Reviewerly/exHarmony huggingface dataset:
An Anti-Collusion Fingerprinting based on CFF Code and RS Code

Data security is becoming more and more important in data exchange. Once the data is leaked, it will pose a great threat to the privacy and property security of users. Copyright authentication and data provenance have become an important requirement of the information security defense mechanism. In order to solve the collusion leakage of the data distributed by organization and the low efficiency of tracking the leak provenance after the data is destroyed, this paper proposes a concatenated-group digital fingerprint coding based on CFF code and Reed-solomon (RS) that can resist collusion attacks and corresponding detection algorithm. The experiments based on an asymmetric anti-collusion fingerprint protocol show that the proposed method has better performance to resist collusion attacks than similar non-grouped fingerprint coding and effectively reduces the percentage of misjudgment, which verifies the availability of the algorithm and enriches the means of organization data security audit.
On the Implementation Efficiency of Linear Regression-Based Side-Channel Attacks

Cryptographic protocol implementations in both software and hardware leak sensitive information during their execution. Side-channel attacks (SCA) consist in analyzing this information in order to reveal the secret parameters of the protocols. Among the different SCA introduced in the literature, the Linear Regression Analysis (LRA) has been argued to be particularly interesting when few information is available on the hardware architecture of the device executing the protocol (e.g. if the so called Hamming weight model does not hold). However, the computing complexity of the existing LRA implementation is high, which explains why other techniques like e.g. the Correlation Power Analysis (CPA) is often preferred in practice. This paper aims improving the LRA implementation complexity (in memory space and computation) against both unprotected and protected implementations in uni- and multi-variate contexts. In addition we exhibit the relationship between the LRA and the Numerical Normal Form (NNF), which has been originally introduced in the field of Boolean functions. Thanks to this relationship, we deduce the polynomial degree of the normalized product combination of the arithmetic masking. Our improvements have been assessed using simulated leakage of a running AES.
Mining Opinion and Sentiment from Arabic Text

At a big data level there are voluminous amounts of posts in Arabic that can be found on social media and on various product/service review sites. This provides a rich source of information at a number of levels that can be useful both commercially and at government level. Analyzing media in English, which uses sentiment lexicons, is well established. Posts written in Arabic is a challenging task primarily due to Arabic’s rich morphology. There have been a number of efforts to build Arabic sentiment lexicons. However, they suffer from being of a limited size, unclear usability plan or publicly not available. This project, ASAM, aims to develop tools that will opinion mine media written in Gulf Arabic, Modern Standard Arabic and Arabizi (Arabish) or a mixture of these. In addition to mining opinion, the project will aim at demographically profiling the opinions.
Deep Learning Optimization for Many-Core Virtual Platforms

The rapid development of deep learning technology has made deep learning models widely used in image processing, speech recognition, and target tracking. However, the model becomes larger and larger, and it is difficult to deploy on a stand-alone device, usually on a distributed computing platform. As a high-performance digital signal processor developed by the 38th Research Institute of China Electronics Technology Group, HXDSP has strong computing power and rich computing resources, and is suitable for computing-intensive applications such as deep learning. Design the many-core virtual platform based on the HXDSP simulator, and provide the parallel communication interface MPIRIO to realize fast communication and task synchronization between the HXDSPs, and provide basic conditions for the deployment of deep learning models. At the same time, the parallel computing capability and pipeline mechanism provided by the virtual platform are used to accelerate the operation of the model. Aiming at the problem that the traditional gradient descent algorithm needs to be manually set, the meta-learning optimization algorithm is used to realize the adaptive fine-tuning of the model on the virtual platform, forming a deep learning optimization framework based on the CPU/HXDSP heterogeneous system.
Challenge report: Compound Emotion challenge

This paper is a brief introduction to our submission to the Joint Challenge on Compound Emotion Recognition and Multimodal (Audio, Facial, and Gesture) based Emotion Recognition (CER & MMER) held in conjunction with the IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2020. Our method combine texture information and landmark, which is the geometric representation of emotions as the input of the model. Moreover, we proposed a network based on the Alexnet network. To better deal with the recognition of micro emotions, we combine cross-entropy loss and triplet loss as our loss function. Finally, we get better performance than the baseline on the test set.
Triple-Channel Feature Mixed Sentiment Analysis Model Based on Attention Mechanism

Sentiment analysis is an important branch of the field of natural language processing, and opinion sentiment analysis is the current research hotspot. With the development of artificial intelligence, it is very challenging to effectively extract important emotional information from a large amount of text information and analyzes the emotional tendency of views in response to the increasing number of emotional opinions with rich connotations. Today’s methods mostly use shallow emotional factors, while ignoring deeper text semantic information, and cannot explore the semantic connection between words. To make up for this deficiency, this paper proposes a triple-channel feature-mixed sentiment analysis model Tri-BiGRU-Atten based on attention mechanism. This model combines different semantic feature mixed modeling to enable it to mine deeper emotional information in the context. Compared with the traditional attention mechanism, LSTM, Bi-LSTM, and other models, the emotion classification effect is more effective.
DFPNet:Dislocation Double Feature Pyramid Real-time Semantic Segmentation Network

The feature pyramid structure has almost become a standard configuration in the object detection network. This paper introduces a dislocation double feature pyramid structure and configure it in a lightweight segmentation network. We also use the classic module (atrous spatial pyramid pooling) in the segmentation network to extract rich contextual information. Our network is called DFPNet. In order to fully verify the gain of the dislocation double feature pyramid structure for network performance, we perform a wealth of experiments on different datasets (CitySpaces and CamVid) to show that DFPNet can obtain competitive results using our novel feature pyramid module. In particular, DFPNet achieves 73.1% Mean IoU(mIoU) on the CamVid validation set with only 5.5M parameters and runtime of 117 milliseconds per image on a single RTX 2080Ti. Our code and model have been open sourced at https://github.com/Fang789/pytorch_seg.
Performance of two redundant quantum channels for single qubits under indefinite causal order

Abstract Indefinite causal order has introduced novel procedures to improve the quality of quantum communication. The superposition of paths on a set of consecutive quantum channels has demonstrated enhancement in communication on some of them. This work models a generic quantum channel for single qubits in terms of Kraus operators in the form of Pauli operators. The output state going through the channel can be obtained analytically to then analyse the quality by means of the quantum fidelity.
Simulation of SAR signal processing system in telescopic mapping

At the present stage of development of surveillance tools, the most popular are radar tools. A special place among such systems is occupied by radars with a synthesized antenna aperture, which make it possible to obtain high-resolution radar images with a relatively small size of the antenna system. The creation of such radar facilities has a number of features, which increases the cost of the development process and increases the time frame, and verification of work can be complicated by the inability to form the required conditions. To eliminate such difficulties, it is advisable to create adequate mathematical models of systems that would allow you to conduct all the necessary tests and work out algorithms, with minimal time spent. In this paper, a phenomenological model of the reflected signal in SAR is proposed, and a SAR model is developed that has a block structure and can be supplemented with modules to identify new dependencies.
Bayesian detection of event spreading pattern from multivariate binary time series

This paper concerns the inference of the event spreading path from noisy event occurrence time series. We propose a probabilistic tree model for the event spreading path, and provide a full Bayesian approach with a Metropolis-Hastings (MH) algorithm to detect the tree path from multivariate binary time series. Our approach allows a node to spread the event in a probabilistic way to an arbitrary number of downstream nodes, the input data to contain an unknown number of nodes which are not inside the event spreading path, and the algorithm to sample only tree spreading structures. Efficient MH moves and iterative procedures guarantee the detection even if none or multiple event spreading paths are presented in the data. Simulations on synthesized data sets showed that our method can accurately detect the event spreading topologies, has good one-step-ahead prediction accuracy and outperforms existing approaches. We also analyzed the influence of model parameters on estimation accuracy in depth and obtained some interesting conclusions. Furthermore, we applied our method to the PM2.5 data in provincial capitals of north and east China. The inferred path is largely consistent with the known pollutant spreading trend.
Cloud Computing Inhibitors Among Small and Medium Enterprises

Cloud computing is an emerging information technology (IT) paradigm which undoubtedly has received significant attention from different spheres of influence. The cloud computing concept entails the provisioning of computing resources from remote locations to individuals or organizations over the internet at anytime. The technology is associated with several benefits but the reduction in capital and operational expenditure is the major one. The importance of IT innovations to Small and Medium Enterprises (SMEs) and the role of SMEs in economic growth have been demonstrated empirically. However, literature indicate that SMEs in developing economies are not taking the advantages of emerging technologies like cloud computing to improve operational excellence within their organizations. An in-depth review of academic publications, technical reports, and industry white papers on cloud computing has confirmed security and privacy is-sues as some of the key inhibitors to cloud computing adoption among SMEs. This is probably because the customer data is released to remote locations, which are invisible, and data is accessed over the internet, which is already over-whelmed with malicious attackers. This study explores the reasons for the lack of cloud computing adoption among SMEs in developing economies. When the rea-sons for this lag are identified, cloud computing service providers, technology policy makers, entrepreneurs and SMEs executives will be provided with the opportunity to develop appropriate solutions and strategies that will specifically meet the needs of SMEs in developing economies. This will promote and accelerate the acceptance rate of cloud services. When SMEs utilize cloud computing as an IT strategy, business growth becomes inevitable and the country's economic growth is witnessed.
Professionally Oriented Training in the School-University System

The article investigates a problem of organizing professionally oriented training in the system “school-university”. The authors identified approaches to the study of the continuity principle and defined their objectives; analyzed the essence of the continuity principle in professional-oriented education in the system “school-university”; specified scientific understanding of the concept of “readiness of high school graduates to continue their education in high school”; justified the structure of school graduates’ readiness to continue their education in a higher education institution and determined the content of its components. The results of the experimental work allow making conclusions that the implemented organizational and pedagogical conditions contribute to increasing the level of school graduates’ readiness to continue their education, successful adaptation in new conditions, and thus provide professionally-oriented training in the “school-university” system.
Quantum annealing for semi-supervised learning

Recent advances in quantum technology have led to the development and the manufacturing of programmable quantum annealers that promise to solve certain combinatorial optimization problems faster than their classical counterparts. Semi-supervised learning is a machine learning technique that makes use of both labeled and unlabeled data for training, which enables a good classifier with only a small amount of labeled data. In this paper, we propose and theoretically analyze a graph-based semi-supervised learning method with the aid of the quantum annealing technique, which efficiently utilize the quantum resources while maintaining a good accuracy. We illustrate two classification examples, suggesting the feasibility of this method even with a small portion (20%) of labeled data is involved.
Addressing Military Sexual Violence by Proposing a Social Media Influencer Model

Military sexual violence (MSV) is a prevalent issue that uniquely affects mission readiness. Although research on MSV and social media is growing, examinations of possible interventions like those employing social media in this population are scant. Given the growing interest in targeting MSV, the present systematic review was conducted. The PRISMA framework was used to conduct a systematic review of MSV and social media ( N = 71). Queries were limited to articles published between 2010 and 2020. SAGE Journals, PubMed, and JSTOR were utilized. Terms and potential combinations were entered into the databases in varying Boolean combinations. Additional recorders were identified for inclusion via the reference sections of relevant records. After removing duplicates from the query results, we selected records of suspected relevance by title and screened abstracts. Finally, articles with relevant abstracts were reviewed thoroughly to determine whether they met inclusion criteria for the review. The employments of military leaders in a social media intervention puts into practice the military’s central values and development of its leadership core. This intervention promotes group solidarity while maximizing conversations around meaningful messages. Findings in this review suggest military leaders could feasibly employ a cost-effective global intervention using social media, as a tool to help actively address MSV.
Cause I’m a Genial Imprecise Point: Outlier Detection for Uncertain Data

In this paper, we introduce the outlier detection problem in a set of uncertain points. We study two variants of the problems based upon the definition of the outlier. For a given positive integer $$k(<n)$$ and a set $$\mathfrak {R}$$ of n regions as the imprecise points, the first type of the outlier detection problem that we study is to locate $$n-k$$ points on distinct regions, such that the size of the smallest axis-aligned bounding box (AABB), the diameter or the smallest enclosing circle (SEC) of the resulting points gets minimized. The uncertainty regions we study are squares or disks, and the excluded k regions are considered as outliers. We also study the covering versions in which the objectives of the SEC and the AABB problems are to find the smallest circle or axis-aligned bounding box, respectively, that covers the area of at least $$n-k$$ regions. In the second-type of outliers, the outliers are those k regions that mostly reduce the uncertainty-induced gap between the lower bound and the upper bound on the size of the output. We give polynomial time algorithms for several variants of the mentioned problems, ranging in running time from $$O(n \log n)$$ to $$O(n^{5.5}\log n)$$ .
Rational Belief in Lottery- and Preface-Situations

Rational agents represent their beliefs about the world in two rather different ways. One way is by means of qualitative beliefs, or yes-or-no beliefs: Here a proposition p is either believed, or disbelieved (i.e., ¬p is believed), or belief about p is suspended. Thereby, with “rational belief that p” it is meant that the proposition p is believed to be true, or accepted as being true, whereby the system of beliefs satisfies certain rationality conditions.
Finding Real-Life Doppelgangers on Campus with MTCNN and CNN-Based Face Recognition

Face recognition has been widely used in areas such as security informatics, forensic investigation, customer tracking and mobile payment. This project is inspired by a photography artwork by Francois Brunelle, where he spent 12 years tracking people who are completely strangers but lookalikes, or doppelgangers. We aim to use face recognition techniques to mine doppelgangers on a school campus. We developed a face processing system which includes four steps, face detection, image processing (alignment and cropping), feature extraction and classification. We trained Multi-task Cascaded Convolutional Networks (MTCNN) and traditional CNNs with joined softmax loss and Center Loss on the Caffe framework. Finally, cosine similarity is used to detect similar faces. By exhibiting the results, we demonstrate the potential to adopt CV technology in art-related domains, in this case mimicking a photographer’s human eyes. This project provides an example for cross-disciplinary study between art and technology and will inspire researchers from both domains to establish further collaboration channels.
A Theory on AI Uncertainty Based on Rademacher Complexity and Shannon Entropy

In this paper, we present a theoretical discussion on AI deep learning neural network uncertainty investigation based on the classical Rademacher complexity and Shannon entropy. First it is shown that the classical Rademacher complexity and Shannon entropy is closely related by quantity by definitions. Secondly based on the Shannon mathematical theory on communication [3], we derive a criteria to ensure AI correctness and accuracy in classifications problems. Last but not the least based on Peter Barlette's work in [1], we show both a relaxing condition and a stricter condition to guarantee the correctness and accuracy in AI classification. By elucidating in this paper criteria condition in terms of Shannon entropy based on Shannon theory, it becomes easier to explore other criteria in terms of other complexity measurements such as Vapnik-Cheronenkis, Gaussian complexity by taking advantage of the relations studies results in other references such as in [2]. A close to 1/2 criteria on Shannon entropy is derived in this paper for the theoretical investigation of AI accuracy and correctness for classification problems.
Use of Apple iPad in Education in Primary Schools in the Czech Republic

This study focuses on the use of Apple iPads in lessons in primary schools in the Czech Republic. Despite the fact that in Western countries, both the manner and the efficiency of the use of iPads in various educational stages are already being researched, there are no such studies thin the context of the Czech Republic. In this regard, this study provides yet unpublished and innovative findings.
A Sub-linear Lattice-Based Submatrix Commitment Scheme

Subvector commitment is a recently proposed cryptographic primitive that provides the underlying cryptographic tool to design many interesting security systems such as succinct non-interactive arguments of knowledge (SNARK), verifiable database, dynamic accumulators, etc. In this paper, we present a generalization of subvector commitment, a public-coin-setup lattice-based submatrix commitment, which allows a commitment of a message matrix to be opened on multiple entries of the matrix simultaneously. It exploits a conceptual similarity between Single-Instruction Multiple-Data (SIMD) in homomorphic encryption and submatrix commitment, and develops a novel position binding technique based on the Chinese Remainder Theorem. We show that the position binding property can be reduced to module-based short integer solution (SIS) problem, a standard assumption that is believed to be post-quantum secure. We also show that the commitment and opening size of our commitment scheme are both sublinear, i.e., proportional to the square root of the message size. As far as we know, this is the first public-coin-setup and post-quantum secure subvector commitment scheme.
“Opinion Target: Opinion Word” Pairs Extraction Based on CRF

With the spread of mobile applications and online interactive platforms, the number of user reviews are increasing explosively and becoming one of the most important ways for users to voice opinions. Opinion target extraction and opinion word extraction are two key procedures used to determine the helpfulness of reviews. In this paper, we implement a system to extract “opinion target:opinion word” pairs based on the Conditional Random Field (CRF). Firstly, we used the CRF model to extract opinion targets and opinion words, then combined these into pairs in order. In addition, Node.js was used to build a visualization system to display “opinion target:opinion word” pairs. In order to verify the effectiveness of the system, experiments were conducted on the Laptop and Restaurant datasets of SemEval-2014-task4, and the accuracy of the F value extracted by the model reached 86% and 90%, respectively. All the code and datasets for this experiment are available on GitHub.
A Neural Network Featured System for Learning Performance of Students

The system proposed by this article is a combination of association rules and decision tree which is utilized to construct the early warning model of student learning performance reflected by scores achieved by students. Because Apriori algorithm achieves low efficiency of frequent item set traversal, this model uses FP-growth to search for frequent itemsets and to construct through association rules and decision trees. Inter-course dependencies, analysis and early warning of learning courses related to both computer and student evaluated by student’s school. Proposed system provides students with accurate academic guidance, which can effectively improve the quality of learning management of individuals. It is of great significance to the development and guidance of students. It helps understand the situation of students in all aspects and improve the overall level of students. Compared with other machine learning algorithms which can only predict performance through scores the students get, proposed system with decision tree and association rules has achieved the combination of horizontal and vertical prediction, which suggest it does not rely upon available scores to predict performance. In the meanwhile, horizontal relationship between decision tree and association rules has been found, which has been proven that it has achieved the comprehensive prediction.