Reviewerly/exHarmony huggingface dataset:
Humanitarization of University and Social Engagement Mission

The paper focuses on substantiating the institutional significance of the humanitarian component of University education and demonstrating opportunities for its implementation through non-profit activities of the University community. Transition to the new technological order accentuates the relevance of new personal and communicative competencies formed on the basis of education in humanities. Humanitarization is a priority task, which is reflected in the University education practices in the United States and European countries. The idea of upbringing a humanitarianly educated and humanitarianly oriented personality is declared in the discourses of the world leading Universities’ missions, whose activities are aimed at achieving public good for the society and its sustainable development. Russian documents and discussions on higher education emphasize the importance of humanitarization, but in practice, the humanitarian component in Russian universities is clearly being underestimated. In our opinion, this is due to the fact that humanitarization means mainly the strengthening of the cognitive element of University programs – the expansion of humanitarian specialties and humanitarian courses, but socially oriented University practices are not taken into account. Meanwhile, humanitarization includes both the translation of humanitarian knowledge and values – the strategic goals of the development of society, the state, the region, and the activity-based approbation of the knowledge gained in extra-curricular practices. Humanitarization of higher education is considered in the article from the standpoint of social and philosophical analysis, within the ontological aspect as a mode of being of an institutionally organized human activity on knowledge production and translation, which has closely been expressed in creating University 3.0, as well as in the idea and discourse of the third mission of University. The third mission sufficiently strengthens its emphasis on the anthropological and social function – orientation of University activities towards the genesis of a creative personality and the increased good for society. The goal of achieving the good is explicitly present in those social practices that are aimed at participating in the life of society without direct commercial gain and is implemented outside the University. The article examines the main types of socio-humanitarian practices in universities in Western countries.
Set-based Multi-Sensor Data Fusion For Integrated Navigation Systems

This paper presents a novel set-based multisensor data fusion algorithm for combining aircraft 3D position estimates provided by three separate positioning systems: Inertial Reference System (IRS), Global Positioning System (GPS) and Instrument Landing System (ILS). An Extended Zonotopic Kalman Filter (EZKF) is proposed to solve the problem of IRS/GPS/ILS data fusion that rigorously encloses the nonlinearities of ILS measurement equations. Moreover, an adaptive tuning of the overall data fusion filter relies on a lower layer integrating a bank of elementary filters. The latters result from the simplification of firstorder zonotopic Kalman filters optimizing a 1-norm accuracy criterion. Simulations using real flight data provided by Airbus illustrate the effectiveness of the proposed method.
The Power of Scale for Parameter-Efficient Prompt Tuning

In this work, we explore “prompt tuning,” a simple yet effective mechanism for learning “soft prompts” to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3’s few-shot learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method “closes the gap” and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant because large models are costly to share and serve and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed “prefix tuning” of Li and Liang (2021) and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer and enables efficient “prompt ensembling.” We release code and model checkpoints to reproduce our experiments.
Directions Old and New: Palaeomagnetism and Fisher (1953) Meet Modern Statistics

Summary Most modern articles in the palaeomagnetism literature are based on statistics developed by Fisher's 1953 paper ‘Dispersion on a sphere’, which assumes independent and identically distributed (iid) spherical data. However, palaeomagnetic sample designs are usually hierarchical, where specimens are collected within sites and the data are then combined across sites to calculate an overall mean direction for a geological formation. The specimens within sites are typically more similar than specimens between different sites, and so the iid assumptions fail. This article has three principal goals. The first is to review, contrast and compare both the statistics and geophysics literature on the topic of analysis methods for clustered data on spheres. The second is to present a new hierarchical parametric model, which avoids the unrealistic assumption of rotational symmetry in Fisher's 1953 paper ‘Dispersion on a sphere’ and may be broadly useful in the analysis of many palaeomagnetic datasets. To help develop the model, we use publicly available data as a case study collected from the Golan Heights volcanic plateau. The third goal is to explore different methods for constructing confidence regions for the overall mean direction based on clustered data. Two bootstrap confidence regions that we propose perform well and will be especially useful to geophysics practitioners.
Nonce@Once: A Single-Trace EM Side Channel Attack on Several Constant-Time Elliptic Curve Implementations in Mobile Platforms

We present the first side-channel attack on full-fledged smartphones that recovers the elliptic curve secret scalar from the electromagnetic signal that corresponds to a single scalar-by-point multiplication in current versions of Libgcrypt, OpenSSL, HACL* and curve25519-donna. To avoid leaking information via side channels, these implementations follow the recommendations of RFC 7748 and use a constant-time conditional swap operation. Our attack targets signal differences created by systematic changes in operand values during this conditional swap operation. We deploy the attack, using low-cost equipment (<$800), against two Android-based mobile phones and against a Linux-based IoT development board. We repeat the attack 100 times, each time with a different scalar, on each device. In all of the implementations considered in this work, our attack successfully recovers the full secret key within seconds. To mitigate the attack we suggest randomizing the exclusive-or mask in the conditional swap operation. We show that this countermeasure is effective in preventing this and similar attacks.
A Control Injection Attack against S7 PLCs -Manipulating the Decompiled Code

In this paper, we discuss an approach which allows an attacker to modify the control logic program that runs in S7 PLCs in its high-level decompiled format. Our full attack-chain compromises the security measures of PLCs, retrieves the machine bytecode of the target device, and employs a decompiler to convert the stolen compiled bytecode (low-level) to its decompiled version (high-level) e.g. Ladder Diagram LAD. As the LAD code exposes the structure and semantics of the control logic, our attack also manipulates the LAD code based on the attacker’s understanding to the physical process causing abnormal behaviors of the system that we target. Finally, it converts the infected LAD code to its executable version i.e. machine bytecode that can run on the PLC using a compiler before pushing the malicious code back to the PLC. For a real scenario, we implemented our full attack-chain on a small industrial setting using real S7-300 PLCs, and built the database (for our decompiler and compiler) using 108 different control logic programs of varying complexity, ranging from simple programs consisting of a few instructions to more complex ones including multi functions, sub-functions and data blocks. We tested and evaluated the accuracy of our decompiler and compiler on 5 random programs written for real industrial applications. Our experimental results showed that an external adversary is able to infect S7 PLCs successfully. We eventually suggest some potential mitigation approaches to secure systems against such a threat.
The Invisible Shadow: How Security Cameras Leak Private Activities

This paper presents a new privacy threat, the Invisible Infrared Shadow Attack (IRSA), which leverages the inconspicuous infrared (IR) light emitted by indoor security cameras, to reveal in-home human activities behind opaque curtains. The key observation is that the in-home IR light source can project invisible shadows on the window curtains, which can be captured by an attacker outside using an IR-capable camera. The major challenge for IRSA lies in the shadow deformation caused by a variety of environmental factors involving the IR source position and curtain shape, which distorts the body contour. A two-stage attack scheme is proposed to circumvent the challenge. Specifically, a DeShaNet model performs accurate shadow keypoint detection through multi-dimension feature fusion. Then a scene constructor maps the 2D shadow keypoints to 3D human skeletons by iteratively reproducing the on-site shadow projection process in a virtual Unity 3D environment. Through comprehensive evaluation, we show that the proposed attack scheme can be successfully launched to recover 3D skeleton of the victims, even under severe shadow deformation. Finally, we propose potential defense mechanisms against the IRSA.
Pedestrian-Aware Panoramic Video Stitching Based on a Structured Camera Array

The panorama stitching system is an indispensable module in surveillance or space exploration. Such a system enables the viewer to understand the surroundings instantly by aligning the surrounding images on a plane and fusing them naturally. The bottleneck of existing systems mainly lies in alignment and naturalness of the transition of adjacent images. When facing dynamic foregrounds, they may produce outputs with misaligned semantic objects, which is evident and sensitive to human perception. We solve three key issues in the existing workflow that can affect its efficiency and the quality of the obtained panoramic video and present Pedestrian360, a panoramic video system based on a structured camera array (a spatial surround-view camera system). First, to get a geometrically aligned 360○ view in the horizontal direction, we build a unified multi-camera coordinate system via a novel refinement approach that jointly optimizes camera poses. Second, to eliminate the brightness and color difference of images taken by different cameras, we design a photometric alignment approach by introducing a bias to the baseline linear adjustment model and solving it with two-step least-squares. Third, considering that the human visual system is more sensitive to high-level semantic objects, such as pedestrians and vehicles, we integrate the results of instance segmentation into the framework of dynamic programming in the seam-cutting step. To our knowledge, we are the first to introduce instance segmentation to the seam-cutting problem, which can ensure the integrity of the salient objects in a panorama. Specifically, in our surveillance oriented system, we choose the most significant target, pedestrians, as the seam avoidance target, and this accounts for the name Pedestrian360 . To validate the effectiveness and efficiency of Pedestrian360, a large-scale dataset composed of videos with pedestrians in five scenes is established. The test results on this dataset demonstrate the superiority of Pedestrian360 compared to its competitors. Experimental results show that Pedestrian360 can stitch videos at a speed of 12 to 26 fps, which depends on the number of objects in the shooting scene and their frequencies of movements. To make our reported results reproducible, the relevant code and collected data are publicly available at https://cslinzhang.github.io/Pedestrian360-Homepage/ .
Clear Vision - Obstacle detection using Bat Algorithm Optimization Technique

As per a survey which was conducted in mid-2014 by WORLD HEALTH ORGANISATION (WHO), around 39 million people are blind out of total 288 million across the word in which, 15 million (21%) people are from INDIA. Majority of the blind people are dependent on others for their day today activities. In this paper, we are proposing a concept “CLEAR VISION’ an assistive shoe which acts as object detection device for the blind people to deal with the problems faced by them. The proposed smart shoes detect objects or obstacles in nearby surroundings and provides real time update to the visually impaired person in vibrational form, henceforth helps the visually impaired person to have information about the object without any other human being making them more independent.
Remote Attestation as a Service for Edge-Enabled IoT

The Internet of Things integrates multiple hardware appliances from large cloud data centres to constrained devices embedded within the physical reality, from multiple vendors and providers, under the same infrastructure. These appliances are subject to different restrictions, have different available resources and show different risk profiles and vulnerabilities. In these scenarios, remote attestation mechanisms are essential, enabling the verification of a distant appliance’s internal state before allowing it to access sensitive data or execute critical workloads. This work proposes a new attestation approach based on a Trusted Platform Module (TPM), devoted to performing Remote Attestation as a Service (RAaaS) while guaranteeing essential properties such as flexibility, generality, domain separation and authorized initiation. The proposed solution can prove both edge devices and IoT devices reliability to services running on cloud data centres. Furthermore, the first prototype of this service has been validated and evaluated via a real use case.
Real-time Facemask Detector using Deep Learning and Raspberry Pi

Medical staffs wear face masks to prevent the spread of the disease. Nowadays, with the coronavirus pandemic everyone must wear a facemask for the same reason. When a person near to you coughs, talks, sneezes he could release germs into the air that may infect you or anyone nearby. Wearing a facemask is a part of an infection control strategy to avoid and eliminate cross-contamination. Even so, people are getting tired of wearing facemasks or they are not conscious enough of the seriousness of the actual covid19. In this paper, we propose a facemask detector based on IoT embedded devices and deep learning algorithm. Our main goal is to warn people in real-time if they are not wearing a facemask or they are not wearing it correctly. The proposed solution generates loud vocal alerts after detection disrespect of facemask wear in real-time for a fast reaction. To have the most efficient detector in real-time we tested the facemask detection model using various versions of the Raspberry Pi and NCS2. As a result, the facemask detector works perfectly on powerful devices, however its performance decrease in realtime using less powerful devices such as an old version of the Raspberry Pi.
VSEC: Transformer-Based Model for Vietnamese Spelling Correction

Spelling error correction is one of topics which have a long history in natural language processing. Although previous studies have achieved remarkable results, challenges still exist. In the Vietnamese language, a state-of-the-art method for the task infers a syllable’s context from its adjacent syllables. The method’s accuracy can be unsatisfactory, however, because the model may lose the context if two (or more) spelling mistakes stand near each other. In this paper, we propose a novel method to correct Vietnamese spelling errors. We tackle the problems of mistyped errors and misspelled errors by using a deep learning model. The embedding layer, in particular, is powered by the byte pair encoding technique. The sequence to sequence model based on the Transformer architecture makes our approach different from the previous works on the same problem. In the experiment, we train the model with a large synthetic dataset, which is randomly introduced spelling errors. We test the performance of the proposed method using a realistic dataset. This dataset contains 11,202 human-made misspellings in 9,341 different Vietnamese sentences. The experimental results show that our method achieves encouraging performance with 86.8% errors detected and 81.5% errors corrected, which improves the state-of-the-art approach 5.6% and 2.2%, respectively.
New Communication Models and Decoding of Maximum Rank Distance Codes

In this paper an interpolation-based decoding algorithm to decode Gabidulin codes transmitted through a new communication model is proposed. The algorithm is able to decode rank errors beyond half the minimum distance by one unit. Also the existing decoding algorithms for generalized twisted Gabidulin codes and additive generalized twisted Gabidulin codes are improved.
Path-RotatE: Knowledge Graph Embedding by Relational Rotation of Path in Complex Space

We study the problem of learning knowledge representations of entities and relations in knowledge graphs to predict missing links. The key to precisely accomplish a such task is modeling and inferring the diverse patterns of the relations. In this paper, we present a new rotation-based knowledge representation learning model named Path-RotatE, which considers additional paths to model rich inference patterns between entities. In addition, this paper considers the correlation between the path and the direct relation. In this way, we improve reliability of the path, making it more suitable to train. Finally, this paper conducts entity prediction experiments on datasets such as FB15k, FB15-237, WN18 and WN18RR. The results show that the Path-RotatE model has a certain improvement in MR, MRR and Hits@N compared to RotatE, PTransE and other baseline models.
Improved Text Summarization of News Articles Using GA-HC and PSO-HC

Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.
A Martingale-Based Approach for Flight Behavior Anomaly Detection

The timely detection of anomalous flight behavior is critical to ensure a prompt and appropriate response to mitigate any dangers to flight safety or hindrance of logistics operations. Most previous approaches focused on anomaly detection, leading them to only be able to raise an alert after an occurrence of an anomaly. A more effective approach is to predict a potential anomaly based on current observations, thus cutting down on detection time and allowing for a more expedient response. We propose a novel martingale-based approach to predict anomalous flight behavior in the near future as data points are observed one by one in real-time. The proposed anomaly prediction method consists of two components: (i) utilization of regression to model the historical full flight behavior and (ii) monitoring of the real-time flight behavior using a martingale (stochastic) process. The latter component consists of two prediction steps: (i) first to predict future values of multiple target variables (e.g., latitude, longitude, and altitude) using regression models, and (ii) then to decide whether the predicted values exhibit anomalies. In particular, our proposed method uses martingale tests on multiple Gaussian process regression (GPR) predictive models of target variables. The main advantages of the proposed method are: (i) the use of multiple martingale tests allows one to have a tighter false positive bound for anomaly detection/prediction, and (ii) the prediction steps reduce the delay time for anomaly detection. Experimental results on real-world data show that the performance (mean delay time, recall, and precision) of our proposed approach is competitive against other compared methods.
Evaluation of Cache Attacks on Arm Processors and Secure Caches

Timing-based side and covert channels in processor caches continue to be a threat to modern computers. This work shows for the first time, a systematic, large-scale analysis of Arm devices and the detailed results of attacks the processors are vulnerable to. Compared to x86, Arm uses different architectures, microarchitectural implementations, cache replacement policies, etc., which affects how attacks can be launched, and how security testing for the vulnerabilities should be done. To evaluate security, this paper presents security benchmarks specifically developed for testing Arm processors and their caches. The benchmarks are evaluated with sensitivity tests, which examine how sensitive the benchmarks are to having a correct configuration in the testing phase. Further, to evaluate a large number of devices, this work leverages a novel approach of using a cloud-based Arm device testbed for architectural and security research on timing channels and runs the benchmarks on 34 different physical devices. In parallel, there has been much interest in secure caches to defend the various attacks. Consequently, this paper also investigates secure cache architectures using proposed benchmarks. Especially, this paper implements and evaluates secure PL and RF caches, showing the security of PL and RF caches, but also uncovers new weaknesses.