Reviewerly/exHarmony huggingface dataset:
Multi-Label Hashing for Dependency Relations Among Multiple Objectives

Learning hash functions have been widely applied for large-scale image retrieval. Existing methods usually use CNNs to process an entire image at once, which is efficient for single-label images but not for multi-label images. First, these methods cannot fully exploit independent features of different objects in one image, resulting in some small object features with important information being ignored. Second, the methods cannot capture different semantic information from dependency relations among objects. Third, the existing methods ignore the impacts of imbalance between hard and easy training pairs, resulting in suboptimal hash codes. To address these issues, we propose a novel deep hashing method, termed multi-label hashing for dependency relations among multiple objectives (DRMH). We first utilize an object detection network to extract object feature representations to avoid ignoring small object features and then fuse object visual features with position features and further capture dependency relations among objects using a self-attention mechanism. In addition, we design a weighted pairwise hash loss to solve the imbalance problem between hard and easy training pairs. Extensive experiments are conducted on multi-label datasets and zero-shot datasets, and the proposed DRMH outperforms many state-of-the-art hashing methods with respect to different evaluation metrics.
A tutorial on data mining for Bayesian networks, with a specific focus on IoT for agriculture

We are seeing a steady build up in momentum of two trends that will lead to a significant, and necessary, transformation of agriculture in the 21st Century. Firstly, the move to digital with IoT facilitating the use of sensor networks to support precise decision making. Secondly, the move to “ecological intensification”; working with natural processes to lower the carbon footprint of agricultural processes and increase the biodiversity of agricultural units without sacrificing yield. Clearly data mining and machine learning have an important role to play in supporting this transformation. However, given the range of biotic, abiotic and social contexts that need to inform the development of models for decision support in agriculture, we need data mining techniques that support the use of qualitative and unstructured data as well as hard numerical data. In this tutorial we show how Bayesian Networks can be built from a wide range of sources of expert knowledge and data. Whist we use digital agriculture as a key beneficiary of these techniques, this tutorial will also be of interest to all those with an interest in building IoT systems that require combinations of social, technical and environmental understanding.
Detecting abnormal seismic activity areas of Anatolian plate and deformation directions using Python Geospatial libraries

Various inaccurate traditional models have resulted in major ambiguities and gaps in the interpretation of Anatolian plate deformation directions. To address this issue, a GIS-based spatial statistical analysis method was used for the first time to detect the directional distribution of deformation along the Anatolian Plate in Turkey. Two strategies were used in this study: firstly, identifying the abnormally active seismic areas by detecting significant hotspot and cold spot clusters and confirming this detection using optimized hotspot analysis for earthquake events that occurred from 1900 to the end of 2019. Secondly, detecting the directional distribution of deformation using a Standard Deviational Ellipse (SDE) by calculating the standard deviation of the x and y coordinates from the mean center for each set of earthquake events in the Anaconda Python Platform and ArcGIS 10.8 software. Our improved geostatistical analysis results confirmed the existence of abnormal seismic hazard zones within the study area and three deformation directions: the east-west trend, the southeast-northwest trend, and the south-north trend.
EETC: An extended encrypted traffic classification algorithm based on variant resnet network

With the advent of big data, encrypted traffic is widely used, and it is gradually becoming a new challenge for network security and management. Many researchers have obtained good results by converting encrypted traffic into images and feeding them to deep learning-based classification models. However, these methods have some limitations in that they cannot do sustainable learning. They have to retrain a new classifier when new traffic is encountered. To tackle this issue, we propose an extended encrypted traffic classification algorithm based on incremental learning. This approach extracts content and statistical information from encrypted traffic and supports multi-label prediction for VPN channels and applications. We can add new classes to the model without completely retraining and accelerate the update cycle of the model. The results show that the proposed model performs well in classification experiments, which can achieve 98.1% and 96.0% classification accuracy on ISCXVPN2016 and self-collected dataset respectively. In addition, our method can retain high accuracy under the situation of limited memory space while the number of new classes of data increases gradually. It demonstrates the superiority of constructing a generic unforgetting basis for classifying encrypted communication.
Witnessing entangled two-photon absorption via quantum interferometry

Recent investigations have suggested that the use of non-classical states of light, such as entangled photon pairs, may open new and exciting avenues in experimental two-photon absorption spectroscopy. Despite several experimental studies of entangled two-photon absorption (eTPA), there is still a heated debate on whether eTPA has truly been observed. This interesting debate has arisen mainly because it has recently been argued that single-photon-loss mechanisms, such as scattering or hot-band absorption, may mimic the expected entangled-photon linear absorption behavior. In this work, we focus on transmission measurements of eTPA and explore three different two-photon quantum interferometers in the context of assessing eTPA. We demonstrate that the so-called N00N-state configuration is the only one among those considered insensitive to linear (single-photon) losses. Remarkably, our results show that N00N states may become a potentially powerful tool for quantum spectroscopy, placing them as a strong candidate for the certification of eTPA in an arbitrary sample.
Economic Growth, Financial Development and Bank Failure

Corruption is one of the main causes of inefficiency and poor productivity. This chapter looks at the relationship between banking failure, corruption, financial development, and economic growth in Nigeria during the period from 1989 to 2019. The work uses the autoregressive distributed lag (ARDL) cointegration and error correction model and the Granger causality test for the analysis of data. The results of the empirical analyses show a negative relationship between corruption and both financial development and economic growth. This shows that corruption is bad for both the financial sector and the economy. The result also shows that corruption is a good reason for causing bank failures in Nigeria. Hence, for financial development and economic growth, corruption must be reduced as much as possible if not eliminated. It is recommended to put the fight against all forms of corruption in Nigeria in top gear.
High Concurrency Response Strategy based on Kubernetes Horizontal Pod Autoscaler

Abstract The automatic scaling of resources in the cluster can reduce costs while satisfying resource usage. This is one of the salient features of cloud computing. The current service scenarios are diverse, and the default policy of Horizontal Pod Autoscaling (HPA) is difficult to meet the demand. In the face of high concurrent traffic, the default strategy has the problem of long expansion time and slow speed. Based on HPA, this paper proposes the High Concurrency Response Strategy. It can cope with high concurrency usage scenarios and ensure that services can still be accessed in the face of large traffic concurrency. Compared with the default HPA, the customized HPA strategy reduces the number of service requests by over 20 ms or 19%.
An Improved SSD Object Detection Algorithm Based on Attention Mechanism and Feature Fusion

Abstract The Single Shot MultiBox Detector (SSD) is a well-known object detection method, but its detection of small objects is not effective. This paper makes modifications to the SSD object detection method to address its insufficient semantic information in low-level feature maps, thus enhancing the detectability for small objects. First, the Feature Pyramid Network (FPN) is incorporated into the SSD so that the shallow feature map, which is primarily utilized for detecting small objects, contains more semantic information in addition to rich location information. Second, the Convolutional Block Attention Module (CBAM) is introduced to reinforce the SSD network’s capability to learn key features and thus improve missed detections. The experimental data indicate that this algorithm achieves 78.1% mAP in the PASCAL VOC2007test, a 3.9% improvement compared with the conventional SSD, and also has a great improvement compared with the Fast R-CNN and Faster R-CNN. As well as,this algorithm is better for small object detection and also meets the real-time requirements.
On metrics and prioritization of investments in hardware security

Abstract The security risks posed by electronics are numerous. There are typically a variety of risk‐reducing countermeasures for a given system or across an enterprise. Each countermeasure is associated with both a level of risk reduction and its lifecycle costs. Given budgetary constraints, risk managers and systems engineers must determine what combinations of countermeasures cost‐effectively maximize risk reduction, and what metrics best guide the investment process. In this paper, we seek to answer these questions through exploration of risk reduction metrics from the field of security economics, including the benefit/cost ratio, return on security investment (ROSI), expected benefit of information security (EBIS), and expected net benefit of information security (ENBIS). The results suggest that ratio‐based metrics are not strongly correlated with risk reduction, while EBIS is equivalent to risk reduction and ENBIS is equal to risk reduction minus cost.
SAND-2: An optimized implementation of lightweight block cipher

The development of ARX-based lightweight block ciphers has been plagued by the difficulty of theoretical security analysis. SAND solves this problem better by obtaining an equivalent representation based on a synthetic S-box. This paper analyzed SAND in terms of diffusivity and found that it can be optimized. SAND has the issue of slow diffusion after the initial plaintext and key are all 0. On the other hand, it takes at least 11 rounds for SAND to reach full diffusion without AddRoundKey. Thus, this paper proposes a high-diffusion architecture SAND-2 to address the above issues. Firstly, issue 1 is solved by replacing the AND operation with a NAND operation and calling the round function dynamically. Then, in order to solve issue 2, P1 and P2 permutations are introduced into G0 and G1, respectively. The full diffusion speed of SAND-2 is 63.7% increased compared to SAND. In addition, the comparison results of hardware indicators show that the hardware resources of SAND-2 are slightly lower, and the throughput is 10% higher than SAND. Finally, the security analysis shows that SAND-2 reaches the upper bound of the resisting differential analysis in fewer rounds.
Dynamics of multipartite quantum steering for different types of decoherence channels

Multipartite quantum steering, a unique resource for asymmetric quantum network information tasks, is very fragile to the inevitable decoherence, which makes it useless for practical purposes. It is thus of importance to understand how it decays in the presence of noise channels. We study the dynamic behaviors of genuine tripartite steering, reduced bipartite steering, and collective steering of a generalized three-qubit W state when only one qubit interacts independently with the amplitude damping channel (ADC), phase damping channel (PDC) or depolarizing channel (DC). Our results provide the region of decoherence strength and state parameters that each type of steering can survive. The results show that these steering correlations decay the slowest in PDC and some non-maximally entangled states more robust than the maximally entangled ones. Unlike entanglement and Bell nonlocality, the thresholds of decoherence strength that reduced bipartite steering and collective steering can survive depend on the steering direction. In addition, we find that not only one party can be steered by a group system, but also two parties can be steered by a single system. There is a trade-off between the monogamy relation involving one steered party and two steered parties. Our work provides comprehensive information about the effect of decoherence on multipartite quantum steering, which will help to realize quantum information processing tasks in the presence of noise environments.
Automatic modulation classification in optical wireless communication systems based on cancellable biometric concepts

Automatic modulation classification (AMC) has recently acquired a lot of interest in the optical wireless communication (OWC) community. The OWC channel has variable characteristics. Hence, there is a need for an adaptive modulation scheme to cope with the varying channel characteristics. Adaptive modulation requires the implementation of AMC at the receiver side. Instead of using complex classification with deep learning (DL) techniques, a simple proposed scheme for AMC is introduced in this paper. This scheme is based on chaotic Baker map (CBM), wavelet image fusion, and autocorrelation estimation. It depends on constellation diagrams for eight modulation formats, including (2/4/8/16 PSK), and (8/16/32/64 QAM). First, the constellation diagrams are acquired and scrambled through the CBM, and they are merged using the wavelet image fusion and stored as reference templates in the system database. After that, the classification of each modulation format depends on estimated correlation scores and a thresholding strategy similar to the strategy adopted in cancellable biometric systems that depend on encrypted templates. Simulation results prove good classification for all studied modulation formats.
Probabilistic Coarsening for Knowledge Graph Embeddings

Knowledge graphs have risen in popularity in recent years, demonstrating their utility in applications across the spectrum of computer science. Finding their embedded representations is thus highly desirable as it makes them easily operated on and reasoned with by machines. With this in mind, we propose a simple meta-strategy for embedding knowledge graphs using probabilistic coarsening. In this approach, a knowledge graph is first coarsened before being embedded by an arbitrary embedding method. The resulting coarse embeddings are then extended down as those of the initial knowledge graph. Although straightforward, this allows for faster training by reducing knowledge graph complexity while revealing its higher-order structures. We demonstrate this empirically on four real-world datasets, which show that coarse embeddings are learned faster and are often of higher quality. We conclude that coarsening is a recommended prepossessing step regardless of the underlying embedding method used.
A New Reliability Coefficient Using Betting Commitment Evidence Distance in Dempster–Shafer Evidence Theory for Uncertain Information Fusion

Dempster-Shafer evidence theory is widely used to deal with uncertain information by evidence modeling and evidence reasoning. However, if there is a high contradiction between different pieces of evidence, the Dempster combination rule may give a fusion result that violates the intuitive result. Many methods have been proposed to solve conflict evidence fusion, and it is still an open issue. This paper proposes a new reliability coefficient using betting commitment evidence distance in Dempster-Shafer evidence theory for conflict and uncertain information fusion. The single belief function for belief assignment in the initial frame of discernment is defined. After evidence preprocessing with the proposed reliability coefficient and single belief function, the evidence fusion result can be calculated with the Dempster combination rule. To evaluate the effectiveness of the proposed uncertainty measure, a new method of uncertain information fusion based on the new evidence reliability coefficient is proposed. The experimental results on UCI machine learning data sets show the availability and effectiveness of the new reliability coefficient for uncertain information processing.
Synchronization of spatiotemporal chaos and reservoir computing via scalar signals

Reservoir computing turns out to be a powerful data-driven tool for model-free prediction and synchronization of chaotic systems. However, recent studies of synchronization using reservoir computing mainly focus on low-dimensional chaotic systems, the relevant research on spatiotemporally chaotic systems that are more common in nature is still missing. Here, we investigate synchronization of spatiotemporally chaotic systems via reservoir computing instead of dynamical equations. By transmitting scalar driving signals at a finite number of spatial points, we realize three types of synchronization, including the one between the trained reservoir computer and its learned spatiotemporally chaotic system, the one between the trained reservoir computers, and the cascading one among the learned system and the trained reservoir computers. Our method can not only be used to confirm whether the trained reservoir computer has captured the features of the spatiotemporally chaotic system but also is of great significance for application in complex networks and secure communication. The results are illustrated numerically with two benchmark dynamical systems.
On the concept of university competitiveness management

Operating under market economy conditions, higher education institutions should develop a set of strategic measures to ensure their competitiveness. The system of views on solving the problems of achieving and keeping competitive advantage is mainly represented by the concept of managing the competitiveness of higher education institutions. The article is devoted to identifying the features and clarifying the content of this concept. It is determined that its content is a set of goals, principles, functions, methods, and tools that ensure the competitiveness of a university in the educational services market. The aforementioned content components are analysed in detail. Methods of managing university competitiveness are substantiated and classified based on the type of controlling influence. Tools for managing competitiveness are described, including in terms of their impact on end users of scientific and educational products (services).
Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them

Linked Open Data (LOD) is the largest, collaborative, distributed, and publicly-accessible Knowledge Graph (KG) uniformly encoded in the Resource Description Framework (RDF) and formally represented according to the semantics of the Web Ontology Language (OWL). LOD provides researchers with a unique opportunity to study knowledge engineering as an empirical science: to observe existing modelling practices and possibly understanding how to improve knowledge engineering methodologies and knowledge representation formalisms. Following this perspective, several studies have analysed LOD to identify (mis-)use of OWL constructs or other modelling phenomena e.g. class or property usage, their alignment, the average depth of taxonomies. A question that remains open is whether there is a relation between observed modelling practices and knowledge domains (natural science, linguistics, etc.): do certain practices or phenomena change as the knowledge domain varies? Answering this question requires an assessment of the domains covered by LOD as well as a classification of its datasets. Existing approaches to classify LOD datasets provide partial and unaligned views, posing additional challenges. In this paper, we introduce a classification of knowledge domains, and a method for classifying LOD datasets and ontologies based on it. We classify a large portion of LOD and investigate whether a set of observed phenomena have a domain-specific character.
Multi-Agent Action Graph Based Task Allocation and Path Planning Considering Changes in Environment

Task allocation and path planning considering changes in the mobility of robots in the environment allows the robots to efficiently execute tasks with smaller travel times.A lunar base construction is one of the situations in which robots can more efficiently accomplish its goal by taking such environment changes into account when performing tasks.For the construction, we assumed that when a robot executes a task of building a road, the environment changes such that aisles that were unusable before the task become usable post execution.If such changes in environment are considered in advance, the robot can efficiently plan to wait until the environment changes and can move before executing the task.However, previous studies have not considered such changes, resulting in inefficient planning.To solve this problem, we developed a multi-agent action graph that consists of multiple layers and expresses the environment changes associated with task execution in terms of changes in these layers.In this graph, task allocation and path planning are formulated as a combinatorial optimization problem and are optimized using mixed-integer programming.Multi-agent action graphs and the proposed formulation enable efficient planning considering changes in the robots' mobility in advance.Through simulations, we confirmed that the proposed method completed the construction of the lunar base approximately 16.4% earlier than the conventional method, while consuming approximately 16.0% less total energy of the robots.
Building Maritime Cybersecurity Capacity Against Ransomware Attacks

Ransomware is considered among the top threats that organizations have to face, and one that is not expected to go away anytime soon. Cyber criminals have turned ransomware into a profitable business by targeting environments in which they can maximize the attack's impact and their profits. The Maritime domain is a lucrative environment as it supports many aspects of the supply chain, making it a high priority target for cyber criminals. Recent ransomware incidents in the Maritime domain have demonstrated the necessity to increase the cyber risk awareness and readiness levels, to effectively address this threat. To defend and minimize the risk to get infected and/or recover if impacted by a ransomware, the required cybersecurity capacity needs to be developed. This can be achieved by educating and training all Maritime stakeholders, according to their role and responsibilities, across a strategic, operational, and/or tactical level. The challenge is to determine the capabilities that the Maritime stakeholders need to develop across the different levels, so they can exercise sound judgement and procedures when faced with a ransomware incident. This work presents an innovative training curriculum that was developed to build cybersecurity capacity in the Maritime domain and defend against ransomware attacks. A highlight of the proposed curriculum is that it specifies structured walkthrough practice to promote active learning and make education memorable and actionable. The proposed curriculum targets to provide design directions to the cybersecurity community to develop new training curricula to address future ransomware attacks.