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The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer from general deep learning issues and are hard to properly train. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos. [ABSTRACT FROM AUTHOR]

In just a few years, the efficiency of perovskite-based solar cells (PSCs) has risen to 25.8%, making them competitive with current commercial technology. Due to the inherent advantage of perovskite thin films that can be fabricated using simple solution techniques at low temperatures, PSCs are regarded as one of the most important low-cost and mass-production prospects. The lack of stability, on the other hand, is one of the major barriers to PSC commercialization. The goal of this review is to highlight the most important aspects of recent improvements in PSCs, such as structural modification and fabrication procedures, which have resulted in increased device stability. The role of different types of hole transport layers (HTL) and the evolution of inorganic HTL including their fabrication techniques have been reviewed in detail in this review. We eloquently emphasized the variables that are critical for the successful commercialization of perovskite devices in the final section. To enhance perovskite solar cell commercialization, we also aimed to obtain insight into the operational stability of PSCs, as well as practical information on how to increase their stability through rational materials and device fabrication. [ABSTRACT FROM AUTHOR]

With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. The proposed framework can discover and model intrusion scenarios, and the constructed model keeps evolving with the variance of the data. Additionally, a series of data preprocessing methods are introduced to enhance its adaptability to the noisy and twisted data. The experimental results show that the framework is effective in intrusion scenario discovery, and the discovered scenario is more concise and accurate than our previous work. [ABSTRACT FROM AUTHOR]

Hybrid organometal halide perovskites have attracted much attention these past four years as the new active layer for photovoltaic applications. Researches are now intensively focused on the stability issues of these solar cells, the process of fabrication and the design of innovative materials to produce efficient perovskite devices. In this review, we highlight the recent progress demonstrated in 2015 in the design of new π-conjugated organic materials used as hole transporters in such solar cells. Indeed, several of these "synthetic metals" have been proposed to play this role during the last few years, in an attempt to replace the conventional 2,2′,7,7′-tetrakis-(N,N-di-4-methoxyphenylamino)-9,9′-spirobifluorene (Spiro-OMeTAD) reference. Organic compounds have the benefits of low production costs and the abundance of raw materials, but they are also crucial components in order to address some of the stability issues usually encountered by this type of technology. We especially point out the main design rules to reach high efficiencies. [ABSTRACT FROM AUTHOR]

This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody's (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb's three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy. [ABSTRACT FROM AUTHOR]

Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time series predictions, has helped to solve various types of problems. This paper aims to introduce a new theory, Hierarchical Temporal Memory (HTM), that applies to stock market prediction. HTM is based on the biological functions of the brain as well as its learning mechanism. The results are of significant relevance and show a low percentage of errors in the predictions made over time. It can be noted that the learning curve of the algorithm is fast, identifying trends in the stock market for all seven data universes using the same network. Although the algorithm suffered at the time a pandemic was declared, it was able to adapt and return to good predictions. HTM proved to be a good continuous learning method for predicting time series datasets. [ABSTRACT FROM AUTHOR]

Copper oxide (CuxO) films are considered to be an attractive hole-transporting material (HTM) in the inverted planar heterojunction perovskite solar cells due to their unique optoelectronic properties, including intrinsic p-type conductivity, high mobility, low-thermal emittance, and energy band level matching with the perovskite (PS) material. In this study, the potential of reactive sputtered CuxO thin films with a thickness of around 100 nm has been extensively investigated as a promising HTM for effective and stable perovskite solar cells. The as-deposited and annealed films have been characterized by using X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Photoluminescence (PL), UV-Vis spectroscopy, and Hall-effect measurement techniques. The significant change in structural and optoelectronic properties has been observed as an impact of the thermal annealing process. The phase conversion from Cu2O to CuO, including grain size increment, was observed upon thermal annealing. The transmittance and optical bandgap were found to vary with the films' crystallographic transformation. The predominant p-type conductivity and optimum annealing time for higher mobility have been confirmed from the Hall measurement. Films' optoelectrical properties were implemented in the complete perovskite solar cell for numerical analysis. The simulation results show that a 40 min annealed CuxO film yields the highest efficiency of 22.56% with a maximum open-circuit voltage of 1.06 V. [ABSTRACT FROM AUTHOR]

Planar perovskite solar cells were fabricated on F-doped SnO2 (FTO) coated glass substrates, with 4,4'-((1E,1'E)-((1,2,4-thiadiazole-3,5-diyl)bis(azaneylylidene))bis(methaneylylidene))bis(N,N-di-p-tolylaniline) (bTAThDaz) as hole transport material. This imine was synthesized in one step reaction, starting from commercially available and relatively inexpensive reagents. Electrochemical, optical, electrical, thermal and structural studies including thermal images and current-voltage measurements of the full solar cell devices characterize the imine in details. HOMO-LUMO of bTAThDaz were investigated by cyclic voltammetry (CV) and energy-resolved electrochemical impedance spectroscopy (ER-EIS) and were found at −5.19 eV and −2.52 eV (CV) and at −5.5 eV and −2.3 eV (ER-EIS). The imine exhibited 5% weight loss at 156 °C. The electrical behavior and photovoltaic performance of the perovskite solar cell was examined for FTO/TiO2/perovskite/bTAThDaz/Ag device architecture. Constructed devices exhibited good time and air stability together with quite small effect of hysteresis. The observed solar conversion efficiency was 14.4%. [ABSTRACT FROM AUTHOR]

This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody's (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb's three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy.