Reviewerly/exHarmony huggingface dataset:
Energy-Efficient Stream Compaction Through Filtering and Coalescing Accesses in GPGPU Memory Partitions

Graph-based applications are essential in emerging domains such as data analytics or machine learning. Data gathering in a knowledge-based society requires great data processing efficiency. High-throughput GPGPU architectures are key to enable efficient graph processing. Nonetheless, irregular and sparse memory access patterns present in graph-based applications induce high memory divergence and contention, which result in poor GPGPU efficiency for graph processing. Recent work has pointed out the importance of stream compaction operations, and has proposed a Stream Compaction Unit (SCU) to offload them to a specialized hardware. On the other hand, memory contention caused by high divergence has been tackled with the Irregular accesses Reorder Unit (IRU), delivering improved memory coalescing. In this paper, we propose a new unit, the IRU-enhanced SCU (ISCU), that leverages the strengths of both approaches. The ISCU employs the efficient mechanisms of the IRU to improve SCU stream compaction efficiency and throughput limitations, achieving a synergistic effect for graph processing. We evaluate the ISCU for a wide variety of state-of-the-art graph-based algorithms and applications. Results show that the ISCU achieves a performance speedup of 2.2x and 90 percent energy savings derived from a high reduction of 78 percent memory accesses, while incurring in 8.5 percent area overhead.
Federated Trace: A Node Selection Method for More Efficient Federated Learning

Federated Learning (FL) is a learning paradigm, which allows the model to directly use a large amount of data in edge devices for training without heavy communication costs and privacy leakage. An important problem that FL faced is the heterogeneity of data at different edge nodes, resulting in a lack of convergence efficiency. In this paper, we propose Federated Trace (FedTrace) to address this problem. In FedTrace, we define the time series of some performance metrics of the global model on the edge node as the training trace of this node, which can reflect the data distribution of the edge node. By clustering the training traces, we can know which nodes have similar data distribution, which can guide the selection of nodes in each round of training. Here, we use a simple but effective method, that is, randomly selecting nodes from each cluster evenly. Experiments on various settings demonstrate that our method significantly reduces the number of communication rounds required in FL.
Modeling and Use of Inter-Criteria Decision Analysis for Selecting Growth Rate Models for Batch Cultivation of Yeast Kluyveromyces marxianus var. lactis MC 5

Ten unstructured models of Monod, Mink, Tessier, Moser, Aiba, Andrews, Haldane, Luong, Edward, and Han-Levenspiel are considered in this paper to explain the kinetics of cell growth for batch cultivation of the yeast Kluyweromyces marxianus var. lactis MC 5. For the first time, two independent kinetic models are used to model the process for the two basic substrates—lactose and oxygen. The selection of the most appropriate growth rate models has been made through a new multi-criteria decision-making approach called the Inter-Criteria Decision Analysis (ICDA) method. The application of ICDA to the growth rate of lactose and oxygen alone has shown that there have been many correlations between the studied models. Thus, the models for the growth rate, depending only on lactose, are reduced to one—Monod model and there are two models—Monod and Mink—depending on oxygen only. Separate kinetic process models have been developed for the combination of Monod–Monod and Monod–Mink models. For the first time, in addition to the multiplicative form, the additive form of a specific growth rate has been studied. The comparison of the obtained results has shown that the additive form has shown better results than the multiplicative one. For this reason, the additive form of the Monod–Monod model will be used to model the process.
A multivariate normal approximation for the Dirichlet density and some applications

In this short note, we prove an asymptotic expansion for the ratio of the Dirichlet density to the multivariate normal density with the same mean and covariance matrix. The expansion is then used to derive an upper bound on the total variation between the corresponding probability measures and rederive the asymptotic variance of the Dirichlet kernel estimators introduced by Aitchison and Lauder (1985) and studied theoretically in Ouimet (2020). Another potential application related to the asymptotic equivalence between the Gaussian variance regression problem and the Gaussian white noise problem is briefly mentioned but left open for future research.
Baby Search Algorithm

In this paper, we propose a novel meta-heuristic algorithm, called Baby Search Algorithm (BSA), for solving complex optimization problems. BSA mimics the behavior of babies who have strong curiosity about external things and can actively look for things of interest. Generally speaking, babies' search behaviors are divided into two types, one is to find another interesting thing, which is similar to exploration in the optimization problem; the other is to approach the most interesting thing at present, which is similar to exploitation. In order to balance exploration and exploitation, we utilize two probability thresholds to control it. In addition, we further combine Levy flight and mutation operations in BSA, which can make the babies' search get closer to the nature of searching behavior. We validate the proposed algorithm against test functions and then compare its performance with those of twelve state-of-the-art algorithms. Finally, we discuss the implication of the results and suggestion for further research.
SHACL: A Description Logic in Disguise

SHACL is a W3C-proposed language for expressing structural constraints on RDF graphs. In recent years, SHACL’s popularity has risen quickly. This rise in popularity comes with questions related to its place in the semantic web, particularly about its relation to OWL (the de facto standard for expressing ontological information on the web) and description logics (which form the formal foundations of OWL). We answer these questions by arguing that SHACL is in fact a description logic. On the one hand, our answer is surprisingly simple, some might even say obvious. But, on the other hand, our answer is also controversial. By resolving this issue once and for all, we establish the field of description logics as the solid formal foundations of SHACL.
Impact Analysis of Digital Transformation of the Energy Sector Economy on Hospitality Industry

The article is devoted to an overview of current trends in the development of digital technologies in the state’s economic energy sector and an analysis of their impact on the hospitality industry. In the course of the study, criteria of practical importance were determined for the hospitality industry business structures. The same is done with the digital transformation of the energy sector economy. Methods. A study of the digital energy development features and their impact on the structure of enterprise expenses in the hospitality industry was carried out. An analysis of the energy complex activities was done within the framework of the Russian economy digital transformation concept, and an assessment of the hospitality industry risks was given, within the framework of the chosen research area. Results. A model was developed for assessing the impact of the digital transformation of the energy sector economy on the hospitality industry. The points of the interconnection of domestic power grid companies with enterprises in the hospitality industry are highlighted. The key tools for supporting enterprises in the hospitality industry are clustered, depending on their degree of influence by the digital transformation tools of the economy of the country’s energy sector. Conclusions. It is possible to take advantage of promising technological trends in the energy sector. The country’s economy can allocate effort to the transition of the hospitality industry business structures. It would transition the digitization of individual tasks to the digital transformation of the whole industry. Based on the analysis of the digital transformation impact of the energy sector economy on the hospitality industry development, recommendations were specified on the establishment of a policy of soft influence of digital energy on the development of the hospitality industry.
An anchor-free detector and R-CNN integrated neural network architecture for environmental perception of urban roads

Environmental perception of urban roads is a critical research goal in intelligent transportation technology and autonomous vehicles, and pedestrian location is key to many relevant algorithms. Because anchor-free detectors are faster and region-based convolutional neural networks have a higher accuracy in object detection and classification, we propose an integrated convolutional networking architecture combining an anchor-free detector with a region-based convolutional neural network in the environmental perception task. The proposed network achieves higher precision and increases inference speed by up to 30%. To acquire more accurate region boundaries than a coarse bounding box method, a semantic segmentation sub-network is adopted to predict an instance segmentation mask for each object, and more accurate segmentation results are obtained by using the Dice loss. Moreover, we present an assignment strategy using a modified feature pyramid structure and show that it improves mean average precision of pedestrian detection by 2% on average. Finally, we verify that the pretrained neural network is beneficial for small datasets. Overall, the results show that our model achieves higher precision than the approaches used for comparison.
The Impact of Different Data Mining Classification Techniques in Different Datasets

Data Mining is the process of finding knowledge through the processing of massive amounts of data from different viewpoints and combining them into valuable information; data mining has been a crucial part in various aspects of human life. It is used to recognize the covered up patterns in a huge amount of data. Classification methods are supervised learning methods that categorize the data item into known categories. Creating classification models from an input dataset is one of the most beneficial techniques in data mining; these methods typically create models that are used to forecast future patterns in data. This work has been done to assess the effectiveness of different classifiers algorithms such as Support Vector Machine (SVM), Naïve Bayes (NB), J48, and Neural Network (NN), these algorithms were applied on several datasets to determine the performance of the algorithm. All techniques were used with 10-fold cross-validation in the machine learning platform WEKA. According to the study’s findings, no algorithm has consistently performed best for each dataset.
Identification Of In-Field Sensor Defects In The Context Of Image Age Approximation

Image sensor defects that develop in field over a camera’s lifetime are at the core of temporal image forensics, as by knowing their onset time a temporal order can be assigned among pieces of evidence. In this context, only defects that have developed within the time interval of the available data set are relevant. The available methods for defect detection, based on regular scene images, aim to identify all present defects (e.g., to conceal them). In this paper, we introduce two novel defect detection techniques. Because of their properties, these methods only detect defects relevant for image age approximation. This is important since defects that do not provide additional age information can negatively affect the process of image age approximation.
Native multiqubit Toffoli gates on ion trap quantum computers

We examine the detailed scenario for implementing n-control-qubit Toffoli gates and select gates on ion-trap quantum computers, especially those that shuttle ions into interaction zones. We determine expected performance of these gates with realistic parameters for an ion-trap quantum computer and taking into account the time variation of the exchange integrals. This allows us to estimate the errors due to spin-phonon entanglement as well. While there are challenges with implementing these gates, because their performance always has some degree of error, they should be feasible on current hardware, but they may be too slow to be used efficiently in quantum codes on noisy intermediate scale quantum computers.
Graph matching based reasoner: A symbolic approach to question answering

Text understanding and reasoning are among the core areas of artificial intelligence. Even a total solution to automatic text understanding and reasoning is still beyond the current techniques, thus it is time to build the stepping stones to solve a more straightforward problem set like question-answering (QA) without ambiguous utterances in the contexts or questions. The reported state-of-the-art approaches to this kind of problem are nearly all connectionist models based on neural networks. Significant progress has even been made in this direction. It is still hard for the pure connectionist models to handle logical reasoning. They generally suffer from the longstanding drawback of poor explainability and sensitivity to data noise and distribution. In this paper, we propose a complementary symbolic approach, GMR (Graph Matching based Reasoner) to QA — it automatically generates reasoning rules in the form of graphs from the training set and uses the generated rules to infer answers to the questions in the test set via graph matching. By employing this symbolic approach, 20 tasks in bAbI are solved with an average accuracy of 99.38%, and it outperforms the state-of-the-art for a real-life QA dataset WikiTableQuestions. After analyzing the accuracy of the basic evaluation indicators, we studied the generalization ability of the model in the paper, including the anti-noise ability, the convergence of the model, the stability of the model, the complexity of the algorithm, and the uncertainty of the parameters. Through comprehensive analysis and comparison, our model is stronger than the neural network model regarding anti-noise interference. Compared with the neural network model, our model performs very well in multi-tasking, and the stability of the model is quite high. The diversity of tasks did not reduce the stability of the model. We have conducted a comprehensive analysis and comparison of the parameter uncertainties. Our model can optimally select parameter configurations and will not cause a sharp drop in performance due to the parameter uncertainties. Finally, we describe the complexity of the GMR method and the optimal configuration of its parameters.
SoK: Remote Power Analysis

In recent years, numerous attacks have appeared that aim to steal secret information from their victim using the power side-channel vector, yet without direct physical access. These attacks are called Remote Power Attacks or Remote Power Analysis, utilizing resources that are natively present inside the victim environment. However, there is no unified definition about the limitations that a power attack requires to be defined as remote. This paper aims to propose a unified definition and concrete threat models to clearly differentiate remote power attacks from non-remote ones. Additionally, we collect the main remote power attacks performed so far from the literature, and the principal proposed countermeasures to avoid them. The search of such countermeasures denoted a clear gap in preventing remote power attacks at the technical level. Thus, the academic community must face an important challenge to avoid this emerging threat, given the clear room for improvement that should be addressed in terms of defense and security of devices that work with private information.
Recognizing creative visual design

Multiscale design is the widely practiced use of space and scale to visually explore and articulate relationships. Free-form web curation (FFWC) is an approach to supporting multiscale design, involving creative strategies of collecting content, assembling it to juxtapose and organize, sketching, writing, shifting perspective to navigate, and exhibiting to share and collaborate. Our long term goal is to support design students with automatic, on demand feedback.
Visual Exploration of Anomalies in Cyclic Time Series Data with Matrix and Glyph Representations

The digitalization of manufacturing involves machines equipped with sensors that collect, produce, and exchange data machine-to-machine and machine-to-human in real-time. As the data generated within a production process can be massive and overwhelming for human users, support is needed to understand and explore this data, and drive decisions from it. First, the data has to be monitored and recorded using methods that can handle massive datasets. Next, the collected data has to be analyzed (often in real-time) to, e.g., (i) identify undetected process correlations, (ii) forecast the product quality, and (iii) perform root-cause analysis of failures or problems. The analysis becomes even more valuable when the production process is divided into repeating tasks, producing a vast amount of comparable data. For instance, in automotive durability tests, engineers investigate an engine's condition using multiple sensors, recording data from repeating test cycles. Tests can span dozens or hundreds of cycles, and thousands of runtime hours, making it difficult for engineers to collect and monitor each iteration's data to detect interesting data, such as anomalies. We propose an interactive visual analytics approach that displays the iterations of durability tests as a collection of color-encoded cycle glyphs to tackle this issue. With our approach, domain users including test engineers can readily monitor the test, detect potential anomalies, and intuitively analyze, report and document the detected anomalies. This research is conducted in close collaboration with our partner from the automotive sector and shows the effectiveness and efficiency of a prototype with a pair analytics evaluation study. We open up directions for future work, including a visual interactive labeling concept for anomaly classification.
Identifying constitutive articles of cumulative dissertation theses by bilingual text similarity. Evaluation of similarity methods on a new short text task

Abstract Cumulative dissertations are doctoral theses comprised of multiple published articles. For studies of publication activity and citation impact of early career researchers, it is important to identify these articles and link them to their associated theses. Using a new benchmark data set, this paper reports on experiments of measuring the bilingual textual similarity between, on the one hand, titles and keywords of doctoral theses, and, on the other hand, articles’ titles and abstracts. The tested methods are cosine similarity and L1 distance in the Vector Space Model (VSM) as baselines, the language-indifferent methods Latent Semantic Analysis (LSA) and trigram similarity, and the language-aware methods fastText and Random Indexing (RI). LSA and RI, two supervised methods, were trained on a purposively collected bilingual scientific parallel text corpus. The results show that the VSM baselines and the RI method perform best but that the VSM method is unsuitable for cross-language similarity due to its inherent monolingual bias.
A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling

To provide more accurate and stable recommendations, it is necessary to combine display information with implicit information and to dig out potential information. Existing methods only consider explicit feedback information or implicit feedback information unilaterally and ignore the potential information of explicit feedback information and implicit feedback information, which is also crucial to the accuracy of the recommendation system. However, the traditional Heterogeneous Information Networks (HIN) recommendation ignores the attribute information in the meta-path and the interaction between the user and the item and, instead, only considers the linear characteristics of the user-object often ignoring its non-linear characteristics. Aiming at the potential information acquisition problem from assorted feedback, we propose a new top-N recommendation method MFDNN for Heterogeneous Information Networks (HINs). First, we consider explicit and implicit feedback information to determine the potential preferences of users and the potential features of the product. Then, matrix factorization (MF) and a deep neural network (DNN) are fused to learn independent feature embeddings through MF and DNN, and fully considering the linear and non-linear characteristics of the user-object. MFDNN was tested on several real data sets, such as Movie-Lens, and compared with benchmark experiments. MFDNN significantly improved the hit ratio (HR) and normalized discounted cumulative gain (NDCG). Further research showed that the meta-path bias had an excellent effect on the gain of potential information mining and the fusion of explicit and implicit information in the accuracy and stability of user interest classification.
Chinese Relation Extraction with Flat-Lattice Encoding and Pretrain-Transfer Strategy

Relation Extraction (RE) aims to assign a correct relation class holding between entity pairs in context. However, many existing methods suffer from segmentation errors, especially for Chinese RE. In this paper, an improved lattice encoding is introduced. Our structure is a variant of the flat-lattice Transformer. The lattice framework can combine character-level and word-level information to avoid segmentation errors. We optimize the position encoding scheme to embrace the relative distance between spans and target entities. Moreover, to reduce the classification errors between positive instances and negative instances in our model, we propose a pretrain-transfer strategy. Specifically, the main idea is to migrate the classification ability of the binary classifier to multi-class identification. Experiments on SanWen, FinRE, and ACE-2005 corpora demonstrate that our methods are effective and outperform other relation extraction models in performance.
Implementation Mini (Advanced Encryption Standard) by Substitution Box in Galois Field (2<sup>4</sup>)

In this paper, a different method was used to find the Substitution Box, By the fourth exponent of the inverse of a function irreducible polynomial which is instead the first exponent of the inverse of the usual method, which is to use this different method after several experiments led to good results increased the complexity and security. the affine transformation period in GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> )increased to the max value of 16 periods .the distance of Strict Avalanche Criterion decreased to perfect value zero. The fixed point decreased to perfect value zero.
Federating trust

Zero Trust is an emerging security paradigm that does away with implicit zones of trust commonly employed within static, defense-in-depth, enterprise architectures. One of the core tenets of Zero Trust is that resource access is determined by dynamic policy - an intersection of trust in a user, the supporting application or service, the underlying network, and the devices which hold or process data. Establishing this overall assessment of trust serves well for centralized architectures where an administrator can establish and assess each of these trust enablers, such as in an enterprise network. However, shifting workloads to remote access, bring your own device (BYOD), and cloud hosting of collaborative services, to name a few, all challenge the ability of an administrator to effectively establish a complete Zero Trust architecture due to the inability to fully trust each component.