Reviewerly/exHarmony huggingface dataset:
MPWFRFT Secure Communication Scheme Based on Chaotic CP Insertion

Security is one of the key issues in the development of wireless communication systems, and wireless communication is highly susceptible to security threats, such as eavesdropping and interference due to its openness and broadcast nature. Traditional security is achieved by cryptography above the physical layer, which lacks effective security measures. In this paper, we propose a dynamic cyclic prefix (CP) insertion scheme by using one-dimensional chaotic mapping and multiple-parameter weighted fractional Fourier transform (MPWFRFT) communication method, chaotic mapping is used to control the length of the inserted CP, as CP length changes at each block, and eavesdroppers can not fully get information about the chaotic mapping, physical layer security can be achieved. Simulation analysis shows that the proposed method can maintain good BER performance, and the generated signal has a low interception property.
An empirical study of DeFi liquidations: incentives, risks, and instabilities

Financial speculators often seek to increase their potential gains with leverage. Debt is a popular form of leverage, and with over 39.88B USD of total value locked (TVL), the Decentralized Finance (DeFi) lending markets are thriving. Debts, however, entail the risks of liquidation, the process of selling the debt collateral at a discount to liquidators. Nevertheless, few quantitative insights are known about the existing liquidation mechanisms. In this paper, to the best of our knowledge, we are the first to study the breadth of the borrowing and lending markets of the Ethereum DeFi ecosystem. We focus on Aave, Compound, MakerDAO, and dYdX, which collectively represent over 85% of the lending market on Ethereum. Given extensive liquidation data measurements and insights, we systematize the prevalent liquidation mechanisms and are the first to provide a methodology to compare them objectively. We find that the existing liquidation designs well incentivize liquidators but sell excessive amounts of discounted collateral at the borrowers' expenses. We measure various risks that liquidation participants are exposed to and quantify the instabilities of existing lending protocols. Moreover, we propose an optimal strategy that allows liquidators to increase their liquidation profit, which may aggravate the loss of borrowers.
Safe Learning of Linear Time-Invariant Systems.

We consider safety in simultaneous learning and control of discrete-time linear time-invariant systems. We provide rigorous confidence bounds on the learned model of the system based on the number of utilized state measurements. These bounds are used to modify control inputs to the system via an optimization problem with potentially time-varying safety constraints. We prove that the state can only exit the safe set with small probability, provided a feasible solution to the safety-constrained optimization exists. This optimization problem is then reformulated in a more computationally-friendly format by tightening the safety constraints to account for model uncertainty during learning. The tightening decreases as the confidence in the learned model improves. We finally prove that, under persistence of excitation, the tightening becomes negligible as more measurements are gathered.
CIRAL: a hybrid active learning framework for plankon taxa labeling

With the complex structure of planktonic species and an immense amount of data captured from autonomous underwater vehicles (AUVs), a large burden is placed on the domain experts for plankton taxa labeling. At the same time, the most prominent machine learning (ML) methods for classification rely heavily on a massive amount of labeled datasets to create and train neural network classifier models that perform their tasks accurately. Active Learning (AL) is an ML paradigm that reduces this manual effort by proposing algorithms that support the construction of the training datasets, thus enlarging the sets while minimizing human involvement. To build the training set, AL methods apply heuristics to select a subset of images, i.e., samples, from the entire data. The selected samples that capture the common statistical patterns or feature space are likely to include all the information needed for the training and the learning processes. In addition, the algorithm should prioritize samples that are likely belonging to multiple classes, i.e., having close inter-class boundaries, and might lead to model confusion. Many of the current AL approaches fail to incorporate both types of samples representing the statistical pattern and the samples in which the particular machine learning model is uncertain about. In this paper, we extend our framework which addresses these challenges with an augmentation module to increase the robustness of the model and ensure its adaptability to the planktonic domain. We compare the framework with existing hybrid AL techniques and test an adaption of our extended framework on the planktonic domain. The empirical results from the experiments exerted in this paper confirm higher accuracy achieved by the new extended framework.
Messenger Phishing Modus Operandi in South Korea

The purpose of this study is to identify the messenger phishing modus operandi in South Korea. Messenger phishing is a serious crime in which offenders make profits by deceiving victims through messenger conversations, and the damage is increasing each day. Despite the seriousness of messenger phishing, existing research has not clarified the sequence of processes by which it occurs. To fill this gap, the researchers collected messenger phishing cases in open spaces to secure a sufficient number of vivid damage cases and employed content analysis and crime script analysis. The findings demonstrated that the messenger phishing modus operandi consists of preparation, developing specific plans, sending a phishing message without revealing one’s identity, explaining the urgency of the situation to the target, requesting money from the target, and acquiring the target’s money. In particular, there were four main techniques used by criminals to acquire the target’s money: requesting remittance to the account presented by the offender, requesting PINs for gift certificates after their purchase, inducing installation of a remote-control application on the target’s smartphone, and requesting sensitive information. Further, based on these results, this study suggested preventive measures against messenger phishing using situational crime prevention techniques.
Classification of Turbulence-Degraded Imagery Using Neural Networks

A more realistic simulation is used to evaluate claims made in a recently published work on object classification for turbulence degraded imagery.
STUDI PEMANFAATAN SERAT ECENG GONDOK DAN IJUK PADA KOMPOSIT SEBAGAI MATERIAL ALTERNATIF KAPAL NON-CLASS

Nowadays, natural fibers are widely used in the industrial field. This is because natural fibers are easy to obtain and cost more economical when compared to synthetic fibers. Therefore many researchers want to test the feasibility of natural fibers for composite materials. A lot of natural fibers have been researched feasibility for various needs. Natural fibers in Indonesia are very many kinds, in this study researchers used natural fibers from water hyacinth and palm fiber. In addition to water hyacinth fibers and palm fibers, there are still many natural fibers that can be used in the manufacture of composites. Water hyacinth fiber and palm fiber are widely used in the field of industry and handicrafts for household appliances. This is because water hyacinth fibers and palm fibers have strong properties, corrosion resistant, easy to obtain, durable and also economically priced. With the characteristics possessed by water hyacinth fibers and palm fibers, it encourages researchers to make composites made from water hyacinth and palm fibers. Specimen making using hand lay-up method in accordance with ASTM standards, then this specimen is tested with tensile and bending testing. After testing it was discovered that water hyacinth and palm fibers did not meet the mechanical standards specified by BKI.
Students' Speaking Anxiety During Online Learning: Causes and Overcoming Strategies

Anxiety has been considered as one of the obstacles in learning English and a common issue in the EFL context. In speaking, it also debilitates the opportunity of English Foreign Language (EFL) students to speak English. This study was designed to reveal the causal factors of the students’ speaking anxiety and strategies to overcome them during online learning. Narrative inquiry approach as the part of qualitative research was employed through interview as the instrument. The participants were four students at MAN Insan Cendekia Gowa whom selected through purposive sampling. The result of this study establishes two main findings. Firstly, the causal factors of students' speaking anxiety during online learning which are divided into internal and external factor. The internal factor causes by lack of confidence, shyness, fear of making mistake, and feeling insecure. Then, the external factor causes by lack of preparation, limited vocabulary, friends or classmates, embarrassment, teacher characteristic. In the causal factors, external factors are indicated as the most dominant causes of students' speaking. Secondly, strategies used by the students to overcome their speaking anxiety, they are preparation (lecture note), peer seeking, keeping silent or calm, speaking themselves at home, turning the camera off, using ‘google translator’, limiting speaking duration.
The Distributed Adaptive Control Theory of the Mind and Brain as a candidate Standard Model of the Human Mind

This article presents the Distributed Adaptive Control (DAC) theory of mind and brain as a candidate standard model of the human mind. DAC is defined against a reformulation of the criteria for unified theories of cognition advanced by Allen Newell, or the Unified Theories of Embodied Minds – Standard Model benchmark (UTEM-SM) that emphasizes real-world and real-time embodied action. DAC considers mind and brain as the function and implementation of a multi-layered control system and addresses the fundamental question of how the mind, as the product of embodied and situated brains, can obtain, retain and express valid knowledge of its world and transform this into policies for action. DAC provides an explanatory framework for biological minds and brains by satisfying well-defined constraints faced by theories of mind and brain and provides a route for the convergent validation of anatomy, physiology, and behavior in our explanation of biological minds. DAC is a well validated integration and synthesis framework for artificial minds and exemplifies the role of the synthetic method in understanding mind and brain. This article describes the core components of DAC, its performance on specific benchmarks derived from the engagement with the physical and the social world (or the H4W and the H5W problems) and lastly analyzes DAC’s performance on the UTEM-SM benchmark and its relationship with contemporary developments in AI.
Platform tailored co-design of gate-based quantum simulation.

The utility of near-term quantum computers and simulators is likely to rely upon software-hardware co-design, with error-aware algorithms and protocols optimized for the platforms they are run on. Here, we show how knowledge of noise in a system can be exploited to improve the design of gate-based quantum simulation algorithms. We concretely demonstrate this co-design in the context of a trapped ion quantum simulation of the dynamics of a Heisenberg spin model. Specifically, we derive a theoretical noise model describing unitary gate errors due to heating of the ions' collective motion, finding that the temporal correlations in the noise induce an optimal gate depth. We then illustrate how tailored feedforward control can be used to mitigate unitary gate errors and improve the simulation outcome. Our results provide a practical guide to the co-design of gate-based quantum simulation algorithms.
Book Review - Copyright and Collective Authorship: Locating the Authors of Collaborative Work, Daniela Simone (Cambridge University Press, 2019)

In her book Copyright and Collective Authorship — Locating the Authors of Collaborative Work, Dr. Daniela Simone identifies root causes of the deficiencies in the law’s treatment of (joint) authorship, and provides an admirable roadmap and analytical framework to orient the judiciary and all others concerned about ascertaining who should be the authors of collective works. Simone’s analytical framework and prescriptions rely on four case studies that involve large amounts of collaboration: Wikipedia, Australian indigenous art, scientific collaborations and film. Throughout the book, I was struck by Simone’s commitment to integrity, ethics and fairness in her treatment of contributors of creative processes, pointing to power imbalances, misguided practicality and efficiency rationales, and how those factors may have the law turn its back away from otherwise deserving authorial contributions.
Personalized and Explainable Models of Text and Sequences

Author(s): Ni, Jianmo | Advisor(s): McAuley, Julian | Abstract: Understanding and modeling heterogeneous human interaction data is essential to build next-generation natural (e.g. conversational, personalized) interactive systems. Textual data (e.g. dialogues, product reviews), relational data (e.g. knowledge bases) and behavioral data (e.g. purchases, clicks) are ubiquitous sources of data describing human interactions. Recent advances in natural language processing and human behavior modeling have led to drastic development in interactive systems as recommender systems, search engines, conversational agents, fitness trackers, etc. However, it remains challenging for such systems to provide personalized and explainable outputs, which are critical to improving the user experience. In this thesis, we aim to study two main sources of human interaction data, i.e., text and sequences, to improve behavioral prediction, enhance model interpretability, and increase model usability. In particular, my work focuses on studying personalized models to estimate and interpret user interactions and exploring explainable models to understand and learn from user-generated text. The primary contributions are 1) a personalized text generation model which can facilitate assistive writing; 2) a personalized model that generates textual justification for modern recommender systems; 3) a personalized algorithm that models workout activity sequences, predicts user heart rate profile and provides fitness-aware recommendations; 4) an algorithm that learns to attend on essential terms in complex questions and retrieves relevant evidence to boost performance in open-domain question answering; 5) a visual-semantic embedding based method to report abnormal findings on chest X-rays.
The Effect of Strategic Surveillance Radar in Reducing Strategic Drift Analytical Research of the Opinions of a Sample of Workers in Private Universities in the Holy Karbala Governorate

This research seeks to determine the effect of Strategic Surveillance Radar based on (Khalil, 2019) in reducing Strategic drift based on (Hussein &amp; Abdul Hassan, 2020). Private universities in Karbala governorate-Iraq chosen as a field for research through a questionnaire that included (75) members of the research community, represented by deans, their assistants, and department heads. Some descriptive statistics, correlation analysis and regression analysis used. A set of recommendations submitted to guide them in limiting Strategic drift through adopting Strategic Surveillance Radar.
An Efficient Algorithm for Enumerating Longest Common Increasing Subsequences

The longest common increasing subsequence (LCIS) problem is the combination of two classic problems in algorithms: the longest increasing subsequence (LIS) problem and the longest common subsequence (LCS) problem. In this paper, we propose an algorithm that finds every LCIS of two sequences a, b of length n in \(O(n+\sigma +I_a)\) time and space, where \(\sigma \) denotes the size of the alphabet set and \(I_a\) the total number of increasing subsequences contained in a (thus, the running time is output-sensitive). Our algorithm employs the trie and some simple data structures, and thus is implementation-wise simple. In addition, it can be proved that our algorithm is optimal in time complexity when \(\sigma \le {\log _2 {n}}\).
Assessing Scientific Conferences through Knowledge Graphs

Springer Nature is the main publisher of scientific conferences in Computer Science and produces several well-known series of proceedings books, such as LNCS. The editorial team needs to take critical decisions about which conferences to publish as well as actively scan the horizon for identifying emerging ones. In this short paper, we present the Conference Dashboard, a new web application based on a large knowledge graph of scholarly data (1.3B triples) for assessing scientific conferences and informing editorial decisions.
Quantum Tensor Networks

Tensor networks take a central role in quantum physics as they can provide an efficient approximation to specific classes of quantum states. [33] With their insensitivity to decoherence, Majorana particles could become stable building blocks of quantum computers. [32] A team of researchers at the University of Maryland has found a new way to route photons at the micrometer scale without scattering by building a topological quantum optics interface. [31] Researchers at the University of Bristol's Quantum Engineering Technology Labs have demonstrated a new type of silicon chip that can help building and testing quantum computers and could find their way into your mobile phone to secure information. [30] Theoretical physicists propose to use negative interference to control heat flow in quantum devices. [29] Particle physicists are studying ways to harness the power of the quantum realm to further their research. [28] A fundamental barrier to scaling quantum computing machines is qubit In new research published in Science Advances, engineers and physicists from Rigetti Computing describe a breakthrough that can expand the size of practical quantum processors by reducing interference. [26] The search and manipulation of novel properties emerging from the quantum nature of matter could lead to next-generation electronics and quantum computers. [25]
Detection of Breast Cancer by using Deep Learning Algorithm

When a woman is in her 30s and beginning to think about her future, getting breast cancer is hard. Every 4 minutes in India, one woman gets diagnosed with breast cancer, and another 13 are killed from it. The prognosis for women in India is usually diagnosed at a later, more advanced level. More than one in 28 females is likely to be diagnosed with breast cancer. It is estimated that by the year 2030, breast cancer will be the leading killer of women in India. The main form of breast cancer occurs when the cells develop out of control. Tumours are often formed from these cells, which can show up on an x-ray or may often be noticed by the patient. It is malignant if it spreads to nearby tissues and/tissues or spreads (invades) into other regions of the body. Due to the increased accuracy and progress in deep neural networks, for example, which are able to learn from the ultrasonic images by using the model that associates each with a different image, it is now possible to distinguish tumour types using the images of this method with high precision. In this paper we discuss the use of deep learning algorithms on biopsy images for detection of breast cancer.
Improve Chit-Chat and QA Sentence Classification in User Messages of Dialogue System using Dialogue Act Embedding.

In recent years, dialogue system is booming and widely used in customer service system, and has achieved good results. Viewing the conversation records between users and real customer service, we can see that the user’s sentences are mixed with questions about products and services, and chat with customer service. According to the experience of professionals, it is helpful in improving the user experience to mix some chats in customer service conversations. However, users’ questions are expected to be answered, while chatting is expected to interact with customer service. In order to produce an appropriate response, the dialogue system must be able to distinguish these two intentions effectively. Dialog act is a classification that linguists define according to its function. We think this information will help distinguishing questioning sentences and chatting sentences. In this paper, we combine a published COVID-19 QA dataset and a COVID-19-topic chat dataset to form our experimental data. Based on the BERT (Bidirectional Encoder Representation from Transformers) model, we build a question-chat classifier model. The experimental results show that the accuracy of the configuration with dialog act embedding is 16% higher than that with only original statement embedding. In addition, it is found that conversation behavior types such as “Statement-non-opinion”, “Signal-non-understanding” and “Appreciation” are more related to question sentences, while “Wh-Question”, “Yes-No-Question” and “Rhetorical-Question” questions are more related to chat sentences.
Research on College English Teaching Based on Wechat Platform

WeChat platform has been widely used in various fields due to its rich functions and mature technology. In addition to being used for timely communication, the integration with education and teaching has also been explored. College English teaching based on WeChat platform plays an active role in the construction of teaching resources, innovation in teaching methods and improvement of teaching efficiency. Based on the function and characteristics of WeChat platform, this paper proposes a college English teaching scheme, fully considering the reality of college English teaching.
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing

Unsupervised domain adaptation which aims to adapt models trained on a labeled source domain to a completely unlabeled target domain has attracted much attention in recent years. While many domain adaptation techniques have been proposed for images, the problem of unsupervised domain adaptation in videos remains largely underexplored. In this paper, we introduce Contrast and Mix (CoMix), a new contrastive learning framework that aims to learn discriminative invariant feature representations for unsupervised video domain adaptation. First, unlike existing methods that rely on adversarial learning for feature alignment, we utilize temporal contrastive learning to bridge the domain gap by maximizing the similarity between encoded representations of an unlabeled video at two different speeds as well as minimizing the similarity between different videos played at different speeds. Second, we propose a novel extension to the temporal contrastive loss by using background mixing that allows additional positives per anchor, thus adapting contrastive learning to leverage action semantics shared across both domains. Moreover, we also integrate a supervised contrastive learning objective using target pseudo-labels to enhance discriminability of the latent space for video domain adaptation. Extensive experiments on several benchmark datasets demonstrate the superiority of our proposed approach over state-of-the-art methods. Project page: https://cvir.github.io/projects/comix