Reviewerly/exHarmony huggingface dataset:
STD-TR: End-to-End Spatio-Temporal Action Detection with Transformers

Spatio-temporal action detection methods locate human actions in both spatial and temporal dimension, which usually follow a two-stage structure. In this paper, We propose STD-TR, a novel spatio-temporal action detection framework with an end-to-end transformer structure. STD-TR employs two branches to extract feature from video clip and key frame concurrently, then sends the aggregated feature to the transformer encoder-decoder. View spatio-temporal action detection as a set matching and prediction problem, STD-TR employs learned object queries to model the relation of feature context, and directly outputs all predictions at one inference time. Our method remove all hand-designed and can be optimized by a joint loss. Besides, a Hungarian algorithm and a upgraded linking strategy are used for bipartite set matching and action tube generation respectively. Convincing experiment result on challenging dataset demonstrates the superiority of our method.
Making an Almanac

Abstract This chapter employs critical design practice to examine a specific form in the history of prediction—almanacs—and ultimately interrogates the nature of current forms of algorithmic prediction. The chapter revolves around the concept of monism—the idea that the same universal laws govern both natural and social worlds—and focuses on the role monism plays in predictions within almanac publications. It draws on empirical evidence from the Monistic Almanac—an ongoing practice-based research project which revisits the almanac as a site for experiments across the blurry boundary between data science and astrology—in order to conceptualize almanacs as precursors to the current regime of algorithmic prediction. The chapter experiments with computational astrologies and reflects upon the diagrammatic operations of data science. Benqué argues that divination opens up opportunities for critical design practice to question the authority of current notions of predictions.
Authorship Attribution

Binary authorship attribution refers to the process of discovering information related to the author(s) of anonymous binary code on the basis of stylometric characteristics extracted from the code. However, in practice, authorship attribution for binary code still requires considerable manual and error-prone reverse engineeringReverse engineering analysis, which can be a daunting task given the sheer volume and complexity of today’s malware. In this chapter, we propose BinAuthor, a novel and the first compiler-agnostic Compiler-agnostic method for identifying the authors of program binaries. Having filtered out unrelated functions (compiler and library) to detect user-related functions, it converts user-related functions into a canonical form Canonical form to eliminate compiler/compilation effects. Then, it leverages a set of features based on collections of authors’ choices made during coding. These features capture an author’s programming coding habits.
Futurama

Accessibility has, at its heart, the aim of removing developer and content creator preconceptions as to the technology, people, working environment, and use their systems support. The move to mobile has been nothing but positive for the accessibility of people with a visual disability. Similarly in the next 10 years as the Web becomes a utility, imbedded—embedded and invisible—and as wearables, sensors and the Internet of Things (and the Web, IoW) become more prevalent accessibility will be even more crucial. As the Web becomes more seamless, we will need to look for new ways to convey information in many different environments, and here lessons from accessibility will contribute. We have seen over the last 10 years the expansion from physical and sensory disability through cognitive and ageing to inclusion, and situational impairments. Over the next 10 years, our understanding of assistive technology will change because we cannot build for the many combinations of interactions, technologies, environments and users then assistive technology will just become ubiquitous. More attention will be paid to previously under-represented areas such as cognitive and autism spectrum conditions as well as those surrounding comprehension and people with learning difficulties. These groups have often been overlooked in the past; this is changing and will change significantly in the next 10 years. So in 10 years, we predict that new solutions for emerging technologies will be required, that accessibility research will focus more on cognitive and learning spectrum conditions, that access technology will become more ubiquitous, and that this ubiquity will require automated tooling created by experts to assist in the removal of barriers at scale.
InterCorp

This chapter presents the current version of InterCorp, a parallel corpus created at the Faculty of Arts, Charles University in Prague. The corpus contains texts in Czech aligned with one or more foreign-language version(s), including Czech and 39 other languages. The chapter analyses its structure and technical parameters, and describes some technical tools used with the corpus (Kontext, a corpus query interface, and InterText, a parallel text alignment editor created specifically for the project). Similarly, the contribution discusses Treq (Translation Equivalents Database), a collection of bilingual Czech-foreign language dictionaries built automatically from InterCorp. In the last section of the chapter, the possibilities for methodological and linguistic exploitation of the corpus are discussed.
Translating Presuppositions

Expressing source language (SL) presuppositions as presuppositions in the receptor language (RL) is sometimes impossible, due to linguistic differences between the languages. In other cases it can cause problems of comprehension or naturalness for RL readers, especially when the “presupposition” constitutes new information to the reader. The most common solution to such problems is to express the presupposed content as a separate assertion. This strategy preserves the propositional content of the original but distorts the information packaging. Another strategy that may be useful in such cases is to render the problematic SL presupposition as a conventional implicature, preserving the “backgrounded” status of the presupposed information without triggering an inference that this information is already known to the addressee.
Towards an unsupervised morphological segmenter for isiXhosa

In this paper, branching entropy techniques and isiXhosa language heuristics are adapted to develop unsupervised morphological segmenters for isiXhosa. An overview of isiXhosa segmentation issues is given, followed by a discussion on previous work in automated segmentation, and segmentation of isiXhosa in particular. Two unsupervised isiXhosa segmenters are presented and compared to a random minimum baseline and Morfessor-Baseline, a standard in unsupervised word segmentation. Morfessor-Baseline outperforms both isiXhosa segmenters at 79.10% boundary identification accuracy. The IsiXhosa Branching Entropy Segmenter (XBES) performance varies depending on the segmentation mode used, with a maximum of 73.39%. The IsiXhosa Heuristic Maximum Likelihood Segmenter (XHMLS) achieves 72.42%. The study suggests that unsupervised isiXhosa morphological segmentation is feasible with better optimization of the current attempts.
Set Classification

The classification problem where each object is given by a set of multidimensional measurements that is associated with an unknown dependence is considered. Intersection of sets that define objects from different classes is allowed. In this case, it is natural to found classification algorithms based on the difference between dependencies for the objects belonging to different classes. Two algorithms to convert the set classification problem solution from the initial feature space into (1) the parameters space of the common model structure for all the objects and (2) the parameters spaces of the best structures for each class are proposed, along with a classification algorithm based on the accuracy of object representation by the models based on the structures found for each class. If the objects are described with big data, the approach can be used to transform data into a compact form (model parameters) that preserves the characteristics that are necessary to separate the classes. An approach to solve a problem of clustering sets is proposed. Some examples are given.
Interactive Information Retrieval: Models, Algorithms, and Evaluation

Since Information Retrieval (IR) is an interactive process in general, it is important to study Interactive Information Retrieval (IIR), where we would attempt to model and optimize an entire interactive retrieval process (rather than a single query) with consideration of many different ways a user can potentially interact with a search engine. This tutorial systematically reviews the progress of research in IIR with an emphasis on the most recent progress in the development of models, algorithms, and evaluation strategies for IIR, ending with a brief discussion of the major open challenges in IIR and some of the most promising future research directions.
Efficient web service selection with uncertain QoS

The QoS-based service selection in a highly dynamical environment is becoming a challenging issue. In practice, the QoS fluctuations of a service composition entail major difficulties in measuring the degree to which the user requirements are satisfied. In addition, the search space of feasible compositions (i.e., the solutions that preserve the requirements) is generally large and cannot be explored in a limited time; therefore, we need an approach that not only copes with the presence of uncertainty but also ensures a pertinent search with a reduced computational cost. To tackle this problem, we propose a constraint programming framework and a set of ranking heuristics that both reduce the search space and ensure a set of reliable compositions. The conducted experiments show that the ranking heuristics, termed 'fuzzy dominance' and 'probabilistic skyline', outperform almost all existing state-of-the-art methods.
Buying Bitcoin

Using live trade execution data from an institutional money manager, we measure the real-world costs and challenges a trader faces when demanding significant liquidity of Bitcoin. We provide a novel characterization of the market to purchase digital assets across exchanges and over-the-counter (“OTC”) dealers. We find that most displayed digital asset liquidity on exchanges is not readily accessible to institutional investors. However, by purchasing digital assets OTC from regulated counterparties, institutional investors can achieve comparable or better execution quality while fulfilling their fiduciary obligations.
Designing augmented reality sibi sign language as a learning media

Abstract Sign language is language used by deaf people to exchange information. Indonesia has two sign languages used in its application, namely SIBI and BISINDO. SIBI is an adaptation of the American Sign Language which is currently being applied in a special school (SLB) environment. To provide support in learning SIBI sign language in technological advances, the design of Augmented Reality to introduce the letters SIBI will help in the learning process. The black box test results showed all the features and functions of the application can run well. In observing the response time when scanning markers using the Xiaomi PocoF1 marker on the letters SIBI, it had an average time of 6.05 seconds. Observation of the scan distance of the SIBI letter, the average scan distance of the marker can be scanned at a distance of 5 cm to 50 cm.
Research on English Teaching Ability Evaluation Algorithm Based on Particle Swarm Optimization Algorithm

English grade examination is regarded as the main test to evaluate students' English ability. How to find out the relationship between English grades and grade examination results in order to guide different students to improve the passing rate of grade examination is of great significance to English teaching. In the process of English teaching post competency assessment, there are many advantages that have an impact on it. It is necessary to quantitatively test and analyze the English teaching post competency level. On this basis, the parametric model of constraint competency can be constructed, and then the big data analysis model can be obtained. In this paper, the particle follows the optimal particle in the problem state space to search the optimal solution. Its convergence speed is fast, but there are problems such as premature convergence and poor local search ability. The algorithm is applied to the analysis of College English grade examination data, and the generated fuzzy decision tree is analyzed to find the relationship between English grade examination and daily teaching, It provides a reference for future teaching work.
HRSE: A novel high-speed random sampling encryption scheme on big data

The security and privacy protection of big data have attracted lots of attentions in recent years. The volume characteristic of big data puts forward the higher requirements in terms of encryption speed and processing performance. Existing data encryption schemes usually bring huge computing overhead, and it is difficult to guarantee the confidentiality of big data. In this paper, we propose an improved random sampling encryption scheme, which can realize a high-speed random sampling encryption and provide sufficient security for big data. A comprehensive evaluation, including security analysis and efficiency analysis, is presented to show the security and feasibility of the proposed scheme.
The Study of the Barriers to Digital Transformation in Higher Education: A Preliminary Investigation in Indonesia

The implementation of digital transformation has been carried out in various industrial sectors, including the higher education sector. Many countries have considered changing their education system by doing digital transformation. Although digital transformation has the potential to improve the education system, developing countries still find it difficult to reap the benefits caused by certain barriers. The purpose of this study is to investigate the barriers faced when implementing digital transformation in higher education, in this case in Indonesia. The results of the questionnaire survey ensured that of the twenty-two barriers identified based on a literature review, eleven barriers significantly affecting the implementation of digital transformation in Indonesian higher education. This study contributes by revealing initial set barriers of digital transformation in the higher education sector. The findings of this study will help identify barriers that influence the direction of decision strategies for implementing digital transformation in higher education institutions in Indonesia.
Employing chatbots for data collection in participatory journalism and crisis situations

The journalistic profession has long since entered an age where technology and audience participation are two of its most defining factors. Changes that were brought about by the advent of WEB 2.0 transformed journalism – among other professions – fundamentally, and opened the gates to a more connected future, one in which the lines between content producer and content consumer are far less defined than they used to be. One of the more promising technologies of this new era is that of chatbots and conversational agents. These multifaceted programs have proven to be extremely useful in many aspects of modern journalism, with some of them getting used in scenarios that go as far as trying to replace the traditional role of the journalist as we know it. As such, the influence of these programs has also spread to the field of audience interaction and participatory journalism. This article aims to underline the integral role that chatbots play within the confines of the journalistic profession, while at the same time explore the significant effects they have in the field of audience participation and communication between the journalist and the public in general. To achieve this goal a model chatbot was created in order to demonstrate the benefits of automating the process of filing and transferring a report on account of the public to the news organization.
Path Planning for Welding Robot based on Ant colony Optimization Algorithm

Since welding robots have to traverse a large number of welding points during welding operations, the length of the welding path directly affects the working time and production efficiency of the automatic line. In respect to the problem that traditional path planning methods are not suitable for multi-target points, a path planning method based on ant colony optimization algorithm (ACO) is proposed. Firstly, the robot's working environment is simulated with the grid method, then the ACO algorithm model is established, and finally an optimal welding path is obtained through algorithm convergence. Taking the shortest welding path as the planning goal, the simulation experiment is carried out in MATLAB software. The results show that the method obtains the optimal path planning effect under the condition of a small number of iterations, and verifies the feasibility of applying the ACO algorithm to the path planning for welding robots.
Efficient Density-Based Partitional Clustering Algorithm

Clustering is an important data mining technique that helps to detect hidden structures and patterns in the data. K-means algorithm is one of the most popular and widely used partitional clustering algorithms. It is a simple and efficient method but has several shortcomings. One major drawback of traditional K-means is that it selects initial centroids randomly, resulting in low-quality clusters. Various K-means extensions are designed to solve the issue of the random initial centroid. A novel density-based K-means (DK-means) algorithm is recently proposed that uses density-parameters for selecting initial centroids. It outperforms K-means in terms of accuracy at the cost of time. In this research, we present an efficient density-based K-means algorithm (EDK-means) that uses advance data structures and significantly reduces the DK-means algorithm's execution time. Furthermore, we rigorously evaluated the performance of density-based K-means on different challenging real-world datasets and compared it with traditional K-means. The experimental results are promising and show that density-based K-means outperforms K-means. It converges more rapidly than basic K-means, and it works well for the datasets with different cluster sizes.
A novel domain adaption approach for neural machine translation

Neural machine translation has been widely adopted in modern machine translation as it brings the state-of-the-art performance to large-scale parallel corpora. For real-world applications, high-quality translation for text in a specific domain is crucial. However, performances of general neural machine models drop when being applied in a specific domain. To alleviate this issue, this paper presents a novel method of machine translation, which explores both model fusion algorithm and logarithmic linear interpolation. The method can improve the performance of in-domain translation model, while preserving or even improving the performance of out-domain translation model. This paper has carried out extensive experiments on proposed translation model using the public United Nations corpus. The bilingual evaluation understudy (BLEU) score of the in-domain corpus and the out-domain corpus reaches 30.27 and 43.17 respectively, which shows a certain improvement over existing methods.
Optimal-speed unitary quantum time evolutions and propagation of light with maximal degree of coherence

It is recognized that Grover arrived at his original quantum search algorithm inspired by his comprehension of the interference of classical waves originating from an array of antennas. It is also known that quantum-mechanical characterization of electromagnetic radiation is isomorphic to the treatment of the orientation of a spin-$1/2$ particle. In this paper, motivated by Grover's original intuition and starting from this mathematical equivalence, we present a quantitative link between the geometry of time-independent optimal-speed Hamiltonian evolutions on the Bloch sphere and the geometry of intensity-preserving propagation of light with maximal degree of coherence on the Poincar\'e sphere. Finally, identifying interference as the fundamental physical ingredient underlying both physical phenomena, we propose that our work can provide in retrospect a quantitative geometric background underlying Grover's powerful intuition.
Entropy estimation in bidimensional sequences

We investigate the performance of entropy estimation methods, based either on block entropies or compression approaches, in the case of bidimensional sequences. We introduce a validation data set made of images produced by a large number of different natural systems, in the vast majority characterized by long-range correlations, which produce a large spectrum of entropies. Results show that the framework based on lossless compressors applied to the one-dimensional projection of the considered data set leads to poor estimates. This is because higher dimensional correlations are lost in the projection operation. The adoption of compression methods which do not introduce dimensionality reduction improves the performance of this approach. By far, the best estimation of the asymptotic entropy is generated by the faster convergence of the traditional block-entropies method. As a by-product of our analysis, we show how a specific compressor method can be used as a potentially interesting technique for automatic detection of symmetries in textures and images.
Foundations for Bayesian inference with engineered likelihood functions for robust amplitude estimation

We present mathematical and conceptual foundations for the task of robust amplitude estimation using engineered likelihood functions (ELFs), a framework introduced by Wang et al. [PRX Quantum 2, 010346 (2021)] that uses Bayesian inference to enhance the rate of information gain in quantum sampling. These ELFs, which are obtained by choosing tunable parameters in a parametrized quantum circuit to minimize the expected posterior variance of an estimated parameter, play an important role in estimating the expectation values of quantum observables. We give a thorough characterization and analysis of likelihood functions arising from certain classes of quantum circuits and combine this with the tools of Bayesian inference to give a procedure for picking optimal ELF tunable parameters. Finally, we present numerical results to demonstrate the performance of ELFs.