Reviewerly/exHarmony huggingface dataset:
Quantum Hacking on an Integrated Continuous-Variable Quantum Key Distribution System via Power Analysis

In quantum key distribution (QKD), there are some security loopholes opened by the gaps between the theoretical model and the practical system, and they may be exploited by eavesdroppers (Eve) to obtain secret key information without being detected. This is an effective quantum hacking strategy that seriously threatens the security of practical QKD systems. In this paper, we propose a new quantum hacking attack on an integrated silicon photonic continuous-variable quantum key distribution (CVQKD) system, which is known as a power analysis attack. This attack can be implemented by analyzing the power originating from the integrated electrical control circuit in state preparation with the help of machine learning, where the state preparation is assumed to be perfect in initial security proofs. Specifically, we describe a possible power model and show a complete attack based on a support vector regression (SVR) algorithm. The simulation results show that the secret key information decreases with the increase of the accuracy of the attack, especially in a situation with less excess noise. In particular, Eve does not have to intrude into the transmitter chip (Alice), and may perform a similar attack in practical chip-based discrete-variable quantum key distribution (DVQKD) systems. To resist this attack, the electrical control circuit should be improved to randomize the corresponding power. In addition, the power can be reduced by utilizing the dynamic voltage and frequency scaling (DVFS) technology.
The improved TLD algorithm that combines CAMShift and orientation prediction to realize face tracking in low illumination

TLD(Tracking-Learning-Detection)algori thm acquires the target characteristics through continuous learning of the initial target. It can carry out long-term target Tracking. However, during the Tracking process, illumination, occlusion and target movement have a great impact on the Tracking effect. At the same time, the algorithm has a relatively large amount of calculation in the calculation process, which leads to the poor real-time performance of tracking. In order to reduce the influence of low illumination and occlusion on the algorithm, an improved algorithm integrating LBP algorithm into CAMshift algorithm was adopted to replace the original algorithm of tracking module in the algorithm. At the same time, in order to eliminate the defect of tracking loss caused by the target movement, Markov method is used to predict the direction and position of target movement , which can reduce the detection range of the algorithm and improve the real-time performance of the algorithm. The experiment shows that: compared with the traditional TLD algorithm, the improved algorithm has greatly improved tracking accuracy and faster tracking speed.
Deep Co-Training Active Learning for Mammographic Images Classification

Breast cancer becomes one of the most serious women's diseases. With help of early screen, the deep learning based computer-aided diagnosis (CAD) could reduce the mortality rate, which has become increasingly concerned. Building a deep learning based CAD system requires a large labeled dataset, however the labeling procedure is highly time consuming and labor-intensive. The active learning algorithm is applied to reduce the labeling cost by selecting the most valuable sample, which improve the model performance with smaller amount of data and limited annotation. However, the informative unlabeled data will be discarded. To make use of unlabeled data, the co-training approach is a suitable option apply in breast mammogram classification. We propose the co- training active learning (COAL) framework combine with the co-training and active learning which not only leverage the unlabeled data and import the annotation efficiency but also simultaneously utilizes two-view images to improve the classification performance.The experimental results proved the effectiveness and efficiency of the framework, where only 30% labeled datasets could achieve a comparable performance to the fully-supervised learning framework. Besides, the proposed algorithm fully utilizes the breast images two-view information, which could improve 2% prediction accuracy.
Multi-Swarm Optimization for Extracting Multiple-Choice Tests From Question Banks

In this study, a novel method for generating multiple-choice tests is presented, which extracts the required number of tests of the same levels of difficulty in a single attempt and approximates the difficulty level requirement given by users. We propose an approach using parallelism and Pareto optimization for multi-swarm migration in a particle swarm optimization (PSO) algorithm. Multi-PSO is proposed for shortening the computing time. The proposed migration of PSOs increases the diversity of tests and controls the overlap of extracted tests. The experimental results show that the proposed method can generate many tests from question banks satisfying predefined levels of difficulty. Additionally, the developed method is shown to be effective in terms of many criteria when compared with other methods such as manually extracted tests, a simulated annealing algorithm (SA), random methods and PSO-based approaches in terms of the number of successful solutions, accuracy, standard deviation, search speed, and the number of questions overlapping between the exam questions, as well as for changing the search space, changing the number of individuals, changing the number of swarms, and changing the difficulty requirements.
Research on Unmanned Surface Vehicle Path Planning Based on Improved Intelligent Water Drops Algorithm

Unmanned Surface Vehicle (USV) becomes the key point of the development of intelligent marine equipment at home and abroad with its wide application prospects. Path Planning is one of the key technologies for autonomous control of USV. In this paper, a global path planning method for USV based on intelligent water drops (IWD) algorithm is proposed. Aiming at the disadvantage of slow convergence speed caused by the lack of heuristic ability of IWD in USV path planning, an improved IWD (IIWD) algorithm based on multi-objective heuristic function is put forward. Experiments have been conducted, and the results show that IIWD algorithm converges faster and obtains shorter path than IWD algorithm and that IIWD algorithm is very fit to solve the USV path planning problem.
Integration of Explainable AI and Blockchain for Secure Storage of Human Readable Justifications for Credit Risk Assessment

Automated credit risk assessment is one of the most important applications of artificial intelligence. However, the traditional black-box AI models are no longer suitable due to the regulations imposed all over the world to ensure security, privacy and interpretability of the data. A notable example is the General Data Protection Regulation (GDPR). The problem is twofold; firstly, human-understandable explanations are required and secondly, these explanations must be stored securely to ensure the privacy of the user and should be accessible in the future if required. In this paper, we propose a system that leverages the secure and immutable nature of Blockchain to store machine learning model explanations for credit-scoring. Our proposed system aims to enable local interpretations of the global model to be publicly available to customers to access in a secure manner. A chronological account of the model decisions can be obtained per user, helping the user further understand the series of events leading to their loan approval decisions. Our results demonstrate the trustworthiness of an explained model prediction, with the security, reproducibility, traceability and transparency of Blockchain, providing the end-user with a way to securely request an explanation for the credit-scoring decision.
ICE-Talk 2: Interface for Controllable Expressive TTS with perceptual assessment tool

In this paper, we present open-source 1https://github.com/noetits/ICE-Talk.1https://github.com/noetits/ICE-Talk. tools that facilitates the use of controllable TTS systems in experiments, towards the democratization of TTS systems across domains. ICE-Talk is a web-based GUI that allows the use of a TTS system with controllable parameters via a text field and a clickable 2D plot. It enables the study of latent spaces for controllable TTS. A tool to design a perceptual experiment is provided and consists of three steps: pre-synthesizing samples covering the 2D plot representing controllable dimensions, including this interface inside a template question, and integrate it in a Mechanical Turk system called turkle.
Pipe Routing of Reactor Based on Adaptive A* Algorithm Combined With Genetic Algorithm

To solve the complex 3-D space pipe routing problem, an algorithm framework combining genetic algorithm and adaptive A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm (AAGA) is proposed. The initial population of the genetic algorithm is generated using the adaptive A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm combined with the concept of intermediate join points, this method is also used in the generation of sub-paths in the crossover and mutation operations of genetic algorithms. On the one hand, this method improves the quality of the initial population and speeds up the optimization of genetic algorithm, on the other hand, it solves the problem that the genetic algorithm combined with the traditional A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm (AGA) is prone to redundant inflection points. Finally, on the premise of ensuring pipeline connectivity, pipeline length and number of inflection points, the constraints of pipeline center of gravity and minimum bending radius are added to carry out simulation experiments. Simulation results show that the AAGA has better performance than adaptive A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm and AGA.
Classifier Invariant Approach to Learn from Positive-Unlabeled Data

Learning from positive ( P) and unlabeled ( U) data has a rich history as it finds use in multiple applications. In this paper, we provide a novel framework to tackle this problem in a model agnostic fashion. We say model agnostic since, our solution involves identifying and weighting positive as well as negative examples in an unsupervised manner which could then be passed as input to any standard classification algorithm. Moreover, based on our framework we provide approximation guarantees for our algorithm in terms of how well the identified positive examples from U along with their weights match the distribution of P. Such a principled approach has been missing for other methods that belong to the model agnostic category, not to mention that the current state-of-the-art are model dependent strategies that involve modifying the training algorithm. For Kernel Support Vector Machines, trained on a (non-negative) weighted dataset that as such is the output of our method, we derive generalization bounds. Given the advantages of having model agnostic methods (viz. use with (almost) any classifier, one time running cost), we show that our algorithm which possesses these benefits, is competitive with the best methods based on experiments on three real datasets. In fact, in a couple of cases we observe that our approach has better test performance than even standard supervised learning which has access to all positive as well as negative labels.
Adversarial Resilience for Sampled-Data Systems Under High-Relative-Degree Safety Constraints

Control barrier functions (CBFs) have recently become a powerful method for rendering desired safe sets forward invariant in single-agent and multiagent systems. In the multiagent case, prior literature has considered scenarios where all agents cooperate to ensure that the corresponding set remains invariant. However, these works do not consider scenarios where a subset of the agents are behaving adversarially with the intent to violate safety bounds. In addition, prior results on multiagent CBFs typically assume that control inputs are continuous and do not consider sampled-data dynamics. This article presents a framework for normally behaving agents in a multiagent system with heterogeneous control-affine, sampled-data dynamics to render a safe set forward invariant in the presence of adversarial agents. The proposed approach considers several aspects of practical control systems including input constraints, clock asynchrony and disturbances, and distributed calculation of control inputs. Our approach also considers functions describing safe sets having high relative degree with respect to system dynamics. The efficacy of these results are demonstrated through simulations.
Phase-shift determination for a 4 × 4 intelligent photonic neural network with compatible learning

Intelligent photonic circuits (IPCs) tuned with an appropriate phase-shift vector could enable a photonic intelligent matrix possibly implemented in multiple neural layers for a task-oriented topologies. A photonic Mach–Zehnder Interferometer (MZI) is a fundamental photonic component in IPCs, whose matrix representation could be broadcasted into an arbitrary matrix that is equipped with an optimized phase-shift vector. The initialized MZIs’ phases are tentatively probed between analytical elements and a digital weight matrix that is learned from samples with efficient compatible learning for complex-valued neural networks. Nonlinear least squares is utilized to formulate a phase determination system to refine the optimal phase-shift solutions. The robustness of phase determination system for photonic neural networks is discussed in detail. For a preliminary implementation, a basic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>4</mml:mn> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mn>4</mml:mn> </mml:math> intelligent photonic neural network is utilized to verify the proof of concept on phase-shift determination in IPC through numerical experiments.
Neural architecture search and weight adjustment by means of Ant Colony Optimization

In recent years, many different areas of research have utilized neural networks (NNs). Many have investigated weights in NNs as well as structural optimization of NNs via neural architecture search. In this paper, we apply weight training during neural architecture search by Ant Colony Optimization(ACO) to two problems from OpenAI Gym and one problem from pybullet-gym controlled by NNs. We also compare the timing of when the weight training is performed, which is before the architecture search, during architecture search and after architecture search. It was found that performing architecture search by ACO and weight training simultaneously is effective for increasing the score of NNs and that by performing weight training before or at the same time as architecture search, the score was increased statistically significantly for all problems compared with fully-connected NN and the score by performing weight training after architecture search was increased statistically significantly for only one problem from pybullet-gym.
Real-time Detection of Cache Side-channel Attack Using Non-cache Hardware Events

Cache side-channel attack is a class of attacks to retrieve sensitive information from a system by exploiting shared resource in CPUs. As the attacks are delivered to wide range of environments from mobile systems to cloud recently, many detection strategies have been proposed. Since the conventional cache side-channel are likely to incur tremendous number of cache events, most of the previous detection mechanisms were designed to carefully monitor cache events. However, recently proposed attacks tend to incur less cache events during the attack. PRIME+ABORT attack, for example, leverages the Intel TSX instead of accessing cache to measure access time. Because of the characteristic, cache event based detection mechanisms may hardly distinguish the attack. In this paper, we conduct an in-depth analysis of the PRIME+ABORT attack to identify the other useful hardware events for detection rather than cache events. Based on our finding, we present a novel mechanism called PRIME+ABORT Detector to detect the PRIME+ABORT attack and demonstrate that the detection mechanism can achieve 99.5% success rates with 0.3% performance overhead.
Exhaust Fan Speed Controller Using Fuzzy Logic Controller

Abstract Fuzzy logic is a reasoning method for solving linguistic problems that cannot be solved mathematically. The application of fuzzy logic from our development program regarding the development of exhaust fans with fuzzy logic controllers to regulate air flow velocity in accordance with air conditions in a room. Using a gas sensor and a temperature sensor will create a number of conditions to then be treated in a fuzzy logic system using the Mamdani method. The process that occurs in the Mamdani fuzzy includes four stages including the process of calculating the fuzzy set (fuzzification), the application of the implication function, the composition of the rules to the last stage, the defuzzification process. The inter elements of the mamdani fuzzy process are interconnected in the sense that the order determined by fuzzy mamdani cannot be removed or replaced by another. Development that has been done related to the exhaust fan uses two sensors namely the temperature sensor and gas sensor. Used as a detector for the amount of harmful gas in a room so that it activates the exhaust fan to eliminate it at a speed consistent with the gas content in the room. Also used to measure the temperature of the room and then activate the exhaust fan so that the room can be inhabited comfortably. Fuzzy logic controller system is used to adjust the rotational speed of the exhaust fan.
Natural Language Processing on Marketplace Product Review Sentiment Analysis

Marketplace is a platform that bridges buyer and seller to transact. In January 2020, Central Bank reported that total transactions in 14 Indonesian marketplaces reached 23.27 trillion rupiahs. At the end of the transaction process, the buyer has the opportunity to give a review of the product that has been purchased. This research aims to sentiment analysis on marketplace product reviews. This research applies Natural Language Processing as a pre-processing of text in the sentiment analysis of marketplace product reviews using a machine learning approach with the Naive Bayes and K-NN algorithms. The test scenario obtained an average Naive Bayes accuracy of 52.4% on the Unigram dataset and an average K-NN accuracy of 79.4% on the Bigram dataset. The utilization of NLP, particularly the word normalizer, can increase the accuracy of 10% for Naive Bayes and 4% for K-NN. Sentiment analysis on marketplace product reviews is useful as an opportunity for product improvement for both seller and competitor, in the other side it is also used as a reference for other users before buying a product.
Which aspects of the Open Science agenda are most relevant to scientometric research and publishing? An opinion paper

Abstract Open Science is an umbrella term that encompasses many recommendations for possible changes in research practices, management, and publishing with the objective to increase transparency and accessibility. This has become an important science policy issue that all disciplines should consider. Many Open Science recommendations may be valuable for the further development of research and publishing, but not all are relevant to all fields. This opinion paper considers the aspects of Open Science that are most relevant for scientometricians, discussing how they can be usefully applied.
Gesture Agreement Assessment Using Description Vectors

Participatory design is a popular design technique that involves the end-users in the early stages of the design process to obtain user-friendly gestural interfaces. Guessability studies followed by agreement analyses are often used to elicit and comprehend the preferences (gestures/proposals) of the participants. Previous approaches to assess agreement, grouped the gestures into equivalence classes and ignored the integral properties that are shared between them. In this work, we represent the gestures using binary description vectors to allow them to be partially similar. In this context, we introduce a new metric referred to as a soft agreement rate (SAR) to quantify the level of consensus between the participants. In addition, we performed computational experiments to study the behavior of our partial agreement formula and mathematically show that existing agreement metrics are a special case of our approach. Our methodology was evaluated through a gesture elicitation study conducted with a group of neurosurgeons. Nevertheless, our formulation can be applied to any other user-elicitation study. Results show that the level of agreement obtained by SAR metric is 2.64 times higher than the existing metrics. In addition to the most agreed gesture, SAR formulation also provides the mostly agreed descriptors which can potentially help the designers to come up with a final gesture set.
A Novel Low-Illumination Image Enhancement Method Based on Dual-Channel Prior

Low-illumination image enhancement is the important part of computer vision research, and defogging model is widely applied to image enhancement. However, there are still some problems in this model, such as the different depth of field of the window segmentation and blurring of edge details. In this paper, we propose a novel low-illumination image enhancement method based on dual-channel prior. First, the low-illumination image is divided into regions with the same depth of field in the local window using the superpixel segmentation method. Then the bright and dark channels of the image are obtained through the maximum/minimum filtering, and after the bright channel is introduced, the adaptive threshold method is used to obtain the atmospheric light value. The initial transmission rate is obtained by combining the dual-channel image with pixel-level image fusion and determining the optimal combination coefficient for mutual correction. The light map with clearer texture and brightness information is used as the guide map for multi-scale gradient domain guided filtering to refine the transmittance. Finally fast BM3D removes noise and obtains the final enhanced image. Experimental results prove that the method proposed in this paper has improved in removing halo artifacts and restoring detailed information.
IdleSR: Efficient Super-Resolution Network with Multi-scale IdleBlocks

In recent years, deep learning approaches have achieved impressive results in single image super-resolution (SISR). However, most of these models require high computational and memory resources beyond the capability of most mobile and embedded devices. How to significantly reduce the number of operations and parameters while maintaining the performance is a meaningful and challenging problem. To address this problem, we propose an efficient super-resolution network with multi-scale IdleBlocks called IdleSR. Firstly, inspired by information multi-distillation blocks and hybrid composition of IdleBlocks, we construct efficient multi-scale IdleBlocks at the granularity of residual block. Secondly, we replace two 3 $$\times $$ 3 kernels in residual blocks by a 5 $$\times $$ 1 kernel and a 1 $$\times $$ 5 kernel, decreasing parameters and operations dramatically. Thirdly, we use gradient scaling, large input patch size and extra data during training phase to compensate dropped performance. The experiments show that IdleSR can achieve a much better tradeoff among parameter, runtime and performance than start-of-the-art methods.