Reviewerly/exHarmony huggingface dataset:
Defensive Distillation Based End-to-end Auto-encoder Communication System

The new generation of wireless communication systems proposes the vision that artificial intelligence should play a more significant role in the development of techniques, thus the application of deep learning (DL) in communications has attracted much attention. DL based auto-encoders have been considered to model an end-to-end communication system. In the situation of DL based communication systems, security problems also need to be concerned. Attackers may utilize the broadcast nature of wireless channels to add perturbations which lead to error transmission. In order to defend against such attacks, a defensive distillation based end-to-end system is proposed. Distillation is introduced to modify the structure of the neural network so that it assists to enhance the robustness of the system model against perturbation and decrease error transmission. Additionally, the robustness against channel noise is also demonstrated in the research.
Can Unhappy Pictures Enhance the Effect of Personas? A User Experiment

There has been little research into whether a persona's picture should portray a happy or unhappy individual. We report a user experiment with 235 participants, testing the effects of happy and unhappy image styles on user perceptions, engagement, and personality traits attributed to personas using a mixed-methods analysis. Results indicate that the participant's perceptions of the persona's realism and pain point severity increase with the use of unhappy pictures. In contrast, personas with happy pictures are perceived as more extroverted, agreeable, open, conscientious, and emotionally stable. The participants’ proposed design ideas for the personas scored more lexical empathy scores for happy personas. There were also significant perception changes along with the gender and ethnic lines regarding both empathy and perceptions of pain points. Implications are the facial expression in the persona profile can affect the perceptions of those employing the personas. Therefore, persona designers should align facial expressions with the task for which the personas will be employed. Generally, unhappy images emphasize realism and pain point severity, and happy images invoke positive perceptions.
REN: Receiver-Driven Congestion Control Using Explicit Notification for Data Center

In recent years, receiver-driven transport protocols have been proposed to use proactive congestion control to meet the stringent latency requirements of large-scale applications in data center. However, the receiver-driven proposals face the challenges brought by network dynamic. First, when the bursty flows start, the aggressive and blind line-rate transmission in the first RTT easily leads to persistent queue backlog. Second, when some flows finish transmissions, the remaining ones cannot increase their sending rates to seize the available bandwidth. To address these problems, this article presents a new receiver-driven congestion control design, called REN, which uses the under- and over-utilization notifications from switch to handle the dynamic traffic. With the aid of explicit feedback, REN alleviates the traffic burstiness due to aggressive start, mitigates the conservativeness in utilizing available bandwidth, and still retains the receiver-driven feature to achieve ultra-low latency. We implement the prototype of REN using DPDK. The experimental results of real testbed and large-scale NS2 simulation show that REN effectively reduces the average flow completion time (AFCT) by up to 68% over the state-of-the-art receiver-driven transmission schemes.
Harmonization of Systems of Discretionary Differentiation of Access to Information Systems at the Stage of Modernization

Integrated information and telecommunication systems operate subsystems with their own systems of access differentiation. At the stage of subsystems modernization there is a possibility to change the parameters or structure of their access differentiation systems. This fact is a prerequisite for violation of the information properties, contained in the general field of the integrated information and telecommunications system. Analysis of the existing ways of the unauthorized information flow appearance during usage of discretionary models in the access differentiation systems has shown a possibility of information properties violation. A method for harmonization of access matrices of various options of the discretionary differentiation of access systems has been developed. The security theorem of joint functioning of different options of access differentiation systems, built on the bases of a discretionary model, is formulated and proved.
Deep Supervised Hashing by Classification for Image Retrieval

Hashing has been widely used to approximate the nearest neighbor search for image retrieval due to its high computation efficiency and low storage requirement. With the development of deep learning, a series of deep supervised methods were proposed for end-to-end binary code learning. However, the similarity between each pair of images is simply defined by whether they belong to the same class or contain common objects, which ignores the heterogeneity within the class. Therefore, those existing methods have not fully addressed the problem and their results are far from satisfactory. Besides, it is difficult and impractical to apply those methods to large-scale datasets. In this paper, we propose a brand new perspective to look into the nature of deep supervised hashing and show that classification models can be directly utilized to generate hashing codes. We also provide a new deep hashing architecture called Deep Supervised Hashing by Classification (DSHC) which takes advantage of both inter-class and intra-class heterogeneity. Experiments on benchmark datasets show that our method outperforms the state-of-the-art supervised hashing methods on accuracy and efficiency.
Eye movement analysis in simple visual tasks

The small eye movements in the process of fixation on an image element give us knowledge about the human visual information perception. An indepth analysis of these movements can reveal the influence of personality, mood and mental state of the examined subject on the process of perception. The modern eye tracking technology provides us with the necessary technical means to study these movements. Nevertheless, still a lot of problems remains open. In the present paper two approaches for noise cancellation in the eye-tracker signal and two approaches for microsaccade detection are proposed. The analysis of the obtained results can be a good starting point for interpretation by neurobiologists about the causes of different types of movement and their dependence on the individuality of the observed person and the specific mental and physical condition.
Automatic Extrinsic Rotational Calibration of LiDAR Sensors and Vehicle Orientation Estimation

Whether a vehicle is being used for an autonomous mission or in support of data collection and research, extrinsic calibration to align the vehicle's sensors to a reference point on the vehicle is integral to ensuring that quality data is available to the system. This is particularly true for vision and distance sensors, such as LiDAR, which must be well-located with respect to the vehicle body before they can provide meaningful localization or environmental modeling assistance. This paper outlines an automatic approach for calibrating a LiDAR to the body of a ground vehicle using only the data from the LiDAR itself. This approach assumes that the LiDAR is able to sample at least a one meter square section of level ground, and that the vehicle travels along a straight section of roadway with a well-marked road edge at some point during the calibration, while closely following the trajectory of the road edge. This method has been able to automatically calibrate a LiDAR to yield 0.08 and 0.12 degrees of error in roll and pitch respectively when comparing estimated ground pitch and roll to the orientation estimates from a truth sensor.
A multi-preference integrated algorithm (MPIA) for the deep learning-based recommender framework (DLRF)

Purpose The deep learning-based recommender framework (DLRF) is based on an improved long short-term memory (LSTM) structure with additional controllers; thus, it considers contextual information for state transition. It also handles irregularities in the data to enhance performance in generating recommendations while modelling short-term preferences. An algorithm named a multi-preference integrated algorithm (MPIA) is proposed to have dynamic integration of both kinds of user preferences aforementioned. Extensive experiments are made using Amazon benchmark datasets, and the results are compared with many existing recommender systems (RSs). Design/methodology/approach RSs produce quality information filtering to the users based on their preferences. In the contemporary era, online RSs-based collaborative filtering (CF) techniques are widely used to model long-term preferences of users. With deep learning models, such as recurrent neural networks (RNNs), it became viable to model short-term preferences of users. In the existing RSs, there is a lack of dynamic integration of both long- and short-term preferences. In this paper, the authors proposed a DLRF for improving the state of the art in modelling short-term preferences and generating recommendations as well. Findings The results of the empirical study revealed that the MPIA outperforms existing algorithms in terms of performance measured using metrics such as area under the curve (AUC) and F1-score. The percentage of improvement in terms AUC is observed as 1.3, 2.8, 3 and 1.9% and in terms of F-1 score 0.98, 2.91, 2 and 2.01% on the datasets. Originality/value The algorithm uses attention-based approaches to integrate the preferences by incorporating contextual information.
Change management scenario to improve Webometrics ranking

Webometrics ranking of world universities (WRWU) is recognized as a trusted ranking system for the world’s universities. Previous study shows that there is a relationship between the electronic Word of Mouth (e-WOM) and Web Content Accessibility Guidelines (WCAG) 2.0 variables on the increase of the webometrics ranking indicator value. This research is improving the previous result by mapping the recommendations into element based on the Awareness, Desire, Knowledge, Ability, Reinforcement (ADKAR) method. The goal of this research is creating recommendation activities and change management scenario in ADKAR method to improve university webometrics ranking. The result of this study is 75 recommended activities to improve university webometrics ranking.
New roles and competencies of teachers in the ICT-mediated learning environment of Russian universities

Introduction . The research considers organisational and functional changes in the learning environment of Russian universities mediated by the spread of ICTs in education. ICTs affect the structure and content of curriculum and the relationship of participants in the educational process, which actualises the need to revise a place, roles, knowledge and competencies of humanities teachers. External and internal factors induce teachers to change approaches, methods and teaching technologies in accordance with the social request for high quality training of future professionals in the ICT-mediated realities and improve their ICT proficiency to meet the demands of the day. The best model of ICT-mediated learning environment will function on the basis of technologisation, interdisciplinarity, practice orientation, student focus and multicultural diversity. Aim . The purpose of this study was to identify, analyse and assess the determining factors that accompany, actualise and influence the process of transformation of the place, roles, skills and competencies of teachers in the ICT-mediated learning environment of universities in Russia. Methodology and research methods . The study was conducted within a framework of social, pedagogical, integrative, contextual, competency-based and comparative approaches, covering all aspects of academic activities of teachers and students in the ICT-mediated learning environment. Both qualitative and quantitative research methods were used to collect and analyse the relevant data. Analysis, synthesis and comparison were used to study and summarise scientific information; observations, questionnaires and survey were carried out within the experimental section, followed by a subsequent statistical analysis. A holistic approach allowed to create a classification of the ICT-based roles and skills of teachers and to develop the authors’ scale of gradation of ICT proficiency. Results . The conceptual apparatus is concretised; conditions for the ICTs inclusion in education are defined; the authors’ classification of the roles, skills and competencies of teachers for ICT-based professional activity, growth and development is given; the authors’ diagnostic scale (toolkit) for rating and grading humanities teachers’ level of ICT proficiency is designed and presented. Scientific novelty . The scientific novelty of the work lies in the definition and description of the systemic integrity of educational content based on knowledge and technology, the appropriate conditions for integration of ICT in the curriculum, the level of teachers’ ICT proficiency sufficient for the learning needs of their students and the expected learning outcomes. Practical significance . The authors’ classification of the roles, skills and competencies and the diagnostic scale (toolkit) for rating and grading the level of ICT-proficiency can be used in refresher courses for humanities teachers for their professional training, growth and development.
The Cost of Incidents in Essential Services—Data from Swedish NIS Reporting

Abstract The NIS Directive aims to increase the overall level of cyber security in the EU and establishes a mandatory reporting regime for operators of essential services and digital service providers. While this reporting has attracted much attention, both in society at large and in the scientific community, the non-public nature of reports has led to a lack of empirically based research. This paper uses the unique set of all the mandatory NIS reports in Sweden in 2020 to shed light on incident costs. The costs reported exhibit large variability and skewed distributions, where a single or a few higher values push the average upwards. Numerical values are in the range of tens to hundreds of kSEK per incident. The most common incident causes are malfunctions and mistakes, whereas attacks are rare. No operators funded their incident costs using loans or insurance. Even though the reporting is mandated by law, operator cost estimates are incomplete and sometimes difficult to interpret, calling for additional assistance and training of operators to make the data more useful.
Assessing farmer perceptions on livestock intensification and associated trade‐offs using fuzzy cognitive maps; a study in mixed farming systems in the mid‐hills of Nepal

Abstract Intensified livestock production is considered as a promising pathway for smallholder farmers. Nevertheless, this pathway may entail prohibitive investment requirements of labour, capital or trade‐offs at farm level that preclude sustainable intensification. We used fuzzy cognitive mapping (FCM) to assess farmers' perceptions of changes in the farm household system resulting from adding livestock to their mixed farms. Farmers identified trade‐offs between the increased income and farmyard manure production versus increases in labour requirements for fodder imports. Furthermore, a sensitivity analysis performed on the FCMs showed that an increase in milk market demand could have strong positive effects on livestock production and income. We conclude that FCM is a good tool to rapidly identify trade‐offs and analyse perceptions of farmers which revealed that although they consider intensification a promising strategy, the perceived deepening of labour constraints and increasing dependency on fodder import makes a concurrent (sustainable) intensification of these farm systems unlikely.
Weakly-Supervised Semantic Segmentation Network With Iterative dCRF

This Autonomous driving methods driven by big data are becoming more and more perfect, but the cost of existing data labeling is too high, so how to reduce or even not label data has attracted more and more attention. Semantic segmentation networks supervised by image-level annotations are all trained using pseudo-labels. Most methods use image classification networks to generate class activation maps (CAMs) and start with CAMs to diffuse features to other parts of the target to obtain pseudo-labels. However, due to its weak supervision information, it is difficult for the existing methods to obtain better results. Therefore, we propose a weakly-supervised semantic segmentation network with iterative dCRF based on graph convolution. Specifically, we use ResNet to generate CAMs and node features and then use graph convolution for feature propagation and merge the low-level and high-level semantic information of the image. Then execute dCRF in an iterative manner, and finally obtain refined pseudo-labels. On the PASCAL VOC 2012 data set, our model achieves an mIoU of 63.5%, which is 0.3% higher than the graph convolutional network method.
A Deep Learning Approach to Invisible Watermarking for Copyright Protection

Watermarking has been an age-old process serving a multitude of purposes from copyright protection to maintaining confidentiality. Invisible watermarking is a subcategory of watermarking which involves the use of a digital signature embedded in the image to be protection in such a way that keeps the watermark imperceptible to the naked eye. The proposed technique embeds an invisible and ineffaceable watermark to any image, without any distortion or loss in image quality. This is done using autoencoder neural network which is trained for the process of embedding the watermark onto the image to be protected. This technique provides a lower loss after extraction and makes sure the watermark is invisible with minimal distortion to original image. Easy extraction of watermark is also ensured using the model specifically designed for extraction.
Prediction of Voice Sentiment using Machine Learning Technique

Sentiment Analysis or we can say the study of any person’s attitudes or their emotions for an event, discussion on any random thing. It has been evolving over the recent decades; mainly the work done in the past decades is in the area of text sentiment analysis with many text mining methods. But Voice sentimental analysis remains during a growing stage within the research communities. In this paper, we had performed a sentimental analysis on user’s voice to detect the emotions of the users. By using the Librosa python library we will perform speaker discrimination and sentiment analysis. Interpreting the mood of any person is very useful. Like, if computers gain the power to recognize and replying to human non-verbal discussion such as human feelings. In this situation, after recognizing a person’s feelings, the machine can change its own settings in accordance with user’s mood or emotion and preferences.
On minimal representations of shallow ReLU networks

The realization function of a shallow ReLU network is a continuous and piecewise affine function f:Rd→R, where the domain Rd is partitioned by a set of n hyperplanes into cells on which f is affine. We show that the minimal representation for f uses either n, n+1 or n+2 neurons and we characterize each of the three cases. In the particular case, where the input layer is one-dimensional, minimal representations always use at most n+1 neurons but in all higher dimensional settings there are functions for which n+2 neurons are needed. Then we show that the set of minimal networks representing f forms a C∞-submanifold M and we derive the dimension and the number of connected components of M. Additionally, we give a criterion for the hyperplanes that guarantees that a continuous, piecewise affine function is the realization function of an appropriate shallow ReLU network.
MC-OCR Challenge 2021: End-to-end system to extract key information from Vietnamese Receipts

In the information age, how to quickly obtain information and extract key information from massive and complex re-sources has become challenging. Extracting information from scanned or captured document is one of the most demanding process in many areas such as finance, accounting, and taxation. The current achievement in the computer vision field has shown a substantial improvement in the field of Optical Character Recognition (OCR), including text detection and recognition tasks. However, there are two challenges for current OCR. The first one is the quality of the input data which is captured by mobile phone. The quality is greatly affected by external factors like light condition, dynamic environment or blurry content. Secondly, Key Information Extraction (KIE) from documents, which is a downstream task of OCR, had been a largely under explored domain because the input documents have not only textual features extracting from OCR systems but also semantic visual features which are not fully utilized and play a critical role in KIE. In this paper, we propose an end-to-end system based on several state-of-the-art models from both computer vision and natural language processing areas to deal with the Mobile captured receipts OCR (MC-OCR) challenge, including two tasks: (1) evaluating the quality of the captured receipt, and (2) recognizing required fields of the receipt. Our code is publicly available at https://github.com/ndcuong9/MC_OCR
SqueezeCapsNet – Transfer Learning-Based ASL Interpretation Using SqueezeNet With Multi-Lane Capsules

Gesture recognition and interpretation systems broadly implement deep learning architectures to facilitate human system interaction, including communication through sign language. This paper deals with the automatic interpretation of American Sign Language (ASL) gestures using computer vision with image processing. A hybrid SqueezeNet Multi-Lane CapsNet architecture is introduced where the features are learnt by a SqueezeNet convolutional architecture and the relative pose estimation of the features is carried out by capsules in multiple lanes. Multi-Lane CapsNet setup ensures parallel identification of distinct features that are then mapped by using a dynamic routing algorithm. Transfer learning was implemented on the SqueezeNet model with weights from pre-training the ImageNet dataset. This ensures optimized initialization of weights and reduced time for convergence during training. The hybrid architecture is trained on a benchmark dataset containing hand gestures pertaining to the English alphabets from ASL convention. The SqueezeCapsNet model performed with a high accuracy of 91.6%, a false positive rate of 0.0033%, and a comparative analysis of this model is presented with the existing state-of-the-art ASL gesture recognition models. The weights from the SqueezeNet model were extracted to present a Grad-CAM++ visualization of the gesture interpretation system.