Reviewerly/exHarmony huggingface dataset:
Reinforcement learning with dynamic completion for answering multi-hop questions over incomplete knowledge graph

Text-enhanced and implicit reasoning methods are proposed for answering questions over incomplete knowledge graph (KG), whereas prior studies either rely on external resources or lack necessary interpretability. This article desires to extend the line of reinforcement learning (RL) methods for better interpretability and dynamically augment original KG action space with additional actions. To this end, we propose a RL framework along with a dynamic completion mechanism, namely Dynamic Completion Reasoning Network (DCRN). DCRN consists of an action space completion module and a policy network. The action space completion module exploits three sub-modules (relation selector, relation pruner and tail entity predictor) to enrich options for decision making. The policy network calculates probability distribution over joint action space and selects promising next-step actions. Simultaneously, we employ the beam search-based action selection strategy to alleviate delayed and sparse rewards. Extensive experiments conducted on WebQSP, CWQ and MetaQA demonstrate the effectiveness of DCRN. Specifically, under 50% KG setting, the [email protected] performance improvements of DCRN on MetaQA-1H and MetaQA-3H are 2.94% and 1.18% respectively. Moreover, under 30% and 10% KG settings, DCRN prevails over all baselines by 0.9% and 1.5% on WebQSP, indicating the robustness to sparse KGs.
Poisoning Attacks in Federated Learning: A Survey

Federated learning faces many security and privacy issues. Among them, poisoning attacks can significantly impact global models, and malicious attackers can prevent global models from converging or even manipulating the prediction results of global models. Defending against poisoning attacks is a very urgent and challenging task. However, the systematic reviews of poisoning attacks and their corresponding defense strategies from a privacy-preserving perspective still need more effort. This survey provides an in-depth and up-to-date overview of poisoning attacks and corresponding defense strategies in federated learning. We first classify the poisoning attacks according to their methods and targets. Next, we analyze the differences and connections between the various categories of poisoning attacks. In addition, we classify the defense strategies against poisoning attacks in federated learning into three categories and analyze their advantages and disadvantages. Finally, we discuss the privacy protection problem in poisoning attacks and their countermeasure and propose potential research directions from the perspective of attack and defense, respectively.
Seagull optimization algorithm based multi-objective VM placement in edge-cloud data centers

Edge-Cloud Datacenters (ECDCs) have been massively exploited by the owners of technology and industrial centers to satisfy the user demand. At the same time, the amount of energy used by these data centers is considerable. To address this challenge, Virtual Machine (VM) placement of the ECDCs plays an important role; therefore, assigning VM properly to physical machines (PM) can significantly decrease the amount of energy consumption. The applied assigning technique simultaneously must consider additional objectives involving traffic and power usage of the network elements, which makes it a challenging problem. This paper proposes a multi-objective VM placement approach in edge-cloud data centers, which uses Seagull optimization to optimize power and network traffic together. In this strategy, the network traffic among PMs is reduced by concentrating the communications of VMs on the same PMs to reduce the amount of transferred data through the network and reduce the PMs' power consumption by consolidating VMs to fewer PMs, which consumes less energy. We evaluate with simulations in CloudSim and test two different network topologies, VL2 (Virtual Layer 2) and three-tier, to validate that the proposed approach can effectively reduce traffic and power consumption in ECDCs. The experimental results show that our proposed method can decrease energy consumption by 5.5% while simultaneously reducing network traffic by 70% and the power consumption of the network components by 80%.
Bidirectional Translation Between UHD-HDR and HD-SDR Videos

With the popularization of ultra high definition (UHD) high dynamic range (HDR) displays, recent works focus on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">upgrading</i> high definition (HD) standard dynamic range (SDR) videos to UHD-HDR versions, aiming to provides richer details and higher contrasts on advanced modern displays. However, joint considering the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">upgrading</i> & <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">downgrading</i> translations between two types of videos, which is practical in real applications, is generally neglected. On the one hand, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">downgrading</i> translation is the key to showing UHD-HDR videos on HD-SDR displays. On the other hand, considering both translations enables joint optimization and results in high quality translation. To this end, we propose the bidirectional translation network (BiT-Net), which jointly considers two translations in one network for the first time. In brief, BiT-Net is elaborately designed in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">invertible</i> fashion that can be efficiently inferred along forward and backward directions for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">downgrading</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">upgrading</i> tasks, respectively. Based on this framework, we divide each direction into three sub-tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , decomposition, structure-guided translation, and synthesis, to effectively translate the dynamic range and the high-frequency details. Benefiting from the dedicated architecture, our BiT-Net can work on 1) downgrading UHD-HDR videos, 2) upgrading existing HD-SDR videos, and 3) synthesizing UHD-HDR versions from the downgraded HD-SDR videos. Experiments show that the proposed method achieves state-of-the-art performances on all these three tasks.
A Deep Analysis of Textual Features Based Cyberbullying Detection Using Machine Learning

Today's internet advancements boost our electronic connectivity to one another through the use of social media platforms. Using social media has facilitated us in many ways, but it has also negatively impacted us. One of the negative repercussions of utilizing social media is cyberbullying, which harms our reputation, privacy, and feelings, or harasses us. Cyberbullying can be controlled by early detection and legal action. By using machine learning and natural language processing (NLP), it is possible to automatically identify tweets, images, and videos that contain offensive language associated with bullying. In this study, we analyzed five distinct machine learning models, including LightGBM, XGBoost, Logistic Regression, Random Forest, and AdaBoost, to detect cyberbullying using the textual feature-based tweeters dataset. We used more than 47,000 tweets from our dataset, which were divided into six classes. We analyzed the machine learning model and observed that LightGBM performed significantly better than other models, reaching accuracy rates of 85.5%, precision rates of 84%, recall rates of 85%, and an F-1 score of 84.49%.
Dual-branch adaptive attention transformer for occluded person re-identification

Occluded person re-identification is still a common and challenging task because people are often occluded by some obstacles (e.g. cars and trees) in the real world. In order to locate the unoccluded parts and extract local fine-grained features of the occluded human body, State-of-the-Art (SOTA) methods usually use a pose estimation model, which usually causes additional bias and this two-stage architecture also complicates the model. To solve this problem, an end-to-end dual-branch Transformer network for occluded person re-identification is designed. Specifically, one of the branches is the transformer-based global branch, which is responsible for extracting global features, while in the other local branch, we design the Selective Token Attention (STA) module. STA can utilize the multi-headed self-attention mechanism to select discriminating tokens for effectively extracting the local features. Further, in order to alleviate the inconsistency between Softmax Loss and Triplet Loss convergence goals, Circle Loss is introduced to design the Goal Consistency Loss (GC Loss) to supervise the network. Experiments on four challenging datasets for Re-ID tasks (including occluded person Re-ID and holistic person Re-ID) illustrate that our method can achieve SOTA performance.
Capacity of Linguistic Communication Channels in Literary Texts: Application to Charles Dickens’ Novels

In the first part of the article, we recall our general theory of linguistic channels—based on regression lines between deep language parameters—and study their capacity and interdependence. In the second part, we apply the theory to novels written by Charles Dickens and other authors of English literature, including the Gospels in the King James version of the Bible. In literary works (or in any long texts), there are multiple communication channels. The theory considers not only averages but also correlation coefficients. The capacity of linguistic channels is a Gaussian stochastic variable. The similarity between two channels is measured by the likeness index. Dickens’ novels show striking and unexpected mathematical/statistical similarity to the synoptic Gospels. The Pythagorean distance, defined in a suitable Cartesian plane involving deep language parameters, and the likeness index correlate with an inverse proportional relationship. A similar approach can be applied to any literary corpus written in any alphabetical language.
Analysis of Post-Mining Vegetation Development Using Remote Sensing and Spatial Regression Approach: A Case Study of Former Babina Mine (Western Poland)

The vegetation of the post-mining areas is subject to constant and significant changes. Reclamation works, carried out after the cessation of mineral extraction, contribute to the intensive development of new plant species. However, secondary deformations, occurring even many years after the end of exploitation, may cause the degradation of the vegetation cover. It is, therefore, an important issue to identify changes in flora conditions and to determine whether and to what extent past mining has a negative impact on the plant cover state. The objectives of this research have been as follows: (1) analysis of the flora condition in the post-mining area in the 1989–2019 period, (2) identification of sites with significant changes in vegetation state, and (3) modeling of the relationship between the identified changes in vegetation and former mining activities. The research was carried out in the area of the former opencast and underground lignite mine “Friendship of Nations—Babina Shaft,” which is located in the present-day Geopark (Western Poland), using Landsat TM/ETM+/OLI derived vegetation indices (NDVI, NDII, MTVI2) and GIS-based spatial regression. The results indicate a general improvement in flora condition, especially in the vicinity of post-mining waste heaps and former opencast excavations, with the exception of the northwestern part of the former mining field where the values of all of the analyzed vegetation indices have decreased. Also, four zones of statistically significant changes in the flora condition were identified. Finally, the developed GWR models demonstrate that former mining activities had a significant influence on changes in the plant cover state of the analyzed region.
Software Fault Prediction Using an RNN-Based Deep Learning Approach and Ensemble Machine Learning Techniques

Alongside the modern software development life cycle approaches, software testing has gained more importance and has become an area researched actively within the software engineering discipline. In this study, machine learning and deep learning-related software fault predictions were made through a data set named SFP XP-TDD, which was created using three different developed software projects. A data set of five different classifiers widely used in the literature and their Rotation Forest classifier ensemble versions were trained and tested using this data set. Numerous publications in the literature discussed software fault predictions through ML algorithms addressing solutions to different problems. Some of these articles indicated the usage of feature selection algorithms to improve classification performance, while others reported operating ensemble machine learning algorithms for software fault predictions. Besides, a detailed literature review revealed that there were few studies involving software fault prediction with DL algorithms due to the small sample sizes in the data sets and the low success rates in the tests performed on these datasets. As a result, the major contribution of this research was to statistically demonstrate that DL algorithms outperformed ML algorithms in data sets with large sample values via employing three separate software fault prediction datasets. The experimental outcomes of a model that includes a layer of recurrent neural networks (RNNs) were enclosed within this study. Alongside the aforementioned and generated data sets, the study also utilized the Eclipse and Apache Active MQ data sets in to test the effectiveness of the proposed deep learning method.
Analysis of Lightweight Cryptographic Algorithms on IoT Hardware Platform

Highly constrained devices that are interconnected and interact to complete a task are being used in a diverse range of new fields. The Internet of Things (IoT), cyber-physical systems, distributed control systems, vehicular systems, wireless sensor networks, tele-medicine, and the smart grid are a few examples of these fields. In any of these contexts, security and privacy might be essential aspects. Research on secure communication in Internet of Things (IoT) networks is a highly contested topic. One method for ensuring secure data transmission is cryptography. Because IoT devices have limited resources, such as power, memory, and batteries, IoT networks have boosted the term “lightweight cryptography”. Algorithms for lightweight cryptography are designed to efficiently protect data while using minimal resources. In this research, we evaluated and benchmarked lightweight symmetric ciphers for resource-constrained devices. The evaluation is performed using two widely used platform: Arduino and Raspberry Pi. In the first part, we implemented 39 block ciphers on an ATMEGA328p microcontroller and analyzed them in the terms of speed, cost, and energy efficiency during encryption and decryption for different block and key sizes. In the second part, the 2nd-round NIST candidates (80 stream and block cipher algorithms) were added to the first-part ciphers in a comprehensive analysis for equivalent block and key sizes in the terms of latency and energy efficiency.
UAV Path Planning and Obstacle Avoidance Based on Reinforcement Learning in 3D Environments

This study proposes using unmanned aerial vehicles (UAVs) to carry out tasks involving path planning and obstacle avoidance, and to explore how to improve work efficiency and ensure the flight safety of drones. One of the applications under consideration is aquaculture cage detection; the net-cages used in sea-farming are usually numerous and are scattered widely over the sea. It is necessary to save energy consumption so that the drones can complete all cage detections and return to their base on land. In recent years, the application of reinforcement learning has become more and more extensive. In this study, the proposed method is mainly based on the Q-learning algorithm to enable improvements to path planning, and we compare it with a well-known reinforcement learning state–action–reward–state–action (SARSA) algorithm. For the obstacle avoidance control procedure, the same reinforcement learning method is used for training in the AirSim virtual environment; the parameters are changed, and the training results are compared.
An Improved Probabilistic Roadmap Planning Method for Safe Indoor Flights of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been widely used in industry and daily life, where safety is the primary consideration, resulting in their use in open outdoor environments, which are wider than complex indoor environments. However, the demand is growing for deploying UAVs indoors for specific tasks such as inspection, supervision, transportation, and management. To broaden indoor applications while ensuring safety, the quadrotor is notable for its motion flexibility, particularly in the vertical direction. In this study, we developed an improved probabilistic roadmap (PRM) planning method for safe indoor flights based on the assumption of a quadrotor model UAV. First, to represent and model a 3D environment, we generated a reduced-dimensional map using a point cloud projection method. Second, to deploy UAV indoor missions and ensure safety, we improved the PRM planning method and obtained a collision-free flight path for the UAV. Lastly, to optimize the overall mission, we performed postprocessing optimization on the path, avoiding redundant flights. We conducted experiments to validate the effectiveness and efficiency of the proposed method on both desktop and onboard PC, in terms of path-finding success rate, planning time, and path length. The results showed that our method ensures safe indoor UAV flights while significantly improving computational efficiency.
Ecovisor: A Virtual Energy System for Carbon-Efficient Applications

Cloud platforms' rapid growth is raising significant concerns about their carbon emissions. To reduce carbon emissions, future cloud platforms will need to increase their reliance on renewable energy sources, such as solar and wind, which have zero emissions but are highly unreliable. Unfortunately, today's energy systems effectively mask this unreliability in hardware, which prevents applications from optimizing their carbon-efficiency, or work done per kilogram of carbon emitted. To address the problem, we design an "ecovisor", which virtualizes the energy system and exposes software-defined control of it to applications. An ecovisor enables each application to handle clean energy's unreliability in software based on its own specific requirements. We implement a small-scale ecovisor prototype that virtualizes a physical energy system to enable software-based application-level i) visibility into variable grid carbon-intensity and local renewable generation and ii) control of server power usage and battery charging and discharging. We evaluate the ecovisor approach by showing how multiple applications can concurrently exercise their virtual energy system in different ways to better optimize carbon-efficiency based on their specific requirements compared to general system-wide policies.
Real-time Assamese Sign Language Recognition using MediaPipe and Deep Learning

People lacking the sense of hearing and the ability to speak have undeniable communication problems in their life. People with hearing and speech problems communicate using sign language with themselves and others. Sign language is not essentially known to a more significant portion of the human population who uses spoken and written language for communication. Therefore, it is a necessity to develop technological tools for interpretation of sign language. Much research have been carried out to acknowledge sign language using technology for most global languages. But there are still scopes of development of tools and techniques for sign language development for local dialects. There are 22 modern Indian languages and more than 19000 languages that are spoken regionally as mother tongue. This work attempts to develop a technical approach for recognizing Assamese Sign Language, which is one of the 22 modern languages of India. Using machine learning techniques, this work tried to establish a system for identifying the hand gestures from Assamese Sign Language. A combination of two-dimensional and three-dimensional images of Assamese gestures has been used to prepare a dataset. The MediaPipe framework has been implemented to detect landmarks in the images. An Assamese Sign Language dataset of 2094 data points has been generated, including nine static gestures with vowels and consonants (অ, ই, ঈ, উ, এ, ও, ক, জ, ল) from the Assamese Sign Language. The dataset was used for training of a feed-forward neural network. The model yielded an accuracy of 99%. The results reveal that the method implemented in this work is effective for the recognition of the other alphabets and gestures in the Assamese Sign Language. This method could also be tried and tested for the recognition of signs and gestures for various other local languages of India.
A Survey on Cross-chain Technologies

Blockchain has attracted more and more attention of academia, industry, and government in recent decades. Different usage demands have inspired various blockchain designs, forming different blockchain systems, which leads to information islands. Many cross-chain technologies have been proposed to link different blockchains together and expand the utility of blockchain. Nevertheless, the cross-chain technology is still in its infancy, which faces many problems that retard its wide application, for example, the issues related to security, privacy, and effectiveness. In order to further investigate cross-chain technologies, it is essential to understand its current state of arts. Although there are some surveys about cross-chain technologies driven by specific demands, the literature still lacks a comprehensive survey focusing on security, privacy, and effectiveness of cross-chain technologies. In this paper, we provide a review on existing cross-chain technologies based on a comprehensive set of criteria on security, privacy, and other performance. We first propose a blockchain interoperability architecture for the purpose of analyzing potential threats and problems regarding security, privacy, and effectiveness. We then summarize a set of criteria regarding these quality attributes. Next, we comprehensively review the representative works on cross-chain technologies according to a taxonomy based on applied types of techniques and cross-chain purposes. In each work review, we provide a serious discussion on its pros and cons by employing our proposed criteria. Finally, based on our review and analysis, we figure out a number of open issues and step ahead to direct future research directions on cross-chain technologies.
Design Tradeoffs in CXL-Based Memory Pools for Public Cloud Platforms

Dynamic random-access memory (DRAM) is a key driver of performance and cost in public cloud servers. At the same time, a significant amount of DRAM is underutilized due to fragmented use across servers. Emerging interconnects such as Compute Express Link (CXL) offers a path toward improving utilization through memory pooling. However, the design space of CXL-based memory systems is large, with key questions around the size, reach, and topology of the memory pool. At the same time, using pools require navigating complex design constraints around performance, virtualization, and management. This article discusses why cloud providers should deploy CXL memory pools, key design constraints, and observations in designing toward practical deployment. We identify configuration examples with significant positive return of investment.