Reviewerly/exHarmony huggingface dataset:
Potential of Robust Face Recognition from Real-Time CCTV Video Stream for Biometric Attendance Using Convolutional Neural Network

Face recognition is one of the most bothersome research issues in security systems due to various challenges like constantly changing poses, facial expressions, lighting conditions, and resolution of the image. The wellness of the recognition technique firmly depends on the accuracy of extracted features and also on the ability to deal with the low-resolution face images. The mastery to learn accurate features from raw face images makes deep convolutional neural networks (DCNNs) a suitable option for facial recognition. The DCNNs utilizes Softmax for evaluating model accuracy of a category for associate degree input image to create a forecast. However, the Softmax probabilities do not depict the real representation of model accuracy. The main aim of this paper is to maximize the accuracy of face recognition systems by minimizing false positives. The complete procedure of building a face recognition prototype is defined very well. This prototype consists of many vital steps built using most advanced methods: CNN cascade for detection of face and HOG for generating face embeddings. The primary aim of this analysis was the sensible use of those developing deep learning techniques for face recognition work, because of the reason that CNNs give almost accurate results for huge datasets. The proposed face recognition prototype can be used together with another system by making some minor changes or without making any changes as an assisting or a primary element for surveillance functions.
Image Block Compression Based on Dual-Learning Dictionaries

To improve the nonlinear reconstruction performance of sparse representation in image compression and reconstruction process, a new image compression algorithm based on dual synthesis and analysis dictionaries is proposed. Firstly, the dual synthesis and analysis dictionaries learning algorithm is analyzed, including penalty function analysis and the algorithm of dictionary learning with gradient decent. Then, the compression model based on synthesis and analysis dictionaries is introduced and the properties of the model is also analyzed. Experimental results demonstrate that the proposed method outperforms many other algorithms in image compression.
Multi-dimensional data aggregation utilizing extended partitioned Bonferroni mean Operator

In this contribution, we develop the concept of an Extended Partitioned Bonferroni Mean (εPBM) operator, which is efficient enough to aggregate input vectors with a varying number of components integrated with some dependence pattern. The global monotonicity for the εPBM is analyzed by defining a new partition for each arity. Further to illustrate the applicability and feasibility of the proposed extended aggregation operator, an example based on medical device selection is demonstrated. Finally, we present a way to obtain the weights associated with the corresponding εPBM operator employing the Max-Entropy technique.
Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices

A novel deep neural network is proposed, for accurate and robust crowd counting. Crowd counting is a complex task, as it strongly depends on the deployed camera characteristics and, above all, the scene perspective. Crowd counting is essential in security applications where Internet of Things (IoT) cameras are deployed to help with crowd management tasks. The complexity of a scene varies greatly, and a medium to large scale security system based on IoT cameras must cater for changes in perspective and how people appear from different vantage points. To address this, our deep architecture extracts multi-scale features with a pyramid contextual module to provide long-range contextual information and enlarge the receptive field. Experiments were run on three major crowd counting datasets, to test our proposed method. Results demonstrate our method supersedes the performance of state-of-the-art methods.
Result attack: a privacy breaching attack for personal data through K-means algorithm

Protecting data privacy concerns the most significant challenge of the present era. This paper is an attempt to demonstrate how machine learning can be used by an attacker to compromise data privacy. To demonstrate, we have chosen an attack on handwritten signatures. The attacker utilizes available signatures for training and appends malicious signatures to be used in the testing process until he gets the desired result. The attacker manipulates the achieved result to perform the malicious attack. We propose, result attack to highlight the need for ensuring the secrecy of the genuine signature. An illustration is performed by applying the K-means algorithm to the MNIST dataset. Differential Privacy (DP) is adopted for defense discussion. The illustration of DP is produced by aggregating red or white noise to the MNIST dataset. Observation shows, the aggregation of noise to personal data successfully delivers defense against the result attack. We get the area under the receiver operating characteristic curve for the original dataset as 0.878719, original dataset vs aggregated red noise as 0.4999901, and original dataset vs white noise as 0.4448475. This concludes for the defense model, aggregating white noise is better than red noise, i.e. white noise aggregation is 11% better than red noise.
Transparent object segmentation from casually captured videos

Abstract Segmentation of transparent objects from sequences can be very useful in computer vision applications. However, without additional auxiliary information it can be hard work for traditional segmentation methods, as light in the transparent area captured by RGB cameras mostly derive from the background and the appearance of transparent objects changes with surroundings. In this article, we present a from‐coarse‐to‐fine transparent object segmentation method, which utilizes trajectory clustering to roughly distinguish the transparent from the background and refine the segmentation based on combination information of color and distortion. We further incorporate the transparency saliency with color and trajectory smoothness throughout the video to acquire a spatiotemporal segmentation based on graph‐cut. We conduct our method on various datasets. The results demonstrate that our method can successfully segment transparent objects from the background.
A Preliminary Approach to Referring to Groups of Objects in Images

In order to talk about an image it is of special interest to be able to refer to the groups of objects that appear in it. To do that, we have to form noun phrases in which properties are combined to discriminate the target group from the rest. These phrases are part of what is known as plural referring expressions in the NLG field. The construction of this type of expressions is a complex problem, given the graduality of both the visual properties that can be used to make them up and the concept of group itself, among other reasons. In this work we propose the use of distance as the inducing element of the notion of group of objects in the image and we also show how results from the theory of fuzzy sets and the related theory of representation by levels (RL) can be used to assess the referential success of referring expressions taking into account the aforementioned graduality of the involved concepts.
UA-Miner: Deep Learning Systems for Expose Unprotected API Vulnerability in Source Code

Application Programming Interface (API) documentation only describes the functions of the corresponding API and does not indicate how to use it safely. So, there are many unprotected API vulnerabilities in programs. Mining unprotected API (UA) vulnerabilities is a significant issue related to system security. In this paper, a system named UA-Miner was proposed that can be automatically mine the unprotected API vulnerabilities by a series of techniques based on Deep Learning. The problem in which APIs need to be protected was solved by analyzing the published vulnerability information. The challenge of how to protect API correctly was solved by Deep Learning. In order to improve the effect of Deep Learning, the methods normalization, reducing the dimension of tokens were adopted. Heuristic Method was designed to solve the problem of sample imbalance, and samples were constructed from actual programs. We use 56000 samples to train and test UA-Miner. The experimental results show that UA-Miner can effectively mine unprotected API vulnerabilities, and is superior to the existing methods in effectiveness and accuracy.
Picking Trajectory Planning of Citrus Based on Improved Immune Algorithm and Binocular Vision

Aiming at the phenomenon that to pick citrus by robot arm is in low efficiency, this paper chooses improved immune algorithm (IIA) for picking path planning of the citrus fruits on the surface of the tree canopy after many experiments. IIA is proposed by improving the neighborhood structure of basic immune algorithm (BIA) and using tabu search strategy to search the neighborhood structure of the current optimal solution which is already got by immune search in the final stage intensively. The world coordinates of citrus fruits are obtained by processing the photos taken by a ZED camera based on the principle of binocular vision. The experiment results show that when picking 6, 20 and 31 citrus fruits, the average planning time of IIA are 13.33%, 21.49% and 23.96% less than BIA, and the average picking distance are 0%, 0.66% and 0.67% shorter than BIA. This shows that IIA can not only effectively shorten the time of trajectory planning, but also shorten the distance of picking path, which provides theoretical support of improving the working efficiency of picking robot.
State Will do

The main strength of purely functional languages like Haskell is that they make reasoning about programs easy with a technique called equational reasoning. This ease of reasoning also extends to effectful programs provided the side-effects are modelled in a purely functional manner, for instance with monads. This paper uses equational reasoning to reason about the modelling of the side-effects themselves. In particular, we consider non-determinism, which is a key side-effect in Prolog and other logic-programming systems. Non-determinism is typically modelled by the list monad, but efficient implementations of Prolog and other non-deterministic systems use a much more low-level approach based on mutable state. In this way we model those lower-level implementations with the state monad and use equational reasoning to show its correctness with respect to the high-level list-based model. We also show how this result can be further generalized to other models of non-determinism.
Computer Forensics and Cyber Attacks

During investigative activities in the field of contrasting tax evasion and fraud, it is known that law enforcement agencies are increasingly encountering digital documents, which are slowly replacing the paper ones. The chapter has the purpose to explain as data, extracted from an electronic device, turns into evidence in court. The authors describe how hidden data (metadata) can become forensic evidence. In particular, the chapter examines the metadata contained in digital photos, which conceal a mass of data whose existence is not normally suspected. The second part of the chapter consists of miscellaneous cyber-attack descriptions in which computer forensics can be applied. It is finally described how one can protect systems against a cyber-attacks.
Compression by and for Deep Boltzmann Machines

We answer two questions in this work: what Deep Boltzmann Machines (DBMs) can do for compression and vise versa. We show that (1) DBMs can be applied to learn the rate distortion approaching posterior as in the Blahut-Arimoto (BA) algorithm, and to construct a lossy source compression scheme based on the Deep AutoEncoder; (2) compression can improve DBMs’ training performances via compression-based denoising algorithms. The implementation of the BA algorithm in the form of DBMs is the foundation of the two applications.
Promoting Awareness on Sustainable Behavior Through an AR-Based Art Gallery

This paper presents "Augmented Gallery", an art gallery in the form of a networked, AR-based, audio-visual system, which was devised to convey the visitors a clear message about the urgency of taking action to address environmental threats such as pollution, climate change, and biodiversity loss. The system was designed to both entertain and educate the visitors, empowering them to understand how unsustainable behaviours may affect our lives in the future and emphasize the need to take relevant actions for building a more environmentally sustainable world. Augmented Gallery consists of an AR app running on networked smartphones used by visitors. The app displays, at visual and auditory level, scenes representing various healthy environments as well as, via user interactions, their counterpart affected by the consequences of an unaddressed climate change and unsustainable human behaviour, exposing predictions based on the Intergovernmental Panel on Climate Change climate report. A user study was conducted, where the gallery was evaluated during two public events. The results showed that opinions about Augmented Gallery were generally very positive. Furthermore, visitors admitted that attending the gallery increased their awareness on environmental issues and their consequences on the planet.
On the Distributed Jamming System of Covert Timing Channels in 5G Networks

To build the fifth generation (5G) mobile network, the sharing structure in the 5G network adopted in industries has gained great research interesting. However, in this structure data are shared among diversity networks, which introduces the threaten of network security, such as covert timing channels. To eliminate the covert timing channel, we propose to inject noise into the covert timing channel. By analyzing the modulation method of covert timing channels, we design the jamming strategy on the covert channel. According to the strategy, the interference algorithm of the covert timing channel is designed. Since the interference algorithm depends heavily on the memory, we construct a distributing jammer. Experiments results show that these covert time channel can be blocked under the distributing jammer.
Practice of physical exercises as a non-pharmacological strategy in the treatment of alzheimer's disease treatment

Studies show that the life expectancy of the world population is increasing and consequently the elderly population. With aging is observed a loss of cognitive skills that can occur pathologically such as Alzheimer's disease (AD). AD is a neurodegenerative disease characterized by the accumulation of extraneuronal amyloid plaques and intraneuronal neurofibrillar entanglements located in regions of the temporal lobe, which result in progressive cognitive decline. Physical activity programs can reduce the risk of AD development. Thus, this study aimed to portray the pathophysiological aspects of AD, and the practice of physical exercise as a non-pharmacological method in its prevention. The research was carried out from a bibliographic review of specialized literature, being consulted scientific articles located in the databases Medline, Scielo, Lilacs, Google Academic and the portal of CAPES Journals published until 2019 and additionally consultation of academic books to complement the research. Physical exercises can prevent and delay the disease, in addition to physical benefits such as improved psychological state, behavior disorders, improved ability to perform daily life activities, reduced risk of falls and the social relationship of the patient.
Question Answering System Using LSTM and Keyword Generation

Question answering system is an area under natural language processing and information retrieval which automatically answers the questions generated by humans. This work represents an approach for building a system that generates answers for the question based on deep learning neural network which has the competence of processing the information present inside the dataset and enables the user to obtain an insight from the SQuAD dataset by inviting questions in natural language form. Key stages of this approach cover corpus pre-processing, question pre-processing, answer generation, deep neural network for answer extraction and keyword generation. The concept of keyword generation is a novel idea implemented to enable naïve user of the system to apprehend the passage. The system is competent in interpreting the question, responding to the user’s query in natural language form along with generating the keywords. The performance was measured on SQuAD dataset using EM and F1 score.
Design and Implementation of Heterogeneous Identity Alliance Risk Assessment System

Heterogeneous identity alliance consists of multiple identity management platforms cross architectures and applications, provides unified identity management and services. This paper focuses on the heterogeneous alliance risk assessment framework, The network behavior and identity attributes of users in the alliance system are chosen as the main evaluated factors. Combined with the information security risk assessment, the risk assessment system of the alliance system is designed and implemented by Web. Finally, The development of the system shows the results of the risk assessment in a visual way, which assistants the regulators to visually find out the dangerous factors of the alliance system and take corresponding measures.
Quaternionic keypoint description for multispectral imaging

In this work, we propose a new keypoint descriptor that is suitable for multispectral inputs comprising up to 4 channels. Color images with or without an additional infrared or depth channel are some of the use-cases that can be handled by the proposed descriptor. Standard keypoint descriptors employ single-channel input gradients, thereby discarding potentially useful content. The proposed descriptor is based on a quaternionic representation of the input image thereby treating each pixel multispectral value holistically. Coupled with a suitable multispectral quaternionic detector, we show that the proposed detector leads to superior experimental results on a keypoint matching scenario.
Two dependent probabilistic chip-collecting games

Alice and Bob take turns to collect chips in the following manner. In each turn, Alice tosses a fair coin, which decides whether she collects a or b chips, where a and b are positive integers. If Alice collects a chips, then Bob collects b chips, and vice versa. We consider two variants of game play that have different rules in determining the winner. Namely, the winner of Game 1 is the first player to collect at least n chips, while the winner of Game 2 is the first player to collect a positive number of chips congruent to 0 modulo n. We fully determine the formula for the winning probabilities of each player in Game 1, and determine the best and worst case scenarios in terms of winning probabilities in Game 2.
Data Compression Method Based on Operation Condition Identification

In response to the requirement of a large amount of historical data storage and the difficulty of data analysis and visualization in the industrial equipment data acquisition system, a data compression method based on operation condition identification is proposed in this paper. The method firstly sets the dead zone limit value for each dimensional time series according to the change of multi-dimensional time series data collected from industrial equipment; then, on the grounds of whether each piece of data meets the dead zone limit, collecting equipment operation condition information and marking operation condition labels; finally, based on the operation condition label, the data is compressed into a data matrix composed of time and operation condition information. Our storage method has good universality and realizes efficient data storage by reducing the dimension of historical data.
Speaker recognition based on BP neural network

Speaker recognition is a newly developed recognition field in the field of speech recognition. In recent years, with the increasingly wide use of artificial neural networks, a speaker recognition method based on the BP neural network has been proposed. The main purpose of this paper is to use BP neural network to train and classify the extracted Mel Frequency Cepstrum Coefficients (MFCC) which can be seen as a kind of speech feature information, so as to achieve the function of speaker recognition.
Research on Computer Network Information Security System Based on Big Data

In order to effectively improve the computer network security defence capability in the era of big data, a comprehensive analysis of the functions of big data centre applications is performed to create a comprehensive computing network security defence system. First of all, a comprehensive analysis of the hidden dangers of modern computer network security is carried out, and then corresponding technologies such as modern network security technology and solutions, intrusion detection technology are introduced to realize the design of computer network security defence system in the context of the big data era. After the system design is implemented, the system is tested accordingly. According to the test results, the computer network security defence system designed in this paper can actively discover and effectively prevent security threats in the network, thereby ensuring that the network can Normal and safe operation. The computing network security defence system can also provide effective ideas for future network security protection and achieve further expansion of security defence.
Visual object tracking based on siamese network with a deeper and more powerful backbone

Siamese tracker has become an important branch of single object tracking. It balanced speed and accuracy on the tracking task. The network of feature extraction is the key to performance of Siamese tracker. However, the recent trackers used classic backbone networks, such as AlexNet and ResNet. But there is a gap between these networks and the modern deep networks in performance. In order to improve the performance of Siamese tracker, we use a deeper and powerful modern backbone network to redesign the framework of Siamese Tracker. In this work, we did some convincing experiments on authoritative datasets, such as OTB2015, VOT2017 and VOT2018, which are frequently used by other trackers. The evaluation results on these datasets show that our tracker has better performance and robustness.