Reviewerly/exHarmony huggingface dataset:
Coupled variable‐input LCG and clock divider‐based large period pseudo‐random bit generator on FPGA

The authors present a new method for the generation of pseudorandom bits, based on coupled variable input linear congruential generator (LCG) and a clock divider. To prevent the system from falling into short-period orbits as well as increasing the randomness of the generated bit sequences, the proposed algorithm periodically changes the seed parameters of the LCG blocks. The proposed clock divider-based pseudorandom bit generator is compared with other LCG-based realisations, showing great improvement. First, a clock divider is utilised for generating a maximum length of 22n pseudorandom bits for n-bit operands size which leads to lowering the hardware cost. Secondly, it generates high-speed random bits at a uniform clock rate with one initial clock latency. Third, the proposed technique provides good statistical properties. The proposed architecture is implemented using Verilog HDL and further prototyped on commercially available field programmable gate array (FPGA) devices Virtex-5, Virtex-7, and Artix-7. The realisation of the proposed architecture in these FPGA devices accomplishes an improved data throughput and utilises minimum FPGA resources (in terms of look-up-tables and flip-flops) which are compared with the existing techniques. The generated bit sequence from the experiment is further analysed briefly for sequence size and verified for randomness by using the National Institute of Standards and Technology benchmark test.
Bench-Top Cooling of a Microwave Mode Using an Optically Pumped Spin Refrigerator

We experimentally demonstrate the temporary removal of thermal photons from a microwave mode at 1.45 GHz through its interaction with the spin-polarized triplet states of photo-excited pentacene molecules doped within a p-terphenyl crystal at room temperature. The crystal functions electromagnetically as a narrow-band cryogenic load, removing photons from the otherwise room-temperature mode via stimulated absorption. The noise temperature of the microwave mode dropped to $50^{+18}_{-32}$ K (as directly inferred by noise-power measurements) while the metal walls of the cavity enclosing the mode remained at room temperature. Simulations based on the same system's behavior as a maser (which could be characterized more accurately) indicate the possibility of the mode's temperature sinking to $\sim$10 K (corresponding to $\sim$140 microwave photons). These observations, when combined with engineering improvements to deepen the cooling, identify the system as a narrow-band yet extremely convenient platform -- free of cryogenics, vacuum chambers and strong magnets -- for realizing low-noise detectors, quantum memory and quantum-enhanced machines (such as heat engines) based on strong spin-photon coupling and entanglement at microwave frequencies.
Optimizing the optimizer: decomposition techniques for quantum annealing

Although quantum computing hardware has evolved significantly in recent years, spurred by increasing industrial and government interest, the size limitation of current generation quantum computers remains an obstacle when applying these devices to relevant, real-world problems. In order to effectively exploit the potential benefits of quantum computing, heterogeneous approaches that combine both classical and quantum computing techniques are needed. In this work, we explore multiple heterogeneous algorithms for solving a variety of industry-relevant benchmark problems in order to understand how best to leverage quantum computers given current constraints. Our results indicate the following: (1) that solver performance is highly dependent on the structure (size and edge density) of the problem graph; (2) that reusing a single fixed problem embedding, as opposed to dynamically searching for problem embeddings, is key to avoiding computational bottlenecks; (3) that solutions of better quality are produced by algorithms that iteratively propagate the influence that solving an individual sub-problem has to the remainder of the larger problem; and (4) that the Qbsolv algorithm (which implements the aforementioned techniques) is, at this time, the state-of-the-art in producing quality solutions, in a timely fashion, to a variety of theoretical and real-world problems too large to directly embed onto a quantum annealing device.
So Many Phish, So Little Time: Exploring Email Task Factors and Phishing Susceptibility

The present studies examine how task factors (e.g., email load, phishing prevalence) influence email performance.Phishing emails are a paramount cybersecurity threat for the modern email user. Research attempting to understand how users are susceptible to phishing attacks has been limited and has not fully explored how task factors (e.g., prevalence, email load) influence accurate detection.In three experiments, participants classified emails as either legitimate or not legitimate and reported on a variety of other categorizations. The first two experiments examined how email load and phishing prevalence influence phishing detection independently. The third experiment examined the interaction of these two factors to determine whether they have compounding effects. All three experiments utilized individual difference variables to examine how cognitive, behavioral, and personality factors may influence classifications.Experiment 1 suggests that high email load can make the task appear more challenging. Experiment 2 indicates that low phishing prevalence can decrease sensitivity for phishing emails. Experiment 3 demonstrates that high levels of email load can decrease classification accuracy under 50/50 prevalence rates. Notably, performance was poor across all experiments, with phishing detection near chance levels and low discriminability for emails. Participants demonstrated poor metacognition with over confidence, low self-reported difficulty, and low perceived threat for the emails.Overall, the present studies suggest that high email load and low phishing prevalence can influence email classifications.Organizations and researchers should consider the influences of both email load and phishing prevalence when implementing phishing interventions.
Optimizing Voice Conversion Network with Cycle Consistency Loss of Speaker Identity

We propose a novel training scheme to optimize voice conversion network with a speaker identity loss function. The training scheme not only minimizes frame-level spectral loss, but also speaker identity loss. We introduce a cycle consistency loss that constrains the converted speech to maintain the same speaker identity as reference speech at utterance level. While the proposed training scheme is applicable to any voice conversion networks, we formulate the study under the average model voice conversion framework in this paper. Experiments conducted on CMU-ARCTIC and CSTR-VCTK corpus confirm that the proposed method outperforms baseline methods in terms of speaker similarity.
Multiple pedestrian tracking under first-person perspective using deep neural network and social force optimization

Multiple pedestrian tracking in the first-person perspective is a challenging problem, obstacles of which are mainly caused by camera moving, frequent occlusions, and collision avoidance. To solve the mentioned issues, we proposed a novel deep learning-based approach. Firstly, a dense connection and attention based YOLO (DCA-YOLO) is proposed for ameliorating the detection performance. Then, the detection results are sent to a wide residual network for feature extraction. We use the Kuhn–Munkres algorithm to construct a similarity matrix and find the best match of two detection boxes. To tackle the frequent occlusion and ID-switch issues caused by collision avoidances or grouping behavior, we introduce a social force model into the proposed network to optimize the tracking results. The experimental results on widely used challenging MOT2015 and MOT2016 benchmarks demonstrate the effectiveness of our proposed algorithm.
Deep Co-Attention Network for Multi-View Subspace Learning

Many real-world applications involve data from multiple modalities and thus exhibit the view heterogeneity. For example, user modeling on social media might leverage both the topology of the underlying social network and the content of the users' posts; in the medical domain, multiple views could be X-ray images taken at different poses. To date, various techniques have been proposed to achieve promising results, such as canonical correlation analysis based methods, etc. In the meanwhile, it is critical for decision-makers to be able to understand the prediction results from these methods. For example, given the diagnostic result that a model provided based on the X-ray images of a patient at different poses, the doctor needs to know why the model made such a prediction. However, state-of-the-art techniques usually suffer from the inability to utilize the complementary information of each view and to explain the predictions in an interpretable manner.
Significance of Elliptic Curve Cryptography in Blockchain IoT with Comparative Analysis of RSA Algorithm

In the past few years, the blockchain emerged as peer-to-peer distributed ledger technology for recording transactions, maintained by many peers without any central trusted regulatory authority through distributed public-key cryptography and consensus mechanism. It has not only given the birth of cryptocurrencies, but it also resolved various security, privacy and transparency issues of decentralized systems. This article discussed the blockchain basics overview, architecture, and blockchain security components such as hash function, Merkle tree, digital signature, and Elliptic curve cryptography (ECC). In addition to the core idea of blockchain, we focus on ECC's significance in the blockchain. We also discussed why RSA and other key generation mechanisms are not suitable for blockchain-based IoT applications. We also analyze many possible blockchain-based applications where ECC algorithm is better than other algorithms concerning security and privacy assurance. At the end of the article, we will explain the comparative analysis of ECC and RSA.
Compositional Non-Interference for Fine-Grained Concurrent Programs

Non-interference is a program property that ensures the absence of information leaks. In the context of programming languages, there exist two common approaches for establishing non-interference: type systems and program logics. Type systems provide strong automation (by means of type checking), but they are inherently restrictive in the kind of programs they support. Program logics support challenging programs, but they typically require significant human assistance, and cannot handle modules or higher-order programs.To connect these two approaches, we present SeLoC—a separation logic for non-interference, on top of which we build a type system using the technique of logical relations. By building a type system on top of separation logic, we can compositionally verify programs that consist of typed and untyped parts. The former parts are verified through type checking, while the latter parts are verified through manual proof.The core technical contribution of SeLoC is a relational form of weakest preconditions that can track information flow using separation logic resources. SeLoC is fully machine-checked, and built on top of the Iris framework for concurrent separation logic in Coq. The integration with Iris provides seamless support for fine-grained concurrency, which was beyond the reach of prior type systems and program logics for non-interference.
Functional Symmetry and Statistical Depth for the Analysis of Movement Patterns in Alzheimer’s Patients

Black-box techniques have been applied with outstanding results to classify, in a supervised manner, the movement patterns of Alzheimer’s patients according to their stage of the disease. However, these techniques do not provide information on the difference of the patterns among the stages. We make use of functional data analysis to provide insight on the nature of these differences. In particular, we calculate the center of symmetry of the underlying distribution at each stage and use it to compute the functional depth of the movements of each patient. This results in an ordering of the data to which we apply nonparametric permutation tests to check on the differences in the distribution, median and deviance from the median. We consistently obtain that the movement pattern at each stage is significantly different to that of the prior and posterior stage in terms of the deviance from the median applied to the depth. The approach is validated by simulation.
Deep ConvLSTM Network with Dataset Resampling for Upper Body Activity Recognition Using Minimal Number of IMU Sensors

Human activity recognition (HAR) is the study of the identification of specific human movement and action based on images, accelerometer data and inertia measurement unit (IMU) sensors. In the sensor based HAR application, most of the researchers used many IMU sensors to get an accurate HAR classification. The use of many IMU sensors not only limits the deployment phase but also increase the difficulty and discomfort for users. As reported in the literature, the original model used 19 sensor data consisting of accelerometers and IMU sensors. The imbalanced class distribution is another challenge to the recognition of human activity in real-life. This is a real-life scenario, and the classifier may predict some of the imbalanced classes with very high accuracy. When a model is trained using an imbalanced dataset, it can degrade model’s performance. In this paper, two approaches, namely resampling and multiclass focal loss, were used to address the imbalanced dataset. The resampling method was used to reconstruct the imbalanced class distribution of the IMU sensor dataset prior to model development and learning using the cross-entropy loss function. A deep ConvLSTM network with a minimal number of IMU sensor data was used to develop the upper-body HAR model. On the other hand, the multiclass focal loss function was used in the HAR model and classified minority classes without the need to resample the imbalanced dataset. Based on the experiments results, the developed HAR model using a cross-entropy loss function and reconstructed dataset achieved a good performance of 0.91 in the model accuracy and F1-score. The HAR model with a multiclass focal loss function and imbalanced dataset has a slightly lower model accuracy and F1-score in both 1% difference from the resampling method. In conclusion, the upper body HAR model using a minimal number of IMU sensors and proper handling of imbalanced class distribution by the resampling method is useful for the assessment of home-based rehabilitation involving activities of daily living.
Mapping alteration minerals in the Pulang porphyry copper ore district, SW China, using ASTER and WorldView-3 data: Implications for exploration targeting

Identifying alteration minerals of porphyry Cu deposits by using multispectral remote sensing data is a significant approach for ore prospecting. However, obtaining refined alteration information related to a porphyry Cu system is still challenging, especially in an area with complex geological background. In this study, the effectiveness of spectroscopic techniques for mapping alteration minerals associated with the Pulang porphyry copper deposit, SW China, which is located in high altitude and with tough terrain, were discussed by using ASTER and WorldView-3 data. The method of Principal Component Analysis (PCA) + Spectrum-Area (S-A) was developed to enhance the remote sensing information extraction of the alteration zones with quartz, sericite, chlorite and epidote. The results suggested that the extraction accuracy of porphyry alteration minerals based on WorldView-3 data with the method of PCA + S-A is significantly improved comparing with that of ASTER data. Using the method, we identified a new alteration zone caused by sericite anomalies in the Pulang deposit, which was afterward confirmed by field investigation. Hence, the PCA + S-A method based on WorldView-3 data is an effective approach for mapping alteration zones and ore prospecting in the porphyry copper system.
Generating Diverse and Competitive Play-Styles for Strategy Games

Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research community has not found super-human performance yet, like strategy games. These require the AI to deal with large action spaces, long-term planning and partial observability, among other well-known factors that make decision-making a hard problem. On top of this, achieving distinct play-styles using a general algorithm without reducing playing strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) and show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
Light scattering pattern specific convolutional network static cytometry for label‐free classification of cervical cells

Abstract Cervical cancer is a major gynecological malignant tumor that threatens women's health. Current cytological methods have certain limitations for cervical cancer early screening. Light scattering patterns can reflect small differences in the internal structure of cells. In this study, we develop a light scattering pattern specific convolutional network (LSPS‐net) based on deep learning algorithm and integrate it into a 2D light scattering static cytometry for automatic, label‐free analysis of single cervical cells. An accuracy rate of 95.46% for the classification of normal cervical cells and cancerous ones (mixed C‐33A and CaSki cells) is obtained. When applied for the subtyping of label‐free cervical cell lines, we obtain an accuracy rate of 93.31% with our LSPS‐net cytometric technique. Furthermore, the three‐way classification of the above different types of cells has an overall accuracy rate of 90.90%, and comparisons with other feature descriptors and classification algorithms show the superiority of deep learning for automatic feature extraction. The LSPS‐net static cytometry may potentially be used for cervical cancer early screening, which is rapid, automatic and label‐free.
On the adoption of static analysis for software security assessment–A case study of an open-source e-government project

Static Application Security Testing (SAST) is a popular quality assurance technique in software engineering. However, integrating SAST tools into industry-level product development for security assessment poses various technical and managerial challenges. In this work, we reported results from a case study of adopting SAST as a part of a human-driven security assessment process in an open-source e-government project. We described how SASTs are selected, evaluated, and combined into a novel approach and adopted by security experts for software security assessment. The approach was preliminarily evaluated using semi-structured interviews. Our results show that while some SAST tools out-perform others, it is possible to achieve better performance by combining more than one SAST tools. The combined approach has the potential to aid the security assessment process for open-source software.
Design of a Hybrid Classroom Teaching Quality Evaluation System Based on Information Technology

Abstract With the rapid development of today’s information technology, it is an inevitable trend for the reform and development of education and teaching in colleges and universities to promote education modernization through education informatization. Information management has penetrated into the teaching and management of colleges and universities. Based on information technology, this paper studies the design of a data mining algorithm-based hybrid classroom teaching quality improvement evaluation system model, which fully reflects the quality of hybrid classroom teaching based on information technology in universities. In order to accurately grasp the current status of the current mixed classroom teaching quality, this experiment has been collected, sorted, and studied in recent years for analysis. A neural network algorithm is used to construct a model for it. This algorithm is combined with a questionnaire survey method to screen the mixed classroom teaching quality evaluation indicators of college information technology. Finally, a BP model is constructed through the classroom teaching quality and the model calculation samples are selected., The teaching quality can be obtained after calculation. Experimental research results show that with the advent of the information age, the development of blended teaching is divided into three stages. The first is based on technology, and the second is based on the interactive method of online and offline technology teaching. The third is to focus on the learning experience of learners. Blended teaching is a sublimation of traditional teaching mode and a return to traditional concepts. This brand-new teaching model will surely become the new normal of the education model in the future.
Prediction of the Consolidation Delay in Blockchain-based Applications

In the last years, blockchains have become a popular technology to store immutable data validated in a peer-to-peer way. Software systems can take advantage of blockchains to publicly store data (organised in transactions) which is immutable by design. The most important consensus algorithm in public blockchains is the proof-of-work in which miners invest a huge computational power to consolidate new data in a ledger. Miners receive incentives for their work, i.e., a fee decided and paid for each transaction. Rational miners aim to maximise the profit generated by the mining activity, and thus choose the transactions offering the highest fee per byte for their consolidation. In this paper, we propose a queueing model to study the relation between the fee offered by a transaction and its expected consolidation time, i.e., the time required to be added to the blockchain by the miners. The solution of the queueing model, although approximate, is computationally and numerically efficient and software systems can use it online to analyse the trade-off between costs and response times. Indeed, a static configuration of the model would not account for the high variations in the blockchain workload and fees offered by other users.
UserSim: User Simulation via Supervised GenerativeAdversarial Network

With the recent advances in Reinforcement Learning (RL), there have been tremendous interests in employing RL for recommender systems. However, directly training and evaluating a new RL-based recommendation algorithm needs to collect users’ real-time feedback in the real system, which is time/effort consuming and could negatively impact users’ experiences. Thus, it calls for a user simulator that can mimic real users’ behaviors to pre-train and evaluate new recommendation algorithms. Simulating users’ behaviors in a dynamic system faces immense challenges – (i) the underlying item distribution is complex, and (ii) historical logs for each user are limited. In this paper, we develop a user simulator based on a Generative Adversarial Network (GAN). To be specific, the generator captures the underlying distribution of users’ historical logs and generates realistic logs that can be considered as augmentations of real logs; while the discriminator not only distinguishes real and fake logs but also predicts users’ behaviors. The experimental results based on benchmark datasets demonstrate the effectiveness of the proposed simulator.