Reviewerly/exHarmony huggingface dataset:
Commands 4 Autonomous Vehicles (C4AV) Workshop Summary

The task of visual grounding requires locating the most relevant region or object in an image, given a natural language query. So far, progress on this task was mostly measured on curated datasets, which are not always representative of human spoken language. In this work, we deviate from recent, popular task settings and consider the problem under an autonomous vehicle scenario. In particular, we consider a situation where passengers can give free-form natural language commands to a vehicle which can be associated with an object in the street scene. To stimulate research on this topic, we have organized the Commands for Autonomous Vehicles (C4AV) challenge based on the recent Talk2Car dataset. This paper presents the results of the challenge. First, we compare the used benchmark against existing datasets for visual grounding. Second, we identify the aspects that render top-performing models successful, and relate them to existing state-of-the-art models for visual grounding, in addition to detecting potential failure cases by evaluating on carefully selected subsets. Finally, we discuss several possibilities for future work.
A Study on Korean-to-English Machine Translation of Newspaper Headlines, Focusing on Linguistic Features

The purpose of this study was to examine problems in machine translation output of newspaper headlines with a focus on linguistic features, in comparison with human translation output. For this purpose, 200 headlines from Infostock Daily (machine translation) and 200 headlines from Pulse, Maeil Business Newspaper’s English-language daily (human translation), were collected and analyzed in terms of five different linguistic features between Korean and English newspaper headlines. The analysis results showed a range of problems in machine-translated headlines. Ellipsis, quotation marks, commas used instead of subject case markers, and verb-less noun phrases – a common strategy for Korean newspaper headlines – negatively affected readability of Infostock Daily headlines, while unnecessary be-verbs, present perfect tense to report past events, and ‘will’ for future tense – a feature not common in English newspaper headlines – were frequently found in Infostock Daily. All in all, these findings suggest the need for light post-editing of machine-translated headlines, providing some useful insights into the post-editing guidelines for newspaper headlines.
Insights and Future Directions

In this chapter, we attempt to go beyond a restatement of the content of the book. First, we consider the broader impacts of this research and the lessons learned during its completion. These insights are situated within the current state of cybersecurity research, and more importantly, within current societal challenges. Second, we outline an ambitious range of future research which could be carried out using this book as a foundation.
custom: Evaluating the Impact of Network Latency on the Safety of Blockchain Transactions

Blockchain technology has lately become widely regarded, partially because of the surge in cryptocurrencies such as Bitcoin and their ability to be a force for economic and financial shift. While tokenomics also helped push blockchain in mainstream, this technology’s strengths are far more than crypt-monetary. Often known as distributed leader technologies, it is hypothesized that blockchain would serve like a catalyst for global disruptions and that blockchain-based applications in many industries such as the supply chain; the medical and legal fields are now being developed and implemented. By simultaneous research, we prove that the six confirms convention is vulnerable to peer-to-peer latency in the network and show just how readily PoW mining is broken. The divergences between these latest blocks open the transactions in question to the possibility that the blockchain fork will not be used. We concentrate on evaluating block detection accuracy and the breach of the six confirmed blockchain agreement.
DenseFuseNet: Improve 3D Semantic Segmentation in the Context of Autonomous Driving with Dense Correspondence

With the development of deep convolutional networks, autonomous driving has been reforming human social activities in the recent decade. The core issue of autonomous driving is how to integrate the multi-modal perception system effectively, that is, using sensors such as lidar, RGB camera, and radar to identify general objects in traffic scenes. Extensive investigation shows that lidar and cameras are the two most powerful sensors widely used by autonomous driving companies such as Tesla and Waymo, which indeed revealed that how to integrate them effectively is bound to be one of the core issues in the field of autonomous driving in the future. Obviously, these two kinds of sensors have their inherent advantages and disadvantages. Based on the previous research works, we are motivated to fuse lidars and RGB cameras together to build a more robust perception system. It is not easy to design a model with two different domains from scratch, and a large number of previous works (e.g., FuseSeg [10]) has sufficiently proved that merging the RGB camera and lidar models can attain better results on vision tasks than the lidar model alone. However, it cannot adequately handle the inherent correspondence between the RGB camera and lidar data but rather arbitrarily interpolates between them, which quickly leads to severe distortion, heavy computational burden, and diminishing performance. To address these problems, in this paper, we proposed a general framework to establish a connection between lidar and RGB camera sensors, matching and fusing the features of the lidar and RGB models. We also defined two kinds of inaccuracies (missing pixels and covered points) in spherical projection and conducted a numerical analysis on them. Furthermore, we proposed an efficient filling algorithm to remedy the impact of missing pixels. Finally, we proposed a 3D semantic segmentation model, DenseFuseNet, which incorporated our techniques and achieved a noticeable 5.8 and 14.2 improvement in mIoU and accuracy on top of vanilla SqueezeSeg [24]. All code is already open-source on https://github.com/IDl0T/DenseFuseNet.
Integrating Cardinality Constraints into Constraint Logic Programming with Sets

Formal reasoning about finite sets and cardinality is an important tool for many applications, including software verification, where very often one needs to reason about the size of a given data structure and not only about what its elements are. The Constraint Logic Programming tool {log} provides a decision procedure for deciding the satisfiability of formulas involving very general forms of finite sets, without cardinality. In this paper we adapt and integrate a decision procedure for a theory of finite sets with cardinality into {log}. The proposed solver is proved to be a decision procedure for its formulas. Besides, the new CLP instance is implemented as part of the {log} tool. In turn, the implementation uses Howe and King's Prolog SAT solver and Prolog's CLP(Q) library, as an integer linear programming solver. The empirical evaluation of this implementation based on +250 real verification conditions shows that it can be useful in practice.
Image Retrieval Using Intensity Gradients and Texture Chromatic Pattern

Methods to retrieve images involve retrieving images from the database by using features of it. They are colour, shape, and texture. These features are used to find the similarity for the query image with that of images in the database. The images are sorted in the order with this similarity. The article uses intra- and inter-texture chrominance and its intensity. Here inter-chromatic texture feature is extracted by LOCTP (local oppugnant colored texture pattern). Local binary pattern (LBP) gives the intra-texture information. Histogram of oriented gradient (HoG) is used to get the shape information from the satellite images. The performance analysis is land-cover remote sensing database, NWPU-VHR-10 dataset, and satellite optical land cover database gives better results than the previous works.
SLPal: Accelerating Long Sequence Alignment on Many-Core and Multi-Core Architectures

Biological Sequence alignment is a fundamental application in bioinformatics. It can be used to identify functionally conserved sequences and find evolutionary relationships between species. To compare entire genomes from different species, biologists increasingly need alignment methods that are efficient enough to handle long sequences, and accurate enough to correctly align the conserved biological features between distant species. Global alignments are important because they reveal the shared order of biological features in the compared species, and produce a more accurate alignment at the base-pair level when the features are in the same order. The best known global alignment algorithm is Needleman-Wunsch, later, BitPAl, a bit parallel algorithm for general, integer scoring global algorithm, provides a new implementation of Needleman-Wunsch algorithm (BitNW). Compared with original Needleman-Wunsch algorithm, BitNW is significantly faster by exploiting bit parallelism. A number of parallel strategies have been proposed to accelerate exact alignment methods. However, most of them failed to align long biological sequences due to quadratic time complexity. In this paper, we propose SLPal, a fast bit-parallel algorithm for accelerating long DNA sequence comparison on Intel manycore and multi-core architectures. In order to fully exploit the computing power of many cores and the 512-bit vector processing units (VPUs), we use a two-level parallelism scheme: coarsegrained thread level and fine-grained VPU level approaches. In thread level, the alignment scoring matrix will be split into small tiles and multiple threads will process these small tiles currently by using Intel TBB library. In the VPU level, the computing kernels are implemented using the Single Instruction Multiple Data (SIMD) instructions, thus, 16 independent integers reside in a 512-bit vector register can be processed simultaneously. The evaluation reveals that our algorithm achieves a stable performance for all benchmark data and yields a performance of up to 511.7 (617.2) GCUPS on a server with single Xeon Phi 7210 processor (dual Xeon Gold 614820-core processors). Furthermore, our test shows that SLPal can align two sequences with about 5 million bps in 50 seconds on our server equipped with dual Xeon Gold 6148 CPUs.
National Project Education: Advantages and Risks

The main goal of the National Project ‘Education’ – to ensure Russia’s placement in the top ten countries in general education quality ranking – is quite achievable. However, there exist some risks that the motivation of the project participants may dwindle, and the approach to implementing the project will be purely formal.
Graph Self-Attention Network for Image Captioning

Most state-of-the-art methods for image captioning highly depend on an attention mechanism on the object regions within the encoder-decoder framework. Generally, existing attention models are based on simple addition or multiplication operations and may not fully discover the complex relationships between the visual features and the target words. In this paper, we propose a novel attention model, named graph self-attention (GSA), that incorporates graph networks and self-attention for image captioning. GSA constructs a star-graph model to dynamically assign weights to the detected object regions when generating the words step-by-step. The central node is represented by the semantic feature and the visual features of the object regions are used as edge nodes. Through propagating messages among the center and edge nodes, GSA explicitly captures the relationships between the current target word and the image features. To generate conjunctions and attributives that are not directly related to visual information, GSA introduces self-attention so that such words are allowed to focus more on the semantic information. Moreover, the GSA model is also generic and can be applied to tasks that require attention to multiple features. The experiments show the effectiveness and potentiality of our proposed GSA.
Fixed-Time Distributed Cooperative Learning for Stochastic Configuration Networks

In this paper, we present a fixed-time convergent distributed cooperative learning algorithm for stochastic configuration network, namely FixD-SCN, to solve 'Big Data' problem. The algorithm is proposed by using the fixed-time stability. In SCN, parameters that involve input weights and biases of new hidden nodes, are obtained at random via a supervisory mechanism with inequality constraints. And in particular, compared to the finite- time algorithms, the optimization of output weights are achieved within fixed time which is independent of the initial conditions of agents. Different from the traditional centralized learning algorithm, the FixD-SCN requires that each agent only cooperates with its nearest neighbors to share local information and learn unknown patterns. Numerical examples given corroborate effectiveness of the FixD-SCN algorithm.
Status of Organization Citizen Behavior (OCB) in Nepalese Commercial Banks

The paper aims to analyze the status of organization citizen behavior in Nepalese Commercial Banks. This study is based on Likert scale data. Twenty-seven questions are entertained in the set of questionnaires containing major demographic variables such as gender, experience, designation and qualifications of employees in sampled commercial banks. Primary data are collected through convenient and snowball sampling. Collected data are analyzed by using descriptive statistics, Karl Pearson coefficient of correlation, regression model by Andrew Haney’s and Welch‘s ANOVA. The Statistical Package for Social Science (SPSS) 25 versions is used for the analysis of data. The result indicates that OCB favorably exits in Nepalese commercial banks. All dimensions of OCB have positive correlations among each other. The Welch’s ANOVA posits that all employees of commercial banks have same levels of OCB irrespective of their nature. Further, the study proves that OCB at individual and organizational level significantly impact the composite OCB. Gender and education strengthen the relationship between OCB at individual level and composite OCB but these and other variables do not influence in the relationship of OCB at organizational level and composite OCB.
Rethinking Ranking-based Loss Functions: Only Penalizing Negative Instances before Positive Ones is Enough.

Optimising the approximation of Average Precision (AP) has been widely studied for retrieval. Such methods consider both negative and positive instances before each target positive one according to the definition of AP. However, we argue that only penalizing negative instances before positive ones is enough, because the loss only comes from them. To this end, instead of following the AP-based loss, we propose a new loss, namely Penalizing Negative instances before Positive ones (PNP), which directly minimizes the number of negative instances before each positive one. Meanwhile, limited by the definition of AP, AP-based methods only adopt a specific gradient assignment strategy. We wonder whether there exists better ones. Instead, we systematically investigate different gradient assignment solutions via constructing derivative functions of the loss, resulting in PNP-I with increasing derivative functions and PNP-D with decreasing ones. Because of their gradient assignment strategies, PNP-I tries to make all the relevant instances together, while PNP-D only quickly corrects positive one with fewer negative instances before. Thus, PNP-D may be more suitable for real-world data, which usually contains several local clusters for one class. Extensive evaluations on three standard retrieval datasets also show that PNP-D achieves the state-of-the-art performance.
NARRATIVE STRUCTURE AND ECOLOGICAL WISDOM OF MAK UNGKAI MALAY STORY IN RIAU ISLANDS

The spirit of Mak Ungkai is begun to be forgotten and not widely known by the Malay people. The forbidden Malay traditions of the folklore are begun to fade. The society awareness and habits can be formed by inspiring and upholding of folklore as a local wisdom. Greimas ’ Structuralism (Culler. 2004) and Garrard ’ s Ecocritics (2004) are two important theories dealing to this study. The study of narrative structure was used to dissect the spirit of the sea story. Eco-criticism was used to observe the interconnection between Malay folklore and the ecological wisdom. The coastal locals of Tanjung Kertang, Sebulang, Batam, Riau Islands were the informants. The results showed that the spirit of the sea ‘ Mak Ungkai ’ story was inseparable from the local wisdom. The sea, as the central life of nature, was inseparable from human. The spirits of Mak Ungkai , Datuk Jerampang, and Awang Jangkung were not only as the antagonists but also as a part of nature. This story does not only present a frightening myth but it also shows how to maintain the balance of nature.
Fast differentiable evolution of quantum states under Gaussian transformations

In a recent work we presented a recursive algorithm to compute the matrix elements of a generic Gaussian transformation in the photon-number basis. Its purpose was to evolve a quantum state by building the transformation matrix and subsequently computing the matrix-vector product. Here we present a faster algorithm that computes the final state without having to generate the full transformation matrix first. With this algorithm we bring the time complexity of computing the Gaussian evolution of an $N$-dimensional $M$-mode state from $O(MN^{2M})$ to $O(M(N^2/2)^M)$, which is an exponential improvement in the number of modes. In the special case of high squeezing, the evolved state can be approximated with complexity $O(MN^{M})$. Our new algorithm is differentiable, which means we can use it in conjunction with gradient-based optimizers for circuit optimization tasks. We benchmark our algorithm by optimizing circuits to produce single photons, Gottesman-Kitaev-Preskill states and NOON states, showing that it is up to one order of magnitude faster than the state of the art.
Performance Analysis on x86 Architecture Microprocessor for Lightweight Encryption

Encryption is a process to replace information with an unreadable code using specific algorithm so that only the people who have the key and algorithm can understand the original information. The encryption, and its follow up decryption process, requires high computational power, including transposition, substitution, and iteration, which affects the speed and various aspects of performance, especially when applied to a generic processor. This research tries to find the optimum machine instruction composition for lightweight encryption to establish a design for Application-specific Instruction Set Processor (ASIP). The lightweight encryption algorithms used in this research are the Data Encryption Standard (DES) and Advanced Encryption Standard (AES), implemented using x86 instruction set architecture and compare the results for the machine instruction composition and the computational speed between the two programs. For machine instruction composition, the result shows that AES requires fewer instructions with 1537 instructions on separate I/O data scenarios and 1487 instructions on data overwrite scenarios. Both algorithms show that the most widely used machine instruction type is the Data Transfer. From performance point of view, AES is faster than DES with an average computational time in separate I/O data scenarios is 0.0544653 seconds and for the data overwrite scenario is 0.0520902 seconds.
Behavioral Economics Approach to Interpretable Deep Image Classification. Rationally Inattentive Utility Maximization Explains Deep Image Classification.

Are deep convolutional neural networks (CNNs) for image classification consistent with utility maximization behavior with information acquisition costs? This paper demonstrates the remarkable result that a deep CNN behaves equivalently (in terms of necessary and sufficient conditions) to a rationally inattentive utility maximizer, a model extensively used in behavioral economics to explain human decision making. This implies that a deep CNN has a parsimonious representation in terms of simple intuitive human-like decision parameters, namely, a utility function and an information acquisition cost. Also the reconstructed utility function that rationalizes the decisions of the deep CNNs, yields a useful preference order amongst the image classes (hypotheses).
Building a Chatbot

In the last chapter, you have learned about designing a chatbot including the conversation flow and the user experience. This chapter will cover the topic of building a chatbot using a tool called “Bot Framework Composer,” which is part of the Bot Framework ecosystem, for building chatbots using a graphical user interface instead of programming a chatbot using code. Moreover, the topics covered in previous chapters will be covered in detail within a real-life scenario for building a chatbot, such as using language understanding and QnA Maker, for enhancing natural language processing routines. In addition to that, this chapter will also describe the process of how to use custom code to include certain capabilities into a chatbot built with Composer, which is not available out of the box.
When Will I Return to the Uni Workshop? Studying Industrial Design in Colombia during the Pandemic

My university affords an education for students who come from remote areas of the country, indigenous people, and those with scarce financial resources. For these reasons, distance education has been a real challenge for us, but not the only one. Lockdowns thrust students into new realities devoid of the normal hard work and hard‐won joys of college life. But such times can also present new opportunities for learning.
Localised 3D disparity regularisation for improving contour propagation in Adaptive Radiotherapy

Identifying target volumes is a key component in image-guided adaptive radiotherapy. Furthermore, propagating contours from treatment planning images to images acquired during treatment is a difficult image registration problem due to organ motion. Among many deformable image registration techniques, block matching has been studied extensively in prostate and bladder cancer. Here, we propose a two-pass, three-dimensional disparity regularisation that accounts for anatomical constraints by distance and neighbouring motion vectors' orientation. This approach improves the Dice similarity score of the delineation/contour propagation and results in a non- iterative and non-pyramidal method with reduced computational time.