Miracle-dz/UniFAR-FTUs huggingface dataset:
The New Division of Labor: How Computers Are Creating the Next Job Market Princeton: Princeton Unive

A separating chamber for a magnetic separator is disclosed, which includes a rigid canister having an inlet and an outlet for fluid, and a packing of magnetizable material disposed within the canister so that, in use, fluid supplied to the inlet passes through the magnetizable material towards the outlet. Plates are spaced apart within the packing material and disposed substantially along the general direction of flow of the fluid through the packing material, whereby the plates act to limit permanent deformation of the packing material by magnetic forces acting on said material upon the chamber being moved axially into and out of a zone in which a magnetic field is established.
On the Speed of Social Learning

We study the speed of social learning, when two players learn from private signals as well as the actions of the other. Our main finding is that increased interaction between the agents can lower the rate of learning: learning is significantly slower when both players observe each other, than when one only observes the other. JEL classifications: C73, D82, D83
Three-dimensional identification of the Black Sea mesoscale eddies according to nemo numerical model calculations

A new method of three-dimensional identification of mesoscale eddies based on the results of the Black Sea hydrodynamics modeling is applied. It is based on identification of the closed streamlines in the velocity fields. This method and the data resulted from the NEMO calculations for 2005–2008 are used to identify more than 1000 mesoscale eddies in the basin. The results permit to define eddy characteristic trajectories, calculate spatial variability of their velocities and radii and frequency of detection of cyclones and anticyclones. The obtained results are in good agreement with the earlier published studies of the eddy characteristics derived from satellite and in situ data. The modeling results permit to investigate the features of vertical distribution of eddy characteristics. The eddies are most often detected in the 0–150 m layer (the highest detection frequency F is within 20–50 m). In the 150–300 m layer this value is two times smaller, but still is substantial. Below 300 meters F quickly decreases with depth. The analogous distribution is characteristic of the eddy radii and orbital velocity: the highest values are observed in the upper 0–150 m layer, and in the lower layers they sharply decrease with depth. The analysis of seasonal variability of the eddy characteristics shows that the anticyclonic dynamics intensifies in summer, whereas the cyclonic one – in winter, that is consistent with the previous studies. The developed method provides additional opportunities for investigating the features of the eddy generation and evolution in the Black Sea.
Sensor planning for mobile robot localization using Bayesian network representation and inference

We propose a novel method to solve a kidnapped robot problem. A mobile robot plans its sensor actions to localize itself using Bayesian network inference. The system differs from traditional methods such as the simple Bayesian decision or top-down action selection based on a decision tree. In contrast, we represent the contextual relation between the local sensing results and beliefs about the global localization using Bayesian networks. Inference of the Bayesian network allows us to classify ambiguous positions of the mobile robot when the local sensing evidences are obtained. By taking into account the trade-off between the global localization belief degree and local sensing cost, we define an integrated utility function to decide the local sensing range, and obtain an optimal sensing plan and optimal Bayesian network structure based on this function. We conducted simulation and real robot experiments to validate our planning concept.
Real Time Smart Door System for Home Security

This paper presents a real time smart door system for home security. Most of the proposed smart home systems try to bring solutions for this security problem, but many of those systems use numerous sensor devices. With the increasing security issues, it is necessary to use new technologies. Video based smart home security systems have recently become an efficient approach with the development of video technology and Raspberry Pi is a strong and reliable embedded system device for the complex and challenging tasks. Using these technologies in the proposed system will bring several advantages in providing safety and security in terms of visualizing and identifying people who visit the home. In the overall proposed system, there will be two different significant techniques to provide home security. One is to use video technology to see the front door in real time even if the home is empty and another is to provide communication between the door system and smart phone device. By connecting the smart door system with the mobile phone through the cellular network, the owner of the house may have several opportunities such as controlling the house, getting instant video streaming, receiving and sending message, talking to the visitor and starting the alarm system. The experimental results indicate that the proposed system may provide a consistent support and assistance for safe and secure life.
Combinatorial Fusion Analysis: Methods and Practices of Combining Multiple Scoring Systems

Combination methods have been investigated as a possible means to improve performance in multi-variable (multi-criterion or multi-objective) classification, prediction, learning, and optimization problems. In addition, information collected from multi-sensor or multi-source environment also often needs to be combined to produce more accurate information, to derive better estimation, or to make more knowledgeable decisions. In this chapter, we present a method, called Combinatorial Fusion Analysis (CFA), for analyzing combination and fusion of multiple scoring. CFA characterizes each Scoring system as having included a Score function, a Rank function, and a Rank/score function. Both rank combination and score combination are explored as to their combinatorial complexity and computational efficiency. IDEA GROUP PUBLISHING This paper appears in the publication, Advanced Data Mining Technologies in Bioinformatics edited by Hui-Huang Hsu Reichgelt © 2006, Idea Group Inc. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB12938
Chapter III Combinatorial Fusion Analysis : Methods and Practices of Combining Multiple Scoring Systems

Combination methods have been investigated as a possible means to improve performance in multi-variable (multi-criterion or multi-objective) classification, prediction, learning, and optimization problems. In addition, information collected from multi-sensor or multi-source environment also often needs to be combined to produce more accurate information, to derive better estimation, or to make more knowledgeable decisions. In this chapter, we present a method, called Combinatorial Fusion Analysis (CFA), for analyzing combination and fusion of multiple scoring. CFA characterizes each Scoring system as having included a Score function, a Rank function, and a Rank/score function. Both rank combination and score combination are explored as to their combinatorial complexity and computational efficiency. IDEA GROUP PUBLISHING This paper appears in the publication, Advanced Data Mining Technologies in Bioinformatics edited by Hui-Huang Hsu Reichgelt © 2006, Idea Group Inc. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB12938
Early Permian Tabulate Corals from the Jamal Formation, East-Central Iran

Seven species of tabulate corals, Sutherlandia jamalensis sp. nov., Pseudofavosites exiguus Flügel, 1972, P. fusiforme (Flügel, 1972), Michelinia sp. indet. , Gertholites? diversaporus (Flügel, 1972), G. sp. indet. , and Thamnoptychia directa (Flügel, 1972), are described from the Bagh-e Vang Member (late early Permian) of the Jamal Formation at the Tabas area, East-Central Iran. The discoveries of Sutherlandia and Thamnoptychia mark the first records of these genera in Iran. This assemblage inhabited on the southern shelves of the Paleotethys along northern margin of Gondwana.
A Novel Approach for Protein Classification Using Fourier Transform

throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily-and thereby functionally-related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.
MOSS REVEGETATION- A PROCESS TO INITIATE RESTORATION AND REPAIR NATURAL RESOURCES DAMAGED BY MINING

Mosses are small plants within the phylum Bryophyta known for their ability to grow on acidic, metal laden soil. This project proposes the restoration of moss to barren mine waste areas which will stabilize the soil, thus reducing heavy metal contaminants released to the aquifer and the surrounding environment. In general, the mine waste areas can be naturally recolonization by native and non-native vegetation as a remediation process. This process is very slow. The removal of the mine waste, capping and replacing it with clean soil, and subsequently replanting is a common remediation method. However, this approach is very expensive and involves high maintenance. When a responsible party cannot be identified, the sites remain barren. Consequently, precipitation and wind events could transport the contaminants into the environment. Mosses are known as pioneer plants, it is the first plant to establish in barren and disturbed soil areas. They are reproduced by spores or plant fragments, and do not have roots. Therefore, it draws water from the air or soil directly into its leaves. The propagation of moss and its associated organisms accelerate the natural processes that restore damaged areas to normal, steady state and biodiverse ecosystems. The advantages of moss are that, it requires minimum amount of water to propagate, low cost and low maintenance and it does not require superior soil quality to grow. Mosses increase the alkalinity of the soil, hence allowing all other plant forms to grow. They create rich soil by building up organic material from their growth. Moss community organisms also fix atmospheric nitrogen into bioavailable nitrogen. They are not only resistant to metals and metalloids, but they also adsorb these toxic substances and eliminate them from the surrounding soil. Ultimately, mosses provide a foundation for natural succession of other plant communities to build a stable ecosystem. The goal of this project is to develop a replicable method to revegetate and restore mineimpacted areas by the propagation of moss and to study the growth of moss by using different hydrophilic polymers and the bacteria Beijerinckia indica (Starkey and De 1939) Derx 1950. Experiments on growing moss were done in the culture chamber and at the outdoor heavy metal contaminated sites. Heavy metal sequestration by moss was determined by digestion and analysis and by the X-ray fluorescence (XRF) handgun.
ASAP: Adaptive stall-aware pacing for improved DASH video experience in cellular networks

The dramatic growth of video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent characteristics of wireless networks and varying channel conditions as well as variability in the video bitrate that can degrade streaming performance. In this paper, we propose stall aware pacing as a novel MPEG DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. These goals are achieved by leveraging both network and client state information to optimize the pacing of individual video flows. We evaluate the performance of two versions of stall aware pacing techniques extensively, including stall aware pacing (SAP) and adaptive stall aware pacing (ASAP), using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparisons with SAP and ASAP. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric across clients. We also show that ASAP achieves additional performance gains by adaptively pacing video streams based on the application buffer state.
A Time Interval Memory Protection System

Abstract : Time is often a critical factor for making decisions regarding access to information. To manage and protect critical data in this regard implies that information systems need to enforce temporal security policies. However, we lack operating system and hardware support for implementing temporal protection mechanisms. A time interval memory protection architecture to support enforcement of temporal policies is presented. It implements a Time Interval Access Control model in which authorizations are determined by time attributes associated with subjects and objects, and constraints on the relations of those attributes relative to the time of access. The policy is enforced at the memory page level by kernel mechanisms and related hardware extensions. The notion of memory access phases and the principle of efficiently mediated access are articulated to support the analysis of the design. A simulator-based prototype shows that the architecture is both feasible and practical.
Dynamic pharming attacks and the locked same-origin policies for web browsers

We describe a new attack against web authentication, which we call dynamic pharming. Dynamic pharming works by hijacking DNS and infecting the victim’s browser with malicious Javascript, which then exploits the name-based same-origin policy to hijack a legitimate session after authentication has taken place. As a result, the attack works regardless of the authentication scheme used. Dynamic pharming enables the adversary to eavesdrop on sensitive content, forge transactions, key log secondary passwords, etc. To counter dynamic pharming attacks, we propose two locked same-origin policies for web browsers. In contrast to the legacy same-origin policy, which regulates cross-object access control in browsers using domain names, the locked same-origin policy enforces access using servers’ X.509 certificates and public keys. We show how our policies help two existing web authentication mechanisms, client-side SSL and SSL-only cookies, resist both pharming and stronger active attacks. Also, we present a deployability analysis of our policies based on a study of 14651 SSL domains. Our results suggest one of our policies can be deployed today and interoperate seamlessly with the vast majority of legacy web servers. For our other policy, we present a simple incrementally deployable “opt in” mechanism for legacy servers using policy files, and show how web sites can use policy files to support self-signed and untrusted certificates, shared subdomain objects, and key updates.
PREDICTION REPORT Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8

A correct alignment is an essential requirement in homology modeling. Yet in order to bridge the structural gap between template and target, which may not only involve loop rearrangements, but also shifts of secondary structure elements and repacking of core residues, high-resolution refinement methods with full atomic details are needed. Here, we describe four approaches that address this ‘‘last mile of the protein folding problem’’ and have performed well during CASP8, yielding physically realistic models: YASARA, which runs molecular dynamics simulations of
A modular Takagi-Sugeno-Kang (TSK) system based on a modi(cid:12)ed hybrid soft clustering for stock selection

This study presents a new hybrid intelligent system with ensemble learning for stock selection using the fundamental information of companies. The system uses the selected (cid:12)nancial ratios of each company as input variables and ranks the candidate stocks. Due to the di(cid:11)erent characteristics of the companies from di(cid:11)erent activity sectors, modular system for stock selection may show a better performance than an individual system. Here, a hybrid soft clustering algorithm was proposed to eliminate the noise and partition the input dataset into more homogeneous overlapped subsets. The proposed clustering algorithm bene(cid:12)ts from the strengths of the fuzzy, possibilistic and rough clustering to develop a modular system. An individual Takagi-Sugeno-Kang (TSK) system was extracted from each subset using an arti(cid:12)cial neural network and genetic algorithm. To integrate the outputs of the individual TSK systems, a new weighted ensemble strategy was proposed. The performance of the proposed system was evaluated among 150 companies listed on Tehran Stock Exchange (TSE) regarding information coe(cid:14)cient, classi(cid:12)cation accuracy, and appreciation in stock price. The experimental results show that the proposed modular TSK system signi(cid:12)cantly outperforms the single TSK system as well as other ensemble models using di(cid:11)erent decomposition and combination strategies. ' 2021 Sharif University of Technology. All rights reserved.
Fundamentals of Algebraic Graph Transformation

This is the first textbook treatment of the algebraic approach to graph transformation, based on algebraic structures and category theory. It contains an introduction to classical graphs. Basic and advanced results are first shown for an abstract form of replacement systems and are then instantiated to several forms of graph and Petri net transformation systems. The book develops typed attributed graph transformation and contains a practical case study.
Combination of a Hidden Tag Model and a T Study in Czech Speech

A speech recognition system targeting high inflective languages is described that combines the traditional trigram language model and an HMM tagger, obtaining results superior to the trigram language model itself. An experiment in speech recognition of Czech has been performed with promising results. 1. Speech Recognition of Inflective Languages Inflective languages pose a hard problem in speech recognition due to two phenomena: highly inflective nature (causing data sparseness problem and excessive vocabulary growth), and free word order (causing the traditional speech recognition systems, such as n-gram Hidden Markov Models (HMMs) on word forms to be less accurate than for English). Specific methods targeting speech recognition of inflective languages have been already introduced in [1], [2] and [3]. The authors mainly focus on improving the language model by decomposing words from the vocabulary into stems and endings. This approach has mainly helped in reducing the size of the vocabulary of the speech recognizer reducing the WER slightly. 2. Combining Taggers with Language Models Tagger has been to our best knowledge first introduced as a speech recognition language model component in [4] without improving results over the baseline bigram model. The idea has been further explored in [5] where the author proposes the interpolation with a trigram model. P (W ) = λP (wi|wi−2, wi−1)+ (1 − λ)Q(wi|g(wi−2), g(wi−1)), (1) where g(wi) is the tagging function. The importance of formula (1) for languages with the data sparseness problem is that the new component Q can have enough evidence to give us reliable statistics about the word sequence W as the size of tag set tends to be much smaller then the size of the vocabulary itself. The problem with approach (1) is that the tagging function g(wi) depends on all words of the utterance (supposing that the tagging component is performed by an HMM tagger). The standard solution is to replace the probability Q by a new probability Q∗: Q∗(wi|w1, . . . , wi−1) = ∑ Q(wi|g1, g2)T (g(wi−2) = g2, g(wi−1) = g1) (2) The new probability T is the corresponding forward probability of the HMM tagger. The calculation of the forward probabilities is an seque words we do stand becom where proba are th that e inform us som the m the lis
Technical Report UW-CSE-2018-12-02

Technology is increasingly used — unintentionally (mis-information) or intentionally (disinformation) — to spread false information at scale, with potentially broad-reaching societal effects. For example, technology enables increasingly realistic false images and videos, and hyper-personal targeting means different people may see different versions of reality. This report is the culmination of a PhD-level special topics course 1 in Computer Science & Engineering at the University of Washington’s Paul G. Allen School in the fall of 2018. The goals of this course were to study (1) how technologies and today’s technical platforms enable and support the creation and spread of such mis-and disinformation, as well as (2) how technical approaches could be used to mitigate these issues. In this report, we summarize the space of technology-enabled mis-and disinformation based on our investigations, and then surface our lessons and recommendations for technologists, researchers, platform designers, policymakers, and users.