Reviewerly/exHarmony huggingface dataset:
ContextWing: Pair-wise Visual Comparison for Evolving Sequential Patterns of Contexts in Social Media Data Streams

Understanding and comparing the evolution of public opinions on a social media event is important. However, such a task requires summarizing rich semantic information and an in-depth comparison of semantics and dynamics at the same time, which is difficult for the analysis. To tackle these challenges, we propose ContextWing, an interactive visual analytics system to support pair-wise comparison for evolving sequential patterns of contexts between two data streams. The computational model of ContextWing generates dynamic topics and sequential patterns, and characterizes public attention and pair-wise correlations. A novel multi-layer bilateral wing metaphor is designed to intuitively visualizes sequential patterns merged by different contexts to reveal the similarities and differences in both temporal and semantic aspects between two streams. Interactive tools support the selection of a central keyword and its contexts to iteratively generate patterns for a focused exploration. The system supports analysis on both static and streaming settings that enables a wider range of application scenarios. We verify the effectiveness and usability of ContextWing from multiple facets, including three case studies, two expert interviews, and a user study.
Hate Raids on Twitch: Echoes of the Past, New Modalities, and Implications for Platform Governance

In the summer of 2021, users on the livestreaming platform Twitch were targeted by a wave of "hate raids," a form of attack that overwhelms a streamer's chatroom with hateful messages, often through the use of bots and automation. Using a mixed-methods approach, we combine a quantitative measurement of attacks across the platform with interviews of streamers and third-party bot developers. We present evidence that confirms that some hate raids were highly-targeted, hate-driven attacks, but we also observe another mode of hate raid similar to networked harassment and specific forms of subcultural trolling. We show that the streamers who self-identify as LGBTQ+ and/or Black were disproportionately targeted and that hate raid messages were most commonly rooted in anti-Black racism and antisemitism. We also document how these attacks elicited rapid community responses in both bolstering reactive moderation and developing proactive mitigations for future attacks. We conclude by discussing how platforms can better prepare for attacks and protect at-risk communities while considering the division of labor between community moderators, tool-builders, and platforms.
"I Have to Use My Son's QR Code to Run the Business": Unpacking Senior Street Vendors' Challenges in Mobile Money Collection in China

Mobile payment systems have become an infrastructural component in citizens' socio-economic life in China. The rapid shift to a cashless society demands vendors of all ages to quickly adapt themselves to the ubiquitous mobile payment era. However, how this trend may impact senior vendors, a group that typically uses less technology, remains unknown despite its significance in inclusive mobile payment design. This work aims to address this gap by investigating the challenges and strategies of senior vendors in mobile payment adoption. Particularly, we focus on a traditional low-resource setting with a large volume of senior vendors: street vending. We conduct a qualitative study incorporating field observations on 33 senior street vendors and semi-structured interviews with 15 of them (aged 53-78), and take Moneywork as an analytical lens to unpack their challenges in physical and social interactions. We find that senior street vendors are a group passively adopting mobile payments due to business requirements instead of recognizing their advantages. Vendors with relatively low digital literacy have to take an alternative method - using family members' QR codes - to run the business as family-dependent money receivers. With limited considerations for senior vendors' situational vulnerabilities, unexpected difficulties of payment confirmation emerge during transactions, such as reduced confirmation efficacy under noisy surroundings and degraded hearing. Transaction security issues also appear when mobile payment-based frauds target both confirmation interfaces (e.g., fake sounds of successful payment) and trust systems (e.g., showing half-done proof to flee without paying) in street vending. Finally, we raise a less visible yet critical concern of family-dependent vendors on the lost money freedom when their income flows into their families' wallets. We propose design implications for mobile payment systems to support more secure, efficient and accessible money collection services to underrepresented groups.
Resource Utilization Tracking for Fine-Tuning Based Event Detection and Summarization Over Cloud

A huge volume of data generated by the video surveillance creates a variety of problems when utilized for different purposes such as video indexing, retrieval, analysis and summarization. The exponential growth of video content needs an effective video summarization which can help in efficient indexing and retrieval of data. The video summariza-tion is a challenging task due to huge amounts of data, redundancy, interview correlation and lighting variation. To overcome these problems, the proposed work presents object of interest-based event detection summa-rization using YOLOv5 which first detects the target object from the extracted frames and discards the frames which have no objects. The frames with the target objects then used for the creation of video sum-mary. The proposed method can detect the single and multiple objects in the frames and capable to detect the objects directly in the videos. This study also compared the different versions of the Yolov5 (YoloVs, YoloVm, YoloVl and YoloVn) against the resource utilization during the model training over cloud. The experimental study shows the better performance of our approach compared in terms of precision, recall and resource utilization to the state-of-the-art approaches.
Trend Prediction in Finance Based on Deep Learning Feature Reduction

One of the main features of Deep Learning is to encode the information content of a complex phenomenon in a latent representation space. This represents an element of definite interest in Finance, as it allows time series data to be compressed into a smaller feature space. Among the different models that are used to accomplish this task are Restricted Boltzmann Machines (RBM) and Auto-Encoders (AE). In this paper we present a preliminary comparative study in the use of these techniques in predicting the trend of time series finance. We attempt to outline the impact of architectural and input space characteristics have on the quality of prediction.
Analysis of Bangla Transformation of Sentences Using Machine Learning

In many languages, various language processing tools have been developed. The work of the Bengali NLP is getting richer day by day. Sentence pattern recognition in Bangla is a subject of attention. Additionally, our motivation was to work on implementing this pattern recognition concept into user-friendly applications. So, we generated an approach where a sentence (sorol, jotil and jougik) can be correctly identified. Our model accepts a Bangla sentence as input, determines the sentence construction type, and outputs the sentence type. The most popular and well-known six supervised machine learning algorithms were used to classify three types of sentence formation: Sorol Bakko (simple sentence), Jotil Bakko (complex sentence) and Jougik Bakko(compound sentence). We trained and tested our dataset, which contains 2727 numbers of data from various sources. We analyzed our dataset and got accuracy, precision, recall, f1-score and confusion matrix. We get the highest accuracy with the decision tree classifier, which is 93.72%.
Bi-RNN and Bi-LSTM Based Text Classification for Amazon Reviews

Electronic Commerce (E-Commerce) enables the effective implementation of product-based online business transactions. In this paper, we categorize the dataset consisting of Amazon reviews into positive and negative. Consumers are able to choose a product solely on these binary text categorizations, neglecting the manual rating provided in the reviews. Recently, Deep Learning (DL) approaches started gaining popularity in E-Commerce applications. DL Techniques offers a powerful and effective approach for data analysis through filtering humongous E-Commerce data and finding hidden patterns and valuable details. Here, the research focuses on investing DL techniques for categorizing the text into positive and negative reviews. In this paper, we implemented two different DL approaches and found the accuracy of both approaches. DL models used here are bidirectional recurrent neural networks (Bi-RNN), and bidirectional long-short-term memory (Bi-LSTM). The accuracy obtained with Bi-RNN and Bi-LSTM is 89.60% and 92.80% respectively. The result shows that Bi-LSTM performs best on the Amazon review dataset.
MEVSS: Modulo Encryption Based Visual Secret Sharing Scheme for Securing Visual Content

In recent years, there has been a rapid development in the digital world of image communication over unsecured networks. Many domains, including military, medical, government, and education, require transmitting sensitive data through images. Hence, it raises the primary concern of its secrecy with the advancement of technologies. Traditional methods such as Advanced Encryption Standard (AES) and Data Encryption Standard (DES) have been widely used for image security. However, key management is the main concern in protecting image data. This work uses Visual Secret Sharing Scheme (VSS) or Visual Cryptography (VC) to handle the above mentioned issue. Modulo encryption-based VSS scheme (MEVSS) is proposed to encrypt the secret image shared among n number of users. The secret image is recovered loss-less by XORing n shares followed by modulo decryption. Experimental results show that the proposed technique outperforms the exiting techniques Binary tree-based Secret Sharing Scheme and RSA-based VSS in Unified average changing intensity (UACI) and Number of Pixel Change Rate (NPCR), While, the proposed model outperforms the existing techniques in term of Pixel to Signal Noise Ratio (PSNR). Moreover, it is faster than traditional techniques as it works directly on color image without converting it into another form like binary or grayscale.
Upaya Meningkatkan Hasil Belajar Matematika dengan Metode Penugasan Siswa Kelas VB SD Negeri Model Mataram

The purpose of this study was to determine the increase in learning outcomes of Mathematics with the Assignment Method for Class V.B SD Negeri Mataram Model Even Semester for the 2020/2021 Academic Year. The subjects of this study were students of class V.B SD Negeri Model Mataram, totaling 20 people, consisting of 7 female students and 13 male students. The stages of this research include: (1) planning, (2) implementing actions, (3) observing, and (4) reflecting in each cycle. The results of the study show that the learning outcomes of class VB students at the Mataram Model State Elementary School for the 2020/2021 academic year, using the assignment method can be improved with the final result of the study being the average grade before the action 75.85 or classical completeness 65.00%, to 77 .70 or 75.00% classical completeness in the first cycle, increased to an average of 82.45 or 80.00% in the second cycle and increased to a test average of 91.65 or 100% in the third cycle. From the results of student observations showed that there was an increase in student activity which in the first cycle obtained a score of 18 or 64.29% to 21 or 75.00% in the second cycle and 25 or 89.29% in the third cycle. Increased student activities in learning Mathematics are supported by increased teacher activities in maintaining and improving the learning atmosphere using the assignment method, this can be seen from the observations of teacher activities where in the first cycle a score of 23 or 63.89% was obtained, to 28 or 77.78 % in the second cycle and increased to 32 or 88.89% in the third cycle.
Type-safe Quantum Programming in Idris

Abstract Variational Quantum Algorithms are hybrid classical-quantum algorithms where classical and quantum computation work in tandem to solve computational problems. These algorithms create interesting challenges for the design of suitable programming languages. In this paper we introduce Qimaera, which is a set of libraries for the Idris 2 programming language that enable the programmer to implement hybrid classical-quantum algorithms where the full power of the elegant Idris language works in synchrony with quantum programming primitives. The two key ingredients of Idris that make this possible are (1) dependent types which allow us to implement unitary quantum operations; and (2) linearity which allows us to enforce fine-grained control over the execution of quantum operations so that we may detect and reject many physically inadmissible programs. We also show that Qimaera is suitable for variational quantum programming by providing implementations of two prominent variational quantum algorithms – QAOA and VQE.
Contextual Modal Type Theory with Polymorphic Contexts

Abstract Modal types—types that are derived from proof systems of modal logic—have been studied as theoretical foundations of metaprogramming, where program code is manipulated as first-class values. In modal type systems, modality corresponds to a type constructor for code types and controls free variables and their types in code values. Nanevski et al. have proposed contextual modal type theory , which has modal types with fine-grained information on free variables: modal types are explicitly indexed by contexts —the types of all free variables in code values. This paper presents $$\lambda _{\forall []}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>λ</mml:mi> <mml:mrow> <mml:mo>∀</mml:mo> <mml:mo>[</mml:mo> <mml:mo>]</mml:mo> </mml:mrow> </mml:msub> </mml:math> , a novel extension of contextual modal type theory with parametric polymorphism over contexts . Such an extension has been studied in the literature but, unlike earlier proposals, $$\lambda _{\forall []}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>λ</mml:mi> <mml:mrow> <mml:mo>∀</mml:mo> <mml:mo>[</mml:mo> <mml:mo>]</mml:mo> </mml:mrow> </mml:msub> </mml:math> is more general in that it allows multiple occurrence of context variables in a single context. We formalize $$\lambda _{\forall []}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>λ</mml:mi> <mml:mrow> <mml:mo>∀</mml:mo> <mml:mo>[</mml:mo> <mml:mo>]</mml:mo> </mml:mrow> </mml:msub> </mml:math> with its type system and operational semantics given by $$\beta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>β</mml:mi> </mml:math> -reduction and prove its basic properties including subject reduction, strong normalization, and confluence. Moreover, to demonstrate the expressive power of polymorphic contexts, we show a type-preserving embedding from a two-level fragment of Davies’ $$\lambda _{\bigcirc }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>λ</mml:mi> <mml:mo>◯</mml:mo> </mml:msub> </mml:math> , which is based on linear-time temporal logic, to $$\lambda _{\forall []}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>λ</mml:mi> <mml:mrow> <mml:mo>∀</mml:mo> <mml:mo>[</mml:mo> <mml:mo>]</mml:mo> </mml:mrow> </mml:msub> </mml:math> .
System $$F^\mu _\omega $$ with Context-free Session Types

Abstract We study increasingly expressive type systems, from $$F^\mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>F</mml:mi> <mml:mi>μ</mml:mi> </mml:msup> </mml:math> —an extension of the polymorphic lambda calculus with equirecursive types—to $$F^{\mu ;}_\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>F</mml:mi> <mml:mi>ω</mml:mi> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>;</mml:mo> </mml:mrow> </mml:msubsup> </mml:math> —the higher-order polymorphic lambda calculus with equirecursive types and context-free session types. Type equivalence is given by a standard bisimulation defined over a novel labelled transition system for types. Our system subsumes the contractive fragment of $$F^\mu _\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>F</mml:mi> <mml:mi>ω</mml:mi> <mml:mi>μ</mml:mi> </mml:msubsup> </mml:math> as studied in the literature. Decidability results for type equivalence of the various type languages are obtained from the translation of types into objects of an appropriate computational model: finite-state automata, simple grammars and deterministic pushdown automata. We show that type equivalence is decidable for a significant fragment of the type language. We further propose a message-passing, concurrent functional language equipped with the expressive type language and show that it enjoys preservation and absence of runtime errors for typable processes.
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing

Abstract We study the foundations of variational inference, which frames posterior inference as an optimisation problem, for probabilistic programming. The dominant approach for optimisation in practice is stochastic gradient descent. In particular, a variant using the so-called reparameterisation gradient estimator exhibits fast convergence in a traditional statistics setting. Unfortunately, discontinuities, which are readily expressible in programming languages, can compromise the correctness of this approach. We consider a simple (higher-order, probabilistic) programming language with conditionals, and we endow our language with both a measurable and a smoothed (approximate) value semantics. We present type systems which establish technical pre-conditions. Thus we can prove stochastic gradient descent with the reparameterisation gradient estimator to be correct when applied to the smoothed problem. Besides, we can solve the original problem up to any error tolerance by choosing an accuracy coefficient suitably. Empirically we demonstrate that our approach has a similar convergence as a key competitor, but is simpler, faster, and attains orders of magnitude reduction in work-normalised variance.
Representation of the inferred relationships in a map‐like space

Abstract A cognitive map is an internal representation of the external world that guides flexible behavior in a complex environment. Cognitive map theory assumes that relationships between entities can be organized using Euclidean‐based coordinates. Previous studies revealed that cognitive map theory can also be generalized to inferences about abstract spaces, such as social spaces. However, it is still unclear whether humans can construct a cognitive map by combining relational knowledge between discrete entities with multiple abstract dimensions in nonsocial spaces. Here we asked subjects to learn to navigate a novel object space defined by two feature dimensions, price and abstraction. The subjects first learned the rank relationships between objects in each feature dimension and then completed a transitive inferences task. We recorded brain activity using functional magnetic resonance imaging (fMRI) while they performed the transitive inference task. By analyzing the behavioral data, we found that the Euclidean distance between objects had a significant effect on response time (RT). The longer the one‐dimensional rank distance and two‐dimensional (2D) Euclidean distance between objects the shorter the RT. The task‐fMRI data were analyzed using both univariate analysis and representational similarity analysis. We found that the hippocampus, entorhinal cortex, and medial orbitofrontal cortex were able to represent the Euclidean distance between objects in 2D space. Our findings suggest that relationship inferences between discrete objects can be made in a 2D nonsocial space and that the neural basis of this inference is related to cognitive maps.
Texts as points: Scene text detection with point supervision

Scene text detection is challenging due to the diverse text appearance, the complex background, and the expensive labeling of training data. For detecting arbitrary-shaped texts, most existing methods require heavy data labeling efforts to produce polygon-level annotations for supervised training. In order to reduce the cost in data labeling, we propose to combine center point annotation into mixed-supervised scene text detection, in which the dataset comprises small number of fully annotated images and large number of weakly annotated images by center points. For better incorporating point supervision, we adopt self-training strategy based on a detector which locates texts by predicting their centers. Besides, in order to weight the pseudo labels generated during self-training, we also propose a novel regression uncertainty estimation module to measure the quality of detection results. Extensive experiments on five benchmark datasets (ICDAR2015, C-SVT, CTW1500, Total-Text and ICDAR-ArT) show that using small amount of polygon annotated data and large amount of center point annotated data, our detector can achieve competitive detection performance.
An identity-based dynamic group signature scheme for reputation evaluation systems

As many Internet service platforms have launched reputation evaluation systems for applications, some malicious merchants are trying to disrupt the market competition environment by harassing evaluators or hiring netizens to give many untrue praises. Protecting evaluators’ identity privacy and the evaluation system’s fairness and justice is essential. Group signatures allow group members to sign messages anonymously in the name of a group. The group manager can find the actual signer in case of a dispute. Group signatures are widely used in reputation evaluation systems, digital cash, etc. However, most existing solutions are expensive, difficult to meet complex application requirements, and need more privacy protection or regulatory compliance. This paper proposes an identity-based dynamic group signature scheme for reputation evaluation systems standardized in IEEE P1363. We prove our scheme is secure in the random oracle model. Moreover, we provide a theoretical analysis of our scheme and conduct experiments by using the Miracl library on the PC platform to verify the analysis result. The analysis demonstrates that the size of the group public key and signatures do not increase with the number of group members. Our solution is efficient in reputation evaluation systems.