Reviewerly/exHarmony huggingface dataset:
Distinguishment of Greenberger–Horne–Zeilinger States in Rydberg Atoms via Noncyclic Geometric Quantum Computation

Abstract Since quantum entanglement plays a crucial role in quantum information processing, analyzing entangled states is indispensable for practical applications. Here a simple and direct protocol to distinguish arbitrary N ‐qubit Greenberger–Horne–Zeilinger (GHZ) states by novel nonadiabatic noncyclic geometric quantum computation using Rydberg atoms is put forward. The Bell states can also be distinguished completely using this way. The protocol is justified by numerical simulation for Bell states and N ‐qubit GHZ states, which evaluates the robustness and shows gratifying results. In addition, the idea is flexible, that is, available in any platform. Due to its simplicity and easy operation, the scheme would be very useful in quantum information science both theoretically and experimentally.
Construction and enumeration of left dihedral codes satisfying certain duality properties

Let Fq be the finite field of q elements and let D2n=〈x,y|xn=1,y2=1,yxy=xn−1〉 be the dihedral group of 2n elements. Left ideals of the group algebra Fq[D2n] are known as left dihedral codes over Fq of length 2n, and abbreviated as left D2n-codes. Let gcd(n,q)=1. In this paper, we give an explicit representation for the Euclidean hull of every left D2n-code over Fq. On this basis, we determine all distinct Euclidean LCD codes and Euclidean self-orthogonal codes which are left D2n-codes over Fq. In particular, we provide an explicit representation and a precise enumeration for these two subclasses of left D2n-codes and self-dual left D2n-codes, respectively. Moreover, we give a direct and simple method for determining the encoder (generator matrix) of any left D2n-code over Fq, and present several numerical examples to illustrative our applications.
Make labs, not war: Rethinking library creative technology services through a critical making lens

Libraries are increasingly building makerspaces and technology labs which are aimed at portraying them as innovative, progressive, and future-oriented to meet the changing need of a technology-centered society. The Auraria Library designed several spaces with these ideals in mind, yet after running them for several years, the authors have redesigned and re-prioritized its services, staffing, and values. Using critical making as a framework, the authors interrogate some of the implicit logic of library makerspaces that contributes to sexism and ableism and outline how libraries can create inclusive creative technology spaces and services that center people over technology.
Attention based Long-Short Term Memory Model for Product Recommendations with Multiple Timesteps

A recommender system is an engine that helps users find information on products and services. These products and services can be anything from books, digital content, music, videos, essentials, and more. Shuffling through numerous pages on the web may be hectic, that is where a recommender system may come handy in suggesting them products or services based on their past browsing history. The key motivation of this research is to improve the accuracy of existing benchmark model (Long-Short Term Memory with Multi Period - LSTM_MP) based on the previous purchase history. In the previous studies, limitations are seen in certain areas for e.g., usage of very less amount of data for evaluations due to which changing preferences of customers have not been identified. Also, they fail to accurately predict products properly. To overcome such shortfalls, researcher has come up with ALSTMM which is attention based Long Short-Term Memory Model which tells network where attention should be paid in input sequence of items corresponding to output sequence. The proposed model uses enormous and diversified real transactional data from Amazon, identifies changing preferences of customers over period and recognizes more patterns based on past purchase transactions. Our research focuses on suggesting a multi-time stamp product recommender system that is efficient enough to study past purchase patterns and accurately predict the next purchase order. With the help of RNN (Recurrent Neural Networks) for time series data analysis, the model segments recommendation periods into numerous time stamps which can then be used by the system to recommend products. Several experiments have been performed using datasets from Amazon and Instacart, which reveal that this model is a substantially improved version of the older systems and excel in accuracy and diversity when compared to CF-based models. Based on the results shown at the end of this study, the proposed model is found to be best for multi-time-based product recommendation purposes. In terms of applications, this model can effectively contribute towards reducing the shopping times and manual effort.
A Combined Simulation and Machine Learning Approach for Real-Time Delay Prediction for Wait-listed Neurosurgery Candidates

In this study, we present a method to predict whether a patient seeking admission to the neurosurgery ward of a large public tertiary care hospital in north India receives admission within a pre-specified duration. The prediction needs to be made at the time the patient is seeking admission at the ward, so that they can then decide whether to wait for admission into the neurosurgery ward or seek care elsewhere. We accomplish this by simulating the admission and patient stay processes at the neurosurgery ward, and use the simulation to generate data to train machine learning algorithms to predict whether the patient is admitted as a function of the state of the simulation at the time the patient is seeking admission at the ward. With ensemble tree classifiers, we achieve generalization area under the curve scores of 95% for all patients taken together and between 80-95\% depending upon patient sub-type.
Design Patterns for Smart Contract in Ethereum

Recently, we witnessed the advent of blockchain technology. Initially a means of cryptocurrency management, it later evolved into the concept of Smart Contracts, real programs running on the blockchain. The most important ecosystem for the development and distribution of SCs, is currently the Ethereum blockchain through the Solidity programming language. It is enjoying increasing popularity, and several applications in the real world have already been developed [1].
LiNet: A Lightweight Network for Image Super Resolution

This paper proposes a new lightweight network, LiNet, that enhancing technical efficiency in lightweight super resolution and operating approximately like very large and costly networks in terms of number of network parameters and operations. The proposed architecture allows the network to learn more abstract properties by avoiding low-level information via multiple links. LiNet introduces a Compact Dense Module, which contains set of inner and outer blocks, to efficiently extract meaningful information, to better leverage multi-level representations before upsampling stage, and to allow an efficient information and gradient flow within the network. Experiments on benchmark datasets show that the proposed LiNet achieves favorable performance against lightweight state-of-the-art methods.
Safety and reliability of deep learning

Robotics and Autonomous Systems (RAS) become ever more relying on deep learning components to support their perception and decision making. Given RAS will inevitably be applied to safety critical applications, efforts are needed to ensure that the deep learning is safe and reliable. In this lecture, I will give a brief overview on recent progress in the verification and validation techniques for deep learning, focusing on two major safety and reliability risks, i.e., robustness and generalisation. We consider formal verification, statistical evaluation, reliability assessment, and runtime monitoring techniques, all of which complement with each other in providing assurance to the reliability of deep learning in operation. The challenges and future directions will also be discussed.
A Hybrid Feature Enhancement Method for Gland Segmentation In Histopathology Images

Accurate and automatic gland segmentation can help pathologists diagnose the malignancy of colorectal cancers. However, it remains a challenging task because of the large morphological differences between the glands and the presence of sticky glands. In this paper, a hybrid feature enhancement network (HFE-Net) for glandular segmentation is proposed, which includes a multi-scale local feature extraction block (MSLFEB) and a global feature enhancement block (GFEB). Specifically, the MSLFEB is used to extract multiscale features through different sizes of the receptive field to reduce the loss of the local information and effectively alleviate glandular adhesion. The GFEB is used to transfer the underlying features to the decoder by considering the global semantic information. Furthermore, we design a focal and variance (FV) loss function to alleviate the class imbalance and constraint the pixels within the same instance. Finally, we evaluate the proposed method on the 2015 MICCAI GlaS challenge dataset and the CRAG colorectal adenocarcinoma dataset. The results show that our HFE-Net can achieve competitive results with fewer computing resources when compared with the state-of-the-art gland segmentation methods.
Identity-based outsider anonymous cloud data outsourcing with simultaneous individual transmission for IoT environment

The integration of the Internet of Things (IoT) and cloud computing has become an attractive cloud-oriented big data processing paradigm, which is playing an important role in efficiency and productivity for digitalization of numerous IoT enabled industries. However, cloud-assisted IoT is also becoming an increasingly attractive target for various cyber-attacks, including the authenticity of outsourcing data, untrustworthiness of third parties, and data security and privacy. As a potential and promising solution to securely outsource data, we present the first construction for an efficient cloud data outsourcing system with simultaneous individual transmission preserving outsider anonymity of the subscribed consumer set. In our system, a data owner generates personalized data for each of the consumers in a group and transmits a common encrypted data for the group in such a way that the subscribed consumer set is completely hidden from the outsiders. Personalized data can be recovered only by an authorized consumer, while the common data can be decrypted by all the authorized consumer in that group. The communication bandwidth is compact in our construction, and the decryption algorithm requires significantly less computation cost. We design our scheme using asymmetric bilinear map over the prime order group to prevent fault attacks on symmetric bilinear map. Our construction is built in identity-based setting without any non-standard q-type security assumption and does not use random oracles. Our scheme enjoys adaptive security against an indistinguishable chosen-plaintext attack under the hardness of the standard decisional bilinear Diffie–Hellman exponent problem. Furthermore, our design supports an exponential number of consumers as the size of the valid identity set grows exponentially with the security parameter, whereas it is only polynomial in the security parameter for the existing cloud data outsourcing systems. In particular, the implementation and performance analysis explicates the advantages of our design for resource-constrained IoT enabled frameworks.
Deep neural network pruning algorithm based on particle swarm

A powerful neural network consumes a lot of storage space and computing resources, which is unacceptable for mobile devices and embedded devices with limited resources. To solve this problem, PSOPruner was proposed. Firstly, The algorithm randomly initializes a series of particles as the pruned network structure; secondly, calculates the threshold corresponding to each convolutional layer according to the particles, deletes the convolution kernels with corresponding values less than the threshold; then, adapts the pruned network structure evaluation, update the particles and the global optimal fitness particles; finally, retrain the network structure corresponding to the global optimal fitness particles to restore the network accuracy. The experimental results show that the algorithm is applied to VGG16 on the cifar-10 data set. After 100 iterations of the algorithm, the test accuracy is improved by 0.04%, the model size is reduced by 94%, and the running speed is increased by 36% compared with the untrimmed model. The pruned network model is more conducive to deployment in mobile devices and embedded devices.
PAMELA: An annotation-based Java modeling framework

This article presents PAMELA, an annotation-based Java modeling framework. PAMELA provides a smooth integration between model and code and enables Java developers to handle software development both at conceptual level and at source-code level, without code transformation and/or generation, avoiding round-trip-related issues.
Cross-Modal Knowledge Distillation For Fine-Grained One-Shot Classification

Few-shot learning can recognize a novel category based on only a few samples because it learns to learn from a lot of labeled samples during the training process. When data is insufficient, the performance is affected. And it is expensive to obtain a large-scale finegrained dataset with annotation. In this paper, we adopt domain- specific knowledge to fill the gap of insufficient annotated data. We propose a cross-modal knowledge distillation (CMKD) framework to do fine-grained one-shot classification and propose the Spatial Relation Loss (SRL) to transfer cross-modal information, which can tackle the semantic gap between multimodal features. The teacher network distills the spatial relationship of the samples as a soft target for training a unimodal student network. Notably, the student network makes predictions only based on a few samples without any external knowledge in the application. This model-agnostic framework will be well adapted to other few-shot models. Extensive experimental results on benchmarks demonstrate that CMKD can make full use of cross-modal knowledge in image and text few-shot classification. CKMD improves the performances of the student networks significantly, even if it is a state-of-the-art student network.
Flush-Reload Attack and its Mitigation on an FPGA Based Compressed Cache Design

Several micro-architectural components such as caches, branch predictors and prefetchers are known to assist in side-channel data leaks. Side-channel attacks recover secret data by observing the timing behavior while the victim process accesses the cache or the memory. In this paper, we explore the impact of a Flush-Reload attack in the presence of a cache compression scheme. Cache compression technique increases the effective size of the cache through compression and appropriate placement of the compressed data blocks. We integrate the compressed cache design with a RISC-V processor core on an FPGA. We implement the Flush-Reload attack and find that the impact of the attack on a compressed cache scheme is likely to be weaker than that of an uncompressed design. To the best of our knowledge, this is the first attempt at understanding the role of side-channel attacks on a compressed cache design in hardware. We further explore the possibility of using a variable clock logic and observe that it reduces the effectiveness of the attack by 30 to 50%. However, this scheme could impact the cache performance by up to 2.9 times.
Social Bot Detection Using "Features Fusion"

Social robots are fake users who are active in social networks. Usually, social robots imitate real users and publish a lot of tweets. Malicious social robots are destroying the security of social networks. Existing research uses machine learning technology to detect social robots from social users' attributes and tweets. The features extracted from tweets include emotional features, thematic features, etc. However, the redesigned intelligent robot can imitate a certain feature of normal users' tweets, which makes the detection of social robots more difficult. The purpose of this paper is to propose several methods of fusing the old features to obtain more complex features, which makes the design of social robots more difficult. The results show that the new feature fusion method has better accuracy than the single feature for social robot detection.
FPGA-Based Logistic Chaotic Pseudo-Random Sequence Generator Design

Pseudo-random sequence is widely used in the field of information security. This design uses Verilog HDL to complete the construction of Logistic chaotic pseudo-random sequence generator with modular programming ideas. The test results show that the correlation performance and test statistics of the output sequence can meet the requirements of chaotic pseudo-sequence. The effect of applying it to image encryption also verifies that the sequence has good randomness.
2-SPGAL: 2-Phase Symmetric Pass Gate Adiabatic Logic for Energy-Efficient Secure Consumer IoT

The adaptation of the Internet-of-Things (IoT) for consumer electronics has enabled us to uplift everyday life. Low-power smart and secure computing devices are needed to sustain the expected growth of consumer IoT. Adiabatic switching is a modern approach that recycles the energy stored in load capacitance to save energy. Further, the cryptographic circuit designed using adiabatic switching is secure against the Correlation Power Analysis (CPA) attack in contrast to the same circuit designed using standard CMOS. In this paper, we propose 2-SPGAL, a 2-phase sinusoidal signal based clocking implementation of Symmetric Pass Gate Adiabatic Logic (SPGAL). As a case study, we simulated the design of PRESENT-80 (a lightweight cryptographic scheme) one round with an in-built Power Clock Generator (PCG) with 45nm technology. The 2-SPGAL shows on an average 82.76% and 67.35% better energy saving compared to standard CMOS, and 2-EE-SPFAL (another 2-phase adiabatic logic), respectively at a frequency range from 100 kHz to 25 MHz with a load of 1 fF. The 2-SPGAL has 16.78% savings of the number of transistors compared to 2-EE-SPFAL for implementation of one round PRESENT-80. Further, the CPA attacks reveal the key in standard CMOS, however, 2-SPGAL PRESENT-80 adiabatic logic design was successful to protect the key.
Dense U-Net for Limited Angle Tomography of Sound Pressure Fields

Tomographic reconstruction allows for the recovery of 3D information from 2D projection data. This commonly requires a full angular scan of the specimen. Angular restrictions that exist, especially in technical processes, result in reconstruction artifacts and unknown systematic measurement errors. We investigate the use of neural networks for extrapolating the missing projection data from holographic sound pressure measurements. A bias flow liner was studied for active sound dampening in aviation. We employed a dense U-Net trained on synthetic data and compared reconstructions of simulated and measured data with and without extrapolation. In both cases, the neural network based approach decreases the mean and maximum measurement deviations by a factor of two. These findings can enable quantitative measurements in other applications suffering from limited angular access as well.
Multi-label Contrastive Focal Loss for Pedestrian Attribute Recognition

Pedestrian Attribute Recognition (PAR) has received extensive attention during the past few years. With the advances of deep convolutional neural networks (CNNs), the performance of PAR has been significantly improved. Existing methods tend to acquire attribute-specific features by designing various complex network structures with additional modules. Such additional modules, however, dramatically increase the number of network parameters. Meanwhile, the problems of class imbalance and hard attribute retrieving remain underestimated in PAR. In this paper, we explore the optimization mechanism of the training processing to account for these problems and propose a new loss function called Multi-label Contrastive Focal Loss (MCFL). This proposed MCFL emphasizes the hard and minority attributes by using a separated re-weighting mechanism for different positive and negative classes to alleviate the impact of the imbalance. MCFL is also able to enlarge the gaps between the intra-class of multi-label attributes, to force CNNs to extract more subtle discriminative features. We evaluate the proposed MCFL on three large public pedestrian datasets, including RAP, PA-100K, and PETA. The experimental results indicate that the proposed MCFL with the ResNet-50 backbone is able to outperform other state-of-the-art approaches in term of mean accuracy.
Optimized Classification for Organizational Workload

The classification of workload for companies or organizations consumes a lot of resources. Especially for smaller organizations, excessive resource use can lead to an imbalance in systems. Machine learning principles such as Naive Bayes can effectively reduce costs and manageability as it can reuse the organization's sources or statistics and can be reused without any administration. However, the performance of such an algorithm may deteriorate by itself due to improper deployment during data analysis. This study aims to optimize the Naive Bayes algorithm for classifying workload levels focusing on the distributions and the relevant kernels, including Gaussian, Kernel, Box, Epanechnikov, and Triangle. The optimized classification method offers better accuracy along with other performances. The results of the optimized method can be well applied to models that are similar to the proposed model.
Search Efficient Binary Network Embedding

Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned continuous vector representations are inefficient for large-scale similarity search, which often involves finding nearest neighbors measured by distance or similarity in a continuous vector space. In this article, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations using a stochastic gradient descent-based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support faster node similarity search than using Euclidean or other distance measures. Extensive experiments and comparisons demonstrate that BinaryNE not only delivers more than 25 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods. The binary codes learned by BinaryNE also render competitive performance on node classification and node clustering tasks. The source code of the BinaryNE algorithm is available at https://github.com/daokunzhang/BinaryNE.
Requirements Gathering for VR Simulators for Training: Lessons Learned for Globally Dispersed Teams

We report an empirical study on the use of current VR technologies for requirements gathering in the field of simulation and training. We used synchronous and asynchronous traditional techniques plus collaborative virtual environments such as MozillaHubs and AltspaceVR. Our results show that requirements gathering in VR makes a difference in the process of requirements identification. We report advantages and shortcomings that can be useful for future practitioners. For example, we found that VR sessions allowed for better identification of dimensions and sizes. VR sessions for requirements gathering could also benefit from better pointers and better sound.