Reviewerly/exHarmony huggingface dataset:
Optimal Placement of UAV for Coverage Maximization with Minimum Path Loss

Unmanned Aerial Vehicle (UAV) act as a flying Base Station (BS) is ascertained an auspicious way to cater the problems of system coverage and capacity.There are some constraints that must be considered to set out a UAV in place of a Base Station (BS).The accessibility of a reliable wireless backhaul link is one of the aforementioned limits and it is considered in this work.The paper explores diverse sort of wireless backhauls that delivers unlike data rates, and their impact on the served users.We present a dual model network0centric's and user0centric's and the optimum 3 dimensional assignment of a UAV is calculated for each model.We then maximized the quantity of attended consumers and sum rates for both models.Furthermore it is preferred to lessen the UAV movements which results in increasing flying time & decreases channel variants and the performance of the network is analyzed in correspondence to the user's translations.
Deep Adversarial Social Recommendation

Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life. The majority of existing social recommendation methods unify user representation for the user-item interactions (item domain) and user-user connections (social domain). However, it may restrain user representation learning in each respective domain, since users behave and interact differently in the two domains, which makes their representations to be heterogeneous. In addition, most of traditional recommender systems can not efficiently optimize these objectives, since they utilize negative sampling technique which is unable to provide enough informative guidance towards the training during the optimization process. In this paper, to address the aforementioned challenges, we propose a novel deep adversarial social recommendation framework DASO. It adopts a bidirectional mapping method to transfer users' information between social domain and item domain using adversarial learning. Comprehensive experiments on two real-world datasets show the effectiveness of the proposed framework.
Reducing file size and time complexity in secret sharing based document protection

Recently, Tu and Hsu proposed a secret sharing based document protecting scheme. In their scheme, a document is encrypted into $n$ shares using Shamir's $(k, n)$ secret sharing, where the $n$ shares are tied in with a cover document. The document reconstruction can be accomplished by acknowledgement of any $k$ shares and the cover document. In this work, we construct a new document protecting scheme which is extended from Tu-Hsu's work. In Tu-Hsu's approach, each inner code of secret document takes one byte length, and shares are generated from all inner codes with the computation in $GF(257)$, where $257$ is a Fermat Prime that satisfies $257 = 2^{2^{3}}+1$. However, the share size expands when it equals to $255$ or $256$. In our scheme, each two inner codes of document is combined into one double-bytes inner code, and shares are generated from these combined inner codes with the computation in $GF(65537)$ instead, where $65537$ is also a Fermat Prime that satisfies $65537 = 2^{2^{4}}+1$. Using this approach, the share size in our scheme can be reduced from Tu-Hsu's scheme. In addition, since the number of combined inner codes is half of the inner codes number in Tu-Hsu's scheme, our scheme is capable of saving almost half running time for share generation and document reconstruction from Tu-Hsu's scheme.
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation

A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity.Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information.However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts.To solve these problems, we propose a head-modifier template-based method to ensure the readability and data fidelity of generated type descriptions.We also propose a new dataset and two automatic metrics for this task.Experiments show that our method improves substantially compared with baselines and achieves stateof-the-art performance on both datasets.
Domain Adaptation of Neural Machine Translation by Lexicon Induction

It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift. In this paper, we argue that this is a dual effect of the highly lexicalized nature of NMT, resulting in failure for sentences with large numbers of unknown words, and lack of supervision for domain-specific words. To remedy this problem, we propose an unsupervised adaptation method which fine-tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus. Specifically, we perform lexicon induction to extract an in-domain lexicon, and construct a pseudo-parallel in-domain corpus by performing word-for-word back-translation of monolingual in-domain target sentences. In five domains over twenty pairwise adaptation settings and two model architectures, our method achieves consistent improvements without using any in-domain parallel sentences, improving up to 14 BLEU over unadapted models, and up to 2 BLEU over strong back-translation baselines.
Spilltime: Designing for the relationship between QS, CO2e and climate goals

(2019). Spilltime: Designing for the relationship between QS, CO2e and climate goals. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 1087-1100.
Effective writing style imitation via combinatorial paraphrasing

Stylometry can be used to profile or deanonymize authors against their will based on writing style. Style transfer provides a defence. Current techniques typically use either encoder-decoder architectures or rule-based algorithms. Crucially, style transfer must reliably retain original semantic content to be actually deployable. We conduct a multifaceted evaluation of three state-of-the-art encoder-decoder style transfer techniques, and show that all fail at semantic retainment. In particular, they do not produce appropriate paraphrases, but only retain original content in the trivial case of exactly reproducing the text. To mitigate this problem we propose ParChoice: a technique based on the combinatorial application of multiple paraphrasing algorithms. ParChoice strongly outperforms the encoder-decoder baselines in semantic retainment. Additionally, compared to baselines that achieve non-negligible semantic retainment, ParChoice has superior style transfer performance. We also apply ParChoice to multi-author style imitation (not considered by prior work), where we achieve up to 75% imitation success among five authors. Furthermore, when compared to two state-of-the-art rule-based style transfer techniques, ParChoice has markedly better semantic retainment. Combining ParChoice with the best performing rule-based baseline (Mutant-X) also reaches the highest style transfer success on the Brennan-Greenstadt and Extended-Brennan-Greenstadt corpora, with much less impact on original meaning than when using the rule-based baseline techniques alone. Finally, we highlight a critical problem that afflicts all current style transfer techniques: the adversary can use the same technique for thwarting style transfer via adversarial training. We show that adding randomness to style transfer helps to mitigate the effectiveness of adversarial training.
Attention: A Big Surprise for Cross-Domain Person Re-Identification

In this paper, we focus on model generalization and adaptation for cross-domain person re-identification (Re-ID). Unlike existing cross-domain Re-ID methods, leveraging the auxiliary information of those unlabeled target-domain data, we aim at enhancing the model generalization and adaptation by discriminative feature learning, and directly exploiting a pre-trained model to new domains (datasets) without any utilization of the information from target domains. To address the discriminative feature learning problem, we surprisingly find that simply introducing the attention mechanism to adaptively extract the person features for every domain is of great effectiveness. We adopt two popular type of attention mechanisms, long-range dependency based attention and direct generation based attention. Both of them can perform the attention via spatial or channel dimensions alone, even the combination of spatial and channel dimensions. The outline of different attentions are well illustrated. Moreover, we also incorporate the attention results into the final output of model through skip-connection to improve the features with both high and middle level semantic visual information. In the manner of directly exploiting a pre-trained model to new domains, the attention incorporation method truly could enhance the model generalization and adaptation to perform the cross-domain person Re-ID. We conduct extensive experiments between three large datasets, Market-1501, DukeMTMC-reID and MSMT17. Surprisingly, introducing only attention can achieve state-of-the-art performance, even much better than those cross-domain Re-ID methods utilizing auxiliary information from the target domain.
Resolutions and Network Latencies Concerning a Voxel Telepresence Experience

Recent advancements in computing research and technology will allow future immersive virtual reality systems to be voxel-based, i.e. entirely based on gap-less, spatial representations of volumetric pixels.The current popularity of pixel-based videoconferencing systems could turn into true telepresence experiences that are voxel-based.Richer, non-verbal communication will be possible thanks to the three-dimensional nature of such systems.An effective telepresence experience is based on the users' sense of copresence with others in the virtual environment and on a sense of embodiment.We investigate two main quality of service factors, namely voxel size and network latency, to identify acceptable threshold values for maintaining the copresence and embodiment experience.We present a working prototype implementation of a voxel-based telepresence system and can show that even a coarse 64 mm voxel size and an overall round-trip latency of 542 ms are sufficient to maintain copresence and embodiment experiences.We provide threshold values for noticeable, disruptive, and unbearable latencies that can serve as guidelines for future voxel and other telepresence systems.
Implicit Discourse Relation Classification with Syntax-Aware Contextualized Word Representations

Automatically identifying implicit discourse relations requires an in-depth semantic understanding of the text fragments involved in such relations. While early work investigated the usefulness of different classes of input features, current state-of-the-art models mostly rely on standard pretrained word embeddings to model the arguments of a discourse relation. In this paper, we introduce a method to compute contextualized representations of words, leveraging information from the sentence dependency parse, to improve argument representation. The resulting token embeddings encode the structure of the sentence from a dependency point of view in their representations. Experimental results show that the proposed representations achieve state-of-the-art results when input to standard neural network architectures, surpassing complex models that use additional data and consider the interaction between arguments.
A survey of Object Classification and Detection based on 2D/3D data

Recently, by using deep neural network based algorithms, object classification, detection and semantic segmentation solutions are significantly improved. However, one challenge for 2D image-based systems is that they cannot provide accurate 3D location information. This is critical for location sensitive applications such as autonomous driving and robot navigation. On the other hand, 3D methods, such as RGB-D and RGB-LiDAR based systems, can provide solutions that significantly improve the RGB only approaches. That is why this is an interesting research area for both industry and academia. Compared with 2D image-based systems, 3D-based systems are more complicated due to the following five reasons: 1) Data representation itself is more complicated. 3D images can be represented by point clouds, meshes, volumes. 2D images have pixel grid representations. 2) The computation and memory resource requirement is higher as an extra dimension is added. 3) Different distribution of the objects and difference in scene areas between indoor and outdoor make one unified framework hard to achieve. 4) 3D data, especially for the outdoor scenario, is sparse compared with the dense 2D images which makes the detection task more challenging. Finally, large size labelled datasets, which are extremely important for supervised based algorithms, are still under construction compared with well-built 2D datasets such as ImageNet. Based on challenges listed above, the described systems are organized by application scenarios, data representation methods and main tasks addressed. At the same time, critical 2D based systems which greatly influence the 3D ones are also introduced to show the connection between them.
Fuzzi: A Three-Level Logic for Differential Privacy

Curators of sensitive datasets sometimes need to know whether queries against the data are differentially private [Dwork et al. 2006]. Two sorts of logics have been proposed for checking this property: (1) type systems and other static analyses, which fully automate straightforward reasoning with concepts like "program sensitivity" and "privacy loss," and (2) full-blown program logics such as apRHL (an approximate, probabilistic, relational Hoare logic) [Barthe et al. 2016], which support more flexible reasoning about subtle privacy-preserving algorithmic techniques but offer only minimal automation. We propose a three-level logic for differential privacy in an imperative setting and present a prototype implementation called Fuzzi. Fuzzi's lowest level is a general-purpose logic; its middle level is apRHL; and its top level is a novel sensitivity logic adapted from the linear-logic-inspired type system of Fuzz, a differentially private functional language [Reed and Pierce 2010]. The key novelty is a high degree of integration between the sensitivity logic and the two lower-level logics: the judgments and proofs of the sensitivity logic can be easily translated into apRHL; conversely, privacy properties of key algorithmic building blocks can be proved manually in apRHL and the base logic, then packaged up as typing rules that can be applied by a checker for the sensitivity logic to automatically construct privacy proofs for composite programs of arbitrary size. We demonstrate Fuzzi's utility by implementing four different private machine-learning algorithms and showing that Fuzzi's checker is able to derive tight sensitivity bounds.
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums

Modern large-scale finite-sum optimization relies on two key aspects: distribution and stochastic updates. For smooth and strongly convex problems, existing decentralized algorithms are slower than modern accelerated variance-reduced stochastic algorithms when run on a single machine, and are therefore not efficient. Centralized algorithms are fast, but their scaling is limited by global aggregation steps that result in communication bottlenecks. In this work, we propose an efficient \textbf{A}ccelerated \textbf{D}ecentralized stochastic algorithm for \textbf{F}inite \textbf{S}ums named ADFS, which uses local stochastic proximal updates and randomized pairwise communications between nodes. On $n$ machines, ADFS learns from $nm$ samples in the same time it takes optimal algorithms to learn from $m$ samples on one machine. This scaling holds until a critical network size is reached, which depends on communication delays, on the number of samples $m$, and on the network topology. We provide a theoretical analysis based on a novel augmented graph approach combined with a precise evaluation of synchronization times and an extension of the accelerated proximal coordinate gradient algorithm to arbitrary sampling. We illustrate the improvement of ADFS over state-of-the-art decentralized approaches with experiments.
Assessing the Ability of Self-Attention Networks to Learn Word Order

Self-attention networks (SAN) have attracted a lot of interests due to their high parallelization and strong performance on a variety of NLP tasks, e.g. machine translation. Due to the lack of recurrence structure such as recurrent neural networks (RNN), SAN is ascribed to be weak at learning positional information of words for sequence modeling. However, neither this speculation has been empirically confirmed, nor explanations for their strong performances on machine translation tasks when "lacking positional information" have been explored. To this end, we propose a novel word reordering detection task to quantify how well the word order information learned by SAN and RNN. Specifically, we randomly move one word to another position, and examine whether a trained model can detect both the original and inserted positions. Experimental results reveal that: 1) SAN trained on word reordering detection indeed has difficulty learning the positional information even with the position embedding; and 2) SAN trained on machine translation learns better positional information than its RNN counterpart, in which position embedding plays a critical role. Although recurrence structure make the model more universally-effective on learning word order, learning objectives matter more in the downstream tasks such as machine translation.
MENTORING ON SCIENCE TEACHER EDUCATION IN RUSSIA AND INTERNATIONAL PERSPECTIVES

Nowadays, mentoring as an educational form of Russia’s occupational activity is undergoing fundamental changes due to the variety of interrelated factors affecting the mentoring system. The requirements for the mentoring system itself are affected by these changes as well. Nature of the mentoring activity influences socio-economic, political, socio-cultural, pedagogical, methodical and individual factors. Socio-economic factors that determine not only the enterprises' state order, but also modern educational institutions’ order to revive and improve the traditions of mentoring receive the highest priority among these factors (Antipin, 2011).
Constructive Type-Logical Supertagging With Self-Attention Networks

We propose a novel application of selfattention networks towards grammar induction.We present an attention-based supertagger for a refined type-logical grammar, trained on constructing types inductively.In addition to achieving a high overall type accuracy, our model is able to learn the syntax of the grammar's type system along with its denotational semantics.This lifts the closed world assumption commonly made by lexicalized grammar supertaggers, greatly enhancing its generalization potential.This is evidenced both by its adequate accuracy over sparse word types and its ability to correctly construct complex types never seen during training, which, to the best of our knowledge, was as of yet unaccomplished.
Development and Application of Programming Education Model Based on Visual Thinking Strategy for Pre-service Teachers

In the software-oriented society, the importance of computational thinking for problem solving is increasing day by day.In particular, programming is a good way to improve this kind of computational thinking, and beginners are having difficulties in creating and programming procedures for problem solving.In this study, we developed a Thinking-Revise-Encoding-Evaluating (TREE) model based on a visual thinking strategy for pre-service teachers and applied a 15-week curriculum.The results of the study showed that pre-service teachers' SW education perception, block programming ability, computational thinking, and satisfaction were significant.
Application of Interactive Multiple Model Adaptive Five-Degree Cubature Kalman Algorithm Based on Fuzzy Logic in Target Tracking

Aiming at the shortcomings of low precision, hysteresis, and poor robustness of the general interactive multimodel algorithm in the “snake-like” maneuver tracking of anti-ship missiles, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic (FLIMM5ACKF) is proposed. The algorithm mainly includes adaptive five-degree cubature Kalman algorithm (A5CKF) and fuzzy logic algorithm (FL). A5CKF uses the Sage–Husa noise estimation principle to propose a state error covariance adaptive five-degree cubature Kalman algorithm to improve the performance of state estimation. Then, the fuzzy logic algorithm (FL) is added to the model probability update module to control the model probability update module. Finally, by setting the same tracking model simulation analysis, the algorithm has better convergence speed, tracking effect and robustness than the interactive multimodel cubature Kalman algorithm (IMMCKF), the interactive multimodel five-degree cubature Kalman algorithm (IMM5CKF) and the interactive multimodel adaptive five-degree cubature Kalman (IMMA5CKF).
Penerapan Model Pembelajaran Multilierasi Informasi Untuk Meningkatkan Pemahaman Konsep Jual Beli

This article aimed to improve the understanding of the concept of purchase and sale in class III Elementary School of Mekarjaya III through the application of information multiliterasi learning models. This research was classroom action research. This research was conducted in three cycles. The results showed that the percentage of classical completeness in the first cycle has average 63.2 with the percentage of classical completeness which was 48.5%. In the second cycle the average score of students were 70 with the percentage of classical completeness 63.6%. In the third cycle the average value of students were 81.2 with the percentage of classical completeness 87.9%. Then it can be concluded that the application of the multiliteration learning model of information on social studies subjects has success to increase the understanding of the concept purchase and sale in class III Elementary School of Mekarjaya III District Majalengka.&#x0D; Key words: Multiliteration Information Model, Understanding of Purchase and Sale
Building a Computer Mahjong Player via Deep Convolutional Neural Networks

The evaluation function for imperfect information games is always hard to define but owns a significant impact on the playing strength of a program. Deep learning has made great achievements these years, and already exceeded the top human players' level even in the game of Go. In this paper, we introduce a new data model to represent the available imperfect information on the game table, and construct a well-designed convolutional neural network for game record training. We choose the accuracy of tile discarding which is also called as the agreement rate as the benchmark for this study. Our accuracy on test data reaches 70.44%, while the state-of-art baseline is 62.1% reported by Mizukami and Tsuruoka (2015), and is significantly higher than previous trials using deep learning, which shows the promising potential of our new model. For the AI program building, besides the tile discarding strategy, we adopt similar predicting strategies for other actions such as stealing (pon, chi, and kan) and riichi. With the simple combination of these several predicting networks and without any knowledge about the concrete rules of the game, a strength evaluation is made for the resulting program on the largest Japanese Mahjong site `Tenhou'. The program has achieved a rating of around 1850, which is significantly higher than that of an average human player and of programs among past studies.