Reviewerly/exHarmony huggingface dataset:
HIGH SCHOOL STUDENTS’ DIFFICULTIES IN ENGLISH SPEAKING SKILLS OF NAMLEA, BURU ISLAND

The study aims at exploring the speaking difficulties encountered by high school students and investigating the causes of such difficulties at Namlea , Buru Island The researcher implemented a descriptive qualitative method to describe the speaking difficulties found by high school students at Namlea, Buru Island . The researcher designed an interview to find the data and observed the students speaking. The interview was conducted for each student to investigate speaking difficulties and the causes of such difficulties. The results showed there some difficulties in faced by student in speaking due to some reasons such some students have inadequate English vocabulary, spelling pronunciation, problem. Students are lack grammatical understanding. This research also indicated several factors that cause the students difficulties, such as; Students’ paradigm, motivation, the lack of language environment support, few chances to practice speaking in the learning process.
Transmittance Surface Detection and Material Identification Using Multitask ViT-SIFT Fusion

The automatic detection of transmittance surfaces (transparency and translucency) and material class identification (glass, plastic, and so on) from an image is crucial for applications, including domestic service robotics and handling fragile instruments in experimental or industrial settings. However, this is a difficult task due to the lack of expressive textures in such materials. Although previous approaches for detecting transmittance surfaces propose handcrafted features, they disregard global material information and are limited to a single item (usually glass). Material or object recognition algorithms based on convolutional neural networks (CNNs) focus on optimizing performance that requires large labeled datasets. These algorithms are better for detecting opaque objects because large architectures tend to generalize the texture from an image’s background layer. Furthermore, creating large-scale off-the-shelf dataset labeled with transmittance surface is an extremely tedious task. Thus, we propose a simple yet effective and efficient learning model that combines a shallow multitask vision transformer (ViT) with a scale-invariant feature transform (SIFT) and a shallow CNN as the backbone network. SIFT and ViT are used to capture local and global discriminative features, respectively, and their fusion can increase the performance on a small dataset. The backbone network is utilized to introduce the desirable properties of CNN. Using limited training data, this model detects transmittance surfaces and determines their material type. We conducted tests on a new benchmark dataset containing a variety of transmissive materials and three publicly available datasets for robust comparison. Results show that the proposed model achieved a new state-of-the-art accuracy of 81.78% and 77.21% on our new dataset for transmittance surface recognition and material class identification, respectively.
Traceable Ring Signature Schemes Based on SM2 Digital Signature Algorithm and Its Applications in the Evidence-Storage System

A high-quality evidence-storage service is crucial for many existing applications. For example, judicial or arbitral authorities need to guarantee that their systems are available and trustworthy to conduct the arbitration. Such a system should protect witnesses’ privacy from potential adversary threats. Ring signatures can be employed in blockchain-based systems to conceal the witness’s identity among a group of persons while guaranteeing the availability and trustworthy of evidence. However, the strong anonymity of ring signature makes regulation tough and shields criminals. The traceable ring signature (TRS) is a de-anonymization mechanism that, unlike group signatures, does not rely on centralized trust, making it suitable for the blockchain system. Unfortunately, no SM2-based designs could be discovered in the TRS public literature. To fill the gap, this paper proposes a traceable ring signature scheme based on SM2 digital signature algorithm. It is shown that SM2 traceable ring signature (STRS) satisfies integrity, unforgeability, anonymity, and traceability. Moreover, we present an STRS-based blockchain evidence-storage system, in which users upload evidence with traceable ring signature generated by themselves, and regulators can learn the true identity of the signer if necessary.
Blockchain based e-voting system

The process of establishing democracy in a country is defined by election. Elections might just be a significant event in today’s democracy, however many segments of population throughout the world lack faith in their electoral system, which is a major source of concern for democracy. Even the world’s most powerful democracies, such as the Republic of India, the United States, and Japan, have a flawed legal system. To eliminate these drawbacks of the electoral system, Blockchain Technology is considered as an ultimate solution. With the present surge in sales and use of blockchain technology for a number of purposes, including banking, medical, and identity, a lot of attention has been focused on the legal concerns rather than its practical uses in administration. In this paper, we discuss the concept of Blockchain in a detailed manner making the reader understand the working of blockchain, its characteristics etc. everything from the scratch, as well as how this concept can be implemented as an efficient solution for public voting and how it is more beneficial from the traditional voting methods, with the goal of eliminating the drawbacks of India’s current electoral system while also providing a better, more trustable, safe, and transparent means of public governance. Blockchain is really an emerging technology that promises to improve the resiliency of electronic voting systems. This method offers a way to profit from blockchain’s advantages, such as crypto logical foundations and transparency, to achieve an efficient theme for the e-voting system.
EaT-PIM: Substituting Entities in Procedural Instructions Using Flow Graphs and Embeddings

When cooking, it can sometimes be desirable to substitute ingredients for purposes such as avoiding allergens, replacing a missing ingredient, or exploring new flavors. More generally, the problem of substituting entities used in procedural instructions is challenging as it requires an understanding of how entities and actions in the instructions interact to produce the final result. To support the task of automatically identifying viable substitutions, we introduce a methodology to (1) parse instructions, using NLP tools and domain-specific ontologies, to generate flow graph representations, (2) train a novel embedding model which captures flow and interaction of entities in each step of the instructions, and (3) utilize the embeddings to identify plausible substitutions. Our embedding strategy aggregates nodes and dynamically computes intermediate results within the flow graphs, which requires learning embeddings for fewer nodes than typical graph embedding models. Our rule-based flow graph generation method shows comparable performance to machine learning-based work, while our embedding model outperforms baselines on a link-prediction task for ingredients in recipes.
A New Approach to Efficient Non-Malleable Zero-Knowledge

Non-malleable zero-knowledge, originally introduced in the context of man-in-the-middle attacks, serves as an important building block to protect against concurrent attacks where different protocols may coexist and interleave. While this primitive admits almost optimal constructions in the plain model, they are several orders of magnitude slower in practice than standalone zero-knowledge. This is in sharp contrast to non-malleable commitments where practical constructions (under the DDH assumption) have been known for a while. We present a new approach for constructing efficient non-malleable zero-knowledge for all languages in $$\mathcal{N}\mathcal{P}$$ , based on a new primitive called instance-based non-malleable commitment ( $$\textsf{IB}\text {-}\textsf{NMC}$$ ). We show how to construct practical $$\textsf{IB}\text {-}\textsf{NMC}$$ by leveraging the fact that simulators of sub-linear zero-knowledge protocols can be much faster than the honest prover algorithm. With an efficient implementation of $$\textsf{IB}\text {-}\textsf{NMC}$$ , our approach yields the first general-purpose non-malleable zero-knowledge protocol that achieves practical efficiency in the plain model. All of our protocols can be instantiated from symmetric primitives such as block-ciphers and collision-resistant hash functions, have reasonable efficiency in practice, and are general-purpose. Our techniques also yield the first efficient non-malleable commitment scheme without public-key assumptions.
Spin Chemistry Simulation via Hybrid-Quantum Machine Learning

The Radical Pair Mechanism, a process by which magnetic fields modulate the kinetics of chemical reactions by influencing spin dynamics, is thought to underlie a variety of biological mechanisms and is inherently both quantum and noisy. Model simulations with limited electron-nuclear interactions have been conducted on classical devices resulting in experimentally verified final state results, however these simulations become too complex to simulate as the systems scale in size. This motivates us to propound the simulation of the relevant spin dynamics in a noisy quantum device to probe the currently unexplored open quantum systems aspects of the radical pair mechanism and to provide a novel theoretical tool for us to be able to tackle bottlenecks in simulating larger and more physically relevant biological spin systems. We employ a hybrid quantum machine learning approach where we evaluate a parametrized quantum circuit representing the evolution of the system on the quantum device and then update the parameters according to our objective cost function on a classical computer using a gradient optimizer and a noisy gradient free (SPSA) optimizer. We show that a 3 qubit circuit with a series of parametrized rotation gates representing the Hyperfine and Zeeman interactions can be optimized, resulting in a circuit that evolves a singlet state under different magnetic field angles. Moreover, we show that the noisy gradient free optimizer is better suited for optimizing on a noisy quantum device. However, the precision in the quantum circuit outputs is only sub-par at present, leaving room for further improvements.
Impact Evaluation of Multimodal Information on Sentiment Analysis

Text-based sentiment analysis is a popular application of artificial intelligence that has benefited in the past decade from the growth of digital social networks and its almost unlimited amount of data. Currently, social network users can combine different types of information in a single post, such as images, videos, GIFs, and live streams. As a result, they can express more complex thoughts and opinions. The goal of our study is to analyze the impact that incorporating different types of multimodal information may have on social media sentiment analysis. In particular, we give special attention to the interaction between text messages and images with and without text captions. To study this interaction we first create a new dataset in Spanish that contains tweets with images. Afterwards, we manually label several sentiments for each tweet, as follows: the overall tweet sentiment, the sentiment of the text, the sentiment of the individual images, the sentiment of the caption, if present, and—in cases where a single tweet has several images—the aggregate sentiment of all images present in the tweet. We conclude that incorporating visual information into text-based sentiment analysis raises the performance of the classifiers that determine the overall sentiment of a tweet by an average of 25.5%.
Research on Management Optimization of Industry-University-Research Cooperation Projects in Universities and Colleges from the Perspective of Collaborative Management

In the current university talent education and training, industry-university-research collaboration has become a very important teaching philosophy and concept.At present, there is certain room for innovation and development in industry-university-research collaborative management in colleges and universities.How to better realize innovative management and adapt to the needs of talent training in the current new period is an issue and content that we must pay attention to and explore.In this paper, the construction of project-based management mode of industry-university-research collaborative innovation in colleges and universities was analyzed and discussed.
Annotating Argument Schemes

Argument schemes are abstractions substantiating the inferential connection between premise(s) and conclusion in argumentative communication. Identifying such conventional patterns of reasoning is essential to the interpretation and evaluation of argumentation. Whether studying argumentation from a theory-driven or data-driven perspective, insight into the actual use of argumentation in communicative practice is essential. Large and reliably annotated corpora of argumentative discourse to quantitatively provide such insight are few and far between. This is all the more true for argument scheme corpora, which tend to suffer from a combination of limited size, poor validation, and the use of ad hoc restricted typologies. In the current paper, we describe the annotation of schemes on the basis of two distinct classifications: Walton’s taxonomy of argument schemes, and Wagemans’ Periodic Table of Arguments. We describe the annotation procedure for each, and the quantitative characteristics of the resulting annotated text corpora. In doing so, we extend the annotation of the preexisting US2016 corpus of televised election debates, resulting in, to the best of our knowledge, the two largest consistently annotated corpora of schemes in argumentative dialogue publicly available. Based on evaluation in terms of inter-annotator agreement, we propose further improvements to the guidelines for annotating schemes: the argument scheme key, and the Argument Type Identification Procedure.
Xception Network for Weather Image Recognition Based on Transfer Learning

Weather conditions are closely related to our lives. Traditional methods have some problems, such as small scale of weather image data set, difficult to optimize the model, and similar image factors of different weather types, which may lead to recognition errors. Therefore, this paper proposes a weather image recognition algorithm based on transfer learning and uses Xception algorithm to realize the network architecture. The Xception model was improved by adding a full connection layer, Dropout layer and Softmax layer. Combined with the functional requirements of weather image recognition, the identification of six different weather images was realized and compared with other models under the same parameter background. According to the results, the final accuracy of this method is up to 94.19%, which can effectively solve the problems of insufficient training samples and low accuracy in data sets.
Sequestered Encryption: A Hardware Technique for Comprehensive Data Privacy

Data breaches that penetrate web-facing servers and exfiltrate sensitive user data have become pervasive. Insulating these systems from attack is seemingly impossible due to the ubiquity of software vulnerabilities within cloud applications. It is simply insurmountable to adequately address all such vulnerabilities, and therefore imprudent to rely on software applications to protect user data. Rather, the ideal systems solution upholds data confidentiality, even in the presence of vulnerable or compromised software. Homomorphic encryption (HE) provides these capabilities, but its limited expressiveness and significant runtime overheads have inhibited its adoption. In this work, we explore how trusted hardware can be leveraged to provide data confidentiality in the presence of vulnerable software while achieving practical performance overheads. We present Sequestered Encryption (SE)—a hardware technique for data privacy that sequesters sensitive plaintext data into a small hardware root of trust and encrypts this data in all external microarchitectural structures, thereby rendering secret values inaccessible to software. With optimizations, SE achieves <2.5× performance slowdowns on average compared to native execution, demonstrating that architectural approaches can emerge as data privacy solutions that possess zero trust in software while being dynamic, expressive, and performant.
Tor Traffic’s Representation and Classification Based on Packet Timing Characteristics

Tor provides users with great anonymity due to its multi-layer proxy operating mechanism, but also provides living space for illegal and criminal activities. It is necessary to further analyze users' behaviors based on the identification of Tor traffic to effectively supervise Tor users' online conduct. We focus on the differences in traffic patterns when users use different applications, and propose a Tor traffic classification method based on the timing distribution characteristics of packets, which treats each packet as an object with several attributes, analyzes its distribution in unit time, and generates visual samples, finally use CNN to identify the application type. Focusing on the transmission stage of application data, this method can effectively avoid the influence of Tor’s existing defense mechanism, and the processed samples can intuitively present the traffic features of a specific application that are visible to the naked eye. The experiment al results show that the proposed method can not only provide higher recognition accuracy but also effectively improve the problem of poor recognition ability of previous models for certain application types.
Prediction Number of Passenger Ships from Port X to the Kepulauan Seribu Using SARIMA

Indonesia is the world’s biggest archipelago country. Kepulauan Seribu is a famous marine tourist destination. Tourists can use passenger ship in Port X as their sea transport. The number of tourists visiting the thousand islands can’t be confirmed as it is always fluctuating. The lack of information causes overload and inadequate safety equipment. Therefore, this research wants to predict the number of passengers aboard ship. The researcher determines a good initial SARIMA model based on RMSE value. This can be seen from the lags in the ACF and PACF plots to determine the AR and MA parameters. Seasonal parameters can be identified through the ACF plot, seen from the significant first lag. The prediction uses SARIMA method as it is [effective] for short-term predictions containing seasonal elements. The model used is model (3,0,1) (1,0,1)7 with RMSE value of 645,296. This model is used to predict 90 days ahead of the number of passengers abroad. This research results in a dashboard of total ship passengers and its prediction.
Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width

Recently, sketches have been introduced as a general language for representing the subgoal structure of instances drawn from the same domain. Sketches are collections of rules of the form C -&gt; E over a given set of features where C expresses Boolean conditions and E expresses qualitative changes. Each sketch rule defines a subproblem: going from a state that satisfies C to a state that achieves the change expressed by E or a goal state. Sketches can encode simple goal serializations, general policies, or decompositions of bounded width that can be solved greedily, in polynomial time, by the SIW_R variant of the SIW algorithm. Previous work has shown the computational value of sketches over benchmark domains that, while tractable, are challenging for domain-independent planners. In this work, we address the problem of learning sketches automatically given a planning domain, some instances of the target class of problems, and the desired bound on the sketch width. We present a logical formulation of the problem, an implementation using the ASP solver Clingo, and experimental results. The sketch learner and the SIW_R planner yield a domain-independent planner that learns and exploits domain structure in a crisp and explicit form.
DBkWik++- Multi Source Matching of Knowledge Graphs

Large knowledge graphs like DBpedia and YAGO are always based on the same source, i.e., Wikipedia. But there are more wikis that contain information about long-tail entities such as wiki hosting platforms like Fandom. In this paper, we present the approach and analysis of DBkWik++, a fused Knowledge Graph from thousands of wikis. A modified version of the DBpedia framework is applied to each wiki which results in many isolated Knowledge Graphs. With an incremental merge based approach, we reuse one-to-one matching systems to solve the multi source KG matching task. Based on this alignment we create a consolidated knowledge graph with more than 15 million instances.
Deep-Learning Based Modulation Identification in Wireless Communication System

This paper studies the performance of deep learning (DL) for the identification of analog and digital modulation in orthogonal frequency-division multiplexing (OFDM) based wireless communication systems. OFDM is the key enabling technology for 4G and going to be part of 5G communication system implementation. The conventional methods for signal modulation identification (SMI) are discrete wavelet transform (DWT), adaptive wavelet transform (AWT) and, mixed parameters. These are based on statistical models and therefore encounter the bottleneck of accuracy. To handle this problem, machine learning (ML) algorithms were proposed which uses support vector machine (SVM), k nearest neighbors (KNN), decision tree (DT), etc. However, the feature extraction has to be done manually which makes these methods very difficult to implement in practical OFDM systems. Therefore many DL based SMI methods are proposed which handles the limitations of the mathematical models by making use of the availability of large datasets. In this paper, we have used a Convolutional Neural Network (CNN) for identification of both analog and digital modulation in 4G/5G wireless system. This proposed deep learning based method achieves accuracy higher than 95% which is better than the traditional methods.
Block-Based Image Reversible Data Hiding Based on the Visual Feature and Edge Entropy

Due to the widespread usage of the Internet, electronic information can be quickly altered, duplicated, and shared across interconnection. As a result, the unauthorized reproduction of digital material became a severe issue. Reversible data hiding (RDH) was a practical way of protecting digital media's copyright. This research presents an enhanced DCT-based RDH method that considers human visual features. A less uniform block was used for the information embedding to achieve high imperceptibility for a block based RDH technique. The testing findings of the suggested technique shown a better increase in robustness and non - repudiation under various kinds of image alteration procedures.