Reviewerly/exHarmony huggingface dataset:
Real Time Emotion Recognition and Gender Classification

The proposed prototype aims to classify the gender and the emotions of a person in real time or using the image of the person in smartphone or hard copy of the photograph. The gender classification would be implemented by simple yet robust real time convolutional neural network. Whereas for the emotion recognition, instead of fully connected layers, this model boasts and consists of depth wise separable convolution. It not only reduces the number of parameters and computation utilized in convolution but further increasing the efficiency. It has proved to achieve success in image classification models in terms of both, i) in obtaining better models than previously possible for a given parameter count required to perform at a given level and ii) acquire state-of-the-art results. The output is provided in the form of classes and these are seven classes of emotion recognition (angry, fear, sad, happy, surprise, neutral, disgust) and two classes of gender classification (Male and Female). The present accuracy for gender classification is 95%, whereas the accuracy for facial emotion recognition is around 67%. Also, a large reduction of hyperparameters is the main goal to reduce the model size.
Ending youth unemployment in sub‐saharan Africa: Does ICT development have any role?

Abstract This study investigates the role of information and communication technology (ICT) development towards ending youth unemployment in the 48 sub‐Saharan Africa (SSA) countries from 1991 to 2018. Using a dynamic panel modelling technique, the study employed the instrumental variable (IV) regression, within the framework of the system generalized method of moment (GMM‐SYS) estimator. The results show that the low level of mobile phone subscriptions, broadband internet subscriptions, Wi‐Fi internet subscriptions and internet bandwidth exert a significant negative effect on youth unemployment. This means that ICT development reduces youth unemployment in SSA. Also, it was found that the number of households with access to computers has a negative but insignificant effect on youth unemployment; this shows the ineffective role of household computers in reducing youth unemployment in SSA. Based on these findings, we recommend among other things that, for government to optimize the expanding youth population, there is the need for further telecommunication reforms to reduce the cost of mobile phone technologies and improved ultra‐modern internet facilities in the region.
Subversion-resilient signatures: Definitions, constructions and applications

We provide a formal treatment of security of digital signatures against subversion attacks (SAs). Our model of subversion generalizes previous work in several directions, and is inspired by the proliferation of software attacks (e.g., malware and buffer overflow attacks), and by the recent revelations of Edward Snowden about intelligence agencies trying to surreptitiously sabotage cryptographic algorithms. The main security requirement we put forward demands that a signature scheme should remain unforgeable even in the presence of an attacker applying SAs (within a certain class of allowed attacks) in a fully-adaptive and continuous fashion. Previous notions—e.g., the notion of security against algorithm-substitution attacks introduced by Bellare et al. (CRYPTO '14) for symmetric encryption—were non-adaptive and non-continuous. In this vein, we show both positive and negative results for the goal of constructing subversion-resilient signature schemes. Negative results. We show that a broad class of randomized signature schemes is insecure against stateful SAs, even if using just a single bit of randomness. On the other hand, we establish that signature schemes with enough min-entropy are insecure against stateless SAs. The attacks we design are undetectable to the end-users (even if they know the signing key). Positive results. We complement the above negative results by showing that signature schemes with unique signatures are subversion-resilient against all attacks that meet an undetectability requirement. A similar result was shown by Bellare et al. for symmetric encryption, who proved the necessity to rely on stateful schemes; in contrast unique signatures are stateless, and in fact they are among the fastest and most established digital signatures available. As our second positive result, we show how to construct subversion-resilient identification schemes from subversion-resilient signature schemes. We finally show that it is possible to devise signature schemes secure against arbitrary tampering with the computation, by making use of an un-tamperable cryptographic reverse firewall (Mironov and Stephens-Davidowitz, EUROCRYPT '15), i.e., an algorithm that "sanitizes" any signature given as input (using only public information). The firewall we design allows us to successfully protect so-called re-randomizable signature schemes (which include unique signatures as a special case).
Avatars rendering and its effect on perceived realism in Virtual Reality

Immersive virtual environments have proven to be a plausible platform to be used by multiple disciplines to simulate different types of scenarios and situations at a low cost. When participants are immersed in a virtual environment experience presence, they are more likely to behave as if they were in the real world. Improving the level of realism should provide a more compelling scenario so that users will experience higher levels of presence, and thus be more likely to behave as if they were in the real world. This paper presents preliminary results of an experiment in which participants navigate through two versions of the same scenario with different levels of realism of both the environment and the avatars. Our current results, from a between subjects experiment, show that the reported levels of quality in the visualization are not significantly different, which means that other aspects of the virtual environment and/or avatars must be taken into account in order to improve the perceived level of realism.
Bi-directional Encoder Representation of Transformer model for Sequential Music Recommender System

A recommendation system is a set of programs that utilize different methodologies for relevant item selection for the user. In recent years deep neural networks have been used heavily for improving recommendation quality in every domain. We describe a model for music recommendation system that uses the BERT (Bidirectional Encoder Representations from Transformers) model. In the past, other deep neural networks have been used for music recommendation, which capture the the unidirectional sequential nature of a user’s data. Unlike other sequential techniques of recommendation, BERT uses bidirectional training of a user’s sequence for better recommendation. BERT uses the encoder part of the Transformer model, which uses an attention mechanism to learn contextual relations between a user’s past interactions. The proposed model relies on a user’s previous interaction to determine the bidirectional encoding for the model, which considers both the left and the right contexts. We evaluated our model with a baseline deep sequential model using two different datasets, and comparative results show that the model outperforms other sequential models.
Deep learning for general game playing with Ludii and Polygames

Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games. The training and search algorithms are not game-specific, but every individual game that these approaches are applied to still requires domain knowledge for the implementation of the game’s rules, and constructing the neural network’s architecture – in particular the shapes of its input and output tensors. Ludii is a general game system that already contains over 1,000 different games, which can rapidly grow thanks to its powerful and user-friendly game description language. Polygames is a framework with training and search algorithms, which has already produced superhuman players for several board games. This paper describes the implementation of a bridge between Ludii and Polygames, which enables Polygames to train and evaluate models for games that are implemented and run through Ludii. We do not require any game-specific domain knowledge anymore, and instead leverage our domain knowledge of the Ludii system and its abstract state and move representations to write functions that can automatically determine the appropriate shapes for input and output tensors for any game implemented in Ludii. We describe experimental results for short training runs in a wide variety of different board games, and discuss several open problems and avenues for future research.
Cogno-Vest: A Torso-Worn, Force Display to Experimentally Induce Specific Hallucinations and Related Bodily Sensations

Recent advances in virtual reality and robotic technologies have allowed researchers to explore the mechanisms underlying bodily aspects of self-consciousness which are largely attributed to the multisensory and sensorimotor processing of bodily signals (bodily self-consciousness, BSC). One key contribution to BSC, that is, currently poorly addressed due to the lack of a wearable solution, concerns realistic collision sensations on the torso. Here, we introduce and validate a proof-of-concept prototype of the torso-worn force display, the Cogno-vest, that provides mechanical touch on the user’s back in a sensorimotor perception experiment. In a first empirical study, we characterized human finger poking ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=28$ </tex-math></inline-formula> ). In order to match these poking characteristics and meet the wearability criteria, we used bidirectional, push–pull solenoids as a force actuator in the Cogno-vest. Subsequently, and based on an iterative design procedure, a body-conforming, unisex, torso-worn force display was prototyped. Finally, we conducted a behavioral study that investigated BSC in 25 healthy participants by introducing conflicting sensorimotor signals between their hands and torso (back). Using the final reiteration of the Cogno-vest, we successfully replicated previous findings on illusory states of BSC, characterized by presence hallucinations (PH) and passivity symptoms, and achieved higher illusion ratings compared to static conditions used in prior studies.
Coronavirus: Case for Digital Money?

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Atomic Cross-Chain Swaps: Development, Trajectory and Potential of Non-Monetary Digital Token Swap Facilities

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The Effects of Cyber-security Risks on Added Value of Consulting Services for IT-security Management Systems in Holding Companies

Many efforts in IT-security management systems implementation do not always have the expected results. Sometimes they actually require more integrated costs than planned. One of the reasons of this is the lack of robust cyber risk management practices. This statement is particularly applicable to the correct choice and application of robust IT-security management tools when providing the consulting services. This article considers a case of a cyber-security system implementation. A well-known consulting company was involved in the implementation of this project. The provided examples illustrate the choice of IT-security metrics and the use of various IT-security management tools when providing consulting services in a Holding companies.
FPGA Implementation of 3-bit Quantized CNN for Semantic Segmentation

Abstract Semantic segmentation is a task of inputting an image and performing category classification for each pixel. Semantic segmentation by CNN has high accuracy but its calculation using floating-point numbers consumes a large amount of power. We adopted UNET as the semantic segmentation CNN and improved it for FPGA implementation. We quantized both weights and activations of the network up to 3-bit. Then, we devised a dedicated hardware architecture for the quantized CNN and implemented it on an FPGA. This circuit uses only internal memory to perform forward propagation calculations, that eliminates high-power external memory accesses. This circuit is a stall-free pixel-by-pixel pipeline, and performs 8 rows, 16 input channels, 16 output channels, 3 by 3 pixels convolution calculations in parallel. The convolution calculation performance at an operating frequency of 300 MHz is 11 TOPs/s.
Advanced self-compensated, high-accuracy optical extensometer based on field-of-view splitting and dual-reflector imaging techniques

Optical extensometers realized using a telecentric lens and field-of-view (FOV) splitting technique can achieve a high strain measurement accuracy. However, the telecentric lens has some disadvantages such as high cost, fixed FOV, and fixed working distance. In this study, we have developed an advanced optical extensometer using a low-cost imaging lens and FOV splitting device. Dual-reflector imaging technique is implemented in the proposed optical extensometer to reduce the strain errors induced by out-of-plane motion. With the FOV splitting device, the proposed extensometer exhibits a gauge length of 4659 pixels, while the initial gauge length is only 1700 pixels. The effectiveness of this extensometer is verified by static and tensile tests. The experimental results indicate that the values of axial strain obtained using the proposed extensometer in uniaxial tensile tests are in excellent agreement with those obtained using a strain gauge, and the standard deviation of corresponding strain errors is 4.6 microstrain, which validates the high accuracy of the extensometer.
Using a smartphone camera to analyse rotating and vibrating systems: Feedback on the SURVISHNO 2019 contest

A smartphone is a low-cost pocket wireless multichannel multiphysical data acquisition system: the use of such a device for noise and vibration analysis is a challenging task. To what extent is it possible to carry out relevant analysis from it? The Survishno conference, held in Lyon in July 2019, proposed a contest to participants based on this subject. Two challenges were proposed, wherein each a mute video showing an object moving/excited at different frequencies was provided. Due to the frequencies set and the video sampling characteristics, special effects occurred and are visible on both videos. From the first video, participants were asked to estimate the Instantaneous Angular Speed (IAS) of a rotating fan. From the second video, they were asked to perform the modal analysis of a cantilever beam. This paper gathers the interesting ideas proposed by the contestants and proposes a global method to solve these two problems. One major point of the paper might be the advantageous use of the rolling shutter effect, a well-known artefact of smartphone videos, to perform advanced mechanical analyses: the consideration of the unavoidable slight phase shift between the acquisition of each pixel opens up the possibility to perform a dynamic analysis at frequencies that are much higher than the video frame rate.
Hour-Ahead Global Horizontal Irradiance Forecasting Using Long Short Term Memory Network

Energy management is an evolving challenge these days and deployment of renewable energy sources is an effective way out. It is imperative to specifically recognize the quantity from diverse resources and at distinct horizons: minutes, hours, and days, in order to achieve an effective deployment. Based on horizon, the approaches needed to be utilized for solar irradiance forecasting can be classified in two main categories: statistical time series prediction techniques for short to medium prospects and numerical weather forecasting techniques for medium to long-term prospects. This study mainly accentuates on statistical time series forecasting techniques for short term horizons (1 hour). A forecasting model has been introduced in this paper for solar irradiance depending upon the meteorological variables and historical data analysis. In the anticipated prototype, the Recurrent Neural Network (RNN) variation, specifically Long Short-Term Memory (LSTM) is employed. In order to develop best models that suit the global horizontal irradiance data, hyper-parameter tuning is being employed for achieving optimal parameters. After this, the quantity of data is being enhanced to discover the impact of increased data on this optimized model. The experiment is being performed on dataset gathered in Meteorological Station of National University of Sciences and Technology (NUST) Islamabad, Pakistan. Finally, the results of these models are compared by calculating errors.
Impact of Task on Attentional Tunneling in Handheld Augmented Reality

Attentional tunneling describes a phenomenon in Augmented Reality (AR) where users excessively focus on virtual content while neglecting their physical surroundings. This leads to the concern that users could neglect hazardous situations when using AR applications. However, studies have often confounded the role of the virtual content with the role of the associated task in inducing attentional tunneling. In this paper, we disentangle the impact of the associated task and of the virtual content on the attentional tunneling effect by measuring reaction times to events in two user studies. We found that presenting virtual content did not significantly increase user reaction times to events, but adding a task to the content did. This work contributes towards our understanding of the attentional tunneling effect on handheld AR devices, and highlights the need to consider both task and context when evaluating AR application usage.
Data-Driven Approach to Dual Service Failure Monitoring From Negative Online Reviews: Managerial Perspective

Monitoring of dual service failures (e.g., trends in service failures and consecutive service failures) in business is emphasized for service quality management. Previous studies analyzing negative online reviews to conduct dual service failure monitoring from a managerial perspective are scarce. Numerous negative online reviews are useful sources for dual service failure monitoring because they can be easily collected at a low cost. This article proposes a data-driven approach to monitor service failure trends and consecutive service failures from negative online reviews. In the proposed approach, first a classifier is developed to categorize newly collected negative reviews into service failures by Latent Dirichlet allocation. Subsequently, a threshold value is provided to identify a new type of service failure, which was not achieved previously using a control chart. Finally, the probability of consecutive service failures is obtained by association rule mining. A case study of Uber is conducted to validate the proposed approach. The results exhibit that the proposed approach can perform dual service failure monitoring. This study can increase marketing intelligence for dynamic management of service failure and allow rapid responses to service failures.
Foundations of information technology based on Bunge’s systemist philosophy of reality

General ontology is a prominent theoretical foundation for information technology analysis, design, and development. Ontology is a branch of philosophy which studies what exists in reality. A widely used ontology in information systems, especially for conceptual modeling, is the BWW (Bunge-Wand-Weber), which is based on ideas of the philosopher and physicist Mario Bunge, as synthesized by Wand and Weber. The ontology was founded on an early subset of Bunge's philosophy; however, many of Bunge's ideas have evolved since then. An important question, therefore, is: do the more recent ideas expressed by Bunge call for a new ontology? In this paper, we conduct an analysis of Bunge's earlier and more recent works to address this question. We present a new ontology based on Bunge's later and broader works, which we refer to as Bunge's Systemist Ontology (BSO). We then compare BSO to the constructs of BWW. The comparison reveals both considerable overlap between BSO and BWW, as well as substantial differences. From this comparison and the initial exposition of BSO, we provide suggestions for further ontology studies and identify research questions that could provide a fruitful agenda for future scholarship in conceptual modeling and other areas of information technology.
Performance Measurement of Processes and Threads Controlling, Tracking and Monitoring Based on Shared-Memory Parallel Processing Approach

In this paper, a professional integrated operating system performance measurement system is proposed, designed, and implemented in order to monitor processes and threads. Because monitoring itself is not enough, so this system provides full control of the CPU forcing, Priority-changing, and pausing-resuming-killing of these processes/threads. During these complex operations, it provides the ability to measure/compute the details of Total-execution-time, CPU-time, User-time, Kernel-time, context switching, CPU -usage, and the priority of processes/threads. The proposed system can be used with any architecture of multicore processors. Despite that measuring all these parameters in one system is very difficult and complex especially the Kernel-time and Context-switching, but all of them have been applied and determined successfully. Adding to the above features that never been integrated (by any previous work) in one system, and in order to illustrate the real abilities of this system, a shared-memory parallel processing approach has been proposed and applied. The proposed system prepared to run all possible cases of processes and threads that construct under-tested-program which may be one of the: Single-Process-Single-Thread, Single-Process-Multi- Thread, Multi-Process-Single-Thread, Multi-Process-Multi- Thread, and Multi- Process-Single-Multi-Thread. The algorithms of this system are designed and implemented via C++ programming language(using C++ Standard Library Thread) which is the nearest programming to the OSs provides a more increasing processing speed.