Reviewerly/exHarmony huggingface dataset:
Analyzing D-Wave Quantum Macro Assembler Security

As we enter the quantum computing era, security becomes of at most importance. With the release of D-Wave One in 2011 and most recently the 2000Q, with 2,000 qubits, and with NASA and Google using D-wave Systems quantum computers, a thorough examination of quantum computer security is needed. Quantum computers underlying hardware is not compatible with classical boolean and binary-based computer systems and software. Assemblers and compliers translate modern programming languages and problems into quantum-annealing methods compatible with quantum computers. This paper presents a vulnerability assessment utilizing static source code analysis on Qmasm Python tool. More specifically, we use flow-sensitive, inter-procedural and context-sensitive data flow analysis to uncover vulnerable points in the program. We demonstrate the Qmasm security flaws that can leave D-Wave 2X system vulnerable to severe threats.
Modified Anti-Monotone Support Pruning on FP Tree for Improved Frequent Pattern Generation

Pattern discovery plays a very important role in mining interesting frequent patterns from any data sources. Besides, it also includes determining whether the pattern is interesting. The study applied the modified anti-monotone support constraint to the Frequent Pattern-Growth (FP-Growth) algorithm to enhance its frequent pattern generation. Besides, the study applied the alteration after constructing the FP tree, thereby traversing and searching the nodes of the tree for possible nodes not satisfying the minimum support. The mining process resulted to a comparable difference in the number of generated itemsets and association rules. The second pruning showed more interesting frequent patterns compared to those generated only from the first pruning. The study used the confidence measure in assessing the algorithm's performance. The result of the evaluation showed that of the two itemsets used in the testing, only very few frequent patterns generated after the first pruning obtained most interesting measures, having 1 out of 6 for itemset S40 and 5 out of 20 for itemset S82. On the other hand, all the frequent patterns generated after the second pruning obtained the most interesting measures. Hence, the dual pruning removes infrequent and weak patterns, leaving only those interesting and strong ones.
Internal and External Implementation Strategy of Collaboration Management Model in Hasanuddin University Educational Forest

Abstract The management of educational forest with multiple stakeholder approaches is expected to contribute to the success of educational forest management. The results of previous studies recommended the need to strengthen multiple parties to build a collaborative management model by considering aspects of needs and clarifying the role of stakeholders, so that common perceptions were developed in managing the area and anticipating overlapping in carrying out their respective roles. This study aims to analyze the internal and external strategies for implementing the collaboration management model in the Hasanuddin University Educational Forest area. The methodological approach using descriptive policy analysis, internal and external strategy analysis (review of IFAS-EFAS) were chosen to achieve the expected goals. The results of IFAS-EFAS strategy analysis proposed four main strategies. The first one is a strategy which maximizes the power to seize opportunities (SO), namely a plan for empowering the surrounding forest community through the Social Forestry scheme in a strategic plan and utilizing the human resources to promote and jointly developing eco-tourism potential. The second one is a strategy that utilizes maximum use of power to anticipate or deal with threats and try to make threats as opportunities (ST), namely to make experts and other human resources as mediators or facilitators to relevant parties so that mutual understanding can be developed regarding management objectives. The third one is a strategy that minimizes weaknesses to seize opportunities (WO), namely improvements in the funding area through fundraising to improve the quality of educational forest management, including the improvement of supporting facilities and infrastructure through the concept of partnership with the community, and massive promotion of eco-tourism potential. The fourth one is a strategy that minimizes weaknesses to avoid threats well (WT), namely managing budget that is right on target, accountable, and transparent, to avoid negative stigma from the community.
Search efficient blockchain-based immutable logging and querying

Blockchain offers the ability to create distributed databases that can be trusted, even if some actors on the network may be malicious. We consider the problem of reducing the read overhead of a ledger built using blockchains as part of the IDASH 2018 competition. In this scenario, we have multiple nodes granted to access a server. The goal is to store the activity logs of the nodes accessing the server in a secure fashion, using a blockchain-based ledger. To increase search speed, we propose splitting the ledger into groups based on expected search terms, and storing each group on a separate blockchain. By doing so, a search for all records of a specific type is transformed from a linear search on all records, to a linear search on a small subset of records. In our solution, this increases search efficiency by a factor of 8, at the cost of increasing storage overhead by a factor of 4. The system can be adjusted based on what types of searches are expected to reduce this overhead.
Gradient based histogram equalization in grayscale image enhancement

This paper presents a new method of histogram equalization for grayscale images, which is called the gradient based histogram equalization. The histogram equalization is performed on the image filtered by means of gradient operators. The proposed method is simple, fast, and the preliminary experimental examples with different images show that the method is effective for image enhancement. While preserving the range and mean intensity of the image, the new method allows for reducing the standard deviation and significantly straitening the graph of the histogram, when comparing with the traditional (or global) histogram equalization.
Probing the quantum depolarizing channel with mixed Indefinite causal order

Indefinite causal ordering (ICO) refers to quantum channels arranged so that the path through them is a super- position of different possible paths. The ICO is called pure when the path superposition is a pure quantum state. Channel probing, in which probes in prepared quantum states are passed through copies of the channel to estimate one or more channel parameters, is aided by ICO. Specifically, pure ICO is known to increase the quantum Fisher information (QFI) in the processed probe state about the unknown parameter(s). We consider the case complementary to pure ICO in which the path state is maximally mixed for a given indefiniteness. Deriving the QFI under this condition by a new approach, we find that mixed ICO-assisted probing, while not as beneficial as pure ICO probing, still yields greater QFI than does the comparable scheme with definite causal order. The d-dimensional quantum depolarizing channel is used as the channel model for this study. While mixed ICO-assisted probing, like pure ICO-assisted probing, is generally advantageous for probing the depolarizing channel, mixed ICO's relative effectiveness decreases with probe dimension, just as for pure ICO-assisted probing, each being most effective for qubit probes.
How readable are data breach notifications?

Data breaches – broadly defined as incidents where protected data that is considered sensitive and confidential has been disclosed, accessed and/or altered in an unauthorised manner – present a growing threat to society and organisations. While much of the focus to date has been on technical countermeasures, particularly the ways to prevent and detect security threats associated with data breach incidents, we also need greater insights into the readability of the notification response used by firms to alert affected consumers after a suspected incident has taken place. Data breaches – where protected data that is considered sensitive and confidential has been accessed in an unauthorised manner – present a growing threat to society and organisations. While much of the focus to date has been on technical countermeasures, we also need greater insights into the readability of the notification response used by firms to alert affected consumers after a suspected incident has taken place. Stephen Jackson of the University of London examines ways in which we can judge how comprehensible breach notifications are and how we can go about improving them.
Evaluation of Feature Detectors, Descriptors and Match Filtering Approaches for Historic Repeat Photography

This work analyzes the suitability of classic feature detectors and descriptors as well as different match filters for matching historical to modern images. A variety of prominent detector and descriptor combinations are evaluated on a new dataset, that is composed of repeat photographs of various scenes exposed to tremendous change across years. Results show that a dense keypoint sampling is more effective than classic feature detection, while several descriptors achieve comparable performances. Yet, more important is an adequate match filtering approach that identifies correct correspondences despite their large descriptor distances. We show that the standard ratio test is unsuitable for matching historic to modern image pairs, while other filters based on keypoint geometry boost performance. Finally, we establish a complete pipeline including detectors, descriptors and match filtering methods. Our pipeline shows high performance on challenging image pairs beyond repeat photography, as our tests on another dataset show.
Integrating Bayesian and Neural Networks for Discourse Coherence

In dialogue systems, discourse coherence is an important concept that measures semantic relevance between an utterance and its context. It plays a critical role in determining the inappropriate reply of dialogue systems with regard to a given dialogue context. In this paper, we present a novel framework for evaluating discourse coherence by seamlessly integrating Bayesian and neural networks. The Bayesian network corresponds to Coherence-Pivoted Latent Dirichlet Allocation (cpLDA). cpLDA concentrates on generating the fine-grained topics from dialogue data and takes both local and global semantics into account. The neural network corresponds to Multi-Hierarchical Coherence Network (MHCN). Coupled with cpLDA, MHCN quantifies discourse coherence between an utterance and its context by comprehensively utilizing original texts, topic distribution and topic embedding. Extensive experiments show that the proposed framework yields superior performance comparing with the state-of-the-art methods.
Minimization of Percent Root-Mean-Square Difference in the Generation of Wavelets Using Genetic Algorithm

This paper proposes the minimization of the distortion measure of signals reconstructed by wavelets using genetic algorithm. The minimization happens between the original study object and the reconstructed one—Percent Root-Mean-Square. This distortion measure is analyzed in wavelet creation that are responsible of signals reconstruction with the best approximation from its originals, and these signals, used as study object, are electrocardiograms signals. This proposal solves the problem main commonly found in signals compressing area which is exactly the reconstruction of signals without loss of informations. The proposed method provided satisfactory results compared to those found in the literature.
APPROACH OF PROCESSING, CLASSIFICATION AND DETECTION OF NEW CLASSES AND ANOMALIES IN HETEROGENIOUS AND DIFFERENT STREAMS OF DATA

Objectives. The aim of the study is to search for effective methods and approaches to the processing of heterogeneous data streams and the management of problems of infinite length, conceptual evolution and conceptual drift. A heterogeneous data stream can have infinite length and contain structured or unstructured data. Processing a heterogeneous and multi-scale data flow is a major challenge for researchers. Most of the research focuses on solving problems of infinite length and concept-drift. Method. New class detection strategies are classified as parametric and non-parametric. This work is based on a non-parametric approach. The classifier works on the ensemble of three models. The separation generates a different number of classes in each fragment. Classes are calculated by applying the K-Medoid clustering method on each fragment. The effectiveness of the K-media clustering method is more suitable for a data set containing anomalies. Result. The developed algorithm is capable of processing heterogeneous and multi-scale data. Each instance that is present in the model belongs to only one class. Experimental work was performed on four samples of stream data of 2000 lines each. After performing the pre-processing, the multi-valued characteristics of the data were found in the data set. Conclusion. This paper presents an effective approach for processing heterogeneous data streams and managing tasks of infinite length, conceptual evolution and conceptual drift. The developed approach is based on the string matching parameter instead of the distance for processing the four tasks of data streams. The level of false positives in the developed algorithm is rather low and can be considered insignificant. The approach does not classify a new instance of the class as an existing class, but can effectively handle the functional evolution.
Similarity learning for person reidentification using projected gradient method

The problem of learning a similarity measure for cross-view person identification in a disjointed camera network is addressed. Our learning framework is based on the projected gradient approach and is suitable for large-scale applications. The hinge loss with the triplet constraints is used as the objective function. Contrary to other approaches where a dual formulation is used, we propose to optimize the objective function in the primal form, with the help of a minibatch gradient descent algorithm. The choice of learning rate and its schedule are nontrivial tasks in such optimization schemes. We have studied empirically the effect of using different strategies for learning rate schedules. Our experimental study includes conventional strategies such as fixed or diminishing learning rates and recently developed adaptive gradient methods such as root-mean-square propagation (RMSProp) for learning rate schedule. The experimental results are presented on three benchmark datasets of person reidentification, namely VIPeR, CUHK Campus, and CUHK03. Our experimental results demonstrate that the RMSProp is well suited for the proposed learning framework. A comparison with recent methods in the domain presents the justification of the proposed approach.
Emojis: Langue or Parole?

Abstract The phenomenon of emojis has had many implications for the future course of writing, literacy, communications, and the nature of representation itself. This paper looks at the implications of emoji use through the filter of Saussurean semiotics and through the lens of theories of visuality, which claim that visual writing is having radical effects on literacy and cognition. The historical background to the rise of visual writing is used as a backdrop to the semiotic analysis of the emoji phenomenon. The way we read and write messages today with visual elements such as emoji may indicate a radical shift away from a linear mode of processing information, as imprinted in alphabetic forms of writing, toward a more holistic and imaginative mode. However, because emoji usage and creativity depend on specific technologies, it remains to be seen if such writing can survive as technologies change. The main argument in this paper is that emojis are more part of parole than they are a separate langue , but they nonetheless reveal changes that the latter is undergoing in an age of digital multimodal communication.
Creative Leadership of Teachers as a Resource of Pedagogical Influence

The article presents results of an empirical study of the teachers’ creative leadership as a resource of pedagogical influence. The study involved 11 teachers and 63 high school students of the school No. 94 inChelyabinsk. It has been established that the creative leadership of teachers as a set of influences on students in joint activities (inter-effects of the personal style) and beyond (meta-effects of the personal style) is associated with the individual characteristics of their activity (personal style). In teachers with pronounced attitudes of transformational leadership stimulating meta-effects for students are dominated.
An Efficient ID-based Fair Off-line Electronic Cash Scheme without Bilinear Pairings

Due to the rapid development of mobile internet and intelligent terminal, the Scale of electronic commerce expands sharply. As an important means of payment, electronic cash has received extensive attention and application. In this paper, a new and efficient ID-based fair off-line electronic cash scheme is proposed. The scheme effectively constructs double-spending detection protocol by using signature of proof of knowledge and achieves coin tracing protocol and owner tracing protocol. The scheme is proved to be non-forgeable, anonymous, fair and effective. Because the scheme avoids using bilinear parings and Map-to-point hash functions, the scheme needs more less computing power as compared with other off-line electronic cash schemes based on elliptic curve.
Argumentation-Based Explanations for Answer Sets Using ADF

This paper presents so-called asl-explanation graphs for answer set programming based on a translation of extended logic programs to abstract dialectical frameworks (ADF). The graphs show how a literal can be derived from the program, and they evaluate in an argumentative way why necessary assumptions about literals not contained in an answer set hold. With the set of all asl-explanation graphs for a literal and an answer set, it is possible to explain and justify thoroughly why the literal is or is not contained in that answer set. Additionally, we provide a criterion to improve the clarity of explanations by pruning nodes without loss of information and selecting most significant asl-explanation graphs.
Options in Multi-task Reinforcement Learning - Transfer via Reflection

Temporally extended actions such as options are known to lead to improvements in reinforcement learning (RL). At the same time, transfer learning across different RL tasks is an increasingly active area of research. Following Baxter's formalism for transfer, the corresponding RL question considers the benefit that an RL agent can achieve on new tasks based on experience from previous tasks in a common "learning environment". We address this in the specific context of goal-based multi-task RL, where the different tasks correspond to different goal states within a common state space, and we introduce Landmark Options Via Reflection (LOVR), a flexible framework that uses options to transfer domain knowledge. As an explicit analog of principles in transfer learning, we provide theoretical and empirical results demonstrating that when a set of landmark states covers the state space suitably, then a LOVR agent that learns optimal value functions for these in an initial phase and deploys the associated optimal policies as options in the main phase, can achieve a drastic reduction in cumulative regret compared to baseline approaches.
Media experiences in community-driven rural areas: exploring children’s media cultures in rural Gujarat, India

In this paper, we examine how social structures of gender, caste, and religion, within which children in India operate, inform their engagements with media technologies/narratives. We demonstrate our approach using the case of three villages in the Ahmedabad district of Gujarat, India. We examine children’s media cultures using ethnographic methods which occasion long-term immersion and paying attention to children’s narratives. We also argue that children experience and enact their agency while engaging with media from within the limitations or norms imposed by the social structures. This is evident in the simple negotiation strategies they have developed to challenge social structures and norms prevalent in their societies. Based on empirical evidence we conclude that the children’s exposure to and consumption of media narratives under adult management allow the families and communities to reinforce the dominant rationality of patriarchy, caste hierarchy, and religious prejudice.
Modeling and Detecting Drift in User Interest Based on Hierarchical Classification

During participating in Web 2.0 applications, people enjoy the convenience brought by Internet resources, but also encounter the problem of lost in Internet. This paper takes the Douban movie review as the background, and proposed a method for detecting the drift of user's interests. The user's comment and the frequency with which the user watches a certain type of movie are used to determine the extent to which the user is interested in the movie, and the parameters p and q are used to adjust the degree to which the two factors affect the interest weight. A hierarchical user interest classification tree (named HC-tree) and time window table are designed to maintain the interests and their weights in multi-granularities way. Also two functions λ and ψ are designed to quickly identify the drift of user interests. A large number of experimental results show that the proposed method is better on the correctness for user interest drift detection and is prior to other similar algorithms.
Chaotic oscillation of laser diode with pseudorandom signal

We numerically studied the chaotic dynamics of a laser diode (LD) system with optical injection, where a pseudorandom signal is applied to the drive current of the master LD, and compared two different methods of quantifying the orbital instability of the LD system, i.e., calculating the orbital expansion exponent (OEE) and the Lyapunov exponent. We found that both exponents are increased and the orbital instability is enhanced by applying a pseudorandom signal when the inherent system without the applied signal is a “window”. On the other hand, when the inherent system is chaotic, the OEE is increased and the Lyapunov exponent is decreased with increasing standard deviation of the applied signal. Although the two exponents have a different tendency, it is suggested that the variation of the standard deviation of the applied signal can be identified using both exponents. We then investigated the response of the orbital instability to the frequency range of the applied signal and showed that the orbital instability is efficiently enhanced by using the inherent system is a window and by applying a broad signal that does not have a frequency near the spectral peak of the inherent system and has lower-frequency components than this frequency.