Reviewerly/exHarmony huggingface dataset:
Identification and Impact Analysis of Family History of Psychiatric Disorder in Mood Disorder Patients With Pretrained Language Model

Mood disorders are ubiquitous mental disorders with familial aggregation. Extracting family history of psychiatric disorders from large electronic hospitalization records is helpful for further study of onset characteristics among patients with a mood disorder. This study uses an observational clinical data set of in-patients of Nanjing Brain Hospital, affiliated with Nanjing Medical University, from the past 10 years. This paper proposes a pretrained language model: Bidirectional Encoder Representations from Transformers (BERT)-Convolutional Neural Network (CNN). We first project the electronic hospitalization records into a low-dimensional dense matrix via the pretrained Chinese BERT model, then feed the dense matrix into the stacked CNN layer to capture high-level features of texts; finally, we use the fully connected layer to extract family history based on high-level features. The accuracy of our BERT-CNN model was 97.12 ± 0.37% in the real-world data set from Nanjing Brain Hospital. We further studied the correlation between mood disorders and family history of psychiatric disorder.
Authenticated encryption chip implementation against side-channel attack

Abstract An authenticated encryption chip with novel nonce generation circuit was developed. This circuit associates the nonce generation with the contents and receiving time of plaintext. It will also generate overlapping power compensation for the whole chip. The integrated chip was fabricated under SMIC 180nm technology. Under 1.8V VDD, 100MHz global clock, the power consumption is about 14mW within 50k gates. The test result exhibits more than 10 times the strength in resistance to side-channel attack than the unprotected version without increasing hardware cost.
Semiparametric modelling of two-component mixtures with stochastic dominance

In this work, we studied a two-component mixture model with stochastic dominance constraint, a model arising naturally from many genetic studies. To model the stochastic dominance, we proposed a semiparametric modelling of the log of density ratio. More specifically, when the log of the ratio of two component densities is in a linear regression form, the stochastic dominance is immediately satisfied. For the resulting semiparametric mixture model, we proposed two estimators, maximum empirical likelihood estimator (MELE) and minimum Hellinger distance estimator (MHDE), and investigated their asymptotic properties such as consistency and normality. In addition, to test the validity of the proposed semiparametric model, we developed Kolmogorov–Smirnov type tests based on the two estimators. The finite-sample performance, in terms of both efficiency and robustness, of the two estimators and the tests were examined and compared via both thorough Monte Carlo simulation studies and real data analysis.
Distributed architecture design of big data platform

This paper designs a big data platform based on Hadoop (HDFS, MapReduce) and hive. The platform is built on VMware/openstack cloud platform. Firstly, multiple virtual machines are created on the cloud platform(A virtual machine is equivalent to an instance node of big data,instance node), Then deploy Hadoop distributed storage system on the virtual machine (Hadoop Distributed File System)And distributed computing system (MapReduce and spark real-time computing framework), and deployed the query analysis platform of hive data warehouse based on Hadoop. At the same time, we conducted a case study of Internet search and social data analysis based on the big data platform. The experimental results show that in terms of Internet big data loading, mapping, query and statistical analysis, the big data platform cluster built by multiple machines has higher processing efficiency and throughput than a single machine.
Remote Sensing Image Ship Detection Based on Improved YOLOv3

With the improvement of remote sensing image resolution, remote sensing target detection has become a research hotspot, and it plays an important role in military reconnaissance, disaster rescue, urban traffic management, and other scenarios. However, remote sensing target detection still has problems such as a large amount of data and specific differences in shooting by different sensors. In order to meet these challenges and improve the speed and accuracy of remote sensing target detection, this paper proposes a target detection algorithm based on an attention mechanism. Based on the YOLOv3 network, the SE-YOLOv3 network is designed using an attention mechanism to increase expressiveness, focus on essential features, and suppress unnecessary features. The experimental results show that: on the DOTA data set, compared with the YOLOv3 network, adding the squeeze and excitation (SE) model to the bottleneck has the best effect and can better improve the performance of the model. Experiments show that the proposed SE-YOLOv3 network is more efficient and accurate than YOLOv3, so it is more suitable for target detection tasks in complex remote sensing scenes.
Decolonial Re-Existence and the Myths of Knowledge Production

Knowledge production is a taken-for-granted concept in the Social Sciences and Humanities. By revealing the myths surrounding knowledge production, this chapter aims to shows its connection to colonialism, imperialism, and the erasure of other ways of knowing and being. Each myth is dispelled to demonstrate the complicity of knowledge production in continuing the modern colonial order. In doing so, it reveals the impossibility of onto-epistemological re-existence under knowledge production. Calling for a refusal of knowledge production, the chapter then proposes knowledge cultivation (Shilliam 2015) as a life-affirming and epistemically non-violent approach to research. Through pedagogies of positionality, re-existence, and relationality (Vázquez 2020), knowledge cultivation is presented as a pathway that counters the zero-point of western epistemology, the double erasure of modernity/coloniality, and the disciplining of knowledge. In doing so, it creates fertile ground for the emergence of onto-epistemological re-existence.
Comparative study of solar tracking and fixed-tilt photovoltaic modules in Morocco

Nowadays the global energy context is being difficult, looking for other alternatives is obviously observed in many countries, and the expectations in terms of renewable energies are increase continuously. Taking the example of photovoltaic energy with its environmental advantages and especially the fixed-tilt solar panel. This technology is used to optimize radiation received, and more the surface of a solar panel is perpendicular to incident rays, more the efficiency is better, hence the objective of our work. In this context, a comparative study between a fixed-tilt panel and a panel with a 2-axis Open Loop tracking system is performed for the first time in AFRICA, and exactly in the 6 regions of MOROCCO distributed according to their solar potential. The comparative study is done on Matlab/Simulink software, in which the incident radiation for both systems is calculated from a radiation model based on zenith and azimuth angles obtained via the Solar position algorithm; the photovoltaic power is also calculated in our program. As a results, we found that the use of open-loop solar tracking systems in less sunny areas and periods increase the productivity of photovoltaic panels and consequently improves its energy efficiency.
A Specific Emitter Identification Method Based on RF-DNA and XGBoost

Specific emitter identification (SEI) is a promising research direction in artificial intelligence and Internet of Things. Aiming at the fingerprint features extraction and the identification algorithm selection for SEI, a novel method based on RF-DNA feature set and extreme gradient boosting (XGBoost) algorithm is proposed in this paper. Firstly, considering the advantages of RF-DNA in characterizing the fluctuation degree of instantaneous sequences, a RF-DNA feature set is constructed based on statistical features extracted from instantaneous frequency, instantaneous phase and instantaneous amplitude of signals. Then, the XGBoost algorithm is used to perform feature learning on the structured RF-DNA data set. Finally, three civil communication emitters of the same model are used as the identification objects to verify the performance of the identification method. Experimental results show that the RFDNA feature set exhibits satisfactory feature expression performance, and the XGBoost algorithm shows favorable feature learning properties.
Architectural Pen-Test Generation and Vulnerability Prediction for Cyber-Physical Systems

The security of cyber-physical systems (CPSs) gains in significance as they become more open and rely on communication. Penetration testing is a technique to assess the security of such systems. However, existing techniques depend on the experience and domain knowledge of the penetration tester. This paper outlines ideas for supporting penetration testers to reduce the influence of the experience and knowledge on the results of a pen test. Our goal is to generate attack paths and test cases based on a software architecture during design phase. The attack paths and test cases are used to support pen testers in conducting tests on a system under test (SUT) once the system is completely developed. Furthermore, we intend to use the generated test cases to analyze changes made by the software architect during the design phase. Our goal here is to provide feedback in terms of a vulnerability prediction. The prediction supports the software architect in making design decisions towards more secure software architectures.
Enhanced ZSSR for Super-resolution Reconstruction of the Historical Tibetan Document Images

Due to the poor preservation and imaging conditions, the image quality of historical Tibetan document images is relatively unsatisfactory. In this paper, we adopt super-resolution technology to reconstruct high quality images of historical Tibetan document. To address the problem of low quantity and poor quality of historical Tibetan document images, we propose the EZSSR network based on the Zero-Shot Super-resolution Network (ZSSR), which borrows the idea of feature pyramid in Deep Laplacian Pyramid Networks (LapSRN) to extract different levels of features while alleviating the ringing artifacts. EZSSR neither requires paired training datasets nor preprocessing stage. The computational complexity of EZSSR is low, and thus, EZSSR can also reconstruct image within the acceptable time frame. Experimental results show that EZSSR reconstructs images with better visual effects and higher PSNR and SSIM values.
Semi-Measurement-Device-Independent Quantum State Tomography

As one of the fundamental tools in the quantum information field, quantum state tomography can be utilized to reconstruct any unknown state. Usually, it needs a tomographically complete set of measurements and meantime it requires that all measurements are fully characterized. Here we propose a semi-measurement-device-independent quantum state tomography protocol, which only needs one characterized measurement and a trusted ancillary system. Furthermore, we carry out corresponding experimental demonstrations by using linear optics, and obtain the average state fidelity as high as 0.973, verifying the effectiveness of the scheme.
Privacy Leaks Protection in Music Streaming Services Using an Intelligent Permissions Management System

A security violation is referred to as a personal data breach when it leads to unintentional and unlawful destruction, loss, alteration, unauthorized disclosure, or access to personal data that has been communicated, stored, or otherwise processed in some other manner. Based on the principles of information security, we can define a breach of confidentiality as the unauthorized or accidental disclosure or access to personal data, a breach of integrity as the unauthorized or accidental alteration of personal data, and a breach of availability as the unauthorized or accidental loss of access to or destruction of personal data. This paper suggests designing an intelligent consensus policy management system based on the Markov chain approach. It is a novel system that would analyze the present status of the consensus elements for future development and anticipates the possibility of possible breaches of sensitive personal data. The evaluation of the proposed strategy is based on a policy scenario that involves a hypothetical consensus and a data breach of sensitive information to music streaming services.
Software Module Clustering Using the Hierarchical Clustering Combination Method

The scale of software applications has increased dramatically. Hierarchical clustering is a good method for modular recovery of software architecture. Because the different evaluation criteria of software types and clustering results, a single hierarchical clustering algorithm cannot integrate the number of clusters, arbitrary decision-making, recovery quality and other indicators on different evaluation standards, there is no comprehensive clustering algorithm selection method. We propose a hierarchical clustering combination algorithm and use principal component analysis to combine the results of multiple hierarchical clusters, the combined result retain the basic information of each clustering algorithm as much as possible. The experimental results show that compared with the single software hierarchical clustering method and other combined methods, the result of our method is closer to expert decomposition, and it has good performance on a variety of indicators.
What to do with 2.000.000 Historical Press Photos? The Challenges and Opportunities of Applying a Scene Detection Algorithm to a Digitised Press Photo Collection

In 1962, Dutch celebrity Ria Kuyken was attacked by a circus bear. Cees de Boer captured this moment, for which he was awarded both a World Press Photo and the Silver Camera (Zilveren Camera). Though this photo popularised Fotopersbureau De Boer, which Cees had founded in 1945, the importance of the collection lies in its scale. Approximately 2,000,000 photos taken of about 250,000 events in sixty years, accompanied by extensive metadata. Not only major nationwide events are represented, but also subjects of small scale, human interest, such as the shopkeeper around the corner. Our aim is not only the digitisation and publication of all 2,000,000 photo negatives of Fotopersbureau De Boer but also to explore how artificial intelligence can enrich this collection, benefiting both users of the archive and cultural historians studying historical photographs. One of our efforts focuses on scene detection, a method to detect the ‘scene’ represented in an image (Zhou et al, 2018). We will rely on transfer learning to adapt existing computer vision models to our collection and the needs of our users. Existing models can generate labels with high accuracy, however, these labels are ahistorical and more often than not irrelevant to our collection. We will label subsets of the images via crowdsourcing to train and improve existing models. As such, we can add labels relevant to our collection to the model, which are absent in existing models. In this paper, we will highlight the opportunities and challenges of applying artificial intelligence to a collection of historical photographs.
Fast estimation method of boresight errors in nonuniformly heated sapphire windows

Previous work has shown nonuniformly heated windows cause distortions which affect imaging a transmitted point source as the focused spot has an angular displacement. If this angular displacement is not compensated, the processing of the imaging system’s images of the transmitted point source could be misinterpreted. The in-situ estimation technique uses LWIR pyrometry to image the inside surface of the window, material properties, and fast computational techniques. Results from this in-situ estimation technique are presented and compared with experimentally measured results. The front surface temperatures, transmitted wavefront, and angular deviation of a transmitted point source are compared.
Noise resilience in path-polarization hyperentangled probe states

Most quantum systems that are used for generating entanglement and for practical applications are not isolated from the environment, and are hence susceptible to noise. Entanglement in more than one degree of freedom between two systems, known as hyperentanglement, is known to have certain advantages, including robustness against noise over conventional entangled states. Quantum illumination, imaging and communication schemes that involve sending one photon from a pair of entangled photons and retaining the other photon usually involve exposing only the signal photon to environmental noise. The disruptive nature of noise degrades entanglement and other correlations which are crucial for many of these applications. In this paper, we study the advantages of using photon pairs in certain path-polarization hyperentangled states in a noisy interaction where photons in only one of the paths are affected by noise. We model such noise and study the effect of noise on the correlations present in the hyperentangled photons. Three different methods, entanglement negativity, entanglement witnesses and Bell nonlocality are used to show the resilience of path-polarization hyperentangled probe state against noise.
A Comprehensive Exploration of Noise Robustness and Noise Compensation in ResNet and TDNN-based Speaker Recognition Systems

In this paper, a comprehensive exploration of noise robustness and noise compensation of ResNet and TDNN speaker recognition systems is presented. Firstly the robustness of the TDNN and ResNet in the presence of noise, reverberation, and both distortions is explored. Our experimental results show that in all cases the ResNet system is more robust than TDNN. After that, a noise compensation task is done with denoising autoen-coder (DAE) over the x-vectors extracted from both systems. We explored two scenarios: 1) compensation of artificial noise with artificial data, 2) compensation of real noise with artificial data. The second case is the most desired scenario, because it makes noise compensation affordable without having real data to train denoising techniques. The experimental results show that in the first scenario noise compensation gives significant improvement with TDNN while this improvement in Resnet is not significant. In the second scenario, we achieved 15% improvement of EER over VoiCes Eval challenge in both TDNN and ResNet systems. In most cases the performance of ResNet without compensation is superior to TDNN with noise compensation.
Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets

Deep learning (DL) has revolutionized unstructured data analytics. But in most cases, DL needs massive labeled datasets and large compute clusters, which hinders its adoption. These limitations can be overcome using a popular paradigm called deep transfer learning (DTL). With DTL, one adapts a pre-trained DL model instead of training a model from scratch. Thus, DTL reduces the massive training data and compute requirements to train a model. During adaptation, a common practice is to freeze most pre-trained model parts and adapt only the remaining. Since no single adaptation scheme is universally the best, one often evaluates several schemes, which is also called model selection. We also observed that data labeling for DTL is seldom a one-off process. One often updates their labeled data intermittently by adding new labeled data and performs model selection to evaluate the accuracy of the trained models. Today, one executes this workload by performing computations for the entire pre-trained model and repeats it for every model selection cycle. This approach results in redundant computations in frozen model parts and causes usability and system inefficiency issues. In this work, we reimagine DTL model selection in the presence of frozen layers as an instance of multi-query optimization and propose two optimizations that reduce redundant computations and training overheads. We implement our optimizations into a data system called Nautilus. Experiments with end-to-end workloads on benchmark datasets show that Nautilus reduces DTL model selection runtimes by up to 5X compared to the current practice.
A Phenomenological Study of Online Assessment Perceptions of Macao Higher Learning Institutions During the COVID-19 Pandemic

Online assessment is the key issue of studying online learning during the COVID-19 pandemic. Often, online assessments are understood as the mirror to face-to-face learning assessments. In fact, online assessment should start from the premise that all interactive learning strategies are assessment strategies, and as such should be considered for their value in measuring the experiences of students and instructors. This study used the phenomenological method of qualitative research through in-depth interviews with 14 college students and 6 instructors. Based on this, it explored the perception of online assessment in Macao higher learning institutions from the complementary perspectives of college students and instructors, and constructed a theoretical model. The main research findings are that after the assessment shifted from offline to online, some new features appeared, and corresponding changes in form and function occurred. Formally, online assessment is manifested as single subjectivity and flexible diversity, which leads to the assessment dilemma of lack of constructive alignment. At the same time, the functions of assessment have also been extended and even alienated. So the effect of online assessment is not satisfactory. The recommendations for the development of the technological environment and the changes of formative and summative assessment are also discussed in this paper. In terms of the technological environment, efforts need to be made to provide more authentic online learning experiences with synchronous feedback and flexible interaction. In order to help develop a holistic person with online learning, which is the ultimate goal of learning, corresponding criterion-referenced assessment and functions of assessment should be improved accordingly to promote student learning while considering students’ recognition and facilitate the co-construction of knowledge between instructors and students. In addition, future research can focus on the development of the readiness of assessment technology, as well as the form and purpose of assessment. Overall, this study provides a more comprehensive understanding of online learning and assessment from the dual perspectives of instructors and students, which could help further studies in this field.