention of internet banking users in clicking the links in phishing emails. Social implications The findings could contribute to Malaysian banking sectors and relevant government agencies in educating and increasing internet banking users’ awareness towards phishing emails. Originality/value The outcomes demonstrated the individual factors that influenced internet banking users’ intention in responding to phishing emails that are specific and relevant to Malaysia’s context.
Enabling Artificial Intelligence Adoption through Assurance

The wide scale adoption of Artificial Intelligence (AI) will require that AI engineers and developers can provide assurances to the user base that an algorithm will perform as intended and without failure. Assurance is the safety valve for reliable, dependable, explainable, and fair intelligent systems. AI assurance provides the necessary tools to enable AI adoption into applications, software, hardware, and complex systems. AI assurance involves quantifying capabilities and associating risks across deployments including: data quality to include inherent biases, algorithm performance, statistical errors, and algorithm trustworthiness and security. Data, algorithmic, and context/domain-specific factors may change over time and impact the ability of AI systems in delivering accurate outcomes. In this paper, we discuss the importance and different angles of AI assurance, and present a general framework that addresses its challenges.
Patient-Level Cancer Prediction Models From a Nationwide Patient Cohort: Model Development and Validation

Background: Nationwide population-based cohorts provide a new opportunity to build automated risk prediction models at the patient level, and claim data are one of the more useful resources to this end. To avoid unnecessary diagnostic intervention after cancer screening tests, patient-level prediction models should be developed.
Integrating Dialog History into End-to-End Spoken Language Understanding Systems

End-to-end spoken language understanding (SLU) systems that process human-human or human-computer interactions are often context independent and process each turn of a conversation independently. Spoken conversations on the other hand, are very much context dependent, and dialog history contains useful information that can improve the processing of each conversational turn. In this paper, we investigate the importance of dialog history and how it can be effectively integrated into end-to-end SLU systems. While processing a spoken utterance, our proposed RNN transducer (RNN-T) based SLU model has access to its dialog history in the form of decoded transcripts and SLU labels of previous turns. We encode the dialog history as BERT embeddings, and use them as an additional input to the SLU model along with the speech features for the current utterance. We evaluate our approach on a recently released spoken dialog data set, the HarperValleyBank corpus. We observe significant improvements: 8% for dialog action and 30% for caller intent recognition tasks, in comparison to a competitive context independent end-to-end baseline system.
Deep sequence to sequence learning-based prediction of major disruptions in ADITYA tokamak

Major disruptions in tokamak plasmas need to be identified well before their occurrence and appropriately mitigated. Otherwise, it may dump the heat and electromagnetic load to the vessel and its surrounding plasma-facing components. A predictor system based on precursor diagnostics may help in forecasting the disruptive events in tokamak plasma and raise the alert beforehand to take necessary actions to prevent the major damages inside the vacuum vessel. This paper describes a predictor system built with a few selected diagnostic signals from the ADITYA tokamak and trained on a time-sequence long short-term memory network to predict the occurrence of disruption to 7–20 ms in advance with an accuracy of 89% on the testing set of 36 disruptive and 6 non-disruptive shots. This real-time network can infer to one time-step results under 170 µs on an Intel Xeon processor running python, suggesting minimal computation cost and best suited for the real-time plasma control applications.
A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion

We propose a new paradigm for maintaining speaker identity in dysarthric voice conversion (DVC). The poor quality of dysarthric speech can be greatly improved by statistical VC, but as the normal speech utterances of a dysarthria patient are nearly impossible to collect, previous work failed to recover the individuality of the patient. In light of this, we suggest a novel, two-stage approach for DVC, which is highly flexible in that no normal speech of the patient is required. First, a powerful parallel sequence-to-sequence model converts the input dysarthric speech into a normal speech of a reference speaker as an intermediate product, and a nonparallel, frame-wise VC model realized with a variational autoencoder then converts the speaker identity of the reference speech back to that of the patient while assumed to be capable of preserving the enhanced quality. We investigate several design options. Experimental evaluation results demonstrate the potential of our approach to improving the quality of the dysarthric speech while maintaining the speaker identity.
Closed-form expressions and analysis for the slumping effect of a cuboid in the scattering characteristics of quantum radar

The closed-form expressions of a monostatic quantum radar cross-section (QRCS) for a cuboid under illumination from single photon pulses have been successfully derived. An omnidirectional three-dimensional image of the QRCS is given for the first time using the derived expressions. The simulation results for the expressions are consistent with the numerical calculations, which validate the expressions. The simulation results indicate that the QRCS may have a new slumping effect of different degrees near the special visual angle. The slumping effect means that when the irradiation direction of photon pulses deviates from the vertical direction of the cuboid panel, the QRCS in the main lobe direction drops significantly. We then use the expressions to determine the quantitative relationship between the observed slumping effect and the number of illuminated atoms. In addition, the quantum effect is suppressed (amplified) on the electrically large (small) panel side. This new slumping effect provides good design concepts for quantum stealth aircraft.
Complexity Analysis of Generalized and Fractional Hypertree Decompositions

Hypertree decompositions (HDs), as well as the more powerful generalized hypertree decompositions (GHDs), and the yet more general fractional hypertree decompositions (FHDs) are hypergraph decomposition methods successfully used for answering conjunctive queries and for solving constraint satisfaction problems. Every hypergraph $H$ has a width relative to each of these methods: its hypertree width $hw(H)$, its generalized hypertree width $ghw(H)$, and its fractional hypertree width $fhw(H)$, respectively. It is known that $hw(H)\leq k$ can be checked in polynomial time for fixed $k$, while checking $ghw(H)\leq k$ is NP-complete for $k \geq 3$. The complexity of checking $fhw(H)\leq k$ for a fixed $k$ has been open for over a decade. We settle this open problem by showing that checking $fhw(H)\leq k$ is NP-complete, even for $k=2$. The same construction allows us to prove also the NP-completeness of checking $ghw(H)\leq k$ for $k=2$. After that, we identify meaningful restrictions which make checking for bounded $ghw$ or $fhw$ tractable or allow for an efficient approximation of the $fhw$.
The effects of VR in training simulators: Exploring perception and knowledge gain

Although immersive virtual environments have been used for years for training and learning purposes (e.g., flight and surgery simulators), the effects of using VR devices on simulation sessions are yet to be understood. In this work, we explore the effects of different VR devices on virtual environments developed for training, focusing on perception and knowledge gain aspects. We performed two user studies to investigate the influence of these devices on users’ workload, motion sickness, and performance in the domain of work safety training. The first experiment included 61 participants and sought to understand whether and how VR displays providing different fields of view affects the users’ ability to search for risks in an office-like virtual environment (focus on user perception). Subsequently, we conducted a second experiment involving 46 subjects, where we assess whether and how interaction techniques providing different degrees-of-freedom influence users’ ability to learn procedural tasks (focus on knowledge gain). From our results, we learned that users’ knowledge on the simulation’s topic (i.e. work safety) and gaming experience play an important role in VR simulations, and that cybersickness symptoms such as disorientation are likely caused by unawareness of one’s surroundings instead of VR content.
On the Security of Smartphone Unlock PINs

In this article, we provide the first comprehensive study of user-chosen four- and six-digit PINs ( n =1705) collected on smartphones with participants being explicitly primed for device unlocking. We find that against a throttled attacker (with 10, 30, or 100 guesses, matching the smartphone unlock setting), using six-digit PINs instead of four-digit PINs provides little to no increase in security and surprisingly may even decrease security. We also study the effects of blocklists, where a set of “easy to guess” PINs is disallowed during selection. Two such blocklists are in use today by iOS, for four digits (274 PINs) as well as six digits (2,910 PINs). We extracted both blocklists and compared them with six other blocklists, three for each PIN length. In each case, we had a small (four-digit: 27 PINs; six-digit: 29 PINs), a large (four-digit: 2,740 PINs; six-digit: 291,000 PINs), and a placebo blocklist that always excluded the first-choice PIN. For four-digit PINs, we find that the relatively small blocklist in use today by iOS offers little to no benefit against a throttled guessing attack. Security gains are only observed when the blocklist is much larger. In the six-digit case, we were able to reach a similar security level with a smaller blocklist. As the user frustration increases with the blocklists size, developers should employ a blocklist that is as small as possible while ensuring the desired security. Based on our analysis, we recommend that for four-digit PINs a blocklist should contain the 1,000 most popular PINs to provide the best balance between usability and security and for six-digit PINs the 2,000 most popular PINs should be blocked.
FLiText: A Faster and Lighter Semi-Supervised Text Classification with Convolution Networks

In natural language processing (NLP), stateof-the-art (SOTA) semi-supervised learning (SSL) frameworks have shown great performance on deep pre-trained language models such as BERT, and are expected to significantly reduce the demand for manual labeling.However, our empirical studies indicate that these frameworks are not suitable for lightweight models such as TextCNN, LSTM and etc.In this work, we develop a new SSL framework called FLiText, which stands for Faster and Lighter semi-supervised Text classification.FLiText introduces an inspirer network together with the consistency regularization framework, which leverages a generalized regular constraint on the lightweight models for efficient SSL.As a result, FLiText obtains new SOTA performance for lightweight models across multiple SSL benchmarks on text classification.Compared with existing SOTA SSL methods on TextCNN, FLiText improves the accuracy of lightweight model TextCNN from 51.00% to 90.49% on IMDb, 39.8% to 58.06% on Yelp-5, and from 55.3% to 65.08% on Yahoo.In addition, compared with the fully supervised method on the full dataset, FLi-Text just uses less than 1% of labeled data to improve the accuracy by 6.59%, 3.94%, and 3.22% on the datasets of IMDb, Yelp-5, and Yahoo respectively.
Path planning in an unknown environment based on deep reinforcement learning with prior knowledge

Path planning in an unknown environment is a basic task for mobile robots to complete tasks. As a typical deep reinforcement learning, deep Q-network (DQN) algorithm has gained wide popularity in path planning tasks due to its self-learning and adaptability to complex environment. However, most of path planning algorithms based on DQN spend plenty of time for model training and the learned model policy depends only on the information observed by sensors. It will cause poor generalization capability for the new task and time waste for model retraining. Therefore, a new deep reinforcement learning method combining DQN with prior knowledge is proposed to reduce training time and enhance generalization capability. In this method, a fuzzy logic controller is designed to avoid the obstacles and help the robot avoid blind exploration for reducing the training time. A target-driven approach is used to address the lack of generalization, in which the learned policy depends on the fusion of observed information and target information. Extensive experiments show that the proposed algorithm converges faster than DQN algorithm in path planning tasks and the target can be reached without retraining when the path planning task changes.