Reviewerly/exHarmony huggingface dataset:
Cybersecurity Threats Based on Machine Learning-Based Offensive Technique for Password Authentication

Due to the emergence of online society, a representative user authentication method that is password authentication has been a key topic. However, in this authentication method, various attack techniques have emerged to steal passwords input from the keyboard, hence, the keyboard data does not ensure security. To detect and prevent such an attack, a keyboard data protection technique using random keyboard data generation has been presented. This technique protects keyboard data by generating dummy keyboard data while the attacker obtains the keyboard data. In this study, we demonstrate the feasibility of keyboard data exposure under the keyboard data protection technique. To prove the proposed attack technique, we gathered all the dummy keyboard data generated by the defense tool, and the real keyboard data input by the user, and evaluated the cybersecurity threat of keyboard data based on the machine learning-based offensive technique. We verified that an adversary obtains the keyboard data with 96.2% accuracy even if the attack technique that makes it impossible to attack keyboard data exposure is used. Namely, the proposed method in this study obviously differentiates the keyboard data input by the user from dummy keyboard data. Therefore, the contributions of this paper are that we derived and verified a new security threat and a new vulnerability of password authentication. Furthermore, a new cybersecurity threat derived from this study will have advantages over the security assessment of password authentication and all types of authentication technology and application services input from the keyboard.
Detecting defects on corrugated plate surfaces using a differential laser triangulation method

During corrugated-plate manufacturing and associated service processes, the plates are often prone to suffering from defects such as cracks, holes and thinning. However, traditional inspection methods cannot effectively satisfy real-time requirements owing to the complexities of the examined surfaces. To solve this problem, this study develops a differential laser triangulation method based on online detection of variations in wall thicknesses, to identify surface defects (holes and thinning). This integrated system drives a pair of laser triangulation probes to capture the surface data of the plates. To compensate for the measurement errors of the triangulation probe caused by irregular surface curvatures, an empirical error model based on error evaluation is established. To improve the precision of the cross-sectional contour reconstruction, the corrected point data are segmented according to the geometrical characteristics of the examined plates, and the analytical model of the contour data in each subsection is modeled using the least-squares method. The wall thickness of each point can then be calculated using its normal equation, to characterize the surface defects. Experiments on a 203 mm × 176 mm workpiece demonstrate that the developed method not only analyzes the object dimensions, but also provides a non-destructive means to effectively detect defects on the surface of a corrugated plate.
Microwave-based arbitrary cphase gates for transmon qubits

Superconducting transmon qubits are of great interest for quantum computing and quantum simulation. A key component of quantum chemistry simulation algorithms is breaking up the evolution into small steps, which naturally leads to the need for nonmaximally entangling, arbitrary cphase gates. Here we design such microwave-based gates using an analytically solvable approach leading to smooth, simple pulses. We use the local invariants of the evolution operator in SU(4) to develop a method of constructing pulse protocols, which allows for the continuous tuning of the phase. We find cphase fidelities of more than 0.999 and gate times as low as $100\phantom{\rule{4.pt}{0ex}}\text{ns}$.
A New User Identity Based Authentication, Using Security and Distributed for Cloud Computing

Abstract The Cloud based Computing is a trending technology in present IT scenario in which data is stocked in a Scattered or distributed manner &amp; resources are shared among people. The data centers are widely distributed across the globe and are accessed by anyone at any time. Data is fetched, uploaded and manipulated by several users simultaneously hence identification and authentication of users who are fetching the data need to be done mandatorily without any error. In this Research Paper we have presented a new advanced security architecture for user identification which includes two factor authentication, AES based file encryption and decryption of data uploaded on cloud, admin verification and locking of users, fetching IP details of users, distributed database storage i.e. data is stored in tiers which means user login details is stored in one database &amp;encryption/decryption details such as (files uploaded, key) is stored on different database. Whole work deals on enhancing security for cloud computing.
A Novel Hybrid Fuzzy AHP-GA Method for Test Sheet Question Selection

The use of web-based education and e-learning environments has increased with the developments in educational technology. Schools, universities, public institutions, and other private sector companies started deploying these systems to train their students, members, and employees. Exams are carried out during the evaluation process of these trainings. Web-based tests are sometimes used for these exams. When there are so many questions about the same topic, it is a time-consuming and difficult problem to prepare these exams in terms of the best quality, quickly and effectively. In order to overcome this issue, artificial intelligence techniques are utilized as well as conventional methods for producing test papers. In this study, an Intelligent Question Evaluation and Selection Software (I-QUESS), that enables the selection of questions according to desired preferences by using Fuzzy Analytic Hierarchy Process (FAHP) and genetic algorithm (GA) as hybrid, was developed. This proposed hybrid system was used in a case study to create test sheet for web-based environments.
Disentangling Image distortions in deep feature space

Previous literature suggests that perceptual similarity is an emergent property shared across deep visual representations. Experiments conducted on a dataset of human-judged image distortions have proven that deep features outperform classic perceptual metrics. In this work we take a further step in the direction of a broader understanding of such property by analyzing the capability of deep visual representations to intrinsically characterize different types of image distortions. To this end, we firstly generate a number of synthetically distorted images and then we analyze the features extracted by different layers of different Deep Neural Networks. We observe that a dimension-reduced representation of the features extracted from a given layer permits to efficiently separate types of distortions in the feature space. Moreover, each network layer exhibits a different ability to separate between different types of distortions, and this ability varies according to the network architecture. Finally, we evaluate the exploitation of features taken from the layer that better separates image distortions for: i) reduced-reference image quality assessment, and ii) distortion types and severity levels characterization on both single and multiple distortion databases. Results achieved on both tasks suggest that deep visual representations can be unsupervisedly employed to efficiently characterize various image distortions.
Potential legal issues when caring healthcare robot with communication in caring functions are used for older adult care

The issue of older adults’ care is becoming a serious concern in Japan, which has a rapidly aging population and a low birthrate. The development of robots is pushed forward as a measure to compensate for the healthcare worker shortage. The purpose of this paper is to consider the potential legal issues of caring healthcare robot (CHR) for older adults’ care. A CHR must recognize the speech, face, and presence of older adults, and make judgments and relay information based on acquired information. CHRs fulfill the caring function by being close to patients, to know them deeply, and to look after them. Therefore, communication functions by advanced artificial intelligence based on caring in nursing are essential for CHRs. The ability to maintain and improve the activities of daily living (ADL) of older adults is to facilitate activities through bidirectional information relay. Furthermore, without guarantees on the safety and ascription of responsibilities, the introduction of CHRs in clinical practice will not proceed. As laws differ from country to country, it is necessary to examine Japanese policies and related laws when using CHRs in Japan. Currently, there are no central rules on information security. In Japan, the law is made after a case has occurred; thus, dealing with novel issues as they occur will not have the benefit of legal guidance. Creating a broad legal framework or taking preventive measures at an early stage is needed. Therefore, as a first step, establishing guidelines for the use of CHRs will be valuable.
Matching Cross Network for Learning to Rank in Personal Search

Recent neural ranking algorithms focus on learning semantic matching between query and document terms. However, practical learning to rank systems typically rely on a wide range of side information beyond query and document textual features, like location, user context, etc. It is common practice to concatenate all of these features and rely on deep models to learn a complex representation.
Smart Home IoT Anomaly Detection based on Ensemble Model Learning From Heterogeneous Data

Nowadays, internet based home automation is made possible with the advent of intelligent device control. These electronic sensing devices transfer an enormous amount of data into the cloud. It is a challenge to discover hidden information from the massive amount of stored data in the cloud. In addition, privacy, security, and stability could also be a concern for users. Due to these issues becoming ever more prevalent in today’s society, the need to have access to readily anomaly detection becomes crucial for the modern smart home user. In this paper, we design, test and evaluate an ensemble model anomaly detection method. Our method targets the data anomalies present in general smart Internet of Things (IoT) devices, allowing for easy detection of anomalous events based on stored data. We make our method robust through ensemble machine learning model training. We aim to simulate different types of anomaly situations on publicly available smart home data sets, thereby exposing our models to likely real world phenomenons and events that may cause anomalies. Experiments are conducted on the processed data and evaluated for accuracy through validation and testing against independent and identically distributed labeled data.
Video action recognition based on visual rhythm representation

Advances in video acquisition and storage technologies have promoted a great demand for automatic recognition of actions. The use of cameras for security and surveillance purposes has applications in several scenarios, such as airports, parks, banks, stations, roads, hospitals, supermarkets, industries, stadiums, schools. An inherent difficulty of the problem is the complexity of the scene under usual recording conditions, which may contain complex background and motion, multiple people on the scene, interactions with other actors or objects, and camera motion. Most recent databases are built primarily with shared recordings on YouTube and with snippets of movies, situations where these obstacles are not restricted. Another difficulty is the impact of the temporal dimension since it expands the size of the data, increasing computational cost and storage space. In this work, we present a methodology of volume description using the Visual Rhythm (VR) representation. This technique reshapes the original volume of the video into an image, where two-dimensional descriptors are computed. We investigated different strategies for constructing the representation by combining configurations in several image domains and traversing directions of the video frames. From this, we propose two feature extraction methods, Naïve Visual Rhythm (Naïve VR) and Visual Rhythm Trajectory Descriptor (VRTD). The first approach is the straightforward application of the technique in the original video volume, forming a holistic descriptor that considers action events as patterns and formats in the visual rhythm image. The second variation focuses on the analysis of small neighborhoods obtained from the process of dense trajectories, which allows the algorithm to capture details unnoticed by the global description. We tested our methods in eight public databases, one of hand gestures (SKIG), two in first person (DogCentric and JPL), and five in third person (Weizmann, KTH, MuHAVi, UCF11 and HMDB51). The results show that the developed techniques are able to extract motion elements along with format and appearance information, achieving competitive accuracy rates compared to state-of-the-art action recognition approaches.
Building Shadow Detection on Ghost Images

Although many efforts have been made on building shadow detection from aerial images, little research on simultaneous shadows detection on both building roofs and grounds has been presented. Hence, this paper proposes a new method for simultaneous shadow detection on ghost image. In the proposed method, a corner point on shadow boundary is selected and its 3D approximate coordinate is calculated through photogrammetric collinear equation on the basis of assumption of average elevation within the aerial image. The 3D coordinates of the shadow corner point on shadow boundary is used to calculate the solar zenith angle and the solar altitude angle. The shadow areas on the ground, at the moment of aerial photograph shooting are determined by the solar zenith angle and the solar altitude angle with the prior information of the digital building model (DBM). Using the relationship between the shadows of each building and the height difference of buildings, whether there exists a shadow on the building roof is determined, and the shadow area on the building roof on the ghost image is detected on the basis of the DBM. High-resolution aerial images located in the City of Denver, Colorado, USA are used to verify the proposed method. The experimental results demonstrate that the shadows of the 120 buildings in the study area are completely detected, and the success rate is 15% higher than the traditional shadow detection method based on shadow features. Especially, when the shadows occur on the ground and on the buildings roofs, the successful rate of shadow detection can be improved by 9.42% and 33.33% respectively.
To schedule or not to schedule: When no-scheduling can beat the best-known flow scheduling algorithm in datacenter networks

Conventional wisdom for minimizing the average flow completion time (AFCT) in the datacenter network (DCN), where flow sizes are highly variable, would suggest scheduling every individual flow. However, we show that considering scheduling delay (including schedulers computational and communication delays), serving most of the flows without any scheduling and only in first-come-first-served (FCFS) manner significantly improves their performance even when it is compared to the shortest remaining processing time (SRPT) known as optimum algorithm when scheduling delay is zero. To do so, we only require to have two coarse classes of flows categorized based on flows sizes (1st-class including flows smaller than a threshold, H, and 2nd-class including others) and serve 1st-class flows always before serving 2nd-class ones. To show that, we take SRPT scheduling algorithm accompanied by the global knowledge of flows, formulate impact of scheduling delay on its performance, and prove that for any flow size distribution and network load ( < 1), there is always a threshold, H, which guarantees 1st-class flows achieve lower AFCT under FCFS compared to SRPT. Our numerically calculated results and extensive flow-level simulations show that on average, more than 90% of flows could be in 1st-class and consequently do not require any scheduling.
Scaled Conjugate Gradient Backpropagation based SLA Violation Prediction in Cloud Computing

Cloud Services are highly scalable and dynamic. Providing high availability and low latency with high data rate is a big challenging task for service providers. Any deviation in the Service Level Agreement (SLA) between service providers and customers is service level Violation (SLV). In the case of SLV, the cloud service provider has to pay the penalty to the customers in terms of money or additional services as per the SLA. Hence SLA prediction is of paramount importance for both customers and service providers. In this paper, we have used a Scaled conjugate gradient neural network to make predictions on SLA Violations. We have used real-world data which seems to be relevant to the real-world scenarios. Oversampling technique is used to overcome the problem of the biased dataset. Results obtained from the experiments have undergone 3-fold validation. Classification accuracy of SCG algorithm is found to be 96.76% and 93.23% for two datasets which may be considered as an efficient prediction model.
Superconducting nanowire multi-photon detectors enabled by current reservoirs

Single-photon detectors are ubiquitous devices in quantum-photonic-based communication, computation, metrology, and sensing. In these applications, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> -fold coincidence photon counting is often needed, for example, to characterize entanglement. However, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m2"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> -fold coincidence photon counting typically requires <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m3"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> individual single-photon detectors and associated bias and readout electronics, and these resources could become prohibitive if <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m4"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> goes large and the detectors need to work at cryogenic temperatures. Here, to break this limit on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m5"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> , we propose a device architecture based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m6"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> cascaded photosensitive superconducting nanowires and one wider nanowire that functions as a current reservoir. We show that by strategically designing the device, the network of these superconducting nanowires can work in a synergic manner as an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m7"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> -photon detector, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m8"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> can be from 1 to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m9"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> , depending on the bias conditions. We therefore name the devices of this type superconducting nanowire multi-photon detectors (SNMPDs). In addition to its simple one-port bias and readout circuitry, the coincidences are counted internally in the detector, eliminating the need for external multi-channel, time-correlated pulse counters. We believe that the SNMPDs proposed in this work could open avenues towards conveniently measuring high-order temporal correlations of light and characterizing multi-photon entanglement.
Robust ellipse fitting based on Lagrange programming neural network and locally competitive algorithm

Given a set of 2-dimensional (2D) scattering points, obtained from the edge detection process, the aim of ellipse fitting is to construct an elliptic equation that best fits the scattering points. However, the 2D scattering points may contain some outliers. To address this issue, we devise a robust ellipse fitting approach based on two analog neural network models, Lagrange programming neural network (LPNN) and locally competitive algorithm (LCA). We formulate the fitting task as a nonsmooth constrained optimization problem, in which the objective function is an approximated l0-norm term. As the LPNN model cannot handle non-differentiable functions, we utilize the internal state concept of LCA to avoid the computation of the derivative at non-differentiable points. Simulation results show that the proposed ellipse fitting approach is superior to several state-of-the-art algorithms.
A study on rare fraud predictions with big Medicare claims fraud data

Access to copious amounts of information has reached unprecedented levels, and can generate very large data sources. These big data sources often contain a plethora of useful information but, in some cases, finding what is actually useful can be quite problematic. For binary classification problems , such as fraud detection, a major concern therein is one of class imbalance. This is when a dataset has more of one label versus another, such as a large number of non-fraud observations with comparatively few observations of fraud (which we consider the class of interest). Class rarity further delineates class imbalance with significantly smaller numbers in the class of interest. In this study, we assess the impacts of class rarity in big data, and apply data sampling to mitigate some of the performance degradation caused by rarity. Real-world Medicare claims datasets with known excluded providers are used as fraud labels for a fraud detection scenario, incorporating three machine learning models. We discuss the necessary data processing and engineering steps in order to understand, integrate, and use the Medicare data. From these already imbalanced datasets, we generate three additional datasets representing varying levels of class rarity. We show that, as expected, rarity significantly decreases model performance, but data sampling, specifically random undersampling, can help significantly with rare class detection in identifying Medicare claims fraud cases.
Generating Realistic Users Using Generative Adversarial Network With Recommendation-Based Embedding

User data has been used by many companies to understand user behaviors and finding new business strategies. However, common techniques cannot be used when it comes to new products that have not yet been released due to the fact that there are no prior data available. In this work, we propose a framework for generating realistic user data on new products which can then be analyzed for insights. Our model uses Conditional Generative Adversarial Network (CGAN) with the Straight-Through Gumbel estimator which can also handle discrete-valued outputs. The CGAN is conditioned on product features learned using a recommendation system which can better capture the relationship between products. Experiments using a dataset consisting of view logs from a real estate listing website shows that our model outperforms other baselines on four performance metrics, and can effectively predict the finer characteristics of new products.