Reviewerly/exHarmony huggingface dataset:
SCAR-Net: Scalable ConvNet for Activity Recognition with Multimodal Sensor Data

In recent times, context-aware activity recognition has made significant progress due to modern development of machine learning. Yet, it is challenging due to the major difference among recognition tasks due to the change in sensor types, system design, bio-factors of the subjects, etc. In this work, a generalized solution has been presented for the Cooking Activity Recognition Challenge, where the model needs to predict the macro- and micro activities from raw sensor data. The proposed Convolutional Neural Network (SCAR-Net [1,2]) is an end2end, multi-head model which can work with sensor data from multiple sensors at once without truncating or padding the time-series data. SCAR-Net also performs exceptionally well without any kind of preprocessing to the data. The proposed method is tested with leave-one-subject-out cross-validation metrics (accuracy) to validate models’ performance. SCAR-Net 1 achieves 54% average accuracy for the macro-activity recognition task with 3 classes and SCAR-Net 2 achieves 27% average multi-label accuracy for the micro-activity recognition task with 10 classes. The source code for the complete project is available at https://github.com/zabir-nabil/activity-recognition-abc .
Geo-Distributed Driving Maneuver Anomaly Detection

Auto-Encoder has been widely applied to anomaly detection areas. In this paper, we present a geo-distributed driving maneuver anomaly detection system based on auto-encoder. The auto-encoder is trained by using the normal driving data, so it memorizes the feature of normal driving pattern. The well trained auto-encoder is able to work as a classifier during the detection phase, it will tell whether the input data is normal or abnormal. To further improve the detection accuracy, we divide a city into a set of sub-regions by maximizing the spatial contrast within the same sub-region and minimizing the spatial contrast among different sub-regions. To examine performance of the proposed system, we evaluate it using a large dataset of GPS trajectories. The experiment results show our system achieves high detection accuracy.
Attack Models and Scenarios

Collecting knowledge about cyber threat attackers is an important task to develop cyber threat attack models for security analysis of different cyber threat attack scenarios. This allows simulating distinct attack paths or alternative approaches reaching cyber threat attackers attack objectives. This methodological approach can be assumed as profiling cyber-criminal attackers, based on the collection of a-priori knowledge processed by the cyber threat attacker’s scenarios. This also contains the potential cyber threat attack policy which includes answering questions such as “What is the adversary’s objective?”, and “What is the adversary’s goal?”, and “What may be the adversary’s preferred attack method to achieve his attack goal?”, and many others.
Wearable Sanitizer: Design and Implementation of an Open-source, On-body Sanitizer

During the pandemic, wearables such as face masks and face shields have become broadly adopted, these solutions do reduce infection but do not eliminate infectious agents from surfaces and objects the person may touch. Therefore, regular disinfection of hands and frequently touched surfaces is a critical factor in preventing the spread of infectious diseases ranging from the common cold and flu to SARS and COVID-19. This activity of frequent disinfection requires a high degree of discipline and leads to increased cognitive and physical effort involved in frequent washing of hands or use of a pocket sanitizer. We present an open-source, wearable sanitizer that provides just-in-time, automatic dispensing of alcohol to the wearer’s hand or nearby objects using sensors and programmable cues. We systematically explore the design space aiming to create a device that not only seamlessly integrates with the user’s body and behavior but also frees their physical and mental faculties for other tasks.
Near-duplicated Loss for Accurate Object Localization

Multi-class object detection always involves the tasks of accurate target localization which is mainly related to bounding box regression. Smooth L1 loss is the most popular bounding box regression loss used in the current state-of-the-art object detection systems. However, such loss for regressing the parameters of a bounding box can't accurately and consistently regress the bounding box to the associated ground truth well. We instead propose the near-duplicated loss, a loss that better evaluate the disparity between the bounding box and the ground truth consistently. We present an approximate algorithm associated with a kernel function that not only considers the absolute distance but also involves the relative overlap area between the two bounding boxes. The new loss doesn't need additional supervision and is easy to embed into existing networks. Our final result, by incorporating the near-duplicated loss into the state-of-the-art object detection detectors (Faster RCNN, RetinaNet), shows consistent and significant improvements on popular object detection benchmarks (MS COCO and Pascal VOC).
Discrete Spatial Importance-Based Deep Weighted Hashing

Hashing is a widely used technique for large-scale approximate nearest neighbor searching in multimedia retrieval. Recent works have proved that using deep neural networks is a promising solution for learning both feature representation and hash codes. However, most existing deep hashing methods directly learn hash codes from a convolutional neural network, ignoring the spatial importance distribution of images. The loss of spatial importance negatively affects the performance of hash learning and thus reduces its accuracy. To address this issue, we propose a new deep hashing method with weighted spatial information, which generates hash codes by using discrete spatial importance distribution. In particular, to extract the discrete spatial importance information of images effectively, we propose a method to learn the spatial attention map and hash code simultaneously, which makes the spatial attention map more conductive to hash-based retrieval. The experimental results of three widely used datasets show that the proposed deep weighted hashing method is superior to the state-of-the-art hashing method.
Modified method of fuzzy recognition of proteins in electrophoresis in population genetics

We propose the development of a previously created method for fuzzy recognition of proteins by gel electrophoresis of earthworm tissues in population genetics. In case when several proteins have close masses, their traces on the electrophoregram intersect. The visually determined boundaries of the fuzzy membership function carrier are unjustifiably narrowed, as can be seen in the dependence of the boundaries on the mass of the protein. Therefore, it is necessary to clarify the coordinates of the carrier of the fuzzy membership function for each of them. The methodology developed by us involves the construction of an experimental membership function on a narrowed carrier, the approximation of it by selected curve (Gauss or Bezier of fifth order) and, then, the adjustment of the boundaries of the carrier by extrapolating the approximants to such boundaries where the membership function reaches a predetermined level. The effectiveness of the application of the technique in studying the effect of laser radiation on changing the genotype of the Eisenia foetida worm population is shown.
Coupling Deep Models and Extreme Value Theory for Open Set Fault Diagnosis

Existing deep-learning-based fault diagnosis methods assume that all possible fault modes are available during training process, which is sometimes not consistent with real applications. Unknown fault types may occur in the testing phase due to the fact that it is impossible to collect all the fault modes in the training phase. Thus, in this paper, we introduce and define the open set fault diagnosis (OSFD), and handle this problem in both shared-domain and cross-domian scenarios. For shared-domain OSFD, an extreme-value-theory-based method is proposed to build a rejection model to detect samples from the unknown classes. For cross-domain OSFD, weighted domain adversarial neural networks is constructed to obtain domain-invariant features of the shared classes and separate samples of unknown classes by reweighting target samples. Learned features of source data are used to establish a rejection model, such that unknown samples in the target domain can be detected. Experimental results on the Case Western Reserve University dataset demonstrate the effectiveness of the proposed methods.
Performance Evaluation of Pre-Trained CNN Models for Visual Saliency Prediction

Human Visual System (HVS) has the ability to focus on specific parts of the scene, rather than the whole scene. This phenomenon is one of the most active research topics in the computer vision and neuroscience fields. Recently, deep learning models have been used for visual saliency prediction. In this paper, we investigate the performance of five state-of-the-art deep neural networks (VGG-16, ResNet-50, Xception, InceptionResNet-v2, and MobileNet-v2) for the task of visual saliency prediction. In this paper, we train five deep learning models over the SALICON dataset and then use the trained models to predict visual saliency maps using four standard datasets, namely: TORONTO, MIT300, MIT1003, and DUT-OMRON. The results indicate that the ResNet-50 model outperforms the other four and provides a visual saliency map that is very close to human performance.
Automatic Curriculum Generation by Hierarchical Reinforcement Learning

Curriculum learning has the potential to solve the problem of sparse rewards, a long-standing challenge in reinforcement learning, with greater sample efficiency than traditional reinforcement learning algorithms because curriculum learning enables agents to learn tasks in a meaningful order: from simple tasks to difficult ones. However, most curriculum learning in RL still relies on fixed hand-designed sequences of tasks. We present a novel scheme of automatic curriculum learning for reinforcement learning agents. A two-level hierarchical reinforcement learning framework, with a high-level policy called the curriculum generator and a low-level policy called the action policy, is proposed. During training, the curriculum generator automatically proposes curricula for the action policy to learn. Our training methods guarantee that the proposed curricula are always moderately difficult for the action policy. Both levels of policies are trained simultaneously and independently. After training, the low-level policy will be able to finish all tasks without the instructions given by the curriculum generator. Experiment results on a wide range of benchmark robotics environments demonstrate that our method accelerates convergence considerably and improves the training quality compared with the method without the curriculum generator.
Performance Analysis of Various Machine Learning Algorithm for Fall Detection-A Survey

Improper activities appear nowadays for the human (i.e.) falling without aware, and numerous techniques had been developed to reduce them. In this paper, critically analysis of the various proposed methodologies by comparing their strength and their weakness. Due to the diverseness and complexness action recognition become very challenge. The traditional (CCTV) system for monitoring patient activity it is inefficient and costly, then by using sensor based is also difficult due to drained battery life. So we need real time system for activity recognition with more efficiency and accuracy to avoid people from morality problems or it may lead to causes to major injuries. By comparing various algorithm Support vector machine (SVM) is a discriminative classifier belonging to supervised learning. Recurrent neural network (RNN) is one of the concepts in deep neural network. The main intention of the RNN is to minimize the preprocessing. This application will seem like visual imagery analysis. The convolutional neural network (CNN) it is more cost expensive compared wearable and ambience based. Diffusion Convolutional Neural Network (DCRNN) is the branch of artificial intelligence. The neural network was trained by DCRNN algorithm. The objective of the discussion is to be able to implement an automated anomalous human activity recognition system by using Diffusion Convolutional Recurrent Neural network and compression of the video in real times it proves to be more effective than other classification algorithm.
Experimental Comparisons of Verifiable Delay Functions

Verifiable delay function (VDF) has been a hot topic in recent cryptography research since the Ethereum researchers announced that they intended to use it in Ethereum 2.0. VDF has many applications in decentralized systems. This paper tries to organize the development path of VDF and related applications. We compare the performance of the four state-of-art VDFs by theoretical analysis and experimental verification. And through experiments, the influence of different type of groups and different hardware conditions on VDF performance are compared. In the end, we concluded that Wesolowski VDF is more suitable for decentralized clock applications that require higher time accuracy. Meanwhile, modular N multiplicative cyclic group is more suitable for constructing VDF that requires higher time accuracy while the class group is more suitable for applications with limited space. Besides, the effect of hardware on various VDFs is basically the same if the four VDFs use the same group and in the case of the same number of evaluation steps. Generally speaking, it might have a constant multiple improvement on the performance of VDF.
Farmer Call Centre Literature Review and Data Preparation

There is a general understanding that mankind began civilization with the discovery of farming, which is arguably the most crucial profession on this planet. The simple yet scientific definition of farming is growing crops for food and nourishment. Farmers are the people who undertake this noble job. At the start of twenty-first century, 1/3rd of the global workforce was employed in the agricultural job.
Activity recognition through intermittent distributed processing by energy harvesting PIR sensors

As an increasing demand for human activity monitoring in many smart services such as elderly monitoring, there is a keen need of an activities of daily living (ADL) recognition system. which can be easily deployed in ordinary homes and does not require periodic maintenance such as battery replacement for long time. In this paper, we propose an ADL recognition system which can run continuously without feeding power from outlets by intermittent sensing and distributed processing of energy harvesting (EH) sensor modules. Specifically, we have designed and developed an EH sensor node composed of (i) a micro-controller board with an analog PIR sensor which senses human activity as analog signals and form a BLE mesh network with other sensor nodes and (ii) an energy harvest module with solar panels and a rechargeable battery. We have also implemented a simple distributed random forest (RF) classifier consisting of multiple RF classifiers trained independently and running on different nodes which exchange the classification results with each other via BLE and make a final decision based on majority vote. Through experiments with five sensor nodes deployed in our smart home testbed, the distributed RF classifier classified the collected data of up to five different activities with average accuracy of over 90%.
Vector Representation of Words Using Quantum-Like Probabilities

The paper considers the problem of natural language processing and, particularly, representation of word semantics as a vector. In contrary to existing methods of word vectorization based on “bag of words” conception we propose a word representation as an integrated context built as a generalization of all the embeddings of a word into the text. To solve this task, we propose to use the apparatus of the quantum theory of probabilities that allows operating correctly with as clean as entangled states of words. The synthesis of the matrix for generalized context is implementing using the machine learning technique and, for elimination extra degrees of freedom, we developed a set of restrictions ensuring an acceptable convergence of the learning process. The proposed approach provides a probabilistic interpretation of results that allows easy to find a probability that a word is contextually similar to another one and also easy to integrate this model of a word to a number of text data analysis processes that use the theory of probabilities apparatus. The suggested method of formal representation of word sense could be used in many practical tasks of natural language processing including information retrieval.
Eye-tracking Data Analysis for Visual Exploration Assessment and Decision Making Interpretation in Virtual Reality Environments

This study presents an eye-tracking data analysis using ten different metrics aiming to complement the evaluation process of visual attention and decision making. We developed a virtual reality environment and used an eye-tracking device incorporated in a Head-Mounted Display to collect and analyze data. In the virtual environment, we included an activity that requires attention in the decision making process. 10 subjects participated in the activity that consists of solving a set of Raven's Progressive Matrices questions. The data generated in the questions solving process were analyzed. Results suggest that metrics such as spatial density, on-target fixation and time to first fixation provide insights that may help to improve the visual attention analysis, and thus, the decision making process.
Integrated Control of Multiple Data Centre Cooling Units

Energy management is one of the biggest challenges in today's fast growing world of technology. While data centres play a critical role as a facility for storage and IT operations, they also consume a significant amount of energy to operate, especially for cooling. Efficient cooling of data centres involves meeting temperature constraints while minimizing power consumption. We first demonstrate the applicability of a zonal model for a modular data centre. We then leverage this zonal model to demonstrate that a joint workload assignment and cooling control problem has a simple solution: equally load the servers and maximize the setpoints of the cooling units to meet redline temperature constraints.
An Effective Load Balancing Algorithm Based on Deadline Constraint Under Cloud Computing

Abstract Cloud computing is one of the attractive topics in distributed systems. It provides services on-demand based on pay-as-you-go basis. Task scheduling and load balancing are important research issues in cloud computing. Task scheduling means distributing tasks to resources (Virtual Machines) while load balancing refers to distribution tasks among resources to even workload. In this paper, we propose an Effective Load Balancing Algorithm with Deadline constraint (ELBAD). The ELBAD allocates nearest deadline tasks each time to the highest speed Virtual Machines (VMs) then it balances workload among VMs. The proposed algorithm is compared with other existing algorithms such as FCFS, SJF, Min-Min and EDF and experimental results show superiority of ELBAD over others with respect to minimizing makespan and maximizing resource utilization.
Visual Secret Sharing Scheme with Distributed Levels of Importance of Shadows

Dutta, Sabyasachi Bhore, Tamal Sardar, M. Kutubuddin Adhikari, Avishek Sakurai, KouichiWe put forward a construction of visual cryptographic scheme (VCS) to address the scenario, where there are several levels of importance of shadows. Traditional (k, n)-threshold VCS does not distinguish among the shadows—all shadows are of equal importance. Any k of them can recover the secret image whereas less than or equal to $$k-1$$ shadows do not reveal any information about the secret image. We consider the scenario where there are several levels of essential participants (labeled according to their importance) and there must be an agreement across the levels to recover the secret image. This captures the distribution of importance of parties across several levels or compartments. We give a novel construction of visual cryptographic scheme realizing such access structures, where the parties are compartmented into different levels according to their importance. We come up with closed mathematical formulae of pixel expansion and relative contrast. We also consider more efficient probabilistic construction and compare contrasts with several other probabilistic, non-expansible constructions. Numerical evidence shows that our scheme performs better than the available generic constructions.
Technical Analysis of Multi -Text Video Standardization Based on Tag System

In the context of media integration, video standardization plays a crucial role in understanding video content for video recommendation services. The multi-text video contains a lot of text information. The text analysis method can be used to extract the video keywords to assist the personalized recommendation of the video.
Modeling the construction of an individual learning path

Abstract In this paper, the most important aspect is touched upon - the principle of constructing an individual learning path in the system of open education. At the moment, there is no clear implemented construction method that satisfies both the learner and the teacher. The method proposed in this paper is based on the use of neural network technologies to determine significant input parameters that affect the success rate. At the first stage of determining an individual trajectory, proceeding mainly from theoretical ideas about the diagnosed set, a “draft” version of the list of parameters is formed. This option includes parameters that, according to the expert, should reflect the individual psychological differences of the subjects. The definition of a “draft” version of the initial set of diagnostic features is a difficult formalized task.
Fusing Faster R-CNN and Background Subtraction Based on the Mixture of Gaussians Model

Convolutional Neural Networks (CNNs) have made important progress in object detection. Using the internal feature extractor, CNNs perform a refined spatial analysis of the images for acquiring context-dependent information. To improve the object detection performed for streaming data, this paper proposes a fusion between Faster R-CNN and Background Subtraction based on the Mixture of Gaussians model (BSMOG). Faster R-CNN is a widely recommended object image detector using CNNs both for region proposal and classification. The suggested method expands the spatial analysis with the temporal stochastic information acquired by BSMOG. The interaction between Faster R-CNN and BSMOG is cooperative. During background updating, the reliable CNN-based detections define areas where matched Gaussians will be disfavored and replacing Gaussians will be unadvised. Also, BSMOG refines the confidence scores assigned to CNN-based detections. The method is exemplified for a video surveillance application. The experimental results show improved pixel-wise segmentation of the dynamic objects and a reduced number of false-positive detections for the category of objects recognized by Faster RCNN.