Reviewerly/exHarmony huggingface dataset:
Experimental Evaluation of the Defense Capability of ARM-based Systems against Buffer Overflow Attacks in Wireless Networks

Buffer overflow attack is one of the mainstream attacks towards the ARM architecture. It may lead to consequences such as program failure or system privileges loss. The mainstream operating systems deploy multiple defense mechanisms to mitigate such attacks. However, so far there are few reports on evaluation of the defense capability of ARM-based operating systems from buffer overflow attacks. In this paper, firstly, we implemented the Runtime Intrusion Prevention Evaluator on ARM-based operating systems, which we called RIPE-ARM. In that evaluator, 850 kinds of effective buffer overflow attacks are integrated for test. Secondly, by using the QEMU virtual machine, an ARM-based system, Raspberry Pi, was set up for the experiment; and then, the RIPE-ARM was used to test and evaluate the defense capability of Raspberry Pi. We identified the kinds of attacks that each defense or defense combination can successfully prevent, respectively. Among all the defense methods, the Canary + DEP combination turns out to be optimal that is able to make 840 out of the total 850 kinds of attacks fail. Furthermore, for comparison, the defense capability of Ubuntu 16.04 LTS system based on X86 architecture was also tested. The results show that the optimal defense method of that system can prevent only 790 attack kinds.
TRAINABILITY OF ReLU NETWORKS AND DATA-DEPENDENT INITIALIZATION

In this paper, we study the trainability of rectified linear unit (ReLU) networks. A ReLU neuron is said to be dead if it only outputs a constant for any input. Two death states of neurons are introduced; tentative and permanent death. A network is then said to be trainable if the number of permanently dead neurons is sufficiently small for a learning task. We refer to the probability of a network being trainable as trainability. We show that a network being trainable is a necessary condition for successful training and the trainability serves as an upper bound of successful training rates. In order to quantify the trainability, we study the probability distribution of the number of active neurons at the initialization. In many applications, over-specified or over-parameterized neural networks are successfully employed and shown to be trained effectively. With the notion of trainability, we show that over-parameterization is both a necessary and a sufficient condition for minimizing the training loss. Furthermore, we propose a data-dependent initialization method in the over-parameterized setting. Numerical examples are provided to demonstrate the effectiveness of the method and our theoretical findings.
Liquid resource types

This article presents liquid resource types, a technique for automatically verifying the resource consumption of functional programs. Existing resource analysis techniques trade automation for flexibility – automated techniques are restricted to relatively constrained families of resource bounds, while more expressive proof techniques admitting value-dependent bounds rely on handwritten proofs. Liquid resource types combine the best of these approaches, using logical refinements to automatically prove precise bounds on a program’s resource consumption. The type system augments refinement types with potential annotations to conduct an amortized resource analysis. Importantly, users can annotate data structure declarations to indicate how potential is allocated within the type, allowing the system to express bounds with polynomials and exponentials, as well as more precise expressions depending on program values. We prove the soundness of the type system, provide a library of flexible and reusable data structures for conducting resource analysis, and use our prototype implementation to automatically verify resource bounds that previously required a manual proof.
A Case Study of Online Class Operation and Instructor’s Difficulties in Physical Education as a Liberal Arts in University Due to COVID-19

Purpose: The purpose of this study is to examine the difficulties of operating online classes, efforts and solutions to overcome them, and the value of meaning in the process due to COVID-19.BR Method: Using the purposive sampling, six instructors who teach liberal sports classes at universities in the metropolitan area were selected and case study were conducted. Data were collected through interviews and participation observations and so on. The collected data were subject to inductive category analysis and continuous comparative analysis.BR Results: The results of this study are as follows. 1) Participants in the study had difficulties with online lectures. 2) Participants in the study operated the class in a new way with a sense of responsibility. 3) Participants in the study continued to learn, made efforts and challenged to improve their professionalism.BR Conclusion: In summary, instructors should make an effort to develop the professionalism to provide high quality online classes. And universities should provide administrative and financial support for instructors. Also the government should provide various teaching material.
Padding Ain’t Enough: Assessing the Privacy Guarantees of Encrypted DNS

DNS over TLS (DoT) and DNS over HTTPS (DoH) encrypt DNS to guard user privacy by hiding DNS resolutions from passive adversaries. Yet, past attacks have shown that encrypted DNS is still sensitive to traffic analysis. As a consequence, RFC 8467 proposes to pad messages prior to encryption, which heavily reduces the characteristics of encrypted traffic. In this paper, we show that padding alone is insufficient to counter DNS traffic analysis. We propose a novel traffic analysis method that combines size and timing information to infer the websites a user visits purely based on encrypted and padded DNS traces. To this end, we model DNS Sequences that capture the complexity of websites that usually trigger dozens of DNS resolutions instead of just a single DNS transaction. A closed world evaluation based on the Tranco top-10k websites reveals that attackers can deanonymize test traces for 86.1 % of all websites, and even correctly label all traces for 65.9 % of the websites. Our findings undermine the privacy goals of state-of-the-art message padding strategies in DoT/DoH. We conclude by showing that successful mitigations to such attacks have to remove the entropy of inter-arrival timings between query responses.
Building and Interpreting Artificial Neural Network Models for Biological Systems

Biology has become a data driven science largely due to the technological advances that have generated large volumes of data. To extract meaningful information from these data sets requires the use of sophisticated modeling approaches. Toward that, artificial neural network (ANN) based modeling is increasingly playing a very important role. The “black box” nature of ANNs acts as a barrier in providing biological interpretation of the model. Here, the basic steps toward building models for biological systems and interpreting them using calliper randomization approach to capture complex information are described.
A Lossless Watermarking Algorithm Based on Line Pairs for Vector Data

With the increasing demand for copyright protection of high-precision and sensitive vector data in Geographical Information System (GIS), research on lossless watermarking has attracted more and more attention. In this paper, a new lossless watermarking method based on line pairs is proposed. Firstly, the points and polygons are unified into the polylines, and then every two adjacent polylines are combined into a line pair. Besides, the storage direction and the interior angle of the line pairs are analyzed. Secondly, the watermark bit is determined by the interior angle of each line pair. Then, the watermark information is embedded by judging whether the storage direction of each line pair and the watermark bit is the same. Finally, experimental results verify that the proposed method works effectively for the data of the points, polylines and polygons, and has good invisibility without damaging the vector data. In addition, compared with the existing algorithm, the proposed method achieves higher robustness against geometric attacks, such as translating, rotating, and scaling the vector data.
Identifying The Usage Of Known Vulnerabilities Components Based On OWASP A9

Penetration testing is gaining its popularity in the current generation as there has been evidences where the hacker have broken the system. There are standards that are available in the market which help the entire software industry to overcome such scenarios. One of the non-profit organization is Open Web Application Security Project (OWASP). Penetration testing or we say as ethical hacking is one of the important activity in the Cyber Security domain. In this paper we will be discussion about the well-known vulnerability defined by OWASP 2017 which stands in number 9 or A9 in the list i.e. “using components of with known vulnerabilities”. The reason for bringing up this topic is because A9 vulnerability is very frequently seen as per my experience in the pen testing. We will be discussing about how to identity the known vulnerability in a web application from hacker point of view and how remediation would help the developer to close the vulnerability. Please note the practicality that is discussed in this paper should only be performed prior the approval of the application owner.
Vehicle Detection Based on an Imporved Faster R-CNN Method

In this paper, we present a novel method for vehicle detection based on the Faster R-CNN frame. We integrate MobileNet into Faster R-CNN structure. First, the MobileNet is used as the base network to generate the feature map. In order to retain the more information of vehicle objects, a fusion strategy is applied to multi-layer features to generate a fused feature map. The fused feature map is then shared by region proposal network (RPN) and Fast R-CNN. In the RPN system, we employ a novel dimension cluster method to predict the anchor sizes, instead of choosing the properties of anchors manually. Our detection method improves the detection accuracy and saves computation resources. The results show that our proposed method respectively achieves 85.21% and 91.16% on the mean average precision (mAP) for DIOR dataset and UA-DETRAC dataset, which are respectively 1.32% and 1.49% improvement than Faster R-CNN (ResNet152). Also, since less operations and parameters are required in the base network, our method costs the storage size of 42.52MB, which is far less than 214.89MB of Faster R-CNN(ResNet50).
A Novel Deep Forest-Based Active Transfer Learning Method for PolSAR Images

The information extraction of polarimetric synthetic aperture radar (PolSAR) images typically requires a great number of training samples; however, the training samples from historical images are less reusable due to the distribution differences. Consequently, there is a significant manual cost to collecting training samples when processing new images. In this paper, to address this problem, we propose a novel active transfer learning method, which combines active learning and the deep forest model to perform transfer learning. The main idea of the proposed method is to gradually improve the performance of the model in target domain tasks with the increase of the levels of the cascade structure. More specifically, in the growing stage, a new active learning strategy is used to iteratively add the most informative target domain samples to the training set, and the augmented features generated by the representation learning capability of the deep forest model are used to improve the cross-domain representational capabilities of the feature space. In the filtering stage, an effective stopping criterion is used to adaptively control the complexity of the model, and two filtering strategies are used to accelerate the convergence of the model. We conducted experiments using three sets of PolSAR images, and the results were compared with those of four existing transfer learning algorithms. Overall, the experimental results fully demonstrated the effectiveness and robustness of the proposed method.
Postural Instability and Seasickness in a Motion-Based Shooting Simulation

BACKGROUND: Motion sickness is a problem for many; however, it is especially pressing for military personnel who need to operate in life and death environments. The current study investigated the underlying cause of motion sickness by testing postural instability theory. METHODS: Subjects experienced realistic motion profiles while performing a virtual reality shooting task and reporting any motion sickness symptoms. Postural instability was manipulated within 20 subjects across 2 conditions. In one condition, subjects could readily adapt their posture to the motion profile by adjusting their feet on the platform (Free), and in the other condition, their feet were fixed in place on the moving platform (Fixed). This Free condition decreased postural instability by allowing adjustment, while the Fixed condition increased postural instability by restricting adjustment. The same subjects completed both conditions to control for individual differences in motion sickness susceptibility. RESULTS: Overall, motion sickness was mild as measured by SSQ ( M 14.41, Free; M 18.89, Fixed), and no statistically significant differences were observed between the conditions. Performance on the shooting task was reduced in accuracy by approximately 40%, although this result did not differ between conditions. DISCUSSION: The results do not support postural instability as a contributing factor in motion sickness symptomology. They also demonstrate the importance of accounting for motion when conducting training. Pettijohn KA, Pistone DV, Warner AL, Roush GJ, Biggs AT. Postural instability and seasickness in a motion-based shooting simulation . Aerosp Med Hum Perform. 2020; 91(9):703709.
Palindromic k-Factorization in Pure Linear Time

Given a string s of length n over a general alphabet and an integer k, the problem is to decide whether s is a concatenation of k nonempty palindromes. Two previously known solutions for this problem work in time O(kn) and O(nlog n) respectively. Here we settle the complexity of this problem in the word-RAM model, presenting an O(n)-time online deciding algorithm. The algorithm simultaneously finds the minimum odd number of factors and the minimum even number of factors in a factorization of a string into nonempty palindromes. We also demonstrate how to get an explicit factorization of s into k palindromes with an O(n)-time offline postprocessing.
Multi-Stage Quantum Secure Direct Communication Using Secure Shared Authentication Key

The security of Quantum Secure Direct Communication (QSDC) and its authentication procedure based on multiple stages is analyzed. The security analysis shows that the process of authentication is required to be done three times based on the usage of unitary transformation that is only known between Alice and Bob. In the proposed protocol, a secure quantum handshake is utilized to share the secret polarization angle and an authentication key at the initial stage of authentication over the quantum channel. The symmetry key is used in this work to protect user data communication within the QSDC protocol, where the same secret key is used to encrypt and decrypt the message. This work adopts the information travel time (ITT) by allowing the sender to detect any interference from third parties. In addition, the operation of the Pauli-X quantum gate increases Eve’s difficulty in stealing the information. The information transmitted is then continued by sending photons once in the quantum channel, which improves the efficiency without losing the message’s security. In addition, to securely transfer the stream of messages, the proposed protocol is operated in single-stage, and the authentication is applied bit-by-bit, thus reducing the transmission time. Security checks are carried out along the data transmission process. Compared to previous protocols, this new initial authentication protocol has remarkable advantages since it does not require public communication to pre-share the authentication key and secret angles before the onset of the transmission, therefore, reducing the communication cost. Moreover, the secret authentication key and polarization angles are updated after a number of bits are sent to increase the security level. The verification process is also conducted to ensure the symmetry of the sender and receiver. The analyses presented herein demonstrate that the proposed authentication protocol is simple and secure in order to ensure the legitimacy of the users.
COVID-19 PANDEMIC: THE INFLUENCE OF FULL-ONLINE LEARNING FOR ELEMENTARY SCHOOL IN RURAL AREAS

Abstract The Indonesian government launched an online learning program in the education area to break the chainof the coronavirus (covid-19) spreading. But, online learning requires more facilities such as internet access and smartphone/notebook which is not all equipped in rural areas. Therefore, this research has aimed to know the situation of the online learning process in rural areas during the co-19 pandemic. The research was conducted in Purwosari sub-district, Bojonegoro district, East Java with 53 of elementary school teachers as the participant. Online questionnaires that consist of 5 statements were used to collect the data. This research showed that the Whatsapp group was the famous platform for online learning. Besides that, the teachers have been facilitated by the school in online learning. On the other hand, internet access, learning innovation, and students’ facilities for online learning become obstacles in online learning in rural areas. Keywords: covid-19; learning process; elementary school; rural area
Use of Deep Learning Approaches in Cancer Diagnosis

Cancer is among serious health problems with an uncertain and complex structure that causes fatal results. Cancer is a disease that consists of uncontrolled proliferation of cells in different organs, whose clinical appearance, treatment and approach are different from each other and that should be controlled in the early stages. The cancer burden should be estimated in order to determine priorities for cancer control. In this context, there are many studies on diagnosis and treatment methods and a rapid development is observed in this regard. The aim is to increase the survival rate of people with cancer. In order to achieve this goal effectively, early and accurate diagnosis is especially important in the treatment of cancer, as it causes fatal results. It is known that cancer is very difficult to diagnose in the early stages and accurately with traditional diagnostic methods. At this point, the artificial intelligence, a new or current approach, comes to the agenda. Developments in this area offer very important opportunities in cancer diagnosis as in many areas. Therefore, in this study, deep learning approaches which are an artificial intelligence technique in the literature for the diagnosis of cancer disease are examined, and the applications in the literature on how these approaches are used are included. Since the subject of the study is up to date, it is considered that the study will be a guide for people or institutions working in this field.
Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects

Artificial intelligence (AI)-aided decision support has developed rapidly to meet the needs for effective analysis of substantial data sets from electronic medical records and medical images generated daily, and computer-assisted intelligent drug design. In clinical practice, paediatricians make medical decisions after obtaining a large amount of information about symptoms, physical examinations, laboratory test indicators, special examinations and treatments. This information is used in combination with paediatricians’ knowledge and experience to form the basis of clinical decisions. This diagnosis and therapeutic strategy development based on large amounts of information storage can be applied to both large clinical databases and data for individual patients. To date, AI applications have been of great value in intelligent diagnosis and treatment, intelligent image recognition, research and development of intelligent drugs and intelligent health management. This review aims to summarize recent advances in the research and clinical use of AI in paediatrics.
BERT-BIGRU-CRF: A Novel Entity Relationship Extraction Model

Entity name recognition and entity relationship extraction are the most critical foundation for building knowledge graph, and it is also the basic task of NPL. The main purpose of entity relationship extraction is to extract the semantic relationship between the pairs of marked entities in the sentence, that is, to determine the relationship categories between entity pairs in unstructured text based on entity identification, and to form structured data for storage and retrieval. This paper proposes a BERT-BIGRU-CRF entity relationship extraction method, which effectively changes the relationship between the pre-training generated word vector and the downstream specific NLP task, and gradually moves the downstream specific NLP task to the pre-training generated word vector. Our method achieves better performance of relationship extraction and entity name recognition, which helps to construct the knowledge graph more accurately.
FarsTail: a Persian natural language inference dataset

Natural language inference (NLI) is known as one of the central tasks in natural language processing (NLP) which encapsulates many fundamental aspects of language understanding. With the considerable achievements of data-hungry deep learning methods in NLP tasks, a great amount of effort has been devoted to develop more diverse datasets for different languages. In this paper, we present a new dataset for the NLI task in the Persian language, also known as Farsi, which is one of the dominant languages in the Middle East. This dataset, named FarsTail, includes 10,367 samples which are provided in both the Persian language as well as the indexed format to be useful for non-Persian researchers. The samples are generated from 3,539 multiple-choice questions with the least amount of annotator interventions in a way similar to the SciTail dataset. A carefully designed multi-step process is adopted to ensure the quality of the dataset. We also present the results of traditional and state-of-the-art methods on FarsTail including different embedding methods such as word2vec, fastText, ELMo, BERT, and LASER, as well as different modeling approaches such as DecompAtt, ESIM, HBMP, and ULMFiT to provide a solid baseline for the future research. The best obtained test accuracy is 83.38% which shows that there is a big room for improving the current methods to be useful for real-world NLP applications in different languages. We also investigate the extent to which the models exploit superficial clues, also known as dataset biases, in FarsTail, and partition the test set into easy and hard subsets according to the success of biased models. The dataset is available at https://github.com/dml-qom/FarsTail