Reviewerly/exHarmony huggingface dataset:
Unsupervised Learning of the Set of Local Maxima

This paper describes a new form of unsupervised learning, whose input is a set of unlabeled points that are assumed to be local maxima of an unknown value function v in an unknown subset of the vector space. Two functions are learned: (i) a set indicator c, which is a binary classifier, and (ii) a comparator function h that given two nearby samples, predicts which sample has the higher value of the unknown function v. Loss terms are used to ensure that all training samples x are a local maxima of v, according to h and satisfy c(x)=1. Therefore, c and h provide training signals to each other: a point x' in the vicinity of x satisfies c(x)=-1 or is deemed by h to be lower in value than x. We present an algorithm, show an example where it is more efficient to use local maxima as an indicator function than to employ conventional classification, and derive a suitable generalization bound. Our experiments show that the method is able to outperform one-class classification algorithms in the task of anomaly detection and also provide an additional signal that is extracted in a completely unsupervised way.
Faithful Transmission of Single-Photon Qubits Using Error-Rejection Coding

Here, we experimentally demonstrate a faithful single-photon qubits transmission using error-rejecting coding, resorting to neither ancillary photons nor entangled photon resources. Using unbalanced polarization interferometer, the polarization qubits are transferred into the state on time-bin degree of freedom. Additionally, the deterministic transmission of single photon quantum states can be achieved with high fidelity. The immunity of time-bin encoded qubits against collective noise paves the way to practical quantum error-correction and error-rejection in quantum information science.
[Papers] BumpMarker: a 3D-printed tangible marker for simultaneous tagging, tracking, and weight measurement

This paper introduces BumpMarker: a 3D-printed tangible marker that can perform simultaneous tagging, position tracking, and weight measurement of objects on pressure sensor sheets. The markers baseplate features several pins (raised dots) whose locations encode embedded information. A matrix pressure sensor sheet captures the pressure map of a marker-attached object on a sheet. The embedded data and object weight can be retrieved by processing the pressure map. We propose our design to achieve robust detection of the pins. We also show that our system has the ability to monitor weight changes in tagged objects. Through a series of evaluations, we investigate the technical feasibility of BumpMarker.
Review of Feature Extraction and Matching Methods for Drone Image Stitching

Image stitching is the process of combining multiple overlapping images of different views to produce a high-resolution image. The aerial perspective or top view of the terrestial scenes will not be available in the generic 2D images captured by optical cameras. Thus, stitching using 2D images will result in lack of information in top view. UAV (Unmanned Aerial Vehicle) captured drone images tend to have the high aerial perspective, 50–80% of overlapping of information between the images with full information about the scene. This work comprises of discussion about methods such as feature extraction and feature matching used for drone image stitching. In this paper, we compare the performance of three different feature extraction techniques such as SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), and ORB (ORiented FAST and rotated BRIEF) for detecting the key features. Then the detected features are matched using feature matching algorithms such as FLANN (Fast Library for Approximate Nearest Neighbors) and BF (Brute Force). All the matched key points may not be useful for creating panoramic image. Further, RANSAC (Random sample consensus) algorithm is applied to separate the inliers from the outlier set and interesting points are obtained to create a high-resolution image.
Enhance interval width of crime forecasting with ARIMA model-fuzzy alpha cut

With qualified data or information a better decision can be made. The interval width of forecasting is one of data values to assist in the selection decision making process in regards to crime prevention. However, in time series forecasting, especially the use of ARIMA model, the amount of historical data available can affect forecasting result including interval width forecasting value. This study proposes a combination technique, in order to get get a better interval width crime forecasting value. The propose combination technique between ARIMA model and Fuzzy Alpha Cut are presented. The use of variation alpha values are used, they are 0.3, 0.5, and 0.7. The experimental results have shown the use of ARIMA-FAC with alpha=0.5 is appropriate. The overall results obtained have shown the interval width crime forecasting with ARIMA-FAC is better than interval width crime forecasting with 95% CI ARIMA model.
Developing Mobile Tracking Applications for Patient Treatment

Much of the adjunct technology developed for using among medical environments is targeted towards computers. Because the hospitals face increasing demands to participate in a very big selection of quality improvement activities, the role and influence of using mobile applications in these efforts is additionally increasing. The professionals of Healthcare pay abundant of their time wandering between offices and patients, whereas the validator technology stays stationary. This paper presents a study performed using the mobile application for storing and following up patients status. Therefore, mobile application for tracking patient progress is proposed to minimize such challenges and demands, by allowing physicians and nurses to trace the patients&amp;rsquo; conditions a lot of expeditiously and simply. The experimental results conclude that the working environment would be improved by supporting the mobile workers with mobile technology.
Mobile crowd participation to root small-scale piped water supply systems in India and Bangladesh

Abstract In the arsenic-contaminated Ganges-Brahmaputra-Meghna Delta in India and Bangladesh, small-scale piped water supply seems a promising way to provide safe drinking water to households in the region. The use of smartphone applications can support monitoring of the system and enhance local engagement and empowerment. In this paper the scope for mobile crowd participation as a research and monitoring tool for piped water supply systems in Bihar, India and in Khulna and Chittagong, Bangladesh is investigated. In these areas, the use of smartphones and internet access are growing rapidly and smartphone applications would enable real-time water quality monitoring, payment of water bills, awareness creation, and a dialogue between the end-user and the water supplier. To identify the relevance and acceptability of piped water supply and smartphone monitoring, four surveys with potential end-users were conducted. Based on these surveys we conclude that in the investigated areas there is a desire for piped water systems, that households already own smartphones with internet access, and that there is an interest in smartphone monitoring. The enabling environment to deploy mobile crowd participation for piped water system monitoring stimulates further research towards an investigation of potential functionalities and the actual development of such an application.
Optimal Choice of When to Garbage Collect

We consider the ultimate limits of program-specific garbage collector (GC) performance for real programs. We first characterize the GC schedule optimization problem. Based on this characterization, we develop a linear-time dynamic programming solution that, given a program run and heap size, computes an optimal schedule of collections for a non-generational collector. Using an analysis of a heap object graph of the program, we compute a property of heap objects that we call their pre-birth time. This information enables us to extend the non-generational GC schedule problem to the generational GC case in a way that also admits a dynamic programming solution with cost quadratic in the length of the trace (number of objects allocated). This improves our previously reported approximately optimal result. We further extend the two-generation dynamic program to any number of generations, allowing other generalizations as well. Our experimental results for two generations on traces from Java programs of the DaCapo benchmark suite show that there is considerable promise to reduce garbage collection costs for some programs by developing program-specific collection policies. For a given space budget, optimal schedules often obtain modest but useful time savings, and for a given time budget, optimal schedules can obtain considerable space savings.
Extended ForUML for Automatic Generation of UML Sequence Diagrams from Object-Oriented Fortran

Recently, reverse engineering has become widely recognized as a valuable process for extracting system abstractions and design information from existing software. This study focuses on ForUML, a reverse engineering tool developed to extract UML diagrams from modern object-oriented Fortran programs. Generally, Fortran is used to implement scientific and engineering software in various domains, such as weather forecasting, astrophysics, and engineering design. However, methods for visualizing the existing design of object-oriented Fortran software are lacking. UML diagrams of Fortran software would be beneficial to scientists and engineers in explaining the structure and behavior of their programs at a higher level of abstraction than the source code itself. UML diagrams can enhance discussions within development teams and with the broader scientific community. The first version of ForUML produces only UML class diagrams. Class diagrams provide a useful window into the static structure of a program, including the structure and components of each class and the relationships between classes. However, class diagrams lack the temporal information required to understand class behavior and interactions between classes. UML sequence diagrams provide this important algorithmic information. Therefore, herein, an extension for ForUML to extract UML sequence diagrams from the Fortran code is proposed, and this capability is provided using a widely used open-source platform. This study argues that the proposed extension will enable the visualization of object-oriented Fortran software behavior and algorithmic structure and thereby enhance the development, maintenance practices, decision processes, and communications in scientific and engineering software communities worldwide.
Identification Entropy

Shannon (Bell Syst Technol J 27:379–423, 623–656, 1948) has shown that a source $$(\mathcal {U},P,U)$$ with output satisfying Prob (U = u) = P u, can be encoded in a prefix code $$\mathcal {C}=\{c_u:u\in \mathcal {U}\}\subset \{0,1\}^*$$ such that for the entropy $$\displaystyle H(P)=\sum _{u\in \mathcal {U}}-p_u\log p_u\leq \sum p_u\text{ length}(c_u) \leq H(P)+1. $$
DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images

Understanding fashion images has been advanced by benchmarks with rich annotations such as DeepFashion, whose labels include clothing categories, landmarks, and consumer-commercial image pairs. However, DeepFashion has nonnegligible issues such as single clothing-item per image, sparse landmarks (4~8 only), and no per-pixel masks, making it had significant gap from real-world scenarios. We fill in the gap by presenting DeepFashion2 to address these issues. It is a versatile benchmark of four tasks including clothes detection, pose estimation, segmentation, and retrieval. It has 801K clothing items where each item has rich annotations such as style, scale, viewpoint, occlusion, bounding box, dense landmarks and masks. There are also 873K Commercial-Consumer clothes pairs. A strong baseline is proposed, called Match R-CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner. Extensive evaluations are conducted with different criterions in DeepFashion2.
On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks

Recently, there has been an abundance of works on designing Deep Neural Networks (DNNs) that are robust to adversarial examples. In particular, a central question is which features of DNNs influence adversarial robustness and, therefore, can be to used to design robust DNNs. In this work, this problem is studied through the lens of compression which is captured by the low-rank structure of weight matrices. It is first shown that adversarial training tends to promote simultaneously low-rank and sparse structure in the weight matrices of neural networks. This is measured through the notions of effective rank and effective sparsity. In the reverse direction, when the low rank structure is promoted by nuclear norm regularization and combined with sparsity inducing regularizations, neural networks show significantly improved adversarial robustness. The effect of nuclear norm regularization on adversarial robustness is paramount when it is applied to convolutional neural networks. Although still not competing with adversarial training, this result contributes to understanding the key properties of robust classifiers.
What Is Machine Learning?

The first Checkers program from machine learning pioneer Arthur Samuel debuted in 1956, demonstrating artificial “intelligence” capabilities. Since then, not only has the application of artificial intelligence grown, there is now a velocity, volume, and variety of data that has never been seen before. Samuel’s software ran on the IBM 701, a computer the size of a double bed. Data was typically discrete. Almost 70 years later, data is ubiquitous, and computers now more powerful such that 100 IBM 701s can fit into the palm of our hand. This has facilitated the subsets of machine learning and deep learning within AI (see Figure 3-1).
Weakly-supervised Knowledge Graph Alignment with Adversarial Learning

This paper studies aligning knowledge graphs from different sources or languages. Most existing methods train supervised methods for the alignment, which usually require a large number of aligned knowledge triplets. However, such a large number of aligned knowledge triplets may not be available or are expensive to obtain in many domains. Therefore, in this paper we propose to study aligning knowledge graphs in fully-unsupervised or weakly-supervised fashion, i.e., without or with only a few aligned triplets. We propose an unsupervised framework to align the entity and relation embddings of different knowledge graphs with an adversarial learning framework. Moreover, a regularization term which maximizes the mutual information between the embeddings of different knowledge graphs is used to mitigate the problem of mode collapse when learning the alignment functions. Such a framework can be further seamlessly integrated with existing supervised methods by utilizing a limited number of aligned triples as guidance. Experimental results on multiple datasets prove the effectiveness of our proposed approach in both the unsupervised and the weakly-supervised settings.
TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis

Object detection and object tracking are usually treated as two separate processes. Significant progress has been made for object detection in 2D images using deep learning networks. The usual tracking-by-detection pipeline for object tracking requires that the object is successfully detected in the first frame and all subsequent frames, and tracking is done by associating detection results. Performing object detection and object tracking through a single network remains a challenging open question. We propose a novel network structure named trackNet that can directly detect a 3D tube enclosing a moving object in a video segment by extending the faster R-CNN framework. A Tube Proposal Network (TPN) inside the trackNet is proposed to predict the objectness of each candidate tube and location parameters specifying the bounding tube. The proposed framework is applicable for detecting and tracking any object and in this paper, we focus on its application for traffic video analysis. The proposed model is trained and tested on UA-DETRAC, a large traffic video dataset available for multi-vehicle detection and tracking, and obtained very promising results.
LS-Tree: Model Interpretation When the Data Are Linguistic

We study the problem of interpreting trained classification models in the setting of linguistic data sets. Leveraging a parse tree, we propose to assign least-squares based importance scores to each word of an instance by exploiting syntactic constituency structure. We establish an axiomatic characterization of these importance scores by relating them to the Banzhaf value in coalitional game theory. Based on these importance scores, we develop a principled method for detecting and quantifying interactions between words in a sentence. We demonstrate that the proposed method can aid in interpretability and diagnostics for several widely-used language models.
Manganese Deposits of Turkey

Turkey comprises different types of manganese deposits which were identified by the Mineral Research and Exploration Institute of Turkey (MTA) in 1965 (MTA. Manganese ore deposits of Turkey. General Directorate of Mineral Research and Exploration. Pub. No: 120, 38 p. (In Turkish), 1972). After many research works conducted on the formation and genesis of marine pollymetalic oxides that occur in the modern oceanic sediments, a new genetic classification of the marine ferromanganese manganese deposits was made by several workers (Frakes LA, Bolton BR. Geology 12:83–86, 1984; Rona 1992). Based on newly acquired knowledge, the manganese deposits of Turkey were divided into four main types according to host rocks, geological-tectonic settings and formation processes by Öztürk (Geol Eng 43:24–33. (In Turkish with English abstract), 1993a; Öztürk H. Ore Geol Rev 12:187–203, 1997; Öztürk H, Hein JR. Econ Geol 92:733–744, 1997). These types are (1) Black shale – hosted deposits, (2) Radiolarian chert – hosted deposits, (3) Oligocene – hosted deposits, (4) Volcanic arc – hosted and/or vein -type deposits. Although radiolarian chert – hosted type Mn oxides mineralization is widely distributed, they generally are small occurrences associated with ophiolitic mélanges but some of them may be of economic value. Economically important Mn deposits are in the black shale rocks in southwestern Turkey and in the Oligocene sediments in the Thrace Basin. The Oligocene deposits are connected to the Black Sea region deposits, including Varna (Bulgaria), Nikopol (Ukraine) and Chiatura (Georgia). Exploration studies by drilling programs have been made by MTA for the Oligocene – hosted Binkılıç deposit of the Thrace Basin (Bora E. Binkılıç ve Sefaalan Bölgesinin jeolojisi ve manganez yatakları. [The geology and Mn deposits of the Binkılıc¸ and Sefaalan area]. M.Sc. Thesis, Istanbul University, 47 p. (In Turkish, unpublished), 1969) and black shale – hosted Ulukent Mn deposit in Tavas region (Doğan H, Turkmen H. Ulukent – Tavas çevresindeki manganez yataklarının jeoloji Raporu. [Geological report of the manganese deposits of the Ulukent-Tavas and surrounding area]. General Directorate of Mineral Research and Exploration Report No: 345, 87 p. (In Turkish, Unpublished), 1983). The representative manganese deposits or occurrences of each type are identified in this chapter on the basis of their age, host rocks, mineralogy, geochemistry and reserve – grade relationships. The distribution of the manganese mineral systems and the location of the important deposits in Turkey are shown in Fig. 6.1. The Mn ores of the main deposits were sampled and analyzed by ICP- MS at Acme laboratories in Canada, in 2015 exclusive for this chapter.
Defense behavior of real time strategy games: comparison between HFSM and FSM

&lt;span&gt;RTS Game is one of the popular genre in PC gaming, which has been played by various type of players frequently. In RTS game, NPC Defense Building (Tower) has attacking behavior to the closest enemy without considering certain enemy parameters. This causes the NPC Tower to be more predictable by the opponent and easily defeated if NPC attacked by enemies in the group. Thus, this research simulates NPC Tower using Hierarchical Finite State Machine (HFSM) method compared with Finite State Machine (FSM). In this study, NPC Tower detects enemies by seeing at four parameters namely NPC Tower Health, Enemy's Health, Enemy Type, and Tower Distance to enemies. NPC Tower will attack the most dangerous enemy according to the ‘Degree of Danger’ parameter. Then use the decision-making logic of the rule-based system. The output of NPC Tower are three type of behaviors namely Aggressive Attacking, Regular Attacking, and Attack with Special Skill. From the test results of 3 NPC Tower, Kamandaka NPC Tower with HFSM method is winning 8.92% compare to Kamandaka Tower with FSM method. For Gayatri Tower NPC obtained equal results using both HFSM and FSM. Meanwhile, Adikara NPC with HFSM method is 4.62% superior to Adikara Tower with FSM method.&lt;/span&gt;
Limitations of Assessing Active Learning Performance at Runtime

Classification algorithms aim to predict an unknown label (e.g., a quality class) for a new instance (e.g., a product). Therefore, training samples (instances and labels) are used to deduct classification hypotheses. Often, it is relatively easy to capture instances but the acquisition of the corresponding labels remain difficult or expensive. Active learning algorithms select the most beneficial instances to be labeled to reduce cost. In research, this labeling procedure is simulated and therefore a ground truth is available. But during deployment, active learning is a one-shot problem and an evaluation set is not available. Hence, it is not possible to reliably estimate the performance of the classification system during learning and it is difficult to decide when the system fulfills the quality requirements (stopping criteria). In this article, we formalize the task and review existing strategies to assess the performance of an actively trained classifier during training. Furthermore, we identified three major challenges: 1)~to derive a performance distribution, 2)~to preserve representativeness of the labeled subset, and 3) to correct against sampling bias induced by an intelligent selection strategy. In a qualitative analysis, we evaluate different existing approaches and show that none of them reliably estimates active learning performance stating a major challenge for future research for such systems. All plots and experiments are provided in a Jupyter notebook that is available for download.
A Geometric Perspective on Optimal Representations for Reinforcement Learning

We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functions. We leverage this perspective to provide formal evidence regarding the usefulness of value functions as auxiliary tasks. Our formulation considers adapting the representation to minimize the (linear) approximation of the value function of all stationary policies for a given environment. We show that this optimization reduces to making accurate predictions regarding a special class of value functions which we call adversarial value functions (AVFs). We demonstrate that using value functions as auxiliary tasks corresponds to an expected-error relaxation of our formulation, with AVFs a natural candidate, and identify a close relationship with proto-value functions (Mahadevan, 2005). We highlight characteristics of AVFs and their usefulness as auxiliary tasks in a series of experiments on the four-room domain.