Reviewerly/exHarmony huggingface dataset:
Extractive Summarization - A Comparison of Pre-Trained Language Models and Proposing a Hybrid Approach

The automatic summarization of technical articles is a field that has garnered a fair amount of interest, and one that enjoys a significant portion of NLP-related research. As a whole, automatic summarization can be split into two broad categories - extractive and abstractive. Extractive summarization implies that important and relevant sentences are picked from the article as is, and inserted in the summary. Abstractive summarization, on the other hand, requires contextual understanding of the document, and rearranging and shortening the sentences, while maintaining the core essence of the article. Multiple algorithms have been proposed for both these classes of automatic summarization. In the recent past, the emergence of pre-trained language models for NLP tasks have been heralded by the creation of attention mechanisms and Transformers. These models implement encoder-decoder structures, and have far wider applications than previously utilised algorithms. Four such pretrained models are - BERT, BART, XLNet and GPT-2. In this project, we use transfer learning to fit these models on our corpus of Medium articles, fine-tuning them for the task of summarization. Further, we generate online summaries of the data, and use ROUGE metrics to evaluate the accuracy of the model-generated summaries alongside those. We also implement the popular Word2Vec model on the same data, and compare its result with the ones obtained from the attention based models, once again using ROUGE metrics. In addition, we explore the effectiveness of a hybrid approach to the summarization task, by using different combinations of the models on the same article to investigate the results of the same.
Face Detection Using YOLOX with Attention Mechanisms

Because of the rise of convolutional neural networks, face detection has received a lot of attention and research. Especially the face detection affected by image quality, resolution size, and illumination, The detection of small faces has been a major challenge for face detection tasks. Different from general face detectors, we propose an anchor- free face detection method based on the general object detection framework YOLOX. The YOLOX network incorporating the attention mechanism is proposed for the problem of wrong detection and omission of YOLOX in the detection of small faces at low resolution. The proposed method were evaluated on some open benchmark face datasets, promising results were obtained.
Gaze-Dependent Image Re-Ranking Technique for Enhancing Content-Based Image Retrieval

Content-based image retrieval (CBIR) aims to find desired images similar to the image input by the user, and it is extensively used in the real world. Conventional CBIR methods do not consider user preferences since they only determine retrieval results by referring to the degree of resemblance or likeness between the query and potential candidate images. Because of the above reason, a “semantic gap” appears, as the model may not accurately understand the potential intention that a user has included in the query image. In this article, we propose a re-ranking method for CBIR that considers a user’s gaze trace as interactive information to help the model predict the user’s inherent attention. The proposed method uses the user’s gaze trace corresponding to the image obtained from the initial retrieval as the user’s preference information. We introduce image captioning to effectively express the relationship between images and gaze information by generating image captions based on the gaze trace. As a result, we can transform the coordinate data into a text format and explicitly express the semantic information of the images. Finally, image retrieval is performed again using the generated gaze-dependent image captions to obtain images that align more accurately with the user’s preferences or interests. The experimental results on an open image dataset with corresponding gaze traces and human-generated descriptions demonstrate the efficacy or efficiency of the proposed method. Our method considers visual information as the user’s feedback to achieve user-oriented image retrieval.
Improved bounds on some entanglement criteria in bipartite quantum systems

Entanglement detection is one of the core tasks in quantum information processing. Based on a broad class of informationally complete symmetric measurements, a series of separability criteria are proposed for different bipartite quantum systems using Cauchy–Schwarz inequality and ρ−ρA⊗ρB, respectively. In addition, by comparing and analyzing the existing entanglement criteria and detailed examples, it is not difficult to prove that our proposed criteria are more powerful and comprehensive in entanglement detection.
HBSS: (Simple) Hash-Based Stateless Signatures – Hash All the Way to the Rescue!

One-time signatures (originated by Lamport) and their extensions to many signatures has gained recent momentum with the need for Post-Quantum secure signing since they are essentially based on standard hash (one-way) functions (rather than number theoretic assumptions). Such signatures, to date, have been based on an array of commitments that are de-committed (private key/preimage revealed) only based on bits of a representation of the hashed message signed, and their stateless versions have been based on pseudorandom functions. In this work, a new approach to the above is presented, based on probabilistic “set membership data structure,” which in turn is based on hash functions. A signature based on hash access to a suitably long array, where k hash functions which are independent are used for each message to tag an index in the array. The above access is similar to the access performed in Bloom filters. The resulting signature scheme is stateless and can be tuned to support any given upper-bound number of signatures (by tuning the array’s length). The central idea is that a de-committed array is only partially loaded with de-commitments to be valid, a fact that assures correctness (signed message is validated), soundness (unsigned message is not fully validated), and unforgeability of the signature (an attempt to forge a signature is reduced to decommitment without access to the private key/decommited preimage). Constructions that are based on enhancing a one-time or bounded-message construction (such as the Naor-Yung extension from bounded messages to regular signatures) are valid for the new Hash-Based Stateless Signature (HBSS).
Practical Improvements on BKZ Algorithm

Lattice problems such as NTRU and LWE problems are widely used as the security base of post-quantum cryptosystems. And currently, lattice reduction by BKZ algorithm is the most efficient way to solve them. In this paper, we give four further improvements on BKZ algorithm, which can be used for SVP subroutines based on enumeration and sieving. These improvements in combination provide a speed-up of $$2^\text {3-4}$$ in total. So all the lattice-based NIST PQC candidates lose 3–4 bits of security in concrete attacks. Using these new techniques, we solved the 656 and 700 dimensional ideal lattice challenges in 380 and 1787 thread hours, respectively. The cost of the first one (also used an enumeration-based SVP subroutine) is much less than the previous records (4600 thread hours). One can still simulate the improved BKZ algorithm to find the blocksize strategy that makes $$\textrm{Pot}$$ of the basis (defined in Sect. 4.2) decrease as fast as possible, which means the length of the first basis vector decrease the fastest if we accept the GSA assumption. It is useful for analyzing concrete attacks on lattice-based cryptography.
Efficient Extended GCD and Class Groups from Secure Integer Arithmetic

In this paper we first present an efficient protocol for the secure computation of the extended greatest common divisor, assuming basic secure integer arithmetic common to many MPC frameworks. The protocol is based on Bernstein and Yang's constant-time 2-adic algorithm, which we adapt to work purely over the integers. This yields a much better approach for the MPC setting, but raises a new concern about the growth of the Bézout coefficients. By a careful analysis we are able to prove that the Bézout coefficients in our protocol will never exceed $$3\max (a,b)$$ in absolute value for inputs a and b. Next, we present efficient protocols for implementing class groups of imaginary quadratic number fields in the MPC setting. We start from Shanks' original algorithms for the efficient composition of binary quadratic forms and combine these with our particular adaptation of a forms reduction algorithm due to Agarwal and Frandsen. We will formulate this result in terms of secure groups, which are introduced as oblivious data structures implementing finite groups in a privacy-preserving manner. Our results show how class group operations can be run efficiently between multiple parties operating jointly on secret-shared group elements. We have integrated secure class groups in MPyC along with other instances of secure groups such as Schnorr groups and elliptic curves.
Generating One-Hot Maps Under Encryption

One-hot maps are commonly used in the AI domain. Unsurprisingly, they can also bring great benefits to ML-based algorithms such as decision trees that run under Homomorphic Encryption (HE), specifically CKKS. Prior studies in this domain used these maps but assumed that the client encrypts them. Here, we consider different tradeoffs that may affect the client's decision on how to pack and store these maps. We suggest several conversion algorithms when working with encrypted data and report their costs. Our goal is to equip the ML over HE designer with the data it needs for implementing encrypted one-hot maps.
Building Blocks for LSTM Homomorphic Evaluation with TFHE

Long Short-Term Memory (LSTM) is a Neural Network (NN) type that creates temporal connections between its nodes. It models sequence data for applications such as speech recognition, image captioning, DNA sequence analysis, and sentence translation. Applications that are often subject to privacy constraints. This paper thus presents basic building blocks for the homomorphic execution of an LSTM that would respect the privacy of its inputs. By means of TFHE functional bootstrapping, we propose several approaches for homomorphically evaluating discretized flavors of the Sigmoid and Tanh activation functions. Experimental results show that the accuracy of the resulting discretized networks remains comparable to a full precision clear-domain execution. Performance-wise, we are able to homomorphically compute a Sigmoid or Tanh function in 0.3 or 0.15 s (depending on whether or not multivalue bootstrapping is relied on). This paves the way towards evaluating practical LSTMs over encrypted inputs in around 1 to 3 min which is competitive with the state of the art.
Patch or Exploit? NVD Assisted Classification of Vulnerability-Related GitHub Pages

This paper presents a semi-automated approach to distinguish between patches and exploits published on GitHub, by using the National Vulnerability Database (NVD) as a reference. For this purpose, we leverage two interpretable algorithms, FP-Growth rule mining and decision trees, to extract patterns from the data and provide insights into the relationships between variables. To mitigate the risk of overfitting, we use more than 30,000 GitHub pages labeled by NVD as ground truth and focus on simple models. Among our findings, we discover that it is feasible to semi-automatically identify GitHub webpages containing patches and exploits. In particular, after pre-filtering webpages of interest, we discovered that most commits refer to patches, whereas URLs containing screenshots correspond to exploits showcasing how to exploit a given target system. Our results suggest that NVD is valuable to bootstrap machine learning algorithms for assisting in the analysis of increasingly larger amounts of cybersecurity data shared over the Internet.
XInsight: eXplainable Data Analysis Through The Lens of Causality

In light of the growing popularity of Exploratory Data Analysis (EDA), understanding the underlying causes of the knowledge acquired by EDA is crucial. However, it remains under-researched. This study promotes a transparent and explicable perspective on data analysis, called eXplainable Data Analysis (XDA). For this reason, we present XInsight, a general framework for XDA. XInsight provides data analysis with qualitative and quantitative explanations of causal and non-causal semantics. This way, it will significantly improve human understanding and confidence in the outcomes of data analysis, facilitating accurate data interpretation and decision making in the real world. XInsight is a three-module, end-to-end pipeline designed to extract causal graphs, translate causal primitives into XDA semantics, and quantify the quantitative contribution of each explanation to a data fact. XInsight uses a set of design concepts and optimizations to address the inherent difficulties associated with integrating causality into XDA. Experiments on synthetic and real-world datasets as well as a user study demonstrate the highly promising capabilities of XInsight.
BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs

Recent studies have shown that Binary Graph Neural Networks (GNNs) are promising for saving computations of GNNs through binarized tensors. Prior work, however, mainly focused on algorithm designs or training techniques, leaving it open to how to materialize the performance potential on accelerator hardware fully. This work redesigns the binary GNN inference backend from the efficiency perspective. It fills the gap by proposing a series of abstractions and techniques to map binary GNNs and their computations best to fit the nature of bit manipulations on GPUs. Results on real-world graphs with GCNs, GraphSAGE, and GraphSAINT show that the proposed techniques outperform state-of-the-art binary GNN implementations by 8-22X with the same accuracy maintained. BitGNN code is publicly available.1.
The Discrete Fourier Transform Over the Binary Finite Field

The novel methods for binary discrete Fourier transform (DFT) computation over the finite field have been proposed. The methods are based on a binary trace calculation over the finite field and use the cyclotomic DFT. The direct DFT computational complexity has been reduced due to using the binary trace function over the finite field and the functions of trace which are stored in small tables. The computational complexity of the inverse DFT has been reduced due to representation of elements in the finite field with respect to the normal basis. The proposed methods can be used for encoding/decoding subfield subcodes, especially for binary Bose–Chaudhuri–Hocquenghem (BCH) codes. The computational complexity of the direct/inverse DFT computation methods is the smallest of all known methods.
Hardware Implementation of Multi-Layer Photonic Spiking Neural Network With Three Cascaded Photonic Spiking Neurons

Photonic technologies offer great prospects for novel ultrafast, energy-efficient and hardware friendly neuromorphic computing platforms. Moreover, Photonic spiking neural network (PSNN) emerged as a promising approach toward building a low-latency and energy-efficient non-von-Neuman computing system is of particular interest. In this article, a fabricated Fabry–Pérot laser with saturable absorber (FP-SA) is employed as the photonic spiking neuron of the PSNN, and a multi-layer PSNN with such three cascaded photonic spiking neurons is proposed. The cascadability of the proposed multi-layer PSNN is demonstrated experimentally. It is found that the performance of the pattern recognition task using the hardware-algorithm collaborative computing is improved in the multi-layer PSNN. Besides, the nonlinear neuron-like dynamics including temporal integration and threshold in such multi-layer PSNN are also experimentally investigated, and better nonlinear neuron-like dynamics can be achieved. Such proposed multi-layer PSNN potentially opens up the prospect of employing simple hardware structure and cooperating with algorithm design to realize a photonic depth neural network to solve more complex problems.
Enhancing Cryptocurrency Price Forecasting Accuracy: A Feature Selection and Weighting Approach With Bi-Directional LSTM and Trend-Preserving Model Bias Correction

A cryptocurrency is a digitized, encrypted, and decentralized virtual currency, which is impossible to counterfeit or double-spend. It is one of the very popular investment instruments and traded in blockchain based crypto exchanges on ever growing volume. It is quite volatile due to imbalance of supply and demand, government regulations, investor sentiment and above all media hype. Cryptocurrency price forecasting is an active area of research and several approaches have been proposed recently. This study proposed a price forecasting model based on three vital characteristics (i) a feature selection and weighting approach based on Mean Decrease Impurity(MDI) features. (ii) Bi-directional LSTM and (iii) with a trend preserving model bias correction (CUSUM control charts for monitoring the model performance over time) to forecast Bitcoin and Ethereum values for long and short term spans. The data for both currencies were analyzed in three different intervals: (i) April 01, 2013 to April 01, 2016 (ii) April 01, 2013 to April 01, 2017 and (iii) April 01, 2013 to December 31, 2019. Extensive series of experiments were performed and evaluated on Root Mean Square Errors (RMSE). Comparing with the prevalent forecasting models we report a new state of the art in cryptocurrency forecasting.
Image Generation and Recognition Technology Based on Attention Residual GAN

In accordance with the concept of game antagonism, Generative Adversarial Network (GAN) is a popular model in current image generation technology.However, GAN has problems such as unstable training and difficult convergence, which seriously affect the effectiveness of input feature extraction and image recognition.The study introduces residual network structure and self attention mechanism to calculate the weight parameters of features, and then guides image generation through image label information.The improved GAN model classifier is applied to image recognition.The final experimental data shows that the Fréchet Inception Distance (FID) values of the iGAN in facial expressions and behavioral actions are 77.68 and 176.84, respectively, which are closer to the distribution of real image data.In behavioral image recognition, the accuracy of the model is 96.8%, and the required time is 30 seconds.In facial expression recognition, the accuracy and recognition time of the model are 90.1% and 24 seconds, respectively.This indicates that it can generate high-quality images, has stronger feature extraction capabilities, and has higher recognition efficiency.This model provides a new technical reference for the further improvement of image processing technology, and has certain application potential and value.
Marine Organism Detection Based on Double Domains Augmentation and an Improved YOLOv7

The existing object detection methods are facing significant challenges when applied to marine environments, such as underwater image degradation caused by absorption and scattering of light, and domain transfer in water bodies with different water qualities. In this letter, we propose a marine organism detection framework to improve the detection performance and the domain generalization performance. First, a double domains data augmentation is proposed. This method combines underwater image enhancement and water quality transfer to improve the domain diversity of the original dataset. Second, we utilize the self-attention operations and the convolution to improve the detection performance of the YOLOv7, fully utilizing the advantages of self-attention and convolutional computation. Meanwhile, this model uses SIoU loss to accelerate convergence speed and improve the regression. Experiments on the URPC2019 and URPC2020 datasets show that the proposed object detection method achieves a mean average precision of 82.3% and 83.6%, respectively, which is superior to all other methods used for comparison.
A Metadata-Based Approach for Research Discipline Prediction Using Machine Learning Techniques and Distance Metrics

Forecasting research disciplines associated with research projects is a significant challenge in research information systems.It can reduce the administrative effort involved in entering research projectrelated metadata, eliminate human errors, and enhance the quality of research project metadata.It also enables the calculation of the degree of interdisciplinarity of these projects.However, predicting scientific research disciplines and measuring interdisciplinarity in a research endeavor remain difficult.In this paper, we propose a framework for predicting the research disciplines associated with a research project and measuring the degree of interdisciplinarity based on associated metadata to address these issues.The proposed framework consists of several components to improve the performance of research disciplines prediction and interdisciplinarity measurement systems.These include a feature extraction component that utilizes a topic model to extract the most appropriate features.Further, the framework proposes a discipline encoding component that applies a data mapping strategy to lower the dimensionality of the output variables.Furthermore, a distance matrix creation component is proposed to recommend the most appropriate research disciplines and compute interdisciplinarity associated with research projects.We implemented the suggested framework on two separate research information systems databases for research projects, Dimensions and the Flemish Research Information Space.Experimental results demonstrate that the proposed framework predicts the research disciplines associated with research projects more accurately than related work.
Analysis of Students' Multiple Intelligences in Biology Learning Activities

Purpose of the study: The research objective was to determine the multiple intelligences of students in biology learning activities for class XII IPA at SMA Negeri 1 Sarolangun Regency.&#x0D; Methodology: The design used in this research is a descriptive analytic research design. The number of samples in this study were 40 students. The types of data in this study are quantitative and qualitative. The method of collecting data in this study is by interviewing and documentation.&#x0D; Main Findings: In the Linguistic intelligence indicator, the percentage reached 76.15% in the high category, Mathematical-Logical intelligence reached 75% in the medium category, Visual-Spatial intelligence reached 71% in the medium category, Kinesthetic intelligence reached 67.5% in the medium category, Musical intelligence reached 59% in the high category, Interpersonal intelligence reaches 67.5% in the medium category, Intrapersonal intelligence reaches 74.15% in the medium category, and Naturalist intelligence reaches 81.5% in the high category.&#x0D; Novelty/Originality of this study: Provide reference material that can be used as material for consideration in developing learning strategies and tools in carrying out learning activities.