Reviewerly/exHarmony huggingface dataset:
Towards Safety Verification of Direct Perception Neural Networks

We study the problem of safety verification of direct perception neural networks, where camera images are used as inputs to produce high-level features for autonomous vehicles to make control decisions. Formal verification of direct perception neural networks is extremely challenging, as it is difficult to formulate the specification that requires characterizing input as constraints, while the number of neurons in such a network can reach millions. We approach the specification problem by learning an input property characterizer which carefully extends a direct perception neural network at close-to-output layers, and address the scalability problem by a novel assume-guarantee based verification approach. The presented workflow is used to understand a direct perception neural network (developed by Audi) which computes the next waypoint and orientation for autonomous vehicles to follow.
Measuring Effective Temperatures of Qubits Using Correlations

Initialization of a qubit in a pure state is a prerequisite for quantum computer operation. A plethora of ways to achieve this has been proposed in the last decade, from active reset protocols to advances in materials and shielding. An instrumental tool to evaluate those methods and develop new ones is the ability to measure the population of excited states with high precision and in a short period of time. In this Letter, we propose a new technique of finding the excited state population of a qubit using correlations between two sequential measurements. We experimentally implement the proposed technique using a circuit QED platform and compare its performance with previously developed ones. Unlike other techniques, our method does not require high-fidelity readout and does not involve the excited levels of the system outside of the qubit subspace. We experimentally demonstrated measurement of the spurious qubit population with accuracy of up to 0.01%. This accuracy enabled us to perform ``temperature spectroscopy'' of the qubit, which helps to shed light on decoherence sources.
A Semi-Fragile Video Watermarking Algorithm Based on H.264/AVC

With the increasing application of advanced video coding (H.264/AVC) in the multimedia field, a great significance to research in video watermarking based on this video compression standard has been established. We propose a semifragile video watermarking algorithm, which can simultaneously implement frame attack and video tamper detection, herein. In this paper, the frame number is selected as the watermark information, and the relationship of the discrete cosine transform (DCT) nonzero coefficients is used as the authentication code. The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mn>4</mml:mn><mml:mo>×</mml:mo><mml:mn>4</mml:mn></mml:math> subblocks, whose DCT nonzero coefficients are sufficiently complex, are selected to embed the watermark. The parities of these nonzero coefficients in the medium frequency are modulated to embed watermarks. The experimental results show that the visual quality of the embedded watermarked video is virtually unaffected, and the algorithm exhibits good robustness. Furthermore, the algorithm can correctly implement frame attack and video tamper detection.
Developing Students’ Intercultural Competence During the Professional Oriented Course in English as a Foreign Language

Intercultural competence is one of the most important soft skills deemed necessary for future engineers to become competitive specialists at the labor market. We consider problem, intercultural and interdisciplinary approaches to be the most important in developing such skills. The objective of the present study was to determine the effectiveness of active teaching methods implemented into the English teaching practice, and to monitor how intercultural competence components developed over time. We suggest two criteria for determining the level of three components. “Linguistic skills” and “linguo-cultural skills” stand for the cognitive component; “empathy skills” and “reflective skills” refer to the emotional component; “sociability” and “lack of aggressiveness” imply the behavioral component. The implementation of chosen intercultural based and professionally oriented material alongside with active teaching methods (collaborative learning, problem-based learning, project-based learning, role-play) contributed to the increase of students’ intercultural competence level in the treatment group. After examining the behaviour of the students, we could state the qualitative dynamics of its three components as well. The results of this study should be taken into consideration while designing the framework for developing students’ intercultural competence during the extracurricular time.
What’s The Latest? A Question-driven News Chatbot

This work describes an automatic news chatbot that draws content from a diverse set of news articles and creates conversations with a user about the news. Key components of the system include the automatic organization of news articles into topical chatrooms, integration of automatically generated questions into the conversation, and a novel method for choosing which questions to present which avoids repetitive suggestions. We describe the algorithmic framework and present the results of a usability study that shows that news readers using the system successfully engage in multi-turn conversations about specific news stories.
KGAnet: a knowledge graph attention network for enhancing natural language inference

Abstract Natural language inference (NLI) is the basic task of many applications such as question answering and paraphrase recognition. Existing methods have solved the key issue of how the NLI model can benefit from external knowledge. Inspired by this, we attempt to further explore the following two problems: (1) how to make better use of external knowledge when the total amount of such knowledge is constant and (2) how to bring external knowledge to the NLI model more conveniently in the application scenario. In this paper, we propose a novel joint training framework that consists of a modified graph attention network, called the knowledge graph attention network, and an NLI model. We demonstrate that the proposed method outperforms the existing method which introduces external knowledge, and we improve the performance of multiple NLI models without additional external knowledge.
Probabilistic Symmetries and Invariant Neural Networks

Treating neural network inputs and outputs as random variables, we characterize the structure of neural networks that can be used to model data that are invariant or equivariant under the action of a compact group. Much recent research has been devoted to encoding invariance under symmetry transformations into neural network architectures, in an effort to improve the performance of deep neural networks in data-scarce, non-i.i.d., or unsupervised settings. By considering group invariance from the perspective of probabilistic symmetry, we establish a link between functional and probabilistic symmetry, and obtain generative functional representations of probability distributions that are invariant or equivariant under the action of a compact group. Our representations completely characterize the structure of neural networks that can be used to model such distributions and yield a general program for constructing invariant stochastic or deterministic neural networks. We demonstrate that examples from the recent literature are special cases, and develop the details of the general program for exchangeable sequences and arrays.
Ensemble Feature Selection and Classification of Internet Traffic using XGBoost Classifier

Identification and classification of internet traffic is most important in network management to ensure Quality of Service (QoS).However, existing machine learning models tend to produce unsatisfactory results when applied with imbalanced datasets involving multiple classes.There are two reasons for this: the models have a bias towards classes which have more samples and they also tend to predict only the majority class data as features of the minority class are often treated as noise and therefore ignored.Thus, there is a high probability of misclassification of the minority class compared with the majority class.Therefore, in this paper, we are proposing an ensemble feature selection based on the tree approach and ensemble classification model using XGboost to enhance the performance of classification.The proposed model achieves better classification accuracy compared to other tree based classifiers.
Utility-Driven Mining of Trend Information for Intelligent System

Useful knowledge, embedded in a database, is likely to change over time. Identifying recent changes in temporal databases can provide valuable up-to-date information to decision-makers. Nevertheless, techniques for mining high-utility patterns (HUPs) seldom consider recency as a criterion to discover patterns. Thus, the traditional utility mining framework is inadequate for obtaining up-to-date insights about real world data. In this paper, we address this issue by introducing a novel framework, named utility-driven mining of Recent/trend high-Utility Patterns (RUP) in temporal databases for intelligent systems, based on user-specified minimum recency and minimum utility thresholds. The utility-driven RUP algorithm is based on novel global and conditional downward closure properties, and a recency-utility tree. Moreover, it adopts a vertical compact recency-utility list structure to store the information required by the mining process. The developed RUP algorithm recursively discovers recent HUPs. It is also fast and consumes a small amount of memory due to its pattern discovery approach that does not generate candidates. Two improved versions of the algorithm with additional pruning strategies are also designed to speed up the discovery of patterns by reducing the search space. Results of a substantial experimental evaluation show that the proposed algorithm can efficiently identify all recent high-utility patterns in large-scale databases, and that the improved algorithm performs best.
Approximate quantum circuit synthesis using block encodings

One of the challenges in quantum computing is the synthesis of unitary operators into quantum circuits with polylogarithmic gate complexity. Exact synthesis of generic unitaries requires an exponential number of gates in general. We propose a novel approximate quantum circuit synthesis technique by relaxing the unitary constraints and interchanging them for ancilla qubits via block encodings. This approach combines smaller block encodings, which are easier to synthesize, into quantum circuits for larger operators. Due to the use of block encodings, our technique is not limited to unitary operators and can be applied for the synthesis of arbitrary operators. We show that operators which can be approximated by a canonical polyadic expression with a polylogarithmic number of terms can be synthesized with polylogarithmic gate complexity with respect to the matrix dimension.
Psycho-Pedagogical Conditions for Preparing Future Teachers for Their Interaction with Students’ Parents in Inclusive Practice in Primary Schools

The concept of the New Ukrainian School is aimed at organizing effective inclusive education for students, in particular younger students with special educational needs, and their cooperation with healthy peers.The paper aims to theoretically justify and experimentally verify psycho-pedagogical conditions for teaching future primary school teachers how to interact with students' parents in inclusive practice.The experimental group (EG) involved 203 respondents and the control group (CG) 215 respondents, as well as 12 university teachers, 36 primary school teachers and 115 students' parents of students with/without special educational needs.The psycho-pedagogical conditions are determined as follows: developing future primary school teachers' motivational, values-based and responsible attitude to acquiring knowledge, abilities and skills needed for the interaction with students' parents in inclusive practice; using the opportunities of an educational environment in higher education institutions to develop future primary school teachers' certain competencies by improving and integrating the content of professional and specialized academic courses, applying innovative and traditional technologies of teaching and learning; focusing academic training and teaching placement towards improving the experience of future primary school teachers in interacting with students' parents in inclusive practice).The paper employs some author's methodologies (adapted to Raven's (1991) socio-pedagogical methodologies): surveys, interviews, student questionnaires, tests, assignments.The ratio between the levels of future primary school teachers' readiness to interact with students' parents in inclusive learning is the following: the experimental group -a high level 45.32%, an average level 41.87%, a low level 12.81%; the control group a high level 16.74%, an average level 36.75%, a low level 41.51%.The obtained results indicate some dynamic positive changes in the levels of EG respondents' readiness under the influence of the proposed psychopedagogical conditions.
Disentangling Trainability and Generalization in Deep Neural Networks

A longstanding goal in the theory of deep learning is to characterize the conditions under which a given neural network architecture will be trainable, and if so, how well it might generalize to unseen data. In this work, we provide such a characterization in the limit of very wide and very deep networks, for which the analysis simplifies considerably. For wide networks, the trajectory under gradient descent is governed by the Neural Tangent Kernel (NTK), and for deep networks the NTK itself maintains only weak data dependence. By analyzing the spectrum of the NTK, we formulate necessary conditions for trainability and generalization across a range of architectures, including Fully Connected Networks (FCNs) and Convolutional Neural Networks (CNNs). We identify large regions of hyperparameter space for which networks can memorize the training set but completely fail to generalize. We find that CNNs without global average pooling behave almost identically to FCNs, but that CNNs with pooling have markedly different and often better generalization performance. These theoretical results are corroborated experimentally on CIFAR10 for a variety of network architectures and we include a colab notebook that reproduces the essential results of the paper.
CollatriX: A GUI to collate MorphoMetriX outputs

CollatriX is a graphical user interface (GUI) developed using PyQt5 to collate outputs from MorphoMetriX (Torres & Bierlich, 2020), a photogrammetric measurement GUI designed for morphometric analysis of wild animals from aerial imagery.For each image used in MorphoMe-triX, a comma-separated-values sheet (.csv) is produced containing the custom measurements (length, area, angle) and their associated labels created by the user.Hence, projects with a large number of images in their morphometric analysis have a large number of output files, creating time-intensive and tedious workflows to manually combine output files into a single data file to be used in analysis.CollatriX was designed as a user-friendly GUI overcoming this limitation by collating these measurement outputs into a single data sheet (.csv) based on the animal's individual ID (Fig. 1).Furthermore, CollatriX has two add-on functions, one to correct for altitude error from Unoccupied Aerial Systems (UAS or drone) flights and another for calculating different animal body condition metrics, following body volume from Christiansen et al. (2018) and body area index (BAI) from Burnett et al. (2018) (Fig. 1).The framework of CollatriX was also designed to have the flexibility to accommodate and encourage other future add-on functions.
Siamese Neural Networks for Class Activity Detection

Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms. A CAD solution helps teachers get instant feedback on their pedagogical instructions. However, CAD is very challenging because (1) classroom conversations contain many conversational turn-taking overlaps between teachers and students; (2) the CAD model needs to be generalized well enough for different teachers and students; and (3) classroom recordings may be very noisy and low-quality. In this work, we address the above challenges by building a Siamese neural framework to automatically identify teacher and student utterances from classroom recordings. The proposed model is evaluated on real-world educational datasets. The results demonstrate that (1) our approach is superior on the prediction tasks for both online and offline classroom environments; and (2) our framework exhibits robustness and generalization ability on new teachers (i.e., teachers never appear in training data).
Cross-Lingual Speaker Verification with Domain-Balanced Hard Prototype Mining and Language-Dependent Score Normalization

In this paper we describe the top-scoring IDLab submission for the text-independent task of the Short-duration Speaker Verification (SdSV) Challenge 2020.The main difficulty of the challenge exists in the large degree of varying phonetic overlap between the potentially cross-lingual trials, along with the limited availability of in-domain DeepMine Farsi training data.We introduce domain-balanced hard prototype mining to finetune the state-of-the-art ECAPA-TDNN x-vector based speaker embedding extractor.The sample mining technique efficiently exploits speaker distances between the speaker prototypes of the popular AAM-softmax loss function to construct challenging training batches that are balanced on the domain-level.To enhance the scoring of cross-lingual trials, we propose a languagedependent s-norm score normalization.The imposter cohort only contains data from the Farsi target-domain which simulates the enrollment data always being Farsi.In case a Gaussian-Backend language model detects the test speaker embedding to contain English, a cross-language compensation offset determined on the AAM-softmax speaker prototypes is subtracted from the maximum expected imposter mean score.A fusion of five systems with minor topological tweaks resulted in a final MinDCF and EER of 0.065 and 1.45% respectively on the SdSVC evaluation set.
Solving the k-dominating set problem on very large-scale networks

Abstract The well-known minimum dominating set problem (MDSP) aims to construct the minimum-size subset of vertices in a graph such that every other vertex has at least one neighbor in the subset. In this article, we study a general version of the problem that extends the neighborhood relationship: two vertices are called neighbors of each other if there exists a path through no more than k edges between them. The problem called “minimum k -dominating set problem” (M k DSP) becomes the classical dominating set problem if k is 1 and has important applications in monitoring large-scale social networks. We propose an efficient heuristic algorithm that can handle real-world instances with up to 17 million vertices and 33 million edges. This is the first time such large graphs are solved for the minimum k -dominating set problem.
Non-Markovian channel from the reduced dynamics of a coin in a quantum walk

The quantum channels with memory, known as non-Markovian channels, are of crucial importance for a realistic description of a variety of physical systems, and pave ways for new methods of decoherence control by manipulating the properties of an environment such as its frequency spectrum. In this work, the reduced dynamics of a coin in a discrete-time quantum walk is characterized as a non-Markovian quantum channel. A general formalism is sketched to extract the Kraus operators for a $t$-step quantum walk. Non-Markovianity, in the sense of P indivisibility of the reduced coin dynamics, is inferred from the nonmonotonous behavior of distinguishability of two orthogonal states subjected to it. Furthermore, we study various quantum information-theoretic quantities of a qubit under the action of this channel, putting in perspective the role such channels can play in various quantum information processing tasks.
Set Streams: Visual Exploration of Dynamic Overlapping Sets

Abstract In many applications, membership of set elements changes over time. Since each element can be present in multiple sets, the sets also overlap. As a result, it becomes challenging to visualize the temporal change in set membership of elements across several timesteps while showing individual set intersections. We propose Set Streams, a visualization technique that represents changing set structures on a timeline as branching and merging streams. The streams encode the changing membership of elements with set intersections. A query‐based selection mechanism supports a flexible comparison of selected groups of elements across the temporal evolution. The main timeline view is complemented with additional panels to provide details about the elements. While the proposed visualization is an application‐independent visualization technique for dynamic sets, we demonstrate its effectiveness and applicability through three diverse application examples and expert feedback.
Mitigating Insider Threats Using Bio-Inspired Models

Insider threats have become a considerable information security issue that governments and organizations must face. The implementation of security policies and procedures may not be enough to protect organizational assets. Even with the evolution of information and network security technology, the threat from insiders is increasing. Many researchers are approaching this issue with various methods in order to develop a model that will help organizations to reduce their exposure to the threat and prevent damage to their assets. In this paper, we approach the insider threat problem and attempt to mitigate it by developing a machine learning model based on Bio-inspired computing. The model was developed by using an existing unsupervised learning algorithm for anomaly detection and we fitted the model to a synthetic dataset to detect outliers. We explore swarm intelligence algorithms and their performance on feature selection optimization for improving the performance of the machine learning model. The results show that swarm intelligence algorithms perform well on feature selection optimization and the generated, near-optimal, subset of features has a similar performance to the original one.
Item-based Collaborative Filtering with BERT

In e-commerce, recommender systems have become an indispensable part of helping users explore the available inventory.In this work, we present a novel approach for item-based collaborative filtering, by leveraging BERT to understand items, and score relevancy between different items.Our proposed method could address problems that plague traditional recommender systems such as cold start, and "more of the same" recommended content.We conducted experiments on a large-scale realworld dataset with full cold-start scenario, and the proposed approach significantly outperforms the popular Bi-LSTM model.