Reviewerly/exHarmony huggingface dataset:
Visual Analytics for the Representation, Exploration, and Analysis of High-Dimensional, Multi-faceted Medical Data

Medicine is among those research fields with a significant impact on humans and their health. Already for decades, medicine has established a tight coupling with the visualization domain, proving the importance of developing visualization techniques, designed exclusively for this research discipline. However, medical data is steadily increasing in complexity with the appearance of heterogeneous, multi-modal, multi-parametric, cohort or population, as well as uncertain data. To deal with this kind of complex data, the field of Visual Analytics has emerged. In this chapter, we discuss the many dimensions and facets of medical data. Based on this classification, we provide a general overview of state-of-the-art visualization systems and solutions dealing with high-dimensional, multi-faceted data. Our particular focus will be on multi-modal, multi-parametric data, on data from cohort or population studies and on uncertain data, especially with respect to Visual Analytics applications for the representation, exploration, and analysis of high-dimensional, multi-faceted medical data.
Development of value and moral ideas of students in the system school education

Л. Т. Потанина, Т. В
Saliency detection via double nuclear norm maximization and ensemble manifold regularization

In recent years, salient object detection via robust principal component analysis (RPCA) has received a significant amount of attention. Existing methods generally replace the rank function by the nuclear norm to obtain low-rank and sparse matrices, ignores the heavy-tailed distributions of singular values and over-penalizes large singular values of low rank matrices. In addition, although the manifold regularization is introduced into RPCA to obtain satisfactory low-rank representation of an original image, the graph hyperparameters selection lacks the ability to approximate the optimal solution for intrinsic manifold estimation. To solve these issues, we propose a novel low-rank matrix recovery model for salient object detection, which integrates double nuclear norm maximization with ensemble manifold regularization and can be formulated as a tractable optimization problem. By virtue of the alternating direction method (ADM), we develop an efficient algorithm to optimize the proposed model, which not only effectively fits the heavy-tailed distribution of singular values of low-rank matrices, but automatically learns the optimal linear combination of a set of predefined graph Laplacians. Experimental results on five challenging datasets show that our model achieves better performance than state-of-the-art unsupervised methods.
Trade-off Between Energy Consumption and Makespan in the Mapreduce Resource Allocation Problem

Minimizing energy consumption when executing Mapreduce jobs is a significant challenge for data centers; however, it traditionally conflicts with the system performance. This paper aims to address this problem by making a trade-off between energy consumption and performance. In this paper, we design an integer linear bi-objective optimization model and propose a two-phase heuristic allocation algorithm to find a high-quality feasible solution. By adopting Non-dominated Sorting Genetic Algorithm II with the feasible solution, we obtain a set of Pareto optimal solutions to minimize both energy consumption and makespan. Finally, we perform experiments on several real workloads to evaluate the solutions produced by our proposed algorithm and analyze the trade-off relationship between energy and makespan. The results show that the Pareto optimal solutions are close to the lower bound obtained by the relaxation of the integer linear bi-objective optimization model and can also assist system manager to make intelligent resource allocation decisions for Mapreduce applications based on the energy efficiency and performance needs of the system.
Medical image enhancement in F-shift transformation domain

Image enhancement technology plays an important role in the diagnosis and treatment of medical diseases. In this paper, we propose a method to automatically enhance medical images. The proposed method could be used to support clinical medical diagnosis, adjuvant therapy and curative effect diagnosis. This scheme uses contrast limited adaptive histogram equalization (CLAHE) method in F-shift transformation domain. Firstly, we adjust the overall brightness of the underexposed or overexposed image. Secondly, we perform CLAHE to enhance the low-frequency components obtained by one-level two-dimensional F-shift transformation (TDFS) on the adjusted images. At this stage, most of the coefficients in the high-frequency component can be changed to zero through properly setting the error bound. We then use inverse transformation to reconstruct image which is further enhanced with CLAHE. Compared to previous work, this approach takes into account not only the image enhancement, but also the data compression. Experimental results and comparison with state-of-the-art methods show that our proposed method has a better enhancement performance. Moreover, it has a certain data compression ability.
Natural Language Semantics and Computability

This paper is a reflexion on the computability of natural language semantics. It does not contain a new model or new results in the formal semantics of natural language: it is rather a computational analysis, in the context for type-logical grammars, of the logical models and algorithms currently used in natural language semantics, defined as a function from a grammatical sentence to a (non-empty) set of logical formulas—because a statement can be ambiguous, it can correspond to multiple formulas, one for each reading. We argue that as long as we do not explicitly compute the interpretation in terms of possible world models, one can compute the semantic representation(s) of a given statement, including aspects of lexical meaning. This is a very generic process, so the results are, at least in principle, widely applicable. We also discuss the algorithmic complexity of this process.
Task Transfer by Preference-Based Cost Learning

The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action planning, current methods mostly rely on two requirements: exactlyrelevant expert demonstrations or the explicitly-coded cost function on target task, both of which, however, are inconvenient to obtain in practice. In this paper, we relax these two strong conditions by developing a novel task transfer framework where the expert preference is applied as a guidance. In particular, we alternate the following two steps: Firstly, letting experts apply pre-defined preference rules to select related expert demonstrates for the target task. Secondly, based on the selection result, we learn the target cost function and trajectory distribution simultaneously via enhanced Adversarial MaxEnt IRL and generate more trajectories by the learned target distribution for the next preference selection. The theoretical analysis on the distribution learning and convergence of the proposed algorithm are provided. Extensive simulations on several benchmarks have been conducted for further verifying the effectiveness of the proposed method.
Lovasz Convolutional Networks

Semi-supervised learning on graph structured data has received significant attention with the recent introduction of Graph Convolution Networks (GCN). While traditional methods have focused on optimizing a loss augmented with Laplacian regularization framework, GCNs perform an implicit Laplacian type regularization to capture local graph structure. In this work, we propose Lovasz Convolutional Network (LCNs) which are capable of incorporating global graph properties. LCNs achieve this by utilizing Lovasz’s orthonormal embeddings of the nodes. We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs. We validate the proposed method on standard random graph models such as stochastic block models (SBM) and certain community structure based graphs where LCNs outperform GCNs and learn more intuitive embeddings. We also perform extensive binary and multi-class classification experiments on real world datasets to demonstrate LCN’s effectiveness. In addition to simple graphs, we also demonstrate the use of LCNs on hyper-graphs by identifying settings where they are expected to work better than GCNs.
I2PA: An Efficient ABC for IoT

The Internet of Things (IoT) is very attractive because of its promises. However, it brings many challenges, mainly issues about privacy preservation and lightweight cryptography. Many schemes have been designed so far but none of them simultaneously takes into account these aspects. In this paper, we propose an efficient attribute-based credential scheme for IoT devices. We use elliptic curve cryptography without pairing, blind signing, and zero-knowledge proof. Our scheme supports block signing, selective disclosure, and randomization. It provides data minimization and transaction unlinkability. Our construction is efficient since smaller key size can be used, and computing time can be reduced. As a result, it is a suitable solution for IoT devices characterized by three major constraints, namely low-energy power, small storage capacity, and low computing power.
Stepping Stones to Inductive Synthesis of Low-Level Looping Programs

Inductive program synthesis, from input/output examples, can provide an opportunity to automatically create programs from scratch without presupposing the algorithmic form of the solution. For induction of general programs with loops (as opposed to loop-free programs, or synthesis for domain-specific languages), the state of the art is at the level of introductory programming assignments. Most problems that require algorithmic subtlety, such as fast sorting, have remained out of reach without the benefit of significant problem-specific background knowledge. A key challenge is to identify cues that are available to guide search towards correct looping programs. We present MAKESPEARE, a simple delayed-acceptance hillclimbing method that synthesizes low-level looping programs from input/output examples. During search, delayed acceptance bypasses small gains to identify significantly-improved stepping stone programs that tend to generalize and enable further progress. The method performs well on a set of established benchmarks, and succeeds on the previously unsolved “Collatz Numbers” program synthesis problem. Additional benchmarks include the problem of rapidly sorting integer arrays, in which we observe the emergence of comb sort (a Shell sort variant that is empirically fast). MAKESPEARE has also synthesized a record-setting program on one of the puzzles from the TIS100 assembly language programming game.
Implementing the Residue Logarithmic Number System Using Interpolation and Cotransformation

The Residue Logarithmic Number System (RLNS) offers fast multiplication and division, but poses challenges for implementing addition and subtraction because the underlying integer Residue Number System (RNS) has slow sign detection. The conventional Binary Logarithmic Number Systems (BLNS) has benefited from interpolation and cotransformation. We propose a dual-path ALU that speculates about the sign detection to adapt interpolation and cotransformation to the limitations of RLNS. Synthesis shows for the same precision and technology, the area of the proposed RLNS circuit is similar to BLNS and much smaller than prior RLNS methods. We also compare against Floating Point (FP).
A Secret-Key Image Steganography Technique using Random Chain Codes

With the wide range use of digital communication technologies, the Internet has been commonly used as a channel for transmitting various images.Steganography practises have been implemented for achieving such secure transmission.The main focus of steganography is data hiding where, digital images are utilized as the cover image.One of the image steganography techniques is based on LSB method, where the secret message bits are embedded sequentially in LSB of the bytes of the carrier image.This makes the hidden message vulnerable to detection by attackers.Many secret key image steganography techniques have been developed as alternative techniques to achieve a high level of security for the hidden secret message.But, these techniques failed to use the full capacity of the carrier image.In this paper, a secret key image steganography technique has been implemented using chains of a random sequence of indices (codes) of the bytes in the carrier image.These chains have been constructed based on the secret key used.This makes the hidden message more secure and difficult to depict by attackers.Furthermore, the proposed technique uses the full capacity of the carrier image.Visual and numerical tests have been conducted for the performance of the proposed technique, the recorded results proved it can be used effectively in the field of information hiding.
An Uncertainty-Incorporated Approach to Predict the Winner in StarCraft II Using Neural Processes

Real-time strategy (RTS) game is a kind of strategy game, in which the players compete for resources on 2D terrain by establishing the economy, training army, and guiding them into battle in real time. The winner prediction of the RTS games often involves studying a highly uncertain problem in an adversarial environment. In addition, the limit of the number of samples restricts on the application and performance of the prediction models. To obtain better winner prediction accuracy and maintain the prediction uncertainty under an adversarial environment, this paper proposes a neural network-based prediction method incorporated probability inference dealing with a small set of samples. This paper uses a dataset released based on SC2LE, a reinforcement learning environment released jointly by Blizzard Entertainment and DeepMind, and then employed the proposed neural processes model to build a winner prediction model. To verify, this paper implemented different features types’ grouping and different game length grouping experiments for demonstrating better adaptability to such problems. Furthermore, this paper also implemented the SVM model experiments and compared the proposed method with the SVM model. Finally, when making predictions on a 1000 size testing data, the results show that the proposed prediction model achieves an accuracy of 0.811 at 200 and 0.821 at 1000 sizes of training sets, which is better than the SVM model with small training datasets.
Addressing Age-Related Bias in Sentiment Analysis

Recent studies have identified various forms of bias in language-based models, raising concerns about the risk of propagating social biases against certain groups based on sociodemographic factors (e.g., gender, race, geography). In this study, we analyze the treatment of age-related terms across 15 sentiment analysis models and 10 widely-used GloVe word embeddings and attempt to alleviate bias through a method of processing model training data. Our results show significant age bias is encoded in the outputs of many sentiment analysis algorithms and word embeddings, and we can alleviate this bias by manipulating training data.
Challenging Human Supremacy in Skat

After impressive successes in deterministic and fully-observable board games to significantly outclass humans, game playing research shifts towards non-deterministic and imperfect information card games, where humans are still persistently better. In this paper we devise a player that challenges human supremacy in Skat. We provide a complete player for playing selected variants of the game, with effective solutions for bidding and Skat putting, extracting knowledge from several million games. For trick play we combine expert rules with engineered tree exploration for optimal open card play. For dealing with uncertainty especially in Ouvert games we search the belief space.
A Quantitative Analysis Platform for PD-L1 Immunohistochemistry based on Point-level Supervision Model

Recently, deep learning has witnessed dramatic progress in the medical image analysis field. In the precise treatment of cancer immunotherapy, the quantitative analysis of PD-L1 immunohistochemistry is of great importance. It is quite common that pathologists manually quantify the cell nuclei. This process is very time-consuming and error-prone. In this paper, we describe the development of a platform for PD-L1 pathological image quantitative analysis using deep learning approaches. As point-level annotations can provide a rough estimate of the object locations and classifications, this platform adopts a point-level supervision model to classify, localize, and count the PD-L1 cells nuclei. Presently, this platform has achieved an accurate quantitative analysis of PD-L1 for two types of carcinoma, and it is deployed in one of the first-class hospitals in China.
Optical chaotic data-selection logic operation with the fast response for picosecond magnitude

We investigate the evolution of nonlinear dynamic behaviors of two polarization components (x-PC and y-PC), as well as the interplay of polarization bistability, frequency detuning and injection strength in the vertical cavity surface emitting laser with optical injection. Specifically, by encoding two logic inputs and one clock input in the amplitude of the light from a sampled grating distributed Bragg reflector laser, and by decoding two output logic responses from the x-PC and y-PC emitted by the laser, we demonstrate two parallel data-selection computing. The correct logic output encoded in two emitted PCs response for as short as 100 ps bit time and the response bit time of the correct logic output encoded in the y-PC may be 67 ps by the optimization of the injection strength. The probability of a correct response is controlled by the interplay of the bit time, the injection strength and noise strength, and is equal to 1 in a wide region of the injection strength and noise strength. The chaotic data-selection computing in an optically VCSEL offer interesting perspectives for applications where noise is unavoidable and fast switching is required.
Graduation Certificate Verification Model: A Preliminary Study

Graduation certificates issued by universities and other educational institutions are one of the most important documents for a graduate.It is a proof of graduate's qualifications and can be used to advance forward in one's career and life.However, due to advances in software, printing and photocopying technologies, forgery of those certificates is made easy and as good as the original, making them difficult to detect.Several universities and educational institutions as well as businesses started to dedicate resources for verifying certificates however that is usually a tedious and quite costly process and there isn't a clear model that can be adopted by those institutions that could minimize cost and speed up the process.There are many techniques proposed for paper based document verification and this paper analyzes and expatiate the issues on those techniques.Most of the verification techniques require change in the process of certificate generation either by changing template, changing paper, changing printers, adding hardware or even adding extra information.This change may mean that the university or verifier needs the proper knowledge to execute and run the proposed technique.This also means that older certificates may not work with the newly introduced techniques.To also add some proposed techniques require a change that is not always easy or cheap like in creating a third body to verify certificates.
A New Annotation Scheme for the Sejong Part-of-speech Tagged Corpus

In this paper we present a new annotation scheme for the Sejong part-of-speech tagged corpus based on Universal Dependencies style annotation.By using a new annotation scheme, we can produce Sejong-style morphological analysis and part-of-speech tagging results which have been the de facto standard for Korean language processing.We also explore the possibility of doing named-entity recognition and semantic-role labelling for Korean using the new annotation scheme.
Cognitive triaging of phishing attacks

In this paper we employ quantitative measurements of cognitive vulnerability triggers in phishing emails to predict the degree of success of an attack. To achieve this we rely on the cognitive psychology literature and develop an automated and fully quantitative method based on machine learning and econometrics to construct a triaging mechanism built around the cognitive features of a phishing email; we showcase our approach relying on data from the anti-phishing division of a large financial organization in Europe. Our evaluation shows empirically that an effective triaging mechanism for phishing success can be put in place by response teams to effectively prioritize remediation efforts (e.g. domain takedowns), by first acting on those attacks that are more likely to collect high response rates from potential victims.
Is Everything Going According to Plan? Expectations in Goal Reasoning Agents

In part motivated by topics such as agency safety, there is an increasing interest in goal reasoning, a form of agency where the agents formulate their own goals. One of the crucial aspects of goal reasoning agents is their ability to detect if the execution of their courses of actions meet their own expectations. We present a taxonomy of different forms of expectations as used by goal reasoning agents when monitoring their own execution. We summarize and contrast the current understanding of how to define and check expectations based on different knowledge sources used. We also identify gaps in our understanding of expectations.
Guiding Search with Generalized Policies for Probabilistic Planning

We examine techniques for combining generalized policies with search algorithms to exploit the strengths and overcome the weaknesses of each when solving probabilistic planning problems. The Action Schema Network (ASNet) is a recent contribution to planning that uses deep learning and neural networks to learn generalized policies for probabilistic planning problems. ASNets are well suited to problems where local knowledge of the environment can be exploited to improve performance, but may fail to generalize to problems they were not trained on. Monte-Carlo Tree Search (MCTS) is a forward-chaining state space search algorithm for optimal decision making which performs simulations to incrementally build a search tree and estimate the values of each state. Although MCTS can achieve state-of-the-art results when paired with domain-specific knowledge, without this knowledge, MCTS requires a large number of simulations in order to obtain reliable state-value estimates. By combining ASNets with MCTS, we are able to improve the capability of an ASNet to generalize beyond the distribution of problems it was trained on, as well as enhance the navigation of the search space by MCTS.
DSINE: Deep Structural Influence Learning via Network Embedding

Structural representations of user social influence are critical for a variety of applications such as viral marketing and recommendation products. However, existing studies only focus on capturing and preserving the structure of relations, and ignore the diversity of influence relations patterns among users. To this end, we propose a deep structural influence learning model to learn social influence structure via mining rich features of each user, and fuse information from the aligned selfnetwork component for preserving global and local structure of the influence relations among users. Experiments on two real-world datasets demonstrate that the proposed model outperforms the state-of-the-art algorithms for learning rich representations in multi-label classification task.