Reviewerly/exHarmony huggingface dataset:
A preimage attack on reduced <scp>GIMLI‐HASH</scp> with unbalanced squeezing phase

In Conference on Cryptographic Hardware and Embedded System 2017, Bernstein et al. proposed GIMLI, a 384-bit permutation with 24 rounds, which aims to provide high performance on various platforms. In 2019, the full-round (24 rounds) GIMLI permutation was used as an underlying primitive for building AEAD GIMLI-CIPHER and hash function GIMLI-HASH, which were submitted to the NIST Lightweight Cryptography Standardisation process and selected as one of the second-round candidates. In Transactions on Symmetric Cryptology 2021, Liu et al. presented a preimage attack with a divide-and-conquer method on round-reduced GIMLI-HASH, which uses 5-round GIMLI. In this paper, preimage attacks on a round-reduced variant of GIMLI-HASH is presented, in which the message absorbing phase uses 5-round GIMLI and the squeezing phase uses 9-round GIMLI. This variant is called as 5–9-round GIMLI-HASH. The authors’ preimage attack on 5–9-round GIMLI-HASH requires 296.44 time complexity and 297 memory complexity. Also, this method can be reached up to round shifted 10-round GIMLI in the squeezing phase. The authors’ first attack requires the memory for storing several precomputation tables in GIMLI SP-box operations. In the authors’ second attack, a time-memory trade-off approach is taken, reducing memory requirements for precomputation tables but increasing computing time for solving SP-box equations by using SAT solver. This attack requires 266.17 memory complexity and 296+ϵ time complexity, where ϵ is a time complexity for solving SP-box equations. The authors’ experiments using CryptoMiniSat SAT solver show that the maximum time complexity for ϵ is about 220.57 9-round GIMLI.
Integration of Photovoltaic Generation Within a Modeling Framework for Energy Hubs

Energy efficiency is a topic with many publications related to resource exploitation at a local scale; via well-performed energy management, substantial environmental and economic benefits can be achieved. In this article, the models used to forecast the photovoltaic power yield in two distinct facilities are described. These facilities are part of the same production plant, which makes use of different heterogeneous resources (carbon dioxide, water, thermal energy, and electricity) and has already been analyzed in a problem that consists in finding the set of variables that minimize the operation cost. In order to predict the power production for both photovoltaic fields, the expressions for radiation on sloped surfaces and the equivalent circuit for solar cells are employed, and the inverters and wire-transmission loss effects are considered. Furthermore, their integration within a general-purpose modeling framework for energy hubs is demonstrated. The comparison between validation results and production real data shows that despite the slight overestimation of the energy yield, the models are suitable to forecast the production of both facilities with a suitable accuracy, as the R 2 coefficients of both facilities lie between 0.95 and 0.96.
A Generative Model for Topic Discovery and Polysemy Embeddings on Directed Attributed Networks

Combining topic discovery with topic-specific word embeddings is a popular, powerful method for text mining in a small collection of documents. However, the existing researches purely modeled on the contents of documents and led to discovering noisy topics. This paper proposes a generative model, the skip-gram topical word-embedding model (simplified as steoLC) on asymmetric document link networks, where nodes correspond to documents and links refer to directed references between documents. It simultaneously improves the performance of topic discovery and polysemous word embeddings. Each skip-gram in a document is generated based on the topic distribution of the document and the two word embeddings in the skip-gram. Each directed link is generated based on the hidden topic distribution of the beginning document node. For a document, the skip-grams and links share a common topic distribution. Parameter estimation is inferred and an algorithm is designed to learn the model parameters by combining the expectation-maximization (EM) algorithm with the negative sampling method. Experimental results show that our method generates more useful topic-specific word embeddings and coherent latent topics than the state-of-the-art models.
Feasibility of interconnecting the University of Bangui and peripheral areas via the TVWS for e-learning in the context of COVID-19

The campuses and classrooms of the University of Bangui do not have connectivity due to insufficient network infrastructure. However, there are Dynamic Spectrum Access technologies in the unused VHF and UHF analog TV band called TV White Space that can be used to provide high-speed Wi-Fi access over wide coverage and at lower cost. Since March 2020, courses have been suspended at the University of Bangui to effectively limit and combat the COVID-19 pandemic. After a three-month period of containment, academic activities have resumed. The increase in the number of cases of COVID-19 contamination raises concerns about this resumption. Given the rules of social distancing that limit the number of students in classrooms, the risks of travelling by public transport and the lack of motivation of some students following years of war, the University of Bangui can organize distance learning as a transitional measure.This paper is the subject of a feasibility study on the interconnection of surrounding neighborhoods and villages by TV White Space at the University of Bangui that can accommodate students and teachers for distance learning courses in the context of COVID-19.The methodological approach consists in studying the deployment of a White Space TV network to interconnect the peripheral areas at the University of Bangui, then using the Moodle distance learning platform on which we integrate the virtual classroom plugin, deployed locally and running without Internet connectivity. The bandwidth of the White Space TV will be important to practice the educational activities without spending money to pay the Internet credits of the operators’ networks. Students can connect locally via Wi-Fi in the areas covered by the platform and follow the different academic activities according to their geographical location, while others closer to the university can go to the classroom to attend classes with the professor in the rules enacted for protection against COVID-19.Our solution initially helps to break the digital divide and then to promote e-learning at the University of Bangui in the context of COVID-19.
Improving scalability of database systems by reshaping user parallel I/O

Modern database systems suffer from compromised throughput, persistent unfair I/O processing and unpredictable, high latency variability of user requests as a result of mismatches between highly scaled user parallel I/O and the I/O capacity afforded by the database and its underlying storage I/O stack. To address this problem, we introduce an efficient user-centric QoS-aware scheduling shim, called AppleS, for user-level fine-grained I/O regulation that delivers the right amount and pattern of user parallel I/O requests to the database system and supports user SLOs with high-level performance isolation and reduced I/O resource contention. It is designed to enable database systems to proactively regulate user request behaviors based on runtime conditions to reshape user access pattern to hide excessive user parallelism from the I/O stack that has a limited concurrent processing capability. This helps achieve scalable throughput for multi-user workloads in a fair and stable manner. AppleS is implemented as a user-space shim for transparent user-differentiated I/O scheduling, making it highly flexible and portable. Our extensive evaluation, run on real databases (MySQL and MongoDB), demonstrates that, by incorporating AppleS in the existing database systems, our solution can not only improve the throughput (up to 39.2%) in a fairer (3.2× to 40.6× fairness improvement) and more stable (up to 2× lower latency variability) manner, but also support user SLOs with less I/O provisioning.
Deterministic Algorithms

This chapter includes a description of safe trajectory planning algorithms for ships classified as deterministic methods, which are characterized by a feature of solution repeatability for every run of calculations with the same input data. This characteristic results from their operation principle. A deterministic algorithm is constructed in such a way, that it always performs a set of precisely defined steps in order to find a solution. Among algorithms described in this chapter are the following methods: the Trajectory Base Algorithm (Sect. 4.1), graph search methods—the Discrete Artificial Potential Field algorithm (Sect. 4.2), the Wave-Front algorithm (Sect. 4.3) and the Visibility Graph search Algorithm (Sect. 4.4), the Dynamic Programming approach (Sect. 4.5) and Local Search (Sect. 4.6). The last method sometimes can be classified to non-deterministic approaches instead of deterministic ones. It will happen when the local search algorithm applies probabilistic operations.
Efficient modal-aware feature learning with application in multimodal hashing

Many retrieval applications can benefit from multiple modalities, for which how to represent multimodal data is the critical component. Most deep multimodal learning methods typically involve two steps to construct the joint representations: 1) learning of multiple intermediate features, with each intermediate feature corresponding to a modality, using separate and independent deep models; 2) merging the intermediate features into a joint representation using a fusion strategy. However, in the first step, these intermediate features do not have previous knowledge of each other and cannot fully exploit the information contained in the other modalities. In this paper, we present a modal-aware operation as a generic building block to capture the non-linear dependencies among the heterogeneous intermediate features, which can learn the underlying correlation structures in other multimodal data as soon as possible. The modal-aware operation consists of a kernel network and an attention network. The kernel network is utilized to learn the non-linear relationships with other modalities. The attention network finds the informative regions of these modal-aware features that are favorable for retrieval. We verify the proposed modal-aware feature learning in the multimodal hashing task. The experiments conducted on three public benchmark datasets demonstrate significant improvements in the performance of our method relative to state-of-the-art methods.
Hardware-oriented Groupwise Topology for CNN Accelerator

For edge AI inference applications, one of the major challenges is to accomplish huge computation load using limited hardware resources and lowest power consumption. A groupwise architecture is proposed in this paper for accelerating neural network workload via hardware group parallelism and optimized processing element utilization. To achieve better efficiency and higher accuracy, the hardware is optimized by using customized groupwise networks and configurable processing element. The experiment results show that it could improve the accuracy by 2&#x0025;, using CIFAR-10 dataset and MobileNet as the baseline model, at the expense of little growth in the number of parameters. Moreover, the groupwise topology also enhances hardware inference performance, in terms of frames per second, without additional hardware resource for running YOLO-Face network.
Evidence Transfer: Learning Improved Representations According to External Heterogeneous Task Outcomes

Unsupervised representation learning tends to produce generic and reusable latent representations. However, these representations can often miss high-level features or semantic information, since they only observe the implicit properties of the dataset. On the other hand, supervised learning frameworks learn task-oriented latent representations that may not generalise in other tasks or domains. In this article, we introduce evidence transfer, a deep learning method that incorporates the outcomes of external tasks in the unsupervised learning process of an autoencoder. External task outcomes also referred to as categorical evidence, are represented by categorical variables, and are either directly or indirectly related to the primary dataset—in the most straightforward case they are the outcome of another task on the same dataset. Evidence transfer allows the manipulation of generic latent representations in order to include domain or task-specific knowledge that will aid their effectiveness in downstream tasks. Evidence transfer is robust against evidence of low quality and effective when introduced with related, corresponding, or meaningful evidence.
A Novel Approach to Predict Popularity Rating Using KNN and SVD

In this hectic world, every one of us wants some peaceful time by doing certain activities like playing our favorite games, watching movies, watching social media platforms, etc..The most popular among them is watching social media platforms. The stress can be reduced by watching different genre of movies like crime, thrillers, love, and emotions. Many social media platforms are available, like Disney + Hot Star, Amazon Prime Video, Netflix, and so on. Finding TRP for these social platforms is essential for the program broadcaster. It was quite challenging task to predict the TRP of each show. In this work, the TRP prediction is done by using the knowledge of machine learning. This paper imposes on finding the television rating point using K-Nearest Neighbors (KNN) and singular value decomposition (SVD) by using Kaggle, an open-source dataset. The KNN is used to find the TRP by calculating the characteristics of each one. Then, the similar one is taken from KNN, and then ranking is done by using SVD. Finally, result is validated with the previous one, which produces high accuracy.
Book Recommendation System of University Library Based on Data Mining

With the deep integration of information technology and human production and life, and the rapid popularization of the Internet, the problem of global data explosion is becoming more and more serious. Big data mining technology represented by personalized recommendation is becoming an important tool to solve the problem of information overload. This paper designs a Book Recommendation System Based on university library, which uses data mining technology to elaborate and discuss the implementation in detail. Then the performance of this method in the accuracy and coverage of recommendation is discussed through experiments. Then, a hybrid recommendation scheme is proposed based on the features of low accuracy and high coverage of text semantic recommendation and high accuracy and low coverage of collaborative filtering algorithm.
Forecasting of Sales Based on Long Short Term Memory Algorithm with Hyperparameter

Increasingly competitive business competition requires business people to re-design their business strategies, one of which is by applying forecasting methods. Several forecasting techniques have been used in sales prediction research with fairly good accuracy in a short period, the research will focus on optimizing the hyperparameter LSTM algorithm to improve the performance of the model formed over the next 60 days. The method used in this study is the Cross Industry Standard Process for Data Mining (CRISP-DM), in the preprocessing the data cleaning, labeling, summary, and data transformation. The data understanding stage applies the Exploratory Data Analysis method. The development of the LSTM model uses several parameters, namely data partition, number of hidden layers, dropout scenarios to prevent overfitting, number of neurons, epoch describing the number of training iterations, batch size is the amount of training data that must be considered in each process of updating the weights. The experimental results of the best LSTM model after experimenting with different parameters are hyperparameter batch size 30, epoch 150, 3 hidden layers and 3 dropouts, resulting in RMSE training of 0.0855 and RMSE testing of 0.0846.
Categorization of tumor-derived cells from lung cancer with compact deep learning

Cell characterization is key to research medical signaling of cancer-derived cells in the peripheral blood sample under the high-resolution fluorescence microscope. The task has been challenging with traditional image processing and machine learning techniques due to imaging artifacts, noise, debris, de-focusing, shallow depth of field, and high variability in cell morphotypes and fluorescence. We present a compact deep learning method that combines cell component segmentation and grouping with a guided feature learning for categorizing circulating tumor cells from lung cancer liquid biopsy. The method demonstrates a promising performance with a small training dataset. It is effective, efficient, and valuable in low-cost clinical applications. Characterization of cancer-derived cells could provide vital insights into cancer metastasis and contribute to development of novel targeted therapies.
Design and implementation of RESTFUL API based model for vulnerability detection and mitigation

Abstract With the rapid rise of online applications in the contemporary digital world, security concerns are also developing day by day. A web application is a collection of several web pages that are kept on a server. It is stated that as the economy grows, so will the security risks. There are numerous current web vulnerabilities that are still a significant concern, and with time, there will be much more new vulnerability that emerges as technology advances. In this paper, There is a detailed study of various web threats i.e., vulnerabilities that are emerging and proposed a system which detects those vulnerabilities, resolves them for the user and generates a user log for the administrator to monitor the type of vulnerabilities. In the proposed model we have generated the system in the format of API. In the testing phase we’ve detected various web vulnerabilities such as SQL Injections, Cross Site Scripting, Session Hijacking, Unauthorized access, Middle man attack etc.
Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim's blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim's blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.
Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning

Conversational recommender systems (CRS) aim to proactively elicit user preference and recommend high-quality items through natural language conversations. Typically, a CRS consists of a recommendation module to predict preferred items for users and a conversation module to generate appropriate responses. To develop an effective CRS, it is essential to seamlessly integrate the two modules. Existing works either design semantic alignment strategies, or share knowledge resources and representations between the two modules. However, these approaches still rely on different architectures or techniques to develop the two modules, making it difficult for effective module integration. To address this problem, we propose a unified CRS model named UniCRS based on knowledge-enhanced prompt learning. Our approach unifies the recommendation and conversation subtasks into the prompt learning paradigm, and utilizes knowledge-enhanced prompts based on a fixed pre-trained language model (PLM) to fulfill both subtasks in a unified approach. In the prompt design, we include fused knowledge representations, task-specific soft tokens, and the dialogue context, which can provide sufficient contextual information to adapt the PLM for the CRS task. Besides, for the recommendation subtask, we also incorporate the generated response template as an important part of the prompt, to enhance the information interaction between the two subtasks. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach. Our code is publicly available at the link: https://github.com/RUCAIBox/UniCRS.
A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation

Most of existing synthetic aperture radar (SAR) ship instance segmentation models do not achieve mask interaction or offer limited interaction performance. Besides, their multi-scale ship instance segmentation performance is moderate especially for small ships. To solve these problems, we propose a mask attention interaction and scale enhancement network (MAI-SE-Net) for SAR ship instance segmentation. MAI uses an atrous spatial pyramid pooling (ASPP) to gain multi-resolution feature responses, a non-local block (NLB) to model long-range spatial dependencies, and a concatenation shuffle attention block (CSAB) to improve interaction benefits. SE uses a content-aware reassembly of features block (CARAFEB) to generate an extra pyramid bottom-level to boost small ship performance, a feature balance operation (FBO) to improve scale feature description, and a global context block (GCB) to refine features. Experimental results on two public SSDD and HRSID datasets reveal that MAI-SE-Net outperforms the other nine competitive models, better than the suboptimal model by 4.7% detection AP and 3.4% segmentation AP on SSDD and by 3.0% detection AP and 2.4% segmentation AP on HRSID.