Reviewerly/exHarmony huggingface dataset:
Implementing a high-efficiency similarity analysis approach for firmware code

The rapid expansion of the open-source community has shortened the software development cycle, but the spread of vulnerabilities has been accelerated, especially in the field of the Internet of Things. In recent years, the frequency of attacks against connected devices is increasing exponentially; thus, the vulnerabilities are more serious in nature. The state-of-the-art firmware security inspection technologies, such as methods based on machine learning and graph theory, find similar applications depending on the known vulnerabilities but cannot do anything without detailed information about the vulnerabilities. Moreover, model training, which is necessary for the machine learning technologies, requires a significant amount of time and data, resulting in low efficiency and poor extensibility. Aiming at the above shortcomings, a high-efficiency similarity analysis approach for firmware code is proposed in this study. First, the function control flow features and data flow features are extracted from the functions of the firmware and of the vulnerabilities, and the features are used to calculate the SimHash of the functions. The mass storage and fast query capabilities of the SimHash are implemented by the pigeonhole principle. Second, the similarity function pairs are analyzed in detail within and among the basic blocks. Within the basic blocks, the symbolic execution is used to generate the basic block semantic information, and the constraint solver is used to determine the semantic equivalence. Among the basic blocks, the local control flow graphs are analyzed to obtain their similarity. Then, we implemented a prototype and present the evaluation. The evaluation results demonstrate that the proposed approach can implement large-scale firmware function similarity analysis. It can also get the location of the real-world firmware patch without vulnerability function information. Finally, we compare our method with existing methods. The comparison results demonstrate that our method is more efficient and accurate than the Gemini and StagedMethod. More than 90% of the firmware functions can be indexed within 0.1 s, while the search time of 100,000 firmware functions is less than 2 s.
Integrating Trusted Platform Modules in Power Electronics

Trusted Platform Modules (TPMs) are specialized chips that store RSA keys specific to the host system for hardware authentication. The RSA keys refer to an encryption technology developed by RSA Data Security. The RSA algorithm accounts for the fact that there is no efficient way to factor extremely large numbers. Each TPM chip contains an RSA Key pair known as the Endorsement Key that cannot be accessed by software. The TPM contains an additional key, called the Attestation Identity Key that protects the device itself against unauthorized firmware and software modification by implementing hash functions on critical sections of the software and firmware before execution. As a result, the TPM can be used as a chip for handling encryption for a larger system to offer an additional layer of security. Furthermore, the TPM can also be used for managing encryption keys, as a Storage Root Key is created when a user or administrator takes ownership of the system. However, merging the TPM into a system does come with additional costs along with potential benefits. This paper focuses on integrating a TPM into a system implemented on an ARM processor that engages with power electronics, and then presents the security benefits associated with a TPM.
Palpatine: Mining Frequent Sequences for Data Prefetching in NoSQL Distributed Key-Value Stores

This paper presents Palpatine, the first in-memory application-level cache for Distributed Key-Value (DKV) data stores, capable of prefetching data that is likely to be accessed in an immediate future. To predict data accesses, Palpatine continuously captures frequent access patterns to the back store by means of data mining techniques. With these patterns, Palpatine builds a stochastic graph of accessed items, and makes prefetching decisions based on it. Experimental evaluation indicates that Palpatine can improve the latency of a specific DKV store by more that an order of magnitude.
Autonomous Curriculum Generation for Self-Learning Agents

The applicability of deep reinforcement learning algorithms to the domain of robotics is limited by the issue of sample inefficiency. As in most machine learning methods, more samples generally mean better learning effectiveness. Sample collection for robotics application is a time-consuming process in addition to safety issues for both the robot itself and the environment surrounding it that come into play for real-world scenarios. Because of these limitations, sample efficiency plays a very vital role in the field of robotic learning. To deal with this, curriculum learning offers a methodology that allows robots to suffer less from the sample collection burden required, trying to keep it at a minimum. This study aims to tackle the sample inefficiency that deep reinforcement learning algorithms face in the domain of robotics by designing a curriculum. We propose an algorithm which decides on the sequence of tasks that the agent must learn to enable the transfer of knowledge in a sample-efficient manner towards the target task. Our algorithm performs a parameter-space task representation for the purpose of deciding on the difficultiness of the tasks. Once the difficulty level of each is determined, easy tasks are learned first before the final target task. We perform a study on a double inverted pendulum setup. Simulation results showed that transfer of knowledge via curriculum is more sample efficient than a direct transfer.
Siralama Sorunu Olarak Nispi Derinlik Tahmini Relative Depth Estimation as a Ranking Problem

We present a formulation of the relative depth estimation from a single image problem, as a ranking problem. By reformulating the problem this way, we were able to utilize literature on the ranking problem, and apply the existing knowledge to achieve better results. To this end, we have introduced a listwise ranking loss borrowed from ranking literature, weighted ListMLE, to the relative depth estimation problem. We have also brought a new metric which considers pixel depth ranking accuracy, on which our method is stronger.
Evaluating Nonlinear Decision Trees for Binary Classification Tasks with Other Existing Methods

Classification of datasets into two or more distinct classes is an important machine learning task. Many methods are able to classify binary classification tasks with a very high accuracy on test data, but cannot provide any easily interpretable explanation for users to have a deeper understanding of reasons for the split of data into two classes. In this paper, we highlight and evaluate a recently proposed nonlinear decision tree approach with a number of commonly used classification methods on a number of datasets involving a few to a large number of features. The study reveals key issues such as effect of classification on the method's parameter values, complexity of the classifier versus achieved accuracy, and interpretability of resulting classifiers.
A Multiobjective Approach for Nearest Neighbor Optimization of N-Dimensional Quantum Circuits

The Nearest Neighbor (NN) restriction in quantum circuits requires quantum gates to act on geometrically adjacent qubits. Methods that convert generic quantum circuits and allow them to comply with the NN restriction have already been studied in the literature, where the main technique to accomplishing this task is by inserting SWAP gates into the circuit. In previous works, other authors have introduced a two-dimensional multi-objective NN conversion algorithm that takes into account two simultaneous objectives: the minimization of the two-dimensional grid size and the minimization of the number of SWAP gates required to allow any generic circuit to comply with the NN restriction. An extended higher-dimensional version of these previous methods is presented in this work, maintaining the same optimization objectives. We present experimental results for three-dimensional circuits, which show improvements for 52.6% of the tested circuits over all previously published results to date.
Improving Effectiveness and Efficiency in Wagner’s Modularity-Evolving Artificial Gene Regulatory Networks

Although no consensus has been reached on the conditions under which modularity emerges, we lind the idea of specialisation driving modularity highly plausible. Current abstractions have demonstrated emergence of modularity in scenarios evolving Gene Regulatory Networks to show simple targeted behaviours, but these methods are less successful in more complex, real-world-level scenarios. We aim to study methods that are more efficient and effective in these simple scenarios, aiming to extend them to higher complexity. We engineer two modifications to the system that advance this aim. Progressive selection allows more than an order of magnitude reduction in computational cost without damaging the emergence of modularity, while dynamic specialisation produces significant improvements in modularity while also modestly reducing computational costs. We also provide some insight into the first of the two evolutionary stages in this approach, providing some explanation of why dynamic specialisation works.
Recognition capabilities of a Hopfield model with auxiliary hidden neurons

We study the recognition capabilities of the Hopfield model with auxiliary hidden layers, which emerge naturally upon a Hubbard-Stratonovich transformation. We show that the recognition capabilities of such a model at zero temperature outperform those of the original Hopfield model, due to a substantial increase of the storage capacity and the lack of a naturally defined basin of attraction. The modified model does not fall abruptly into the regime of complete confusion when memory load exceeds a sharp threshold. This latter circumstance, together with an increase of the storage capacity, renders such a modified Hopfield model a promising candidate for further research, with possible diverse applications.
Multi-UAV task assignment based on improved Wolf Pack Algorithm

Aiming at the task allocation problem of heterogeneous multi-UAVs with different loads, an improved wolf pack algorithm is proposed. For the heterogeneous UAVs with multiple payloads, this paper divides the complex combat tasks into three subtasks: reconnaissance, strike and evaluation, and builds a task allocation model based on the specific load performance. The Gaussian walking in Stochastic Fractal Search (SFS) after chaos optimization is introduced into the wolf pack algorithm (WPA) through an adaptive mechanism, and a chaotic wolf pack algorithm based on enhanced Stochastic Fractal Search (MSFS-CWPA) is proposed. After the classic benchmark function including single/ multiple peak and the example verification of heterogeneous multi-UAV task allocation, the results show that MSFS-CWPA has higher convergence accuracy and robustness than the standard WPA and other improved WPA.
Field Trial of Meteorological Station Using PV Reference Cells

We report on a field trial of a new meteorological station concept for resource assessment at the site of a prospective utility-scale photovoltaic (PV) power plant to be developed by 7X Energy. Cost savings are achieved by substituting Atonometrics PV reference cells for thermopile pyranometers in a unique multi-reference-cell array configuration that allows accurate determination of global horizontal irradiance (GHI) and by using advanced remote diagnostics to minimize the need for on-site visits for sensor cleaning. Reference data are also obtained from a second station at the site employing thermopile pyranometers and maintained through weekly site visits for pyranometer cleaning. Data from both stations are compared to industry-leading SolarAnywhere <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> satellite-derived irradiance measurements from Clean Power Research and are used for tuning of the satellite model for long-term irradiance prediction and site resource assessment. Results comparing the new station concept employing PV reference cells versus the traditional one based on thermopile pyranometers showed that both compared equivalently with the satellite data and tuning model within instrument uncertainties, although small bias differences require more investigation.
Expert System: Vertigo Disease Diagnosis with Backward Chaining Method

An expert system is a computer system that equals the decision-making ability of an expert. The aim is to transfer expertise or knowledge possessed by an expert poured into a computer application. Vertigo is not a common form of migraine or headache. Vertigo is a disease that causes the sufferer to feel a sudden spinning sensation inside or outside the head and the environment feels spinning. In this study using an expert system to diagnose vertigo which is classified based on damage to the vestibular system, namely Perifer and central vertigo. The process of diagnosis of the disease changes to the website where the user answers several questions raised by the system according to the symptoms felt by the user. The inference method used is the Backward Chaining method and data search using the Depth First Search method. This expert system is in the form of a website so that it can be easily accessed by users anytime and anywhere, this system is built with the Hypertext Preprocessor (PHP) programming language and MySql database. The system built is expected to be able to provide a conclusion based on the diagnosis based on the symptoms that have been answered, and provide information about the causes of the symptoms and solutions to treat the disease.
On the weights of dual codes arising from the GK curve

In this paper we investigate some dual algebraic-geometric codes associated with the Giulietti–Korchmáros maximal curve. We compute the minimum distance and the minimum weight codewords of such codes and we investigate the generalized Hamming weights of such codes.
Invariant moment and learning vector quantization (LVQ NN) for images classification

Abstract Image classification need two main components, i.e., features and classifier. The feature commonly used for classification of images with different scale is invariant moment; its value is invariant against the spatial transformation dealing with translation, scale and rotation. The classifier that is widely used for classification is LVQ NN. It is shallow network containing only two layers, the initial value of its weight is more fixed so that its output is more stable and its algorithm is relatively simple thus both training and testing process are run fast. Based on these facts, therefore, this research proposed a combination method of invariant moment and LVQ NN (IM-LVQ). The ability of the proposed method would be compared with two other methods. Firstly, the combination method of invariant moment and Euclidean distance (IM-ED). Secondly, the combination of invariant moment and principal component analysis (IM-PCA). The performance of the three methods was evaluated quantitatively with several metrics, viz.: Confusion Matrix, Accuracy, Precision, True Positive Rate, False Positive Rate, ROC graph and training time. The evaluation of the metrics was based upon the changing (reduction) of the scale/size of training image. The results showed that IM-LVQ method outperformed the other two methods in aforementioned metrics.
A Comparative Study to Classify Cumulonimbus Cloud Using Pre-trained CNN

Thunderstorm is one of the most dangerous natural phenomenons. Basically clouds can be broadly classified into three types. Among them cumulonimbus cloud is the main ingredient of this type of savage weather. In this paper, we have classified cumulonimbus cloud from natural images of different clouds. We have used watershed transformation to segment only the cloud parts from those images and used two pre-trained convolutional neural network, namely AlexNet and GoogLeNet, to classify them into cumulonimbus and non-cumulonimbus cloud. We have seen that AlexNet is performing much better compared with GoogLeNet in terms of total training time with same parameters for training. We have done simulation on MATLAB and giving promising result of 81.65% accuracy in case of AlexNet.
A Machine Learning Approach for Localization of Suspicious Objects using Multiple Cameras

Surveillance automation of public places assumes an important role in proactively detection of possible threat to public and in maintaining law and order. Based on a review of the existing approaches followed in monitoring of crowd behavior and the techniques applied to nab absconding suspects, especially in public places like bus stands, railway stations and airports, the paper proposes surveillance automation i.e. automating the process of detecting, recognizing the suspects and suspicious behavior of the people in the crowd. The process involves not only automatically detecting and recognizing known criminals, but also tracking of movements of persons and objects and notifying the authorities of any suspicious behaviour on the basis of machine learning algorithms. The proposed system, that has automated the surveillance process with multiple cameras was found to be working in simulated environment, can prevent unfortunate incidents in public places.
Firefly Programming For Symbolic Regression Problems

Symbolic regression is the process of finding a mathematical formula that fits a specific set of data by searching in different mathematical expressions. This process requires great accuracy in order to reach the correct formula. In this paper, we will present a new method for solving symbolic regression problems based on the firefly algorithm. This method is called Firefly Programming (FP). The results of applying firefly programming algorithm to some symbolic regression benchmark problems will be compared to the results of Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods.
Undergraduate orthoptic students’ views of the use of Virtual Reality in teaching and learning

This study ascertains undergraduate perceptions of the use of Virtual Reality (VR) within undergraduate studies. Fifty undergraduate orthoptic students were surveyed through an online questionnaire, where questions were based around students understanding of VR, teaching methods in higher education and the value of VR in learning and teaching. Ninety-two percent of students surveyed reported experience of VR on at least one occasion and 55% of all surveyed felt that VR has a valuable role within higher education. For those who do not use VR regularly, 24% reported this being due to a dislike of the headset, and 14% stated that it was due to cyber-sickness. Twenty-seven per cent indicated the lack of use was due to insufficient content. Overall, perception of VR as a learning tool is generally positive or comes with some uncertainty; however, there are factors identified that may prevent use within the curriculum. Development of educationally specific content to orthoptics and increased access to the technology is indicated to enhance student learning.
A Reversible Data Hiding Scheme in Encrypted Images by Controlled Pixel Modification

Reversible data hiding is widely studied in recent years due to its wide applications in various domains such as medical image transmission and cloud computing. In this manuscript, we propose a novel scheme for performing reversible data hiding in encrypted images. In this scheme, the data hider can embed one-bit additional data bit in a small block ( B × 2 pixels) from the encrypted image. All the non-overlapping regions (blocks) in the encrypted image will be processed by accessing those blocks in a predefined order. To embed a bit value 0 in an encrypted image block, no need to modify any pixel values. If we want to embed bit value 1, all the pixels in the first column of the selected image block will be mapped into a new pixel value based on a predefined function. At the receiver side, the data extraction and image recovery are carried by comparing the closeness between pixels in the adjacent columns of the pixels in each block of the decrypted image.
ADTM

Traditional VPN-based data transmission schemes are facing the threat of malicious tracking and blocking. Moreover, most of the anonymous communication networks, such as Tor, can hardly provide stability and reliability capability for massive data transmission due to the uncontrollability of its nodes. This paper proposes ADTM, a novel asynchronous model for massive data transmission, which builds multiple anonymous transmission channels based on Internet file storage services. The algorithms and mechanism of channel selection, segmented redundant transmission, and cover traffic are all designed based on cryptographic theory, which can prevent stealing communication content through link listening, communication relationships inferring, and communication path tracking. Experimental results in a real Internet environment show that ADTM has high performance and can be used for anonymous transmission of massive data.
A2Cloud-cc: A Machine Learning Council to Guide Cloud Resource Selection for Scientific Applications

We present the A2Cloud-cc suite: a multi-agent recommender system to facilitate Cloud resource selection for scientific applications. The suite comprises two components: A2Cloud framework and Council-of-Classifiers (CC). A2Cloud provides the first level of instance recommendation by profiling the target application and selected Cloud instances using hardware benchmarks. The framework uses this information to generate objective scores that are representatives of an application's execution time and cost on the chosen instances. A2Cloud saves this analysis to a public database for scalable machine-learning (ML). CC comprises three ML agents including collaborative filtering, Naïve Bayes', and multinomial logistic regression classifiers; and one decision-making agent: analytic hierarchy process (AHP). CC downloads the A2Cloud analysis from the database to train the three ML agents for instance recommendation. During testing, AHP pools the instance recommendations from ML agents to yield a single recommendation that satisfies the user's preference for performance and cost. We train the suite using 8 applications and 20 Cloud instances from multiple providers. Our testing with two scientific applications yield prediction accuracy over 90%. The suite's aim is to provide users with a sustainable and user-contributed platform that assists with cost-effective instance selection.
User Authentication Using Password and Hand Gesture with Leap Motion Sensor

User authentication is becoming a significant factor in the field of modern technology. It is a process that permits a device to confirm the recognition of somebody who interfaces with a system asset. In the world of AI, machine learning is currently one of the leading research fields which is looking into practical implementation. In this paper, we propose a method where the user will enter the given password while the leap motion sensor will compare the behavioral data of the user with an existing dataset. Leap motion controller is a sensor which can recognize 3D movement of hands, fingers, and finger-like object with no contact. For our project, we chose to use dynamic time warping and Naive Bayes classifier algorithm. For the experiment, we captured the leap motion data of eight people and stored them in the database for user authentication and another eight people were used as third-party users. Moreover, as the password is being typed into the keyboard, it is important to restrict the intrusion of malicious third-party users. The proposed system demonstrates about 91% accuracy which rises to 93% in the best-case scenario.