Reviewerly/exHarmony huggingface dataset:
Conservative Generator, Progressive Discriminator: Coordination of Adversaries in Few-shot Incremental Image Synthesis

The capacity to learn incrementally from an online stream of data is an envied trait of human learners, as deep neural networks typically suffer from catastrophic forgetting and stability-plasticity dilemma. Several works have previously explored incremental few-shot learning, a task with greater challenges due to data constraint, mostly in classification setting with mild success. In this work, we study the underrepresented task of generative incremental few-shot learning. To effectively handle the inherent challenges of incremental learning and few-shot learning, we propose a novel framework named ConPro that leverages the two-player nature of GANs. Specifically, we design a conservative generator that preserves past knowledge in parameter and compute efficient manner, and a progressive discriminator that learns to reason semantic distances between past and present task samples, minimizing overfitting with few data points and pursuing good forward transfer. We present experiments to validate the effectiveness of ConPro.
Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation

The availability of data in expressive styles across languages is limited, and recording sessions are costly and time consuming. To overcome these issues, we demonstrate how to build low-resource, neural text-to-speech (TTS) voices with only 1 hour of conversational speech, when no other conversational data are available in the same language. Assuming the availability of non-expressive speech data in that language, we propose a 3-step technology: 1) we train an F0-conditioned voice conversion (VC) model as data augmentation technique; 2) we train an F0 predictor to control the conversational flavour of the voice-converted synthetic data; 3) we train a TTS system that consumes the augmented data. We prove that our technology enables F0 controllability, is scalable across speakers and languages and is competitive in terms of naturalness over a state-of-the-art baseline model, another augmented method which does not make use of F0 information.
Orthogonal Spin Current Injected Magnetic Tunnel Junction for Convolutional Neural Networks

We propose that a spin Hall effect driven magnetic tunnel junction device can be engineered to provide a continuous change in the resistance across it when injected with orthogonal spin currents. Using this concept, we develop a hybrid device-circuit simulation platform to design a network that realizes multiple functionalities of a convolutional neural network. At the atomistic level, we use the Keldysh non-equilibrium Green's function technique that is coupled self-consistently with the stochastic Landau-Lifshitz-Gilbert-Slonczewski equations, which in turn is coupled with the HSPICE circuit simulator. We demonstrate the simultaneous functionality of the proposed network to evaluate the rectified linear unit and max-pooling functionalities. We present a detailed power and error analysis of the designed network against the thermal stability factor of the free ferromagnets. Our results show that there exists a non-trivial power-error trade-off in the proposed network, which enables an energy-efficient network design based on unstable free ferromagnets with reliable outputs. The static power for the proposed ReLU circuit is $0.56μW$ and whereas the energy cost of a nine-input rectified linear unit-max-pooling network with an unstable free ferromagnet($Δ=15$) is $3.4pJ$ in the worst-case scenario. We also rationalize the magnetization stability of the proposed device by analyzing the vanishing torque gradient points.
Quantum Vulnerability Analysis to Accurate Estimate the Quantum Algorithm Success Rate

While quantum computers provide exciting opportunities for information processing, they currently suffer from noise during computation that is not fully understood. Incomplete noise models have led to discrepancies between quantum program success rate (SR) estimates and actual machine outcomes. For example, the estimated probability of success (ESP) is the state-of-the-art metric used to gauge quantum program performance. The ESP suffers poor prediction since it fails to account for the unique combination of circuit structure, quantum state, and quantum computer properties specific to each program execution. Thus, an urgent need exists for a systematic approach that can elucidate various noise impacts and accurately and robustly predict quantum computer success rates, emphasizing application and device scaling. In this article, we propose quantum vulnerability analysis (QVA) to systematically quantify the error impact on quantum applications and address the gap between current success rate (SR) estimators and real quantum computer results. The QVA determines the cumulative quantum vulnerability (CQV) of the target quantum computation, which quantifies the quantum error impact based on the entire algorithm applied to the target quantum machine. By evaluating the CQV with well-known benchmarks on three 27-qubit quantum computers, the CQV success estimation outperforms the estimated probability of success state-of-the-art prediction technique by achieving on average six times less relative prediction error, with best cases at 30 times, for benchmarks with a real SR rate above 0.1%. Direct application of QVA has been provided that helps researchers choose a promising compiling strategy at compile time.
Learning idempotent representation for subspace clustering

The critical point for the successes of spectral-type subspace clustering algorithms is to seek reconstruction coefficient matrices which can faithfully reveal the subspace structures of data sets. An ideal reconstruction coefficient matrix should have two properties: 1) it is block diagonal with each block indicating a subspace; 2) each block is fully connected. Though there are various spectral-type subspace clustering algorithms have been proposed, some defects still exist in the reconstruction coefficient matrices constructed by these algorithms. We find that a normalized membership matrix naturally satisfies the above two conditions. Therefore, in this paper, we devise an idempotent representation (IDR) algorithm to pursue reconstruction coefficient matrices approximating normalized membership matrices. IDR designs a new idempotent constraint for reconstruction coefficient matrices. And by combining the doubly stochastic constraints, the coefficient matrices which are closed to normalized membership matrices could be directly achieved. We present the optimization algorithm for solving IDR problem and analyze its computation burden as well as convergence. The comparisons between IDR and related algorithms show the superiority of IDR. Plentiful experiments conducted on both synthetic and real world datasets prove that IDR is an effective and efficient subspace clustering algorithm.
RCA: Ride Comfort-Aware Visual Navigation via Self-Supervised Learning

Under shared autonomy, wheelchair users expect vehicles to provide safe and comfortable rides while following users high-level navigation plans. To find such a path, vehicles negotiate with different terrains and assess their traversal difficulty. Most prior works model surroundings either through geometric representations or semantic classifications, which do not reflect perceived motion intensity and ride comfort in downstream navigation tasks. We propose to model ride comfort explicitly in traversability analysis using proprioceptive sensing. We develop a self-supervised learning framework to predict traversability costmap from first-person-view images by leveraging vehicle states as training signals. Our approach estimates how the vehicle would feel if traversing over based on terrain appearances. We then show our navigation system provides human-preferred ride comfort through robot experiments together with a human evaluation study.
CYBER ETHICS: AN IMPORTANT CONCEPT TO BECOME A RESPONSIBLE CYBER CITIZEN

In this digital era with more and more advancements there has been an increase in cyber security related issues. To address such things cyber ethics related awareness among common people is of utmost importance. While using cyber space we need to be ethically correct. It’s our responsibility to be a good and morally strong cyber citizen. The concept of cyber ethics deals with various code of ethics. There is a great need for cyber ethics as various cyber related issues are increasing like spying, frauds, exploitative conduct. Preventive measures to deal with cyber crime related issues should be known by all. Some awareness programs should be conducted regarding various cyber ethics and cyber crime. Everyone must understand their responsibilities for conducting themselves online. An important component of that is Cyber Ethics. We all should implement cyber ethics to be a good “cyber citizens”.
MODIFIED IMAGE ENCRYPTION APPROACH

Discrete fractional Fourier transform is an extension of Fourier transform with an additional order parameter. It finds application in encryption of digital images. In the present paper the image is first divided into subimages. The cryptographic algorithm is applied independently to each subimage. It uses five keys made up of three fractional orders and two random phase masks for each of the subimage thereby increasing total number of keys used for the image as a whole. All these keys are necessary to properly decrypt the image. The proposed encryption scheme enhances data security and the net bits used in the image transmission are reduced.
Verilog Based Implementation of Efficient Elliptic Curve Processor Galois fields

ECC is an asymmetric cryptographic system that provides an equivalent security to the well-known Rivest, Shamir and Adleman system. The basic operation in ECC is scalar point multiplication, where a point on the curve is multiplied by a scalar. A scalar point multiplication is performed by calculating series of point additions and point doublings. On the other hand GF operations consist of three operations addition, Multiplication and Inversion. In this project implementation of the elliptic curve cryptography processor is proposed. Galois fields play an important role in cryptography. As a result of their carry free arithmetic property, they are suitable to be used in hardware implementation in ECC. Here the multiplier is implemented using a double and add algorithm, to obtain an efficient elliptic curve processor over Galois fields.
ConvNet-based performers attention and supervised contrastive learning for activity recognition

Abstract Human activity recognition based on generated sensor data plays a major role in a large number of applications such as healthcare monitoring and surveillance system. Yet, accurately recognizing human activities is still challenging and active research due to people’s tendency to perform daily activities in a different and multitasking way. Existing approaches based on the recurrent setting for human activity recognition have some issues, such as the inability to process data parallelly, the requirement for more memory and high computational cost albeit they achieved reasonable results. Convolutional Neural Network processes data parallelly, but, it breaks the ordering of input data, which is significant to build an effective model for human activity recognition. To overcome these challenges, this study proposes causal convolution based on performers-attention and supervised contrastive learning to entirely forego recurrent architectures, efficiently maintain the ordering of human daily activities and focus more on important timesteps of the sensors’ data. Supervised contrastive learning is integrated to learn a discriminative representation of human activities and enhance predictive performance. The proposed network is extensively evaluated for human activities using multiple datasets including wearable sensor data and smart home environments data. The experiments on three wearable sensor datasets and five smart home public datasets of human activities reveal that our proposed network achieves better results and reduces the training time compared with the existing state-of-the-art methods and basic temporal models.
Amplitude Reordering Accelerates the Adaptive Variational Quantum Eigensolver Algorithms

The variational quantum eigensolver (VQE) algorithm can simulate the chemical systems such as molecules in the noisy intermediate-scale quantum devices and shows promising applications in quantum chemistry simulations. The accuracy and computational cost of the VQE simulations are determined by the underlying Ansätze. Therefore, the most important issue is to generate a compact and accurate Ansätze which requires a shallower parametric quantum circuit and can achieve an acceptable accuracy. The newly developed adaptive algorithms (AAs) such as the adaptive derivative-assembled pseudo-Trotter VQE (ADAPT-VQE) can solve this issue via generating compact and accurate Ansätze. However, these AAs show very low computational efficiency because they require a large number of additional measurements. Here we propose an amplitude reordering (AR) strategy to accelerate the promising but expensive AAs by adding operators in a "batched" fashion in a way that their order is still quasi-optimal. We first introduce the AR method into ADAPT-VQE and build the AR-ADAPT-VQE algorithm. We then endow the energy-sorting VQE (ES-VQE) algorithm with the adaptive feature, and introduce the AR into AES-VQE to form the AR-AES-VQE algorithm. To demonstrate the performance of these algorithms, we calculate the dissociation curves of three small molecules, LiH, linear BeH2, and linear H6, by using (AR-)ADAPT-VQE and (AR-)AES-VQE algorithms. It is found that all the AR-equipped AAs (AR-AAs) can significantly reduce the number of iterations and subsequently accelerate the calculations with a speedup of up to more than ten times without the obvious loss of the accuracy. The final Ansätze generated by the AR-AAs not only avoids extra circuit depth but also maintains the computational accuracy, sometimes the AR-AAs even outperforms their original counterparts.
Smart Village Crop Planning: Enhancing Farmer’s Decision-Making Culture with Data-Driven Predictive Model

Crop planning prevents inappropriate crop selection and rotation by farmers which can cause economic loss and biodiversity issues. Technology solution for crop planning is a digital innovation in conserving traditional crop varieties and achieving optimal yield. Solutions range from database-oriented crop decision support systems to big data analytics platforms. However, technological capability must match the situated stage of farmers’ digital fluency and skill as traits of their digital culture for decision-making. This research presents a work to explore two aspects of the smart village crop planning application model, which are the pragmatic use of the prediction model and rural community as-is digital culture. As a result, a smart village crop planning application model recommends three components, crop predictor, collaborative tool, and ask expert application, to enhance farmer’s crop planning decision-making culture.
Reinforcement Learning als eine Grundlage für die Cross Domain Fusion heterogener Daten

Abstract We propose to establish a research direction based on Reinforcement Learning in the scope of Cross Domain Fusion. More precisely, we combine the algorithmic approach of evolutionary rule-based Reinforcement Learning with the efficiency and performance of Deep Reinforcement Learning, while simultaneously developing a sound mathematical foundation. A possible scenario is traffic control in urban regions.
Influence of an innovative game on nutrition education for nursing students

Background and objective: The growing global burden of individuals with malnutrition shows no signs of decreasing. Schools are an important arena for health promotion. This study is to develop and to test the innovative game with Good Food cards designed to enable students to acquire knowledge in healthy eating and other influence on their eating behaviors as well as nursing role in dietary education.Methods: Design: A mixed method design was used to evaluate the effectiveness of the game. We conducted pretest-posttest quasi-equivalent groups with cluster sampling design and recruited 109 students at two sites of health center. The game intervention was implemented in two to four 20- to 30-minute sessions for 55 junior nursing students. Method: Univariate analysis of covariance (ANCOVA) was performed to compare knowledge change between the two groups. Additionally, a focus group was performed to collect data from students. Audio-recorded, transcribed verbatim and thematically analyzed were conducted by researchers. Correlation analysis was used to was used to explore factors related to the concept and behavior intention.Results: The knowledge on healthy diet was significantly higher in the experimental group compared to the control group. Effective learning experiences and intention to change eating behaviors were also perceived by participants.Conclusions: The Good Food cards and game mechanism matched the learning objectives of the dietary education. Gaming is a valid learning strategy not just a fun activity. During nursing practicum period, students who are familiar with healthy diet concept perceived the help to facilitate therapeutic relationship building with clients and more willingness to change eating habits. More research is needed to test other children, students, or adult population with this card. Also, factors related to concept and behavior change from playing with this card can be investigated to strengthen students’ competence and focusing more strongly on how best to influence students’ healthy dietary behavior.
The height of record‐biased trees

Abstract Given a permutation , its corresponding binary search tree is obtained by recursively inserting the values into a binary tree so that the label of each node is larger than the labels of its left subtree and smaller than the labels of its right subtree. In this article, we study the height of binary search trees drawn from the record‐biased model of permutations whose probability measure on the set of permutations is proportional to , where . We show that the height of a binary search tree built from a record‐biased permutation of size with parameter is of order , hence extending previous results of Devroye on the height or random binary search trees.
Dependency-Aware Task Offloading for Vehicular Edge Computing with End-Edge-Cloud Collaborative Computing

Abstract Vehicular edge computing (VEC) is emerging as a new computing paradigm to improve the quality of vehicular services and enhance the capabilities of vehicles. It aids vehicles execution tasks with low latency by deploying computing and storage resources close to the vehicle side. However, the traditional task offloading schemes waste the fine-grained offloading opportunities for subtasks by neglecting the decomposability and dependency of tasks. Furthermore, the continuous control problem caused by the learning-based offloading schemes should be taken into account. In this paper, we proposed an efficient dependency-aware task offloading scheme, which minimizes the average processing delay of tasks by fully utilizing the end-edge-cloud collaborative computing. Specifically, first, the directed acyclic graph (DAG) technique is utilized to model the inter-subtask dependency, which establishes the priority of subtask execution. Second, a task offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) in Reinforcement Learning (RL) was proposed to obtain the optimal offloading strategy in a dynamic environment, which efficiently solves continuous control problems and helps reach fast convergence. Finally, we conduct extensive simulation experiments, and the experimental results show that the proposed dependency-aware task offloading scheme can achieve a good performance.
Optical phase contrast microscopy in reporting anisopoikilocytosis in stained blood smears; a pilot study

Introduction: Conventionally, to examine stained blood smears, bright field microscope (BFM) is used. Appreciation of outlines and three-dimensional images of cells is possible with optical phase contrast microscope (OPCM) in wet smears Methods: This study was performed to determine applicability of OPCM in reporting anisopoikilocytosis in stained blood smears. Leishman-stained blood smears were assessed using bright field microscope (BFM) and OPCM and findings related to anisopoikilocytosis were compared with corresponding red cell indices (RI) of complete blood count. Results: There were 25 and 24 samples with normal and abnormal RI respectively. Mean anisopoikilocytosis percentage of both groups reported with OPCM were significantly higher than those of BFM. Anisopoikilocytosis percentage reported with BFM and OPCM were significantly positively correlated. Mean corpuscular volume and microcyte percentage and mean corpuscular haemoglobin and hypochromic cell percentage were significantly negatively correlated (p&lt; 0.05). Conclusion: Optical phase contrast microscopy could significantly improve quantification and reporting of anisopoikilocytosis in stained blood smears.
Smart Manufacturing

Smart manufacturing has gained prominence, thanks to recent industrial developments. Smart manufacturing makes production more efficient and sustainable and provides competitive advantages for manufacturers. We propose five constructs and 29 criteria for an evaluation model of smart manufacturing. The five constructs of smart manufacturing are smart design, smart production, smart factory, smart services, and Industrial Interment of Things (IIoT) management. We use both Analytic Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) in this study. First, we used the AHP to analyze weights between the constructs and between the criteria of smart manufacturing. Second, we used DEMATEL to analyze the causal relationships between the same. Then, we built up an evaluation model for smart manufacturing. Our results show that IIoT is the first priority for developing smart manufacturing in the five constructs. Regarding the criteria, (1) IIoT: sensors are prioritized for hardware development while software development focuses on user privilege management. Software and hardware are combined for device management and the establishment of a standard communication protocol. (2) Smart production: the development of advanced process control is prioritized and machine learning used to optimize the process and create smart machines. (3) Smart factory: the development of digital transformation is prioritized and the resulting content used for update or increase in IT infrastructure. Finally, we provide an evaluation model for building smart manufacturing in the future.