Reviewerly/exHarmony huggingface dataset:
Realization of CSI-Based Human Behavior Recognition: A Survey

With the rapid popularization and development of smart home and Internet of Things (IoT), wireless sensing technique has become more and more popular due to its better privacy protection, insensitivity to light and avoiding obstacles. As one of the most popular wireless sensing devices, Wi-Fi has the advantages of extensive deployment and low cost, which attracts a large number of interests. Thus, this survey mainly focuses on the Wi-Fi-based human behavior recognition systems in recent years, and analyzes its realization process. Concretely, we summary these process from four aspects. First, the collection tool and characteristic of channel state information (CSI) data are simply introduced. Second, the data pre-processing algorithms are described, such as phase correction, low-pass filter, outlier removal, principal component analysis, discrete wavelet transform, activity segmentation and extraction and so on. Third, the recognition modes are outlined, including CSI speed model, Fresnel Zone model, angle of arrival model, machine-learning based mode and deep-learning based model. Forth, applications of human behavior recognition are concluded according to recognition content. Finally, this survey summarizes current challenges and issues of behavior recognition systems, and gives some suggestions for future research.
Security in Big Data: A Succinct Survey

The rapid growth of the Internet, social media, business applications, and IoT devices in the modern era has resulted in a huge rise in data growth where thousands of petabyte are being collected on daily basis. Big data has emerged as an assistive technology for organizations and individuals to process and handle large amounts of mass data in their development path. There are many technologies, frameworks, and tools that play an important role when dealing with big data, some of them are cloud technology, data mining, Hadoop and MapReduce. Protecting important information from invaders, viruses, and worms has been a challenge in the last some decades. Keeping it in mind many, researchers have developed various techniques to secure the data. However, traditional methods and techniques have many limitations such that they are only able to handle structured data, and we need new techniques and methods for processing, securing, and communicating in semi and unstructured data (big data). In this paper, we have concluded some security and privacy measures proposed by various researchers and analyzed the pros and cons of their techniques along with some solutions.
A Length-Scalable Modular Multiplier Implemented with Multi-bit Scanning

Modular division and modular multiplication are the core operations in the elliptic curve cryptography (ECC). In recent years, reducing the hardware area, simplifying the hardware structure, and improving computing efficiency have become research hotspots. This paper proposes a modular multiplier based addition with the technology of Mulit_Bit Scanning, which supports prime field and binary field, and the length of this multiplier is up to 576-bit. This paper first analyzes the advantages and disadvantages of the add-based modular multiplication (AMM) and the multiply-based multiplication (MMM), and optimizes the AMM algorithm based multi-bit scanning; then designs the hardware structure based on the proposed algorithm. Moreover, the modular arithmetic unit is described at the RTL level by Verilog HDL, and based on the 180nm CMOS technology library for logic synthesis of all arithmetic units, the highest clock can reach 371 MHz(1.6~2.7 times of the others ), and the area being 45758 gates (0.29~0.51 times of the related works).
Integrating Graph-Based Document Recommendation in Digital Libraries of Theses Collection

Manuscripts in university local collection such as theses and dissertations are very useful resources for researches and literature reviews. The implementation of full-text search and content recommendation within these collections is limited due to local resources. This work aims to enhance document searching capability of local manuscript collections by implementing document index from abstract and developing graph-based document similarity algorithm for related reading recommendation. The evaluation shows that graph-based ranking performed acceptable precision rate at first 2 recommendation items. To enhance the performance of content-based recommendation system, graph-based filtering can be used to reduce search space for traditional document similarity algorithm. We also developed the prototype of graph-based filtering conjunction with document similarity algorithm in our local university theses and dissertation collection.
Human behavior recognition based on convolutional long and short time memory network

Convolutional long and short time memory network is a kind of fusion model, which inherits the excellent spatial feature extraction ability of convolutional neural network, and can effectively complete the processing and classification of time series data by using the memory ability of long and short time memory network to historical data and the unique gating mechanism. This paper uses the human behavior data set collected by the Wireless Data Mining Laboratory (WISDM) of Fordham University to predict and classify the six daily human behaviors: walking, jogging, going upstairs, going downstairs, sitting and standing. By comparing with long and short time memory network, convolutional neural network and other deep learning models, the experimental results show that the convolutional long and short time memory network has the best performance among them, which the accuracy reaches 97.43% and has a great improvement in real-time and accuracy.
Leveraging CNN and Bi-LSTM in Indonesian G2P Using Transformer

We apply a transformer called tensor2tensor toolkit, which is based on Tensorflow, to overcome the Grapheme-to-Phoneme conversion problem. This study performs conversions to produce pronunciation symbols for certain letter sequences in Indonesian particularly. The unavailability of the G2P conversion system in Indonesian is currently being faced, so research is being carried out to create a system that can solve this problem by applying the Transformer. The transformer has a simple network architecture based solely on the attention mechanism, so we took advantage of eliminating convolution and redundancies—complex recurrent and convolution neural networks including encoders and decoders as the basis for the sequence transduction model. The excellent performance of the model is obtained through the attention mechanism by connecting the encoder and decoder. By using this tool, we carry out to compare among KBBI and CMU dictionary datasets. We attained a word error rate (WER) of 6,7% on the KBBI data set after training for three days on two core CPUs, which has an accuracy of 93,3%, improving over the existing best results CMU dictionary dataset for 26% word error rate. In this study, we carried out a detailed experimental evaluation by assessing the processing time and the error rate of words and then compared it with state of the art. By demonstrating this Transformer, this tool successfully generalizes and then applies it to several Indonesian elements with limited training data and large training data. We concluded that the transformer model is suitable for dealing with the G2P problem at hand for this task.
Chronic Kidney Disease of Uncertain Aetiology and Its Relation with Waterborne Environmental Toxins: An Investigation via Compositional Balances

The occurrence of environmental clusters of Chronic Kidney Disease of uncertain aetiology (CKDu), where there is no known cause for the onset of kidney dysfunction, is a concern globally. Waterborne exposure pathways in the environment may result in indirect or direct ingestion of trace elements with potential health risks. This research examines the relationship between Standardised Incidence Rates (SIRs) of CKDu and the log-ratio balances of Potentially Toxic Elements (PTEs) in regional stream water. Compositional elemental balances were created and regression was used to identify the balances most associated with log-transformed SIR CKDu. At the regional scale, a statistically significant relationship was found between log (CKDu SIR) and the elemental balance Al/As which effectively delineates different geological domains across Northern Ireland. Following stratification by basalt bedrock (the dominant bedrock geology for SIR CKDu), the balance Al/Fe was identified as significantly associated with log (CKDu SIR). Superficial deposits, dissolved organic carbon and pH may act as controls on the balance of Al and Fe. With a high proportion of private water supplies registered in these areas, this research highlights the importance of considering bedrock geology and superficial deposits in understanding multi-element interactions of waterborne environmental toxins and potential links with environmental clusters of CKDu. We would like to acknowledge and thank our esteemed colleague and friend Vera Pawlowsky-Glahn who has been instrumental in the development of this research, pioneering the importance of Compositional Data Analysis (CoDA) in the study of health and the environment. For several of us, Vera provided our first introduction to CoDA and over many years has aided our shared understanding and increased awareness of the need to address the compositional nature of data such as soil and water geochemistry in our research. We are pleased to have this work on the use of compositional balances in exploring the explanatory environmental factors related to chronic kidney disease of uncertain aetiology included in this Festschrift. It represents an exciting field of innovative research where an acknowledgement of CoDA principles is vital if we are to understand fully the critical relationship between our health and the environment.
Integrating electrodes to headsets for human-system interaction and psycho-physiological monitoring

Research in human monitoring led to development of powerful tools for users-systems communicationvia recording electrophysiological data and sending them to the computer system. Eyeand gaze tracking are standing as important methods for a broad range of human monitoring applicationssuch as in neuroscience, psychology, industrial engineering, aeronautics, military, andmedical expertise. Nowadays, the most used eye-tracking technique is video-based tracking basedon infrared illumination. However, the tools using this technique present a certain number of disadvantages.Notably, for the head-mounted tools, such systems obstruct the visual field and thereforeare not suitable for integration in real operational environments. An alternative technique isthe electro-oculography (EOG) which consists in measuring the standing potential between thefront and back of the eyes. This potential increases when the cornea approaches an electrode andit decreases when the cornea moves in the opposite direction. EOG represents one of the easiestmethods to estimate eye movements by using low-cost low-energy consumption devices withoutobstructing the visual field nor handling infrared light. This technique is particularly convenientfor the head-mounted peripherals such as audio or virtual reality headsets. Typical features ofsignal processing, such as the mean of fixation duration, saccadic rates, and blinks are exploredin various environments including air traffic control, pilots and co-pilots of civil and military aircraft.Interestingly, the EOG, as well as electroencephalography (EEG) features were correlated todrivers’ and pilots’ workload, drowsiness and fatigue.
What May Lie Ahead in Reinforcement Learning

The spectacular success enjoyed by machine learning (ML), primarily driven by deep neural networks can arguably be interpreted as only the tip of the iceberg. As neural network architectures, algorithmic methods, and computational power evolve, the scope of the problems that ML addresses will continue to grow. One such direction for growth materializes in reinforcement learning.
An Adaptive Denoising Algorithm for Speckle Noise in DoFP Polarization Images

In this paper, an adaptive algorithm for filtering speckle noise in Division-of-Focal-Plane (DoFP) polarization images is presented. The proposed algorithm involves replacing the first round of operation in the Block-matching and 3D Filtering (BM3D) denoising algorithm with an adaptive thresholding technique based on the smoothness of an image patch. This improves the "Semi-Filtered" image that is passed to the final BM3D denoising round. With this adaptive technique, key image details are preserved and speckle noise is significantly suppressed. Experimental results on four DoFP test images indicate the significant performance of the proposed algorithm in suppressing speckle noise. This significant performance is then compared with the performance of other existing denoising algorithms. Comparison is done numerically using Peak-Signal-to-Noise-Ratio (PSNR) and Structural Similarity (SSIM) Index scores. Thereafter, comparison is extended to visual results to ascertain agreement between the two sets of comparisons.
Research on Power Universal Service Access Gateway Based on Blockchain

With the development of power Internet of things and energy Internet, the traditional centralized access authentication method of power Internet of things terminal is difficult to meet the application needs. At present, the centralized access authentication mode has brought great computing and communication pressure to the authentication center, especially large-scale concurrent access and mobile access have a serious impact on the authentication efficiency of the system. Based on the decentralized technology of block chain and the characteristics of power communication network, a distributed authentication scheme suitable for power Internet of things is proposed. The power ubiquitous service access gateway based on block chain is developed, and the terminal test is carried out in wired environment and wireless environment. The gateway can realize the terminal access authentication of the typical ubiquitous power Internet of things system, such as distribution automation, new energy and so on. It has the characteristics of good universality and convenient configuration, and can significantly improve the security of the terminal system without affecting the original system topology.
Quadratization of gray coded representations, long path problems and needle functions

In Evolutionary Computation, it is informative to ask what happens when well known benchmarks and bit representations are transformed into quadratic pseudo-Boolean optimization problems. Such transforms are commonly used in quantum computing in order to reduce nonlinearity to k-bounded interactions. First we show that Gray code representations are transformed into linear encodings with quadratic constraints. Next we look at Long Path problems which are constructed so that bit flip local search requires exponential time to converge to a global or local optimum. We show that Long Path problems are similar to reflected Gray codes in both construction and complexity. Finally, a basic form of the "Needle in a haystack" problem is transformed into a problem that can be optimally solved in linear time.
A Novel Image Functioned Framework for Steganography Using PS-DCT Approach

This research paper presents a unique approach of image primarily based steganography method, wherever pseudo-random sequence generators operate is employed to randomize the mapping sequence in an exceedingly pseudo-random fashion. The embedding method of the key message is completed per the pseudo-random sequence, and extraction is completed exploitation identical sequence at the opposite side. At the bottom level, 2D-DCT is employed to cover the bits of secret message within the stego image by exchanging the LSBs of DCT coefficients. Since in previous paper, the LSB substitution technique is already used; here during this paper, the previous concept is extended and used DCT rather than LSB technique. A replacement text randomization algorithmic rule (4-2 swap text randomization algorithm) is additionally accustomed to modify the key message before embedding.
A Multi-grained Attention Network for Multi-labeled Distant Supervision Relation Extraction

Distant supervision is an efficient approach to generate labeled data automatically for relation extraction and it is by nature a multi-instance multi-label learning issue. However, previous methods mainly consider that each sentence explicitly express one relation and ignore the relation overlapping problem for which their models are incompetent to capture complete context features. The existing methods for multi-labeled distant supervision relation extraction incorporate with capsule network to handle the highly overlapping relations, but the computational complexity of the model is too high. To this end, we propose a novel multi-grained attention neural network for relation overlapping problem. Further, we devise a adaptive-class-balanced loss function to handle the class-imbalanced problem in distant supervision relation extraction. The experimental results on a widely used dataset show the effectiveness of proposed method to make multi-label predictions and to alleviate class-imbalanced problem.
Gambling: The Italian Communications Authority (AGCOM) fines one of the main online advertising operator for violating the rules on advertising of games and bets (Google)

On 22 October 2020, the Council of the Italian Communications Authority [AGCOM] imposed an administrative fine of EUR 100 000 on Google for violation of the ban on gambling advertising…
An expression detection technique based on multi-input convolutional neural network for incomplete face images

An expression detection technique based on feature fusion multi-input convolutional neural network is proposed. In view of the negative effect of occlusion objects in occlusion face image on expression recognition task, the multi-input convolutional neural network is proposed to use the multi-input property, so that the multi-classifier coupling network can learn more complex prediction model. The local feature level fusion method was used to extract the features from the image, and the local micro-features of the region of interest were taken as the multi-branch input of the multi-input neural network, so as to reduce the influence of the contribution rate of the missing part of the incomplete image and improve the robustness and accuracy of the expression detection.
Securing images on cloud using visual cryptography

Security becomes a major concern for data storage. As observed, the images in the cloud will emerge as a threat to the users. The user considers about the security of stored data device. Henceforth, the storage and maintenance of data on devices became a hectic task and it consumes more effort from the user. To overcome this problem, this research wok has attempted to implement the images on the cloud to waive off the cost problems by implementing security mechanisms for the images present on the cloud with minimal cost and security. The proposed method protects the images on the cloud by using visual cryptograpghy. The image is encrypted with the user specified key by using AES algorithm and it is further divided into multiple pieces called shares. The shares are then moved to the cloud. To decrypt the image, the shares are collected back from the cloud and combined to form a meaningful image using AES algorithm. Finally, the original image gets revealed.
On the landscape of one-hidden-layer sparse networks and beyond

Sparse neural networks have received increasing interest due to their small size compared to dense networks. Nevertheless, most existing works on neural network theory have focused on dense neural networks, and the understanding of sparse networks is very limited. In this paper we study the loss landscape of one-hidden-layer sparse networks. First, we consider sparse networks with a dense final layer. We show that linear networks can have no spurious valleys under special sparse structures, and non-linear networks could also admit no spurious valleys under a wide final layer. Second, we discover that spurious valleys and spurious minima can exist for wide sparse networks with a sparse final layer. This is different from wide dense networks which do not have spurious valleys under mild assumptions.
Projection-free Online Empirical Risk Minimization with Privacy-preserving and Privacy Expiration

Streaming machine learning and data mining problems are prevalent in real world applications, where individual data are collected and revealed consecutively. These problems can often be modeled and solved under Constrained Online Convex optimization (COCO) algorithmic framework. The ever-growing amount of sensitive individual data is posing greater challenge to the contradictory goals of privacy protection and reasonable model usability. Despite its extensive studies via projected/proximal gradient based methods, its projection-free counterpart has not been well-explored. Inspired by the better per-iteration computational efficiency and privacy-utility tradeoff under non-private/non-online settings of the projection-free algorithms, we propose the projection-free COCO with differential privacy guarantee, a de facto standard for privacy preserving. We rigorously analyze its utility in terms of regret rate, which shows that, even without the expensive projection/proximal operators, it still matches the differentially privacy COCO with projection/proximal operations. To the best of our knowledge, it is the first projection-free differentially private COCO, and thus broadens the applicability of COCO with privacy guarantee. Furthermore, since protecting the privacy of all incoming samples will lead to inferior regret rate compared to the nonprivate optimal, we propose a relaxed privacy guarantee which trades the privacy of remote samples for better utility. To achieve this, we adopt a window tree mechanism for maintaining a private gradient summation, which is then used to construct an approximation function for updating the new response variable at each timestamp. It improves the regret bound to O(InT) with respect to the sequence length T, matching the nonprivate optimal regret.
Noise reduction of shot-noise-dominated hyperspectral imagery by combining PCA with existing denoising methods

In this paper, we revisit the effects of principal component analysis (PCA) on hyperspectral imagery denoising. Our previous work combined PCA with wavelet shrinkage and particularly good denoising results has been achieved. We debate that any denoising methods can be used to replace wavelet shrinkage in our PCA+wavelet shrinkage algorithm. The major difference between this work and our previous PCA-based denoising method is that we consider a mixture of Gaussian and shot noise in this work whereas our previous methods studied Gaussian white noise alone. In addition, we retain [Formula: see text] [Formula: see text] PCA output components in our forward PCA transform in this paper whereas we keep all PCA output components [Formula: see text] in our previous works. The [Formula: see text] above is the number of spectral bands in the original hyperspectral imagery data cube. In addition, PCA is much better than nonlinear PCA for hyperspectral imagery denoising when Gaussian white noise and shot noise are introduced as demonstrated in this paper. Extensive experiments demonstrate that the method proposed in this paper outperforms the existing methods significantly in terms of signal-to-noise ratio for two testing hyperspectral imagery data cubes.