Reviewerly/exHarmony huggingface dataset:
Relation-Aware Image Captioning for Explainable Visual Question Answering

Recent studies leveraging object detection models for Visual Question Answering (VQA) ignore the correlations or interactions between multiple objects. In addition, the previous VQA models are black boxes for human beings, which means it is difficult to explain why a model returns correct or wrong answers. To solve the problems above, we propose a new model structure with image captioning for the VQA task. Our model constructs a relation graph according to the relative positions between region pairs and then produces relation-aware visual features with a relation encoder. To make the predictions explainable, we introduce an image captioning module and conduct a multi-task training process. In the meantime, the generated captions are injected into the predictor to assist cross-modal understanding. Experiments show that our model can generate meaningful answers and explanations according to the questions and images. Besides, the relation encoder and the caption-attended predictor lead to improvement for different types of questions.
Optimizing Food Allocation in Food Banks with Multi-agent Deep Reinforcement Learning

Food loss and waste (FLW) is becoming a more serious problem, especially in developed nations. Meanwhile, vegetables with imperfect shapes and/or appearances are often discarded even when their flavor and safety have not been compromised. This study proposes a food allocation method with a multi-agent deep reinforcement learning algorithm (called QMIX) to optimize the food allocation process in food support organizations. Several experiments are conducted in a virtual environment to evaluate the basic characteristics of the proposed method and to understand how agents can behave effectively and cooperatively (i.e., to ensure food supply works fairly) through this method. Empirical results showed that agents using the proposed method can acquire reasonable behaviors that can be applied to simple scenarios in the real world.
A Study on the Use of Runtime Files in Handling Crash Reports in a Large Telecom Company

To minimize service downtime and ensure high system availability, Telecom companies must react quickly to failures and crashes of network services and applications. In this study, the focus is on the crash reporting process that normally goes through different levels of service support in a telecom company. To speed up this this repairing or recovering process, service support engineers and product developers rely on the analysis of runtime files (logs, traces, performance metrics, etc.) that are attached to crash reports submitted when an incident occurs. However, there is no clear understanding how these files are used and what their impact on the crash fixing time is. In this paper, we conduct an empirical study at Ericsson to study the use of runtime files in the crash resolution process. We tackle various research questions that revolve around the proportion of runtime files in a selected set of crash reports, the relationship between the severity of crashes and the type of files they contain, and the impact of different file types on the time to fix the crashes. We also study the prediction of the attachment of runtime files to crash reports during the creation of the reports. Our ultimate goal is to figure out how to collect enough information in the crash report system for support engineers and product developers to resolve the failures occurring in telecom network quickly and efficiently.
The Piranha Foraging Optimization Algorithm Oriented to Numerical Optimization Problems

This paper provides an efficient meta-heuristic optimization algorithm for solving continuous optimization problems in the field of numerical and engineering optimization: Piranha Foraging Optimization Algorithm (PFOA). The algorithm is inspired by the flexible and mobile foraging behavior of piranhas, which are classified into three modes of localized group attack, bloodthirsty swarming attack, and scavenging foraging, and two dynamic search behaviors of exploration and exploitation are constructed by simulating the above behaviors. PFOA allows the population to be diverse at different times of the search by means of a non-linear parameter adjustment strategy, a piranha population survival strategy and a reverse escape search strategy. Using visual means to evaluate the optimization efficiency of PFOA, this paper developed experiments on 23 standard benchmark functions and compared the results with 15 well-known meta-heuristics. Experimental results based on statistical methods such as Wilcoxon rank sum test and Friedman test in multiple dimensions (30, 50, 100 and fixed dimensions) show significant differentiation, stable and significant improvement in algorithm performance compared to other compared algorithms. The unique advantages of PFOA in terms of convergence speed and exploration utilization balance can avoid getting trapped in local optimum regions and effectively solve optimization problems with complex search spaces of multiple dimensions.
Performance of a Neural Network Receiver under Mismatch of Channel Training Samples

Data-driven frameworks for wireless communications systems are currently attracting a lot of attention from researchers and practitioners alike. These frameworks based on machine learning (ML) algorithms and neural networks (NN s) architectures, are capable of solving a broad variety of tasks in the wireless communications domain as, for exam-ple, signal detection, channel estimation, channel coding and modulation classification. Moreover, these tasks are solved at a reduced computational cost in comparison to classic model-driven frameworks such as the maximum likelihood for signal detection, for instance. However, data-driven frameworks depend heavily on the dataset available, so that ML algorithms and NNs could be able to actually learn from data and optimize their parameters to solve such tasks at hand. This contrasts to the model-driven frameworks that inherently impart specialized domain knowledge and thus do not require to learn from data. Therefore, a mismatch between the dataset used for training and the actual data may severely degrade the performance of ML algorithms and NN s, especially in practical scenarios where the data statistics and distribution are unknown. In this work we analyze a recently proposed NN for detecting compressed signals, under practical scenarios of dataset samples mismatch, where channel delay profile and statistics mismatches are considered. Numerical results generated by computer simulations show that the NN is robust to statistics mismatches, whereas a significant degradation in performance is observed for channel delay profile mismatches.
A Human-Robot Interaction Applicution Based on Augmented Reality (AR) for Industrial Robot Grasping Process

The application of Augmented Reality (AR) technology in human-robot interaction is one of the research hotspots in recent years. This paper introduces an AR application that can realize the interaction between humans and industrial robots. Unlike traditional interaction methods, the application in this paper can capture the operator's hand joint points and posture in real-time to recognize the gesture of the operator, and then issue the corresponding instructions to the industrial robot. In addition, it can display model information and track the model in real-time to assist the operator. The application is tested in a case study, and its practical effect is shown.
Dynamics-Aware Context Representation for Domain Adaptation in Reinforcement Learning

A context can be used as an embedding extracted from historical trajectories of dynamic systems to provide meaningful information for reinforcement learning (RL) agents, thus improving the domain adaptability and robustness of RL method. However, the process of context extraction involves two key issues: How to efficiently train an encoder to extract context information from historical trajectories? And how to ensure that context information can distinguish different dynamics clearly? To tackle the problems above, a dynamics-aware context representation reinforcement learning (DacRL) is proposed in this study. We leverage the Cycle-Consistent VAE method to extract a meaningful context from historical trajectories and then divide it into domain-specific and domain-general embedding. Furthermore, we consider the contrastive nature between different tasks and use it to improve the quality of domain-specific information, so that it can represent dynamics more clearly. Finally, the current state combined with the domain-specific information is delivered into the RL agent, so as to improve the generalization of the RL agent. The simulation results illustrate that the proposed DacRL is superior to other baselines.
Reviewing the role of machine learning and artificial intelligence for remote attestation in 5G+ networks

The next generation of communication networks promises an exponential growth in number, low latency, and heterogeneity of devices. Consequently, we need to be able to trust devices and device-to-device interactions in sections of a 5G+ network, commonly known as slices. Devices must be willing and able to remotely attest to their trustworthiness. Although trust has previously been based upon deterministic and hardware-driven protocols, over recent decades it has become more common to incorporate artificial intelligence and machine learning (AI/ML) modeling to supplement those protocols. In this paper we review some key aspects of models used for trust of devices, including important criteria for model selection, model structure and inputs, and advantages and disadvantages of these models. We also examine how these AI/ML models intersect with 5G network architecture. Following that, we discuss what sort of data are expected for these trust models. Finally, we discuss next steps for AI/ML models for remote attestation in 5G+ networks.
Decoupling Statistical Trends from Data Volume on LDP-Based Spatio-Temporal Data Collection

Spatio-temporal data is useful for various applications such as urban planning, epidemiology, and natural disasters, but causes exposure of private information, such as home/workplace addresses, because it involves people's trajec-tories. Local Differential Privacy (LDP) based processing is a promising technology for removing sensitive information from spatio-temporal data. A LDP-based processing adds a certain amount of noise to make each piece of data indistinguishable while keeping its intrinsic value. However, LDP is vulnerable to data amplification. When a data store receives data from any device, the data store only appends the received data to existing data. This allows anyone to inject any amount of data into the data and manipulate the trend of the whole data. To tackle this problem, we design a data collection method enabling a data store to collect statistical trends of data from every device irrespective of the data volume. We utilize an Oblivious Transfer (OT) protocol that performs a packet sampling at the reception side, the data store. This sampling enables the collection of statistical trends but requires adjusting LDP processing because the amount of noise is determined by the assumption that the data store receives every piece of LDP-processed data. We then propose an adjustment method for LDP-based process based on the Euclidean algorithm. We conducted qualitative and experimental overhead analysis and showed that the proposed method decouples the relationship between statistical trend and data volume. We also show the processing load can be acceptable on small devices such as smartphones and loT.
Attack Graphs for Standalone Non-Public 5G Networks

Private Networks (also known as Non-Public Net-works) bring significant benefits to Industry 4.0. These networks are typically deployed on-premises of the enterprises, and their isolation from the public (consumer) networks improves the crucial aspects of security and reliability. Despite the isolation, insider attacks can be mounted on these networks. This paper analyses such attacks using attack patterns from Common Attack Pattern Enumerations and Classifications (CAPEC) database. The analysis uses attack graphs, to combine individual domains, in the context of human, device, and network vulner-abilities. The attack graphs help identify paths, the cumulative impact on the system, and possible defense techniques, including security controls to mitigate the impact. Using three sample attack graphs in the context of standalone private 5G networks, this paper analyses possible security mechanisms and captures the difference among legacy enterprise networks (including WiFi for limited mobility), public networks, and private networks.
On Securing MAC Layer Broadcast Signals Against Covert Channel Exploitation in 5G, 6G &amp; Beyond

In this work, we propose a novel framework to identify and mitigate a recently disclosed covert channel scheme exploiting unprotected broadcast messages in cellular MAC layer protocols. Examples of covert channel are used in data exfiltration, remote command-and-control (CnC) and espionage. Responsibly disclosed to GSMA (CVD-2021-0045), the SPAR-ROW covert channel scheme exploits the downlink power of LTE/5G base-stations that broadcast contention resolution identity (CRI) from any anonymous device according to the 3GPP standards. Thus, the SPARROW devices can covertly relay short messages across long-distance which can be potentially harmful to critical infrastructure. The SPARROW schemes can also complement the solutions for long-range M2M applications. This work investigates the security vs. performance trade-off in CRI-based contention resolution mechanisms. Then it offers a rig-orously designed method to randomly obfuscate CRI broadcast in future 5G/6G standards. Compared to CRI length reduction, the proposed method achieves considerable protection against SPARROW exploitation with less impact on the random-access performance as shown in the numerical results.
Trust-enhanced blockchain-enabled framework for secure and privacy-preserving data sharing systems

As the era of big data continues to evolve, the importance of effective data management and sharing frameworks continues to emerge. While data sharing can play a crucial role in the big data evolution, privacy and security concerns are among the confronting challenges. With the advent of blockchain, many health and financial entities continue to attract blockchain technology due to its tamper-proof and decentralized framework. In this paper, we propose a secure and privacy-preserving data sharing framework by introducing a trust-enhanced proof of authority consensus scheme with the ability to take into consideration users' trust and protection factors. We then provide an implementation of the proposed scheme to demonstrate its ability to ensure safe data sharing process. We showed that the proposed trust-enhanced proof of authority consensus-enabled data sharing framework can facilitate effective data sharing processes among multiple data owners and data requesters in real practical implementations.
Data Streaming over Bitcoin Payment Channels

Bitcoin and blockchain technology allow for value to be transferred in small increments. This property makes Bitcoin suitable for pay-as-you-go data streaming models as an alternative to subscription-based media streaming services. One of the key mechanisms Bitcoin provides to enable such use cases is payment channels. These channels allow two parties to perform value exchanges at high frequency, without settling each individual event on-chain, making them suitable for mediating the purchase of a digital data stream on a packet-by-packet basis. However, some existing designs for Bitcoin payment channels rely on funding transactions to escrow tokens, which increases the operational cost of a data stream and can lead to the funds of peers becoming temporarily stranded in the channel. In this paper, we outline a novel payment channel design that overcomes these challenges. Our construction leverages the inherent malleability of Bitcoin transactions to allow either party to unilaterally terminate the stream at any point, without sacrificing the security of the incremental payments that have occurred up to the termination point.
An Architecture for Autonomic Networks

We elucidate our approach to top-down design of Application Programming Interfaces (APIs) for AI-enabled autonomic network slices. We start with the notion that an API design follows from the underlying software and hardware network architecture and the function and role of each architectural block. We then proceed to describe an adaptive and fully autonomic software architecture for hybrid (software and hardware) network slices, which has recently been a topic of interest for 6G networks. The architecture uses several software design and architectural patterns, which show how the architectural blocks behave and interact with each other. The knowledge of behaviour leads to required APIs. The APIs are further specified in the pattern definitions. We provide two examples of how the architecture is used to achieve network intent with self-adapting network slices.
Autoencoder Communications with Optimized Interference Suppression for NextG RAN

This paper models an end-to-end communications system for the NextG radio access network (RAN) as an autoencoder (AE) subject to interference effects. The transmitter (coding and modulation) and receiver (demodulation and decoding) are represented as deep neural networks (DNNs) of the encoder and decoder, respectively. The AE communications systems is trained with interference training and randomized smoothing to operate under unknown and dynamic interference (jamming) effects. Compared to conventional communications, the AE communications with interference training and randomized smoothing can achieve up to 36 dB interference suppression with a channel reuse of four for the single antenna case. This paper also extends the AE communications formulation to the multiple-input multiple-output (MIMO) case under interference effects and shows the bit error rate (BER) improvement compared to conventional MIMO communications.
Research on Online and Offline Mixed Teaching Quality Evaluation of Higher Mathematics Based on Hierarchical Analysis Method

With the extensive development of network teaching, it has become one of the most important teaching methods at present. It is necessary to evaluate its teaching quality and give better play to its effect. This paper introduces analytic hierarchy process into this field and designs a new online and offline mixed teaching quality evaluation method of higher mathematics. First, the evaluation index is selected. Based on the judgment matrix constructed by AHP, the corresponding hierarchical weights are obtained by maximizing the eigenvalues of different matrices. The evaluation model is established. When the number of iterations reaches the maximum, the evaluation results of teaching quality are obtained. The experiment shows that the average evaluation accuracy of this method is 97.26%, and the effect is good.
Resource Assisted Online Teaching Model of University Library in the Context of Ecological Civilization

As one of the basic educational resources of colleges and universities, university library not only serves the needs of teaching and scientific research of teaching staff, but also an important educational base for cultivating college students. The traditional online teaching mode assisted by university library resources can not meet the needs of current teaching, and can not meet the needs of users for teaching resources at the lowest cost. Therefore, in the context of ecological civilization, A new resource assisted online teaching model of university library is designed. Firstly, the resource assisted online teaching model of university library is designed. Secondly, the resource assisted online teaching principle of university library is optimized in the context of ecological civilization. Finally, the online teaching standard of university library is planned, so as to construct the resource assisted online teaching model of University Library in the context of ecological civilization. The results show that, The designed online teaching model has good teaching effect, effectiveness and certain application value.
Design of Intelligent Accounting Education System Based on Data Processing Technology

The intelligent education system contains a large number of access data and course data, resulting in high load. In order to optimize the response performance of the system, an intelligent accounting education system based on data processing technology is designed. The system is composed of several modules. The main function of the student information management module is to let the administrator have enough operation space to add and delete the student table. The classroom attendance module is used for students’ daily classroom attendance. After the user logs in to the system, the system will judge the user’s role. The data preprocessing module is designed based on data processing technology. Data preprocessing is realized through data cleaning, data normalization and data smoothing. The classroom state monitoring module is used to monitor students’ classroom state during class and make timely early warning. The network lesson preparation and class module can implement the whole classroom teaching process in stages to help students preview before class. The financial knowledge discovery module can realize the mining of financial knowledge. In the run configuration module, configure the hardware for the server and client respectively. The test results show that the user load, operation response load and data load of the system are high, indicating that the system meets the design requirements.
Assistant Teaching System of Human Resource Management Course Based on Data Mining

The traditional assistant teaching system of human resource management course has some problems, such as small data transmission capacity, long system response time and so on. For this reason, this paper proposes an assistant teaching system of human resource management course based on data mining. Establish the snort plug-in mechanism, improve the connection behavior of the course detection engine according to the data mining principle, and then store the teaching information in the database host with the help of the transmission channel organization, so as to build the software execution environment of the teaching system, and complete the design of the auxiliary teaching system of human resource management course based on data mining in combination with the structure of relevant hardware equipment. The experimental results show that, compared with the traditional teaching system, under the action of the data mining assisted teaching system, the response speed of the teacher host and the student host has been effectively improved, which can better solve the problem of obvious accumulation of human resource management course data, and meet the actual application needs.