Reviewerly/exHarmony huggingface dataset:
Unsupervised Adaptive Semantic Segmentation with Local Lipschitz Constraint

Recent advances in unsupervised domain adaptation have seen considerable progress in semantic segmentation. Existing methods either align different domains with adversarial training or involve the self-learning that utilizes pseudo labels to conduct supervised training. The former always suffers from the unstable training caused by adversarial training and only focuses on the inter-domain gap that ignores intra-domain knowledge. The latter tends to put overconfident label prediction on wrong categories, which propagates errors to more samples. To solve these problems, we propose a two-stage adaptive semantic segmentation method based on the local Lipschitz constraint that satisfies both domain alignment and domain-specific exploration under a unified principle. In the first stage, we propose the local Lipschitzness regularization as the objective function to align different domains by exploiting intra-domain knowledge, which explores a promising direction for non-adversarial adaptive semantic segmentation. In the second stage, we use the local Lipschitzness regularization to estimate the probability of satisfying Lipschitzness for each pixel, and then dynamically sets the threshold of pseudo labels to conduct self-learning. Such dynamical self-learning effectively avoids the error propagation caused by noisy labels. Optimization in both stages is based on the same principle, i.e., the local Lipschitz constraint, so that the knowledge learned in the first stage can be maintained in the second stage. Further, due to the model-agnostic property, our method can easily adapt to any CNN-based semantic segmentation networks. Experimental results demonstrate the excellent performance of our method on standard benchmarks.
Training Classifiers that are Universally Robust to All Label Noise Levels

For classification tasks, deep neural networks are prone to overfitting in the presence of label noise. Although existing methods are able to alleviate this problem at low noise levels, they encounter significant performance reduction at high noise levels, or even at medium noise levels when the label noise is asymmetric. To train classifiers that are universally robust to all noise levels, and that are not sensitive to any variation in the noise model, we propose a distillation-based framework that incorporates a new subcategory of Positive-Unlabeled learning. In particular, we shall assume that a small subset of any given noisy dataset is known to have correct labels, which we treat as "positive", while the remaining noisy subset is treated as "unlabeled". Our framework consists of the following two components: (1) We shall generate, via iterative updates, an augmented clean subset with additional reliable "positive" samples filtered from "unlabeled" samples; (2) We shall train a teacher model on this larger augmented clean set. With the guidance of the teacher model, we then train a student model on the whole dataset. Experiments were conducted on the CIFAR-10 dataset with synthetic label noise at multiple noise levels for both symmetric and asymmetric noise. The results show that our framework generally outperforms at medium to high noise levels. We also evaluated our framework on Clothing1M, a real-world noisy dataset, and we achieved 2.94% improvement in accuracy over existing state-of-the-art methods.
Accurate Camouflaged Object Detection via Mixture Convolution and Interactive Fusion

Camouflaged object detection (COD), which aims to identify the objects that conceal themselves into the surroundings, has recently drawn increasing research efforts in the field of computer vision. In practice, the success of deep learning based COD is mainly determined by two key factors, including (i) A significantly large receptive field, which provides rich context information, and (ii) An effective fusion strategy, which aggregates the rich multi-level features for accurate COD. Motivated by these observations, in this paper, we propose a novel deep learning based COD approach, which integrates the large receptive field and effective feature fusion into a unified framework. Specifically, we first extract multi-level features from a backbone network. The resulting features are then fed to the proposed dual-branch mixture convolution modules, each of which utilizes multiple asymmetric convolutional layers and two dilated convolutional layers to extract rich context features from a large receptive field. Finally, we fuse the features using specially-designed multi-level interactive fusion modules, each of which employs an attention mechanism along with feature interaction for effective feature fusion. Our method detects camouflaged objects with an effective fusion strategy, which aggregates the rich context information from a large receptive field. All of these designs meet the requirements of COD well, allowing the accurate detection of camouflaged objects. Extensive experiments on widely-used benchmark datasets demonstrate that our method is capable of accurately detecting camouflaged objects and outperforms the state-of-the-art methods.
Reservation Policies and its Impact on Education System in India: By an Observers Eye

Education can removes injustice, illiteracy, and fulfills the knowledge. Education can also change the capacity of thinking, ideas, recommendations and mental feelings according to the nation, age and circumstances. By which the man recognizes his virtues and gets ability to give them proper expression, without education, all round development is beyond imagination not only of the personality of a person but also the development of the culture and civilization and the progress of the nation and whole society. At present, the Indian education system is one of the fastest developments in the world. This paper presents the reservation policies in India. Impact of reservation on higher education in India also the advantages and disadvantaged o the reservation polices was discussed.
METHODOLOGICAL ASPECTS OF TEACHER TRAINING FOR THEATRICAL ACTIVITIES WITH PUPILS

The research was conducted as part of the project of Erasmus + (Module Jean Monnet) “EU experience of soft skills development of preschool and primary school age children by theatre activities in teacher training”.The purpose of the article is to find out the educational value of theatrical activities, to reveal the methodological aspects of preparing teachers for theatrical activities with pupils. The study used theoretical methods: analysis of psychological and pedagogical literature, synthesis, comparison, method of modeling and generalization of research results; diagnostic methods: interview, survey, observation, interview: methods of processing experimental data for quantitative and qualitative analysis of the results of diagnostic tests.The concept of “theatrical activity” is specified, the educational and developmental potential of theatrical activity is revealed, the technique of introduction of theatrical activity in practice of modern school is developed, methodical bases of preparation of teachers for theatrical activity with schoolchildren are opened.The results of empirical research have shown that most teachers do not use theatrical activities in their own pedagogical practice. Only certain elements are introduced in art and language lessons: staging of literary works or songs. We are convinced that theatrical activities are a powerful effective means of moral, aesthetic, promotes the development of social skills (soft skils), because it allows to involve students in various types of artistic creativity: literary, musical, visual, stage, choreographic. Through participation in theatrical performances, creative potential is revealed, high moral values are formed, sufficient communication skills are needed to model social behavior in the children's team, in the family and society as a whole. The study allowed to confirm the importance and practical feasibility of using theatrical activities as a means of education and development of youth
영논항의 대명사 분석에 대한 재고찰

Korean allows null arguments in finite clauses. According to pro analyses of null arguments, null arguments are silent forms of overt pronouns. Unlike sloppy interpretations of null arguments, those of overt pronouns are somewhat restricted and show speakers’ variation. Accordingly, the presence of sloppy interpretation of null arguments has been considered as weak points of the strict pro analyses. In this paper, we examine two factors that may affect sloppy interpretation of overt pronouns: singular vs. plural on the one hand, and human vs. nonhuman on the other. In addition, we discuss a non-trivial empirical advantage our pro analysis has in comparison with another type of pro analysis such as NP Substitution analysis.
History Of The Development Of Block-Chain Cryptic Technology And Its Future In The Global Economy

This article represents the case of study the block-chain technology evolution across its existing like financial, economical and social instrument. With phenomenal growing in 2017 it creates a new industry of pay instrument Bitcoin has laid the foundation for the evolution of financial instruments and information technologies, this wave may sign new changes that may occur in the near future. In addition, forecasts will be made on the future of new digital coins and block-chain technology Keywords: block-chain, bitcoin, finance, economy, technology.
Positional Artefacts Propagate Through Masked Language Model Embeddings

In this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers. Namely, we find cases of persistent outlier neurons within BERT and RoBERTa's hidden state vectors that consistently bear the smallest or largest values in said vectors. In an attempt to investigate the source of this information, we introduce a neuron-level analysis method, which reveals that the outliers are closely related to information captured by positional embeddings. We also pre-train the RoBERTa-base models from scratch and find that the outliers disappear without using positional embeddings. These outliers, we find, are the major cause of anisotropy of encoders' raw vector spaces, and clipping them leads to increased similarity across vectors. We demonstrate this in practice by showing that clipped vectors can more accurately distinguish word senses, as well as lead to better sentence embeddings when mean pooling. In three supervised tasks, we find that clipping does not affect the performance.
Should we wear a velvet glove to enforce Information security policies in higher education?

The United States higher education is facing a unique challenge in information security management due to its distinctive characteristics, such as decentralised structure, academic freedom, and shared governance. These characteristics sharply distinguish higher education from traditional corporations. Gradually, this is beginning to pose a challenge for higher education institutions to adopt the appropriate information technology governance framework, which, for traditional corporations, mostly addresses security governance and leadership in a top-down manner. To address this issue, we examine the effect of perceived information security management approaches on perceived security practices. Our results show that the perceived flexible-oriented approach of information security management is more effective to use in implementing security controls in high education institutions. This seems to contradict most of the findings in the literature that suggest that a control-oriented approach is more effective in enforcing information security policies. Research contributions and implications are discussed, accordingly.
ReLUSyn: Synthesizing Stealthy Attacks for Deep Neural Network Based Cyber-Physical Systems

Cyber Physical Systems (cps) are deployed in many mission-critical settings, such as medical devices, autonomous vehicular systems and aircraft control management systems. As more and more CPS adopt Deep Neural Networks (Deep Neural Network (dnns), these systems can be vulnerable to attacks. . Prior work has demonstrated the susceptibility of CPS to False Data Injection Attacks (False Data Injection Attacks (fdias), which can cause significant damage. We identify a new category of attacks on these systems. In this paper, we demonstrate that DNN based CPS are also subject to these attacks. These attacks, which we call Ripple False Data Injection Attacks (rfdia), use minimal input perturbations to stealthily change the dnn output. The input perturbations propagate as ripples through multiple dnn layers to affect the output in a targeted manner. We develop an automated technique to synthesize rfdias against DNN-based CPS. Our technique models the attack as an optimization problem using Mixed Integer Linear Programming (Mixed Integer Linear Program (milp)). We define an abstraction for dnnbased cps that allows us to automatically: 1) identify the critical inputs, and 2) find the smallest perturbations that produce output changes. We demonstrate our technique on three practical cps with two mission-critical applications: an (Artifical Pancreas System (aps)) and two aircraft control management systems (Horizontal Collision Avoidance System (hcas) and Collision Avoidance System-Xu (acas-xu)).
Exact PPS Sampling with Bounded Sample Size

Probability proportional to size (PPS) sampling schemes with a target sample size aim to produce a sample comprising a specified number $n$ of items while ensuring that each item in the population appears in the sample with a probability proportional to its specified "weight" (also called its "size"). These two objectives, however, cannot always be achieved simultaneously. Existing PPS schemes prioritize control of the sample size, violating the PPS property if necessary. We provide a new PPS scheme that allows a different trade-off: our method enforces the PPS property at all times while ensuring that the sample size never exceeds the target value $n$. The sample size is exactly equal to $n$ if possible, and otherwise has maximal expected value and minimal variance. Thus we bound the sample size, thereby avoiding storage overflows and helping to control the time required for analytics over the sample, while allowing the user complete control over the sample contents. The method is both simple to implement and efficient, being a one-pass streaming algorithm with an amortized processing time of $O(1)$ per item.
Cognitive-Affective Drivers of Employees’ Daily Compliance with Information Security Policies: A Multilevel, Longitudinal Study

We present a model of employee compliance with information security policy (ISP) that (1) explicates stable, cognitive beliefs regarding the consequences of compliance and non-compliance as well as state-based affective constructs – namely, positive and negative mood states and episodic, security-related work-impediment events and (2) provides an expanded conceptualisation of moral considerations and normative influences regarding employees’ ISP compliance. Because affect is central to this theorisation, we ensure that the model captures and explains differences in day-to-day affective constructs to account for the often fleeting nature of affective states. We test our multilevel model using an experience-sampling methodology design in which employees completed daily surveys over a two-week period, followed by a hierarchal linear modelling statistical assessment. Our contribution to theory is a unique account of ISP compliance that integrates affective factors with constructs from rational choice theory (RCT) and theory of planned behaviour (TPB) and that diverges from prior conceptualisations of ISP compliance as a purely stable and reason-based phenomenon. For practitioners, our results suggest that a combination of cognitive and affective influences may produce discrete episodes of ISP compliance that do not coincide with prior behavioural trends.
BELT: Blockwise Missing Embedding Learning Transfomer.

Matrix completion has attracted a lot of attention in many fields including statistics, applied mathematics and electrical engineering. Most of works focus on the independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in the integration of multiple (point-wise mutual information) PMI matrices, we propose the model {\bf B}lockwise missing {\bf E}mbedding {\bf L}earning {\bf T}ransformer (BELT) to treat row-wise/column-wise missingness. Specifically, our proposed method aims at efficient matrix recovery when every pair of matrices from multiple sources has an overlap. We provide theoretical justification for the proposed BELT method. Simulation studies show that the method performs well in finite sample under a variety of configurations. The method is applied to integrate several PMI matrices built by EHR data and Chinese medical text data, which enables us to construct a comprehensive embedding set for CUI and Chinese with high quality.
ABILITY TO MANAGE THE KNOWLEDGE AND COMPETITIVE ADVANTAGE: Applied to ICT Companies in the Gaza Strip affiliated with the Palestinian Information Systems Association (PITA): قدرة إدارة المعرفة والميزة التنافسية: بالتطبيق على شركات تكنولوجيا المعلومات والاتصالات في قطاع غزة المنضمة لاتحاد نظم المعلومات الفلسطينية (PITA)

The study aims to determine the impact of knowledge management on the competitive advantage of the sector companies by clarifying the impact of different components of ability to manage the knowledge and competitive advantage by applying to the ICT companies in the Gaza Strip of the Palestinian Information Technology Association ( PITA). The research problem was the main question: What is the impact of knowledge management on the competitive advantage of sector companies?. The main objective of the research was to identify the impact of knowledge management on the competitive advantage of sector companies. The researcher used the analytical descriptive approach. To achieve the objectives of the study, an electronic questionnaire made up of two parts: The first part includes the data of the study sample. The second part includes 9 main themes. The data were processed through the application of the Statistical Package for Social Sciences(SPSS). The researcher used the analytical descriptive approach, which tries to describe, assess and interpret the reality of "the impact of knowledge management on the competitive advantage of the sector companies. The study community is composed of officials at various administrative levels (top, middle and low) in ICT companies in the Gaza Strip(PITA), The researcher used an electronic questionnaire was sent to the companies through the e-mail of these companies made up of two parts: The first part includes the data of the study. One of the most important findings of the researcher There is a statistically significant impact of the variable capacity of the social infrastructure to knowledge management (organizational structure, organizational culture, individuals) on the competitive advantage. There is a significant impact of the variable capacity of knowledge management processes (acquisition, conversion, application, protection) on the competitive advantage of the companies subject of study. There is a statistically significant impact of the ability of the IT infrastructure to knowledge management on competitive advantage the most important recommendations recommended by the study. The focus was on the top management of all companies wishing to maintain their competitive advantage adopting the method of knowledge management building Administrations or departments specializing in this field have an active player position in the organizational structure of the company, The need to pay attention in the knowledge; held by the employees and motivate them to retain knowledge. The need for the company to pay attention to knowledge in its environment. Moreover, the need to pay attention to the rules of knowledge and apply these rules to achieve a competitive advantage.
Exploring open-ended gameplay features with Micro RollerCoaster Tycoon

This paper introduces MicroRCT, a novel open source simulator inspired by the theme park sandbox game RollerCoaster Tycoon. The goal in MicroRCT is to place rides and shops in an amusement park to maximize profit earned from park guests. Thus, the challenges for game AI include both selecting high-earning attractions and placing them in locations that are convenient to guests. In this paper, the MAP-Elites algorithm is used to generate a diversity of park layouts, exploring two theoretical questions about evolutionary algorithms and game design: 1) Is there a benefit to starting from a minimal starting point for evolution and complexifying incrementally? and 2) What are the effects of resource limitations on creativity and optimization? Results indicate that building from scratch with no costs results in the widest diversity of high-performing designs.
The revision of syntactic errors related to complex sentences in French L1: strategies of secondary school advanced writers

This article presents a description of the revision strategies targeting complex sentences of 16 secondary school advanced writers (15-17 years old) in the context of French L1 instruction.As the literature indicates, most errors in students' texts are syntactic errors (Boivin & Pinsonneault, 2018), and revising them entails a heavy cognitive load (Roussey & Piolat, 2008).We conducted a multiple case study among these advanced writers to identify their detection, diagnosis and correction strategies targeting syntactic problems.Thinking-aloud (Ericsson & Simon, 1993;Hayes & Flower, 1980), they revised one individual text and one experimental text containing 22 different syntactic errors related to complex sentences.We focused on the revision strategies leading to accurate changes.Our results show that advanced writers make a very limited use of detection strategies.Their diagnosis strategies are mainly reflections, grammaticality judgments and rereadings.Students with high rates of accurate changes in the experimental text use fewer diagnosis strategies than those with average rates.Self-questioning appears to be a strategy most used by students with high rates of accurate changes.The corrections are generally precise and made immediately after a problem is detected.Looking at individual cases, we also present salient profiles based on the students' posture toward revision and syntax.
Safety Metrics for Semantic Segmentation in Autonomous Driving

Within the context of autonomous driving, safety-related metrics for deep neural networks have been widely studied for image classification and object detection. In this paper, we further consider safety-aware correctness and robustness metrics specialized for semantic segmentation. The novelty of our proposal is to move beyond pixel-level metrics: Given two images with each having N pixels being class-flipped, the designed metrics should, depending on the clustering of pixels being class-flipped or the location of occurrence, reflect a different level of safety criticality. The result evaluated on an autonomous driving dataset demonstrates the validity and practicality of our proposed methodology.