Reviewerly/exHarmony huggingface dataset:
Emitter Identification of Digital Modulation Transmitter Based on Nonlinearity and Modulation Distortion of Power Amplifier

Specific transmitter identification (SEI) is a technology that uses a received signal to identify to which individual radiation source the transmitted signal belongs. It can complete the identification of the signal transmitter in a non-cooperative scenario. Therefore, there are broad application prospects in the field of wireless-communication-network security, spectral resource management, and military battlefield-target communication countermeasures. This article demodulates and reconstructs a digital modulation signal to obtain a signal without modulator distortion and power-amplifier nonlinearity. Comparing the reconstructed signal with the actual received signal, the coefficient representation of the nonlinearity of the power amplifier and the distortion of the modulator can be obtained, and these coefficients can be used as the fingerprint characteristics of different transmitters through a convolutional neural network (CNN) to complete the identification of specific transmitters. The existing SEI strategy for changing the modulation parameters of a test signal is to mix part of the test signal with the training signal so that the classifier can learn the signal of which the modulation parameter was changed. This method is still data-oriented and cannot process signals for which the classifier has not been trained. It has certain limitations in practical applications. We compared the fingerprint features extracted by the method in this study with the fingerprint features extracted by the bispectral method. When SNR &lt; 20 dB, the recognition accuracy of the bispectral method dropped rapidly. The method in this paper still achieved 86% recognition accuracy when SNR = 0 dB. When the carrier frequency of the test signal was changed, the bispectral feature failed, and the proposed method could still achieve a recognition accuracy of about 70%. When changing the test-signal baud rate, the proposed method could still achieve a classification accuracy rate of more than 70% for four different individual radiation sources when SNR = 0 dB.
Learning Relation Alignment for Calibrated Cross-modal Retrieval

Despite the achievements of large-scale multimodal pre-training approaches, cross-modal retrieval, e.g., image-text retrieval, remains a challenging task.To bridge the semantic gap between the two modalities, previous studies mainly focus on word-region alignment at the object level, lacking the matching between the linguistic relation among the words and the visual relation among the regions.The neglect of such relation consistency impairs the contextualized representation of image-text pairs and hinders the model performance and the interpretability.In this paper, we first propose a novel metric, Intra-modal Self-attention Distance (ISD), to quantify the relation consistency by measuring the semantic distance between linguistic and visual relations.In response, we present Inter-modal Alignment on Intra-modal Self-attentions (IAIS), a regularized training method to optimize the ISD and calibrate intra-modal self-attentions from the two modalities mutually via inter-modal alignment.The IAIS regularizer boosts the performance of prevailing models on Flickr30k and MS COCO datasets by a considerable margin, which demonstrates the superiority of our approach.
MB-EHR: A Multilayer Blockchain-based EHR

Although Electronic Health Records (EHR) are widely used by healthcare organisations, many EHR systems still face security and privacy problems. Blockchains can provide solutions for many of these problems and there have been attempts to incorporate blockchains in EHR systems; however, inappropriate software architectural structure hinder the the adoption of blockchain in EHR systems. MB-EHR (Multilayer Blockchain-based EHR) aims to solve the aforementioned research gap and proposes a layered blockchain structure to better support the operational hierarchy in health organisations. Furthermore, MB-EHR implements a PDP-like data verification mechanisms to protect data integrity. Experiments results show that MB-EHR achieved good throughput performance.
Traditional Learning vs. Experiential Learning

Traditional ESL/EFL classroom in India is characterized by repetition and memorization of the literary-grammatical content of prescribed textbooks, but experiential language classroom stresses ‘learning by doing’ style. It targets acquisition of communication skills through critical thinking, problem solving, and decision making. Experiential approach accelerates learning. English can be learnt not by learning about it but by doing tasks and projects that promote English communication skills and critical thinking skills. It is personal and effective and it has a positive force on cognitive, affective, and physical domains of learners. It ensures involvement and participation of learners since it treats language as a social activity. The article is an attempt to assess the applicability of experiential approach to ESL in Indian context.
Convolutional Neural Network-Based Natural Image and MRI Classification Using Gaussian Activated Parametric (GAP) Layer

We propose a novel gaussian activated parametric (GAP) layer for deep neural networks specifically for CNN.This layer normalizes the feature vector using the Gaussian filter-based un-sharpening technique.The goal of the proposed method is to normalize and activate the initial and intermediate feature layers of a deep CNN so that a customized layer can make the feature more distinguish and layers are smoothly tuned for the target-domain classification.Our experiment shows the use of the proposed layer for normalization gives a result almost similar to that of batch normalization and in few cases, the result is slightly better at the cost of higher training time.To demonstrate the result of the proposed layer we are using 4 layers of an encoder-based network as a base architecture for classification in which the first two normalization layers are GAP layers or BN layers whereas the remaining are BN layers.The result of the experiment is better in classification with two GAP layer as normalization layer in a bulkier dataset like ADNI MRI (3D CNN accuracy: 93.58% vs. 91.89%)and CIFAR-10 (2D CNN accuracy: 75.21% vs. 75.11%),whereas the result is better with replacement of first BN layer with GAP in a smaller dataset like 5-animals dataset (2D CNN accuracy: 62.92% vs 58.48%).Therefore, we suggest the use of single GAP layer normalization for smaller datasets and two GAP normalization layers for bigger dataset training.Also, the proposed method produced better results than the cross-channel normalization-based AlexNet network under scratch training.
Order Regularization on Ordinal Loss for Head Pose, Age and Gaze Estimation

Ordinal loss is widely used in solving regression problems with deep learning technologies. Its basic idea is to convert regression to classification while preserving the natural order. However, the order constraint is enforced only by ordinal label implicitly, leading to the real output values not strictly in order. It causes the network to learn separable feature rather than discriminative feature, and possibly overfit on training set. In this paper, we propose order regularization on ordinal loss, which makes the outputs in order by explicitly constraining the ordinal classifiers in order. The proposed method contains two parts, i.e. similar-weights constraint, which reduces the ineffective space between classifiers, and differential-bias constraint, which enforces the decision planes in order and enhances the discrimination power of the classifiers. Experimental results show that our proposed method boosts the performance of original ordinal loss on various regression problems such as head pose, age, and gaze estimation, with significant error reduction of around 5%. Furthermore, our method outperforms the state of the art on all these tasks, with the performance gain of 14.4%, 2.2% and 6.5% on head pose, age and gaze estimation respectively.
Data Poisoning on Deep Learning Models

Deep learning is a form of artificial intelligence (AI) that has seen rapid development and deployment in computer software as a means to implementing AI functionality with greater efficiency and ease as compared to other alternative AI solutions, with usage seen in systems varying from search and recommendation engines to autonomous vehicles. With the demand for deep learning algorithms that can perform increasingly complex tasks in a shorter time frame growing at an exponential pace, the developments in the efficiency and productivity of algorithms has far outpaced that of the security of such algorithms, drawing concerns over the many unaddressed vulnerabilities that may be exploited to compromise the integrity of these software. This study investigated the ability of poisoning attacks, a form of attack targeting the vulnerability of deep learning training data, to compromise the integrity of a deep learning model's classificational functionality. Experimentation involved the processing of training data sets with varying deep learning models and the incremental introduction of poisoned data sets to view the efficacy of a poisoning attack under multiple circumstances and correlate such with aspects of the model's design conditions. Analysis of results showed evidence of a decrease of classificational ability correlating with an increase of poison percentage in the training data sets, but the scale of which the decrease occurred varied with the specified parameters in the model design. Based on this, it was concluded that poisoning can provide varying levels of damage to deep learning classificational ability depending on the parameters utilized in the model design, and methods to countermeasure such were proposed, such as increasing epoch count, implementing mechanisms bolstering model fit, and integrating input level filtration systems.
Building a blockchain-based decentralized ecosystem for cloud and edge computing: an ALLSTAR approach and empirical study

Cloud computing has been one of the disruptive technologies to change the traditional application operation for the last decades. The success of Cloud boosts ever more newly-built data centers. Although these data centers are distributed all around the world, the computing resources are managed in a relatively centralized manner within one big data center. For a specific small area, the centralized Cloud lacks the dispersion to satisfy the requirements of collaborative applications, e.g., the nearest data center might still be too far to satisfy the network latency. Through spreading the computing resources at the edge of the network, the emerging Edge computing can complete the data processing before uploading to Cloud. However, Edge computing still stays at the conceptual and experimental stage. Trust and incentive model are missing to motivate the Edge node and micro Cloud owners to share the computing infrastructure resources for building a more generalized and decentralized ecosystem. Traditional method of building trust through authority is not applicable in current edge environment, which is more like peer-to-peer relationship between the customer and provider. To tackle this issue, ALLSTAR is proposed, which is a blockchain-based approach to enhance the trust for equally combining all the Cloud and Edge resources to be seamlessly leveraged by the application. The ALLSTAR approach is a systematic solution to realize decentralized resource management, including Cloud and Edge resource sharing and trading, and target at building the trustworthy ALLSTAR ecosystem. In this paper, we first analyze the challenges of utilizing distributed Cloud and Edge resources, and describe the overall architecture of ALLSTAR, including the related key techniques, detailed application development and operations processes as well as the new business model. Moreover, an empirical study on the permissioned blockchain evaluation is conducted. The study not only demonstrates the ALLSTAR approach is feasible but also provides insights of which blockchain to choose when constructing such an ecosystem.
Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty

Image demosaicking and denoising are the two key fundamental steps in digital camera pipelines, aiming to reconstruct clean color images from noisy luminance readings. In this paper, we propose and study Wild-JDD, a novel learning framework for joint demosaicking and denoising in the wild. In contrast to previous works which generally assume the ground truth of training data is a perfect reflection of the reality, we consider here the more common imperfect case of ground truth uncertainty in the wild. We first illustrate its manifestation as various kinds of artifacts including zipper effect, color moire and residual noise. Then we formulate a two-stage data degradation process to capture such ground truth uncertainty, where a conjugate prior distribution is imposed upon a base distribution. After that, we derive an evidence lower bound (ELBO) loss to train a neural network that approximates the parameters of the conjugate prior distribution conditioned on the degraded input. Finally, to further enhance the performance for out-of-distribution input, we design a simple but effective fine-tuning strategy by taking the input as a weakly informative prior. Taking into account ground truth uncertainty, Wild-JDD enjoys good interpretability during optimization. Extensive experiments validate that it outperforms state-of-the-art schemes on joint demosaicking and denoising tasks on both synthetic and realistic raw datasets.
Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes

The parameters for a Markov Decision Process (MDP) often cannot be specified exactly. Uncertain MDPs (UMDPs) capture this model ambiguity by defining sets which the parameters belong to. Minimax regret has been proposed as an objective for planning in UMDPs to find robust policies which are not overly conservative. In this work, we focus on planning for Stochastic Shortest Path (SSP) UMDPs with uncertain cost and transition functions. We introduce a Bellman equation to compute the regret for a policy. We propose a dynamic programming algorithm that utilises the regret Bellman equation, and show that it optimises minimax regret exactly for UMDPs with independent uncertainties. For coupled uncertainties, we extend our approach to use options to enable a trade off between computation and solution quality. We evaluate our approach on both synthetic and real-world domains, showing that it significantly outperforms existing baselines.
Extracting Zero-shot Structured Information from Form-like Documents: Pretraining with Keys and Triggers

In this paper, we revisit the problem of extracting the values of a given set of key fields from form-like documents. It is the vital step to support many downstream applications, such as knowledge base construction, question answering, document comprehension and so on. Previous studies ignore the semantics of the given keys by considering them only as the class labels, and thus might be incapable to handle zero-shot keys. Meanwhile, although these models often leverage the attention mechanism, the learned features might not reflect the true proxy of explanations on why humans would recognize the value for the key, and thus could not well generalize to new documents. To address these issues, we propose a Key-Aware and Trigger-Aware (KATA) extraction model. With the input key, it explicitly learns two mappings, namely from key representations to trigger representations and then from trigger representations to values. These two mappings might be intrinsic and invariant across different keys and documents. With a large training set automatically constructed based on the Wikipedia data, we pre-train these two mappings. Experiments with the fine-tuning step to two applications show that the proposed model achieves more than 70% accuracy for the extraction of zero-shot keys while previous methods all fail.
A novel multi-task genetic programming approach to uncertain capacitated Arc routing problem

Uncertain Capacitated Arc Routing Problem (UCARP) is an NP-hard optimisation problem with many applications in logistics domains. Genetic Programming (GP) is capable of evolving routing policies to handle the uncertain environment of UCARP. There are many different but related UCARP domains in the real world to be solved (e.g. winter gritting and waste collection for different cities). Instead of training a routing policy for each of them, we can use the multi-task learning paradigm to improve the training effectiveness by sharing the common knowledge among the related UCARP domains. Previous studies showed that GP population for solving UCARP loses diversity during its evolution, which decreases the effectiveness of knowledge sharing. To address this issue, in this work we propose a novel multi-task GP approach that takes the uniqueness of transferable knowledge, as well as its quality, into consideration. Additionally, the transferred knowledge is utilised in a manner that improves diversity. We investigated the performance of the proposed method with several experimental studies and demonstrated that the designed knowledge transfer mechanism can significantly improve the performance of GP for solving UCARP.
GENSYNTH: Synthesizing Datalog Programs without Language Bias

Techniques for learning logic programs from data typically rely on language bias mechanisms to restrict the hypothesis space. These methods are therefore limited by the user's ability to tune them such that the hypothesis space is simultaneously large enough to include the target program but small enough to admit a tractable search. We propose a technique to learn Datalog programs from input-output examples without requiring the user to specify any language bias. It employs an evolutionary search strategy that mutates candidate programs and evaluates their fitness on the examples using an off-the-shelf Datalog interpreter. We have implemented our approach in a tool called GenSynth and evaluate it on diverse tasks from knowledge discovery, program analysis, and relational queries. Our experiments show that GenSynth can learn correct programs from few examples, including for tasks that require recursion and invented predicates, and is robust to noise.
Taming System Dynamics on Resource Optimization for Data Processing Workflows: A Probabilistic Approach

In many data-intensive applications, workflow is often used as an important model for organizing data processing tasks and resource provisioning is an important and challenging problem for improving the performance of workflows. Recently, system variations in the cloud and large-scale clusters, such as those in I/O and network performances and failure events, have been observed to greatly affect the performance of workflows. Traditional resource provisioning methods, which overlook these variations, can lead to suboptimal resource provisioning results. In this article, we provide a general solution for workflow performance optimizations considering system variations. Specifically, we model system dynamics as time-dependent random variables and take their probability distributions as optimization input. Despite its effectiveness, this solution involves heavy computation overhead. Thus, we propose three pruning techniques to simplify workflow structure and reduce the probability evaluation overhead. We implement our techniques in a runtime library, which allows users to incorporate efficient probabilistic optimization into existing resource provisioning methods. Experiments show that probabilistic solutions can improve the performance by up to 65 percent compared to state-of-the-art static solutions, and our pruning techniques can greatly reduce the overhead of our probabilistic approach.
Data Storage System Arrival and Departure Airnav Halim Perdana Kusuma Airport

The Arrival and Departure sheet data storage system is a familiar thing in the LPPNI Public Corporation Halim Perdana Kusuma branch. But the problem of irregular storage and making reports that are not fast and accurate often become obstacles that must be experienced. Data storage Arrival and Departure sheet using a manual system results in frequent errors. In the development of the company to input and make reports Arrival and Departure sheet data still uses a manual system that requires accuracy and a very long process. Therefore the Arrival and Departure sheet data storage system will be able to solve the problem of inputting data and provide data reports quickly and accurately. The system development method in this study uses prototypes or prototypes which include planning, design, implementation and testing. The results of this research are to obtain application and testing on the system that will be developed with the results of black box testing.
Learning Prediction Intervals for Model Performance

Understanding model performance on unlabeled data is a fundamental challenge of developing, deploying, and maintaining AI systems. Model performance is typically evaluated using test sets or periodic manual quality assessments, both of which require laborious manual data labeling. Automated performance prediction techniques aim to mitigate this burden, but potential inaccuracy and a lack of trust in their predictions has prevented their widespread adoption. We address this core problem of performance prediction uncertainty with a method to compute prediction intervals for model performance. Our methodology uses transfer learning to train an uncertainty model to estimate the uncertainty of model performance predictions. We evaluate our approach across a wide range of drift conditions and show substantial improvement over competitive baselines. We believe this result makes prediction intervals, and performance prediction in general, significantly more practical for real-world use.
Privacy-Preserving Reversible Data Hiding for Medical Images Employing Local Rotation

As digitalization becomes more common, patients’ concerns about the leakage of private information, such as electronic medical record, are increasing, and those concerns motivated this case study of secure covert communication. Therefore, in this paper, a novel reversible data hiding method based on pixel rotation is proposed for medical images. Using pixel rotation, a state mapping model is constructed to represent the payload. More specifically, many intermediate states are derived from an image block, and each of them is used to form a one-to-one mapping relationship with a specific sequence of payload bits. In addition, to ensure the visual quality of stego-medical-images, the payload bits are only concealed in the regular blocks and the other blocks are unchanged. Moreover, the smoother regular image block will be priority to be used to embed the payload to enhance the visual quality of stego-medical-image. The experimental results showed that the stego-medical-images generated by the proposed reversible data hiding method have better visual quality with an average PSNR of 47.0307 dB, which is higher than that provided by some state-of-the-art methods.
La gamificación como estrategia de motivación y dinamizadora de las clases en el nivel superior

This investigation focuses on gamification as a teaching -learning strategy that arouse the interest of universities students to learn significantly and allows the teacher to evaluate his methodology and plan the most dynamic and motivating classes to promove environments where students can be motivated so they can participate , increase their learning and be competitive. Also, it describes positive aspects of gamification for teacher and student. In order to be a teacher's good subject, he needs to gamify the class and content considering the elements of the gamification .In addition, It proposes three successful free access tools to gamify classes: kahoot!,Quizizz and Socrative.