Reviewerly/exHarmony huggingface dataset:
Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit

Recent work has noted that all bad local minima can be removed from neural network loss landscapes, by adding a single unit with a particular parameterization. We show that the core technique from these papers can be used to remove all bad local minima from any loss landscape, so long as the global minimum has a loss of zero. This procedure does not require the addition of auxiliary units, or even that the loss be associated with a neural network. The method of action involves all bad local minima being converted into bad (non-local) minima at infinity in terms of auxiliary parameters.
D3TW: Discriminative Differentiable Dynamic Time Warping for Weakly Supervised Action Alignment and Segmentation

We address weakly supervised action alignment and segmentation in videos, where only the order of occurring actions is available during training. We propose Discriminative Differentiable Dynamic Time Warping (D3TW), the first discriminative model using weak ordering supervision. The key technical challenge for discriminative modeling with weak supervision is that the loss function of the ordering supervision is usually formulated using dynamic programming and is thus not differentiable. We address this challenge with a continuous relaxation of the min-operator in dynamic programming and extend the alignment loss to be differentiable. The proposed D3TW innovatively solves sequence alignment with discriminative modeling and end-to-end training, which substantially improves the performance in weakly supervised action alignment and segmentation tasks. We show that our model is able to bypass the degenerated sequence problem usually encountered in previous work and outperform the current state-of-the-art across three evaluation metrics in two challenging datasets.
World Wide Web CBIR Searching Using Query by Approximate Shapes

Nowadays more and more images are stored in the World Wide Web. There are a lot of photo galleries, media portals and social media portals where users add their own content, but also they would like to find the proper ones. The problem of searching for an image is not trivial. Objects present on images may have e.g. different colors, backgrounds or orientations. Moreover, the image may contain many other details which may be hard to be described by words. This paper presents a new system which may be used to query for images from the internet which is based on our Query by Approximate Shapes algorithm. The main idea of the proposed approach is to gather images from the internet. Next, all images are processed using our algorithm which is based on decomposing objects into a set of simple shapes. During the query, depending on its type, an example image or a sketch is used. For both types a graph is constructed which is compared with graphs in the database.
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimate goal is that the background subtraction methods developed in research could be employed in real applications like traffic surveillance. But looking at the literature, we can remark that there is often a gap between the current methods used in real applications and the current methods in fundamental research. In addition, the videos evaluated in large-scale datasets are not exhaustive in the way that they only covered a part of the complete spectrum of the challenges met in real applications. In this context, we attempt to provide the most exhaustive survey as possible on real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions. Thus, challenges are investigated in terms of camera, foreground objects and environments. In addition, we identify the background models that are effectively used in these applications in order to find potential usable recent background models in terms of robustness, time and memory requirements.
Appearance-Based Salient Regions Detection Using Side-Specific Dictionaries

Image saliency detection is a very helpful step in many computer vision-based smart systems to reduce the computational complexity by only focusing on the salient parts of the image. Currently, the image saliency is detected through representation-based generative schemes, as these schemes are helpful for extracting the concise representations of the stimuli and to capture the high-level semantics in visual information with a small number of active coefficients. In this paper, we propose a novel framework for salient region detection that uses appearance-based and regression-based schemes. The framework segments the image and forms reconstructive dictionaries from four sides of the image. These side-specific dictionaries are further utilized to obtain the saliency maps of the sides. A unified version of these maps is subsequently employed by a representation-based model to obtain a contrast-based salient region map. The map is used to obtain two regression-based maps with LAB and RGB color features that are unified through the optimization-based method to achieve the final saliency map. Furthermore, the side-specific reconstructive dictionaries are extracted from the boundary and the background pixels, which are enriched with geometrical and visual information. The approach has been thoroughly evaluated on five datasets and compared with the seven most recent approaches. The simulation results reveal that our model performs favorably in comparison with the current saliency detection schemes.
A Biologically Inspired Visual Working Memory for Deep Networks

The ability to look multiple times through a series of pose-adjusted glimpses is fundamental to human vision. This critical faculty allows us to understand highly complex visual scenes. Short term memory plays an integral role in aggregating the information obtained from these glimpses and informing our interpretation of the scene. Computational models have attempted to address glimpsing and visual attention but have failed to incorporate the notion of memory. We introduce a novel, biologically inspired visual working memory architecture that we term the Hebb-Rosenblatt memory. We subsequently introduce a fully differentiable Short Term Attentive Working Memory model (STAWM) which uses transformational attention to learn a memory over each image it sees. The state of our Hebb-Rosenblatt memory is embedded in STAWM as the weights space of a layer. By projecting different queries through this layer we can obtain goal-oriented latent representations for tasks including classification and visual reconstruction. Our model obtains highly competitive classification performance on MNIST and CIFAR-10. As demonstrated through the CelebA dataset, to perform reconstruction the model learns to make a sequence of updates to a canvas which constitute a parts-based representation. Classification with the self supervised representation obtained from MNIST is shown to be in line with the state of the art models (none of which use a visual attention mechanism). Finally, we show that STAWM can be trained under the dual constraints of classification and reconstruction to provide an interpretable visual sketchpad which helps open the 'black-box' of deep learning.
Optimized ELM based on Whale Optimization Algorithm for gearbox diagnosis

Extreme learning machine (ELM) is a fast and quick learning algorithm with better generalization performance. However, the randomness of input weight and hidden layer bias may affect the overall performance of ELM. This paper proposed a new approach to determine the optimized values of input weight and hidden layer bias for ELM using whale optimization algorithm (WOA), which we call WOA-ELM. An online gearbox vibration signals is used in this study. Empirical mode decomposition (EMD) and complementary mode decomposition (CEEMD) are used to decompose the signals into sub-signals known as intrinsic mode functions (IMFs). Then, statistical features are extracted from selected IMFs. WOA-ELM is used for classification of healthy and faulty condition of gearbox. The result shows that WOA-ELM provide better classification result as compared with conventional ELM. Therefore, this study provide a new diagnosis approach for gearbox application.
Nonclassical properties of two-mode squeezing-enhanced vacuum state

We present a new kind of two-mode squeezed vacuum state with two squeezing parameters. Different from the normal univariate two-mode squeezed vacuum state, this new state shows stronger squeezing effect, anti-bunching and entanglement. It is shown that the new squeezing parameter plays an important role in this state.
Efektifitas E-Learning Dengan Aplikasi Moodle pada Mata Kuliah Penelitian Pengembangan Program Studi Teknologi Pendidikan

This research aims to determine the effectiveness of e-learning with Moodle at educational study program, baturaja university. This research is quantitative research with experimental method. Can be interpreted as the research methods used to find a specific treatment effect against the other in a state of control. This ressearch result in the effectiveness of e-learning for student learning outcomes on science subjects. Effectiveness is the success of an establishment of a system designed to engage students actively and independently in learning. T-test result there are effectiveness of e-learning.
ASSESSMENT OF NATURAL RADIOACTIVITY LEVEL IN SHORE SEDIMENT SAMPLES FROM NASSER LAKE AT ASWAN, EGYPT

Thirty shore sediment samples taken from the side beach of Lake Nasser in south Arab Republic of Egypt to measuring the terrestrial radionuclides radium-226, thorium-232 and potassium-40 and its associated hazard indices.The activity concentration of natural radionuclides 226 Ra, 232 Th and 40 k in shore sediments samples under investigation are ranged from 1.92±0.077to 17.55±0.676Bqkg -1 with average value of 5.02±0.194Bqkg -1 , 5.62±0.281 to 28.77±1.441Bqkg -1 with average value of 13.15±0.641Bqkg -1 and 123.27±10.604 to 277.38±23.861Bqkg -1 with average value of 200.26±17.054Bqkg -1 respectively .The radiation hazard indices which resulting from the presence of natural radionuclides in shore sediment samples were calculated and the obtained results indicate that the values of radium equivalent activity varies from 21.85 to 80.04 Bq/kg with average value of 39.25 Bq/kg, representative level index Iˠr varies from 0.16 to 0.58 with average value of 0.29.Absorbed dose rate varies from 10.63 to 37.541 nGy.h -1 with the average value of 18.83 nGy.h -1 .External hazard index Hix varies from 0.059 to 0.216 with average value of 0.105, internal hazard index Hin varies from 0.070 to 0.263 with average value of 0.119, annual outdoor effective dose varied from 0.013 to 0.046 mSvy -1 with average values 0.023 mSvy -1 andThe indoor effective dose ranged from 0.052 to 0.184 mSvy -1 , with average values 0.092 mSvy -1 .
Need for Anaphoric Resolution towards Sentiment Analysis-A Case Study with Scarlet Pimpernel (Novel)

The problem of resolving references to earlier or later items in the discourse is commonly called as anaphora resolution or pronoun resolution.These items are usually noun phrases representing objects in the real world called referents but can also be verb phrases, whole sentences or paragraphs.Nowadays, anaphora resolution is addressed in numerous NLP (Natural Language Processing) applications.Proper treatment of anaphoric relations improves the performance of applications.Machine translation, information extraction, text summarization, or dialogue systems are some of the common applications of NLP.In early days, the machine translation systems processed on the basis of a sentence-by-sentence level.It did not consider the ties between sentences and resulted in an incoherent text as output.When the researcher forgets to handle the anaphora issue, it results in the striking problem of incorrect facts.It is very much needed to concentrate on the usage of pronoun, as it should match with their antecedents both in number and gender.Assigning inappropriate morphological features to the anaphor often may also lead to an undesirable change in the meaning of the sentence.
Decentralized Autonomous Unmanned Aerial Vehicle Swarm Formation and Flight Control

Unmanned Aerial Vehicles (UAV) have become more popular for usage due to the low cost of deployment and maintenance. Single UAV employment allows remote area monitoring and transferring different payloads to inaccessible or dangerous zones for human. In order to deal with flight tasks that are more complex, UAV swarms are applied. The main challenge of UAV swarm formation and flight control is to avoid vehicle collisions. In this case, artificial intelligence is responsible for flight performance in the airspace in such way that collision is avoided. The main requirements to the method, which will provide conflict-free maneuvers, are safety (collision avoidance), liveness (decentralized control, destination area reachability) and flyability (UAV flight performance constraints are satisfied). Artificial force field method fulfills all of these demands. It allows to detect a potential conflict between multiple UAVs in a swarm and other static or moving obstacles found in airspace, to provide collision resolution by changing UAVs flight parameters through maintaining minimum separation distance, including cases when manned vehicles are found in the same airspace. There can be distinguished by a wide range of obstacles: static (buildings, restricted areas and bed weather conditions) and dynamic ones (other UAVs, manned aircraft). Method allows keeping UAV swarm shape on the flight path, taking into account ground speed and turn bank angle values restrictions according to UAV’s flight performance characteristics.
A Joint Least Squares and Least Absolute Deviation Model

We propose a joint least squares and least absolute deviations (JOLESALAD) model, show that the proposed model can cover least absolute shrinkage and selection operator (LASSO) and two of its variants, namely the generalized LASSO (gLASSO) and the constrained LASSO (cLASSO), and prove that under a full rank condition, the JOLESALAD can be transformed into cLASSO. Based on this equivalency, rich tools currently available for LASSO models can be applied to solve gLASSO and cLASSO. As an example of illustration, we demonstrate an application of the proposed model to the restoration of a noisy ramp signal that needs proper use of penalty term.
A TCB Minimizing Model of Computation

A novel trusted computing base (TCB) minimizing model of computation (TMMC) is proposed for assuring integrity of the outputs of computing processes, by employing Merkle hash tree based two-party (prover-verifier) protocols. The applicability of the TMMC model for assuring integrity of processes is illustrated for two very different scenarios – one leveraging high-integrity-low-complexity hardware modules, and the second leveraging blockchains.
Expert Information Automatic Extraction for IOT Knowledge Base

Abstract With the rapid development of IOT technology, the requirement of effective and accurate retrieval of domain knowledge is growing. Automatically extract various information of expert from the massive web pages and generate a dynamic and wholeness profile model are important for knowledge base. However, the obvious differences in structure and content semantics of web pages between any two websites shows traditional web crawler are hard to understand the semantic of the web page and extract the critical information of expert. Therefore, a six-dimension expert profile model was introduced and then a sequence tagging method with LSTM-CRF model was proposed to automatically extract rich semantic information basing on organization structure, meaning of words and attributes of experts. The results of the experiment on test data sets illustrated that the precision rate and recall rate about the job experience and research field of experts are 67.8%, 66.6% and 82.4%, 79.6%, respectively. In addition, the overall average F value about some obvious features of expert, such as name, title, email, achievement, etc., reaches 82.5%, which is better than the results by MEMM and LSTM algorithms.
Secure logging scheme for forensic analysis in cloud

Summary Cloud computing has emerged as a prominent technology that provides reliable on‐demand cloud services to users. Among the various kind of attacks, Distributed Denial of Service (DDoS) attack is one of the major application layer attacks that target the resources and services running in the cloud. Due to the distributed nature of attacks or crimes in the cloud, the evidence are collected from various components such as the router, switches, hard disk, log traces, and virtual machines. An attacker may collude with the cloud provider or investigators to tamper the log files or by inserting false information or by deleting the malicious activity logs altogether by leaving no trace. Hence, security is the major concern in cloud wherein investigation of crimes and attacks are very difficult. Collecting logs from the cloud providers is very hard since many users share the same network resources and the investigators rely on the providers to access the information. Therefore, in order to preserve the confidentiality, integrity, and authentication of logs, secure logging scheme is proposed to preserve the logs for forensic investigation. This paper highlights the secure logging scheme by extracting the features from logs of all virtual machine instances by double encryption scheme and also deals the integrity of the logs using hashing and verifies the signatures using Bloom filter–based R tree (BR tree). Furthermore, the verification is performed using Shamir Secret Sharing (SSS) scheme, and it is responsible for sharing the secret (private) keys to all the three parties, ie, user, investigator, and cloud providers. It is inferred from the experimental results that the proposed scheme requires minimal log processing time, minimal verification time for inserting logs, stores logs in time, and space efficient manner and scalable than existing methods.
Self-efficacy and Attitude of the Teachers of SMAN Kuansing District towards the Utilization of ICT

The development of Information and Communication Technology (ICT) has a very big influence on education. With the enactment of the 2013 Curriculum, a technology-based curriculum, teachers as learning agents must continue to equip themselves with knowledge related to ICT. This descriptive study aims to describe and analyze self-efficacy and attitudes of the teachers of SMAN Kuansing District towards the utilization of ICT in learning activities. Obstacles faced by the teachers in utilizing ICT and the efforts that have been done to overcome the obstacles are also discussed. The research data was collected in August 2018. By using stratified random sampling technique, 55 teachers were determined as the sample. The instrument used was a questionnaire. Then, the questionnaire was analyzed through descriptive statistics to obtain frequencies and percentages. Finally, it can be concluded that the average score of self-efficacy of the teachers of SMAN in Kuansing District in utilizing ICT is 80.6% and the average score of their attitudes is 92.8%. It means that their self-efficacy and attitude are in very good levels. Although there are many obstacles faced by teachers in terms of facilities, infrastructure and human resources, the teachers have their own strategies to solve those obstacle.
K-Nearest neighbor algorithm on implicit feedback to determine SOP

The availability of a lot of existing Standard Operating Procedures (SOP) document information, users often need time to find SOPs that fit their preference. Therefore, this requires a recommendation system based on user content consumption by personalized usage logs to support the establishment of SOP documents managed according to user preferences. The k-nearest neighbor (KNN) algorithm is used to identify the most relevant SOP document for the user by utilizing implicit feedback based on extraction data by monitoring the document search behavior. From the research results obtained 5 classifications as parameters, with a final value of 3:2 ratio that shows the best distance value with the majority of labels according to the concept of calculation KNN algorithm that sees from the nearest neighbor in the dataset. This shows the precision of applying the KNN algorithm in determining SOP documents according to user preferences based on implicit feedback resulting in 80% presentation for SOPs corresponding to profiles and 20% for SOPs that do not fit the user profile. To establish SOP documents to show more accurate results, it should be used in a broad SOP management system and utilize implicit feedback with parameters not only in search logs and more on performance evaluation evaluations.
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis

In this paper, we study random subsampling of Gaussian process regression, one of the simplest approximation baselines, from a theoretical perspective. Although subsampling discards a large part of training data, we show provable guarantees on the accuracy of the predictive mean/variance and its generalization ability. For analysis, we consider embedding kernel matrices into graphons, which encapsulate the difference of the sample size and enables us to evaluate the approximation and generalization errors in a unified manner. The experimental results show that the subsampling approximation achieves a better trade-off regarding accuracy and runtime than the Nystr\"{o}m and random Fourier expansion methods.
Spectral characteristics of MTF in turbid atmosphere and its application for imaging band selection

The formalized modulation transfer function (MTF) is used to describe the effects induced by turbid atmosphere on image quality. Spectral characteristics of formalized MTF are analyzed in detail. The zero and nonzero spatial frequency components of formalized MTF, which represent different image information, show strong wavelength dependence. Furthermore, both components vary sharply at some wavelengths, which is significantly correlated with molecule absorption. The formalized MTF spectral characteristics are obviously different from atmospheric transmittance. According to its spectral characteristics, turbid atmosphere effects on imaging can be predicted, and a waveband selection method for optimal imaging is proposed. Comparisons between the proposed method and other methods show good consistency at some wavebands. The simulated imaging results and actual imaging results both verify that the optimal imaging band analysis and selection method is effective.
Geochemistry of LCT Pegmatites

The whole rock main and trace element geochemical data of the pegmatites is used to decipher the processes which led to the enormous Cs enrichment in the massive pollucite mineralisation. The geochemical study was also designed for potential prediction if a pegmatite potentially might host a yet not recognised massive pollucite mineralisation. Following these goals, the data from the pollucite-bearing Bikita pegmatite field are used for characterisation of the geochemical behaviour of Cs, and is compared to the Western Australian pegmatites. The whole rock geochemical analyses are supplemented by the mineral modes and assemblages gained by automated SEM-MLA (Mineral Liberation Analysis) from the same samples. The Geochemical investigations of the major and minor elements accentuates the solitary characteristics of the massive pollucite mineralisation at Bikita.