Reviewerly/exHarmony huggingface dataset:
SelfieDroneStick: A Natural Interface for Quadcopter Photography

A physical selfie stick extends the user's reach, enabling the acquisition of personal photos that include more of the background scene. Similarly, a quadcopter can capture photos from vantage points unattainable by the user; but teleoperating a quadcopter to good viewpoints is a difficult task. This paper presents a natural interface for quadcopter photography, the SelfieDroneStick that allows the user to guide the quadcopter to the optimal vantage point based on the phone's sensors. Users specify the composition of their desired long-range selfies using their smartphone, and the quadcopter autonomously flies to a sequence of vantage points from where the desired shots can be taken. The robot controller is trained from a combination of real-world images and simulated flight data. This paper describes two key innovations required to deploy deep reinforcement learning models on a real robot: 1) an abstract state representation for transferring learning from simulation to the hardware platform, and 2) reward shaping and staging paradigms for training the controller. Both of these improvements were found to be essential in learning a robot controller from simulation that transfers successfully to the real robot.
ABAC Implementation Based on Key Pre-Distribution Scheme

Abstract In the article, the KDP key pre-distribution scheme is modified for attribute access control. A scheme for implementing attribute-based access control on symmetric encryption is proposed. There are three participants in the scheme: a key distribution server, a subject, and a storage server. The key distribution server sends key content over secure channels when the system is initialized. The key materials content is determined by the attributes of the object and the subject. The storage server calculates the object ′s encryption key based on the object ′s key materials and attributes. The object is stored encrypted. If the subject has the necessary attributes, it can calculate the encryption key and access to object. If the subject does not have a sufficient set of attributes, the key cannot be computed and access is denied.
The MuTube Dataset for Music Listening History Retrieval and Recommendation System

Recommendation systems are widely used in music streaming services. This paper presents a system to collect user's music preference and music textual features in YouTube as well as to provide music recommendations based on collaborative filtering. As cold start and data sparsity are two severe issues in collaborative filtering, additional features for the item are necessary. We propose a method to aggregate both implicit feedback and textual features collected from YouTube to improve the recommendation performance. Experiment results indicate that the recommendation playlists generated by this system both match individual's and group preference.
Flexible and Privacy-preserving Framework for Decentralized Collaborative Learning

Nowadays, collaborative learning is becoming a new trend to address the data scarcity issue. To prevent potential privacy leakage, some privacy-preservation collaborative learning schemes have been proposed with data encryption, but cannot handle the setting of different data contribution among data nodes and avoid the huge overhead of implementing over encrypted data. In this paper, we design a flexible and secure decentralized collaborative learning to achieve the contribution over data nodes, where each data node can specialize the contribution extent for collaborative learning. Besides, we provide a MPC-friendly collaborative layer for the lightweight privacy preservation. Our security analysis and experimental results demonstrate the security and superiority of our system, respectively.
Hybrid deniable and short-key encryption protocols based on the authentication procedure

To ensure resistance to attacks with coercion to disclose a secret key by an active adversary, the protocol of deniable encryption includes a procedure for mutual authentication of the sender and receiver of a message with their long-term public keys, which is combined with a hidden exchange of single-use public keys used to generate a single-use shared key, on which the secret message is encrypted. Longterm public keys are used to generate a shared secret key, on which a fake message is encrypted. The produced intermediate ciphertexts are converted into a single ciphertext, which is computationally indistinguishable from the ciphertext obtained by probabilistic encryption of a fake message. This approach allows us to build an encryption protocol that is resistant to bi-sided attacks with coercion, since the disclosure of users' private keys gives access only to the fake message and cannot be used to prove the possibility of alternative decryption of the ciphertext transmitted over a public communication channel. The authentication of the single-use public keys is also used to implement protocols for secure encryption using short shared keys.
Challenges and Opportunities in Collaborative Vulnerability Research Workflows

Recently, cyber reasoning systems demonstrated near-human performance characteristics when they autonomously identified, proved, and mitigated vulnerabilities in software during a competitive event. New research seeks to augment human vulnerability research teams with cyber reasoning system teammates in collaborative work environments. However, the literature lacks a concrete understanding of vulnerability research workflows and practices, limiting designers’, engineers’, and researchers’ ability to successfully integrate these artificially intelligent entities into teams. This paper contributes a general workflow model of the vulnerability research process, and identifies specific collaboration challenges and opportunities anchored in this model. Contributions were derived from a qualitative field study of work habits, behaviors, and practices of human vulnerability research teams. These contributions will inform future work in the vulnerability research domain by establishing an empirically-driven workflow model that can be adapted to specific organizational and functional constraints placed on individual and teams.
Using Deconvolutional Variational Autoencoder for Answer Selection in Community Question Answering

Answer selection in community question answering is a challenging task in natural language processing. The main problem is that there is no evaluation for the answers given by the users and one should go through all possible answers for assessing them, which is exhausting and time consuming. In this paper we propose a latent-variable model for learning the representations of the question and answer, by jointly optimizing generative and discriminative objectives. This model uses variational autoencoders (VAE) in a multi-task learning process with a classifier to produces a representation for each answer by which the classifier could classify it's relation with correspond question with a high performance. The experimental results on two public datasets, SemEval 2015 and SemEval 2017, recognize the significance of the proposed framework, especially for the semi-supervised setting. The results showed that the proposed model outperformed F1 of state-of-the-art method up to about 8% for SemEval 2015 and about 5% for SemEva1 2017.
A Minimalistic Perspective on Koblitz Curve Scalar Multiplication for FPGA Platforms

Koblitz Curves offer excellent optimization opportunities for characteristic-2 Elliptic Curve Cryptosystems (ECC). However, porting such choices onto a lightweight and cost-effective FPGA platform is a major challenge. The underlying characteristic-2 algebra is not aligned with the on-chip components like DSP multipliers, which if not utilized leads to large LUT counts of the designs. In this work, we develop several techniques to propose a minimal instruction set, centered around ADDN (Add and Branch if less than zero) based OISC (One-Instruction-Set-Computing) coupled with a lightweight comba characteristic-2 finite field multiplier to ensure the aggressive utilization of the FPGA resources. The paper develops an ADDN based scalar multiplier for Koblitz curves which has a minimal footprint on the LUTs, leveraging methods for utilizing the underlying DSP blocks in FPGAs for performing GF(2) operations, in conjunction with proposed reduced variants of the ADDN instruction to perform the computations. The architecture uses FPGA resources like BRAMs, DSPs effectively to have a minimal requirement for FPGA LUTs, thus leaving room for other peripheral designs to be hosted in a single FPGA. The same design can be configured to realize scalar multiplications by two popular strategies, namely due to Solinas and Montgomery, to demonstrate the modular nature of the design. The minimalistic design style leads to a resource-constrained architecture performing scalar multiplication in less than 1000 slices, needing less than 0.5 ms on a cost-effective Artix-7 FPGA. The design has been compared with reported literature to highlight the area efficiency of the design, however ensuring a competitive AT (area-time) product.
Application of Computer Vision in Face Recognition System in Colleges and Universities

Abstract With the deepening of management studies, face recognition has gradually come into our field of vision. For education in colleges and universities, face recognition is refers to the managers and employees at all levels in order to achieve organizational goals to participate in the performance planning, performance coaching communication, performance evaluation, performance result applied, performance goals continuous cycle of ascension, the purpose of face recognition is individual, the department continued ascension and performance of the organization.
On Metric-based Deep Embedding Learning for Text-Independent Speaker Verification

As a state-of-the-art solution for speaker verification problems, deep neural networks have been usefully employed for extracting speaker embeddings which represent speaker informative features. Objective functions, as the supervisors for the learning of discriminative embeddings, play a crucial role for this purpose. In this paper, motivated by the success of metric learning approaches, we investigate four newly proposed metrics in the literature, specifically for the speaker verification problem. For deeper comparisons, we consider these metrics from both main groups of metric-based objectives, i.e. instance-based and proxy-based ones. By considering embeddings as instances, the first group exploits the instance-to-instance relations, while the latter associates the instances to the proxies as representatives of training samples.Evaluations in terms of Equal Error Rate (EER) are conducted in two conventional manners: end-to-end and modular where cosine similarity and PLDA are applied to embeddings, respectively. Experimental results show that in the case of end-to-end, instances-based metrics outperform proxy-based ones, while interestingly the opposite behavior is gained for the modular case. Finally, the lowest EER is achieved by adopting one of the proxy-based metrics, namely SoftTriple, in the modular manner. It yields relative improvements up to 12% compared to the state-of-the art method, i.e. x-vector.
Q-Value Weighted Regression: Reinforcement Learning with Limited Data

Sample efficiency has emerged as a significant challenge of deep reinforcement learning. We introduce Q-Value Weighted Regression (QWR), a simple RL algorithm that excels in this aspect. QWR builds upon Advantage Weighted Regression (AWR), an off-policy actor-critic algorithm that performs very well on continuous control tasks, but has low sample efficiency and struggles with high-dimensional observation spaces. We perform both theoretical and empirical analyses of AWR, that explain its shortcomings and use these insights to motivate QWR. We show experimentally that QWR either outperforms or matches the state-of-the-art algorithms both on tasks with continuous and discrete actions. In particular, QWR yields results on par with SAC on the MuJoCo suite and - with the same set of hyperparameters - outperforms a highly tuned implementation of Rainbow on a set of Atari games. At the same time, QWR is a much simpler algorithm than both SAC and Rainbow.
Cascaded Transposed Long-Range Convolutions for Monocular Depth Estimation

We study the shape of the convolution kernels in the upsampling block for deep monocular depth estimation. First, our empirical analysis shows that the depth estimation accuracy can be improved consistently by only changing the shape of the two consecutive convolution layers with square kernels, e.g., \((5 \times 5) \rightarrow (5 \times 5)\), to two “long-range” kernels, one having the transposed shape of the other, e.g., \((1 \times 25) \rightarrow (25 \times 1)\). Second, based on this observation, we propose a new upsampling block called Cascaded Transposed Long-range Convolutions (CTLC) that uses parallel sequences of two long-range convolutions with different kernel shapes. Experiments with NYU Depth V2 and KITTI show that our CTLC offers higher accuracy with fewer parameters and FLOPs than state-of-the-art methods.
Fast Segmentation of Complex Texture Image Based on Fractional Order CV Model

To improve the segmentation effect of complex texture images, a fast segmentation method for complex texture images based on fractional CV model is proposed. On the basis of the fractional order CV model, the input image is divided into several compact, uniform and almost the same size super-pixel regions. The saliency value of the super-pixel region is enhanced by smoothing operation, and the global contrast of the complex texture image is calculated. The segmentation strategy from coarse to fine and step by step refinement is adopted. Finally, the segmentation algorithm is used to realize the segmentation of complex texture image. The experimental results show that this method can enhance the image segmentation effect, and the actual application effect is better.
Constant Temperature Control System of Energy Storage Battery for New Energy Vehicles based on Fuzzy Strategy

There is a deviation between the set value of the traditional control system and the actual value, which leads to the maximum overshoot of the system output temperature. Therefore, a constant temperature control system of energy storage battery for new energy vehicles based on fuzzy strategy is designed. In terms of hardware design, temperature sensing circuit and charge discharge circuit are optimized, DC-DC temperature controller and BR20 temperature heat exchanger are designed. In the aspect of software design, the modular program is used to design the temperature change signal acquisition process, and the fuzzy set theory of fuzzy strategy is used to quantify the constant temperature control parameters and optimize the constant temperature control algorithm to realize the system design. The experimental results show that the designed system reduces the maximum overshoot of output temperature and shortens the response time of constant temperature control.
Quantum Computing Model

Quantum computers use processes of a quantum nature manifested with atoms, molecules, molecular clusters, etc. The description of such processes is based on the application of complex numbers and complex matrices. As is well known, the basic notion of classical information theory is a bit []. A classical bit takes the values 0 or 1 (and no other). A qubit (quantum bit) is the smallest element that executes the information storage function in a quantum computer [].
A Two-Layer Moving Target Defense for Image Classification in Adversarial Environment

Deep learning plays an increasingly important role in various fields due to its superior performance, and it also achieves advanced recognition performance in the field of image classification. However, the vulnerability of deep learning in the adversarial environment cannot be ignored, and the prediction result of the model is likely to be affected by the small perturbations added to the samples by the adversary. In this paper, we propose a two-layer dynamic defense method based on defensive techniques pool and retrained branch model pool. First, we randomly select defense methods from the defense pool to process the input. The perturbation ability of the adversarial samples preprocessed by different defense methods changed, which would produce different classification results. In addition, we conduct adversarial training based on the original model and dynamically generate multiple branch models. The classification results of these branch models for the same adversarial sample is inconsistent. We can detect the adversarial samples by using the inconsistencies in the output results of the two layers. The experimental results show that the two-layer dynamic defense method we designed achieves a good defense effect.
Easy-to-Implement Campus Electronic Anonymous Voting System

With the rapid development of internet, the applications requiring unobservable communications increased, such as electronic payment, electronic voting and electronic bidding. The most typical electronic voting protocol was the FOO protocol, which is the first protocol that could be employed in large-scale voting. However, when used on campus, FOO protocol was rather complicated to configure. This paper focuses on scenes of campus voting, such as course evaluation, students' mutual evaluation and students' union election. The character of these scenes was that the interest involved was relatively small and required more attention to anonymity. Therefore, the paper simplified the FOO protocol, removed the counter, concerned about abstention problem and optimized to solve the problem of ballot collision that originally existed due to bit commitment. The experiment showed the correctness, non-reusability and anonymity of our proposed scheme. In addition, because the application used OpenSSL for end-to-end encryption, it can authenticate users and resist eavesdropping attacks and tampering attacks.
A Generative Adversarial Network Framework for JPEG Anti-Forensics

JPEG anti-forensics aims to remove the artifacts left by JPEG compression and recover JPEG compressed images. However, the existing JPEG anti-forensic methods often introduce new traces and cause the degradation of visual quality of the processed images. In this work, JPEG anti-forensics are modelled as an image-to-image translation problem, where a generative adversarial network framework is used to translate a JPEG compressed image to a reconstructed one. Since JPEG compression causes impairment to high-frequency components, a loss function of high-frequency Discrete Cosine Transform (DCT) coefficients is proposed to recover these components. To prevent forensic detection, a calibration loss function is further introduced to mitigate the variance gap in the high-frequency subbands between generated images and their calibrated versions. Our experimental results demonstrate that the proposed method achieves better image quality than the existing state-of-the-art JPEG anti-forensic methods with comparable anti-forensic performance.
More Efficient SM9 Algorithm Based on Bilinear Pair Optimization Processing

Currently, SM9 algorithm has received more and more attention as a new cryptographic product. The SM9 algorithm encryption and decryption principle relies on the mapping relationship on the elliptic curve. Although this mapping relationship improves the security, it will slightly reduce the efficiency. The goal of this article is to improve the efficiency of the SM9 algorithm. Different from the traditional assembly line acceleration method, we decided to start with the basic operation of the algorithm itself. There is a bilinear pairing operation on the elliptic curve, which completes the point to point on the elliptic curve. The calculation complexity directly determines the SM9 algorithm. For this reason, we propose two new bilinear pair processing methods. The former uses the properties of isomorphic mapping to transfer the operations involved in the calculation of bilinear pairs from a large feature domain to a small feature domain, reducing the number of operations on the feature domain. The latter is for special operations in the bilinear pairing process, adding intermediate variables to convert them into low-time-consuming multiplication operations. According to the traditional Miller algorithm, the calculation of bilinear pairs requires 900 multiplication time units. Our solution can reduce this value to 700 and 800 multiplication time units respectively. In addition, the two algorithms have not changed the mapping relationship of the bilinear pair. On the premise of ensuring the correct mapping relationship, the efficiency of the SM9 algorithm is improved.
Organ Segmentation with Recursive Data Augmentation for Deep Models

The precise segmentation of organs from computed tomography is a fundamental and pivotal task for correct diagnosis and proper treatment of diseases. Neural network models are widely explored for their promising performance in the segmentation of medical images. However, the small dimension of available datasets is affecting the biomedical imaging domain significantly and has a huge impact in training of deep learning models. In this paper we try to address this issue by iteratively augmenting the dataset with auxiliary task-based information. This is obtained by introducing a recursive training approach, where a new set of segmented images is generated at each iteration and then concatenated with the original input data as organ attention maps. In the experimental evaluation two different datasets were tested and the results produced from the proposed approach have shown significant improvements in organ segmentation as compared to a standard non-recursive approach.
Deep Learning for Password Guessing and Password Strength Evaluation, A Survey

Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.
On a disclosure probability statement for the 5 safes framework

With increasing demand from the research community for more frequent and unrestricted access to data, national statistical offices (NSOs) are adopting the 5 safes framework to manage the disclosure risk for releasing such data. In this paper, under some mild conditions, we show that the probability of disclosure, given the controls in the 5 safes, is not greater than the product of the smallest conditional disclosure probability amongst the 5 controls and the Risk Ratios of the remaining four safe controls. By computing the disclosure probabilities of all possible configurations of the controls in each of the 5 dimensions of the framework, one can select the set which has the least control on data, but which also meet the confidentiality and privacy requirements of the NSO. Where the required assumption of unconditional independence of the safes cannot be met, the paper proposes a merger of some of the controls to overcome the violation.