Reviewerly/exHarmony huggingface dataset:
Improved File-injection Attacks on Searchable Encryption Using Finite Set Theory

Abstract Searchable encryption (SE) allows the cloud server to search over the encrypted data and leak information as little as possible. Most existing efficient SE schemes assume that the leakage of search pattern and access pattern is acceptable. A series of work was proposed, instructing malicious users to use this leakage to come up with attacks. Especially, with a devastating attack proposed by Zhang et al., the cloud server can reveal the keywords queried by normal users by using some injected files. From the method of constructing uniform $(k,n)$-set of a finite set $A$ proposed by Cao, we put forward a new file-injection attack. In our attack, the server needs fewer injected files than the previous attack when the size of $T$ is larger than 9 and the size of keyword set is larger than $2T$, where $T$ is the threshold of the number of keywords in each injected file. Our attack is more practical and easier to implement in the real scenario.
Blockchain‐based video forensics and integrity verification framework for wireless Internet‐of‐Things devices

Abstract A camera footage which is essential for forensic investigations can easily be modified with advanced video tampering techniques. This makes it necessary to employ novel methods to retain and prove the integrity of captured scene in criminal investigations. In this vein, blockchain technology has received a substantial interest in the last decade as it provides trust among users without a trusted third party, which enabled a myriad of applications. To this end, we propose a framework that utilizes blockchain technology to verify integrity of a camera footage recorded by a resource‐constrained wireless Internet of Things (IoT) device. The proposed approach computes the hash of the video data before it leaves the IoT device to ensure the integrity. The hash is then stored on a permissioned blockchain platform that enables detection of tampering in the video. The continuous stream is segmented efficiently to have periodic hash value to minimize the risk of video loss in case of device failure. The system has been implemented on a Raspberry Pi and Hyperledger to validate its efficiency. We are able to process high resolution videos on a resource‐constrained with reasonable amount of delay. The integrity of recorded video is successfully verified by using the digest kept in permissioned blockchain.
Transfer Learning on Balinese Character Recognition of Lontar Manuscript Using MobileNet

The Balinese lontar manuscripts are cultural heritage written using Balinese characters on dried palm leaves. The conservation of the lontar manuscript is carried out by understanding the meaning contained in it. This research is the first step in the conservation of the lontar manuscript by recognizing Balinese characters. In this study, we recognized Balinese characters on lontar using a transfer learning approach. Transfer learning is done by fine-tuning the number of parameters of the pre-trained model to speed up the model convergence by modifying the number of trainable parameters on the pre-trained model. We modified the number of MobileNet architecture parameters with varying the number of trainable parameters and three optimizers to produce the best performance model. Based on the experimental result, the best recognition model yields 86.23% accuracy with a combination of SGD optimizer and 60% trainable parameters.
Machine-learning-based detection of volcano seismicity using the spatial pattern of amplitudes

SUMMARY We propose a new algorithm, focusing on spatial amplitude patterns, to automatically detect volcano seismic events from continuous waveforms. Candidate seismic events are detected based on signal-to-noise ratios. The algorithm then utilizes supervised machine learning to classify the existing candidate events into true and false categories. The input learning data are the ratios of the number of time samples with amplitudes greater than the background noise level at 1 s intervals (large amplitude ratios) given at every station site, and a manual classification table in which ‘true’ or ‘false’ flags are assigned to candidate events. A two-step approach is implemented in our procedure. First, using the large amplitude ratios at all stations, a neural network model representing a continuous spatial distribution of large amplitude probabilities is investigated at 1 s intervals. Second, several features are extracted from these spatial distributions, and a relation between the features and classification to true and false events is learned by a support vector machine. This two-step approach is essential to account for temporal loss of data, or station installation, movement, or removal. We evaluated the algorithm using data from Mt. Ontake, Japan, during the first ten days of a dense observation trial in the summit region (2017 November 1–10). Results showed a classification accuracy of more than 97 per cent.
Graph-based skeleton data compression

With the advancement of reliable, fast, portable acquisition systems, human motion capture data is becoming widely used in many industrial, medical, and surveillance applications. These systems can track multiple people simultaneously, providing full-body skeletal keypoints as well as more detailed landmarks in face, hands and feet. This leads to a huge amount of skeleton data to be transmitted or stored. In this paper, we introduce Graph-based Skeleton Compression (GSC), an efficient graph-based method for nearly lossless compression. We use a separable spatio-temporal graph transform along with non-uniform quantization, coefficient scanning and entropy coding with run-length codes for nearly lossless compression. We evaluate the compression performance of the proposed method on the large NTU-RGB activity dataset. Our method outperforms a 1D discrete cosine transform method applied along temporal direction. In near-lossless mode our proposed compression does not affect action recognition performance.
Leveraging Prior Concept Learning Improves Generalization From Few Examples in Computational Models of Human Object Recognition

Humans quickly and accurately learn new visual concepts from sparse data, sometimes just a single example. The impressive performance of artificial neural networks which hierarchically pool afferents across scales and positions suggests that the hierarchical organization of the human visual system is critical to its accuracy. These approaches, however, require magnitudes of order more examples than human learners. We used a benchmark deep learning model to show that the hierarchy can also be leveraged to vastly improve the speed of learning. We specifically show how previously learned but broadly tuned conceptual representations can be used to learn visual concepts from as few as two positive examples; reusing visual representations from earlier in the visual hierarchy, as in prior approaches, requires significantly more examples to perform comparably. These results suggest techniques for learning even more efficiently and provide a biologically plausible way to learn new visual concepts from few examples.
A Spatio-temporal Hybrid Deep Learning Architecture for Short-term Solar Irradiance Forecasting

Accurate forecasting of solar irradiance plays an important role in the reliable grid integration of renewable energy. Forecasting of photovoltaic (PV) generation of solar sites is routinely studied in academia and industries, however spatio-temporal weather dependencies amongst regional plants are often ignored. It is intuitively expected that PV generation of neighboring solar plants are correlated and may show similar time-varying patterns. In this paper, we are investigating spatiotemporal correlation between solar sites which are at different geographical locations. We propose a hybrid deep learning model which is a combination of a residual network (ResNet) to learn different representations of data and long short-term memory (LSTM) to capture long temporal dependencies. We measure the performance of the proposed model relative to other deep learning models, such as a convolutional neural network (CNN) and long short-term memory (LSTM), ResNet, and ResNet/multilayer perceptron (MLP) using seventeen years of meteorological data from twelve different sites in Philadelphia. Our experiments show that the proposed architecture integrating special and temporal contexts provides superior performance in solar irradiance forecasting.
A Plan for Developing an Auslan Communication Technologies Pipeline

AI techniques for mainstream spoken languages have seen a great deal of progress in recent years, with technologies for transcription, translation and text processing becoming commercially available. However, no such technologies have been developed for sign languages, which, as visual-gestural languages, require multimodal processing approaches. This paper presents a plan to develop an Auslan Communication Technologies Pipeline (Auslan CTP), a prototype AI system enabling Auslan-in, Auslan-out interactions, to demonstrate the feasibility of Auslan-based machine interaction and language processing. Such a system has a range of applications, including gestural human-machine interfaces, educational tools, and translation.
Lessons Learned from Delivering Sensor Fusion Tools in Support of Autonomous Systems

Autonomous systems continue to be developed and deployed at an accelerated pace across a wide range of applications. The four main components of an autonomous system are sensing, perception, decision making, and action execution and control. In previous work, we described requirements and tools developed to satisfy these requirements to support the perception component of an autonomous system. System developers of autonomous systems in multiple industries including Aerospace and Defense, Automotive, and Industrial Automation and Machinery have used these tools to design, model, and test their systems. In this work, we focus on the key lessons learned based on direct feedback from the system developers in the industries listed above. These lessons span three foundational pillars of a sensor fusion development framework: an integrated test bed, algorithms, and performance analysis. Related to the integrated test bed, this work is focused on sensor modeling, virtual scenario authoring, and easy generation of large datasets for testing. For algorithms, the focus is quick simulation of both early fusion and late fusion schemes, including point object and extended object trackers, grid-based trackers, and track fusers. Finally, in the area of performance analysis, the focus is on integrated metrics that are critical in navigating complex design trade-offs, verification, and validation. Recognizing these lessons and addressing them can help evolve the state of automated testing and system-level robustness of autonomous systems.
Multi-UAV Path Planning in a Spreading Wildfire

This work addresses the problem of path planning for multiple Unmanned Aerial Vehicles (UAVs) tasked to provide situational awareness in a spreading wildfire. While the wildfire's evolution can be estimated using a wildfire forecast model to aid suppression tactics, it is important to update the estimates using in-field measurements to improve the forecast. The information fusion step utilizing the forecast and the ground-level temperature sensors to improve the estimates can often result in disagreement or conflict. By utilizing additional aerial measurements performed by a fleet of UAVs, these conflicts can be resolved. However, the extreme temperatures in the regions immediately above the wildfires pose operational threat to UAVs. The objective of the multi-UAV path planning problem is to allocate conflict locations to the vision-equipped UAVs such that it optimizes the overall energy requirements to fly to high conflict locations. Using the wildfire forecaster and the radiation model, the heat flux at the flight level is estimated. Computationally efficient discrete path planning techniques are presented that provide approximate paths which are used to formulate the assignment problem. Once the assignments are made, kinematically feasible and optimal paths are computed using Gauss Quadrature collocation method. It is shown that by employing this multi-step approach, the path planning can be performed in quick successions allowing recurring implementation in a spreading wildfire.
Grammatical error detection and correction model for Sinhala language sentences

As the national language of Sri Lanka, the greater part of the exercises at most of all the services are completed in Sinhala whereas it is imperative to guarantee the spelling and syntactic accuracy to convey the ideal significance from the perspective of automated materials with the unavailability of resources even though there are enough amount of available materials as hard copy and books. With the high multifaceted nature of the language, it sets aside extensive effort to physically edit the substance of a composed setting. The necessity to overcome this problem has risen numerous years back. But with the complexity of grammar rules in morphologically lavish Sinhala language, the accuracy of the grammar checkers developed so far has been contrastingly lower and thus, to overcome the issue a novel hybrid approach has been introduced. Spell checked Sinhala active sentences being preprocessed, separated nouns and verbs were analyzed with the help of a resourceful part-of-speech-tagger and a morphological analyzer and alongside the sentences were sent through a pattern recognition mechanism to identify its sentence pattern. Then a decision tree-based algorithm has been used to evaluate the verb with the “subject” and output feedback about the correctness of the sentence. To train this decision tree, a dataset consisting of 800 records which included information about 25 predefined grammar rules in Sinhala was used. Finally, the error correction was provided using a machine learning algorithm-based sentence guessing model for the three possible tenses. Conducted research results paved the way to identify the sentence pattern, grammar rules and finally, suggest corrections for identified incorrect grammatical sentences with an acceptable accuracy rate of 88.6 percent which concluded that the proposed hybrid approach was an accurate approach for detecting and correcting grammatical mistakes in Sinhala text.
Hybrid Heuristic Algorithm for Better Energy Optimization and Resource Utilization in Cloud Computing

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a hybrid heuristic algorithm based energy-efficient cloud computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the chaotic based particle swarm optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the hybrid heuristic workflow scheduling algorithm (HHWS) and distributed dynamic VM management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods.
Voice Feature Learning using Convolutional Neural Networks Designed to Avoid Replay Attacks

Speaker verification is a promising Internet of Things (IoT) user authentication model. However, verifying the identity of an individual using speech utterance is challenging especially in the presence of replay spoofing attacks, where attackers can record the speech of the user and use it for unauthorized access. In speaker verification, the voice signature is often polluted by ambient noise. This alone confounds the design of appropriate feature extractors that can distinguish the authentic signal from that of a replay signal. The literature to date reflects very poor performance; notably with state-of-the-art verification errors no less than 11.3% false positives (FPs) from all samples. To address this difficult gap that has, up to now, prevented speaker verification from being a satisfactory IoT authentication alternative we are seeking to develop an improved approach yielding better precision compared to state of the art systems. To accomplish this goal, we have developed a new approach that uses MFCC and CQCC as input features, a CNN based front-end feature extractor, and SVM back-end classifier. Using better training techniques such as batch normalizations, we improved the precision significantly, achieving a state-of-the-art performance. In addition, a formulation that includes knowledge of distinguishing genuine voice from a replay attack is implemented. Experiment has been conducted on ASVspoof 2017 datasets. An improvement in the state-of-the-art performance has been obtained, achieving 7.1 of Equal Error Rate (EER).
Hi-def memories of lo-def scenes

The study of visual memory is typically concerned with an image's content: How well, and with what precision, we can recall which objects, people, or features we have seen in the past. But images also vary in their quality: The same object or scene may appear in an image that is sharp and highly resolved, or it may appear in an image that is blurry and faded. How do we remember those properties? Here six experiments demonstrate a new phenomenon of "vividness extension": a tendency to (mis)remember images as though they are "enhanced" versions of themselves — that is, sharper and higher quality than they actually appeared at the time of encoding. Subjects briefly saw images of scenes that varied in how blurry they were, and then adjusted a new image to be as blurry as the original. Unlike an old photograph that fades and blurs, subjects misremembered scenes as more vivid (i.e., less blurry) than those scenes had actually appeared moments earlier. Follow-up experiments extended this phenomenon to saturation and pixelation – with subjects recalling scenes as more colorful and resolved – and ruled out various forms of response bias. We suggest that memory misrepresents the quality of what we have seen, such that the world is remembered as more vivid than it is.
Explainability and Fairness in Machine Learning: Improve Fair End-to-end Lending for Kiva

Artificial Intelligence is finding its way to ever more applications. Nonetheless, it is increasingly required that decision-making procedures must be explainable and fair. As many applications are based on black-box models, there is a strong need for more explainable AI algorithms. For this reason, our paper explores the practical implications and effectiveness of four bias mitigation algorithms (learning fair representations, reweighing, Equality of Odds, and Reject Option based Classification) based on a standard XGBoost classifier to build an explainable and fair prediction model on a real-world loan dataset. The models were evaluated based on their performance, fairness, and explainability. Potential biases, i.e. fairness, were detected with the use of NLP techniques and evaluated with the AIF360 metrics, whereas the explainability of the model was tested by post-hoc explanations (SHAP method). The best results were obtained by the reweighing algorithm that improved the fairness while maintaining a high model performance and explainability.
Rings for Privacy: An Architecture for Large Scale Privacy-Preserving Data Mining

This article proposes a new architecture for privacy-preserving data mining based on Multi Party Computation (MPC) and secure sums. While traditional MPC approaches rely on a small number of aggregation peers replacing a centralized trusted entity, the current study puts forth a distributed solution that involves all data sources in the aggregation process, with the help of a single server for storing intermediate results. A large-scale scenario is examined and the possibility that data become inaccessible during the aggregation process is considered, a possibility that traditional schemes often neglect. Here, it is explicitly examined, as it might be provoked by intermittent network connectivity or sudden user departures. For increasing system reliability, data sources are organized in multiple sets, called rings, which independently work on the aggregation process. Two different protocol schemes are proposed and their failure probability, i.e., the probability that the data mining output cannot guarantee the desired level of accuracy, is analytically modeled. The privacy degree, the communication cost and the computational complexity that the schemes exhibit are also characterized. Finally, the new protocols are applied to some specific use cases, demonstrating their feasibility and attractiveness.
Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles

This paper presents the results of the design, simulation, and implementation of a virtual vehicle. Such a process employs the Unity videogame platform and its Machine Learning-Agents library. The virtual vehicle is implemented in Unity considering mechanisms that represent accurately the dynamics of a real automobile, such as motor torque curve, suspension system, differential, and anti-roll bar, among others. Intelligent agents are designed and implemented to drive the virtual automobile, and they are trained using imitation or reinforcement. In the former method, learning by imitation, a human expert interacts with an intelligent agent through a control interface that simulates a real vehicle; in this way, the human expert receives motion signals and has stereoscopic vision, among other capabilities. In learning by reinforcement, a reward function that stimulates the intelligent agent to exert a soft control over the virtual automobile is designed. In the training stage, the intelligent agents are introduced into a scenario that simulates a four-lane highway. In the test stage, instead, they are located in unknown roads created based on random spline curves. Finally, graphs of the telemetric variables are presented, which are obtained from the automobile dynamics when the vehicle is controlled by the intelligent agents and their human counterpart, both in the training and the test track.