Reviewerly/exHarmony huggingface dataset:
Simulation of Dynamic Traffic Light Setting Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract Traffic congestion is a common problem in so many big cities. The occurrence of traffic congestion can be caused by various reason, one of them is the increasing number of vehicles while the available facilities are not worth the amount. Congestion can cause many losses, including the amount of time wasted and spent fuel in vain. To solve the problem of congestion can be solved by building a new road. But the solution will make the environment more crowded and require more operational costs. Due to this reason, there is a good solution given that is, the renewal of traffic control system by building intelligent traffic light control system based on road density at each traffic intersection and the width of the road. The method used in this research is Adaptive Neuro-Fuzzy Inference System (ANFIS), this method will process the data of road density and road width to determine the duration of green light to be given. The road density will be calculated based on the video that will be captured by the system and will go through the pre-processing and processing stages until the labelling stage of the object on the road using the Connected Component Labelling (CCL) method, the image pixel of this CCL result will be compared to the image of the empty road condition (no vehicles) to get the percentage of road density. The system testing process used 36 images captured from the video and obtained the accuracy of 98,6%.
Optimization of Evolutionary Algorithm Using Machine Learning Techniques for Pattern Mining in Transactional Database

In recent years, the data analysts are facing many challenges in high utility itemset (HUI) mining from given transactional database using existing traditional techniques. The challenges in utility mining algorithms are exponentially growing search space and the minimum utility threshold appropriate to the given database. To overcome these challenges, evolutionary algorithm-based techniques can be used to mine the HUI from transactional database. However, testing each of the supporting functions in the optimization problem is very inefficient and it increases the time complexity of the algorithm. To overcome this drawback, reinforcement learning-based approach is proposed for improving the efficiency of the algorithm, and the most appropriate fitness function for evaluation can be selected automatically during execution of an algorithm. Furthermore, during the optimization process when distinct functions are skillful, dynamic selection of current optimal function is done.
An Information Theoretic Approach to the Autoencoder

We present a variation of the Autoencoder (AE) that explicitly maximizes the mutual information between the input data and the hidden representation. The proposed model, the InfoMax Autoencoder (IMAE), by construction is able to learn a robust representation and good prototypes of the data. IMAE is compared both theoretically and then computationally with the state of the art models: the Denoising and Contractive Autoencoders in the one-hidden layer setting and the Variational Autoencoder in the multi-layer case. Computational experiments are performed with the MNIST and Fashion-MNIST datasets and demonstrate particularly the strong clusterization performance of IMAE.
Data Fragmentation and Duplication in Cloud for Secure Performance

The Fragmentation and duplication in data sets is used to overcome the increased data overloading issue in the cloud servers.The increase in data usage and processing in cloud has brought new challenges to data management in cloud computing.We propose the idea which helps in reducing the data load in the cloud and reduces the storage and management cost for the users.Duplication finding plays a very major role in data management.Data de-duplication method finds the restricted fingerprint for every data chunk by using hash algorithms such as MD5 and SHA.The recognized fingerprint is then compared touching other available chunks in a database that is dedicated for storing the chunks.Though, there is simply one copy for every file stored in cloud, it will not be immobile if such a file is owned by a massive number.As a part the de-duplication system improves storage consumption whereas dropping reliability.Aiming to contradict with the above safety challenges, this proposed idea makes the first attempt to provide the idea of distributed dependable de-duplication system.This new distributed de-duplication system comes with increased reliability in which the data chunks are distributed diagonally to various cloud servers.This allows the redundancy of all data is eliminated.The security needs of data privacy and tag consistency are also achieve by introducing a deterministic furtive sharing system in distributed storage systems, as an option of using convergent encryption as in foregoing de-duplication system.Proof of Ownership is the major idea that proposed in this paper, which is obtained by two concepts namely proof of reliability and proof of possession.The combination of data integrity and storage is analysed will achieve non-trivial duplicate of metadata which is
Stacked Multi-Target Network for Robust Facial Landmark Localisation

We thoroughly analyse regression-based face alignment methods and introduce a novel stacked multi-target network for robust facial landmark localisation. The primary heatmap regression-based network concentrates on locating the coarse position of pre-defined landmarks while the secondary coordinate regression-based network is responsible for modelling fine sub-pixel features. Specifically, we elaborate the differences among widely-used Cross Entropy related loss functions and propose a new Bilateral Inhibition Cross Entropy loss function, which enlarges the margin between elements in the output heatmaps. Besides, in order to deal with the discrepancy between optimization and evaluation, we propose to dynamically adjust the radius of kernel function during the training process. We demonstrate that training with decreasing radius in temporal order performs much better than assigning it spatially, i.e. decreasing radius along the stages of stacked hourglass networks. Finally, we innovatively limit the output of the secondary coordinate regression network to a reasonable range by importing the hinge loss to refine the coarse coordinate locations for sub-pixel accuracy. Extensive experiments on public datasets such as 300-W, COFW, and AFLW demonstrate that our proposed method performs superiorly to the state-of-the-art approaches.
Dynamic structural operational semantics

We introduce Dynamic Structural Operational Semantics (DSOS or Dynamic SOS) as a framework for describing semantics of programming languages that include dynamic software upgrades, i.e., for upgrading software code during run-time. DSOS is built on top of the Modular SOS of P. Mosses, with an underlying category theory formalization. The idea of Dynamic SOS is to bring out the essential differences between dynamic upgrade constructs and program execution constructs. The important feature of Modular SOS (MSOS) that we exploit in DSOS is the sharp separation of the program execution code from the additional (data) structures needed at run-time. In DSOS we aim to achieve the same modularity and decoupling for dynamic software upgrades. This is partly motivated by the long term goal of having machine-checkable proofs for general results like type safety. We exemplify Dynamic SOS on two languages supporting dynamic software upgrades, namely the C-like Proteus, which supports updating of variables, functions, records, or types at specific program points, and Creol, which supports dynamic class upgrades in the setting of concurrent objects. Existing type analyses for software upgrades can be done on top of DSOS too, as we illustrate for Proteus. As a side contribution we define a general encapsulating construction on Modular SOS useful in situations where a form of encapsulation of the execution is needed. We use encapsulation to give modular semantics to the concurrent object-oriented programming language Creol with active objects and asynchronous method invocations.
A Descriptive Attribute-Based Framework for Annotations in Data Visualization

Annotations are observations made during the exploration of a specific data visualization, which can be recorded as text or visual data selection. This article introduces a classification framework that allows a systematic description of annotations. To create the framework, a real dataset of 302 annotations authored by 16 analysts was collected. Then, three coders independently described the annotations by eliciting categories that emerged from the data. This process was repeated for several iterative phases, until a high inter-coder agreement was reached. The final descriptive attribute-based framework comprises the following dimensions: insight on data, multiple observations, data units, level of interpretation, co-references and detected patterns. This framework has the potential to provide a common ground to assess the expressiveness of different types of visualization over the same data. This potential is further illustrated in a concrete use case.
Detection of genuine n-qubit entanglement via the proportionality of two vectors

In [Science 340:1205, (2013)], via polytopes Michael Walter et al. proposed a sufficient condition detecting the genuinely entangled pure states. In this paper, we indicate that generally, the coefficient vector of a pure product state of n qubits cannot be decomposed into a tensor product of two vectors, and show that a pure state of n qubits is a product state if and only if there exists a permutation of qubits such that under the permutation, its coefficient vector arranged in ascending lexicographical order can be decomposed into a tensor product of two vectors. The contrapositive of this result reads that a pure state of n qubits is genuinely entangled if and only if its coefficient vector cannot be decomposed into a tensor product of two vectors under any permutation of qubits. Further, by dividing a coefficient vector into $$2^{i}$$ equal-size block vectors, we show that the coefficient vector can be decomposed into a tensor product of two vectors if and only if any two nonzero block vectors of the coefficient vector are proportional. In terms of “proportionality,”we can rephrase that a pure state of n qubits is genuinely entangled if and only if there are two nonzero block vectors of the coefficient vector which are not proportional under any permutation of qubits. Thus, we avoid decomposing a coefficient vector into a tensor product of two vectors to detect the genuine entanglement. We also present the full decomposition theorem for product states of n qubits.
Emotional Conversation Generation Orientated Syntactically Constrained Bidirectional-Asynchronous Framework

The field of open-domain conversation generation using deep neural networks has attracted increasing attention from researchers for several years. However, traditional neural language models tend to generate safe, generic reply with poor logic and no emotion. In this paper, an emotional conversation generation orientated syntactically constrained bidirectional-asynchronous framework called E-SCBA is proposed to generate meaningful (logical and emotional) reply. In E-SCBA, pre-generated emotion keyword and topic keyword are asynchronously introduced into the reply during the generation, and the process of decoding is much different from the most existing methods that generates reply from the first word to the end. A newly designed bidirectional-asynchronous decoder with the multi-stage strategy is proposed to support this idea, which ensures the fluency and grammaticality of reply by making full use of syntactic constraint. Through the experiments, the results show that our framework not only improves the diversity of replies, but gains a boost on both logic and emotion compared with baselines as well.
Blockchain Enabled Smart Learning Environment Framework

Smart learning provides a holistic learning to students with a major paradigm shift by using modern day technologies. The Learning Management systems are mostly built using traditional standard frameworks like that of IEEE Learning Technology Systems Architecture (LTSA). These frameworks are not suitable to smart learning environment as they lack several desired features to qualify for smartness levels in learning. In this paper a modified block chain enabled LTSA framework is proposed to realize the sense level of smartness. The proposed framework is evaluated for various security threats suitable in virtual learning. The risk impact factor for several threats is compared for several threat patterns and it is observed that risk impact factor sufficiently reduced in the proposed framework as compared to that of LTSA.
Digital Forensics of Raspberry Pi Used for MITM

Raspberry Pi, a credit card sized, low power consuming and cost-effective computer gained global recognition since its development in 2008. The capability to run GNU-based Linux distribution like Snappy Ubuntu, Kali, Raspbian and even Windows 10, has partly contributed to its popularity. However, these devices can also be exploited. In this chapter, we will discuss how a digital forensic investigator could investigate a case where a Raspberry Pi was used to conduct Man in the middle attack. Using an adversary model, we will demonstrate how one can perform a man-in-the-middle attack against users by creating a rogue wireless access point and capturing critical user information. We will try to analyze different operating system and application log files; files and data recovered using different digital forensic tools to deduce the timeline and what things have happened using recovered evidence. We will also see how different digital forensic analysis tools like Autopsy, FTK AccessData and Bulk Extractor could be helpful in this case by performing benchmarking them against some key factors like recovered web history and cookies files, carved files, etc.
Affective question answering on video

Visual Question Answering (VQA) is an increasingly popular research area in machine learning. Most of the existing VQA tasks only focus on static images, and only a few models are based on videos. The primary purpose of this project is to develop an innovative model that performs Affective Question Answering on Video (AQAV), a multi-tasking architecture that implements a Video QA route and an Affective route. A pre-trained CNN emotion detector recognizes emotions on the frames of a video, and a string of the emotion labels is relayed to the Token-based, Frame-based and Integrated attention mechanisms. The attention model uses the visual features, the question and the emotion labels to focus on relevant frames of the video and relevant regions of the frames. The string of emotion labels is used to generate an emotion caption that will be used by the Text QA module to prepare an affective answer. A conventional answer is generated from processes that take place along the Video QA route, while the affective answer is a product of both the Video QA and the Affective routes. Our model does not only make VQA more analytic by generating an explanatory answer, but also registers quantitative improvement in performance, when compared with previous baselines. We managed to prove that the injection of emotions in the attention mechanism boosts VQA performance. The AQAV model contributes towards efforts in making machines understand sequential and dynamic visual scenes in the real world.
The Relative Edit-Distance Between Two Input-Driven Languages

We study the relative edit-distance problem between two input-driven languages. The relative edit-distance is closely related to the language inclusion problem, which is a crucial problem in formal verification. Input-driven languages are a robust subclass of context-free languages that enable to model program analysis questions within tractable time complexity. For instance, the language inclusion (or equivalence) problem is undecidable for context-free languages whereas the problem is solvable in polynomial time for input-driven languages specified by deterministic input-driven pushdown automata (IDPDAs) and is EXPTIME-complete for nondeterministic IDPDAs. Our main contribution is to prove that the relative edit-distance problem for two input-driven languages is decidable by designing a polynomial time IDPDA construction, based on the edit-distance, that recognizes a neighbourhood of a given input-driven language. In fact, the relative edit-distance problem between two input-driven languages turns out to be EXPTIME-complete when the neighbourhood distance threshold is fixed as a constant.
An Argument for Minimal Logic

The problem of negative truth is the problem of how, if everything in the world is positive, we can speak truly about the world using negative propositions. A prominent solution is to explain negation in terms of a primitive notion of metaphysical incompatibility. I argue that if this account is correct, then minimal logic is the correct logic. The negation of a proposition A is characterised as the minimal incompatible of A composed of it and the logical constant ¬. A rule-based account of the meanings of logical constants that appeals to the notion of incompatibility in the introduction rule for negation ensures the existence and uniqueness of the negation of every proposition. But it endows the negation operator with no more formal properties than those it has in minimal logic.
Distribution Patterns of Ag in Hydrothermal Precipitates from the Okinawa Trough

Abstract The Okinawa Trough is characterized by enrichment of Ag in hydrothermal precipitates; however, the distribution of this enrichment remains poorly constrained. This study presents the results of a field‐emission scanning electron microscopy and electron‐microprobe analysis based mineralogical and geochemical investigation of the spatial distribution of Ag within Ag‐rich sulfide samples from the Okinawa Trough. The tetrahedrite, covellite, and galena in these samples contain high concentrations of Ag (average values of 1.60, 0.78, and 0.23 wt%, respectively) and also various Ag sulfosalts. Examination of the Ag budget of these samples indicates that most of the Ag is hosted by tetrahedrite followed by galena. The Ag within tetrahedrite is incorporated by substitution into the Cu site, whereas galena becomes Ag‐enriched by the coupled incorporation of monovalent Ag, Tl, and Cu, and trivalent Sb and Bi into Pb lattice sites. Tetrahedrite and galena containing higher concentrations of Sb favor increased Ag substitution. Four sets of Ag host minerals are identified with distinct ore formation temperatures. Tetrahedrite and galena concentrate the majority of Ag at medium temperatures (150–300°C). Other Ag host minerals concentrate only minor or trace amounts of Ag, including massive sphalerite, chalcopyrite, and pyrite at high temperatures (&gt;300°C), colloform pyrite and sphalerite at low temperatures (&lt;150°C), and Ag‐sulfosalts at even lower temperatures (&lt;100°C).
BAN: Focusing on Boundary Context for Object Detection

Visual context is one of the important clue for object detection and the context information for boundaries of an object is especially valuable. We propose a boundary aware network (BAN) designed to exploit the visual contexts including boundary information and surroundings, named boundary context, and define three types of the boundary contexts: side, vertex and in/out-boundary context. Our BAN consists of 10 sub-networks for the area belonging to the boundary contexts. The detection head of BAN is defined as an ensemble of these sub-networks with different contributions depending on the sub-problem of detection. To verify our method, we visualize the activation of the sub-networks according to the boundary contexts and empirically show that the sub-networks contribute more to the related sub-problem in detection. We evaluate our method on PASCAL VOC detection benchmark and MS COCO dataset. The proposed method achieves the mean Average Precision (mAP) of 83.4% on PASCAL VOC and 36.9% on MS COCO. BAN allows the convolution network to provide an additional source of contexts for detection and selectively focus on the more important contexts, and it can be generally applied to many other detection methods as well to enhance the accuracy in detection.
Categories of control and visibility in mapping infrastructures

Little attention has been paid to how the infrastructural designs of crowd mapping platforms can result in limited participation and fewer contributions. To this end, we utilize a critical lens to study how OpenStreetMap's (OSM) techno-structure limits users content creation. Drawing upon the field of science, technology, and society studies we show how categorizations used in OSM structurally make certain things visible and others invisible[10]. This results in staggered participation wherein some communities find their efforts stifled, which over time contributes to the digital divide[3]. Through a cultural probe named Mapjack that we constructed, a tool for local mapping communities, we revealed how specific design interventions limit participation and propose some interventions that would improve participation in OSM. The primary interventions were offline mapping and facilitating better metadata. Through semi-structured interviews, we learned that through such improvements, community-based knowledge is enabled, allowing for situated knowledge, to be accessed[20]. By employing these interventions, we shift map making platforms to participatory mapping infrastructures.
“ALPHABET SOUP”: Examining Acronym and Abbreviation Style in Headlines

Through a content analysis and survey, this project explores the prevalence and clarity of acronyms in news headlines. Acronyms popular in a local newspaper were largely unknown to a sample of target readers. When asked about their reactions to unfamiliar published acronyms, about half said they would try to figure it out, and about one-third expressed negative emotions (e.g., frustration, annoyance). Additionally, most said unfamiliar acronyms affect their desire to continue reading, at least some of the time. These findings suggest that an increased focus on comprehension over brevity could help journalists better engage their readers.
A Quantum-Proof Non-malleable Extractor

In privacy amplification, two mutually trusted parties aim to amplify the secrecy of an initial shared secret X in order to establish a shared private key K by exchanging messages over an insecure communication channel. If the channel is authenticated the task can be solved in a single round of communication using a strong randomness extractor; choosing a quantum-proof extractor allows one to establish security against quantum adversaries. In the case that the channel is not authenticated, this simple solution is no longer secure. Nevertheless, Dodis and Wichs (STOC'09) showed that the problem can be solved in two rounds of communication using a non-malleable extractor, a stronger pseudo-random construction than a strong extractor. We give the first construction of a non-malleable extractor that is secure against quantum adversaries. The extractor is based on a construction by Li (FOCS'12), and is able to extract from source of min-entropy rates larger than 1 / 2. Combining this construction with a quantum-proof variant of the reduction of Dodis and Wichs, due to Cohen and Vidick (unpublished) we obtain the first privacy amplification protocol secure against active quantum adversaries.