Reviewerly/exHarmony huggingface dataset:
Implementing RSA for Wireless Sensor Nodes

As wireless sensor networks (WSNs) become more widespread, potential attacks against them also increase and applying cryptography becomes inevitable to make secure WSN nodes. WSN nodes typically contain only a constrained microcontroller, such as MSP430, Atmega, etc., and running public key cryptography on these constrained devices is considered a challenge. Since WSN nodes are spread around in the field, the distribution of the shared private key, which is used in a symmetric key cryptographic algorithm for securing communications, is a problem. Thus, it is necessary to use public key cryptography to effectively solve the key distribution problem. The RSA cryptosystem, which requires at least a 1024-bit key, is the most widely used public key cryptographic algorithm. However, its large key size is considered a drawback for resource constrained microcontrollers. On the other hand, RSA allows for extremely fast digital signature generation which may make it desirable in applications where messages transmitted by sensor nodes need to be authenticated. Furthermore, for compatibility with an existing communication infrastructure, it may be desirable to adopt RSA in a WSN setting. With this work, we show that, in spite of its long key size, RSA is applicable for wireless sensor networks when optimized arithmetic, low-level coding and some acceleration algorithms are used. We pick three versions of the MSP430 microcontroller, which is used widely on wireless sensor network nodes, and implement 1024-bit RSA on them. Our implementation achieves 1024-bit RSA encryption and decryption operations on MSP430 in only 0 . 047 s and 1 . 14 s, respectively. In order to achieve these timings, we utilize several acceleration techniques, such as the subtractive Karatsuba-Ofman, Montgomery multiplication, operand scanning, Chinese remainder theorem and sliding window method. To the best of our knowledge, our timings for 1024-bit RSA encryption and decryption operations are the fastest reported timings in the literature for the MSP430 microcontroller.
EFFECT OF SOCIAL CAPITAL ON FIRM PERFORMANCE: THE ROLE OF ENTREPRENEURIAL ORIENTATION AND DYNAMIC CAPABILITY

Social capital is a concept that describes good quality social relations that can lead to mutu­al benefits. The social capital theory thrives on relationships in networks to gain access to resources, especially information benefits not available to non-members of the network. The study reports the effects of social capital on firm performance. The study further examines the moderating role of entrepreneurial orientation in the relationship between social capital and firm performance; and the mediating role of dynamic capabilities in the relationship between social capital and firm performance. Using SmartPLS software 3.2.8 to analyze primary data collected from 787 small and medium-sized enterprises (SMEs) operating in Ghana, the results show that social capital has a positive and direct relationship with firm performance in Ghana. The findings suggest that EO moderates the relationship between social capital and firm performance. Again, dynamic capabilities mediate the relationship between social capital and firm performance. The study also provides managers with practical ways of building relationships within their networks for achieving competitive advantage in the current business environment.Keywords: Social capital, Entrepreneurial Orientation, Dynamic capabilities and firm performanceJEL Classifications: L31, M1DOI: https://doi.org/10.32479/irmm.8108
Cross-lingual Visual Verb Sense Disambiguation

Spandana Gella, Desmond Elliott, Frank Keller. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Quantum lower bounds for approximate counting via laurent polynomials

We study quantum algorithms that are given access to trusted and untrusted quantum witnesses. We establish strong limitations of such algorithms, via new techniques based on Laurent polynomials (i.e., polynomials with positive and negative integer exponents). Specifically, we resolve the complexity of approximate counting, the problem of multiplicatively estimating the size of a nonempty set S ⊆ [N], in two natural generalizations of quantum query complexity.Our first result holds in the standard Quantum Merlin-Arthur (QMA) setting, in which a quantum algorithm receives an untrusted quantum witness. We show that, if the algorithm makes T quantum queries to S, and also receives an (untrusted) m-qubit quantum witness, then either [MATH HERE]. This is optimal, matching the straightforward protocols where the witness is either empty, or specifies all the elements of S. As a corollary, this resolves the open problem of giving an oracle separation between SBP, the complexity class that captures approximate counting, and QMA.In our second result, we ask what if, in addition to a membership oracle for S, a quantum algorithm is also given QSamples- i.e., copies of the state [MATH HERE] - or even access to a unitary transformation that enables QSampling? We show that, even then, the algorithm needs either [MATH HERE] queries or else [MATH HERE] QSamples or accesses to the unitary.Our lower bounds in both settings make essential use of Laurent polynomials, but in different ways.
Mutual clustering coefficient-based suspicious-link detection approach for online social networks

Online social networks (OSNs) are trendy and rapid information propagation medium on the web where millions of new connections either positive such as acquaintance, or negative such as animosity, are being established every day around the world. The negative links (or sometimes referred to as harmful connections) are mostly established by fake profiles as they are being created by minds with ill aims. Detecting negative (or suspicious) links within online users can better aid in the mitigation of fake profiles from OSNs. A modified clustering coefficient formula, named as MutualClusteringCoefficient represented byMcc, is introduced to quantitatively measure the connectivity between the mutual friends of two connected users in a group. In this paper, a classification system based on mutual clustering coefficient and profile information of users has been presented to detect the suspicious links within the user communities. Profile information helps us to find the similarity between users. Different similarity measures have been employed to calculate the profile similarity between a connected user pair. Experimental results demonstrate that four basic and easily available features such as workw,educatione,home_townhtandcurrent_city(cc) along with MCC play a vital role in designing a successful classification system for the detection of suspicious links.
Modeling Global Syntactic Variation in

This paper evaluates global-scale dialect identification for 14 national varieties of English on both web-crawled data and Twitter data. The paper makes three main contributions: (i) introducing data-driven language mapping as a method for selecting the inventory of national varieties to include in the task; (ii) producing a large and dynamic set of syntactic features using grammar induction rather than focusing on a few hand-selected features such as function words; and (iii) comparing models across both web corpora and social media corpora in order to measure the robustness of syntactic variation across registers.
Blockchain-based Solar Electricity Exchange: Conceptual Architecture and Laboratory Setup

Over the past decade, high penetration of rooftop solar photovoltaics (PV) has been observed in many countries. The availability of rooftop solar PV at customer premises has transformed a house into an energy prosumer that can both consume and export electricity. With proper regulations in place, homeowners can become a market participant who can buy and sell excess solar electricity in a peer-to-peer (P2P) trading environment. With the emerging blockchain technology, it will be possible to securely keep track of such solar electricity exchange without a third party oversight. The objective of this paper is to discuss the conceptual architecture of the blockchain-based platform for exchange of solar electricity in a neighborhood. Several open-source blockchain platforms are reviewed, including Hyperledger, Ethereum and Corda. Experiments developed on one of the well-known open-source blockchain platforms are discussed that describe how a P2P exchange of solar electricity can be set up in a laboratory environment.
Training Objective Image and Video Quality Estimators Using Multiple Databases

Machine learning (ML) is an essential part of recent advances in computer science. To fully exploit its potential, ML-based algorithms require a considerable amount of annotated data to be used for training. This represents a severe limitation in the field of image and video quality assessment since obtaining large-scale annotated databases is time-consuming and expensive. Moreover, the resulting quality estimators are mainly restricted only to the usecases included in the dataset used for their training. This paper proposes a strategy allowing for combination of multiple databases for training of objective image and video quality assessment algorithms. Using this strategy, the algorithms can be trained using all of the existing relevant databases together which allows to increase the amount of data-points and usecases in orders of magnitude. The potential of the proposed method is demonstrated by re-training the combination of features from Video Multimethod Assessment Fusion (VMAF) algorithm resulting in the significant improvement of its performance with respect to 20 video databases.
Measurement of polymers with 3D optical scanners: evaluation of the subsurface scattering effect through five miniature step gauges

In this work, the interaction between 3D optical scanners and objects realized with different materials and colours was investigated. The interaction is mainly related to the translucency of some materials, such as polymers, which is a category of materials in ever-increasing demand by industry. At the same time, the increase of the geometrical complexity of manufactured parts raises the need for the use of 3D optical scanners thanks to their rapidity and effectiveness. Translucency causes a subsurface scattering effect that has been modelled for certain cases and affects the dimensional accuracy of measurements, as well as the quality of reconstruction. Recently, an investigation considered its effect in dimensional verification using a structured light scanner with fringe projection. In this work, the investigation is extended to other 3D optical scanners exploiting different measuring principles: laser, structured light based on phase shifting and gray code, and photogrammetry. This was done in order to study how the subsurface scattering effect can vary with the materials, colours, and also the measuring principle used. To this end, five miniature step gauges made of different polymers and colours were used, and the effects due to their different optical properties investigated. The analysis focused on dimensional verification of bidirectional lengths, first calibrated with a traceable coordinate measuring machine. The quality of the reconstruction was also evaluated through the flatness values.
Simulation-Sound Arguments for LWE and Applications to KDM-CCA2 Security

The Naor-Yung paradigm is a well-known technique that constructs IND-CCA2-secure encryption schemes by means of non-interactive zero-knowledge proofs satisfying a notion of simulation-soundness. Until recently, it was an open problem to instantiate it under the sole Learning-With-Errors ( $$\mathsf {LWE}$$ ) assumption without relying on random oracles. While the recent results of Canetti et al. (STOC’19) and Peikert-Shiehian (Crypto’19) provide a solution to this problem by applying the Fiat-Shamir transform in the standard model, the resulting constructions are extremely inefficient as they proceed via a reduction to an NP-complete problem. In this paper, we give a direct, non-generic method for instantiating Naor-Yung under the $$\mathsf {LWE}$$ assumption outside the random oracle model. Specifically, we give a direct construction of an unbounded simulation-sound NIZK argument system which, for carefully chosen parameters, makes it possible to express the equality of plaintexts encrypted under different keys in Regev’s cryptosystem. We also give a variant of our argument that provides tight security. As an application, we obtain an $$\mathsf {LWE}$$ -based public-key encryption scheme for which we can prove (tight) key-dependent message security under chosen-ciphertext attacks in the standard model.
Can Unconditional Language Models Recover Arbitrary Sentences

Neural network-based generative language models like ELMo and BERT can work effectively as general purpose sentence encoders in text classification without further fine-tuning. Is it possible to adapt them in a similar way for use as general-purpose decoders? For this to be possible, it would need to be the case that for any target sentence of interest, there is some continuous representation that can be passed to the language model to cause it to reproduce that sentence. We set aside the difficult problem of designing an encoder that can produce such representations and, instead, ask directly whether such representations exist at all. To do this, we introduce a pair of effective, complementary methods for feeding representations into pretrained unconditional language models and a corresponding set of methods to map sentences into and out of this representation space, the reparametrized sentence space. We then investigate the conditions under which a language model can be made to generate a sentence through the identification of a point in such a space and find that it is possible to recover arbitrary sentences nearly perfectly with language models and representations of moderate size.
Coverage of Information Extraction from Sentences and Paragraphs

Scalar implicatures are language features that imply the negation of stronger statements, e.g., "She was married twice" typically implicates that she was not married thrice.In this paper we discuss the importance of scalar implicatures in the context of textual information extraction.We investigate how textual features can be used to predict whether a given text segment mentions all objects standing in a certain relationship with a certain subject.Preliminary results on Wikipedia indicate that this prediction is feasible, and yields informative assessments.
Towards Modelling of Visual Saliency in Point Clouds for Immersive Applications

Modelling human visual attention is of great importance in the field of computer vision and has been widely explored for 3D imaging. Yet, in the absence of ground truth data, it is unclear whether such predictions are in alignment with the actual human viewing behavior in virtual reality environments. In this study, we work towards solving this problem by conducting an eye-tracking experiment in an immersive 3D scene that offers 6 degrees of freedom. A wide range of static point cloud models is inspected by human subjects, while their gaze is captured in real-time. The visual attention information is used to extract fixation density maps, that can be further exploited for saliency modelling. To obtain high quality fixation points, we devise a scheme that utilizes every recorded gaze measurement from the two eye-cameras of our set-up. The obtained fixation density maps together with the recorded gaze and head trajectories are made publicly available, to enrich visual saliency datasets for 3D models.
Electronic Scoring Scheme Based on Real Paillier Encryption Algorithms

Paillier algorithm has higher time efficiency than other homomorphic encryption algorithms due to only one power operation is required, but its encryption range is limited to integer, which cannot meet the need of electronic scoring system to encrypt real. To solve the problem, an electronic scoring scheme based on real Paillier algorithm is put forward. In the proposed scheme, compound transformation is introduced to convert real to large integer. The encryption range of Paillier algorithm is extended from integer to real by the large integer operation, which can make the scoring results more precision. In addition, a more secure and efficient two-way identity authentication strategy between clients and servers is designed by using Horner's rule. After hashing the username and password by Horner's rule, the obtained hash value is encrypted and transmitted, which can not only improve system security by avoiding disclosure of user's information during transmission, but also increase the computation efficiency due to the reduction of data size in encryption and decryption. In the experiment, compared with the existing encryption algorithms on different scoring data sets, the whole encryption and decryption time is slightly increased by 0.28% compared with the traditional Paillier, but computing real score is realized in the proposed method. The efficiency of addition and multiplication of the proposed method is increased averagely by 25.99% and 42.75%. The average shorten time of two-way identity authentication is 27.22 milliseconds due to the using of Horner's rule. When the number of clients is more than 105, the time efficiency is improved more obviously.
Aspect-Based Sentiment Analysis Methods in Recent Years

Sentiment Analysis (SA) is the computational treatment of opinions, sentiments and subjectivity of text. Aspect-based Sentiment Analysis (ABSA) is a specific SA that aims to extract most important aspects of an entity and predict the polarity of each aspect from the text. A review of the recent state-of-the-art in ABSA, shows the remarkable growing in finding both aspect, and the corresponding sentiment. Current methods are categorized based on their proposed algorithms and models. For each discussed study, aspect extraction method and sentiment prediction method, the dataset, domain and the reported performance is included. The main goal of this work is to review ABSA techniques with brief details. The main contributions of this paper consist of the refined categorizations of a great number of recent articles, comparing them and the illustration of the recent trend of research in the ABSA.
Hierarchical controlled quantum teleportation

This paper describes a hierarchical controlled quantum teleportation scheme of arbitrary two-qubit based on an eight-particle maximum entanglement state among four parties, one sender and three receivers. In order to prevent the infidelity of the receivers, secret state will be purposefully split and sent to the three receivers. According to the noncloning theorem, only one of the three receivers can recover secret state and the high authorized receiver that recovers the information only requires the cooperation of one of the remaining two receivers. On the contrary, the low authorized party that recovers the secret state requires the cooperation of all remaining receivers.
Time Drift Considerations When Using GPS and Accelerometers

Purpose : To investigate potential time drift between devices when using Global Positioning Systems (GPS) and accelerometers in field-based research. Methods : Six Qstarz BT-Q1000XT GPS trackers, activPAL3 accelerometers, and ActiGraph GT3X+ and GT3X accelerometers were tested over 1–3 waves, each lasting 9–14 days. Once per day an event marker was created on each pair of devices concurrently. The difference in seconds between the time stamps for each event marker were calculated between each pair of GPS and activPAL devices and GPS and ActiGraph devices. Mixed-effects linear regression tested time drift across days and waves and between two rooms/locations (in an inner room vs. on a windowsill in an outer room). Results : The GPS trackers remained within one second of the computer clock across days and waves and between rooms. The activPAL devices drifted an average of 8.38 seconds behind the GPS devices over 14 days ( p &lt; .001). The ActiGraph GT3X+ devices drifted an average of 11.67 seconds ahead of the GPS devices over 14 days ( p &lt; .001). The ActiGraph GT3X devices drifted an average of 28.83 seconds behind the GPS devices over 9 days ( p &lt; .001). Time drift did not differ across waves but did differ between rooms and across devices. Conclusions : Time drift between the GPS and accelerometer models tested was minimal and is unlikely to be problematic when addressing many common research questions. However, studies that require high levels of precision when matching short (e.g., 1-second) time intervals may benefit from consideration of time drift and potential adjustments.
Differential Evolution Algorithm Based on a Competition Scheme

Differential Evolution (DE) is a simple yet powerful population-based metaheuristic algorithm to solve global optimization problems. In this paper, a novel differential evolution is proposed based on the competition among candidate solutions. In the proposed algorithm, the candidate solutions are divided into two groups including losers and winners based on a competition among candidate solutions. Winners generate new candidate solutions based on the DE's standard mutation and crossover operators, whereas losers learn from the winners. To this end, both crossover and mutation are changed for the loser ones. Competition is not performed in each iteration. In this regard, a new control parameter representing the competition period is introduced. We assess the performance of the proposed algorithm on CEC2017 benchmark functions with three dimensions of 30, 50, and 100. The experimental results verify the effectiveness of the proposed algorithm on the majority of the benchmark functions.
Hand segmentation pipeline from depth map: an integrated approach of histogram threshold selection and shallow CNN classification

We propose a new approach which is a three-stage pipeline to fast and accurate segment hand from a single depth image. Firstly, a depth frame is segmented into several regions by histogram-based thresholds selection algorithm and tracing the exterior boundaries of objects. We found that MINIMUM, MEAN and MEDIAN are effective ways to separate objects and the threshold in the valley between two maxima similar to MINIMUM algorithm with a minimum error. Then, each segmentation proposal is evaluated by a 3-layers shallow convolutional neural network (CNN) which is trained as a binary classification function to predict whether it is a partition of hand. Finally, all hand components are merged as our hand segmentation result. In our experiment, we use a set of real data containing more than 200,000 frames of depth images. Compared with the results achieved by approaches based on RDF and SegNet, results demonstrate that our approach achieves better performance in high-accuracy (88.34% mean IoU) within shorter processing time (≤8 ms).