Reviewerly/exHarmony huggingface dataset:
Chapter 2. A dialogue on populism?

Most works on populism framed in a discourse-analytic perspective focus on the features of populist discourse itself, contributing greatly to the understanding of the phenomenon. However, a full understanding of populism should also consider the ways in which notions of populism are constructed, negotiated, reproduced, and popularised in public discourse, as this contributes greatly to forming public opinion at large and peoples responses to populism itself. For this reason, the chapter addresses discourses about populism, with a focus on editorials dealing with Brexit in the British and Italian press. Although their position of supremacy in orienting public opinion has been partly mined by talk shows, blogs, and social media at large, opinion pieces remain one of the most important sites in which intellectuals (generally senior journalists) publicly share their views trying at the same time to influence the opinion of the readers. Based on an original framework integrating categories from critical discourse studies, argumentation theory, and the study of heteroglossia/dialogism, the analysis focuses on the ways in which editorialists define and evaluate populism and populists, the argumentative topoi they employ to support their standpoints, and whether and how they engage alternative viewpoints. In our view, all these aspects concur to expand or reduce the space of dialogue created by the text, and hence, we claim, the ability of the readers to feel included, and see their positions represented, in the broader discussion. The risk is that if no dialogue is opened at all with the people who uphold populist views, intellectual discourse will fail to involve them as interlocutors in a critical discussion, thus making them more receptive (or vulnerable) to populist propaganda.
AntiConcealer: Reliable Detection of Adversary Concealed Behaviors in EdgeAI-Assisted IoT

Internet of Things (IoT) is one of the rapidly developing technologies today that attract huge real-world applications. However, the reality is that IoT is easily vulnerable to numerous types of cyberattacks and anomalies. Detecting them is becoming increasingly challenging day by day due to limitations with IoT devices and threat intelligence. Particularly, one of the most challenging problems is to detect the existence of malicious adversaries that continuously adapt or conceal their behaviors in IoT to hide their actions and to make the IoT security protocol ineffective. In this article, we study this problem at the IoT device level that can be a great idea to avoid potential attacks. We present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AntiConcealer</monospace> , an edge-aided IoT framework, and propose an edge artificial intelligence-enabled approach (EdgeAI) for detecting adversary concealed behaviors in the IoT. We first develop an adversary behavior model and use this to identify mid-attack temporal patterns by learning the multivariate Hawkes process (MHP), a kind of point process as a random and finite series of events (e.g., behaviors) controlled by a probabilistic model. Naturally, learning MHP processed on EdgeAI reveals the influence of the concealed behaviors of adversaries in the IoT. These concealed behaviors are then grouped using a nonnegative weighted influence matrix. To observe the performance of the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AntiConcealer</monospace> framework through evaluation, we employ honeypots integrated with edge servers and verify the usability and reliability of adversary behavioral identification.
Choosing Your Platform for Social Media Drug Research and Improving Your Keyword Filter List

Social media research often has two things in common: Twitter is the platform used and a keyword filter list is used to extract only relevant Tweets. Here we propose that (a) alternative platforms be considered more often when doing social media research, and (b) regardless of platform, researchers use word embeddings as a type of synonym discovery to improve their keyword filter list, both of which lead to more relevant data. We demonstrate the benefit of these proposals by comparing how successful our synonym discovery method is at finding terms for marijuana and select opioids on Twitter versus a platform that can be filtered by topic, Reddit. We also find words that are not on the U.S. Drug Enforcement Agency (DEA) drug slang list for that year, some of which appear on the list the subsequent year, showing that this method could be employed to find drug terms faster than traditional means.
GC2 INC. V. INTERNATIONAL GAME TECHNOLOGY; IGT; AND DOUBLEDOWN INTERACTIVE, LLC Case No. 16 C 8784, 2018 WL 5921315 (N.D. Ill. Nov. 12, 2018)

Gaming Law ReviewVol. 23, No. 2 CasesGC2 INC. V. INTERNATIONAL GAME TECHNOLOGY; IGT; AND DOUBLEDOWN INTERACTIVE, LLC Case No. 16 C 8784, 2018 WL 5921315 (N.D. Ill. Nov. 12, 2018)Published Online:14 Mar 2019https://doi.org/10.1089/glr2.2019.2329AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byCopyright Liability and Music ‘Piracy’: Capitol Records v Thomas-RassetSSRN Electronic Journal, Vol. 153Disrupting Frivolous DefensesSSRN Electronic Journal, Vol. 276Competing through Keyword AdvertisingSSRN Electronic JournalThe Future of Music Trademarks under DastarSSRN Electronic Journal, Vol. 32Contracting for Confidential DiscoverySSRN Electronic JournalCompelled Decryption and the Right Against Self-Incrimination: Obsta PrincipiisSSRN Electronic Journal, Vol. 5The Case Against Product Configuration Trade DressSSRN Electronic JournalSSRN Electronic JournalRecent Developments in Patent Law 2019SSRN Electronic Journal, Vol. 208Naked Price and Trade Secret OverreachSSRN Electronic JournalWhatever Did Happen to the Antitrust Movement?SSRN Electronic JournalIs the Supreme Courtts Patentable Subject Matter Test Overly Ambiguous? An Empirical TestSSRN Electronic JournalRecent Developments in Patent Law 2018SSRN Electronic JournalEmojis and Intellectual Property LawSSRN Electronic JournalAccess Denied Data Breach Litigation, Article III Standing, and a Proposed Statutory SolutionSSRN Electronic JournalResolving Eligibility and Indefiniteness in Proper Context: Applying Alice and AristocratSSRN Electronic JournalPatent Injunctions on Appeal: An Empirical Study of the Federal Circuit's Application of eBaySSRN Electronic Journal, Vol. 730Subjecting Software Subject-Matter to Limitations that MatterSSRN Electronic JournalSocial Enterprise: Choice of Legal EntitySSRN Electronic JournalA Smack on the Chin or a Nibble? Content Analysis of the Impact of the Oakwood TrilogySSRN Electronic Journal, Vol. 123 Volume 23Issue 2Mar 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:GC2 INC. V. INTERNATIONAL GAME TECHNOLOGY; IGT; AND DOUBLEDOWN INTERACTIVE, LLC Case No. 16 C 8784, 2018 WL 5921315 (N.D. Ill. Nov. 12, 2018).Gaming Law Review.Mar 2019.104-111.http://doi.org/10.1089/glr2.2019.2329Published in Volume: 23 Issue 2: March 14, 2019Online Ahead of Print:February 21, 2019PDF download
Stacked Garbling

Secure two party computation (2PC) of arbitrary programs can be efficiently achieved using garbled circuits (GC). The bottleneck of GC efficiency is communication. It is widely believed that it is necessary to transmit the entire GC during 2PC, even for conditional branches that are not taken. This folklore belief is false. We propose a novel GC technique, stacked garbling, that eliminates the communication cost of inactive conditional branches. We extend the ideas of conditional GC evaluation explored in (Kolesnikov, Asiacrypt 18) and (Heath and Kolesnikov, Eurocrypt 20). Unlike these works, ours is for general 2PC where no player knows which conditional branch is taken. Our garbling scheme, $$\textsf {Stack}$$ , requires communication proportional to the longest execution path rather than to the entire circuit. $$\textsf {Stack}$$ is compatible with state-of-the-art techniques, such as free XOR and half-gates. $$\textsf {Stack}$$ is a garbling scheme. As such, it can be plugged into a variety of existing protocols, and the resulting round complexity is the same as that of standard GC. The approach does incur computation cost quadratic in the conditional branching factor vs linear in standard schemes, but the tradeoff is beneficial for most programs: GC computation even on weak hardware is faster than GC transmission on fast channels. We implemented $$\textsf {Stack}$$ in C++. $$\textsf {Stack}$$ reduces communication cost by approximately the branching factor: for 16 branches, communication is reduced by $$10.5{\times }$$ . In terms of wall-clock time for circuits with branching factor 16 over a 50 Mbps WAN on a laptop, $$\textsf {Stack}$$ outperforms state-of-the-art half-gates-based 2PC by more than $$4{\times }$$ .
Abstract Execution

We propose a new static software analysis principle called Abstract Execution, generalizing Symbolic Execution: While the latter analyzes all possible execution paths of a specific program, Abstract Execution analyzes a partially unspecified program by permitting abstract symbols representing unknown contexts. For each abstract symbol, we faithfully represent each possible concrete execution resulting from its substitution with concrete code. There is a wide range of applications of Abstract Execution, especially for verifying relational properties of schematic programs. We implemented Abstract Execution in a deductive verification framework and proved correctness of eight well-known statement-level refactoring rules, including two with loops. For each refactoring we characterize the preconditions that make it semantics-preserving. Most preconditions are not mentioned in the literature.
Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices

Deep learning techniques, particularly convolutional neural networks, have shown great potential in computer vision and medical imaging applications. However, deep learning models are computationally demanding as they require enormous computational power and specialized processing hardware for model training. To make these models portable and compatible for prototyping, their implementation on low-power devices is imperative. In this work, we present the implementation of Modified U-Net on Intel Movidius Neural Compute Stick 2 (NCS-2) for the segmentation of medical images. We selected U-Net because, in medical image segmentation, U-Net is a prominent model that provides improved performance for medical image segmentation even if the dataset size is small. The modified U-Net model is evaluated for performance in terms of dice score. Experiments are reported for segmentation task on three medical imaging datasets: BraTs dataset of brain MRI, heart MRI dataset, and Ziehl-Neelsen sputum smear microscopy image (ZNSDB) dataset. For the proposed model, we reduced the number of parameters from 30 million in the U-Net model to 0.49 million in the proposed architecture. Experimental results show that the modified U-Net provides comparable performance while requiring significantly lower resources and provides inference on the NCS-2. The maximum dice scores recorded are 0.96 for the BraTs dataset, 0.94 for the heart MRI dataset, and 0.74 for the ZNSDB dataset.
An architecture-level analysis on deep learning models for low-impact computations

Abstract Deep neural networks (DNNs) have made significant achievements in a wide variety of domains. For the deep learning tasks, multiple excellent hardware platforms provide efficient solutions, including graphics processing units (GPUs), central processing units (CPUs), field programmable gate arrays (FPGAs), and application-specific integrated circuit (ASIC). Nonetheless, CPUs outperform other solutions including GPUs in many cases for the inference workload of DNNs with the support of various techniques, such as the high-performance libraries being the basic building blocks for DNNs. Thus, CPUs have been a preferred choice for DNN inference applications, particularly in the low-latency demand scenarios. However, the DNN inference efficiency remains a critical issue, especially when low latency is required under conditions with limited hardware resources, such as embedded systems. At the same time, the hardware features have not been fully exploited for DNNs and there is much room for improvement. To this end, this paper conducts a series of experiments to make a thorough study for the inference workload of prominent state-of-the-art DNN architectures on a single-instruction-multiple-data (SIMD) CPU platform, as well as with widely applicable scopes for multiple hardware platforms. The study goes into depth in DNNs: the CPU kernel-instruction level performance characteristics of DNNs including branches, branch prediction misses, cache misses, etc, and the underlying convolutional computing mechanism at the SIMD level; The thorough layer-wise time consumption details with potential time-cost bottlenecks; And the exhaustive dynamic activation sparsity with exact details on the redundancy of DNNs. The research provides researchers with comprehensive and insightful details, as well as crucial target areas for optimising and improving the efficiency of DNNs at both the hardware and software levels.
Entanglement Swapping and Quantum Correlations via Symmetric Joint Measurements

We use hyper-entanglement to experimentally realize deterministic entanglement swapping based on quantum Elegant Joint Measurements. These are joint projections of two qubits onto highly symmetric, iso-entangled, bases. We report measurement fidelities no smaller than $97.4\%$. We showcase the applications of these measurements by using the entanglement swapping procedure to demonstrate quantum correlations in the form of proof-of-principle violations of both bilocal Bell inequalities and more stringent correlation criteria corresponding to full network nonlocality. Our results are a foray into entangled measurements and nonlocality beyond the paradigmatic Bell state measurement and they show the relevance of more general measurements in entanglement swapping scenarios.
The societal impact of the metaverse

Abstract In the 1920s developed a new artistic and cultural movement called “Surrealism”, which not only included the revolutionary paintings and sculptures, but also philosophical texts and novels. Around 100 years later, the arrival of the Metaverse with its independence of physical laws promises to bring Surrealism to a next level. On the other hand, the pure market power of some of the companies developing their concepts may limit this potential freedom for the users. As observed with social media platforms, inside the initial competition, various providers had to close their platforms, leading to today’s oligopoly. This means less choice for the users and a stronger position of the providers to create solutions maximizing their economic benefit. Today, independent companies work on their visions of the Metaverse, but unequal starting positions may make it difficult for them to compete with the established competitors. Similar to the attractivity of social media, also the Metaverse requires a high number of active users and service providers (private companies, but also governmental offices), fostering the tendency to an oligopoly. Based on literature, economy, psychology, and art, this paper discusses the opportunities and risks of the Metaverse, including an outlook on a predicted societal impact, especially laying its focus on the holistic connections of the different points of view. As the Metaverse only appeared in smaller parts yet, academic papers are still lacking. One aim of this article is to start an academic discussion. So far, actual concepts had been widely inspired by popular science fiction novels, ignoring Ray Bradbury’s thoughts: “It’s ‘prevent the future,’ that’s the way I put it. Not predict it, prevent it”.
A Scalable Approach to Joint Cyber Insurance and Security-as-a-Service Provisioning in Cloud Computing

As computing services are increasingly cloud-based, corporations are investing in cloud-based security measures. The Security-as-a-Service (SECaaS) paradigm allows customers to outsource security to the cloud, through the payment of a subscription fee. However, no security system is bulletproof, and even one successful attack can result in the loss of data and revenue worth millions of dollars. To guard against this eventuality, customers may also purchase cyber insurance to receive recompense in the case of loss. To achieve cost effectiveness, it is necessary to balance provisioning of security and insurance, even when future costs and risks are uncertain. To this end, we introduce a stochastic optimization model to optimally provision security and insurance services in the cloud. Since the model we design is a mixed integer problem, we also introduce a partial Lagrange multiplier algorithm that takes advantage of the total unimodularity property to find the solution in polynomial time. We also apply sensitivity analysis to find the exact tolerance of decision variables to parameter changes. We show the effectiveness of these techniques using numerical results based on real attack data to demonstrate a realistic testing environment, and find that security and insurance are interdependent.
An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications Through Security Slicing and Hybrid Constraint Solving

Malicious users can attack Web applications by exploiting injection vulnerabilities in the source code. This work addresses the challenge of detecting injection vulnerabilities in the server-side code of Java Web applications in a scalable and effective way. We propose an integrated approach that seamlessly combines security slicing with hybrid constraint solving; the latter orchestrates automata-based solving with meta-heuristic search. We use static analysis to extract minimal program slices relevant to security from Web programs and to generate attack conditions. We then apply hybrid constraint solving to determine the satisfiability of attack conditions and thus detect vulnerabilities. The experimental results, using a benchmark comprising a set of diverse and representative Web applications/services as well as security benchmark applications, show that our approach (implemented in the JOACO tool) is significantly more effective at detecting injection vulnerabilities than state-of-the-art approaches, achieving 98 percent recall, without producing any false alarm. We also compared the constraint solving module of our approach with state-of-the-art constraint solvers, using six different benchmark suites; our approach correctly solved the highest number of constraints (665 out of 672), without producing any incorrect result, and was the one with the least number of time-out/failing cases. In both scenarios, the execution time was practically acceptable, given the offline nature of vulnerability detection.
Modeling and Analyzing Dynamic Fault-Tolerant Strategy for Deadline Constrained Task Scheduling in Cloud Computing

Cloud computing has been increasingly concerned in scientific computing area. More and more enterprises and research institutes have migrated their applications to the clouds. Due to the complexity of cloud computing system in structural and behavioral aspects, how to design the fault tolerant cloud computing system becomes a challenging problem. This paper investigates the modeling and analysis of fault tolerant strategy for deadline constrained task scheduling in cloud computing. First, a formal description language is defined to accurately model the different components of cloud application, and use it to characterize the operational mechanisms and fault behaviors. Second, we propose a fault tolerant strategy, which includes the scheduling mechanism, synchronization mechanism, and exception mechanism, to dynamically compute the execution mode and required virtual machine for tasks, thus ensuring the reliability and real-time requirement of cloud application. An enforcement algorithm is also designed to realize the proposed strategy. Third, the techniques of Petri nets are provided to analyze and validate the correctness of proposed method. Finally, several experiments are done to illustrate that the reliability of cloud application is improved and its deadline is met.
Achieving Multi-Hop PRE via Branching Program

Proxy re-encryption (PRE) is a fundamental cryptographic primitive in secure data sharing and e-mail forwarding, etc. To our knowledge, most existing efficient lattice-based PRE schemes focus on the construction of single-hop, key-private, multi-bit and chosen-ciphertext attack (CCA), etc. Few works of literature discussed the detailed multi-hop construction over lattices. Very recently, Chandran et al. (PKC'14) proposed a lattice-based PRE scheme that builds upon the key switching mechanism of Brakerski (CRYPTO'12), and pointed out that their scheme can achieve multi-hop PRE scheme by the ideal circuit family for a directed graph G. In this paper, we are still working along this line and achieving multi-hop PRE via the branching program (BP), which is one type of NC1 circuit and can be used to compute encrypted data. To our knowledge, we proposed the first multi-hop PRE scheme via BP which supports homomorphic evaluation. We also analyze the security of our scheme under decisional learning with errors (LWE) assumption.
Collision-Free Path Planning and Delivery Sequence Optimization in Noncoplanar Radiation Therapy

Radiation therapy is among the top three cancer treatments in current medical services. The novel noncoplanar radiation therapy which claimed the best characteristics in almost all dosimetric properties encountered the challenges of the potential collision and the long time delivering. In this paper, we proposed a brand new scheme which uses a combined method of the collision avoidance path planning based on an improved probability roadmap method (PRM) and the delivery sequence optimization based on a modified genetic algorithm (GA) to solve the problems in noncoplanar radiation therapy. A uniform sampling strategy, an improved connection strategy, and an efficient local planner are introduced to optimize the roadmap result and accelerate the roadmap construction. The GA is improved by the elitist selection, the local search strategy, and the similar substitution strategy to achieve a better performance both in convergence rate and optimal solution. Experiments are carried out on the simulation platform with typical therapy system models. The results show that our proposed methods work well with the radiation therapy system in a compact working area. Collision is avoided and time consumption is reduced. We believe that our proposed algorithms could solve the problems in current radiation therapy and promote their clinic applications.