Reviewerly/exHarmony huggingface dataset:
On the past and future of discussing, teaching, and learning the hows and whys of archaeological systematics

Archaeological types have been derogatorily characterized as descriptive, but they must be descriptive in terms of pertinent attributes to be analytically useful. Classifying artifacts has long been (and still is) referred to as “pigeon holing” because some formal variation is masked by the categorization process. Recent discussions of the “tyranny of typologies” echo such concerns. Such caricatures may be the result of inadequate discussion of the hows and whys of archaeological systematics first in the classroom, and second in the professional literature. Seventy-four introductory university-level fundamentals of archaeology textbooks published between 1949 and 2017 devote an average of 2.7% of their pages to archaeological systematics; several recent advanced texts on archaeological method and theory include no discussion of archaeological systematics; over the last 100 years 51 programmatic books on zoological systematics have been published in contrast to eight programmatic volumes on archaeological systematics. After continuous growth since 1900 and a peak in the 1970s, the number of journal articles and book chapters on the programmatics of archaeological systematics published per decade has decreased over the past 40 years; the substantive literature seems to display the same trend. These data suggest reduced consideration by the discipline of the whys (ontologies) and hows (epistemologies) of archaeological systematics. Additional study of the substantive research literature—applications of typologies to particular sets of artifacts—may find discussion has shifted to this sort of venue. Hopefully the future will witness more extended discussions of systematics in new introductory textbooks, and discussions of the relevance of particular mechanics and specific theories of systematics with respect to archaeologists’ favored explanatory theories.
Detect Anomalies in Photovoltaic Systems Using Isolation Forest (Preliminary Results)

Solar energy from Photovoltaic (PV) systems is one of the greatest growing renewable sources of energy. PV systems, although generally quite robust, are subject to failures that can adversely affect energy conversion or even pose safety concerns. Being able to promptly and automatically detect fails/anomalies is essential to improve PV systems reliability while maintaining the expected efficiency. In this paper are investigated five unsupervised Machine Learning (ML) methods for PV plants’ faults detection. The tested methods include Isolation Forest (IF), one-Class Support Vector Machines, Local Outlier Factors, Deep Learning Autoencoders, Gaussian Mixtures Models (GMM). Dealing with anomalies in PV means to handle a highly unbalanced dataset, as anomalies are relatively rare events, hopefully. Moreover, it is usually quite challenging to have precise labels for each fault event. For this reason in this paper are analysed only unsupervised ML models, that does not require labelled data. However, being able to quantify models’ performance precisely is a quite challenging task as it requires expert support or labels existence, even if they can be imprecise. To accurately represents the model’s performance for such a highly unbalanced dataset are reported metrics more suitable for such a task as balanced accuracy, recall, F1, Matthews Correlation Coefficient and Cohen Kappa. For the reason outlined above regarding the available labels, the results summarised in this paper can be considered as preliminary and require a more suitable dataset with precise labels. These preliminary results show that GMM could be highly effective to operate in the PV anomaly detection field; however, some models as IF and Autoencoders, that have proven to be very effective in different but demanding fields, deserve further investigation.
SMOTE: A Tool to Proactively Manage Situations in WoT Environments

The growing number of devices in the Web of Things (WoT) allows larger and more complex smart environments. These environments aim to provide the desired state for the people, adapting the devices to their preferences. The characteristics of the environment, the people and the devices generate a multitude of interconnections and behaviours in specific situations. However, managing these situations is not straightforward because of their changing nature. Tools are needed to identify and automate these interactions according to the desired conditions. In this demo we present SMOTE (Situation Management fOr SmarT Environments), a tool for proactively managing situations in WoT environments, improving the management of different entities and reducing the effort for adapting devices to people’s preferences.
Mutually-Constrained Monotonic Multihead Attention for Online ASR

Despite the feature of real-time decoding, Monotonic Multi-head Attention (MMA) shows comparable performance to the state-of-the-art offline methods in machine translation and automatic speech recognition (ASR) tasks. However, the latency of MMA is still a major issue in ASR and should be combined with a technique that can reduce the test latency at inference time, such as head-synchronous beam search decoding, which forces all non-activated heads to activate after a small fixed delay from the first head activation. In this paper, we remove the discrepancy between training and test phases by considering, in the training of MMA, the interactions across multiple heads that will occur in the test time. Specifically, we derive the expected alignments from monotonic attention by considering the boundaries of other heads and reflect them in the learning process. We validate our proposed method on the two standard benchmark datasets for ASR and show that our approach, MMA with the mutually-constrained heads from the training stage, provides better performance than baselines.
Incorporation of Panoramic View in Fall Detection Using Omnidirectional Camera

Falling is one of the major problems that threaten the health of the elderly and particularly dangerous for people that live alone. Recently, surveillance systems using omnidirectional cameras in general and fisheye cameras particularly have become an attractive choice, as they provide a wide Field of Vision without the need for multiple cameras. However, objects captured by fisheye cameras are highly distorted, therefore computer vision approaches that are developed for conventional cameras require modification to work with such systems . The aim of this work is to incorporate the de-warping of fisheye image using polar to Cartesian transformation to generate a panoramic view. Objects are detected by background subtraction method. Depending on the objects lay inside or outside of a center circle of the omnidirectional frame, features based on contour and rotated bounding box of the objects, are extracted correspondingly on original omnidirectional view or panoramic view. Experiments show that by incorporating both panoramic and omnidirectional view, we can achieve significant improvement in fall detection, particularly in peripheral areas. This result could be a useful reference for further studies.
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition

One-class recognition is traditionally approached either as a representation learning problem or a feature modelling problem. In this work, we argue that both of these approaches have their own limitations; and a more effective solution can be obtained by combining the two. The proposed approach is based on the combination of a generative framework and a one-class classification method. First, we learn generative features using the one-class data with a generative framework. We augment the learned features with the corresponding reconstruction errors to obtain augmented features. Then, we qualitatively identify a suitable feature distribution that reduces the redundancy in the chosen classifier space. Finally, we force the augmented features to take the form of this distribution using an adversarial framework. We test the effectiveness of the proposed method on three one-class classification tasks and obtain state-of-the-art results.
Reducing latency and bandwidth for video streaming using keypoint extraction and digital puppetry

COVID-19 has made video communication one of the most important modes of information exchange. While extensive research has been conducted on the optimization of the video streaming pipeline, in particular the development of novel video codecs, further improvement in the video quality and latency is required, especially under poor network conditions. This paper proposes an alternative to the conventional codec through the implementation of a keypoint-centric encoder relying on the transmission of keypoint information from within a video feed, as shown in Figure 1. The decoder uses the streamed keypoints to generate a reconstruction preserving the semantic features in the input feed. Focusing on video calling applications, we detect and transmit the body pose and face mesh information through the network, which are displayed at the receiver in the form of animated puppets. Using efficient pose and face mesh detection in conjunction with skeleton-based animation, we demonstrate a prototype requiring lower than 35 kbps bandwidth, an order of magnitude reduction over typical video calling systems. The added computational latency due to the mesh extraction and animation is below 120ms on a standard laptop, showcasing the potential of this framework for real-time applications. The code for this work is available at https://github.com/shubhamchandak94/digital-puppetry/ and the full version is available on arXiv [1].
Development of online learning tools based on problem solving on circular motion materials

Abstract Online learning device is a device used in the online learning process. During the Covid-19 pandemic, teachers are required to change the learning system directly into online learning. This research aims to developing online learning tool based on problem solving in effective circular motion material in the midst of the covid-19 pandemic. This learning tool was made online in order to facilitate teachers and students to conduct teaching and learning activities on physics subject matter by using problem solving learning models. Learning tools were validated by three experts, namely media experts, material experts, learning experts and one physics teacher, and tested on students. The trial subjects were carried out in class X SMA as many as 25 students. Data collection instruments use a questionnaire guide through Google’s forms. The development of this research includes ADDIE research with the stages of development: 1. Analysis, 2. Design, 3. Develop, 4. Implement, 5. Evaluate. From the results of research on the development of problem solving based online learning tools on circular motion material obtained learning devices (learning implementation plan, student activity sheet and online media) have been tested for eligibility by experts and field trials with good criteria and are suitable for use.
Research on Robot path Planning algorithm based on Biological heuristic Machine Learning algorithm

Abstract In order to solve the problem that mobile robot is trapped in local convergence and cannot achieve path optimization in ant colony algorithm path planning, an improved variable step size ant colony algorithm is proposed to achieve path optimization with less convergence iterations. According to the relevant characteristics of the application of ant colony algorithm in path planning, the pheromone allocation is optimized, the influence of local pheromone content on the algorithm is reduced, the ant colony is prevented from falling into local optimization when searching the path, and the weight factor is added to the transfer probability formula. improve the probability of the mobile robot moving towards the end point, effectively reduce the number of convergence iterations of the ant colony, and change the mobile robot’s mobile step size. So that it can move freely without collision within 360°, and the path length can be effectively shortened. The simulation results show that in a simple environment, the convergence iteration times and optimal path length of the improved variable step size ant colony algorithm are 2 and 28.042 m, respectively, and the convergence iteration times and optimal path length of the traditional ant colony algorithm are 25 and 29.213 m, respectively. In the complex environment, the convergence iteration times and optimal path length of the improved variable step size ant colony algorithm are 2 and 43.9602m respectively, and the convergence iteration times and optimal path length of the improved potential field ant colony algorithm are 16 and 45.1127m, respectively. Simulation results verify the effectiveness and superiority of the improved variable step size ant colony algorithm.
The Effect of Matching Learning Material to Learners’ Dyslexia Type on Reading Performance

Dyslexia is a universal reading difficulty. Each dyslexic suffers from individual reading problems. Some may not understand what is written, while others may omit or transpose letters while reading. Most research has treated all dyslexics as a single class, especially in Arabic. Therefore, the aim of this research is to overcome these problems by training dyslexics using learning material that matches their individual needs, in order to improve their reading. To achieve this, an online training system for Arabic dyslexics has been developed and evaluated (in terms of learning gain and learner satisfaction).
Unsupervised Co-Segmentation for Athlete Movements and Live Commentaries Using Crossmodal Temporal Proximity

Audio-visual co-segmentation is a task to extract segments and regions corresponding to specific events on unlabeled audio and video signals. It is particularly important to accomplish it in an unsupervised way, since it is generally very difficult to manually label all the objects and events appearing in audio-visual signals for supervised learning. Here, we propose to take advantage of the temporal proximity of corresponding audio and video entities included in the signals. For this purpose, we newly employ a guided attention scheme to this task to efficiently detect and utilize temporal co-occurrences of audio and video information. Experiments using a real TV broadcasts of sumo wrestling, a sport event, with live commentaries show that our model can automatically extract specific athlete movements and its spoken descriptions in an unsupervised manner.
An Intelligent Approach for Food Recipe Rating Prediction Using Machine Learning

In recent times, there are many studies and systems which deal with restaurant rating or individual food rating but rating a recipe using Artificial Intelligence is rare. This study aims to rate recipes based on different attributes using different Machine Learning algorithms. It compares the performance of different classifiers in rating a recipe based on different performance criterion. This can be economically beneficial to restaurants by helping them improve their recipes and getting more customers. It can also be used in a more personal level to improve household recipes and for the customers of restaurants to decide which restaurant is better for a specific dish based on how good their recipe is.
Hole Concealment for Depth Image Using Pixel Classification in Multiview System

As the demand for immersive media increases, the importance of depth image is growing. As we see from the conventional researches, the depth images in the multiview camera system have holes around the objects, and it degrades the quality of the immersive video. To solve this problem, we propose an algorithm to conceal the hole in the depth image where the characteristics of the texture image are utilized. In the proposed algorithm, the holes are filled with the values predicted by the classification and confidence of pixels. The simulation results show that the proposed algorithm outperforms other conventional methods significantly.
How Does DCNN Make Decisions ?

Deep Convolutional Neural Networks (DCNN), despite imitating the human visual system, present no such decision credibility as human observers. This phenomenon, therefore, leads to the limitations of DCNN's applications in the security and trusted computing, such as self-driving cars and medical diagnosis. Focusing on this issue, our work aims to explore the way DCNN makes decisions. In this paper, the major contributions we made are: firstly, provide the hypothesis, “point-wise activation” of convolution function, according to the analysis of DCNN's architectures and training process; secondly, point out the effect of “point-wise activation” on DCNN's uninterpretable classification and pool robustness, and then suggest, in particular, the contradiction between the traditional and DCNN's convolution kernel functions; finally, distinguish decision-making interpretability from semantic interpretability, and indicate that DCNN's decision-making mechanism need to evolve towards the direction of semantics in the future. Besides, the “point-wise activation” hypothesis and conclusions proposed in our paper are supported by extensive experimental results.
Overview of Meta-Reinforcement Learning Research

Machine learning is a method to achieve artificial intelligence, which is divided into three categories: supervised learning, unsupervised earning, and reinforcement learning. The over-reliance of deep learning on big data restricts its development to some extent, so meta-reinforcement learning (meta-RL) research has received more and more attention. The paper introduces the background, research status and development trend of meta-reinforcement learning, which is also considered as one of the most likely ways to realize general artificial intelligence.
Naive bayes methods for rainfall prediction classification in Banyuwangi

Abstract Banyuwangi is the largest district in East Java with an area of 5,782.50 km 2 . It has a long coastline of about 175.8 km which stretches along the southern eastern boundary of Banyuwangi Regency, and there are 10 islands. The BMKG estimates that the dry season in the Banyuwangi area is due to the appearance of the beach having hot weather and rarely rains. Banyuwangi also predicts that the dry season is due to the slight influence of cloud growth. Rainfall is a factor of the rainy season which has a big influence on life such as aviation, plantations and agriculture. Agriculture and plantations in Banyuwangi are mostly located in remote areas. Remote areas are likely to lack weather and climate data information. climate elements of a region cannot be ignored, especially rainfall. Based on data from BMKG (Meteorology, Climatology and Geophysics), the weather data used needs to be classified. Rainfall classification can be categorized into three, namely, light, normal and heavy. There are quite a lot of classification methods, there are several new methods that are quite good such as Naive Bayes (NB). Naive Bayes Classifier (NBC) is an algorithm in data mining techniques that is used to determine the probability of a member of a group. Large and irrelevant datasets can be solved using the Naive Bayes Classifier (NBC) method. The rainfall data used is known first, observed then identified to form a training dataset. Determining the accuracy of rainfall with the Naive Bayes Classifier (NBC) can use several parameters that have a physical relationship between the atmosphere and rainfall. The parameters used to determine rainfall are humidity, rainfall and precipitation. From this study, from 49 data testing, 47 data were predicted correctly with an accuracy of 96%.
Bandwidth Efficient Near-Storage Accelerator for High-Dimensional Similarity Search

Performance of high-dimensional similarity search systems is often determined by the rate of distance calculation between a query and candidate neighbors. This is because as the dimensionality of data becomes higher, it suffers the so-called “curse of dimensionality”, which makes conventional indexing methods inefficient. Since the sheer size of datasets of interest often mandate storing them in secondary storage, this means the performance of a similarity search may be primarily limited by the storage device bandwidth, especially if the distance calculation can be offloaded to a high-performance accelerator built with GPUs or FPGAs. In this paper, we present ZipNN, which moves the bottleneck away from the storage performance in order to make more effective use of available computation resources. We achieve this via two approaches: First, ZipNN is a near-storage accelerator which takes advantage of the high internal bandwidth of storage. Second, it implements multiple pipelines of an application-optimized, wire-speed compression algorithm which can handle full storage bandwidth. We evaluate ZipNN using real-world datasets and an FPGA-based implementation. Compared to a comparable FPGA accelerator deployed in a standalone device, it demonstrates over 6× performance improvements. It also delivers an order of magnitude improvement compared to a purely software implementation.
Distributed Identity Management for Semantic Entities

We propose semDIM, a novel approach for Semantic Distributed Identity Management based on a Semantic Web architecture. For the first time, semDIM provides a framework for a distributed definition and management of entities such as persons being part of an organization, groups, and roles across namespaces. It is suitable for informal, i.e., social networks, as well as for professional networks such as cross-organizational collaborations. Beyond the capabilities of existing Identity Management solutions, we allow distributed identifiers and management of groups (consisting of agents and sub-groups) and roles. semDIM uses owl:sameAs as a central property to represent and verify distributed identities via formal reasoning. This concept enables novel functionalities for Distributed Identity Management, as these entities can be referred to, related to each other, as well as be managed across namespaces. Our semDIM approach consists of a modular software architecture, a process model, as well as a set of state-of-the-art DUL-based OWL ontology patterns. We demonstrate our approach by an example implementation that evaluates its functional fitness.
Machine Learning System Using Modified Random Forest Algorithm

Machine learning algorithms are one of the most advanced failed in computer science, so developing this kind of algorithms is very important subject to work on, also many researches are produced papers about developing these algorithms such as decision tree, random forest, this paper produce development on random forest algorithm, the developed algorithm called hybrid feature selection random forest (HFSRF), the algorithm depend on the accuracy percentage that based on weight of two kinds of measures , information gain (IG) and gain index (GI), the algorithm worked on six different datasets such as ionosphere dataset and lung-cancer dataset, finally the experimental results shows that the proposed HFSRF is better than the classical random forest when applied on all the datasets .