Reviewerly/exHarmony huggingface dataset:
A Call for Clarity in Contemporary Authorship Attribution Evaluation

Recent research has documented that results reported in frequently-cited authorship attribution papers are difficult to reproduce.Inaccessible code and data are often proposed as factors which block successful reproductions.Even when original materials are available, problems remain which prevent researchers from comparing the effectiveness of different methods.To solve the remaining problems-the lack of fixed test sets and the use of inappropriately homogeneous corpora-our paper contributes materials for five closed-set authorship identification experiments.The five experiments feature texts from 106 distinct authors.Experiments involve a range of contemporary non-fiction American English prose.These experiments provide the foundation for comparable and reproducible authorship attribution research involving contemporary writing.
Cryptanalysis and Improvement of Zheng's Signcryption Technique

The signcryption technique was first proposed by Y. Zheng, where two cryptographic operations digital signature and message encryption are made combinedly. We cryptanalyze the technique and observe that the signature and encryption become vulnerable if the forged public keys are used. This paper proposes an improvement using modified DSS (Digital Signature Standard) version of ElGamal signature and DHP (Diffie-Hellman key exchange protocol), and shows that the vulnerabilities in both the signature and encryption methods used in Zheng's signcryption are circumvented. DHP is used for session symmetric key establishment and it is combined with the signature in such a way that the vulnerabilities of DHP can be avoided. The security and performance analysis of our signcryption technique are provided and found that our scheme is secure and designed using minimum possible operations with comparable computation cost of Zheng's scheme.
In-resonator Electro-absorption Modulator for Photonic Computing

A novel microring intensity modulator controlled by in-resonator electro-absorption elements is proposed. By applying different bias voltages, the proposed modulator presents various transmission intensities at a fixed wavelength. This eliminates the crosstalk in large-scale photonic computing systems, where it is proved as a serious issue.
Leveraging Experience in Lazy Search

Lazy graph search algorithms are efficient at solving motion planning problems where edge evaluation is the computational bottleneck. These algorithms work by lazily computing the shortest potentially feasible path, evaluating edges along that path, and repeating until a feasible path is found. The order in which edges are selected is critical to minimizing the total number of edge evaluations: a good edge selector chooses edges that are not only likely to be invalid, but also eliminates future paths from consideration. We wish to learn such a selector by leveraging prior experience. We formulate this problem as a Markov Decision Process (MDP) on the state of the search problem. While solving this large MDP is generally intractable, we show that we can compute oracular selectors that can solve the MDP during training. With access to such oracles, we use imitation learning to find effective policies. If new search problems are sufficiently similar to problems solved during training, the learned policy will choose a good edge evaluation ordering and solve the motion planning problem quickly. We evaluate our algorithms on a wide range of 2D and 7D problems and show that the learned selector outperforms baseline commonly used heuristics. We further provide a novel theoretical analysis of lazy search in a Bayesian framework as well as regret guarantees on our imitation learning based approach to motion planning.
LiFR-Lite (2021-11-05)

Corpus of Czech educational texts for readability studies, with paraphrases, measured reading comprehension, and a multi-annotator subjective rating of selected text features based on the Hamburg Comprehensibility Concept
Exploring the Role of BERT Token Representations to Explain Sentence Probing Results.

Several studies have been carried out on revealing linguistic features captured by BERT. This is usually achieved by training a diagnostic classifier on the representations obtained from different layers of BERT. The subsequent classification accuracy is then interpreted as the ability of the model in encoding the corresponding linguistic property. Despite providing insights, these studies have left out the potential role of token representations. In this paper, we provide a more in-depth analysis on the representation space of BERT in search for distinct and meaningful subspaces that can explain the reasons behind these probing results. Based on a set of probing tasks and with the help of attribution methods we show that BERT tends to encode meaningful knowledge in specific token representations (which are often ignored in standard classification setups), allowing the model to detect syntactic and semantic abnormalities, and to distinctively separate grammatical number and tense subspaces.
Subgroup Generalization and Fairness of Graph Neural Networks

Despite enormous successful applications of graph neural networks (GNNs) recently, theoretical understandings of their generalization ability, especially for node-level tasks where data are not independent and identically-distributed (IID), have been sparse. The theoretical investigation of the generalization performance is beneficial for understanding fundamental issues (such as fairness) of GNN models and designing better learning methods. In this paper, we present a novel PAC-Bayesian analysis for GNNs under a non-IID semi-supervised learning setup. Moreover, we analyze the generalization performances on different subgroups of unlabeled nodes, which allows us to further study an accuracy-(dis)parity-style (un)fairness of GNNs from a theoretical perspective. Under reasonable assumptions, we demonstrate that the distance between a test subgroup and the training set can be a key factor affecting the GNN performance on that subgroup, which calls special attention to the training node selection for fair learning. Experiments across multiple GNN models and datasets support our theoretical results.
Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments

In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle ‘off the path’ scenarios where an agent veers from a reference path. Prior work supervises the agent with actions based on the shortest path from the agent’s location to the goal, but such goal-oriented supervision is often not in alignment with the instruction. Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow. In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation.
What Would it Take to get Biomedical QA Systems into Practice

Medical question answering (QA) systems have the potential to answer clinicians’ uncertainties about treatment and diagnosis on-demand, informed by the latest evidence. However, despite the significant progress in general QA made by the NLP community, medical QA systems are still not widely used in clinical environments. One likely reason for this is that clinicians may not readily trust QA system outputs, in part because transparency, trustworthiness, and provenance have not been key considerations in the design of such models. In this paper we discuss a set of criteria that, if met, we argue would likely increase the utility of biomedical QA systems, which may in turn lead to adoption of such systems in practice. We assess existing models, tasks, and datasets with respect to these criteria, highlighting shortcomings of previously proposed approaches and pointing toward what might be more usable QA systems.
Machine Learning Approach to Find Students' Best Place to Study: Home vs Hostel

Students are a country's backbone. The appropriate surroundings for studying must be provided for them. Of all these criteria, a place where you may locate the appropriate setting for your requirements is the most important. The purpose of the study is to identify the best environment to study among students living with parents and hostels. This research also explores issues such as the life and academic chances of students. Adapted questionnaires were utilized to evaluate the responses of 400 students from different colleges, institutes, and students freshly graduated. According to the findings of the survey, students choose to live and study at home because it is healthy and convenient. A variety of algorithm techniques are used, but the Logistics Regression algorithm was the key preference for this study because it had the highest accuracy score. This leads to the conclusion that students opt to stay at home.
Abnormal Detection of Cash-Out Groups in IoT Based Payment

With the rise of online/mobile transactions, the cost of cash-out has decreased and the cost of detection has increased. In the world of online/mobile payment in IoT, merchants and credit cards can be applied and approved online and used in the form of a QR code but not a physical card or Point of Sale equipment, making it easy for these systems to be controlled by a group of fraudsters. In mainland China, where the credit card transaction fee is, on average, lower than a retail loan rate, the credit card cash-out option is attractive for people for an investment or business operation, which, after investigation, can be considered unlawful if over a certain amount is used. Because cash-out will incur fees for the merchants, while bringing money to the credit cards' owners, it is difficult to confirm, as nobody will declare or admit it. Furthermore, it is more difficult to detect cash-out groups than individuals, because cash-out groups are more hidden, which leads to bigger transaction amounts. We propose a new method for the detection of cash-out groups. First, the seed cards are mined and the seed cards' diffusion is then performed through the local graph clustering algorithm (Approximate PageRank, APR). Second, a merchant association network in IoT is constructed based on the suspicious cards, using the graph embedding algorithm (Node2Vec). Third, we use the clustering algorithm (DBSCAN) to cluster the nodes in the Euclidean space, which divides the merchants into groups. Finally, we design a method to classify the severity of the groups to facilitate the following risk investigation. The proposed method covers 145 merchants from 195 known risky merchants in groups that acquire cash-out from four banks, which shows that this method can identify most (74.4%) cash-out groups. In addition, the proposed method identifies a further 178 cash-out merchants in the group within the same four acquirers, resulting in a total of 30,586 merchants. The results and framework are already adopted and absorbed into the design for a cash-out group detection system in IoT by the Chinese payment processor.
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation

Sequence-to-sequence (seq2seq) problems such as machine translation are bidirectional, which naturally derive a pair of directional tasks and two directional learning signals. However, typical seq2seq neural networks are {\em simplex} that only model one unidirectional task, which cannot fully exploit the potential of bidirectional learning signals from parallel data. To address this issue, we propose a {\em duplex} seq2seq neural network, REDER (Reversible Duplex Transformer), and apply it to machine translation. The architecture of REDER has two ends, each of which specializes in a language so as to read and yield sequences in that language. As a result, REDER can simultaneously learn from the bidirectional signals, and enables {\em reversible machine translation} by simply flipping the input and output ends, Experiments on widely-used machine translation benchmarks verify that REDER achieves the first success of reversible machine translation, which helps obtain considerable gains over several strong baselines.
Submission in Response to the WIPO’s ‘Draft Issues Paper on Intellectual Property Policy and Artificial Intelligence’ (Ref. No. WIPO/IP/AI/2/GE/20/1)

Before considering the aspect of inventorship, we should first consider the meaning of an “invention” and “inventor”. TRIPS didn’t provide any specific definition of what is an invention or who is an inventor. Patent laws of some jurisdictions define an invention as a human creation. Here, the question that should be asked is how an AI can be identified as an inventor? and how its autonomously developed invention can be identified as an invention?
Indian Education Policy: A Journey after Independence

Education is the process of supporting to the learning process, or gaining of knowledge, morals, skills, beliefs, values, and habits. After Independence, educational methods reframed as teaching, training, storytelling, discussion and directed research. But, now days, there is a need of improvising the educational methods by allowing the innovative approaches such as cooperative learning; problem based learning; competency based learning; manufacturing based learning; small modification based learning; earned based learning etc., to be impacted equally on the school and colleges education which decide the future of the nation, in terms of growth and development. Comparing the pre-independence and post-independence era of education, we observed the comprehensive changes in the framework for elementary education to higher education as well as vocational training in both rural and urban India. This paper outlines the salient features of national policy of education including (technical and non-technical) and its improvising amendment from 1968 to 2020.
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism

We study the offline reinforcement learning (offline RL) problem, where the goal is to learn a reward-maximizing policy in an unknown Markov Decision Process (MDP) using the data coming from a policy $\mu$. In particular, we consider the sample complexity problems of offline RL for finite-horizon MDPs. Prior works study this problem based on different data-coverage assumptions, and their learning guarantees are expressed by the covering coefficients which lack the explicit characterization of system quantities. In this work, we analyze the Adaptive Pessimistic Value Iteration (APVI) algorithm and derive the suboptimality upper bound that nearly matches \[ O\left(\sum_{h=1}^H\sum_{s_h,a_h}d^{\pi^\star}_h(s_h,a_h)\sqrt{\frac{\mathrm{Var}_{P_{s_h,a_h}}{(V^\star_{h+1}+r_h)}}{d^\mu_h(s_h,a_h)}}\sqrt{\frac{1}{n}}\right). \] In complementary, we also prove a per-instance information-theoretical lower bound under the weak assumption that $d^\mu_h(s_h,a_h)>0$ if $d^{\pi^\star}_h(s_h,a_h)>0$. Different from the previous minimax lower bounds, the per-instance lower bound (via local minimaxity) is a much stronger criterion as it applies to individual instances separately. Here $\pi^\star$ is a optimal policy, $\mu$ is the behavior policy and $d_h^\mu$ is the marginal state-action probability. We call the above equation the intrinsic offline reinforcement learning bound since it directly implies all the existing optimal results: minimax rate under uniform data-coverage assumption, horizon-free setting, single policy concentrability, and the tight problem-dependent results. Later, we extend the result to the assumption-free regime (where we make no assumption on $ \mu$) and obtain the assumption-free intrinsic bound. Due to its generic form, we believe the intrinsic bound could help illuminate what makes a specific problem hard and reveal the fundamental challenges in offline RL.
LEUKEMIA DIAGNOSIS IN BLOOD MICROSCOPIC IMAGES USING LOCAL BINARY PATTERN AND SUPERVISED CLASSIFIER

Acute lymphoblastic leukemia (ALL) is a group of hematologicalneoplastic during childhood that gets characterized by a huge number of lymphoid blasts in the human blood. It makes 80 percentage of childhood leukemia. It occurs between the ages of 3-7 years. Diagnosis of this disease is really hard. It may end up in wrong diagnosis. It has signs and symptoms similar to other disorders. The effective way of diagnosing leukemia is examining the stained blood smear or bone marrow aspirate. Techniques that can be used for identification of particular type of leukemia are fluorescence in situ hybridization, immunophenotyping, analysis of cytogenic and cytochemistry. Since the above methods are not that much affordable and also it takes more time to complete the process, there is a need of an automated detection of leukemia. Important features that can be useful for detecting the leukemia are nucleus shape and texture. In the existing paper, hausdorff dimension along with contour signature is implemented for grouping a lymphoblastic cell nucleus. Neural network classifier can be used for classification.
Multi-Modeshape Reservoir Computing Using a Continuous MEMS Microbeam

Abstract Delay-based Reservoir computing (RC) offers great potential in time-series problems, especially when applied in hardware due to its low computational power and its compact nature. However, this approach suffers from a large computational delay because of the serial probing of virtual nodes. To address this disadvantage, this paper presents the use of a continuous MEMS arch for Delay-based RC. This novel approach reduces the computational delay by using fewer virtual nodes through maintaining sufficient virtual node coupling and nonlinear complexity. As a demonstration, we show that a single MEMS arch is capable of performing a binary waveform classification task of a multi-frequency square-and-triangle waveform problem with a success rate &amp;gt; 96% using only 10 virtual nodes compared to 40 virtual nodes in a typical implementation. The reduction in the number of virtual neurons is achieved by biasing the MEMS device using an AC source around its second modeshape.
Random Integer Lattice Generation via the Hermite Normal Form

Lattices used in cryptography are integer lattices. Defining and generating a "random integer lattice" are interesting topics. A generation algorithm for a random integer lattice can be used to serve as a random input of all the lattice algorithms. In this paper, we recall the definition of the random integer lattice given by G. Hu et al. and present an improved generation algorithm for it via the Hermite normal form. It can be proven that with probability ≥0.99, this algorithm outputs an n-dim random integer lattice within O(n2) operations.
A bayesian naïve Bayes classifier for dating archaeological sites

Dating is a key element for archaeologists. We propose a Bayesianapproach to provide chronology to sites that have neither radiocarbon dating norclear stratigraphy and whose only information comes from bifacial flint arrowheads. This classifier is based on the Dirichlet-multinomial inferential process andposterior predictive distributions. The procedure is applied to predict the periodof a set of undated sites located in the east of the Iberian Peninsula during theIVth and IIIrd millennium cal. BC
Selected technology-critical elements as indicators of anthropogenic groundwater contamination

Groundwater contamination originating from anthropogenic industrial activities is a global concern, adversely impacting health of living organisms and affecting natural ecosystems. Monitoring contamination in a complex groundwater system is often limited by sparse data and poor hydrogeological delineation, so that numerous indicators (organic, inorganic, isotopic) are frequently used simultaneously to reduce uncertainty. We suggest that selected Technology-Critical Elements (TCEs), which are usually found in very low concentrations in the groundwater environment, might serve as contamination indicators that can be monitored through aquifer systems. Here, we demonstrate the use of selected TCEs (in particular, Y, Rh, Tl, Ga, and Ge) as indicators for monitoring anthropogenic groundwater contamination in two different groundwater systems, near the Dead Sea, Israel. Using these TCEs, we show that the sources of local groundwater contamination are phosphogypsum ponds located adjacent to fertilizer plants in two industrial areas. In addition, we monitored the spatial distribution of the contaminant plume to determine the extent of well and spring contamination in the region. Results show significant contamination of the groundwater beneath both fertilizer plants, leading to contamination of a series of wells and two natural springs. The water in these springs contains elevated concentrations of toxic metals; U and Tl levels, among others, are above the maximum concentration limits for drinking water.
On Memorization in Probabilistic Deep Generative Models

Recent advances in deep generative models have led to impressive results in a variety of application domains. Motivated by the possibility that deep learning models might memorize part of the input data, there have been increased efforts to understand how memorization can occur. In this work, we extend a recently proposed measure of memorization for supervised learning (Feldman, 2019) to the unsupervised density estimation problem and simplify the accompanying estimator. Next, we present an exploratory study that demonstrates how memorization can arise in probabilistic deep generative models, such as variational autoencoders. This reveals that the form of memorization to which these models are susceptible differs fundamentally from mode collapse and overfitting. Finally, we discuss several strategies that can be used to limit memorization in practice.
Wine is not v i n. On the Compatibility of Tokenizations across Languages.

The size of the vocabulary is a central design choice in large pretrained language models, with respect to both performance and memory requirements. Typically, subword tokenization algorithms such as byte pair encoding and WordPiece are used. In this work, we investigate the compatibility of tokenizations for multilingual static and contextualized embedding spaces and propose a measure that reflects the compatibility of tokenizations across languages. Our goal is to prevent incompatible tokenizations, e.g., “wine” (word-level) in English vs. “v i n” (character-level) in French, which make it hard to learn good multilingual semantic representations. We show that our compatibility measure allows the system designer to create vocabularies across languages that are compatible – a desideratum that so far has been neglected in multilingual models.
Comparing Euclidean and Hyperbolic Embeddings on the WordNet Nouns Hypernymy Graph.

Nickel and Kiela (2017) present a new method for embedding tree nodes in the Poincare ball, and suggest that these hyperbolic embeddings are far more effective than Euclidean embeddings at embedding nodes in large, hierarchically structured graphs like the WordNet nouns hypernymy tree. This is especially true in low dimensions (Nickel and Kiela, 2017, Table 1). In this work, we seek to reproduce their experiments on embedding and reconstructing the WordNet nouns hypernymy graph. Counter to what they report, we find that Euclidean embeddings are able to represent this tree at least as well as Poincare embeddings, when allowed at least 50 dimensions. We note that this does not diminish the significance of their work given the impressive performance of hyperbolic embeddings in very low-dimensional settings. However, given the wide influence of their work, our aim here is to present an updated and more accurate comparison between the Euclidean and hyperbolic embeddings.