Reviewerly/exHarmony huggingface dataset:
The Best Model of Convolutional Neural Networks Combined with LSTM for the Detection of Interpersonal Physical Violence in Videos

Citizen insecurity is directly related to interpersonal physical violence, there are algorithms that allow detecting violence in videos; therefore, it is necessary to know which is the best model for detecting violence. For this research, three convolutional neural network models were compared: Xception, InceptionV3 and VGG16 each together with a recurrent LSTM network, to find out which of the models is the best for the detection of interpersonal violence in videos. The three models were trained using the Real Life Violence Situations dataset, then violence and non-violence were classified, as a result, the InceptionV3 model is the best model, managing to classify with an accuracy of 94% compared to the VGG16 and Xception models, which obtained 88% and 93% respectively. Therefore, we recommend the InceptionV3 model for the detection of interpersonal physical violence in citizen security videos.
Sounding out the System: Multidisciplinary Web Science Platforms for Creative Sonification

In this paper, we present our initial findings in using digital methods to consider the way that different devices can connect to the same object. We take a more experimental view of the ways in which network data might be used in compositions to help us to move beyond traditional sonification techniques into more musical territories which enables us to start to understand the ways in which archival data and tools might be used as a creative response to the data and provide a more human way of engaging with the data archive. Such approaches can inform the ways in which future research platforms for Web Science can be developed in a truly multidisciplinary way which matches the needs of the wider research community and supports public engagement.
Types Are Internal ∞-Groupoids

By extending type theory with a universe of definitionally associative and unital polynomial monads, we show how to arrive at a definition of opetopic type which is able to encode a number of fully coherent algebraic structures. In particular, our approach leads to a definition of ∞-groupoid internal to type theory and we prove that the type of such ∞-groupoids is equivalent to the universe of types. That is, every type admits the structure of an ∞-groupoid internally, and this structure is unique.
RANCANG BANGUN KOMUNIKASI DATA MENGUNAKAN Li-Fi

Light Fidelity or Li-Fi is a technology from Visible Light Communication to transmit character data by converting it to binary and then applying it by blinking the light to be received by the receiver. By using the Op-amp as an amplifier on LED light and solar panel as the receiver, data can be transmitted through visible light. With three variables that will be adjusted, specifically the amount of light power, distance, and time delay. The amount of light power affects the number of characters that can be sent correctly. Distance does not influence the range of 30 to 60 cm. And delay greatly affects how many characters would be sent correctly. This research will give the quality of data sent with the Bit Error Rate as a parameter. The value of the Bit Error Rate with a 20-watt lamp at a distance of 200 cm and a time delay of 1000 ms is equal to 4.25%.
국방시설분야의 4차 산업혁명 기술관련 현황파악

The essence of 4th industrial revolution is not in the elemental technologies but in the a revolution in speed on the collection, analysis, and processing of data. In particular, the 4th industrial revolution in the construction and civil engineering is the realization of digital twin through digitalization of real world, processing of digital information, and realization of digital information. From this point of view, the status of defense facilities on domestic and foreign trends related to 4th industrial revolution technology are surveyed by interview in this study. Based on the management status, questionnaire survey to gather detailed opinions of the person in charge by life cycle is conducted. As a result, it was identified that BIM-based digital environment is needed in planning and design phase, and improved PMIS, internet-based data stores and servers is needed in construction and maintenance phase. The introduction of 4th industrial revolution technology in field of defense facilities is expected to be reviewed after establishing the digital environment as above. The results of this study are expected to be used to draw up the guidelines for applying 4th industrial revolution technology in the field of defense facilities in the future.
Integrating SSIM in GANs to Generate High-Quality Brain MRI Images

Application of machine learning (ML) techniques in medical imaging is not new, but modern techniques like deep learning require a lot of quality data to perform effectively. Data, present in the field of medical imaging, imposes two main issues: first is the lack of anomalous data. For instance, only a few medical resonance images of the brain with tumor are present as compared to that of a healthy human brain. This makes the task of anomaly detection using ML techniques suffer from a lack of data. Second, the data that is present is highly confidential, and most of the time, the patient does not want it published anywhere. Many data augmentation techniques have been proposed in the past, but such techniques do not create data that is completely different from the given original data. In our work, we focus on using a novel extension of deep convolutional generative adversarial networks (DCGANs) to generate high-resolution brain magnetic resonance (MR) images of both anomalous and normal brain. This task is a tricky one as a tumor is present at random spots of the brain and has random sizes. For this reason, vanilla generative adversarial networks (GANs) cannot generate realistic tumor images. We propose a novel approach that integrates structural similarity index (SSIM) score in the objective function which, as we shall see, does a much better job at generating anomalous brain MR images as compared to vanilla GANs.
Online Rare Category Identification and Data Diversification for Edge Computing

Identifying rare categories is an important data management problem in many application fields, including video surveillance, ecological environment monitoring, and precision medicine. Previously, solutions in the literature require all data instances to be first delivered to the server. Then, the rare category identification algorithms are executed on the pool of data to find informative instances for human annotators to label. This incurs large bandwidth consumption and high latency. To deal with the problems, we propose a lightweight rare category identification framework. At the sensor side, the designed online algorithm filters less informative data instances from the data stream and only sends the informative ones to the servers for annotating. After labeling, the server only sends labels of the corresponding data instances in response. The sensor-side algorithm is extended to enable cooperation between embedded devices for the cases that data are collected in a distributed manner. For enhancing diversity of selected data, a representative selection algorithm is proposed to run during the idle time of the system or after the execution of a rare category identification algorithm. Experiments are conducted to show that our framework dramatically outperforms the baseline. The network traffic is reduced by 75% on average.
I progetti SPRAR attraverso la lente del <i>Linguistic Landscape</i> Uno studio di casi

This contribution aims to investigate uses and characteristics of the ‘Sprarscape’, defined as the set of linguistic and semiotic objects that contribute to the visual organization of the centres where Sistema di Protezione per Richiedenti Asilo e Rifugiati (SPRAR) projects are active. Adopting an interdisciplinary perspective, which refers to studies on linguistic landscaping and language policy and planning, we describe these spaces through the use of innovative research tools. We also investigate their linguistic composition and the functions of the signs that structure them from an intercultural perspective. These include visual data and field notes, collected through ‘linguistic walks’, integrated with videos and a tourist-guide technique, analysed by means of a specifically-designed observation scheme.
A Comparative Study on Google App Engine Amazon Web Services and Microsoft Windows Azure

In today Internet has grown to be persistent in daily livelihood furthermore Cloud computing is a rising model where computing resources offered over the Internet as scalable, on-demand (Web) services. An association deploy internet service needs to use enormous amounts of money on infrastructure needs to serve feasible users which is not a problem for large venture but when it comes to Small and Medium Enterprises or Enterprises affordability becomes a huge factor with the huge infrastructure come problems like machines failure, hard drive noises, software bugs, etc. Here might be a big problem for such a community. Cloud Computing is the ultimate solution to this problem. Rather than buying, installing and operating its own systems, an organization can rely on a cloud provider to do this for them. Cloud Computing key market leaders like Google, Amazon and Microsoft etc, these providers introduce new operating and business models that allow customers to pay for the resources they completely use, instead of making tremendous upfront investments. The purpose of this paper is to analyze most popular platforms, The Google App Engine, Amazon Web Services, and Windows Azure Platform.
A Matrix for Systematic Selection of Authentication Mechanisms in Challenging Healthcare related Environments

Passwords continue to dominate the authentication landscape, while One Time Passwords (OTPs) provided by apps are increasingly used as second factor. Even though several alternatives are developed, very few regard usability. Even fewer alternatives consider special conditions of authentication, like disabilities and other input restrictions, typical for healthcare workers. In this paper, we show shortcomings by the example of different stages within the care cycle. Generalized requirements are used to evaluate existing authentication mechanisms. These findings result in the design of a matrix showing different authentication methods and requirements. The matrix can be used to identify the best fitting authentication mechanisms based on the needs of the scenario. Not only the first factor can be identified, but the matrix also helps to select additional well-fitting authentication mechanism for a specific scenario. The designed matrix is practically underlined by applying it to the care cycle with different cyber-physical systems (CPS).
Guess-and-Determine Attacks on AEGIS

Abstract AEGIS is one of the authenticated encryption with associated data designs selected for the final portfolio of the CAESAR competition. It combines the AES round function and simple Boolean operations to update its large state and extract a keystream to achieve an excellent software performance. The AEGIS family consists of AEGIS-128, AEGIS-256 and AEGIS-128L, which use 5, 6 and 8 parallel AES round functions to process 128, 128 and 256 bits message block per step with slightly different output functions separately. Surprisingly, very few cryptanalytic results on AEGIS have been published so far. This paper presents the first guess-and-determine attacks on AEGIS family. Firstly, we propose a new observation on the structure of AEGIS that the relations of fixed variables remain in the outputs at consecutive steps under some conditions on the AND operations, and the vectorial bitwise AND operation is biased, which is able to derive the additional variables added directly. Secondly, we add several techniques, such as divide and conquer on byte-based columns, reduction by meet in the middle and simplification through constraints on variables, for each AEGIS member. Finally, we conduct guess-and-determine attacks on AEGIS-128, AEGIS-256 and AEGIS-128L and result in a complexity of $2^{309}$, $2^{437}$ and $2^{384}$ to $2^{416}$, respectively. Although neither attack threatens the practical security of AEGIS, it has great significance to evaluate the resistance of such structure compared with their large internal state exploited of 640, 768 and 1024 bits. It is also the first internal state recovery attack on AEGIS without nonce reusing, while only distinguishing attacks on AEGIS exist up to now.
Missed Opportunities in Digital Investigation

A recent strategic review of policing published by the Police Foundation (Barber in The first report of the strategic review on policing in England and Wales. Police Foundation (2020) []) claimed that the police service in England and Wales is not equipped to meet the scale and complexity of the various challenges it faces, one of which involves the digital elements within crime investigation. Drawing upon data gathered for an MSc dissertation evaluating practices across investigators in the South of England, monthly samples from two years of serious crime investigations established that 50% of enquiries missed all digital investigative opportunities. Where a digital opportunity was identified, potential subsequent digital enquiries were missed 47% of the time. Whilst consistent with the Her Majesty’s Inspectorate of Constabulary and Fire and Rescue Services (HMICFRS) (State of Policing—The Annual Assessment of Policing in England and Wales (2018) []) and the Information Commissioners Office (ICO) (Mobile phone data extraction by police forces in England and Wales (2018) []) reports which highlight that policing capability is lagging behind modern technology and affecting public confidence; these matters will be developed and discussed leading to the conclusion that, consistent with the police foundation report, loss of public confidence will undoubtedly damage police legitimacy.
Context-based Feature Fusion Network for Object Detection

For object detection, the method based on multi-scale feature fusion are widely used to improve performance. However, performance improvements are limited due to the absence of sufficient global context information and the presence of various background noises in low-level features. Regarding the above issues, we propose the Context Extraction Module (CEM) and Context Refinement Module (CRM). The CEM enables the network can learn abstract global context information from various receptive fields; the CRM enhances the discriminative ability of low-level features representations by using the attention mechanism. In order to evaluate the effectiveness of CEM and CRM, we built a new network called Context-based Feature Fusion Network (CFFN) based on FSSD (Feature Fusion Single Shot Multibox Detector) and conducted experiments. In the Pascal VOC 2007 test, the detection performance can achieve 81.4% mAP with the input size 320×320. In addition, our results in COCO also have greater advantages than conventional FSSD.
Epilepsy Websites in Africa: State of the Art

PurposeEpilepsy is one of the main brain disorders in the world and should be considered a priority for healthcare in Africa.Internet is a growing source of health information for healthcare and patients with epilepsy (PWEs).In this short communication, we tried to describe the status of websites on epilepsy in Africa in terms of availability and pertinence, and to suggest ideas on how to make them more effective. MethodsThe existence of epilepsy websites and their distribution in Africa were acquired using a questionnaire sent to different neurologists in the African continent. ResultsBased on the survey answers and the web search, our results show that out of 54 African countries, only 16 countries (≈30%) have an active epilepsy website. ConclusionThe need for reliable, clearly written, and easily comprehended information about epilepsy is considered as an important element to enhance the quality of care, for this all African countries should create and improve their epilepsy websites in order to promote education and awareness.
Classification and Synthesis of Emotion in Sign Languages Using Neutral Expression Deviation Factor and 4D Trajectories

3D Avatars are an efficient solution to complement the representation of sign languages in computational environments. A challenge, however, is to generate facial expressions realistically, without high computational cost in the synthesis process. This work synthesizes facial expressions with precise control through spatio-temporal parameters automatically. With parameters compatible with the gesture synthesis models for 3D avatars, it is possible to build complex expressions and interpolations of emotions through the model presented. The built method uses independent regions that allow the optimization of the animation synthesis process, reducing the computational cost and allowing independent control of the main facial regions. This work contributes to the definition of non-manual markers for 3D Avatar facia expression and its synthesis process. Also, a dataset with the base expressions was built where 4D information of the geometric control points of the avatar built for the experiments presented is found. The results of the generated outputs are validated in comparison with other expression classification approaches using Spatio-temporal data and machine learning, presenting superior accuracy for the base expressions. The rating is reinforced by evaluations conducted with the deaf community showing a positive acceptance of the facial expressions and synthesized emotions.
Place of Reports of Nizhny Novgorod Fair Administration in Document Circulation of Russia in the XIX Century

The purpose of the study is to identify the place that reports of Nizhny Novgorod Fair Administration took in document circulation of Russia. Scientific novelty of the work lies in the fact that reports of Nizhny Novgorod Fair Administration have become a subject of source analysis for the first time. As a result, it is found that the reports were a primary source for the reports that were compiled by the Governor and Ministers of Internal Affairs and Finance. The information was included in customs and police reports, statistical descriptions of Russia, published in central and local periodicals. Reports of Nizhny Novgorod Fair Administration were created on the basis of reports written by district supervisors and row elders.
Transforming human nature in the digital age

The article analyzes the problem of transformation of human nature in a modern digital society. The explication of the concept of “transformation” is given, the types of transformation are discussed, the connections between the concepts of “society” and “human being as a generic being” are revealed. The concepts of “human essence” and “human nature” are analyzed and compared, the uncertainty in the understanding of sociality is revealed, and the conclusion is made that the very concept of the essence of a person is largely amorphous. The complexity and multidimensionality of human nature is demonstrated, the relevance of its research is shown. This reveals the possibilities of the most diverse and, in the limit - inexhaustible - transformations of human nature. At the same time, the authors draw attention to the fact that the measure of possible transformations must be observed, since its violation inevitably turns into the disintegration of both man and society. The article notes that the most accessible and effective is the impact on the biological component of human nature. The connection between the transformation of human nature and modifications of the human worldview is stated. In this regard, the possibilities of transforming societies following the transformation of human nature are discussed. The authors come to the conclusion that as a result of ongoing transformations, a new type of society is emerging, which can be called a singular society, combining almost incompatible characteristics.
The expediency of bad habits

Waste of resources is usually on the spot in software applications for residential sustainability, whose intervention model consists of detecting excessive consumption and helping the consumer reduce it. Excessive (and then ‘inefficient’) consumption, however, is more than a foe to track down and defeat: it reveals where sustainability collides with other priorities and constraints of the user. In the present paper, we discuss the hidden value of “inefficient” practices with the help of data from qualitative and quantitative studies conducted by the authors. We conclude suggesting how 'bad habits' could offer valuable insights to inform transformative interventions in line with the current emphasis on users’ empowerment.
Speech Synthesis Method Based on Tacotron2

Compared with traditional speech synthesis systems, end-to-end speech synthesis systems based on deep learning (such as DeepVoice3, Tacotron2) not only reduce the requirements for linguistic knowledge, but the synthesis effect is almost close to the level of human pronunciation. However, the end-to-end speech synthesis system based on deep learning has disadvantages such as missing words, repeated pronunciation, and slow synthesis speed. In view of the local information preference of the Tacotron2 model in the decoder, this paper proposes to maximize the interactive information between the text and the predicted acoustic features and use the WaveGlow synthesizer to reduce the local information preference and the problem of slow synthesis speed, pronunciation in the Tacotron2 model. Experimental results show that the improved model subjective evaluation MOS (Mean Opinion Score) score is 3.94, and the synthesis speed is significantly improved.
Collaborating to Build the Software That Government Policies Need

In 2020, government policy priorities can succeed or fail based on the quality of their software implementation. Improving the odds of success requires more than just building engineering capacity in government to, or near to, private-sector levels. We suggest that the critical factor in more successful software is sustained cross-disciplinary understanding and collaboration. We outline a framework for this collaboration to be effective: redefining “hard” skills to be broader than core “technical” skills, prioritizing hiring for strength in a range of collaborative skills, and actively shaping a work environment that promotes collaboration. We identify aspects of technical and social science working cultures that can impede effective collaboration and suggest ways to mitigate the cultural gaps.
Research on Embedding Environment of Digital Watermark Resistant to Print-Scan and Print-Camera (PSPC)

Digital watermarking technology is a kind of cryptography. The application of it in the field of printing anti-counterfeiting has the problem of weak resistance to PSPC or uncertain watermark embedding environment. This paper proposes a method for determining the embedding environment of digital watermarking to solve the above problems. This method uses discrete cosine transform (DCT) andsingular value decomposition (SVD) algorithms to embed and extract images multiple times in different embedding environments, and then determine the best environment. Theresult of the test can make the peak signal-to-noise ratio greater than 30 and the normalization coefficient greater than 0.8. The method proposed in this paper balances the contradictory relationship between watermark robustness and transparency to a certain extent.