Reviewerly/exHarmony huggingface dataset:
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining

Anomaly detection is critically important for intelligent surveillance systems to detect in a timely manner any malicious activities. Many video anomaly detection approaches using deep learning methods focus on a single camera video stream with a fixed scenario. These deep learning methods use large-scale training data with large complexity. As a solution, in this paper, we show how to use pre-trained convolutional neural net models to perform feature extraction and context mining, and then use denoising autoencoder with relatively low model complexity to provide efficient and accurate surveillance anomaly detection, which can be useful for the resource-constrained devices such as edge devices of the Internet of Things (IoT). Our anomaly detection model makes decisions based on the high-level features derived from the selected embedded computer vision models such as object classification and object detection. Additionally, we derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method. We use two UCSD datasets to demonstrate that our approach with relatively low model complexity can achieve comparable performance compared to the state-of-the-art approaches.
DESAIN KATALOG MENGUNAKAN APLIKASI COREL DRAW X7 SEBAGAI PENGEMBANGAN PROMOSI PADA PT SWADAYA GRAHA DI GRESIK

The purpose of conducting this research is to add promotional media when conducting a tender process. This study was an a ction r esearch, employing Corel Draw X7 as the  catalog  creation.  Data collection method s  used  in this study w ere  observation, interviews, questionnaires, and documentation. The chosen respondents were  2 computer experts,  3  marketing experts, and 2 employees and 3 consumers who have used the sevices .  The researcher used the EPIC (Emphaty, Persuasion, Impact, Communication) and  as a measurement scale for data analysis. The results of the research in the first cycle according to design experts were considered effective with value 4 obtained by respondents, with acquisition of Empathy dimensions 3.75, Persuasion dimensions 4.25, Impact dimensions 3.75, and Communication dimensions of 4.25. The results show the dimensions of the entire catalog could be said to be worthy of being used as an additional promotional media.
Deliberative Acting, Online Planning and Learning with Hierarchical Operational Models

In AI research, synthesizing a plan of action has typically used descriptive models of the actions that abstractly specify what might happen as a result of an action, and are tailored for efficiently computing state transitions. However, executing the planned actions has needed operational models, in which rich computational control structures and closed-loop online decision-making are used to specify how to perform an action in a complex execution context, react to events and adapt to an unfolding situation. Deliberative actors, which integrate acting and planning, have typically needed to use both of these models together -- which causes problems when attempting to develop the different models, verify their consistency, and smoothly interleave acting and planning. As an alternative, we define and implement an integrated acting-and-planning system in which both planning and acting use the same operational models. These rely on hierarchical task-oriented refinement methods offering rich control structures. The acting component, called Reactive Acting Engine (RAE), is inspired by the well-known PRS system. At each decision step, RAE can get advice from a planner for a near-optimal choice with respect to a utility function. The anytime planner uses a UCT-like Monte Carlo Tree Search procedure, called UPOM, (UCT Procedure for Operational Models), whose rollouts are simulations of the actor's operational models. We also present learning strategies for use with RAE and UPOM that acquire, from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. We demonstrate the asymptotic convergence of UPOM towards optimal methods in static domains, and show experimentally that UPOM and the learning strategies significantly improve the acting efficiency and robustness.
SDMTL: Semi-Decoupled Multi-grained Trajectory Learning for 3D human motion prediction

Predicting future human motion is critical for intelligent robots to interact with humans in the real world, and human motion has the nature of multi-granularity. However, most of the existing work either implicitly modeled multi-granularity information via fixed modes or focused on modeling a single granularity, making it hard to well capture this nature for accurate predictions. In contrast, we propose a novel end-to-end network, Semi-Decoupled Multi-grained Trajectory Learning network (SDMTL), to predict future poses, which not only flexibly captures rich multi-grained trajectory information but also aggregates multi-granularity information for predictions. Specifically, we first introduce a Brain-inspired Semi-decoupled Motion-sensitive Encoding module (BSME), effectively capturing spatiotemporal features in a semi-decoupled manner. Then, we capture the temporal dynamics of motion trajectory at multi-granularity, including fine granularity and coarse granularity. We learn multi-grained trajectory information using BSMEs hierarchically and further capture the information of temporal evolutional directions at each granularity by gathering the outputs of BSMEs at each granularity and applying temporal convolutions along the motion trajectory. Next, the captured motion dynamics can be further enhanced by aggregating the information of multi-granularity with a weighted summation scheme. Finally, experimental results on two benchmarks, including Human3.6M and CMU-Mocap, show that our method achieves state-of-the-art performance, demonstrating the effectiveness of our proposed method. The code will be available if the paper is accepted.
MOVE, EAT, SLEEP, REPEAT: RHYTHMIC STRUGGLES IN PROACTIVE SELF-TRACKING PRACTICES

In this paper, we trace the rhythmicities of activity tracking by applying the method of technograhical collaborative autoethnography (cf. Bucher, 2012; Chang, Hernandez &amp; Ngunjiri, 2012). The focus is on on proactive self-tracking that has become a proliferating digital media practice involving the gathering of data about the body and one’s everyday patterns of being outside the context of clinical health care. In the paper, we address the gap that still exists in research of exploring the mechanisms by and through which the human-technology attachments operate in practice. Drawing from a new materialist understanding of media technologies combined with Lefebvrian rhytmanalysis, we focus on the rhythmic aspects of self-tracking by asking how specific self-tracking devices and interfaces attract and prescribe repetition, rhythms, beats, pulses, and cycles into everyday lives. In so doing, we elaborate how human bodies and technical systems of self-tracking interact rhythmically. The analysis sheds light on the rhythms and beats that we interpret as illustrations of the ’technological unconscious’ (Thrift &amp; French, 2002). We bring our observations in dialogue with the contemporary critical arguments pointing to the design of products that are made as addictive as possible in the race for ‘hijacking minds’ and capturing attention. Our research contributes empirically to the lack of research that has systematically examined these seductive and yet controlling properties of software, and complements the theory of rhythmanalysis with theorization on the hierarchical organisation of rhythms and related power dynamics in the digital context.
Practical use of university students as instructors for high school club activity:

In 2017, a system was introduced in Japan aimed at reducing the burden on school teachers by delegating their roles as instructors in school club activities. However, the issue of securing suitable human resources to fill this role was identified as being problematic. It has been suggested that the active use of university students as club instructors might be one method of resolving this shortage. Accordingly, in the present study, we sent university students to participate as instructors in a high school rowing club. Our aim was to examine their effectiveness and any related issues, as well as to highlight any challenges, so that solutions might be proposed to resolve them. For this purpose, we measured the students’ rowing movements and rowing distance using rowing ergometers (hereinafter referred to as the “ergo rowing distance”), and conducted semi-structured interviews with the students and teachers. This revealed the following items: (1) Instruction by university students was shown to improve the students’ rowing movements and significantly increased their ergo rowing distances. The burden on teachers was also reduced, and this had a positive impact on the teachers’ work. (2) It was suggested that utilization of university students for club activities could be one way to resolve the issues highlighted by previous research. (3) Improvement of instruction methods was shown to be one issue related to the use of university students for school club activities. One possible solution was considered to be provision of professional support with effective instruction methods for club activities by physical education and sports-related universities, as well as the creation of a system that would enable the smooth transfer of club activity instruction to university students.
Interlocking Backpropagation: Improving depthwise model-parallelism

The number of parameters in state of the art neural networks has drastically increased in recent years. This surge of interest in large scale neural networks has motivated the development of new distributed training strategies enabling such models. One such strategy is model-parallel distributed training. Unfortunately, model-parallelism can suffer from poor resource utilisation, which leads to wasted resources. In this work, we improve upon recent developments in an idealised model-parallel optimisation setting: local learning. Motivated by poor resource utilisation in the global setting and poor task performance in the local setting, we introduce a class of intermediary strategies between local and global learning referred to as interlocking backpropagation. These strategies preserve many of the compute-efficiency advantages of local optimisation, while recovering much of the task performance achieved by global optimisation. We assess our strategies on both image classification ResNets and Transformer language models, finding that our strategy consistently out-performs local learning in terms of task performance, and out-performs global learning in training efficiency.
INCREASING INTERNATIONAL ACADEMIC MOBILITY OF STUDENTS: CASE STUDY OF NORTH-CAUCASUS FEDERAL UNIVERSITY

The article is devoted to methods of financial and non-financial stimulation of academic mobility of students of higher educational institutions. Differences in the concepts of the term "academic mobility" in the countries of the European higher education area and in Russia are considered. The drivers of students academic mobility in European Union are presented. The authors describe case study of NCFU: the main barriers to implementation of credit mobility and detailed description of the institutional system stimulating students mobility.
Learning Clusterable Visual Features for Zero-Shot Recognition

In zero-shot learning (ZSL), conditional generators have been widely used to generate additional training features. These features can then be used to train the classifiers for testing data. However, some testing data are considered "hard" as they lie close to the decision boundaries and are prone to misclassification, leading to performance degradation for ZSL. In this paper, we propose to learn clusterable features for ZSL problems. Using a Conditional Variational Autoencoder (CVAE) as the feature generator, we project the original features to a new feature space supervised by an auxiliary classification loss. To further increase clusterability, we fine-tune the features using Gaussian similarity loss. The clusterable visual features are not only more suitable for CVAE reconstruction but are also more separable which improves classification accuracy. Moreover, we introduce Gaussian noise to enlarge the intra-class variance of the generated features, which helps to improve the classifier's robustness. Our experiments on SUN,CUB, and AWA2 datasets show consistent improvement over previous state-of-the-art ZSL results by a large margin. In addition to its effectiveness on zero-shot classification, experiments show that our method to increase feature clusterability benefits few-shot learning algorithms as well.
Asymmetric Prior in Wavelet Shrinkage

In bayesian wavelet shrinkage, the already proposed priors to wavelet coefficients are assumed to be symmetric around zero. Although this assumption is reasonable in many applications, it is not general. The present paper proposes the use of an asymmetric shrinkage rule based on the discrete mixture of a point mass function at zero and an asymmetric beta distribution as prior to the wavelet coefficients in a non-parametric regression model. Statistical properties such as bias, variance, classical and bayesian risks of the associated asymmetric rule are provided and performances of the proposed rule are obtained in simulation studies involving artificial asymmetric distributed coefficients and the Donoho-Johnstone test functions. Application in a seismic real dataset is also analyzed.
PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline.

The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting tumor progression and its treatment response. The value of the Ki-67 index and TILs in approach to heterogeneous tumors such as Breast cancer (BC), known as the most common cancer in women worldwide, has been highlighted in the literature. Due to the indeterminable and subjective nature of Ki-67 as well as TILs scoring, automated methods using machine learning, specifically approaches based on deep learning, have attracted attention. Yet, deep learning methods need considerable annotated data. In the absence of publicly available benchmarks for BC Ki-67 stained cell detection and further annotated classification of cells, we propose SHIDC-BC-Ki-67 as a dataset for the aforementioned purpose. We also introduce a novel pipeline and a backend, namely PathoNet for Ki-67 immunostained cell detection and classification and simultaneous determination of intratumoral TILs score. Further, we show that despite facing challenges, our proposed backend, PathoNet, outperforms the state of the art methods proposed to date in the harmonic mean measure.
Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones?

Previous work suggests that RNNs trained on natural language corpora can capture number agreement well for simple sentences but perform less well when sentences contain agreement attractors: intervening nouns between the verb and the main subject with grammatical number opposite to the latter. This suggests these models may not learn the actual syntax of agreement, but rather infer shallower heuristics such as `agree with the recent noun'. In this work, we investigate RNN models with varying inductive biases trained on selectively chosen `hard' agreement instances, i.e., sentences with at least one agreement attractor. For these the verb number cannot be predicted using a simple linear heuristic, and hence they might help provide the model additional cues for hierarchical syntax. If RNNs can learn the underlying agreement rules when trained on such hard instances, then they should generalize well to other sentences, including simpler ones. However, we observe that several RNN types, including the ONLSTM which has a soft structural inductive bias, surprisingly fail to perform well on sentences without attractors when trained solely on sentences with attractors. We analyze how these selectively trained RNNs compare to the baseline (training on a natural distribution of agreement attractors) along the dimensions of number agreement accuracy, representational similarity, and performance across different syntactic constructions. Our findings suggest that RNNs trained on our hard agreement instances still do not capture the underlying syntax of agreement, but rather tend to overfit the training distribution in a way which leads them to perform poorly on `easy' out-of-distribution instances. Thus, while RNNs are powerful models which can pick up non-trivial dependency patterns, inducing them to do so at the level of syntax rather than surface remains a challenge.
Gender domain adaptation for automatic speech recognition task

This paper is focused on the finetuning of acoustic models for speaker adaptation goals on a given gender. We pretrained the Transformer baseline model on Librispeech-960 and conduct experiments with finetuning on the gender-specific test subsets and. In general, we do not obtain essential WER reduction by finetuning techniques by this approach. We achieved up to ~5% lower word error rate on the male subset and 3% on the female subset if the layers in the encoder and decoder are not frozen, but the tuning is started from the last checkpoints. Moreover, we adapted our base model on the full L2 Arctic dataset of accented speech and fine-tuned it for particular speakers and male and female genders separately. The models trained on the gender subsets obtained 1-2% higher accuracy when compared to the model tuned on the whole L2 Arctic dataset. Finally, we tested the concatenation of the pretrained x-vector voice embeddings and embeddings from a conventional encoder, but its gain in accuracy is not significant.
Visualizing Color-wise Saliency of Black-Box Image Classification Models

Image classification based on machine learning is being commonly used. However, a classification result given by an advanced method, including deep learning, is often hard to interpret. This problem of interpretability is one of the major obstacles in deploying a trained model in safety-critical systems. Several techniques have been proposed to address this problem; one of which is RISE, which explains a classification result by a heatmap, called a saliency map, which explains the significance of each pixel. We propose MC-RISE (Multi-Color RISE), which is an enhancement of RISE to take color information into account in an explanation. Our method not only shows the saliency of each pixel in a given image as the original RISE does, but the significance of color components of each pixel; a saliency map with color information is useful especially in the domain where the color information matters (e.g., traffic-sign recognition). We implemented MC-RISE and evaluate them using two datasets (GTSRB and ImageNet) to demonstrate the effectiveness of our methods in comparison with existing techniques for interpreting image classification results.
From Intuitionism to Brouwer's Modal Logic

We try to translate the intuitionistic propositional logic INT into Brouwer's modal logic KTB. Our translation is motivated by intuitions behind Brouwer's axiom p →☐◊p The main idea is to interpret intuitionistic implication as modal strict implication, whereas variables and other positive sentences remain as they are. The proposed translation preserves fragments of the Rieger-Nishimura lattice which is the Lindenbaum algebra of monadic formulas in INT. Unfortunately, INT is not embedded by this mapping into KTB.
Cycles in the Permutation Decoding System

Currently, there is an increased interest in permutation decoding systems for systematic redundant codes due to the possibility of using cognitive methods for processing digital data. This allows you to give the decoding procedure of such codes a significant acceleration. The resulting time gain is in demand in many application areas. The gain is achieved by pre-calculating the generating matrix of the equivalent code, the structure of which is set by the current permutation of characters of the code vector. The result of this calculation is stored in the decoder's cognitive map. Due to this, the time required for organizing the search for equivalent code is replaced by the amount of memory on the cognitive map.
Un caso de estudio relacionado con la importancia de educar sobre la seguridad informática a nivel personal y empresarial

Currently, with the massification of electronic media such as cell phones and computers, computer security is more in force than ever before; nevertheless, the level of culture in the matter of the users is far from being the ideal. The present work shows a study conducted with two different types of computer attacks, one remotely and the anther one connecting a physical device to a computer. Subsequently, the execution and validation of the proposal in a social environment is shown, finally the conclusions obtained are shown.
An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks

Typically, tokenization is the very first step in most text processing works. As a token serves as an atomic unit that embeds the contextual information of text, how to define a token plays a decisive role in the performance of a model.Even though Byte Pair Encoding (BPE) has been considered the de facto standard tokenization method due to its simplicity and universality, it still remains unclear whether BPE works best across all languages and tasks. In this paper, we test several tokenization strategies in order to answer our primary research question, that is, "What is the best tokenization strategy for Korean NLP tasks?" Experimental results demonstrate that a hybrid approach of morphological segmentation followed by BPE works best in Korean to/from English machine translation and natural language understanding tasks such as KorNLI, KorSTS, NSMC, and PAWS-X. As an exception, for KorQuAD, the Korean extension of SQuAD, BPE segmentation turns out to be the most effective.
Fast Fourier Transformation for Optimizing Convolutional Neural Networks in Object Recognition

This paper proposes to use Fast Fourier Transformation-based U-Net (a refined fully convolutional networks) and perform image convolution in neural networks. Leveraging the Fast Fourier Transformation, it reduces the image convolution costs involved in the Convolutional Neural Networks (CNNs) and thus reduces the overall computational costs. The proposed model identifies the object information from the images. We apply the Fast Fourier transform algorithm on an image data set to obtain more accessible information about the image data, before segmenting them through the U-Net architecture. More specifically, we implement the FFT-based convolutional neural network to improve the training time of the network. The proposed approach was applied to publicly available Broad Bioimage Benchmark Collection (BBBC) dataset. Our model demonstrated improvement in training time during convolution from $600-700$ ms/step to $400-500$ ms/step. We evaluated the accuracy of our model using Intersection over Union (IoU) metric showing significant improvements.