Reviewerly/exHarmony huggingface dataset:
Recognition of Transforming Behavior of Human Emotions from Face Video Sequence: A Triangulation-Induced Circumradius-Incenter-Circumcenter Combined Approach

Usage of the system for human emotion recognition has been increased in various types of applications of affective computing fields such as human sign language understanding, identification of human mental disorder, and human-computer interaction. Here, we report a video frame-based procedure for the estimation of human emotional behavior. We introduce Circumradius-Incenter-Circumcenter combined geometric signature (CIC) induced from our proposed triangulation method. The method first includes the step of salient landmark identification from face image frames by using the Active Appearance Model (AAM). Here, we fetch geometric features from triangles drawn by landmark points, thereafter core triangles are found based on the CIC feature which plays an important role to get an interpretation of changing information of human emotions. In the end, the extracted core features from core triangles are employed into the Multilayer Perceptron (MLP) classifier to get recognition accuracy. The discrimination power of our proposed system is evaluated on well-known three benchmark video face frame databases, viz., CK+, MMI, and MUG. Moreover, the performance of the proposed procedure is validated by presenting the comparison task with other existing methods.
Short-Term Prediction of Generated Power from Small Roof Photovoltaic Power Plant

In recent years, a significant increase in smart home installations can be observed. These installations provide their users with high comfort and make life easier in almost all everyday activities. Users of these systems also expect a reduction in energy costs. The optimal use of photovoltaic rooftop power plants in these systems is therefore essential. This work deals with a short-term prediction of the generated power of a rooftop photovoltaic power plant. The data set of the generated energy for five years obtained from a specific rooftop photovoltaic power plant was used for the prediction process. The prediction model is based on Formal Concept Analysis as a cluster analysis method, and the prediction itself is then performed by traversing the concept lattice.
A Device Framework for IoT using Web Services

As IoT is making in-roads into field, many chip companies are incorporating IoT resources into processors. This allows development of IoT frameworks using which commercial devices can be integrated with web servers, reducing the overall time to market. We have implemented such a framework for IoT using Web services. The framework is based on Texas Instruments' CC3200 microcontroller and PHP + Apache 2.2 web server. The framework provides graphical interface to monitor and control IoT devices. The microcontroller supports Internet connection through WiFi routers as they come equipped with on-chip support for RF communications. Digital/analog input to the microcontroller will be sent to the server using a simple web service. This data is saved and accessed using various REST API and applications implemented on Apache-2 web server. APIs for HTML-5 and Android are available in this framework. The paper includes interaction of some hardware and software components which have to be integrated to achieve desired implementation and performance.
Pixinwav: Residual Steganography for Hiding Pixels in Audio

Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images, or hiding audio in audio), PixInWav targets the multimodal case of hiding images in audio. To this end, we propose a novel residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. Among our results, we find that the residual steganography setup we propose allows an encoding of the hidden image that is independent from the host audio without compromising quality. Accordingly, while previous works require both host and hidden signals to hide a signal, PixInWav can encode images offline—which can be later hidden, in a residual fashion, into any audio signal.
Detection of Human Facial Expression Using CNN and Deployment in Desktop Application

Facial appearance is a basic but noteworthy non-verbal excited procedure. The affirmation of outward appearances is unquestionably not a basic issue. Undoubtedly, even photos of a comparative individual in a comparable outward appearance can change in wonder, establishment, and these assortments are emphasized while considering different subjects (taking into account assortments perfectly healthy, personality among others). Therefore, outward appearance affirmation is at this point a troublesome issue in PC vision. This research work aims to propose a response for outward appearance affirmation that uses a mix of convolution neural network organizations and express picture pre-planning steps. It has portrayed that, the inventive course of action that gives beneficial face appearance and significant learning with convolution neural associations has gained exceptional headway in the gathering of various face sentiments like lively, enraged, shocking, and fair. Henceforth, the collection of neuron-wise and layer-wise insight strategies was applied to use CNN, which is arranged with an openly available picture dataset. Along these lines, it's seen that neural associations can get the tones and surfaces of bruises unequivocal to specific sentiments after discovering, which resembles the human dynamics.
Dijkstra’s Algorithm to Find Shortest Path of Tourist Destination in Bandung

Bandung is one of the many islands in Indonesia and is known as the “City of Flowers”. Bandung has a variety of tourist destinations eg. The Asian-African Museum, Alun Alun Kota, Puncak Dago, and many other tours in the area around Lembang Dijkstra's algorithm is one of the algorithms that can solve the problem of finding the shortest route which is a problem in graph theory. Implementation of the system that we use will result in a website application that uses the Javascript programming language. This system uses the Dijkstra algorithm which is very useful for visitors because tourists can find the shortest route that can be taken to and from a tourist destination in the area of Bandung that you want to visit using this system. With the research of the shortest route using the Djikstra algorithm to find the fastest route of tourist attractions in the city of Bandung, the tourists can add information about the fastest route and finally the tourists can choose the best route that can streamline all costs and find out all the information on tourist objects in This city of Bandung.
Multi-object Detection in Night Time

This paper discusses the work on detecting multi-objects such as person and car in thermal image captured during night time using deep learning architecture. Thermal images are superior to the visible images when it comes to the amount of useful information required to detect the objects during night time. Thermal imager uses radiation emitted by the objects to create an image and improve the visibility of objects in a dark environment. Contrast to that, visible image does not provide useful information in darkness. Hence, it is better to use thermal images to detect objects present in darkness. The state-of-the-art, Yolo-v3, deep learning convolutional neural network model is the latest version of the Yolo model in which the feature extraction layer contains a much deeper network. The results of detecting person and car in the thermal images obtained by the proposed model are compared with the results of Yolo- v3. Experimental results show that there is a significant improvement in detecting person and car in the thermal images in terms of mean average precision (mAP) using the proposed method.
Liquid Interactions: A Conceptual Interaction Framework for the Future Now

Gartner predicted that by 2020, customers would manage 85% of their relationship with an enterprise, without human interactions. Here we are—more than halfway past 2020 and in times where this prediction is becoming a reality. To minimize human interactions with customers, the common (mis)perception and low-hanging fruit is the adoption of conversational technologies such as Chatbots and AI via a multitude of channels. Brands are already cognizant that customer journeys have become dynamic, fluid, and unpredictable, as they consider usefulness, stay mindful and design for rapid changes in consumer desires. Customers tend to act on their needs of the moment—their expectations high and patience low. This new-age customer behavior alludes to the relevance and usefulness of making interactions flow like liquids through swift, seamless, and sticky micro-moments for a unified Omnichannel and multimodal ecosystem encompassing online and offline. The framework attempts to evolve a modern layer over known popular Interaction Design Principles to support liquidity in user experience.
Pengembangan Modul Pembelajaran Biologi Berbasis Reading, Questioning, And Answering (RQA) Materi Virus Kelas X

This study aims to: (1) describe the feasibility of a biology learning module based on Reading, Questioning, and Answering (RQA) virus material based on the value of validation results by material experts; (2) describe the feasibility of a biology learning module based on Reading, Questioning, and Answering (RQA) virus material based on the value of the validation results by instructional media experts; and (3) describe the students' responses to the readability of the biology learning module based on Reading, Questioning, and Answering (RQA) of the virus material developed. This research is a development research that uses the ADDIE development model. The research procedure refers to the stages in the ADDIE development model, namely: Analysis, Design, Develop, Implement, and Evaluate. The analysis stage is a needs analysis activity for the development of an RQA-based module on virus material. The design stage is carried out to compile the design of the module being developed, including technical and substantive specifications. The develop stage is the creation of modules and expert validation. The implement stage is a small group trial stage to determine the readability of students. The last stage (evaluate) is an activity to evaluate the results of all the stages that have been carried out. The data collection techniques used were interviews and questionnaires. The instruments used include: interview guides, student needs analysis questionnaires, material expert validation sheets, learning media expert validation sheets, and student response questionnaires. Validators are material experts and learning media experts who validate each one who has expertise in their field. The number of respondents who responded to the readability of the module developed was 10 people. The data obtained were analyzed descriptively. The results of this study indicate that the RQA-based biology learning module on virus material is considered very feasible. The results of this eligibility were obtained from the results of the validation of material experts with an average percentage of 92.18% (very feasible criteria), the results of the validation of learning media experts with an average percentage of 93.42% (very feasible criteria), and the average percentage of student responses. amounting to 93% (very good criteria).
Appropriation and design-after-design as tools to shape playful museum installations

Over recent years, the Design Research field has been increasingly focusing on how the design of an object is affected after its deployment through the various unpredicted ways of use that occur when user appropriate it—called “design-after-design”. At the same time, that appropriation of hybrid experiences in the museum space can be challenging for institutions which are not prepare to address it through their design processes. This article presents a study that employs a research-through-design method by conducting design experiments in-the-wild. Its aim is to explore a design process which focuses on deploying an open and ambiguous object, thus support acts of design-after-design to emerge and using those acts to re-design the original object. Through the research findings, I aim to produce further knowledge, through field trials, on design processes that deal with designing for leisure and pleasure rather than performance, and address challenges associated with such goals.
Cascaded Head-colliding Attention

Transformers have advanced the field of natural language processing (NLP) in many ways.At the heart of the Transformer architecture is the multi-head attention (MHA) mechanism which models pairwise interactions between the elements of the sequence.Despite its massive success, the current framework ignores interactions among different heads, leading to the problem that many of the heads are redundant in practice, which underutilizes the capacity of the model.To improve parameter efficiency, we re-formulate the MHA as a latent variable model from a probabilistic perspective.We present cascaded head-colliding attention (CODA) which explicitly models the interactions between attention heads through a hierarchical variational distribution.We conduct extensive experiments and demonstrate that CODA outperforms the transformer baseline, by 0.6 perplexity on Wikitext-103 in language modeling, and by 0.6 BLEU on WMT14 EN-DE in machine translation, due to its improvements on the parameter efficiency. 1
Talking with signs A simple method to detect nouns and numbers in a non-annotated signs language corpus

People with deafness or hearing disabilities who aim to use computer based systems rely on state-of-art video classification and human action recognition techniques that combine traditional movement pattern recognition and deep learning techniques. In this work we present a pipeline for semi-automatic video annotation applied to a non-annotated Peruvian Signs Language (PSL) corpus along with a novel method for a progressive detection of PSL elements (nSDm). We produced a set of video annotations indicating signs appearances for a small set of nouns and numbers along with a labeled PSL dataset (PSL dataset). A model obtained after ensemble a 2D CNN trained with movement patterns extracted from the PSL dataset using Lucas Kanade Opticalflow, and a RNN with LSTM cells trained with raw RGB frames extracted from the PSL dataset reporting state-of-art results over the PSL dataset on signs classification tasks in terms of AUC, Precision and Recall.
METHODOLOGICAL ASPECTS OF TEACHING THE BASICS OF PROGRAMMING IN THE PASCALABC.NET (DRAWMAN)

The article describes the methodological rationale for using the virtual robot Drawman in the PascalABC.NET environment for teaching the basics of programming. There is a didactic material on the basics of working with the virtual robot Drawman, which can be used in informatics lessons or in additional classes. Detailed instructions can be both an assistant to the teacher for working in the lesson, and a guide for students working at an individual pace, and schoolchildren in self-study of the basics of programming. However, under teacher guidance, this training will be more effective. The article examines tasks from various topics — linear programs, loop operators, nested loops, procedures and procedures with a parameter — with a detailed description of how to solve these problems, which will allow students to solve other tasks from these topics on their own. For the first linear program, step-by-step instructions are given on how to implement the task and write the first program in the PascalABC.NET environment for the virtual robot Drawman, as well as to automatically check the completed task. Depending on the interests and level of preparation of the students, the material may be useful for students in the sixth or seventh grades.
Features of designing tutors for education master's training

The article discusses and details the features of designing the contents of the educational program of tutors’ master's training, implementing the accompaniment of students in the conditions of individualization of the educational process. Separately, factors affecting the content of the training curriculum at the university are considered. In addition, a significant segment of the study is devoted to the identification of topical areas which are "requesting" tutorials for students to implement their individual educational routes. The role of the modular approach in the design of the training curriculum for tutors in the master's degree for the education system has been revealed and updated. A special place in the structure of the study is occupied by the results of the development of professional competences of a tutor, based on professional functions formulated by employers in the professional standard, as well as the designed modular content of the training curriculum for the education system. This modular training design will ensure the formation of a set of professional competencies that ensure the tutor's readiness to implement design, methodical and accompanying activities in the space of modern education, including remote.
Implementation of Elliptic Net Scalar Multiplication Computation for NIST P-192 Curve using Python

Abstract Elliptic curve cryptography is one of the most efficient public-key cryptosystems compared to the Rivest-Shamir-Addleman scheme. One of the methods to compute elliptic curve scalar multiplication is division polynomials which utilize the non-linear recurrence relation also known as the elliptic net. Previous studies were related to elliptic curve scalar multiplication via elliptic net which implemented the use of C++, GP/PARI or Java programming. This study aims to implement the Python programming in the SageMath compiler for computing elliptic net scalar multiplication based on the National Institute of Standards and Technology curve of the type P-192. The implementation could be seen at three main processes which are the scalar decomposition, the blocks of elliptic net generation namely the initial and final blocks, as well as scalar multiplication computation. The base point and prime field of the short Weierstrass curve with a large scalar are the parameters used in the process. The results showed that the implemented programming was easier while working with a large prime number field, vulnerable to errors and can be easily verified. The implemented programming can also be used to compute scalar multiplication on other standardizations of elliptic curve cryptography such as Brainpool 384-bit prime field or NUMS 256-bit prime field curves.
Mobile Application Behavior Recognition Based on Dual-Domain Attention and Meta-learning

Mobile smart devices and mobile applications carry a lot of personal information and office entertainment functions. By means of analyzing the network traffic generated by mobile applications during use, it can provide valuable information in terms of network management, privacy protection, and behavior recognition. This paper designs a recognition model based on dual-domain attention mechanism and meta-learning. First, feature extraction is performed through a deep separable convolution module, and secondly, attention is extracted from the channel and space dimensions through the attention mechanism module to enhance behavior recognition samples. At the same time, the meta-learning strategy is used for multi-task learning, so that the model can have a faster and more efficient recognition effect when facing new small sample recognition tasks. Experimental results show that compared with other small sample recognition models, the model in this paper can effectively recognize mobile application behavior.
Hierarchical Representation of Plain Areas of Post-Interpolation Residuals for Image Compression

We propose an algorithm for encoding quantized post-interpolation residuals within the framework of hierarchical image compression. This coding algorithm is based on a hierarchical representation of the plain areas of quantized post-interpolation residuals to improve the coding efficiency of these areas. The proposed algorithm reorders the post-interpolation residuals to increase the size of the plain areas. We embed the proposed coding algorithm for post-interpolation residuals into a hierarchical image compression method. This method is based on interpolation the image scale levels using more resampled scale levels of the same image. The errors of this interpolation (post-interpolation residuals) are then quantized and encoded. We use the proposed algorithm to encode the quantized post-interpolation residuals of the hierarchical compression method. We perform computational experiments to study the effectiveness of the proposed algorithm for a set of natural images. We experimentally confirm that the use of the proposed coding algorithm for post-interpolation residuals makes it possible to increase the efficiency of the hierarchical method of image compression.
Span Classification Based Model for Clinical Concept Extraction

Recently, how to structuralize electronic medical records (EMRs) has attracted considerable attention from researchers. Extracting clinical concepts from EMRs is a critical part of EMR structuralization. The performance of clinical concept extraction will directly affect the performance of the downstream tasks related to EMR structuralization. However, the mainstream method, sequence labeling model has some shortcomings. The clinical concept extraction method based on sequence labeling does not conform to the human cognitive model of language. At the same time, the extraction results produced by this method are difficult to couple with downstream tasks, which will cause error propagation and affect the performance of downstream tasks. To deal with these problems, we propose a span classification based method to improves the performance of clinical concept extraction tasks by considering the overall semantics of the token sequence instead of the semantics of each token. We call this model as span classification model. Experiments show that the span classification model achieves the best micro-average F1 score (81.22%) on the corpora of the 2012 i2b2 NLP challenges, and obtained an F1 score (89.25%) comparable to SOTA in the 2010 i2b2 NLP challenges. Furthermore, the performance of our approach is always better than the sequence labeling model such as BiLSTM-CRF model and softmax classifier.
Reputation and cyber: why it matters

Abstract Matthew Mackay CITP MBCS, Principal Consultant at Logiq Consulting, explains how a failure to take into account the defence of an organisation's assets from a cyber-attack could have an adverse effect on their reputation.